a spatial dynamic framework to integrate regional water …

A SPATIAL DYNAMIC FRAMEWORK TO INTEGRATE

REGIONAL WATER USE EFFICIENCY AND ENERGY

CONSUMPTION

Aftab Ahmad

B.Sc. (Agricultural Engineering)

University of Agriculture, Faisalabad

M.Eng. (Water Resource Engineering & Management)

Asian Institute of Technology, Bangkok

A thesis submitted to Charles Sturt University for the degree of Doctor of Philosophy

School of Environmental Sciences, Faculty of Science,

Charles Sturt University

August 2013

i

Table of contents

Certificate of authorship ............................................................................. xvii Acknowledgements ....................................................................................... xix Ethics approval ............................................................................................ xxi Glossary ..................................................................................................... xxiii Acronyms and abbreviations ....................................................................... xxv

Research publications and contributions ............................................................................ xxvi Refereed conference proceedings............................................................... xxvi Journal papers .......................................................................................... xxvii Abstract ...................................................................................................... xxix

Chapter 1 : Introduction ......................................................................................................... 33 1.1 Background and Problem Overview .................................................. 33 1.2 Setting the Scene: The Context for This Research .............................. 34 1.3 Research Objectives ........................................................................... 39 1.4 Research Scope and Limitations ........................................................ 42

Chapter 2 : Literature Review ................................................................................................ 43 2.1 Introduction ............................................................................................. 43

2.1.1 Irrigation in Australia ....................................................................... 44 2.2 Exploring Energy and Water Nexus ........................................................ 46

2.2.1 Water and Energy Indicators ............................................................ 50 2.2.2 Water Footprints of Energy Production/Use .................................... 50 2.2.3 Environmental Footprints of Crop Production ................................. 54 2.2.4 Water Market as a Driver in Water-Energy Nexus .......................... 58 2.2.5 Implications of Introduction of ‘Cap’ .............................................. 61

2.3 Greenhouse Gas Emissions from Agriculture ......................................... 62 2.3.1 Direct and Indirect Emissions .......................................................... 62

2.4 Water Efficiency in Irrigation ................................................................. 65 2.4.1 Irrigation Project Efficiency ............................................................. 66 2.4.2 Whole-of-System Approach ............................................................. 68

2.5 Water-energy nexus for irrigation supply systems .................................. 71 2.6 Conversion to efficient irrigation systems ............................................... 75

2.6.1 Efficient Irrigation Technologies and Controlling Groundwater Rise 77

2.7 Water-energy nexus for horticulture in Australia ................................... 78 2.8 Energy availability and food security ..................................................... 80 2.9 Fertigation – a better way of saving energy input .................................. 82 2.10 Irrigation Management Strategies ........................................................ 84

2.10.1 Demand-based irrigation strategy .................................................. 84 2.10.2 Supply-based irrigation strategy .................................................... 85

2.11 Application of System Dynamics in Agriculture ................................... 86 2.12 Up-scaling Water and Energy Use ....................................................... 86 2.13 Testing economic viability of irrigated systems .................................... 87 2.14 Reliability of Irrigation Supply ............................................................. 87

Chapter 3 : Methodology ........................................................................................................ 89 3.1 Description of Study Region .................................................................... 89

3.1.1 The Murrumbidgee River Catchment .............................................. 90 3.1.2 Study Area Selection ........................................................................ 94 3.1.3 The Case Study Site ....................................................................... 108 3.1.4 Data Collection/Collation and Analysis ......................................... 110

3.2 The Overall Approach ........................................................................... 115

ii

3.2.1 Application of System Dynamics Approach .................................. 118 3.3 Node-link model Development .............................................................. 119

3.3.1 Modules of the Developed Node-link model ................................. 122 3.4 Node-link model Mass Balance Test ..................................................... 174 3.5 Demand-based verses fixed interval scheduling for different irrigation methods ....................................................................................................... 175 3.6 Calculating water and energy efficiency and productivity indicators .. 176 3.7 Structure of the Thesis Report ............................................................... 180 3.8 Chapter Summary ................................................................................. 182

Chapter 4 : Water and Energy Nexus for Demand Based Irrigation Methods and Conveyance Systems .............................................................................................................. 183

4.1 Rationale of this chapter ....................................................................... 183 4.2 Scenario 1 - Flood irrigation with open channel supply system........... 188

4.2.1 Irrigation demand versus irrigation delivery .................................. 189 4.2.2 Estimation of water losses.............................................................. 190 4.2.3 Effect on crop yield ........................................................................ 191 4.2.4 Irrigation Application Rate ............................................................ 193 4.2.5 Accounting for Energy Use and GHG Emissions in Crop Production for Scenario 1 ....................................................................... 194

4.3 Scenario 2 - Furrow irrigation with open channel supply system ........ 200 4.3.1 Irrigation demand versus irrigation delivery .................................. 201 4.3.2 Water losses estimation .................................................................. 202 4.3.3 Effect on crop yield ........................................................................ 203 4.3.4 Irrigation application rate ............................................................... 203 4.3.5 Accounting for energy use and GHG emissions in crop production for Scenario 2 ........................................................................ 204

4.4 Scenario 3 - Flood irrigation with pipe supply system ......................... 211 4.4.1 Optimization of pipe diameters and why ....................................... 211 4.4.2 Irrigation supply, losses and irrigation application rates ............... 213 4.4.3 Accounting for energy use and GHG emissions in crop production for Scenario 3 ........................................................................ 214

4.5 Scenario 4 - Furrow irrigation with pipe supply system ...................... 217 4.5.1 Optimization of pipe diameters ...................................................... 217 4.5.2 Irrigation supply, losses and irrigation application rates ............... 218 4.5.3 Accounting for energy use and GHG emissions in crop production for Scenario 4 ........................................................................ 219

4.6 Scenario 5 - Sprinkler irrigation with pipe supply system .................... 221 4.6.1 Irrigation demand versus irrigation delivery .................................. 222 4.6.2 Water losses estimation .................................................................. 223 4.6.3 Effect on crop yield ........................................................................ 224 4.6.4 Irrigation application rate ............................................................... 224 4.6.5 Accounting for energy use and GHG emissions in crop production for Scenario 5 ........................................................................ 225

4.7 Scenario 6 – Drip irrigation with pipe supply system .......................... 234 4.7.1 Irrigation demand versus irrigation delivery .................................. 235 4.7.2 Water losses estimation .................................................................. 236 4.7.3 Effect on crop yield ........................................................................ 237 4.7.4 Irrigation application rate ............................................................... 237 4.7.5 Accounting for energy use and GHG emissions in crop production for Scenario 6 ........................................................................ 238

iii

4.8 Comparison of the demand-based irrigation scenarios ........................ 248 4.8.1 Comparison of water and energy use rates .................................... 249 4.8.2 Comparison of efficiency and productivity indicators for water and energy ............................................................................................... 250 4.8.3 Comparison of greenhouse gas emissions for modelled scenarios 260

4.9 Sensitivity analysis ................................................................................ 262 4.9.1 Sensitivity of energy use in irrigation ............................................ 262

4.10 Chapter summary ................................................................................ 266 Chapter 5 : Water and Energy Nexus for Supply Based Irrigation Methods and Conveyance Systems .............................................................................................................. 269

5.1 Description of modelled scenarios ........................................................ 269 5.1.1 Scenario 1: Flood irrigation supplied with an open channel system 269 5.1.2 Scenario 2: Furrow irrigation supplied with an open channel system 269 5.1.3 Scenario 3: Sprinkler irrigation system connected with communal piped supply ............................................................................................ 270 5.1.4 Scenario 4: Drip irrigation system connected with communal piped supply ............................................................................................ 270

5.2 Modifications made in the node-link model .......................................... 270 5.2.1 Modifications in crop water use module ........................................ 270 5.2.2 Modifications in irrigation supply/conveyance module ................. 271 5.2.3 Modifications in irrigation application rate and irrigation interval 273

5.3 Determining irrigation application rate ............................................... 274 5.4 Water use and yield comparison of supply-based and demand-based irrigation ..................................................................................................... 276

5.4.1 Comparison of total irrigation water use ........................................ 277 5.4.2 Comparison of net irrigation rate ................................................... 278 5.4.3 Comparison of crop yield ............................................................... 278 5.4.4 Comparison of water losses ........................................................... 279

5.5 Energy and GHG emissions for the supply-based scenarios ................ 281 5.5.1 Comparison of energy use and energy output ................................ 281 5.5.2 Energy efficiency and energy productivity indicators ................... 286 5.5.3 Comparison of greenhouse gas emissions ...................................... 289

5.6 Sensitivity analysis of pressurized irrigation scenarios ........................ 291 5.6.1 Sensitivity of irrigation supply, pumping energy and yield to irrigation interval ..................................................................................... 291 5.6.2 Sensitivity of crop yield and energy use to irrigation water use .... 295

5.7 On-farm storages: water-energy analysis ............................................. 302 5.7.1 Function of on-farm storages ......................................................... 302 5.7.2 Incorporating on-farm storages into supply-based model .............. 303 5.7.3 Comparison of with and without on-farm storage scenarios ......... 304

5.8 Chapter summary .................................................................................. 315 5.8.1 Summary of the key variables ........................................................ 317 5.8.2 Pros and cons of demand-based versus supply-based irrigation strategy 320

Chapter 6 : Up-scaling Water and Energy Linkages from Case Study to Irrigation Scheme Level 323

6.1 Prerequisites for up-scaling demand-based irrigation system ............. 323 6.1.1 Data preparation and approach for up-scaling ............................... 325 6.1.2 Limitations regarding up-scaling water and energy use................. 328

iv

6.2 Node-link model run for representative area unit ................................ 333 6.3 Up-scaling the model results using mosaic approach .......................... 334

6.3.1 Water and energy use at representative area unit scale .................. 334 6.3.2 Water and energy use at MIA scale ............................................... 338 6.3.3 Water and energy use under different climatic conditions ............ 342

6.4 Estimating and mapping water and energy savings for MIA – using GIS-Based distributed approach ................................................................. 343 6.5 Estimating water and energy use at different levels of technology adoption ...................................................................................................... 352 6.6 Chapter Summary ................................................................................. 354

Chapter 7 : Is Irrigation Conversion Worthwhile? ............................................................ 357 7.1 Need for water saving irrigation technologies ..................................... 358

7.1.1 Water availability ........................................................................... 359 7.1.2 Water markets ................................................................................ 360 7.1.3 Crop yield improvement ................................................................ 363

7.2 Representative node-link model ............................................................ 364 7.2.1 Modelled water and energy use ..................................................... 364

7.3 Capital cost for conversion to pressurized irrigation system ............... 365 7.3.1 Assumptions for the economic analysis ......................................... 366 7.3.2 Capital costs of the irrigation systems ........................................... 367 7.3.3 Capital costs of pressurized pipe irrigation supply system ............ 369

7.4 Economic analysis of conversion to sprinkler or drip system for citrus370 7.4.1 Operating costs for furrow irrigation with citrus ........................... 371 7.4.2 Operating costs for low head sprinkler irrigation with citrus ........ 372 7.4.3 Operating costs for surface drip irrigation with citrus ................... 373 7.4.4 Financial benefits/returns from citrus with the three irrigation systems 375 7.4.5 Discounted payback period and financial viability of the three irrigation systems for citrus..................................................................... 376

7.5 Economic analysis of conversion to sprinkler or drip system for wine grapes .......................................................................................................... 382

7.5.1 Operating costs for furrow irrigation with wine grapes ................. 382 7.5.2 Operating costs for sprinkler irrigation with wine grapes.............. 383 7.5.3 Operating costs for drip irrigation with wine grapes ..................... 384 7.5.4 Financial benefits/returns from wine grapes irrigated with the three irrigation systems ........................................................................... 385 7.5.5 Discounted payback period and financial viability of the three irrigation systems for growing wine grapes ............................................ 386

7.6 Sensitivity analysis ................................................................................ 389 7.7 Chapter summary .................................................................................. 393

Chapter 8 : Integrated Analysis, Discussion and Policy Implications ............................... 397 8.1 Understanding and representing the dynamics of the system ............... 397

8.1.1 Water availability versus water saving feedback loop ................... 398 8.1.2 Water savings versus energy use feedback loop ............................ 399 8.1.3 Water savings versus environmental benefits feedback loop ........ 400 8.1.4 Analysis of the feedback dynamics of the integrated system ........ 401

8.2 Discussion on main findings and policy implications ........................... 403 8.2.1 Modelling of water and energy for irrigation systems ................... 403 8.2.2 Water and energy nexus for irrigation strategy .............................. 404 8.2.3 Water and energy nexus for irrigation methods ............................. 404

v

8.2.4 Up-scaling modelled water and energy use.................................... 406 8.2.5 Effectiveness of on-farm storages versus centralized irrigation supply 407 8.2.6 Long-term viability of irrigation conversion .................................. 408 8.2.7 View from system dynamics lens .................................................. 411

Chapter 9 : Conclusions and the Way Forward .................................................................. 413 9.1 Major recommendations ....................................................................... 416

9.1.1 Recommendations for policy makers ............................................. 416 9.1.2 Recommendations for irrigators ..................................................... 417 9.1.3 Recommendations for irrigation providers .................................... 418

9.2 The Way Forward ................................................................................. 418 9.3 Changes in Developed Model for Application in Other Areas ............. 419

References 421 Appendix A: Excerpts from Vensim code for calculation of different model variables .. 447 Appendix B: A snapshot of developed Vensim model in dynamic simulation mode ....... 450 Appendix C: Fertilizer and chemicals input costs .............................................................. 451 Appendix D: Tractor operating costs as per 2008 .............................................................. 452

vii

List of figures Figure 1.1: Water and energy efficiency feedback loop diagram ........................................ 39

Figure 2.1: Energy use and energy intensity by each sector in Australia in 2009-10 (Source: ABS, 2011) .......................................................................................................................... 47

Figure 2.2: Water use by each sector in Australia (Source: ABS, 2012) ............................. 48

Figure 2.3: Natural and regulated average monthly flows in Murrumbidgee River recorded at Balranald station before it joins the Murray River ........................................................... 57

Figure 2.4: Relative distribution of Australia’s direct greenhouse gas emissions by economic sector for 2009-10 (Source: DCC&EE, 2012)..................................................... 65

Figure 2.5: Monthly irrigation application rates to citrus using drip irrigation and low-level micro-sprinklers (Source: Falivene et al., 2006) .................................................................. 76

Figure 3.1: Major rivers and their tributaries in the Murray Darling Basin. (Source: www.mdba.gov.au) .............................................................................................................. 90

Figure 3.2: Dominant land uses of the Murrumbidgee region and its location in MDB (Source: CSIRO, 2008) ........................................................................................................ 92

Figure 3.3: Location of Murrumbidgee Irrigation Area in MDB and its five irrigation districts (Source: Murrumbidgee Irrigation Ltd.) ................................................................ 96

Figure 3.4: Irrigation supply and drainage network of MIA in its five irrigation districts (Source: Murrumbidgee Irrigation Ltd.) .............................................................................. 96

Figure 3.5: Rainfall zones of the Murrumbidgee catchment (Khan at al., 2005) ................. 97

Figure 3.6: Average annual rainfall for each decade since 1950 (Source: Patched Point Dataset from Silo at: http://www.longpaddock.qld.gov.au/silo/) ....................................... 98

Figure 3.7: Monthly Potential Evapotranspiration in the Murrumbidgee Catchment .......... 99

Figure 3.8: Soil groups and their percentage area in MIA (Source: Geoff Beecher’s soils database, unpublished) ....................................................................................................... 100

Figure 3.9: Percentage of irrigation area used by different irrigation systems in the Murrumbidgee Valley (Source: Hope and Wright, 2003) ................................................. 103

Figure 3.10: Irrigation systems in use as per cent of total irrigated area in MIA (Source: Ahmad and Khan, 2009) .................................................................................................... 104

Figure 3.11: Soil types map of the study area (Downloaded from http://www.irrigateway.net/tools/soilmaps/) ...................................................................... 109

Figure 3.12: Daily observed rainfall and evaporation and calculated potential evapotranspiration at Griffith CSIRO gauge for 2007-08 .................................................. 112

Figure 3.13: Daily observed maximum and minimum temperature at Griffith CSIRO gauge for 2007-08 ........................................................................................................................ 112

Figure 3.14: An inventory of factors involved in water and energy consumption and greenhouse gas emissions in irrigation supply systems: open channel network (left), pressurized pipe network (right) (Variables in dotted box are optional). .......................... 117

Figure 3.15: Hypothetical curves of water savings and associated energy use .................. 118

Figure 3.16: Schematic of farm nodes and supply channels/pipes (in parenthesis: channel/pipe length in metres) ........................................................................................... 120

Figure 3.17: Flowchart of interaction among different modules of the node-link model .. 124

Figure 3.18: Steps involved in calculation of crop cover fraction for citrus and stonefruit132

Figure 3.19: Steps involved in calculation of ETc using dual crop coefficient as implemented in the model ................................................................................................. 137

Figure 3.20: Causes tree for ETc adjusted for water stress for Farm No. 6 ....................... 138

viii

Figure 3.21: Schematic of root zone with water balance components (Adapted from Allen et al. (1998). ....................................................................................................................... 139

Figure 3.22: Vensim screen for setting optimisation parameters including optimisation decision variables .............................................................................................................. 141

Figure 3.23: Flowchart of parameter optimisation process as setup in Vensim optimisation framework .......................................................................................................................... 144

Figure 3.24: Setup screen for the objective function definition in Vensim ....................... 145

Figure 3.25: User interface of the developed dynamic model in Vensim model development environment ....................................................................................................................... 152

Figure 3.26: Causes Tree for flow volume at Node 9 of the open channel supply system 156

Figure 3.27: Schematic of supply pipe with outlet pipes to two farms .............................. 160

Figure 3.28: Flowchart of steps to account for energy use, productivity indicators as well as carbon footprint of energy use in irrigation and crop production ...................................... 167

Figure 3.29: Plot between applied water (including rainfall) and yield for citrus crops in South Australia (Source: Skewes, 2010) ........................................................................... 171

Figure 3.30: Feedback loops identified and quantified through integration of modelled variables ............................................................................................................................. 174

Figure 3.31: Flowchart of supply based irrigation strategy as implemented in the node-link model (n=days since start of simulation, d=days since crop gone in water stress) ............ 177

Figure 3.32: Water use accounting components at field scale (Adapted from Molden et al., 2003). ................................................................................................................................. 178

Figure 4.1: Seepage and evaporation losses from channel system of Murrumbidgee Irrigation Area (MIA) ........................................................................................................ 184

Figure 4.2: Comparison of daily irrigation demand and supply and cumulative shortage in irrigation supply (ML shown on y-axis) for Scenario 1 ..................................................... 190

Figure 4.3: Cumulative irrigation water losses (ML shown on y-axis) for Scenario 1 ...... 191

Figure 4.4: Normal and water deficit affected cumulative evapotranspiration (mm shown on y-axis) for the three crops for Scenario 1 ........................................................................... 192

Figure 4.5: Comparison of daily irrigation demand and supply and cumulative shortage in irrigation supply (ML shown on y-axis) for Scenario 2 ..................................................... 202

Figure 4.6: Cumulative irrigation water losses (ML shown on y-axis) for Scenario 2 ...... 203

Figure 4.7: Daily number of parallel pumps turned on to supply irrigation water for Scenario 3 .......................................................................................................................... 214

Figure 4.8: Comparison of daily irrigation demand and supply and cumulative shortage in irrigation supply (ML/day on Y-axis) for Scenario 5 ........................................................ 223

Figure 4.9: Time series of the daily number of pumps turned on in parallel configuration to supply irrigation water for Scenario 5 ............................................................................... 233

Figure 4.10: Comparison of daily irrigation demand and supply and cumulative shortage in irrigation supply (ML/day on Y-axis) for Scenario 6 ........................................................ 236

Figure 4.11: Daily number of pumps turned on in parallel configuration to supply irrigation water for Scenario 6 ........................................................................................................... 247

Figure 4.12: Irrigation application rates (ML/ha) for each crop for the six scenarios ....... 249

Figure 4.13: Energy use per hectare (KWh/ha) for each crop for the six scenarios ........... 250

Figure 4.14: Total greenhouse gas emissions per hectare (kg-CO2e) of each crop for the six scenarios (line graph shows GHG emissions from irrigation only and not other factors of crop production) ................................................................................................................ 261

ix

Figure 4.15: Cumulative probability distribution plots for the delivery pressure head for sprinkler (left) and drip system (right) ............................................................................... 263

Figure 4.16: Sensitivity of cumulative energy use (kWh) for sprinkler irrigation pumping to ±10% change in delivery pressure head (m) ...................................................................... 263

Figure 4.17: Sensitivity of cumulative energy use (kWh) for drip irrigation pumping to ±10% change in delivery pressure head (m) ...................................................................... 264

Figure 4.18: Cumulative probability distribution plots for the irrigation deficit factor for sprinkler (left) and drip system (right) ............................................................................... 265

Figure 4.19: Sensitivity of cumulative energy use (kWh) for sprinkler irrigation pumping to ±50% change in deficit factor ............................................................................................ 265

Figure 4.20: Sensitivity of cumulative energy use (kWh) for drip irrigation pumping to ±50% change in deficit factor ............................................................................................ 266

Figure 5.1: Process of triggering irrigation application events for a given irrigation method ........................................................................................................................................... 272

Figure 5.2: Layout of the module for optimization of the irrigation application rate for each crop .................................................................................................................................... 275

Figure 5.3: Maximum-minimum range and the optimized rates of irrigation for the three crops under the four scenarios ........................................................................................... 277

Figure 5.4: Percentage exceedance plots of total duty flow for pumps for demand-based and supply based irrigation (top plot: drip system, bottom plot: sprinkler system) .................. 285

Figure 5.5: Sensitivity graphs of cumulative irrigation supply (ML) for sprinkler (top) and drip (bottom) systems to irrigation interval (days) ............................................................ 293

Figure 5.6: Sensitivity graphs of cumulative pumping energy use (kWh) for sprinkler (top) and drip (bottom) to irrigation interval (days) ................................................................... 294

Figure 5.7: Sensitivity graphs of yield (t/ha) for sprinkler (left) and drip (right) to irrigation interval (days) for the three crops ...................................................................................... 295

Figure 5.8: Sensitivity of crop yield to irrigation water use for the three modelled crops with drip system ................................................................................................................. 297

Figure 5.9: Sensitivity of irrigation pumping energy consumption (kWh/ha) to irrigation water use (ML/ha) for the three modelled crops with drip system .................................... 298

Figure 5.10: Sensitivity of crop yield to irrigation water use for the three modelled crops with sprinkler system ......................................................................................................... 300

Figure 5.11: Sensitivity of irrigation pumping energy consumption to irrigation water use for the three modelled crops with sprinkler system ........................................................... 301

Figure 5.12: Flowchart of steps to execute supply-based model with on-farm storages ... 306

Figure 6.1: Map showing horticultural farm boundaries and their soil textural classes in the Murrumbidgee Irrigation Area........................................................................................... 325

Figure 6.2: Map of the five soil groups in the MIA horticultural area ............................... 327

Figure 6.3: Sensitivity of cumulative water use (ML) when the irrigated area is uniformly varied by ±50% for the node-link model with a drip system (blue line refers to the baseline value) ................................................................................................................................. 329

Figure 6.4: Sensitivity of cumulative energy use (KWh) when the irrigated area is uniformly varied by ±50% for the node-link model with a drip system (blue line refers to the baseline value) ............................................................................................................. 330

Figure 6.5: Water use (ML) and energy use (kWh) up-scaled from the model results for the whole MIA horticulture area for different climatic conditions .......................................... 342

Figure 6.6: Map of water use rate (ML/ha) for each horticultural farm in MIA for flood irrigation ............................................................................................................................ 349

x

Figure 6.7: Map of water use rate (ML/ha) for each horticultural farm in MIA for sprinkler irrigation ............................................................................................................................ 349

Figure 6.8: Map of water use rate (ML/ha) for each horticultural farm in MIA for drip irrigation ............................................................................................................................ 350

Figure 6.9: Map of water savings (ML/ha) for each horticultural farm in MIA for sprinkler irrigation ............................................................................................................................ 351

Figure 6.10: Map of water savings (ML/ha) for each horticultural farm in MIA for drip irrigation ............................................................................................................................ 351

Figure 6.11: Total water use (ML) and total energy use (MWh) for the two irrigation systems at various level of roll out in MIA horticultural area ........................................... 354

Figure 7.1: Per cent exceedance plot of announced allocation in MIA from 1993-94 to 2009-10 .............................................................................................................................. 359

Figure 7.2: Time series of announced allocation in MIA from 1993-94 to 2009-10 ......... 360

Figure 7.3: Announced percentage allocation versus water trade price ($/ML) in market for MIA from 1998-99 to 2010-11 .......................................................................................... 362

Figure 7.4: Per cent exceedance plots of water trade price ($/ML) and announced allocation (%) for MIA ....................................................................................................................... 363

Figure 7.5: Net present value plots of furrow irrigation with citrus over a period of 30 years ........................................................................................................................................... 379

Figure 7.6: Net present value plots of sprinkler irrigation with citrus connected with an integrated supply system over a period of 30 years ........................................................... 380

Figure 7.7: Net present value plots of drip irrigation with citrus connected with an integrated supply system over a period of 30 years ........................................................... 381

Figure 7.8: Net present value plots of drip, sprinkler and furrow irrigation with wine grapes connected (excluding furrow) with integrated supply system over a period of 30 years ... 388

Figure 8.1: Water availability, investment and water savings negative feedback loop ..... 399

Figure 8.2: Feedback loop between water savings and energy use .................................... 400

Figure 8.3: Positive feedback loop between water savings and environmental benefits ... 401

Figure 8.4: Representation of the integrated system and the constituent causal feedback loops .................................................................................................................................. 402

Figure 8.5: Annual costs and returns for the three irrigation systems with citrus on per hectare basis (capital cost includes the cost of integrated irrigation supply system, except for furrow irrigation).......................................................................................................... 410

Figure 8.6: Annual costs and returns for the three irrigation systems with wine grapes on a per hectare basis (capital cost includes cost of integrated irrigation supply system, except for furrow irrigation).......................................................................................................... 411

xi

List of tables Table 2.1: Final (end-of-water-year i.e. June) percentage general security irrigation allocations for Murrumbidgee valley ................................................................................... 46

Table 2.2: Distribution of water use (ML/year) by each industry under agriculture in Australia during 2009-10 (Source: ABS, 2012)................................................................... 48

Table 2.3: Global average water footprint of primary energy carriers (Gerbens-Leenes, et al., 2008) .............................................................................................................................. 52

Table 2.4: Water footprint of electricity generation in Australia in 2004‐05 (adapted from ABS, 2006) .......................................................................................................................... 53

Table 2.5: Global warming potential of major greenhouse gases (Source: DCC&EE, 2010) ............................................................................................................................................. 63

Table 2.6: Fuel combustion emission factors for selected fuels .......................................... 64

Table 2.7: Potential water saving options to improve water use/irrigation efficiencies (adapted from Khan et al., 2005a) ....................................................................................... 68

Table 2.8: Terms and definitions of irrigation efficiency at different scales as proposed by different researchers............................................................................................................. 69

Table 2.9: Accounted losses and potential water savings in MIA (Source: Khan et al., 2004) ............................................................................................................................................. 70

Table 2.10: Length of earthen irrigation channels in irrigation areas of Australia (Source: ANCID 2000) ...................................................................................................................... 72

Table 2.11: Crop water use (ML/ha) for horticultural crops and water saving potential by high tech irrigation technologies (Source: Khan et al., 2004) .............................................. 76

Table 2.12: Area and economic output of different agriculture industries in MIA (Source: Singh et al., 2005) ................................................................................................................ 80

Table 2.13: Rice and maize production by modern, transitional and traditional methods ... 82

Table 2.14: Approximate nutrient removals based on tonnes of grapes removed per hectare (Source: Giddings, 2004) ..................................................................................................... 83

Table 3.1: Land use distribution in the Murrumbidgee Valley in the year 2000 (Source: BRS, 2005) .......................................................................................................................... 94

Table 3.2: Water entitlements (licenses) in MIA and the Murrumbidgee Valley .............. 101

Table 3.3: Water balance for irrigation delivery system of MIA (all values in GL. source: MIA 2010) ......................................................................................................................... 106

Table 3.4: Information on basic features of the case study area ........................................ 110

Table 3.5: Details of Horticultural farms in the case study area ........................................ 111

Table 3.6: Summary of climatic data used in this study (Griffith CSIRO) ........................ 113

Table 3.7: Average irrigation application data for the three crops in the case study area .. 114

Table 3.8: Soil-water characteristics of WSL and LCL for the case study area ................ 115

Table 3.9: Monthly basal crop coefficients (Kcb) for modelled horticultural crops (Allen et al., 1998) ............................................................................................................................ 129

Table 3.10: Soil water characteristics used in calculation of soil evaporation reduction coefficient, Kr .................................................................................................................... 130

Table 3.11: Values of wetted and vegetative covered soil fraction for irrigation methods and crops modelled in this study .............................................................................................. 132

Table 3.12: Effective root zone and depletion fraction values used for the case study area ........................................................................................................................................... 136

xii

Table 3.13: Calibration variables and their calibrated model values for years 2003-04, 2004-05 and 2005-06 ......................................................................................................... 146

Table 3.14: Comparison of irrigation application rates (ML/ha) between the actual and the calibrated model ................................................................................................................ 146

Table 3.15: Soil-water availability parameters using calibrated model data for the three crops .................................................................................................................................. 147

Table 3.16: Model validation by comparing actual and modelled drip irrigation application rates (ML/ha) for horticultural crops on 13 farms in the study area (Figure in brackets is total number of irrigation days) ......................................................................................... 148

Table 3.17: Reported water use (ML/ha) for fruit and vines (Figures in braces are total crop area in ha) (Sources: MIA 2005, 2006, 2007, 2008, 2009). ............................................... 150

Table 3.18: Physical features of the open channel system in the case study area .............. 152

Table 3.19: Maximum flow capacities of the open channels in the case study area .......... 154

Table 3.20: Main characteristics of the pipe system .......................................................... 157

Table 3.21: Indicative pressure head requirement at each farm outlet ............................... 159

Table 3.22: Pipe size variations and the corresponding sudden contraction loss coefficient Cc values ............................................................................................................................ 161

Table 3.23: Energy equivalent values for different farm inputs and outputs ..................... 166

Table 3.24: CO2 equivalent emissions factors for various farm inputs .............................. 170

Table 3.25: and values for the modelled crops ..................................................... 172

Table 3.26: Mass balance components as computed by model run for 2007-08 ............... 175

Table 3.27: Indicators of water and energy use efficiency and productivity ..................... 178

Table 3.28: Summary of key topics of the thesis ............................................................... 180

Table 4.1: Details about the crops in the modelled case study area ................................... 186

Table 4.2: Wetted area (m2/ha) for the modelled irrigation methods and the crops .......... 188

Table 4.3: Effect of water deficit due to channel capacity constraint on ETc (transpiration only) and crop yield ........................................................................................................... 192

Table 4.4: Average irrigation application rate for the three crops for the modelled Scenario 1 ......................................................................................................................................... 193

Table 4.5: Estimated time and fuel expended by channel operators to manage the irrigation orders for the farms in the case study area in a year .......................................................... 195

Table 4.6: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of citrus crop for Scenario 1 ........................................................ 196

Table 4.7: Nutrient contents in major fertilizers and their application rates to supply 1kg of N, P or K ............................................................................................................................ 197

Table 4.8: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of stone fruit crop for Scenario 1 ................................................. 198

Table 4.9: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of wine grape crop for Scenario 1 ............................................... 199

Table 4.10: Average irrigation application rates for the three crops for the modelled Scenario 2 .......................................................................................................................... 204

Table 4.11: Estimated time and fuel expended by channel operators to the manage irrigation orders for the farms in the case study area in a year .......................................................... 205


xiii


Table 4.14: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of wine grapes crop for Scenario 2 .............................................. 209

Table 4.15: Original and optimized diameters for supply pipe network ............................ 212

Table 4.16: Comparison of losses and irrigation application rates for Scenario 3 and Scenario 1 .......................................................................................................................... 213

Table 4.17: Irrigation water applied and corresponding energy consumption in irrigation pumping for Scenario 3 ..................................................................................................... 215

Table 4.18: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of citrus, stone fruit and wine grapes for Scenario 3 ................... 216

Table 4.19: Original and optimized diameters for supply pipe network for Scenario 4 .... 218

Table 4.20: Comparison of losses and irrigation application rates for Scenario 4 and Scenario 2 .......................................................................................................................... 219


Table 4.22: Accounts for energy inputs and corresponding greenhouse gas emissions on per hectare basis in the yearly production cycle of citrus, stone fruit and wine grapes for Scenario 4 .......................................................................................................................... 221




Table 4.26: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of wine grapes crop for Scenario 5 .............................................. 230


Table 4.28: Accounts for energy inputs and corresponding greenhouse gas emissions on per hectare basis in the yearly production cycle of citrus, stone fruit and wine grapes for Scenario 5 .......................................................................................................................... 234


Table 4.30: Accounts for energy inputs and GHG emissions on per hectare basis in the yearly production cycle of citrus crop for Scenario 6 ........................................................ 241

Table 4.31: Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of stone fruit crop for Scenario 6 .............................................................................. 242

Table 4.32: Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of wine grapes crop for Scenario 6 ........................................................................... 244


Table 4.34: Energy inputs and corresponding greenhouse gas emissions on per hectare basis in the production cycle of citrus, stone fruit and wine grapes for Scenario 6 .................... 248

Table 4.35: Computed overall/project level irrigation efficiency for the six scenarios ..... 254

Table 4.36: Water productivity (kg/m3) indicators for the six scenarios ........................... 255

Table 4.37: Energy productivity (kg/kWh) indicators for the six scenarios ...................... 256

xiv

Table 4.38: Energy efficiency (kWh/kWh) indicators for the six scenarios ...................... 258

Table 4.39: Specific energy (kWh/kg) indicators for the six scenarios ............................. 259

Table 4.40: Water – energy productivity (g/m3/kWh) indicators for the six scenarios ...... 259

Table 4.41: Water – energy ratio (kWh/kWh) for the six scenarios .................................. 260

Table 5.1: Irrigation intervals used in the model for the four supply-based irrigation scenarios ............................................................................................................................ 274

Table 5.2: Comparison of total irrigation water use (ML) between supply-based and demand-based irrigation scenarios ..................................................................................... 278

Table 5.3: Net irrigation rate (ML/ha) for three crops for demand-based and supply-based scenarios ............................................................................................................................ 278

Table 5.4: Comparison modelled crop yield (t/ha) between supply-based and demand-based irrigation systems ............................................................................................................... 279

Table 5.5: Comparison of total water losses (ML) for supply-based and demand-based irrigation scenarios ............................................................................................................ 280

Table 5.6: Energy use for the three crops under demand-based scenarios and the computed energy use for the corresponding supply based scenarios ................................................. 283

Table 5.7: Total equivalent energy output (kWh/ha) from each crop for supply-based and demand-based irrigation scenarios ..................................................................................... 286

Table 5.8: Energy indicators for supply-based irrigation scenarios ................................... 287

Table 5.9: Greenhouse gas emissions rates (kgCO2-Eq/ha) for the three crops under demand-based and supply-based (computed) scenarios .................................................... 290

Table 5.10: Comparison of drip and sprinkler system in terms of yield response to water use ........................................................................................................................................... 299

Table 5.11: Irrigation rates for 4-day irrigation interval and on-farm storage capacity for each farm with sprinkler system ........................................................................................ 307

Table 5.12: Computation of final capacity of each on-farm storage for sprinkler system . 308

Table 5.13: Key variables for with and without on-farm storage scenarios for sprinkler system ................................................................................................................................ 309

Table 5.14: Irrigation rates for 4-day irrigation interval and on-farm storage capacity for each farm with drip system ................................................................................................ 311

Table 5.15: Computation of final capacity of each on-farm storage for drip system ......... 312

Table 5.16: Key variables for with and without on-farm storage scenarios for drip system ........................................................................................................................................... 313

Table 5.17: Comparison of use of on-farm storages and the common piped supply ......... 314

Table 5.18: Summary of important variables for all scenarios modelled in Chapter 5 under supply-based irrigation strategy ......................................................................................... 318

Table 5.19: Comparison of demand-based and supply-based irrigation strategies (the “high” or “low” refers to comparison with each other) ................................................................. 320

Table 6.1: Soil groups and their equivalent USDA soil types ........................................... 327

Table 6.2: Comparison of increase in pumping energy use with increase in total irrigated area for a supply-based drip irrigation strategy ................................................................. 330

Table 6.3: Distribution of different soil classes in MIA horticultural farms and percentage distribution of area of each horticultural crop for each soil type ....................................... 331

Table 6.4: Distribution of different soil groups in MIA horticultural farms and percentage distribution of area of each horticultural crop for each soil group ..................................... 332

xv

Table 6.5: Distribution of number of farms in the representative unit for each model run using a given soil group and crop area (in parentheses, ha) ............................................... 333

Table 6.6: Water and pumping energy uses for different soil groups with sprinkler irrigation system as computed by the demand-based node-link model for the representative unit area (300 ha) for 2007-08 .......................................................................................................... 336

Table 6.7: Water and pumping energy uses for different soil groups with drip irrigation system as computed by the demand-based node-link model for the representative unit area (300 ha) for 2007-08 .......................................................................................................... 337

Table 6.8: Water use for sprinkler and drip irrigation system for five soil groups at model scale and at the scale of the MIA irrigated horticulture area ............................................. 340

Table 6.9: Energy use in irrigation pumping and conveying for sprinkler and drip irrigation system for five soil groups at model scale and at the scale of the MIA irrigated horticulture area .................................................................................................................................... 340

Table 6.10: Water and Energy use for flood irrigation at the model scale for each crop for average climatic conditions ............................................................................................... 345

Table 6.11: Water and Energy use for sprinkler system at the model scale for each crop for average climatic conditions ............................................................................................... 346

Table 6.12: Water and Energy use for drip system at the model scale for each crop for average climatic conditions ............................................................................................... 346

Table 6.13: Total and unit area based water and energy use for sprinkler and drip systems for average climatic conditions for MIA horticultural area ............................................... 353

Table 6.14: Comparison of the two up-scaling methods for water and energy use over 28,970 ha area of MIA ....................................................................................................... 355

Table 7.1: Yield (t/ha) of citrus and wine grapes for various irrigation systems ............... 363

Table 7.2: Node-link model output for a modelled area of 550 ha .................................... 365

Table 7.3: Assumed values of various parameters for economic analysis ......................... 366

Table 7.4: Capital cost for furrow irrigation system (baseline case) ................................. 367

Table 7.5: Capital cost for conversion to low head sprinkler irrigation system ................. 368

Table 7.6: Capital cost for conversion to drip irrigation system ........................................ 368

Table 7.7: Capital costs of pressurized irrigation supply system (Source: MIA per. com.) ........................................................................................................................................... 369

Table 7.8: Values of common cost items for the three irrigation systems ......................... 371

Table 7.9: Annual operating costs per hectare for citrus with furrow irrigation ................ 372

Table 7.10: Annual operating costs per hectare for citrus with low head sprinkler irrigation ........................................................................................................................................... 373

Table 7.11: Annual operating costs per hectare for citrus with surface drip irrigation system ........................................................................................................................................... 374

Table 7.12: Annual financial returns per unit area per for the three irrigation systems growing citrus .................................................................................................................... 376

Table 7.13: Summary of initial and annual costs and annual returns for the three irrigation systems for citrus ............................................................................................................... 377

Table 7.14: Annual operating costs per hectare for wine grapes with furrow irrigation .... 383

Table 7.15: Annual operating costs per hectare for wine grapes with low-head sprinkler irrigation system ................................................................................................................ 384

Table 7.16: Annual operating costs per hectare for wine grapes with surface drip irrigation system ................................................................................................................................ 385

xvi

Table 7.17: Annual financial returns per unit area for the three irrigation systems growing wine grapes ........................................................................................................................ 386

Table 7.18: Summary of initial and annual costs and annual returns for the three irrigation systems for wine grapes ..................................................................................................... 387

Table 7.19: Profitability indicators for the three irrigation systems irrigating wine grapes over the case study area of 550 ha ..................................................................................... 389

Table 7.20: Sensitivity of present values of benefits, costs and B-C ratio to different cost and revenue items for citrus crop (–ve sign shows decrease with respect to original value) ........................................................................................................................................... 390

Table 7.21: Sensitivity of present values of benefits, costs and B-C ratio to different cost and revenue items for wine grapes crop (–ve sign shows decrease with respect to original value) ................................................................................................................................. 391

Table 7.22: Summary of selected profitability indicators for the three irrigation systems 394

Table 8.1: Greenhouse gas emissions cost as percentage of the total annual operational cost per hectare.......................................................................................................................... 411

xvii

Certificate of authorship

CERTIFICATE OF AUTHORSHIP OF THESIS & AGREEMENT FOR THE RETENTION &

USE OF THE THESIS

DOCTORAL AND MASTER BY RESEARCH APPLICANTS

I Aftab Ahmad

Hereby declare that this submission is my own work and that, to the best of my knowledge and belief, it contains no material previously published or written by another person nor material which to a substantial extent has been accepted for the award of any other degree or diploma at Charles Sturt University or any other educational institution, except where due acknowledgment is made in the thesis. Any contribution made to the research by colleagues with whom I have worked at Charles Sturt University or elsewhere during my candidature is fully acknowledged.

I agree that the thesis be accessible for the purpose of study and research in accordance with the normal conditions established by the University Librarian for the care, loan and reproduction of the thesis.*

Signature Date

* Subject to confidentiality provisions as approved by the University

xix

Acknowledgements

First and foremost, I am grateful to Allah, the most beneficial and the most

merciful, for granting me courage and intellect to undertake and complete

this study.

I also tender great respect and profound gratitude to my supervisor Professor

Shahbaz Khan for intelligible guidance, research training, moral support and

never-ending encouragement during the course of this study. His valuable

persistence, technical support, timely guidance and positive reinforcement

during my prolonged study are acknowledged and greatly appreciated.

I should also acknowledge the useful discussions with Associate Professor

John Louis from Charles Sturt University and Dr Kumar Narayan from

CSIRO regarding this PhD research. I am also grateful to my employer

Murray-Darling Basin Authority for granting me study leave to meet my

supervisor and to attend conferences when needed. Particularly, I am

thankful to Mr Andy Close for his continued support in this regard. Also big

thanks to my colleagues Mr Nadeem Samnakay and Dr Tariq Rana for their

support through the course of this study.

I gratefully acknowledge the financial support received from (former) Land

and Water Australia for this research. The success in bringing this thesis

into current form is also attributable to the officials of Murrumbidgee

Irrigation, who provided free access to data and useful field information.

Thanks to my late mother, may her soul rest in peace, for her love and

unreserved prayers for my success. Thanks to my lovely boys Rayyan and

Raed for their patience and my brother, sister, nephews, nieces, brothers-

xx

and-sisters-in-law and their family members for benevolent prayers and

sacrifices to achieve my study objectives.

Honest thanks to all my friends and well-wishers in Australia, New Zealand,

England, Thailand, Nepal, Middle East, USA, Canada and Pakistan who are

keen to see me achieve this milestone.

Finally, I would like to thank the special woman in my life, my wife Fozia,

who in every way has provided me with the inspiration, love and care,

which is vital for the long journey of a PhD.

xxi

Ethics approval

This doctoral research work did not involve any direct communications with the farmers or any other group and therefore ethics approval was not sought.

xxiii

Glossary

The meaning of terms can differ across disciplines. This glossary clarifies

the use of specific terms within the thesis

Channel seepage: Channel seepage can be defined as “loss of water

from a channel via infiltration through micro-pores

and soil processes (i.e. not via preferential flow

through macropores).

Energy efficiency: Energy efficiency of the agricultural production

system can be defined as the ratio of total energy

output from agricultural produce to the total energy

input to engender that produce.

Energy intensity: Measure of the energy consumed by an industry to

produce one unit of economic output

Evapotranspiration: The combined net effect of two processes:

evaporation and transpiration

Fertiliser use (or

recovery) efficiency:

The ratio of the amount of nutrient removed with

the crop to the amount of nutrient applied.

CO2-equivalent

emissions:

A universal measurement of greenhouse gas

emissions; the concentration of CO2 that would

cause the same amount of radiative forcing as a

given mixture of another greenhouse gas. It is

normally expressed in tonnes of CO2.

Net present value: The difference between the present value of cash

inflows (returns) and the present value of cash

outflows (costs) and is widely used for analysing

profitability of long-term projects.

Productivity: The ratio between agricultural output and resource

inputs, e.g. tonnes of product/ML water applied

xxiv

Runoff: Runoff is the movement of water, usually from

precipitation or gravity based irrigation, across the

soil surface towards stream channels, lakes,

depressions, or low points in the soil surface. It

affects the rate of runoff include rainfall duration

and intensity as well as the ground's slope, soil type

and ground cover.

Specific energy: The specific energy of an agricultural production

system is defined as the total energy input per unit

of marketable yield and is expressed as kWh/kg.

Total dynamic

head:

The total dynamic head is the total equivalent

height that a fluid is to be pumped, taking into

account all friction losses in the pipe.

Water intensity: Measure of the water consumed by an industry to

produce one unit of economic output

Water footprint

(WF):

The WF of energy generation is the amount of

water used to produce a unit of energy (m3/GJ).

Watertable: The upper surface of the saturated zone in aquifers

that are not confined by impermeable geologic

material where the water pressure is equal to

atmospheric pressure.

Water use

efficiency:

Water use efficiency is linked with consumptive use

(i.e. evapotranspiration) of water by a given crop. It

is defined as “the ratio between volume of water

consumptively used in evapotranspiration and the

volume of water actually applied.

xxv

Acronyms and abbreviations

ABARE Australian Bureau of Agricultural and Resource Economics ABS Australian Bureau of Statistics ASTM American Society for Testing and Materials B-C ratio Benefit – Cost ratio CIA Coleambally Irrigation Area CoAG Council of Australian Governments CO2-e Carbon dioxide equivalent

CSIRO Commonwealth Scientific and Industrial Research Organisation

DCC&EE Department of Climate Change and Energy Efficiency ET Evapotranspiration ETo Reference Evapotranspiration ETc Crop Evapotranspiration FAO Food and Agriculture Organization GHG Greenhouse Gas GJ Giga joules 109 joules) GL Giga litres (109 litres) ICID International Commission on Irrigation and Drainage KWh Kilowatt-hours MWh Megawatt-hours (1000 KWh) MDB Murray-Darling Basin MDBA Murray-Darling Basin Authority MIA Murrumbidgee Irrigation Area MIL Murray Irrigation Limited ML Mega litres (106 litres) NPB Net Present Benefit NPV Net Present Value NSW New South Wales NVIRP Northern Victoria Irrigation Renewal Project NWC National Water Commission PVC Polyvinyl chloride RAW Readily Available Moisture SILO Specialised Information for Land Owners SD System Dynamics

SEWPaC Sustainability, Environment, Water, population & Communities

SWAGMAN Salt Water and Groundwater MANagement WF Water footprint

xxvi

Research publications and contributions

The following papers were either fully or partly based on analysis of my

collected data, methods and approaches used in this research and developed

water-energy node-link model.

Refereed conference proceedings

1. Ahmad, A., Khan, S., and Louis, J. (2010). Water–energy nexus in

irrigation supply systems using a demand based dynamic node-link

model. In ed. Khan, S., Savenije, H., Demuth, S., Hubert, P. (2010).

Hydrocomplexity: new tools for solving wicked water problems;

proceedings of the Xth Kovacs Colloquium, Paris, 2-3 July 2010;

International Association of Hydrological Sciences (IAHS) Publ. 338;

(2010) ISBN 978-1-907161-11-7, 272 pp.

2. Ahmad, A., and Khan, S. (2009). Comparison of water and energy

productivities in pressurized irrigation systems. Proceedings of

MODSIM, International Congress on Modelling and Simulation, 13-17

July 2009, Cairns, Australia. ISBN: 978-0-9758400-7-8

3. Ahmad, A., Khan, S., and Rana, T. (2007). System dynamics approach

for modelling seasonality of river flows. Proceedings of MODSIM,

International Congress on Modelling and Simulation, 10-13 December

2007, Christchurch, New Zealand.

4. Jackson, T.M., Khan, S., and Ahmad, A. (2007). Exploring energy

productivity for a groundwater dependent irrigated farm using a system

dynamics approach. MODSIM, International Congress on Modelling

and Simulation, 10-13 December 2007, Christchurch, New Zealand.

5. Ahmad, A., and Khan, S. (2008). Systems approach for modelling

dynamics of water and energy inputs in groundwater dependent

xxvii

irrigation areas. West. Pac. Geophys. Meet. Suppl. Trans. AGU,

89(23), 29 July – 1 August 2008, Cairns, Australia.

6. Khan, S., Yufeng, L., and Ahmad, A. (2007). System dynamics

modelling for water savings and conjunctive water management.

ASIMMOD, second international conference on simulation and

modelling 2007, 9 – 11 Jan. 2007, Chang Mai, Thailand.

Journal papers

1. Khan, S., Ahmad, A., Malano, M.H. (2009). Managing irrigation

demand to improve seasonality of river flows. Irrigation and

Drainage Vol 58, Issue 2, pages 157 – 170 April 2009.

2. Khan, S., Hafeez, M., Abbas, A., and Ahmad, A. (2009). Spatial

assessment of water use in an environmentally sensitive wetland.

Ambio Vol. 38, No. 3, May 2009.

3. Khan, S., Mushtaq, S., Ahmad, A., and Hafeez, M. (2008). Tradeoff

analysis for restoring environmental flows through irrigation demand

management. Australian Journal of Water Resources 12(2).

4. Khan, S., Yufeng, L., and Ahmad, A. (2007). Analysing complex

behavior of hydrological systems through a system dynamics

approach. Special Issue, Environmental Modelling and Software.

Elsevier UK. doi:10.1016/j.envsoft.2007.06.006

xxviii

xxix

Abstract

Water and energy are the principal inputs in agricultural production systems.

Efficient use of surface and ground water and energy resources is vital in

terms of productivity and economic competitiveness of agriculture as well

as for environmental sustainability. The need to reduce dependency on

increasingly scarce energy resources, prevent water quality and

environmental deterioration and the opportunity to develop the agricultural

potential for producing high yielding crops, demand an integrated

understanding of hydrologic, economic and environmental dynamics of

high-input farming systems, especially high energy consuming pressurized

irrigation technologies. Analysing the nexus of water and energy dynamics

is a complex scientific and policy issue.

The thesis recognizes the fact that the water saved from on-farm irrigation

efficiency gains and that saved from conveyance losses not only reduces

cost per megalitre to the irrigators; it can also be made available to the

environment and therefore helps achieve both environmental and economic

benefits. Improving water use efficiency by adopting measures and methods

that reduce seepage, evaporation and deep percolation etcetera results in

increased energy consumption. Investments to boost water efficiency and to

improve energy productivity are two possible pathways to reduce the

environmental footprints of crop production. However, these two pathways

may pose conflicting outcomes. This PhD research is primarily aimed at

investigating the both pathways and to recommend a mix of policy options

that are likely to result in optimal outcome. This is also important in the

context of new Murray-Darling Basin Plan which may result in significant

reductions in irrigation diversions for the Murrumbidgee Irrigation Area

(MIA).

This thesis has modelled and analysed water use, energy consumption and

greenhouse gas emissions linkages for two irrigation strategies namely

demand-based and supply-based irrigation in details mainly for three

irrigation methods including furrow, sprinkler and drip irrigation for the

major horticultural crops in the MIA. This study has looked into benefits

and energy implications of using a centralized piped supply system to pump

xxx

pressurized water from source to the individual farms to operate pressurized

irrigation systems i.e. sprinkler and drip irrigation. The study also analysed

the water and energy use for having on-farm private irrigation storages and

compared this option against the centralized piped supply system.

The study found that for drip irrigated citrus under supply-based irrigation

the water use per hectare is as low as 46% of that of demand-based

irrigation but at the same time the yield is found to be as low as 66%.

Similar trend prevails for wine grapes. Although it is evident that demand-

based irrigation produces more yields but at the same time the cost of

energy and its environmental impacts should not be ignored. Demand-based

irrigation involves lesser labour and relies more on technology. This

research addresses the policy question on whether to invest in demand-

based irrigation, especially for horticulture. The water application rate for

flood and furrow irrigated citrus is 12 ML/ha and 10 ML/ha, respectively.

On the other hand it is around 8 ML/ha and 6 ML/ha for sprinkler and drip

irrigation, respectively, representing up to 50% water savings with

conversion from flood to drip irrigation. The corresponding water savings

for wine grapes are as high as 60%. Results show that water productivity of

drip irrigation is 5.7 kg/m3 as compared to just 1.99 kg/m3 for flood irrigated

horticultural crops. Furthermore, the energy productivity of drip irrigation is

4.38 kg/kWh as compared to 3.3 kg/kWh for flood irrigation of horticultural

crops in the case study area.

It is estimated that at 100% adoption (i.e. whole horticultural area of 28,970

ha in MIA is converted) of drip irrigation technology around 137.5 GL of

water would be used every year while around 45,400 MWh of electricity

would be consumed in pumping that irrigation water over the year. For

sprinkler irrigation at 100% adoption level the total water and total energy

use are roughly 30% and 64% higher than that of drip irrigation,

respectively. These results highlight that drip irrigation outperforms

sprinkler irrigation both in terms of water savings and energy consumption

for horticultural crops.

The economic analyses of various options in this thesis indicate that the drip

irrigation system with wine grapes has the lowest payback period of 2 years

xxxi

followed by 3 years for the drip system with citrus. The sprinkler system

and furrow irrigation with citrus have payback periods of 18 years to over

30 years, respectively. The reason for long payback period for furrow

irrigation is the fact that its annual operational costs are higher than the

annual returns. Similarly, the longer payback period for sprinkler system

owes to higher initial capital costs, higher energy costs and relatively lower

annual returns. It is noticed that the profitability indicator; the benefit-cost

ratio, for citrus crop is highly sensitive to the sale price of the yield,

followed by the trade price of water, followed by the labour cost which is

followed by the energy/electricity prices. For wine grapes the benefit-cost

ratio is most sensitive to the sale price of the yield, followed by the labour

cost.

Using a price of $23 per tonne of CO2 equivalent emissions from all energy

inputs, the drip system operated by centralized irrigation supply system for

growing wine grapes has the highest greenhouse gas emissions cost of

roughly 1.8% of the annual operational cost followed by 1.3% for drip

irrigated citrus.

33

Chapter 1: Introduction

1.1 Background and Problem Overview

Ever increasing dependency on fossil fuel inputs is being viewed as a

potential threat to the growth and stability of world food production. On one

side food production practices need to be modernized to meet growing and

pressing food demand; mainly driven by increasing world population and

economic development, while on the other hand the available non-

renewable energy sources to support such practices are limited and finite.

The remaining sources of fossil fuel on earth are either becoming less

productive or situated at inaccessible locations like deep sea offshore

locations or Polar Regions. Moreover the world has started to realize that

increasing consumption of fossil fuels for energy, especially since the

middle of the 20th century, is a major driver to accelerate global warming

and climate change; a phenomenon that is deteriorating our environment.

Particularly in Australia, the energy system faces a number of

environmental issues that need government policy interventions. These

issues include the long-term depletion of national reserves of oil hence

increasing dependence on foreign sources, water scarcity coupled with high

variability, competing demands for water, declining water and air quality

and concerns over the impacts of global warming due to greenhouse gas

emissions (Graham and Williams, 2005).

Water and energy are the chief inputs in agricultural production systems.

Efficient use of the surface and groundwater systems and energy resources

is vital in terms of productivity and competitiveness of agriculture as well as

for environmental sustainability. The need to reduce dependency on

increasingly scarce energy resources, prevent water quality and

environmental deterioration and the opportunity to develop the agricultural

potential for producing high yielding crops, demand an integrated

understanding of the hydrologic, economic and environmental dynamics of

34

high-input farming systems, especially the high energy demanding

pressurized irrigation technologies.

1.2 Setting the Scene: The Context for This Research

Agriculture is itself an energy conversion process, which involves the

conversion of solar energy through photosynthesis to food and fibre for

humans and feed for animals. Prehistoric agriculture involved little more

than scattering seeds on the land and accepting the scant yields that resulted.

With the start of the green revolution, the consumption of both direct and

indirect energy inputs increased. Modern agriculture requires an energy

input at all stages of agricultural production such as direct use of energy in

farm machinery, water management, irrigation, cultivation and harvesting.

Post-harvest energy use includes energy for food processing, storage and in

transport to markets. In addition, there are many indirect or sequestered

energy inputs used in agriculture in the form of mineral fertilizers and

chemical pesticides, insecticides and herbicides (FAO, 2000). Some of these

energy inputs are fixed while others are relatively variable and depend on

other factors. For example, trickle irrigation is used to save irrigation water

but at the same time it consumes more energy to pump and pressurize the

water for effective delivery. Energy input in irrigated agriculture is directly

related to the irrigation technology adopted and the level of production

(Hatirli et al., 2006). Agricultural modernization which requires increasing

amounts of energy inputs is, at the same time, essential to providing enough

food for growing populations (Stout, 1990) and changing preferences.

Efficient irrigation practices are important means for boosting crop

productivity; however the benefits of improved yields may be at the cost of

increased water and energy inputs and associated environmental impacts.

Modern agricultural practices have a significant environmental footprint, as

a result of expansion in cropland at the expense of native forests, grasslands,

and ecotones (Pimental et al., 2004). Crop intensification through high

inputs of water, energy and macro nutrients has been articulated as the way

forward, especially in land scarce regions, but this has profound

35

implications for global water and energy budgets (Khan and Hanjra, 2009).

At the same time one must keep in mind that irrigation is a strong driver of

economic activity. For example, in the Murray-Darling Basin (MDB)

irrigation has an economic multiplier of 3.5, indicating that for every $1,000

of farm gate revenue generated there is an additional $3,500 of dependent

economic activity (Meyer, 2005).

Australia is a naturally drought prone country. Despite relatively high

rainfalls in far north, south and the east coast of the country it is the driest

continent on earth after Antarctica. The prevalence of persistent and intense

droughts could be exacerbated by climate change in southern Australia

(CSIRO, 2012). Current and projected water scarcity in the mid to long term

has urged countries including Australia to adopt water saving policies across

all sectors including irrigated agriculture. Modern agricultural production is

characterized by the heavy use of fertilizers, pesticides, and labour-saving

and high power consuming machines. Modern production practices

including increasing inputs of agrochemicals, hi-tech irrigation and the

growth of more productive cultivars have led to significant increase in crop

yields. However, these practices have led to a dramatic increase in the input

of fossil energy (Hülsbergen et al., 2001), which has raised many concerns

over sustainable use of energy resources. Pimentel et al. (1973) envisaged

that dependency on fossil-fuel inputs will be a potential threat to the growth

and stability of world food production. Apart from on-farm water losses due

to low irrigation efficiency, a large proportion of water diverted for

irrigation is lost through channel seepage and evaporation in Australia.

According to ANCID (2000) there is more than 17,750 km of earthen

irrigation channels in Australia. For a rough estimate of only the seepage

loss from irrigation distribution systems in Australia with a seepage loss rate

of 5 mm/day to 108 mm/day (Khan et. al., 2005a) and assuming an average

channel width of 2 m and average irrigation days of 300 per year results in a

seepage loss of about 53 GL/year to 1,150 GL/year. Assuming the market

price of water being $25/ML, the water lost in seepage from earthen

channels can be translated into losses of over $28 million per year of water.

36

Realizing the need to lift water use efficiency nationally, the Australian

Federal Government launched $12.9 billion Water for the Future program

(SEWPaC website). This program includes the provision of grants for

seeking independent professional expertise and assistance with system

modernization planning including asset refurbishment and water saving

infrastructure. However, a critique on this program could be its over

emphasis on water use efficiency and lack of emphasis on improving energy

use efficiency and undue greenhouse consequences. Given that our water

resources are fully and in some places over-allocated, the only way to

ensure that we have enough water for irrigation development is to use the

water we have more efficiently at both farm and catchment scales. Water

can be saved through better management of its delivery and application

(Khan et al., 2004; Khan et al., 2005b). The Northern Victoria Irrigation

Renewal Project (NVIRP) is a A$2 billion initiative by the Victorian

Government with an objective to modernize and upgrade ageing irrigation

delivery infrastructure in Northern Victoria to achieve real water savings

(NVIRP website). According to ANCID (2000) Murrumbidgee Irrigation

Limited, which is the study area of this research, has third largest irrigation

channel network in Australian with a total of 2,000 km of earthen channels

for irrigation supply after Murray Irrigation Limited (3,800 km) and

Goulburn-Murray Water (6,952 km). The seepage loss from earthen

channels in Murrumbidgee Irrigation Area can be as high as 108 mm/day

per unit of the wetted area (Khan et al., 2004; Khan et al., 2005a; Khan et

al., 2009b) during the irrigation season.

Changed seasonality of flow is a major river health issue in many Australian

rivers, particularly in the Southern Connected System in the MDB. In the

Lower Murray and the Murrumbidgee rivers of the Southern Connected

System the seasonality of flow has been modified with the dominant flow

period shifting from late winter / early spring to summer-dominated

irrigation releases (Khan et. al., 2009a; NWC, 2012). In those rivers the high

flood flows that connect wetlands to the river have attenuated due to river

regulation to store water for summer crops and thus deteriorating

37

environmental assets. In fact, the latest water stress assessment report

released by National Water Commission has identified Murrumbidgee river

as one of the ten most water stressed rivers in Australia (NWC, 2012) where

there is over-allocation of water, changed flow regime and high risk of

compromising environmental assets, ecosystem functions or the long term

sustainability of the resource. A report by Independent Sustainable Rivers

Audit Group on assessment of condition and ecosystem health ranked

Murrumbidgee valley last on the ecosystem health scale (Davies et al.,

2008). The ‘balancing act’ between crop production and environmental

sustainability involves boosting water productivity (Molden et al., 2007) and

energy productivity (de Fraiture et al., 2007) through a range of measures.

For example, Cummins (1998) ranked horticulture second after rice, almost

a decade ago, for potential water savings of up to 150 GL through adoption

of irrigation technology in the Murray-Darling Basin. The energy required

for installation and operation of so-called hi-tech water efficient irrigation

systems like drip irrigation is significantly higher than traditional systems

like furrow irrigation and as a whole the embodied associated greenhouse

gas emissions are potentially singificant. Although internal and external

environmental and economic benefits increase with improvement in

irrigation efficiency (Beare and Heaney, 2001), a balanced use of water and

energy resources is vital in terms of productivity of agriculture as well as for

environmental sustainability. It is hypothesized in this thesis that unless

energy requirement aspects are not considered, the improvement in

irrigation efficiency is a partial solution for minimizing the environmental

footprint of the consumptive use of water. Irrigation conveyance losses can

be caused by evaporation, seepage, leakage and operational losses but by far

the greatest losses are to seepage (Meyer, 2005). Such losses may fluctuate

with seasonal climatic conditions and diversion volume and can be

eliminated by replacement with piped system.

The water saved from irrigation efficiency gains and that saved from

conveyance losses not only reduces cost per megalitre to the irrigators; it

38

can also be made available to the environment thus helping achieve both

environmental and economic benefits.

Policy makers in water and energy sectors generally do not integrate two-

way linkages between the two sectors and hence fail to consider the issues

and wider implications of standalone initiatives by individual portfolios.

Consequently, some policy reforms address only one side of the water-

energy nexus. Against the backdrop of recent drought, climate change

issues, and declining fossil fuel resources, the non-holistic reforms have

focused on either energy efficiency to minimize water withdrawals for

energy production or on water use efficiency of bio-fuel crops or that of

other water dependent systems like irrigation or domestic water use

(DEWHA website). In the water-energy nexus debate, the missing link,

which is also generally missing from policy reforms, is that while improving

energy efficiency will result in water savings from reduced water use by the

energy production industry, the same does not apply to water use efficiency.

On the contrary, improving water use efficiency by reducing seepage,

evaporation, and deep percolation etcetera requires increased energy input.

A feedback loop diagram in Figure 1.1 further depicts this policy dilemma.

It shows that reduction in energy use through increase in energy efficiency

results in decrease in water use by energy generation systems, thus a

positive feedback (represented by ‘+’ sign), while on the contrary, the

reduction in water use through increase in water use efficiency demands

more energy consumption by water use systems, thus a negative feedback

(represented by ‘-’ sign). Sorting out the nexus of water and energy

dynamics is not a simple task. This research is primarily focused on

exploring the second feedback, i.e. the water and energy use in water

systems, of the water and energy nexus.

39

Figure 1.1: Water and energy efficiency feedback loop diagram

The dependency of the water and energy nexus suggests that the

management of the two resources should be integrated under the same

portfolio.

Investments to boost water productivity and improve energy use efficiency

in crop production operations are the two possible pathways to reduce the

environmental footprints as stressed by Khan and Hanjra (2009). However,

these two pathways may pose conflicting outcomes. For example,

conversion from flood irrigation to drip system may improve water

productivity but at the expense of relatively deteriorated energy efficiency.

This PhD research is primarily aimed at investigating both pathways and to

recommend a mix of options that are likely to result in an optimal outcome.

1.3 Research Objectives

Against the backdrop discussed above, this research provides a framework

for accounting for major energy inputs, associated greenhouse emissions

and potential water savings by using a case study area comprising of three

horticultural crops; citrus, stone fruit and wine grapes, four irrigation

application technologies; flood, furrow, low head fixed sprinklers and drip,

two kinds of irrigation supply systems; the open channel and the pressurized

pipes, and finally two irrigation management strategies; demand-based and

supply-based irrigation, in the Murrumbidgee Irrigation Area (MIA).

However, other than accounting for greenhouse gas emissions from direct

and indirect energy inputs, this research does not examine the CO2

mitigation function that agriculture provides in the forms of carbon storage

-

+

Energy use Water use

Energy efficiency

Water use efficiency

40

in forestry/trees or carbon sequestration in soil. Since the irrigated area in

MIA is dominated by horticulture and that water use and energy use are two

major factors in horticultural production, this thesis is focussed only on

horticulture in MIA. Since, greenhouse gas emissions are linked to energy

use and involve tax bearing; they are also part of the analyses in this

research.

The chief rationale of this research is to find scientific and economic

evidence to support the paradigm shift in large scale irrigated systems from

traditional approach of maximizing water use efficiency to a more integrated

approach to link water use efficiency with energy consumption and

associated environmental consequences. The key research question is how

can a holistic and system-wide approach be applied to investigate water-

energy-greenhouse gas relationships in water and energy intensive irrigated

systems. The proposed research aims to enable policy makers and irrigators

to understand how conversion to hi-tech irrigation and other water saving

infrastructure can help reduce the environmental footprint of irrigation by

finding the balanced nexus between energy consumption, water use and

impacts on aquifers and rivers for different irrigation systems. The major

objectives of this research thesis are outlined below:

1. To synthesise knowledge and future challenges related to energy and

water use efficiency in large irrigation areas.

2. To quantify spatio-temporal trends in energy and water use

efficiency in a major irrigation area using a node-link model.

3. To develop a hydrologic-economic dynamic system framework for

testing the economic viability and for minimising the environmental

footprint of farming operations by exploring system-wide linkages

among water use efficiency and associated costs, irrigation

management strategies, energy-yield relationships, energy

consumption and associated greenhouse gas emissions.

41

The study addresses the following key knowledge gaps and research

questions for improved and environmentally responsible irrigation

management:

1. What is the relationship between various water saving measures and

energy consumption for different irrigation systems in large

irrigation regions like Murrumbidgee Irrigation Area?

2. How do so called irrigation system upgrades affect surface-

groundwater interactions and energy consumption?

3. What is the economically and environmentally optimum

combination of water saving methods/technologies, energy

consumption and associated environmental impacts at various levels

of technology adoption (referred to as irrigation conversion)?

To achieve the objectives and answer the questions mentioned above, this

thesis is structured as follows. Chapter 2 provides a detailed analysis of the

current knowledge and knowledge gaps. It identifies the missing links and

justifies the need for the research carried out in this thesis. Chapter 3

provides the details of the case study area, analysis of key available data and

develops the overall methodology of this research. It also provides the

technical basis for this research work and explains various modules and

governing equations of the developed node-link model for simulation of

irrigation water use and pumping energy consumption at daily time step. It

outlines the procedure for estimation of various energy inputs and

quantification of associated greenhouse gas emissions. Chapter 4

investigates the water-energy-environment nexus for the demand-based

irrigation strategy for flood, furrow, sprinkler and drip irrigation using the

node-link model developed in Chapter 3. The model is capable of modelling

both open channel and pressurised pipe irrigation supply systems. Chapter 5

deals with similar issues as Chapter 4 but for supply-based irrigation

strategy. It also includes water-energy analysis of on-farm water storages

versus pumping from centralised irrigation water sources. Chapter 6 is

focused on up-scaling the water-energy use from the case study scale to the

42

entire horticultural area of Murrumbidgee Irrigation Area. In Chapter 7 the

detailed economic analysis of irrigation conversion from furrow to sprinkler

or drip is carried out. It tests the economic viability of the irrigation

conversion over the long-term. Chapter 8 synthesises what is learnt from

analyses in previous chapters by taking a system-wide holistic approach.

During this process various feedback loops among inter-dependent variables

are explored. It also briefly discusses the key findings of this research and

possible policy implications. Chapter 9 reiterates the main conclusions of

this work and any recommendations for further work on this topic.

1.4 Research Scope and Limitations

The scope of this research is from a case study area of a few hundred

hectares to the entire horticultural area of over 28,000 hectares in the

Murrumbidgee Irrigation Area. The research was limited to the major

horticultural crops produced in the area. The research relied on data mainly

collected from publically available sources and reports, past projects, and

through personal communications with the local irrigation company.

Although, it looked only at MIA, the research methodology and the

modelling framework can be applied to any irrigation area to explore the

water-energy-environment nexus.

43

Chapter 2: Literature Review

2.1 Introduction

Water has the utmost importance in the life and growth of a plant. A mature

fruit consists of about 85% water, so a 200 kg crop on a tree contains around

170 kg (170 litres) of pure water. To grow this crop the tree would require a

water supply over the growing season of around 17,000 litres. In other

words about 1% of the water a plant uses is retained in the fruit, and less

than 0.5% in the remaining parts of the tree, the leaves, shoots and roots.

Water regulates plant functions including photosynthesis, stomata

movement, and nutrients uptake etcetera (Goodwin 2000). This signifies

how vital the role of water is in plant growth.

Useable energy is a finite resource on earth and most common and easily

accessible sources of energy are depleting very quickly. During the energy

utilisation process which essentially represents the conversion of energy

from one form to the other, a part of the energy is lost to unintended or non-

usable form. Hence energy input is always greater than energy output for

any energy conversion system. This discrepancy between input and output

energy for a given system leads to the term “energy efficiency” which is

essentially a ratio between energy output and energy input. Cropping

systems convert solar energy and other energy inputs to energy output in the

form of food and other biomass. Like any isolated system, cropping systems

also follow the law of conservation of energy which states that energy

cannot be created or destroyed. Solar energy, a major input, is normally

ignored while computing the energy multiplier for cropping systems which,

shows that energy output from cropping systems is usually equivalent to

several multiples of the input energy. Hence, cropping systems are in a

sense “multipliers” of energy however, there is still a vast scope to improve

system efficiency particularly at the input side of the crop production

process. The system efficiency encompasses mainly water use, energy use

and system output (yield, other biomass etc.).

44

Australia is a water scarce country, where water resources are fully

developed, particularly so in southern systems and financial and storage

capacities are relatively unconstrained, enhancing the efficiency of available

water through structural measures (such as more efficient delivery systems

and on-farm irrigation and production technologies) which hold a key to

managing water scarcity issues. Other options may include non-structural

policy measures, such as water trading to promote the transfer of water to

higher value uses; however, this may lead to water allocations moving to

environmentally undesirable locations with greater buying power but

unsuitable hydrogeological settings in the long run. A detailed assessment of

social, economic and environmental impacts of water trading in Southern

Murray-Darling Basin is carried out by the NWC (2010). Significant gains

in on-farm water use efficiency and water productivity are possible through

appropriate interventions. These gains are often assumed rather than

identified at various spatial scales and across reaches within an irrigation

system. Without proper water accounting for the whole irrigation system,

misguided investments to ‘save’ water can reduce return flows and can be

detrimental to the environment and to other users (Khan et al., 2010).

Furthermore, poorly conceived reforms may also impact upon water and

energy productivity.

The overall aim of this chapter is to critically analyse existing methods and

approaches related to water and energy use in irrigated agriculture,

particularly in horticulture and to identify missing links between water and

energy use efficiency. Such links may be characterised as dynamic

relationships which are governed by underlying feedback mechanisms and

become more conspicuous by adopting a system-wide approach. This

research has strong emphasis on holistic approaches otherwise called

systems thinking (Checkland, 1981; Sterman, 2000; Forrester, 1995; Gerald,

2001; Meadows, 2008).

2.1.1 Irrigation in Australia

45

Irrigation development in Australia dates back to the late 1880s. The

Mildura Irrigation Colony was the first scheme established on the Victorian

side of the Murray River in 1887 (Proust, 2003). Pseudo irrigation schemes

were initiated in the 1890s in New South Wales. The Murrumbidgee

Irrigation Scheme was the first intensive irrigation project in Australia. The

scheme officially opened in June 1912 when water was first made available

in the Yanco irrigation area (Blackmore, 1995). Inexperienced farmers

tended to over-irrigate crops. Rising watertables and waterlogging became

evident by 1914, prompting the first government inquiry into the scheme.

The issue covered a wide area by the 1920s, and the first signs of surface

salinity appeared in 1931 (Proust, 2003). Today irrigated agriculture covers

about 2.33 million hectares (Mha) and contributes just over a quarter of the

value of agricultural production in Australia or about $9.6 billion per annum

(Khan et al., 2006).

Days of abundant and over allocated water resources are gone in Australia.

There has been a continuing decline in water allocations during the last 15

years due to a limit (cap) on maximum water use introduced in 1995 in the

Murray-Darling Basin and a prolonged drought (Khan, 2006). The

Murrumbidgee Irrigation Area (MIA) and Coleambally Irrigation Area

(CIA) have not been immune to this decline, with general security

allocations reducing from 100% to just 8% in 2006–07 (Table 2.1). This

reduction is attributed to many factors including: policy reform in water

allocation; climate change; climate shift, decreased catchment runoff etc. It

is generally accepted that there will be less water available for irrigated

agriculture in future, and the only way to provide enough water for

irrigation will be to use the available resources more efficiently at both farm

and irrigation system level (Khan and Abbas, 2007). However, this

improvement in water use efficiency comes at the greater expense from

increased energy use.

46

Table 2.1: Final (end-of-water-year i.e. June) percentage general security irrigation allocations for Murrumbidgee valley

Year 91/92

92/93

93/94

94/95

95/96

96/97

97/98

98/99

99/00

00/01

01/02

02/03

03/04

04/05

05/06

06/07

Allocation (%)

120 120 120 100 105 100 90 85 78 90 72 40 38 45 55 8

2.2 Exploring Energy and Water Nexus

Energy resources are an essential constituent of global economic growth and

development (Goldemberg, 1995). Energy is the lifeblood of technology and

development at any scale. Yet no primary energy source, be it renewable or

non-renewable, is free of economic or environmental consequences (Chow

et al., 2003). Energy consumption has implications for economic growth;

the local, national, and global environment, and even for global peace and

security (Khan and Hanjra, 2009).

The Modi et al., (2007) reports that on-farm crop production consumes

about 2–5% of the total annual commercial energy in almost all countries,

irrespective of their level of development. Agricultural operations make a

fairly small contribution to the overall energy use. For instance, the use of

farm machinery, irrigation, fertilization and chemical pesticides amounts to

merely 3.9% of the commercial energy use. Of this, 70% is associated with

the production and use of chemical fertilizers (Vlek et al., 2004). Moreover,

energy inputs in agriculture have increased disproportionately over time

when compared to crop yields. Globally, agriculture is one of the five major

energy dependent sectors. According to ABS (2011), in Australia, total

energy consumption by seven major industries was around 2,824 PJ during

the year 2009-10. The total energy intensity (measure of the energy

consumed by an industry to produce one unit of economic output) of the

seven industries was 2,613 GJ/M$ IGVA (Industry Gross Value Added).

The energy use by agriculture was 4% of total energy use and the energy

intensity of agriculture was 12% of the total energy intensity during 2009-10

as shown in Figure 2.1.

47

Figure 2.1: Energy use and energy intensity by each sector in Australia in 2009-10 (Source: ABS, 2011)

Stabilizing the carbon dioxide induced component of anthropogenic climate

change is an energy problem (Raupach et al., 2007) and a major pathway to

reducing the environmental footprint of energy use. From an agricultural

standpoint this includes optimal use of fertilizer energy; soil carbon

sequestration projects; and biofuel crops. Williams (2007) reports that

Australian farmers have made some headway towards carbon sequestration

(also referred to as carbon farming). A conservative estimate by Williams

(2007) suggests that farmers could earn $25 per tonne for carbon dioxide

stored in soil, plants and trees, native vegetation and sustainable cropping

techniques. More recently, the Commonwealth of Australia (2011) has

imposed a tax of $23 per tonne of CO2-e emissions effective from July 2012

in efforts to reduce greenhouse gas emissions.

Australian annual mean rainfall was 503 mm in 2009–10, a 4% decrease

from the 522 mm reported in 2008–09 and a total accounted water use of

13,476 GL (ABS, 2012). Water use by agriculture (industry view) is highest

(52%) among all sectors in Australia as shown in Figure 2.2. The water

intensity i.e. the industry gross value added (IGVA) of agriculture industry

is M$ 3 per GL of water use while that of electricity and gas production is

M$ 64 per GL. Hence, water use for energy (electricity and gas) production

has incomparably high economic return.

4%

19%

37%

1%

5%

19%

15%

Energy use

Agriculture Mining Manufacturing Water supply and waste servicesConstruction Transport Commercial and services

12%

18%

30%

7%

5%

26%

2%Energy intensity

Agriculture Mining Manufacturing Water supply and waste servicesConstruction Transport Commercial and services

48

Figure 2.2: Water use by each sector in Australia (Source: ABS, 2012)

Table 2.2 shows that sheep, beef cattle and grain growing industry

consumes a major share of water use in agriculture followed by irrigation

for crop growing and dairy farming. Horticulture (fruit and tree nut

growing) is the fourth largest user of water (16% of total use) in agriculture

and is the major focus of this thesis.

Table 2.2: Distribution of water use (ML/year) by each industry under agriculture in Australia during 2009-10 (Source: ABS, 2012)

Agriculture industry Water use (ML) % Total

Nursery and floriculture production 60,555 0.9

Mushroom and vegetable growing 439,059 6.3

Fruit and tree nut growing 1,115,883 16.0

Sheep, beef cattle and grain farming 2,648,630 37.9

Other crop growing 1,409,189 20.2

Dairy cattle farming 1,215,678 17.4

Poultry farming 16,644 0.2

Deer farming 574 0.0

Other livestock farming 81,122 1.2

Total 6,987,334 100.0

52%

1%

4%5%2%

14%

8%

14%

Water use

Agriculture

Forestry and fishing

Mining

Manufacturing

Electricity & gas supply

Water supply

Other

Household

49

Researchers have so far emphasized the energy and environmental footprint

of groundwater pumping for irrigation, as evident from studies by Lal

(2004), Chandrakanth and Arun (1997), Scot and Shah (2004), Kumar

(2005), Jackson (2009) and Ahlfeld and Laverty (2011). The carbon

footprint of energy input in groundwater irrigation is substantial, but

government policies have supported groundwater use to enhance food

security while the negative externalities associated with the pumping have

often been ignored (Khan and Hanjra, 2009). This thesis focuses on surface

water use and pumping energy relationships as leaky irrigation supply

channels are being replaced by pipes and gravity based irrigation practices

are replaced by pressurized irrigation systems in Australia.

Sorting out the nexus of water and energy dynamics is not a simple task. In

the water-energy nexus debate, the missing link is that while improving

energy efficiency will result in water savings from reduced water use by

energy production systems, the same does not apply to water use efficiency.

On the contrary, improving water use efficiency by reducing seepage,

evaporation, deep percolation etcetera results in increased energy input.

Modern civilization is heavily dependent on energy from sources such as

coal, petroleum, and natural gas. Yet, despite energy’s many benefits, most

of which are reflected in energy market prices, the production, distribution,

and use of energy also causes negative effects. Beneficial or negative effects

that are not reflected in energy market prices are termed “external effects”

(NAS 2010). These external effects also include non-climatic damage which

occurs, for example, in the form of costs to treat diseases caused by air

pollutants, mainly sulphur oxides, nitrogen oxides, particulate matter, and

other products of fossil fuel combustion, costs of reduced grain harvests and

timber yield, and damage to buildings. The NAS (2010) report estimated

that in 2005 the total external damages from electricity production by

burning coal amounted to about US$62 billion in USA alone. The key to

minimise these negative impacts is to use less energy and the key to reduce

energy use is to improve energy use efficiency in each energy use system.

50

Irrigated agriculture contributes more than one third of the food supply to

the world population and it will have to continue to play a critical role in the

coming century. Irrigated areas are the major food basket of the world,

producing 40% of the world’s food from just 18% of the global cropland

(Khan and Hanjra, 2008). Although the total irrigated area of the world is

increasing, per capita availability of irrigated area is dwindling due to rapid

population growth. Many irrigation projects built in the past are no longer

irrigating their command area as originally envisaged. Construction costs of

new irrigation schemes are increasing. Thus, improvement of productivity

both, per unit of land and per unit of water are becoming equally important.

Rehabilitation and/ or modernization of irrigation projects have been

considered as one of the alternative to achieve the aforementioned twin

objectives (Price, 1999).

This thesis brings in the third and relatively less explored dimension which

is to investigate the significance and interplay between energy efficiency

and water use efficiency in the irrigation modernisation process.

2.2.1 Water and Energy Indicators

Water and energy efficiency and productivity indicators are the

mathematical representation of water and energy nexus in the context of

agricultural production. These indicators include but are not limited to water

use efficiency, energy efficiency, energy productivity, water productivity,

specific energy and, water-energy ratio etc. These indicators depict

relationships among water and energy inputs, energy outputs, yield etc for a

given system. Some of the well-known indicators are defined in Chapter 3

and are explored and used detail in Khan et al., (2009a); Koctürk and

Engindeniz, (2009), Pereira (2006), Pereira (2007) and others. These

indicators are computed for each scenario considered in this study where

applicable to capture the water and energy footprints. This study relies

heavily on these indicators as the most useful and valid tools to test the

effectiveness of improving water and energy systems.

2.2.2 Water Footprints of Energy Production/Use

51

It is now a well-established fact that water is closely coupled to energy

production and use. For example, we depend on electricity right from

lighting a house to manufacturing a car and electricity generation involves

water. According to US Geological Survey (Kenny et al., 2009), 49% of

water withdrawn from US water sources in 2005 was used in thermoelectric

generation (fuelled by coal, oil, natural gas or uranium), albeit a large part of

this is recovered with degraded quality and higher temperatures, followed

by irrigation which utilises about 31% of water withdrawn. In Australia,

thermal power plants, primarily coal-fired power stations, are responsible

for around 1.4 per cent of total water consumption (ABS, 2006).

Energy in the form of electricity, diesel or gas is used to pump irrigation

water from groundwater sources, to deliver water through pipes and to

operate pressure irrigation equipment like sprinklers. Hence water use in

irrigation has an energy footprint and likewise energy generation has a water

footprint (Smart and Aspinall, 2009). The term water footprint (WF) was

introduced by Hoekstra and Hung (2002) and has been developed further by

Chapagain and Hoekstra (2004). They established that WF of an individual

or a country consists of the total volume of water used (m3 per year),

directly or indirectly, to produce goods and services consumed by the

individual or the country. The WF of energy generation can be defined as

the amount of water used to produce a unit of energy (m3/GJ). The global

average WF of primary energy carriers is given in Table 2.3. Processes that

make these primary energy carriers available, almost always require water

in varying amounts. The water footprint of crude oil is highest among the

fossil fuels due to a large quantity of water use in exploration and

refinement. Renewable energy carriers show large differences in their WF.

The WF for wind energy is negligible, for solar thermal energy 0.30 m3/GJ,

but for hydropower 22.3 m3/GJ. For biomass, the WF depends on crop type,

agricultural production system and climate (Gerbens-Leenes, et al., 2008).

The water footprint of electricity generation in Australia by different fuel

types as calculated from ABS (2006) is given in Table 2.4 for the year 2004-

52

05. The proportion of water footprint of hydro-electricity generation is

obviously significant.

Table 2.3: Global average water footprint of primary energy carriers (Gerbens-Leenes, et al., 2008)

The energy/carbon footprint of water use can be defined as the amount of

energy (normally electricity or fossil fuel) consumed and greenhouse gas

emissions produced in moving and/or pressurising water for purposes like

irrigation, desalination etcetera. Here, water use refers to water use by water

related industries. The water footprint of energy generation/utilisation has

been well researched and quantified (Winnie et al., 2008; Chapagain and

Hoekstra, 2004; Gleick, 1993; Gleick, 1994; Kenny et al., 2009; NAS 2010;

Marsh, 2008). However, energy/carbon footprint of water use, especially in

irrigated agriculture, has not been explored to such a deep extent. Some of

the studies related to energy/carbon footprint of water include; Bevan and

Wendy (2009), Pimentel (1991, 1992, 1998), Pimentel and Heichel (1991),

and Jackson (2009).

Primary energy carriers Global average water footprint (m3/GJ)

Non-renewable

Natural gas 0.11

Coal 0.16

Crude oil 1.06

Uranium 0.09

Renewable

Wind energy 0.00

Solar thermal energy 0.27

Hydropower 22

Biomass energy 70 (range: 10-250)

53

Table 2.4: Water footprint of electricity generation in Australia in 2004‐05 (adapted from ABS, 2006)

Fuel Water use

(ML) Electricity

generated (GWh) Water footprint

(ML/GWh)

Hydro electricity

59 867 227 15 991 3743.81

Black coal 153 021 102 180 1.50

Brown coal 81 887 54 041 1.52

Gas 11 606 20 786 0.56

Other 810 1473 0.55

Total 60 114 551 194 471

It is worth noting that the figures mentioned in Table 2.3 and Table 2.4 refer

to water use rather than water consumption. Only a minor portion of the

water diverted for thermoelectric plants is consumed in cooling system of

the plants and the rest is returned back to the source. Evaporative water loss

from the reservoir surfaces accounts as water being consumed for electrical

production in hydroelectric plants. In thermoelectric plants 1.8 litres of fresh

water is evaporated per kWh of electricity consumed at the point of end use,

whereas the hydroelectric plants evaporate an average of 68 litres of fresh

water per kWh used by the consumer in USA. The national weighted

average for thermoelectric and hydroelectric water use is 7.6 litres of

evaporated water per kWh of electricity consumed at the point of end use

(Torcellini, et al., 2003). In Australia in 2004-05, the total water

consumption by electricity generators amounted to 271.035 GL to produce a

total of 194471 GWh thus consuming 1.4 litres of water per kWh of

electricity at its point of use on average (ABS, 2006). The water

consumption per unit of electricity use is significantly lower for Australia

than that of USA. It is due to the fact that the majority of electricity

generated in Australia is by coal (thermal power plants) and that the water

54

consumption for thermal plants is significantly lower than hydroelectric

plants.

2.2.3 Environmental Footprints of Crop Production

Water and energy are essential inputs for the crop production process.

Continued and disproportional water use may result in land salinisation and

water logging. Similarly, energy consumption in crop production processes

contributes to greenhouse gas emissions. Hence, water and energy use for

crop production has environmental footprints including water logging, land

salinisation, saline return flows from irrigated areas to receiving

streams/rivers and greenhouse gas emissions which cause global warming

(IPCC, 2007). The environmental footprints associated with increased water

use for food production are often not taken in account partly because the

links between crop production processes and the environment are poorly

understood; agricultural water input often does not reflect the full

opportunity cost of water use to society and the environment (Khan and

Hanjra, 2009).

Crop intensification through high inputs of water, energy and macro

nutrients has been articulated as the way forward, especially in land scarce

regions, but this has profound implications for global water and energy

cycles. For instance, in many of the world’s most important crop producing

regions (Brazil, China, India, Iran, Pakistan, and Western Europe) the

historical sources of growth in agricultural productivity are being rapidly

exhausted, yield growth is stagnating or decelerating, and a significant share

of irrigated land is now jeopardized by scarce water resources, groundwater

depletion, a fertility-sapping build up of salts in the soil, or some

combination of these factors (Khan et al., 2008; Postel, 2000). These factors

have adverse impacts on land and water quality and thus worsen their

environmental footprint. Although modern agriculture has increased food

production manifolds by making use of high yielding varieties, fertilizers,

pesticides, water, and increased energy input due to agricultural

mechanization; it has also caused extensive environmental damage. Khan

55

and Hanjra (2009) have collated the key environmental footprints of

agricultural production and Khan et al., (2009a) analysed feasible pathways

to reduce environmental footprints of water and energy use in crop

production. The most prominent environmental footprints relevant to this

current study are discussed below:

2.2.3.1 Greenhouse gas (GHG) Emissions from energy use

In addition to energy use in irrigation pumping, energy is also consumed

directly or indirectly in many other farming operations to grow crops

including cultivation, labour, agro-chemicals, pruning, fertigation,

harvesting etcetera. Greenhouse gas (GHG) emissions occur due to use of

fossil fuel energy in irrigation pumping and other mechanical processes in

agriculture. The GHG emissions accelerate global warming which

contributes to extreme hydrological events such as storms, droughts and

floods (Illangasekare et al., 2006). Hence, energy use efficiency and GHG

emissions linkages in agriculture should be investigated. In Australia, the

agricultural sector is the second highest GHG emissions producer after

electricity and gas generation. Agriculture emitted 107.1 Mt CO2-e direct

and indirect GHG emissions during 2009-10 (DCC&EE, 2012).

2.2.3.2 Impacts on Freshwater Ecosystems

Changed seasonality of flow is a major issue in many Australian rivers,

particularly in the Southern Connected System in the MDB. In the Lower

Murray and the Murrumbidgee rivers of the Southern Connected System the

seasonality of flow has been modified with the dominant flow period

shifting from late winter / early spring to summer-dominated irrigation

releases (Khan and Hanjra, 2009; NWC, 2012). In those rivers the high

flood flows that connect wetlands to the river have attenuated due to river

regulation to store water for summer crops and thus deteriorating

environmental assets. In fact, the latest water stress assessment report

released by the National Water Commission has identified the

Murrumbidgee river as one of the ten most water stressed rivers in Australia

(NWC, 2012) where there is over-allocation of water, changed flow regime

56

and a high risk of compromising environmental assets, ecosystem functions

or the long term sustainability of the resource. This highlights the need for a

‘balancing act’ between crop production and environmental sustainability

which includes boosting water productivity (Molden et al., 2007) and

energy productivity (de Fraiture et al., 2007) through a range of measures.

Loss of freshwater fauna populations and habitat for native fish, plummeting

population of aquatic birds due to inadequate water flows, and loss of

riverine biodiversity due to changed seasonality of river flows caused by

river flow regulation and over extraction is of concern. For example, the

Murrumbidgee River caters for irrigation demand from over 200,000 ha of

crop area by storing water in upstream reservoirs. As a result the seasonality

of the end of system flows at Balranald is completely altered from natural

flows as shown in Figure 2.3. It is reported in Frazier and Page (2006) that

the significantly reduced river flows reduce the area of floodplain wetland

inundation by 40% in the Murrumbidgee catchment. Therefore, different

approaches and options to save irrigation water by improving water use

efficiency and returning more water back to the river should be investigated.

Realizing the need to lift water use efficiency, the Australian Federal

Government launched the $12.9 billion Water for the Future program

(DEWHA website). A component of this program provides grants for

seeking independent technical expertise and assistance with system

modernization including asset refurbishment and water saving

infrastructure. However, this program puts too much emphasis on water use

efficiency and lacks focus on improving energy use efficiency and reduction

of greenhouse consequences.

2.2.3.3 Watertable Response and Soil Productivity

Global water use efficiencies are low – typical irrigation system efficiencies

are reported at 40 - 50% - and vary widely across regional delivery systems.

Khan et al., (2008) reported that one of the major factors for inefficient use

of water is the individual planning horizons that are generally shorter than

socially optimal perspectives, such as net aquifer recharge due to excessive

57

irrigation in rice paddies inducing waterlogging and salinity in addition to

inefficient use of water resources in general. Salinity and waterlogging

reduce crop yields and degrade the productivity of agricultural land in many

irrigated settings (Conyers et al., 2008; Khan and Hanjra, 2008; Khan et al.,

2008; Wichelns, 2005; Wichelns et al., 2002).

Figure 2.3: Natural and regulated average monthly flows in Murrumbidgee River recorded at Balranald station before it joins the Murray River

For mature irrigation areas with a deep watertable, a portion of deep

percolation, called the leaching fraction, is needed to remove excess salts

from the root zone to maintain productivity (Kijne, 2006). The energy-

intensive modern irrigation systems like drip may not provide adequate

leaching fraction and build salts in the root zone over time. On the other

hand, excessive/inefficient irrigation and seepage losses from channels and

reservoirs recharge unconfined aquifers. Where recharge exceeds the

combined leakage to deeper aquifers and lateral regional groundwater

outflow, watertables rise. Shallow groundwater areas with high salinity

often are associated with lower productivity (Hussain et al., 2004),

especially in Australian landscapes. Khan et al., (2009b) developed a

conceptual framework to analyse salinity dynamics at a system level. A

dynamic salt and water balance model underpinned by the conceptual

0

50

100

150

200

250

300

350

400

Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

Flo

w (G

L/M

on

th)

Water Year (month)

Natural flows

Regulated flows

58

framework was developed to explore the nexus between water trading and

on-farm shallow groundwater salinity in the Murrumbidgee area.

A study of the Murray-Darling Basin by Gutteridge et al., (1990) has

estimated that the area with a high watertable (within 2m of the land

surface) would increase to 95% of the total irrigated area within 50 years if

no remedial action is taken. Sustainability of irrigated agriculture in the

continuously irrigated areas or rice growing regions of the Murray Darling

Basin (MDB) faces secondary salinisation challenges due to high recharge

levels under current irrigation practices, water trading out of the area and

hydraulic loading not being maintained at sufficient levels to keep root zone

free of salts (Khan et al. 2006). For the rice growing regions in the MDB,

ponded rice contributes 40–50% of the accessions to groundwater, and the

other major sources are other irrigated crops and pastures, inefficient

irrigation methods, channel leakage and rainfall (Leslie 1992).

Among other factors, there are also energy requirement implications to

recover degraded soils or to pump saline groundwater to lower watertables

to safe levels or to remove/intercept saline drainage water from irrigated

areas.

2.2.4 Water Market as a Driver in Water-Energy Nexus

Water trade is one of the possible driving factors behind the trend for water

use efficiency improvement. The water intensity i.e. the industry gross value

added (IGVA) of agriculture industry is M$ 3 per GL of water use while

that of electricity and gas production is M$ 64 per GL of water use (ABS,

2012). Hence, water use for energy (electricity and gas) production has

incomparably high economic return. However, the social value of water use

to produce agricultural products for human consumption is overwhelmingly

high too. Economics, in addition to the push for environmental

sustainability, is the major underlying factor that controls water-energy

nexus. Water is a tradable commodity and has an economic value. With the

development and regulation of water markets in Australia, water can easily

be purchased or sold in the water market. Volumes of water allocated,

59

diverted and traded in the southern Murray-Darling system have varied

greatly over the past decade. Factors contributing to such fluctuation have

been a combination of policy choices, natural circumstances and attitudinal

shifts (Kaczan et al., 2011). Widespread water trading is a relatively recent

activity. Institutional reforms over the past 20 years have been focused on

creating water markets by decoupling water and land property rights, and

allowing water to flow from uses of low value to uses of high value with a

minimum of transaction costs (CoAG, 2004). This reform process is

ongoing. By 2007-08, the value of transactions in water markets was

estimated at $1.68 billion in the southern Murray-Darling system and over

98 per cent of water licenses in New South Wales are now tradable (NWC,

2008). However, a point of caution has been raised by Khan et al., (2009)

that emphasizes that minimum irrigation intensities must be met to flush

salts out of the root zone especially in shallow water table/high salinity

impact areas. Such minimum irrigation intensities are helpful but not

necessarily in deep water table/low salinity impact areas. The author further

suggests that should water markets lead to permanent water transfers out of

mature irrigation areas, minimum irrigation intensity needs might not be met

in high salinity impact areas, causing substantial negative impacts on

resource quality and agricultural productivity. Water trading that adds to

salinity cannot be economically viable in the long run. Therefore, tradeoffs

between water trading and environmental and equity goals need to be

determined.

In the Murrumbidgee Irrigation Area (MIA) the water trade price has been

as high as $1,062/ML during drought periods. The average water trade price

from 2005-06 to 2010-11 in MIA remained at $271/ML. The average annual

estimated market turnover of water allocation trade in four years from 2007-

08 to 2010-11 in the Murrumbidgee valley remained at $152 million per

annum as compared to $283.7 million per annum for the whole state of

NSW (NWC, 2011). The water trade price data is available from various

sources including: Murrumbidgee Water Exchange (2011), Kaczan et al.,

(2011), Watermove (2011) and National Water Commission on request. The

60

water saved through hi-tech irrigation methods can be traded in the market

to recover the capital cost and operating cost that would incur in hi-tech

irrigation. On the other hand, the price of water encourages irrigators to

adopt hi-tech irrigation which in turn would require more energy.

Furthermore, the rollout of the Australian Government’s Restoring the

Balance in the Murray–Darling Basin (‘buyback’) program

(http://www.environment.gov.au/water) to purchase water entitlements for

environmental flows has also provided irrigators incentive to adopt water

efficient irrigation methods and sell the water thus saved to maximize their

returns. Irrigators not only accept water trading: they are increasingly reliant

on it. Trading in both allocations and entitlements grew markedly over the

past five years. Over 30% of announced allocations and 10% of entitlements

on issue are traded in the southern Murray-Darling Basin (Southern-MDB)

in some years.

MDBC (2006) viewed water trading as a foundation in maximizing the

profitable and sustainable use of water, while protecting the environment

and catering for social needs. Later on, the study commissioned by the

National Water Commission (NSW, 2010) demonstrated unequivocally that

water markets and trading are making a major contribution to the

achievement of the National Water Initiative objective of optimizing the

economic, social and environmental value of water. The overwhelming

conclusion of the study is that water trading has significantly benefited

individuals and communities across the Southern-MDB. Water trading has

given individual irrigators more flexibility in their water use and production

decisions. This flexibility has helped them respond to external factors such

as the drought. The benefits of trading to individuals have translated into

benefits for communities, regions and the Basin as a whole. The

environment has also benefited from the net downstream movement of

water during the drought. Water licences for permanent plantings like

horticulture have the characteristic of high security water or high reliability

water. A high security licence has the highest priority from available water

61

resources in the water allocation hierarchy for a given water-year. Water

saved through increased water use efficiency can be sold to government for

environmental application. The average price for high security entitlements

permanently traded during 2008-09 and 2009-10 was A$2,050/ML (NWC

2010).

Water trading is an important tool for irrigators in making production,

investment, adjustment and risk management decisions. It is valuable in a

variety of seasonal conditions, not just as a reactive response to droughts

(NWC, 2012). However, the dynamics of energy use in response to active

water markets should be investigated. This aspect is discussed in Chapter 7

of this thesis.

2.2.5 Implications of Introduction of ‘Cap’

The Australian government imposed a limit on the volume of water that

could be diverted from the rivers in the basin for consumptive use (mainly

irrigation) from 1997 after realising that ongoing increase in consumptive

use of water in the Murray-Darling Basin (MDB) was environmentally and

socially unsustainable. This limit is called the cap and effectively limits the

volume of water diversions to 1993/94 development levels (Cox and Baxter,

1996). This implied that no additional water could be diverted from the river

for new developments. Apart from improving river health, the

implementation of this policy resulted into other positive outcomes namely;

impetus for water saving by improvement in water use efficiency, and water

trading. The improvement in irrigation efficiency helped control rising

watertable in inefficiently irrigated areas, made water available for new

irrigation developments and water trading provided a market mechanism to

temporarily shift water from low value to high value use. However, an

unintended consequence of technology adoption to achieve improved water

use efficiency is the significant increase in energy use and energy cost and

its possible impacts on climate change; as suggested by Hatirli et al. (2006)

that energy input in agriculture is directly related to the level of technology

adoption and the level of production.

62

2.3 Greenhouse Gas Emissions from Agriculture

Greenhouse gases generally include carbon dioxide, methane, nitrous

dioxide and synthetic gases like HFCs. It is now a widely accepted

phenomenon that emissions from greenhouse gases cause global warming

and therefore their emissions must be minimized. Australia has aligned itself

to this position by introducing a tax of $23 per tonne of CO2-e emissions

effective from July 2012. Emissions of greenhouse gases are produced on

agricultural lands as a result of a number of natural and human-induced

processes. These include the decay or burning of biomass, feed digestion by

livestock, the addition of nitrogen fertilizer and animal manure, crop

residues returned to the soil, nitrogen fixation, nitrogen leaching and runoff,

atmospheric deposition, and the anaerobic decomposition of organic matter

during flood irrigation (DCC&EE, 2010). The other associated processes

include burning of fossil fuel or electricity consumption in irrigation

pumping (if any) and other mechanical works. The principal GHG emitted

from agricultural processes are methane (CH4) and nitrous oxide (N2O).

2.3.1 Direct and Indirect Emissions

There are two categories for accounting greenhouse gas emissions namely,

direct emissions and indirect emissions. Direct emissions are produced from

sources within the boundary of an organization and as a result of that

organization’s activities. These emissions mainly arise from the following

activities as indicated in DCC&EE, 2010:

Generation of energy, heat, steam and electricity, including carbon

dioxide and products of incomplete combustion (methane and

nitrous oxide);

Manufacturing processes which produce emissions (for example,

cement, and ammonia production);

Transportation of materials, products, waste and people; for

example, use of vehicles owned and operated by the reporting

organization;

63

Fugitive emissions: intentional or unintentional GHG releases (such

as methane emissions from coal mines, natural gas leaks from joints

and seals); and

On-site waste management, such as emissions from landfill sites.

Emission factors for calculating direct emissions are generally expressed in

the form of a quantity of a given GHG emitted per unit of energy (kg CO2-e

/GJ), fuel (t CH4/t coal) or a similar measure. Emission factors are used to

calculate GHG emissions by multiplying the factor (e.g. kg CO2/GJ energy

in petrol) with activity data (e.g. kilolitres x energy density of petrol used).

There is one emissions factor for each GHG. All emission factors are

standardized by being expressed as a carbon dioxide equivalent (CO2-e).

This is achieved by multiplying the individual gas emission factor by the

respective gas global warming potential (GWP). The GWP is an index used

to convert relevant non-carbon dioxide greenhouse gases to a carbon dioxide

equivalent (CO2-e) by multiplying the quantity of the gas by its GWP as

given in Table 2.5.

Table 2.5: Global warming potential of major greenhouse gases (Source: DCC&EE, 2010)

Gas Chemical formula Global Warming

Potential Carbon dioxide CO2 1

Methane CH4 21 Nitrous oxide N2O 310

Indirect emissions are emissions generated in the wider economy as a

consequence of an organisation‘s activities (particularly from its demand for

goods and services), but which are physically produced by the activities of

another organization (DCC&EE, 2010). The most important category of

indirect emissions is from the consumption of electricity. Indirect emissions

are physically produced by the burning of fuels (coal, natural gas, etc.) at the

power station.

Emissions factors for selected emissions processes are given in Table 2.6.

As greenhouse gases vary in their radiative activity and in their atmospheric

64

residence time, converting emissions into CO2-e allows the integrated effect

of emissions of the various gases to be compared.

Table 2.6: Fuel combustion emission factors for selected fuels

Fuel

Energy

content

factor GJ/t

(solids),

GJ/m3,

KJ/Kl

Emission factor

kg CO2-e/GJ

CO2 CH4 N2O

Black coal 27.0 88.2 0.03 0.2

Brown coal 10.2 92.7 0.01 0.4

Dry wood 16.2 0.0 0.08 1.2

Natural gas 39.3 X 10-3 51.2 0.1 0.03

Compressed

natural gas 39.3 X 10-3 51.2 0.1 0.03

Liquefied

natural gas 25.3 KJ/Kl 51.2 0.1 0.03

Petroleum

based oils e.g.

lubricants

38.8 27.9 0 0

Gasoline 34.2 66.7 .2 .2

Diesel oil 38.6 69.2 0.1 0.2

Liquefied

petroleum gas 25.7 59.6 0.1 0.2

Biodiesel 34.6 0.0 0.06 0.2

Ethanol 23.4 0.0 0.06 0.2

65

Carbon dioxide is the main greenhouse gas emitted from various processes.

As per DCC&EE (2012) the CO2 emissions were 74.7% of the total GHG

emissions in Australia in 2009-10, followed by Methane (CH4) at 19.9%.

Agriculture (includes forestry and fishing) industry maintained its position

of second highest generator of GHG emissions after electricity, gas and

water in 2009-10 in Australia as shows in Figure 2.4.

Figure 2.4: Relative distribution of Australia’s direct greenhouse gas emissions by economic sector for 2009-10 (Source: DCC&EE, 2012)

2.4 Water Efficiency in Irrigation

Just 20% of the world’s croplands are irrigated but they produce 40% of the

global harvest which means that irrigation more than doubles land

productivity (FAO, 2003). Irrigation improves economic returns and can

boost production by up to 400% as compared to dry land cropping. On the

other hand, irrigation can have unwanted environmental consequences.

About one-third of the world’s irrigated lands have reduced productivity as

a consequence of poorly managed irrigation that has caused water logging

and salinity (FAO, 1998). Water efficiency is determined by the extent of

beneficial or intended use of diverted/applied volume of water. It is

probably the most researched area in the field of crop irrigation. However,

little emphasis has been given on understanding the dynamics of the energy

19%

11%

13%37%

11%

9%

Direct greenhouse gas emissions

Agriculture, forestry & fishingMining

Manufacturing

Electricity gas & water

Services, Construction and TransportResidential

66

use with the water efficiency improvement. Austin and Fairweather (2003)

provide insight into water use efficiency in irrigation.

Water efficiency in irrigation is mainly divided into two categories as

described below:

2.4.1 Irrigation Project Efficiency

The definition of “irrigation project efficiency” also commonly referred to

as “irrigation efficiency” as endorsed by the Irrigation Association of

Australia is based on an approach taken by the International Commission on

Irrigation and Drainage (ICID) as per Bos et al., (1993). One of the essential

elements of this approach is that it tracks and accounts for water use from

the point of supply all the way through to the crop. The irrigation project

efficiency is a ratio between total crop water use and total inflow into the

supply system. This definition is suitable for all irrigation systems at an

irrigation case study/scheme/district level and above. Another definition of

irrigation efficiency which is closest to the above definition is given by

Israelsen (1932) as “the ratio of irrigation water transpired by the crops of

an irrigation farm or project during their growth period to the water diverted

from a river or other natural source into the farm or project canal or canals

during the same period of time.” It is usually expressed in percentage terms.

2.4.1.1 Components of Irrigation Project Efficiency

The term irrigation project efficiency covers overall efficiency of the

irrigation scheme and is further broken up into sub-components including;

conveyance efficiency, distribution/farm efficiency, and field application

efficiency as defined below:

Conveyance Efficiency

Conveyance efficiency is defined as “the ratio between supply volume

received at farm inlet to the water volume supplied to the main supply

system”. Factors that affect conveyance efficiency include seepage, leakage,

and evaporation loss from supply channels and from en-route storages (if

any). Other unstated factors that influence conveyance efficiency are water

67

thefts from the main supply channels. For piped supply system, the

conveyance efficiency is almost 100%.

Farm Irrigation Efficiency

Farm irrigation efficiency is defined as “the ratio between field application

volume and the supply volume received at farm inlet”. Farm irrigation

efficiency is affected by evaporation loss from on-farm storage (if any),

seepage loss from on-farm channels and recycling of drainage water (if

installed).

Field Irrigation Efficiency

Field irrigation efficiency is also called irrigation application efficiency. It is

defined as “the ratio between root zone storage and field application

volume”. Field irrigation efficiency varies over a large range from 40% to

99% depending on the method of irrigation application. It depends on

irrigation method (gravity-based or pressurized), soil type, crop, field slope

and irrigation timing and irrigation application rate.

2.4.1.2 Water Use Efficiency

Water use efficiency was broadly introduced as a concept separate from

irrigation efficiency in Kassam and Smith (2001). Water use efficiency is

linked with consumptive use (i.e. evapotranspiration) of water by a given

crop. It is defined as “the ratio between volume of water consumptively

used in evapotranspiration and the volume of water actually applied. Water

use efficiency is controlled by irrigation scheduling and crop growth stages.

Another effective method to improve water use efficiency could be

regulated deficit irrigation or partial rootzone drying as discussed by

Bielorai (1982) and, Kriedemann and Goodwin (2003).

Khan et al., (2005a) conducted a detailed farm survey and analysed potential

improvements in irrigation efficiency at various scales for the

Murrumbidgee catchment as listed in Table 2.7. The current study has

modelled most of these water saving options excluding covering of storages,

68

laser levelling and flow monitoring and have also estimated total energy use

under each option.

Table 2.7: Potential water saving options to improve water use/irrigation efficiencies (adapted from Khan et al., 2005a)

Efficiency Indicator Water savings options

Irrigation efficiency

Conveyance efficiency

Identify and remediate seepage and evaporation losses in supply channels

Farm efficiency

Identify and remediate on-farm seepage losses

On-farm storage and recycling of drainage water

Covering storages

Field efficiency

Laser leveling

Flow monitoring

Matching crop to soil and groundwater depth

Conversion to pressurized irrigation system

Water use efficiency

Soil moisture monitoring and irrigation scheduling

For this study, however, more emphasis is given to the irrigation project

efficiency; mainly the conveyance efficiency and field efficiency as the

focus is to compare water and energy efficiency at a larger case study scale

rather than individual farms.

2.4.2 Whole-of-System Approach

To help identify the opportunities for real water savings and enhancing

water productivity, a whole-of-system water use efficiency framework was

developed and reported by Khan et al., (2010) for the Murrumbidgee

catchment. The analyses conducted by Khan et al., (2010, 2004) and others

suggest that significant reductions in water losses are possible by a targeted

zone by zone approach by improving the river/canal system, the near-farm

zone, and the on-farm zone within the whole catchment. This approach

involves the classification of different flow components into water

accounting categories, on an annual basis, to compute water use efficiency

69

at various spatial scales, including overall irrigation system level (the

highest scale), canal/channel level, farm level, field level, and crop type (the

lowest scale). The terms related to irrigation efficiency proposed by

different irrigation specialists are given in Table 2.8 for further clarification

as noted by Khan et al., (2010).

Table 2.8: Terms and definitions of irrigation efficiency at different scales as proposed by different researchers

Term Definition proposed by

Doorenbos

& Pruitt

(1977)

Jensen et al.

(1990) Bos (1997) IAA (1998)

Overall

project

efficiency

Water made available to crop; water released at headworks

Crop water requirement; total inflow into canal system

Crop water use; total inflow into supply system

Conveyance

efficiency

Water received at inlet to block of fields; water released at headworks

Water delivered to point of use; water supplied to conveyance system

Total outflow from canal; total inflow into canal

Total outflow from supply system; total inflow into supply system

Distribution

efficiency

Field canal efficiency: water received at field inlet; water received at inlet of block of fields

Field level delivery; total inflow into canal system

Water delivered to irrigation field; total inflow into supply system

Field

application

Water directly available to

Unit irrigation efficiency:

Crop water requirement; water delivery

Crop water use; water delivered to

70

efficiency crop; water received at field inlet

irrigation water required for beneficial use in a specified area; water delivered to this area

to field irrigation field

The methodology adopted in the current research is also based on the same

philosophy of conducting a whole-of-system water balance. The surface

water balance of the Murrumbidgee Irrigation Area (MIA) was previously

carried out by van der Lely (1993), Tiwari (1994) and Pendlebury (1994).

The average annual diversions to the MIA are around 1000 GL while the

average annual rainfall is around 400 mm (equivalent water supply of

around 850 GL).

The research for the current thesis is partly inspired by the approach adopted

and the extent and magnitude of estimated water losses and the potential

water savings reported by Khan et al., (2004, 2005a, 2005b) as summarised

in Table 2.9. The current thesis has given a much needed and missing angle;

the estimation of energy use and associated greenhouse gas emissions, to

realize those water savings near-farm and on-farm. It has developed an

energy accounting framework by building on the water accounting

framework at a group-of-farms scale and the whole irrigation scheme scale

of MIA.

Table 2.9: Accounted losses and potential water savings in MIA (Source: Khan et al., 2004)

Item Accounted and Identified Water Savings

(GL/year)

Near-Farm1 On-Farm2

Previous

estimate3

New

Assessment4

Previous

estimate3

New

Assessment4

1. Seepage 211 42 -63 9-36 9-36

71

Item Accounted and Identified Water Savings

(GL/year)

Near-Farm1 On-Farm2

Previous

estimate3

New

Assessment4

Previous

estimate3

New

Assessment4

2. Deep percolation

74-101 74-101

3. Evaporation 62 62*#

4. Irrigation technology conversion

70-86

Total 73 104-125 83-137 153-223

*Includes 40GL evaporation from major storages. # includes 20GL evaporation from channels. 1 refers to water

savings from supply channels within and near the jurisdiction of the irrigation corporations. 2 refer to water

savings from farms within and near the jurisdiction of the irrigation corporations. 3 based on van der Lely (1993),

Tiwari (1994) and Pendlebury (1994). 4 based on Khan et al., (2004).

2.5 Water-energy nexus for irrigation supply systems

Irrigation water is moved from source (surface water or groundwater

pumping station) to the farms either via open channels or through pipes. A

varying fraction of the water which does not reach farms is lost from the

conveyance system through seepage, leakage and evaporation. Channel

seepage can be defined as “loss of water from a channel via infiltration

through micro-pores and soil processes (i.e. not via preferential flow

through macropores). Seepage as measured in pondage tests includes a

leakage component. Generally the term channel seepage refers to both

seeped and leaked water, as the two are not easily separated” (NWC 2009).

Khan et al., (2004) developed a framework to estimate a whole-of-system

water balance for Murrumbidgee catchment and measured different water

balance components including seepage from channels. It reported that

seepage constitutes up to 16% and 50% of total water losses on-farm and

near-farm, respectively as evident from Table 2.9.

72

The length of earthen irrigation channels in Australia was surveyed for the

ANCID Seepage Project by Sinclair Knight Merz (ANCID 2000) with

irrigation authorities. The length of earthen channels in irrigation areas of

Australia is given in Table 2.10. The actual length of earthen channels

should be even higher than 17,752 km as the list in Table 2.10 does not

include many smaller irrigation areas and private schemes that did not

respond to the survey. Murrumbidgee Irrigation Area which is the study

area for this research has the third highest length of earthen channels and

provides an opportunity to achieve real water savings by minimising

conveyance losses from its earthen channels which are as high as 50% of

total conveyance losses. A large number of water saving studies has been

conducted in Murrumbidgee catchment over time; the largest and most

comprehensive study was conducted in 2004 for Pratt Water (Pratt Water

2004).

Table 2.10: Length of earthen irrigation channels in irrigation areas of Australia (Source: ANCID 2000)

Irrigation Company/Authority StateLength of earthen

irrigation channel (km)

Murrumbidgee Irrigation Limited NSW 2,000

Coleambally Irrigation Limited NSW 516

West Corurgan Private Irrigation District NSW 565

Murray Irrigation Limited NSW 3,800

Hay Irrigation Authority NSW 20

Lowbidgee Flood Control & Irrigation District NSW 50

Trangie-Nevertire Irrigation Scheme NSW 250

Marthaguy Irrigation Scheme NSW 60

Nevertire Irrigation Scheme NSW 48

Colly Farms Ltd - Collymongle NSW 200

Namoi Valley Water Users NSW 880

Clyde Agriculture Ltd - Bourke-Brewarrina Irrigation Farms NSW 200

73

Macintyre Irrigation Association NSW 25

Eton Irrigation Area Qld 44

St George Irrigation Area Qld 114

Dawson Valley Irrigation Area Qld 44

Bundaberg Irrigation Area Qld 75

Emerald Irrigation Area Qld 96

Mareeba-Dimbulah Irrigation Area Qld 91

Burdekin River Qld 305

Lower Mary River Irrigation Area Qld 6

Pioneer Valley Qld 35

Yambocully Water Board Qld 3

Condamine Plains Water Board Qld 12

Cubbie Station Qld 70

Cressy-Longford Irrigation Scheme Tas 155

First Mildura Irrigation Trust Vic 4

Sunrasia Rural Water Authority Vic 6

Werribee Irrigation District Vic 2

Goulburn-Murray Water Vic 6,952

Macalister Irrigation District Vic 568

Wimmera-Mallee Water Vic 112

Bacchus Marsh Irrigation Area Vic 0

South West Irrigation WA 284

Ord Irrigation Co-operative WA 160

Loxton Irrigation Area SA 0

Total 17,752

For a rough estimate of only the seepage loss from irrigation distribution

systems in Australia with a seepage loss rate of 5 mm/day to 108 mm/day

(Khan et. al., 2005a) and assuming an average channel width of 2 m and

average irrigation days of 300 per year; equates to a seepage loss of about

53 GL/year to 1,150 GL/year. Assuming the market price of water is

$25/ML, the water lost in seepage from earthen channels can be translated

into a loss of over $28 million per year. Realizing the need to lift water use

74

efficiency, the Australian Federal Government launched the $12.9 billion

Water for the Future program

(http://www.environment.gov.au/water/australia/index.html ). A component

of this program provides funding grants for seeking independent

professional expertise and assistance with system modernization including

asset refurbishment and water saving infrastructure. However, a critique on

this program could be its over emphasis on improving water use efficiency

and lack of emphasis on improving associated energy use efficiency and

undue greenhouse consequences.

Properly conceived investments in river systems could address the

challenges of accurate measurement of water flows and water loss/gain in

the river system zones, as well as the upgrade of the existing measurement

and monitoring infrastructure to best practice levels. They would also

address the capture and storage of water presently lost, look at better

efficiency in the management and delivery of water, and address the reuse

of water for environmental and human use. An improved measurement

system assists in minimising unaccounted flows, optimises the use of the

water distribution network, reduces variability and associated risk for water

users, and improves data integrity and billing systems with attendant

impacts on water security.

Khan et al., (2010) suggested that near-farm investments would target

channel seepage, leakages in the delivery infrastructure, and better storage

and transmission, as well as the reliability of water deliveries. Investments

in the main canal, channels, laterals, storage and replacement works would

help to capture these water savings. Piping of laterals and the installation of

pumps to deliver pressurised water to horticultural farms remains a key

prerequisite for the adoption of drip irrigation systems on-farm. All these

water saving measures including lining of leaky sections of canal, efficient

storage, works and pumping water through pipes etcetera require energy

directly or indirectly. Therefore, this thesis has investigated in greater detail

75

the energy aspects of piped supply of irrigation water in MIA’s horticultural

areas.

2.6 Conversion to efficient irrigation systems

Field irrigation efficiency depends largely on the method of irrigation

application. Gravity based irrigation methods like flood and furrow

irrigation are the least efficient methods due to inappropriate volume, timing

and spatial coverage of irrigation water. Drip irrigation systems are on the

top of the irrigation efficiency ladder with savings of between 40-60% over

furrow and overhead sprinkler systems and up to 30% on micro-sprinkler

systems. Figure 2.5 shows the differences in monthly water application rates

to citrus trees using drip irrigation and a low-level micro-sprinkler system

(Falivene et al., 2006). The total water applied by drip systems is 7.11

ML/ha as compared to 12.4 ML/ha used by the sprinkler system.

Drip irrigation offers the potential for greater water use efficiency, but can

be as inefficient as other irrigation systems if not correctly managed. Given

that drip irrigation wets a smaller volume of soil at each irrigation than a

full-cover system does, there is little margin for error in the timing and

amount of irrigation. Therefore, accurate irrigation scheduling and regular

soil moisture monitoring are vital and if neglected, drip irrigation can cause

greater crop and water losses than other irrigation methods. Therefore, it is

called a hi-tech irrigation method. Trials with drip irrigation in vineyards

have shown that water savings of 25% - 30% over sprinkler systems and

40% over furrow systems are possible (Giddings, 2004).

76

Figure 2.5: Monthly irrigation application rates to citrus using drip irrigation and low-level micro-sprinklers (Source: Falivene et al., 2006)

The potential water savings were estimated by Khan at al., (2004) for each

irrigation method and each crop as given in Table 2.11 for MIA. When

linearly extrapolated, water savings across the total area of arable land in

MIA, without taking into account varying soil types, showed considerable

potential for on-farm water savings of between 70 to 86GL/yr depending on

the irrigation methods used and crops grown. The lower end of these

savings can be made by reducing net recharge to groundwater while the

higher end can be gained through additional investment in pressurized

irrigation technology.

Table 2.11: Crop water use (ML/ha) for horticultural crops and water saving potential by high tech irrigation technologies (Source: Khan et al., 2004)

Irrigation Method

Surface irrigation Sprinkler Drip irrigation

High Low Avg High Low Avg High Low Avg

Citrus 12.0 9.0 10.5 11.0 8.0 9.5 10.0 7.0 8.5

Vineyard 9.0 7.0 8.0 7.5 6.0 6.8 6.0 4.0 5.0

Onions - - 4.5 - - 4.0 - - -

Carrots - - 3.8 - - 3.0 - - -

Melons - - 4.2 - - - - - 3.2

Tomato - - 8.1 - - - - - 6.1

77

The current study further refines these water saving figures by modelling

water use at the irrigation scheme scale for each soil type in MIA and also

estimates additional energy requirements to implement those water saving

options, e.g. the energy required in water pumping for pressurized delivery

of water to operate drip system.

2.6.1 Efficient Irrigation Technologies and Controlling

Groundwater Rise

A small fraction of deep percolation (also referred to as leaching fraction)

from the irrigated area is necessary to flush out excess salts from the root

zone to maintain productivity (Hoffman 1990; Rhoades and Loveday 1990).

At the same time, excessive/uncontrolled irrigation of crops and seepage

losses from irrigation channels and storages result in groundwater recharge

to unconfined aquifers (Rushton 1999). If the groundwater recharge is

greater than the combined groundwater leakage to the deeper aquifers and

lateral regional groundwater outflows, then the watertable will start rising.

When the watertable is less than 2 m from the surface, the root zone of the

plants becomes restricted and capillary up-flows from the watertable start

accumulating salts in the root zone and at the soil surface over time, causing

reduction in crop yields (Kijne, 1998; Kijne et al., 1998).

Similarly a study in the Murray-Darling Basin by Gutteridge et al. (1990)

estimated that the area of high watertable, which refers to watertable within

2 m of the land surface, would increase to 95 per cent of the total irrigated

area within 50 years if no remedial actions were taken. Murrumbidgee

Irrigation Area has similar trends. These observations substantiate the

requirement of finding solutions to minimize levels of groundwater

accessions but at the same time provide enough hydraulic loading to flush

accumulated salts out of the root zone. Conversion to efficient irrigation

application technologies is one of the available solutions that may have

many added benefits. Khan et al. (2004b) and Khan (2005) provided an

overview of regional water balance and benchmark irrigation levels for

control of rising watertables and to minimize the risk of secondary

78

sanitization in Coleambally Irrigation Area which is located adjacent to

Murrumbidgee Irrigation Area. Khan (2007) has further explored the net

recharge management for areas of intensive irrigation through crop rotation.

The paper aims at finding out the appropriate mix of recharging and

discharging land uses which can limit recharge to the regional groundwater

outflow capacity of the underlying aquifer systems.

2.7 Water-energy nexus for horticulture in Australia

Horticulture refers to a vast range of crops that includes vegetables, fruit,

grapes, nuts, mushrooms, nursery, turf, cut flowers and extractive crops.

Horticulture production in Australia is intensive and generally irrigated

agriculture. Horticulture is a diverse industry, spread across the continent in

a wide array of climates. Horticulture is the second-largest and the fastest

growing agricultural industry; with some 30,000 businesses nationally, and

a farm gate value of $9 billion. Total horticulture exports (including fresh

fruit, vegetable, nuts and plants including flowers) were $751m (12 months

to May 2008). As the most labour intensive of all agricultural industries,

horticulture employs around one-third of those employed in agriculture in

Australia (Horticulture Australia website at http://www.horticulture.com.au

accessed in 2011). Horticulture is also a high value user of water.

Horticultural crops account for only 17 per cent of total irrigation but

produce more than 40 per cent of Australia's irrigated production in dollar

terms. Water is a key resource for the horticulture industry for both

permanent and annual plantings, so water efficiency is a strong research

focus. Figures suggest that for every 100 ML of water used in horticulture

$250,000 and four jobs are generated at the farm gate and approximately

$0.5 billion injected into the economy (online: HAL, 2010).

The major growing regions in Australia include the Goulburn Valley of

Victoria, the Murrumbidgee Irrigation Area of New South Wales; the

Sunraysia district of Victoria/NSW; the Riverland of South Australia;

northern Tasmania; southwest Western Australia; the coastal strip of

79

northern New South Wales; and Queensland. In broad terms, approximately

one-third of Australian horticulture is located in Queensland, with another

third along the southern Murray-Darling Basin. Significant research and

extension work has taken place within the industry over the past decade to

ensure that horticulture's water use efficiency and water productivity are the

world's best practice.

Horticulture emissions equate to just 1% of emissions from agriculture, or

some 0.2% of Australia's total emissions. Emissions from horticulture

production result mainly through nitrous oxide release from the use of

nitrogenous fertilizers (www.dpi.vic.gov.au ). Emissions from use of

electricity for irrigation pumping and from machinery use for pruning,

harvesting etcetera are additional. This low level of emissions is due in part

to the type of crops grown. Horticulture is the mixture of perennial crops

such as tree fruits, tree nuts and vine fruits, combined with seasonal

vegetables and herbs. The perennial crops like citrus, stone fruits also

provide an effective storage and capture of carbon.

The MIA has one-third of its area planted to horticultural crops such as

citrus, vineyards and stone fruits. An increasing proportion of this is grown

under drip irrigation. Some large-area farms in the MIA have also been

converted to drip irrigation for horticulture. Data collected from a number of

sources show substantial water savings can be made with the adoption of

pressurized irrigation in horticulture, with drip irrigation providing greater

savings than sprinkler irrigation. However, the premier issue, which is

addressed in this thesis, is the energy requirement of water saving irrigation

technologies. The thesis is based on the assumption that high energy

consumption has its own environmental and economic implications.

As reported by Singh et al. (2005), for the years around 2003- 2004, the

farm gate value of total agricultural production in the MIA was about $404

million which included $150 million from horticultural production, $24

80

million from the vegetable production and $230 million from broadacre

crops (Table 2.12).

Table 2.12: Area and economic output of different agriculture industries in MIA (Source: Singh et al., 2005)

Category Area

(ha)

Proportion

(%)

Value of

production

($M)

Percentage

value of

production

(%)

Horticulture 24,800 13 150 37

Vegetable 3,000 2 24 6

Broadacre

including pasture 157,000 85 230 57

Total 184,800 100 404 100

2.8 Energy availability and food security

The 2010 revision of “World Population Prospects” from the population

division of the United Nations concluded that the world population is

expected to grow over 9 billion people by 2050. The major part of this

increase is expected to take place in developing nations. Based on energy

requirements of 2,500 kcal/day per person, this would give a food demand

of 9,500 TWh, an increase of 34 % compared to the present demand.

Insufficient modern energy is available for agriculture and this is affecting

global food security. The price of food is linked with the price of fossil fuel

and price of fuel is quite dynamic with an upward trend. Fertilizer is an

important energy input to increase/maintain agricultural production. The

prices of all fertilizers have risen quite a bit lately. According to data from

the International Centre for Soil Fertility and Agricultural Development the

price of Urea doubled from January 2007 to January 2008. The price of

DAP (Diammonium phosphate) tripled in the same period and the price of

MOP (Muriate of potash or potassium chloride) almost quadrupled

(Johansson and Liljequist, 2009).

81

Agriculture requires energy as an important input to drive production. It

uses energy directly as fuel or electricity to operate machinery and

equipment, to heat or cool buildings, and for lighting on the farm, while it

indirectly uses the fertilizers and chemicals produced off the farm (Schnepf,

2004). In agriculture, a wide range of modern and traditional energy forms

are used directly on the farm, e.g. as tractor or machinery fuel, and in water

pumping, irrigation and crop drying, and indirectly for fertilizers and

pesticides. Other energy inputs are required for post harvest processing in

food production, packaging, storage, transport and cooking. Direct energy

use in agriculture accounts for only a relatively small proportion of total

final energy demand in national energy accounts. In OECD countries, the

figure is around 3-5%, and in developing countries between 4-8%. Energy

for agricultural practices in many developing countries continues to be

based to a large extent on human and animal energy, and on traditional

woodfuels. Empirical evidence suggests that the potential gains in

agricultural productivity through the deployment of modern energy services

are not being fully realized in developing countries. This reduces both the

quantity of food produced, and also the quality of food. Rural people are

sometimes forced to eat either uncooked food or food that can easily be

cooked but which may not give full nourishment (FAO, 2000).

In general, those regions with higher energy consumption have higher

agricultural yields. However, the relationships between energy input and

agricultural output are also affected by the varying ecological and

environmental conditions around the world; soil fertility and rain-fed water

availability being prime examples. Exact comparisons at the national level

are, therefore, not easily made, but it is possible to use energy inputs for

specific crops to gain further insights into the relationship between energy

use and agricultural productivity (FAO, 2000).

As an example of this, a comparison between the commercial energy

required for rice and maize production by modern methods in the United

States, and transitional and traditional methods used in the Philippines and

82

in Mexico is shown in Table 2.13. These data show that modern methods

give greater productive yields and are much more energy-intensive than

transitional and traditional methods (Stout, 1990). These methods include

the use of fertilizer and other chemical inputs, more extensive irrigation and

mechanized equipment. Similar results on energy use and crop yield are

anticipated for Australian agriculture, especially for horticulture which is a

focus of this research. The recent energy studies available suggest that the

food system consumes close to 16 per cent of the total energy use in the

U.S. (Hendrickson 1996). Furthermore, Heller and Keoleian (2000) estimate

that the manufacturing of mineral fertilizers and pesticides accounts for

almost 40 per cent of the energy use in all of U.S. agriculture.

Table 2.13: Rice and maize production by modern, transitional and traditional methods

Rice production Maize production

Modern

(United States)

Transitional

(Philippines)

Traditional

(Philippines)

Modern

(United States)

Traditional

(Mexico)

Energy input

(MJ/ha) 64,885 6,386 170 30.034 170

Productive yield

(kg/ha) 5,800 2,700 1,250 5,083 950

Energy input yield

(MJ/kg) 11.19 2.37 0.14 5.91 0.18

2.9 Fertigation – a better way of saving energy input

Fertilizer is one of the essential and major indirect energy inputs for crop

production. The world’s fertilizer use per hectare increased from about 60

kg in 1960 to 110 kg in 2002 (FAO, 2007). The delivery of dissolved

mineral fertilizers to the roots of crops in the field using irrigation water is

known as fertigation (NSW I&I, 2011). With pressurized irrigation systems,

83

through fertigation, fertilizers dissolved in the irrigation water can be

applied almost direct to the bulk of rootzone, providing more efficient

uptake of nutrients by trees. This allows easier, controlled, more effective

and more precise application of fertilizers especially Urea which can quickly

leach out of the root zone due to its high solubility, and hence provides an

effective way of saving energy (fertilizer) input.

The amount of nutrient removed from the soil is directly related to the

amount of crop yield obtained. Fertiliser use (or recovery) efficiency is

defined as the ratio of the amount of nutrient removed with the crop to the

amount of nutrient applied. For controlled irrigation application systems like

sprinkler and drip systems, the fertilizer use efficiency can be as high as

75% as the leaching rate is not significant. Hence, fertilizer application rate

can be reduced through fertigation using controlled irrigation methods.

Approximate nutrient removal amounts based on tonnes of grapes removed

per hectare for common nutrients (nitrogen, phosphorus, and potassium) are

given in Table 2.14 (Giddings, 2004). This nutrient removal should be

replaced by application of mineral fertilizers. On one hand fertigation

reduces the amount of required fertilizer thus reducing indirect energy input

and one the other hand more energy is required to operate the pressurised

irrigation systems.

Table 2.14: Approximate nutrient removals based on tonnes of grapes removed per hectare (Source: Giddings, 2004)

Nutrient removed (kg/ha)

Fruit removed (t/ha)

Nitrogen (N)

Phosphorus (P)

Potassium (K)

5 – 10 17 2 19

11 – 15 29 4 32

16 – 20 41 5 45

21 – 25 51 7 56

26 – 30 63 8 69

31 – 35 74 10 82

84

2.10 Irrigation Management Strategies

An irrigation system physically consists of irrigation supply infrastructures

(open channels, pipes, storages etc.), equipment (pumps, metering devices,

sensors, communication devices etc.), on-farm storages (if any) and

irrigation application equipment (drip, sprinklers, pumps etc). On the

management or operations side, there are two strategies of irrigation

management, namely demand-based and supply-based. These two aspects of

large scale irrigation systems are discussed by Merrey (1997), Mandavia

(1999), Sakthivadivel et al. (1999), Horst (1995) and Hatcho and Sagardoy

(1993). On a spatial scale, an irrigation system may be as small as a farm

and as large as an irrigation scheme or even an irrigation district.

One of the underlying objectives of this research study is to investigate and

compare the pros and cons of demand-based and supply-based irrigation

management strategies in the context of water and energy use and associated

greenhouse gas emissions and to provide recommendations on the use of the

two strategies. In contrast to a piece-by-piece and single-focus approach, a

system wide holistic and inclusive methodology is the underlying

philosophy that is adopted throughout this research study.

2.10.1 Demand-based irrigation strategy

A demand-based irrigation system, also termed as “just-on-time” system, is

a modernized and flexible approach and more close to an ideal irrigation

system where irrigation can be applied in whatever quantity and whenever

needed. In Chapter 4, the water and energy aspects of a demand-based

irrigation system were discussed. A demand-based irrigation system is

supported by the irrigation infrastructure which is designed in a way that

facilitates the timely and full replacement of soil water loss due to

evapotranspiration by irrigation application. It also assumes constant

availability of water for irrigation and in sufficient amounts that closely

corresponds to the fluctuating crop water requirement. A demand-based

irrigation system requires larger capacity irrigation supply infrastructure and

is usually supported by automating technology like moisture sensors,

85

telemetric systems and computerized control systems. The irrigation can be

applied anytime as needed to compensate for varying rates of

evapotranspiration due to changing seasonal conditions and crop growth

stages throughout the cropping season. Since the amount of irrigation for

each crop is determined by the crop water requirement, there are little

chances of over or under irrigation. Therefore a demand-based irrigation

system is a preferred system to realize the full production potential of a

given crop and the soil through timely and adequate irrigation application

with minimum water losses. However, it requires high capital investment

and high operating costs and is more suited to situations where water

availability is unconstrained in volume and is accessible anytime. For

example, a drip irrigation system connected with appropriately sized on-

farm storage can be operated as an on-demand irrigation system. The

irrigation systems described in Chapter 4 under Scenario 5 and Scenario 6;

which is sprinkler and drip system respectively, linked with a communal

piped irrigation supply system are operated as demand-based systems.

2.10.2 Supply-based irrigation strategy

In supply-based irrigation systems the irrigation application is bound by the

size of the irrigated area and the availability (both volume and timing) of

irrigation water. A supply-based irrigation system may be constrained by

factors including limited capacity of the irrigation water conveyance

infrastructure, scarcity of water for irrigation and lack of capital investment.

The supply-based irrigation system is relatively simple and normally

implements a fixed-interval irrigation application schedule in rotation with

other users. This may result in occasional over irrigation or under-irrigation

as the crop requires a different amount of water at different growth stages.

Supply based irrigation is more traditional and is a widely practiced

irrigation approach.

For a supply-based irrigation management system, whether it is an open

channel system or piped supply, the total delivery capacity of the supply

system is shared among the irrigators situated along the supply path. The

86

water and energy aspects of supply-based irrigation are explored in Chapter

5.

2.11 Application of System Dynamics in Agriculture

System dynamics (SD) is the theory of system structures and an approach

for representing complex systems and analysing their dynamic behaviour

(Forrester, 1961). System dynamics deals with the study of how the

behaviour of a complex system changes through time. In SD, the relation

between structure and behaviour is based on the concept of information

feedback and control (Simonovic, 2000). Moreover, causal loop diagrams

represent major feedback mechanisms, which reinforce (positive feedback

loop) or counteract (negative feedback loop) a given change in a system

variable (Sterman, 2000).

Some examples of feedback loops are given in Khan et al. (2009) and

Ahmad et al. (2007) for analysing complex behaviour of water systems.

Literature suggests that there are underlying feedback mechanisms that link

water use efficiency, energy consumption and yield in irrigated agriculture.

One of the objectives of this thesis is to explore feedback mechanisms

among these quantities, with specific focus on irrigated horticulture.

2.12 Up-scaling Water and Energy Use

It is a common practice to compute water use at farm or paddock scale and

then upscale it to the entire irrigation area. The most important factor in up-

scaling water use is the variability in soil type. Water use is up-scaled from

case study scale to the whole irrigation scheme using both mosaic and GIS

based approaches. Khan and Abbas (2007) described a detailed

methodology to upscale water savings from a unit area to the irrigation

scheme scale using a biophysical model and geographic information system.

Since there is a non-linear relationship (Darcy-Weisbach formula) between

energy loss and flow rate, up-scaling energy use in irrigation pumping is not

a linear function of area covered. Therefore, a mosaic based approach is

adopted for up-scaling energy use in irrigation in this research.

87

2.13 Testing economic viability of irrigated systems

Testing the economic viability of an option is an important and critical step

in decision making. Therefore, care should be exercised in choosing the

right approach to test economic feasibility of solutions regarding irrigation

systems. A comprehensive book by Mays and Tung (1992) explaining the

economics of hydrosystems was extensively consulted to perform the

widely used financial analysis called benefit cost ratio (also called

profitability index) and net present value (NPV) approach. The net present

value is defined as the difference between the present value of cash inflows

(returns) and the present value of cash outflows (costs) and is widely used

for analysing profitability of long-term projects. The economic methods

used in this analysis are also well documented and applied by Khan at al.

(2005a). Other literature related to economics of irrigation methods

includes: Singh et al. (2005); Giddings (2004); Giddings and Deegenaars

(2008); Cuykendall and White (1998); Texas Cooperative Extension (2001);

New Maxico State University (2000); Malik and Luhach (2002).

2.14 Reliability of Irrigation Supply

Reliability of irrigation supply is an important but often ignored

performance indicator of an irrigation system. Renault and Vehmeyer

(1999) defined reliability of irrigation service the degree to which the

irrigation system, and its water deliveries, conform to the prior expectations

of its users. The timeliness of water availability to fulfil irrigation demand

of the crops also regarded as reliability of supply. Srinivasan et al. (2011)

investigated irrigation reliability in a river-based irrigation scheme in New

Zealand. In this article reliability was defined as the river’s ability to meet

the demand. For the current study irrigation reliability refers to likelihood of

water availability at farm inlet in appropriate quantity when a water order is

placed.

89

Chapter 3: Methodology

This chapter describes the study area, data collection and collation, and the

research methodology. Some existing methods that were applied and some

new approaches that were developed for this research are explained in this

chapter. Since this thesis is about water and energy nexus in irrigated

agriculture, the research methodology entails modelling and simulation of

water use/saving practices and opportunities and energy consumption and

greenhouse gas emissions estimation with a focus on horticultural crops.

The research investigates both on-farm and off-farm water saving options

and energy linkages. Instead of taking the traditional approach of

concentrating on individual farms in isolation, a more holistic approach is

taken where a water-energy model is developed for a cluster of horticultural

farms which is then extended to the scale of a full irrigation scheme. The

methodology for testing economic viability of different scenarios is

discussed briefly in this chapter and in more detail in Chapter 7 on

economic analysis.

3.1 Description of Study Region

The Murray-Darling Basin (MDB) is located in the south-east of Australia

and covers an area of 1,061,469 km2; equivalent to 14% of the country’s

total area. The Basin is defined by the catchment areas of the Murray and

the Darling Rivers and their many tributaries (Figure 3.1). Most of the Basin

is composed of extensive plains and low undulating areas, mostly below 200

m above mean sea level. Of greatest extent are the vast plains, the Darling

Plain in the north, drained by the Darling and its tributaries, and the

Riverine Plain in the south, drained by the Rivers Murray and

Murrumbidgee and their tributaries. The Murray-Darling Basin is spread

over five States and Territories of Australia with areas in: New South Wales

(57%), Victoria (12%), Queensland (25%), South Australia (6%) and the

Australian Capital Territory (ACT) (less than 1% of the Basin). The

Murray-Darling Basin contains more than 20 major rivers catchments as

well as important groundwater systems. It is also an important source of

90

fresh water for domestic consumption, agricultural production and industry.

Although the MDB receives only 6% of Australia’s annual rainfall, around

40% of the value of the nation’s agricultural production is generated here,

and 70% of the value of Australian irrigation occurs in the region, which has

over two million residents (Khan et al. 2009a).

Figure 3.1: Major rivers and their tributaries in the Murray Darling Basin. (Source: www.mdba.gov.au)

3.1.1 The Murrumbidgee River Catchment

The Murrumbidgee valley is regarded as an area of high agricultural

significance. The Murrumbidgee catchment (Figure 3.2) is based around the

Murrumbidgee River in southern New South Wales and covers an area of

87,348 km2 which is about 8.2 per cent of total area of the MDB. The

Murrumbidgee River is the major river in the state of New South Wales and

the Australian Capital Territory originating from the Snowy Mountains; part

91

of the Australian Alps near Mount Kosciusko. Murrumbidgee is the third

largest river in MDB with mainstream of the river being 1,600 km long with

an annual average flow of 4,400 GL/year. It is the major tributary of the

River Murray; Australia’s longest and world’s third longest navigable river

after the Amazon and the Nile. The geographic boundaries of the

Murrumbidgee catchment include the Great Dividing Range in the east, the

Lachlan River Valley to the north and the Murray River Valley to the south.

The population is over half a million or 27 per cent of the total population in

MDB, concentrated in the centres of Canberra, Wagga Wagga, Griffith,

Leeton and Hay. The Murrumbidgee region is the biggest user of water in

the MDB with average consumption of over 22 per cent of surface water

diverted for irrigation and urban use and over 24 per cent of groundwater

use excluding the confined aquifers of the Great Artesian Basin - GAB

(CSIRO, 2008). This catchment has sustained a number of businesses and

regional communities in south-eastern New South Wales. Agricultural

production in the Murrumbidgee region is worth more than $1.9 billion a

year. This amounts to 25% of New South Wales’s fruit and vegetable

production, 42% of the state’s grapes and half of Australia’s rice production.

Irrigated agriculture across the region produces about $200 million worth of

rice a year, $60 million of vegetables, $80 million of grapes, $150 million of

fruit and $10 million of dairy products. While irrigated land accounts for 5%

of total agricultural land area, the value of production from irrigated areas

accounts for about 37% of the gross value of agricultural production (ABS

2009).

92

Figure 3.2: Dominant land uses of the Murrumbidgee region and its location in MDB (Source: CSIRO, 2008)

3.1.1.1 Climate and Water Resources

The Murrumbidgee catchment can be divided into three climatological

zones - upper, middle and lower Murrumbidgee. The average annual rainfall

(1950 – 2000) in the upper part of the Tumut catchment is 768 mm. In the

middle reach at Gundagai it is around 584 mm and in the lower reach

between Darlington Point and Balranald the average annual rainfall is 428

mm. Rainfall in the Murrumbidgee catchment decreases from east to west.

The potential evapotranspiration varies from 1,000 mm in the east to over

1,600 mm per annum in the west. In the lower rainfall zone, January is the

hottest month with average daily maximum and minimum temperatures of

32 °C and 16 °C. In the upper zone, the average daily maximum and

minimum temperatures are 21 °C and 6 °C in January, respectively. July is

the coldest month with average maximum and minimum temperatures of 14

°C and 4 °C respectively for the lower rainfall zone and 4 °C and -4 °C for

most parts of upper rainfall zone (Khan et al., 2004).

Major water resources in the Murrumbidgee region include the

Murrumbidgee River and its tributaries; the Snowy Mountains

Hydroelectric Scheme and its associated storages; alluvial aquifers in the

middle part; wetlands in the lower part and water storages. Both private and

93

public infrastructure is associated with the water resources including the

storages of the Snowy Mountains Hydro-electric Scheme, the storages of the

ACT Water Supply System, the major New South Wales irrigation dams of

Blowering (on the Tumut River) and Burrinjuck (on the Murrumbidgee

River) and on-farm water storages. The total catchment area above

Burrinjuck Dam is 13,000 km2. The storage capacity of Burrinjuck Dam is

1,026 Gigalitre (1 GL = 1 Million Cubic Meter). Below Burrinjuck Dam,

the Murrumbidgee River flows initially through a narrow reach and then a

widening valley near Gundagai. The Tumut River joins the Murrumbidgee

River upstream of Gundagai. The total catchment area of the Tumut River is

4,000 km2. Blowering Dam is the major storage on the Tumut River; it

stores both natural river flows and water that is released from the Snowy

Mountains Hydro-Electric Scheme. The overall capacity of Blowering Dam

is 1,632 million m3. The Murrumbidgee River drains much of southern New

South Wales and all of the Australian Capital Territory, and is an important

source of irrigation water for the Riverina farming area located along the

Mid-Murrumbidgee and Lower Murrumbidgee. With the current level of

development and flow regulation the long-term average surface water

availability is 4270 GL/year with approximately one tenth of this being an

inter-basin transfer from the Snowy Mountains Hydro-electric Scheme. On

average, 2257 GL/year (or 53 per cent) of the available water is diverted for

consumptive uses including irrigation, urban and industry. This indicates an

extremely high level of development. Water is released from Burrinjuck and

Blowering storages based on seasonal irrigation allocations with flows

mostly released between September and March and/or as a result of

operations of Snowy Hydro-electric Scheme. The shallow groundwater in

the area is generally saline while deeper groundwater is of relatively good

quality. The water use in the valley is augmented by groundwater mainly

from the Mid-Murrumbidgee and Lower Murrumbidgee alluvium making

up to 26 per cent of total water use in years of low surface water availability.

3.1.1.2 Land Use

94

Land use distribution in the Murrumbidgee Valley is given in Table 3.1. The

major land use is dryland pasture for livestock grazing. Dryland cropping is

a major enterprise and around 17 per cent of the region is covered with

native vegetation. Approximately 426,400 ha were irrigated in 2000 for

cereals (including rice), pasture and hay production. Citrus and grapes are

grown within the central areas of the Murrumbidgee Irrigation Area near

Griffith and Leeton called Riverina. Irrigated crops which include cereals,

pasture, horticulture and hey production cover 4.9% of the catchment. Two

major irrigation schemes namely Murrumbidgee Irrigation Area (MIA) and

Coleambally Irrigation Area (CIA) were developed in the region in 1912

and 1960’s, respectively. Citrus and grapes are grown within the central

areas of the MIA and constitute 3.6% of the total irrigated area (BRS, 2005).

Cereal crops are mainly grown in CIA. The whole area is covered by more

than 10,000 km of irrigation and drainage channels (Khan et al., 2004)

including some major irrigation canals. In 2005–06, about 2,340 irrigation

farms were in the Murrumbidgee region, representing about 30% of farms

Basin-wide.

Table 3.1: Land use distribution in the Murrumbidgee Valley in the year 2000 (Source: BRS, 2005)

Land use Area Per cent ha Dryland crops

15.7%

1,365,000

Dryland pasture 59.7% 5,213,100 Irrigated crops 4.9% 426,400

Cereals

60.1% 256,100 Cotton 3.6% 15,800 Horticulture 3.2% 13,600 Orchards 3.4% 14,400 Pasture and hay 26.4% 112,400 Vine fruits 3.3% 14,100

Native vegetation

16.8%

1,465,200 Plantation forests 1.6% 136,700 Urban 0.7% 65,300 Water body 0.6% 56,600 Total 100.0% 8,728,300

3.1.2 Study Area Selection

The Murrumbidgee Irrigation Area (MIA), as shown in Figure 3.3, consists

of five irrigation districts namely Mirrool, Yanco, Benerembah, Tabbita,

95

and Wah Wah Irrigation District. The Coleambally Irrigation Area is located

on the southern side of the Murrumbidgee River. The natural drainage-way

of the MIA is the Mirrool Creek. The topography is a flat open plain at an

elevation of 100-135 m above mean sea level. NSW State Water controls

water released from Burrinjuck Dam and Blowering Dam into the

Murrumbidgee River. Murrumbidgee Irrigation Limited, an irrigation

cooperative, is licensed by the NSW Government to divert a bulk volume of

water from the river system and deliver it to its customers. Most of the water

for the MIA is diverted from the Murrumbidgee River at Berembed Weir

(386 river kilometres from Burrinjuck Dam) via Main Canal (6,600 ML/ha)

and further downstream at Gogeldrie weir. From Berembed Weir water

moves into Bundidgery storage which is the intermediate off-stream storage

and marks the start of the system owned and maintained by Murrumbidgee

Irrigation Ltd.

Water is measured onto farm properties and irrigators pay for the water

supply charges. From Gogeldrie Weir water is directed to the Sturt Canal

(2,200 ML/day) to supply farms on the western side of the MIA. Drainage

water from irrigation farms flows through Mirrool Creek to Barren Box

Swamp and then re-regulated into the irrigation districts of Benerembah,

Tabbita and Wah Wah (Khan et al. 2005). Water takes five days to flow

from Burrinjuck Dam to Berembed Weir and a total of seven days to reach

MIA farms. Therefore, farmers have to place their water orders four to

seven days in advance depending on their farm location. The irrigation

supply network in MIA is shown in Figure 3.4 which indicates that other

than Wah Wah irrigation district, all irrigation districts are well covered by

irrigation supply systems, especially the Yanco and the Mirrool irrigation

districts.

96

Figure 3.3: Location of Murrumbidgee Irrigation Area in MDB and its five irrigation districts (Source: Murrumbidgee Irrigation Ltd.)

While the aim of this doctoral study is to a develop research methodology

which is generic and applicable to any irrigation catchment, it is developed

and tested for the Murrumbidgee Irrigation Area (MIA) which is selected as

a study area. Before examining the rationale for focus on MIA for this

study, it is necessary to get readers more familiarized with this area. The

salient features of MIA are discussed in the following sub-sections.

Figure 3.4: Irrigation supply and drainage network of MIA in its five irrigation districts (Source: Murrumbidgee Irrigation Ltd.)

97

3.1.2.1 Climate, Soils, Crop Mix and Water Use

The Murrumbidgee catchment is subdivided into three climatological zones

as suggested by Khan at al. (2005) mainly based on long-term average

annual rainfall, which is shown in Figure 3.5.

Figure 3.5: Rainfall zones of the Murrumbidgee catchment (Khan at al., 2005)

Comparison of Figure 3.4 and Figure 3.5 indicates that a major portion of

the study area is located in middle (Zone 2) part of the Murrumbidgee

valley. Average rainfall for the last six decades for these zones is given in

Figure 3.6 indicating how dry the last nine years have been with below

average rainfall representing the worst drought conditions throughout the

catchment in line with the rest of the Murray-Darling Basin. The long-term

average rainfall in our area of study (Zone 2) is 530 mm/year. The potential

evapotranspiration varies from 1000 mm per annum in the east (Zone 3) to

over 1600 mm per annum in the west (Zone 1). The monthly potential

evapotranspiration (ETo) as calculated by Khan et al., (2005) for upstream

(Burrinjuck) to downstream (Balranald) stations in the Murrumbidgee

catchment are shown in Figure 3.7. There is high irrigation demand due to

high rates of ETo from October to March in irrigated areas like Leeton and

Griffith in Zone 2 as compared to that of Zone 3. The lack of any significant

deviation among monthly ETo values for Zone 2 stations (Griffith, Leeton,

Narrandera) in Figure 3.7 indicates that similar climatic conditions prevail

98

in this part of the catchment and warrants that weather data from any of

these three locations reasonably represents the whole area. Therefore,

weather data from Griffith CSIRO weather station (station ID 75174) was

used for this research except for wind speed data which was only available

at Griffith Airport weather station (station ID 75041).

The Bureau of Meteorology stopped reporting data from Griffith CSIRO

weather station since 2003. However data from this site is still available

from Silo (http://www.longpaddock.qld.gov.au/silo/) which is maintained

by a consortium of state and federal government agencies including the

federal department of Agriculture, Fisheries and Forestry and Queensland's

Department of Environment and Resource Management.

Figure 3.6: Average annual rainfall for each decade since 1950 (Source: Patched Point Dataset from Silo at: http://www.longpaddock.qld.gov.au/silo/)

200

300

400

500

600

700

800

900

1950‐60 1960‐70 1970‐80 1980‐90 1990‐00 2000‐09

Average

Rainfall (m

m/year)

Decade

Zone1

Zone2

Zone3

99

Figure 3.7: Monthly Potential Evapotranspiration in the Murrumbidgee Catchment

There have been more than 80 studies on soils in the Murrumbidgee and

Coleambally irrigation areas in the past 40 years. There are more than 90

soil types in these areas. Often there is relatively minor difference between

two soil types. Geoff Beecher at NSW Agriculture Yanco prepared a

reference database to prior studies undertaken for the soils of Riverina and

described soils of MIA into five broad groups (Figure 3.8); Hornbuckle and

Christen (1999) used slightly different names for one or two of these soil

groups as Clays, Red-brown earths, Transitional red-brown earths, Sands

over clays and Deep sandy soils. Red-brown earths, which cover about 45%

of MIA and sandy soils, are most suitable for horticultural crops.

0

50

100

150

200

250

300

350

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Potential Evapotranspiration (m

m)

Month

Burrinjuck

Gundagai

Wagga

Narrandera

Leeton

Griffith

Hay

Balranald

100

Figure 3.8: Soil groups and their percentage area in MIA (Source: Geoff Beecher’s soils database, unpublished)

Table 3.2 indicates the maximum volume of surface water that can be made

available (also referred to as “water entitlement”) to different users in MIA

and the Murrumbidgee Valley as a whole. The high security water

entitlement license is granted to farms with permanent planting, for example

horticulture crops. High security water licenses have top priority after stock,

domestic and town supply and is guaranteed to stay at least at 95%

allocation in NSW as compared to general security license (e.g. for seasonal

crops). Hence it is relatively more important to improve water use efficiency

of farms with perennial crops to realize long term water savings even during

times of limited water availability than for annual crops. It is worth noting

that about 21 per cent of the total irrigation entitlement (general security

plus high security) is the maximum allowable conveyance loss for MIA.

The data from the Murrumbidgee Licence Compliance Report (MIA 2009)

for 2008-09 indicates that actual conveyance loss varied between 118

GL/year to 225 GL/year from 2001-02 to 2008-09. These fluctuations are

mainly due to variable evaporation loss from delivery channels and

intermediate storages with seasonal climate conditions and total diversion

volume. The costs of allowable conveyance loss and additional conveyance

losses are socialized among the water users and charged as a component of

101

fixed water costs. The conveyance system losses can be minimized by

infrastructure modernization. The water saved from conveyance losses

supposedly not only reduces cost per megalitre to the irrigators; it can also

be used to improve water availability or sold for environmental flow

purposes thus helping achieve both environmental and economic benefits

which is one of the major incentives to save water.

Table 3.2: Water entitlements (licenses) in MIA and the Murrumbidgee Valley

Water Entitlement MIA (GL) Murrumbidgee Valley (GL)

General security 757 1,888

High security 313 356

Stock / domestic / Towns 27 56

Conveyance (Maximum) 223 373

Supplementary 37 198

TOTAL 1357 2,871

3.1.2.2 Groundwater System

Due to continuous irrigation over decades in the MIA the shallow

groundwater aquifers beneath the irrigated areas have become fully

saturated and as a result the watertable response is quite swift to the

hydraulic loading from irrigation. The shallow groundwater is of very poor

quality due to ultra high salinity levels. The shallow groundwater salinity in

the MIA varies from less than 2 dS/m to over 20 dS/m. After regional

groundwater outflow (lateral outflow), most of the remaining shallow

groundwater (within less than 2 m watertable depth) is taken up by the

plants by capillary upflow or is evaporated from the soil surface. This

process creates the risk of salinisation of the soils and is detrimental to

crops. Hence, management of recharge to shallow groundwater from

irrigation is essential and challenging. Most of the irrigation districts of

MIA have very limited groundwater outflow capacities. These areas are at

greater risk of salinity problems from shallow watertables if irrigation and

winter cropping efficiency is not managed within the regional groundwater

flow capacity (Khan et. al., 2004b). Khan et. al., (2004) estimated that in

MIA the deep percolation losses (true losses) are around 110 GL (this is

102

additional to the 100 GL from the supply system) (20 GL from channels and

90 GL from farms) which cause overall recharge to the groundwater. Since

the deep percolated water becomes saline, it is permanently lost for

productive use. There has been an increasing trend in watertable up to 1990

when about 82 % of the MIA had a watertable within 2 meters from the

surface (van der Lely, 1998). This was reduced to 56 % by 1997 mainly due

to dry weather conditions, but perhaps also partially due to improved

irrigation practices and better drainage. It was further reduced to 50% by

2001.

The above discussion highlights the need for efficient water delivery

systems, efficient irrigation application systems and low irrigation demand

crops (e.g. horticulture) to minimize water losses to the saline groundwater.

3.1.2.3 Irrigation System

The net evapotranspiration requirement after taking into account rainfall,

capillary rise (if it occurs) and the runoff water that can be profitably reused

is to be met by irrigation water from the off-farm delivery system (Hafi et

al., 2001). Irrigation application efficiency is the efficiency of delivery of

water to the rootzone from on-farm channels or pipes. It is affected by

processes including surface evaporation, surface runoff and deep

percolation. Irrigation efficiency can be improved by ensuring that water

supplied for irrigation reaches its target, i.e. the rootzone, and that it does

neither move sideways away from the rootzone nor percolate down through

the profile into the groundwater. Management options for improved

irrigation efficiency on farms include laser levelling, monitoring irrigation

rates and irrigation duration, matching crop to soil type and watertable depth

and for both horticultural and large area farms, conversion to pressurised

irrigation systems. Similarly, changing from flood irrigation to alternate

inter-row or twin furrow irrigation will save up to 35% of the water used in

complete flooding. (Beckingham et al., 2004).

Hope and Wright (2003) reported that surface irrigation accounted for

approximately 91% of the total irrigation area in the Murrumbidgee Valley

103

(Figure 3.9). In the last 6 years there has been a rapid adoption of high-

technology irrigation, however, the majority of irrigation systems are still

flood or furrow systems. Similarly in the MIA hi-tech irrigation has been

adopted at a faster rate with more than 34% of the irrigated area using some

form of pressurized irrigation system as shown in Figure 3.10 (Ahmad and

Khan, 2009). Among all high irrigation efficiency technologies, the biggest

increase in conversion has been to drip irrigation systems as it is less labour

intensive and more suitable for controlled irrigation and fertilizer

applications in the horticulture areas with tight soils typical of those found

in the MIA.

Figure 3.9: Percentage of irrigation area used by different irrigation systems in the Murrumbidgee Valley (Source: Hope and Wright, 2003)

0.7

91.2

0.2

5.6

2.3

drip/subsurface/trickle

surface

fixed overhead sprinkler

travelling irrigator

moveable spray

104

Figure 3.10: Irrigation systems in use as per cent of total irrigated area in MIA (Source: Ahmad and Khan, 2009)

The MIA is well suited to drip irrigation given the prevalence of clay soils

coupled with shallow groundwater sitting not very deep from the surface

make MIA perfectly suitable for the drip irrigation. The drip/trickle

irrigation system brings about water savings by reducing soil evaporation,

groundwater accessions and surface runoff by controlled delivery of water

to the rootzone where it is most effective. Low water availability, especially

during the drought in the last decade is one of the major factors that lead to

adoption of hi-tech water efficient modern irrigation systems in MIA. But at

the same time the energy requirement for running the hi-tech water efficient

irrigation systems has also increased by many folds.

3.1.2.4 Rationale for Selection of Study Area

Similar to other irrigated regions in the MDB, the Murrumbidgee region

experience water shortages during the last decade. It is estimated that if the

recent climate (1997 to 2006) trend were to persist, average surface water

availability would reduce by 30%, diversions by 18% and end-of-system

flow by 46%. The best (i.e. least dry) estimate of climate change by 2030 is

less severe than the impacts of the Millennium drought. Under this scenario,

the average surface water availability would reduce by 9 per cent, diversions

by 2 per cent and end-of-system flow by 17 per cent (CSIRO 2008). The

17.2

52.9

23.2

0.90.3 3.5 2.0

Drip

Flood

Furrow

Low head

Microjet

Sprinkler

Overhead

105

recently published Guide to the Basin Plan recommends 32 per cent to 43

per cent reduction in current surface water diversion limits for the

Murrumbidgee valley (MDBA, 2010). In MIA, the overall irrigation supply

system efficiency averaged for the period 2001 to 2009 is 75 per cent (MI

2009). Hence some of the water entitlement cuts can be offset by potential

efficiency gains. If Basin Plan implements these cuts then such reduction in

water availability is likely to require major improvement in overall

irrigation efficiency to irrigate the current crops and/or changes in cropping

system in MIA.

Khan et al., (2009b) have estimated that up to 12.5 GL is lost in evaporation

and up to 42 GL in seepage of water annually from 500 km of surveyed

earthen irrigation supply channels in the Murrumbidgee Irrigation Area at a

rate up to 20 mm/day which is significant and if continued it may result in

raised saline groundwater thus deteriorating soil productivity but also have

negative environmental and economic consequences. In MIA where most of

the irrigation supply network is gravity based, during irrigation season in

summer the supply channels are constantly kept pre-filled with water, which

results in significant losses due to seepage and evaporation. Irrigation

conveyance losses can be caused by evaporation, seepage, leakage and

operational losses but by far the greatest losses are due to seepage (Meyer,

2005).

Some of these component losses, for example, evaporation loss from en-

route storages, may fluctuate with seasonal climatic conditions and

diversion volumes while other loss components, for example, channel

seepage and evaporation, remain relatively constant. Conveyance losses can

be minimized or eliminated by replacement with modern conveyance

systems like pipes and will be discussed in successive chapters. This is

further demonstrated in Table 3.3 (MIA 2010) which presents detailed

accounts for water diversions and conveyance loss components (including

evaporation losses from en-route storages) from 1998 to 2009 from the main

irrigation supply system of MIA. It should be noted that conveyance

106

efficiency dropped from 96% in 2005-06 to 87% in 2007-08. The gross

diversions in 2007-08 were almost one-third of those for 2005-06. The

decrease in gross diversions does not explain for the significant drop in

conveyance efficiency.

Conveyance losses, in particular the constant losses (channel seepage and

evaporation losses) that almost remain unchanged for any level of

diversions. It results in a lesser proportion of the total diverted water being

delivered to irrigation farms for a low water availability year and hence

reduced irrigation conveyance efficiency. A reduction in constant losses

(channel seepage and evaporation) results in real water savings and boosts

irrigation network conveyance efficiency. Channel seepage and channel

evaporation losses can be reduced by investment in irrigation supply

infrastructure like piped supply and/or channel lining etcetera. Investigation

of water and energy aspects of this irrigation water saving option is also

included in this research.

Table 3.3: Water balance for irrigation delivery system of MIA (all values in GL. source: MIA 2010)

1998-99

1999-00

2000-01

2001-02

2002-03

2003-04

2004-05

2005-06

2006-07

2007-08

2008-09

2009-10

Gross Diversions (1) 1036 819 1048 1142 960 862 826 1037 560 336 394 505

Seepage from Channels (2) 21 20 21 21 22 21 21 21 22 21 21 21

Evaporation from Channels (3)

19 18 21 24 26 23 24 23 26 24 24 24

BBS+LW evaporation (4) 31 29 34 39 21 11 4 15 10 8 8 8

Wah Wah (Div-Del) (5) 29 34 19 31 28 28 26 29 18 16 18 19

Leaks, Theft, misc. (6) 10 10 10 10 10 10 10 10 10 10 10 10

Inevitable Real Losses (7) 110 111 105 125 107 93 85 98 86 79 81 82

Total Delivered (8)=(1)-(7) 926 708 943 1017 853 769 741 939 474 257 313 423

Simple Efficiency (8)/(1)*100

89% 86% 90% 89% 89% 89% 90% 91% 85% 76% 79% 84%

Conveyance efficiency (9) = ((2)+(3))/(1)*100

96% 95% 96% 96% 95% 95% 95% 96% 91% 87% 89% 91%

In 1971 there were 935 horticultural farms in the MIA. The total area of

permanent plantings on these farms was 10,405 ha (Kennedy, 1973). In

2003 there were more than 1,000 horticultural farms with a total area of

24,800 ha. Grapes and citrus are the two major horticultural enterprises that

107

accounted for 97 per cent of the total area under fruit crops with 37 per cent

under citrus and 60 per cent area under grapes. The rest of the area was

under prunes and other fruits like apricots, peaches, plums nectarines, nuts

(Singh et al. 2005). Currently about 29,237 ha in MIA is covered with

horticulture crops (MIA 2010). Irrigation data for MIA indicates that water

use for irrigating perennial crops (citrus, vines, other fruits) can be as high

as 65% of total delivered irrigation water when general security allocation to

seasonal crops (cereals, vegetables etc.) is restricted due to low water

availability. Therefore this research project is particularly focused on water

savings in different on-farm irrigation methods for perennial crops. Some of

the irrigation methods are more energy intensive than others.

The level of irrigation system modernization especially, for horticulture

crops in MIA is depicted by the level of irrigation technology adoption as

shown in Figure 3.10. A review of the previous studies indicates that up to 4

ML/ha can be realized in water savings by high-pressure drip irrigation

which is being rapidly rolled out for horticulture areas of MIA. Further

water savings can be achieved by improving conveyance efficiency by

lining the leaky channels and replacing some open-channels with pipes. On

one hand such initiatives can result in significant amount of water savings

while on the other hand they require significant energy inputs in various

forms as compared to traditional methods and practices. In addition, broad

acre farms are gradually being converted into horticultural farms in MIA

that exacerbates the abovementioned issues and opportunities.

Murrumbidgee Irrigation Area was selected as a focus study area for this

PhD research as it is experiencing the abovementioned changes and

challenges. The latest challenge for the irrigators in MIA is to find water

savings to achieve a reduction of up to 320 GL in their annual diversions as

part of the Murray-Darling Basin Plan (MDBA, 2012). One of the real

challenges for water managers is to understand the dynamics and feedback

between water savings and energy use and to strike a balance between water

savings, energy consumption and their environmental footprints.

108

3.1.3 The Case Study Site

A communal irrigation site within the larger irrigated area of MIA was

selected as a case study. It is located in Yanco Irrigation District of MIA.

The case study area mainly consists of horticulture farms with citrus as the

dominant crop. The soil map of the case study area is shown in Figure 3.11.

Although there are 10 soil types in the case study area, they were aggregated

into two representative soil types based on irrigated area. The two soil types

are Sandy Loam (SL) and Clay Loam (CL). Taylor and Hooper (1938)

described SL group profile as, “the sandy loam surface and the somewhat

shallower clay subsoil that appears at about 45 cm. The change to the

medium clay may be more rapid, but it is only occasionally met with above

120 cm. The subsoils have a sticky feel and are apparently slowly

permeable”; and the CL group as, “they are mapped at Leeton, Wamoon and

Stanbridge”. The profile always contains a light clay band, frequently

continuing from 100 to 180 cm without change. The surface loam is always

shallow and is probably the cultivated zone of the original clay loam

surface. The subsoil clay bands are variable in thickness with heavy clay

sometimes absent altogether; the light clay occurs between 90 cm and 123

cm. Occasionally, there is a sandy loam surface, the deep subsoil may go to

a sandy clay approaching the Jondaryan clay loam type”. These soils are

placed into Red Brown Earth group. Both soil types are common and

suitable for horticulture crops in the MIA (Taylor and Hooper, 1938).

109

Figure 3.11: Soil types map of the study area (Downloaded from http://www.irrigateway.net/tools/soilmaps/)

The basic information about the case study area is given in Table 3.3. The

irrigation supply system in the area is being gradually converted from

gravity-fed open channels to high pressure piped supply to each farm. This

conversion will allow farmers to switch from flood or furrow irrigation on

their farms to pressurised irrigation systems, which includes sprinkler and

drip systems. The conversion to pressurised pipe irrigation supply also

eliminates the need for farmers to construct on-farm storages which are

highly inefficient and eliminate the need for the installation of pumps on

individual farms which are expensive to run and maintain.

The irrigation system modernisation of both the supply system and on-farm

irrigation technology can realise potential water savings by preventing

losses through seepage, evaporation and run-off. Under the current roster

system the farmers have to wait up to four days for their turn to irrigate due

to access and channel capacity constraints. This limits the frequency of

access to irrigation water and potentially affects crop yield during critical

crop lifecycles and during extreme hot days. The new system under

investigation ensures timely and on-demand supply of water to farms that

potentially leads to improved crop quality, quantity and better farm

110

management. It also minimises conveyance losses including seepage and

evaporation from the unlined open channels. These upgrades however have

an increased energy cost which this thesis is seeking to analyse.

Table 3.4: Information on basic features of the case study area

Item Data Total area (ha) 291 Vines (ha) 22.6 Citrus (ha) 248.6 Stonefruit (ha) 19.8 Total number of farms 13 Vines 2 Citrus 9 Stonefruit 2 Number of trees per hectare Citrus 550 Stonefruit 225 Average radius of a mature plant canopy (m) Citrus 2.0 Stonefruit 1.95 Average row-to-row distance for wine grapes (m) 2.0 Total length of unlined main supply channels (m) 4,074 Average roster time of irrigation for a farm (days) 4

3.1.4 Data Collection/Collation and Analysis

The data used for this research project was mainly collected from

Murrumbidgee Irrigation, Bureau of Meteorology and other public sources.

It includes data on climate variables, water use, landuse maps, soil types,

soil properties, irrigation types, crop types, input rates, gross margins,

irrigation infrastructure etc. Due to the commercial sensitivities of some

data, the identifiable data fields like Farm Identification Number are not

disclosed here. The farms in the study area are rather identified by

alphabetical notation as given in Table 3.5.

111

Table 3.5: Details of Horticultural farms in the case study area

Farm No. Farm ID Crop Farm Area

1a A Citrus 54.26

3 B Citrus 35.4

4 C Citrus 28.18

5 D Citrus 35.3

6 E Citrus 27.7

7 F Citrus 28.76

7a G Citrus 11.24

8 H Vine 10.17

9 I Vine 12.43

10 J Citrus 6.87

11 K Citrus 16.32

12 L Stonefruit 4.57

13 M Stonefruit 19.77

Total 291

In the Murrumbidgee catchment and in fact the whole of New South Wales

the water year starts in July when the initial announcement of water

availability for general security entitlement holders for the rest of the year is

made by State Water and subsequently revised if water resource conditions

improve. By November farmers get a very good idea of irrigation water

allocation and plan their annual crops accordingly. The water year 2007-08

(July 2007 to June 2008) was selected as study period which was used to

test the developed model. The hydro-climatic data for the study period is

shown in Figure 3.12 and Figure 3.13. A complete series of available

climatic data is given in Appendix A. The average daily potential

evapotranspiration (ETo) for 2007-08 is 4.1 mm/day.

112

Figure 3.12: Daily observed rainfall and evaporation and calculated potential evapotranspiration at Griffith CSIRO gauge for 2007-08

Figure 3.13: Daily observed maximum and minimum temperature at Griffith CSIRO gauge for 2007-08

Table 3.6 summarizes hydro-climatic variables for the daily data for period

from water year 2003-2004 to water year 2008-2009 at Griffith CSIRO

weather station. This data was sourced from SILO patched point database

(http://www.longpaddock.qld.gov.au/silo/). Out of these six years 2006-

2007 was the driest and hottest year with only 186.6 mm of total rain and

0

15

30

45

1/07/2007

15/07/2007

29/07/2007

12/08/2007

26/08/2007

9/09/2007

23/09/2007

7/10/2007

21/10/2007

4/11/2007

18/11/2007

2/12/2007

16/12/2007

30/12/2007

13/01/2008

27/01/2008

10/02/2008

24/02/2008

9/03/2008

23/03/2008

6/04/2008

20/04/2008

4/05/2008

18/05/2008

1/06/2008

15/06/2008

29/06/2008

Rain/Evaporation (m

m/day)

Date

Rain (mm)

Evap (mm)

FAO56 (ETo) (mm)

‐5

0

5

10

15

20

25

30

35

40

45

1/07/2007

15/07/2007

29/07/2007

12/08/2007

26/08/2007

9/09/2007

23/09/2007

7/10/2007

21/10/2007

4/11/2007

18/11/2007

2/12/2007

16/12/2007

30/12/2007

13/01/2008

27/01/2008

10/02/2008

24/02/2008

9/03/2008

23/03/2008

6/04/2008

20/04/2008

4/05/2008

18/05/2008

1/06/2008

15/06/2008

29/06/2008

Daily Temperature (oC)

Date

Max. Temperature (oC)

Min. Temperature (oC)

113

average daily temperature of 18.2oC and highest average daily

evapotranspiration rate of 4.3 mm/day.

The water use data as given in Table 3.7 shows that the water application

rate for citrus and stonefruit is relatively higher for 2006-2007 as compared

to other years with the exception for vine crops which may have gone under

deficit irrigation in 2006-2007. Deficit irrigation in vine crops is a modern

practice to regulate vegetative growth and improve fruit quality while

achieving high irrigation efficiency (CRCV, 2005; Kriedemann and

Goodwin, 2003; Goodwin, 1995; Goodwin and Boland, 2002).

Table 3.6: Summary of climatic data used in this study (Griffith CSIRO)

Water-year (Jul –

Jun)

2003-

2004

2004-

2005

2005-

2006

2006-

2007

2007-

2008

2008-

2009

Long-

term

average

Average maximum

daily temperature

(oC)

24.3 24.8 24.4 25.5 24.8 24.4 24.7

Average minimum

daily temperature

(oC)

9.9 10.6 10.1 10.9 11.1 11.0 10.6

Average daily

temperature (oC) 17.1 17.7 17.3 18.2 17.9 17.7 17.65

Average

evaporation

(mm/day)

5.5 5.4 5.5 5.9 5.4 5.7 5.57

Total potential

evapotranspiration

(mm)

1469 1493 1460 1558 1493 1501 1497

Average potential

evapotranspiration

(mm/day)

4.0 4.1 4.0 4.3 4.1 4.1 4.1

Total rain

(mm/year) 333 263.1 369.3 186.6 336.4 313 300.2

114

The average water use data expressed as megalitres per hectare (ML/ha) for

citrus, stonefruit and vines for the 13 farms in the case study area is given in

Table 3.7. The data is reported for 2003-04 to 2008-09. All farms are either

irrigated with drip/trickle systems. Previous studies indicate that average

water use by flood irrigated citrus and vines in MIA ranged from 9 ML/ha

to 12 ML/ha and 7 ML/ha to 9 ML/ha, respectively (Khan and Abbas, 2007;

Khan et al. 2005a).

Table 3.7: Average irrigation application data for the three crops in the case study area

Crop 2003-

04

2004-

05

2005-

06

2006-

07

2007-

08

2008-

09

Average

(ML/ha)

Citrus

(ML/ha) 5.4 5.5 5.0 6.0 4.2 5.0 5.2

Stonefruit

(ML/ha) 5.6 5.5 5.7 6.0 4.5 5.7 5.5

Vines (ML/ha) 4.1 4.2 4.5 3.5 4.0 3.6 4.0

The soil-water characteristics given in Table 3.8 for both WSL and LCL soil

types were reported in Hornbuckle and Christen (1999) and determined by

Loveday et al. (1978) and Talsma (1963). These values of soil-water

characteristics for the USDA soil textural classes are similar to those

reported in Rawls et al., (1982) and Allen et al., (1998). The amount of

water stored in the soil profile is the difference between field capacity and

wilting point for a given soil texture. This is the total water storage capacity

of the soil. Plant root system extracts water from different depths depending

on crop type, irrigation frequency and weather conditions. Therefore, an

effective root zone depth is used for each crop and multiplied with soil total

storage capacity, which gives the total plant available water. However, only

a portion of the total plant available water can be extracted by plants without

becoming stressed.

115

Table 3.8: Soil-water characteristics of WSL and LCL for the case study area

Soil Type

Moisture content at field capacity, θfc (10Kpa)

(m3/m3) (A)

Moisture content at wilting point, θwp (1500Kpa)

(m3/m3) (B)

Depth of soil

profile (m)

Total soil water

storage (m3/m3) (A – B)

Sandy loam

0.23 (0.18 – 0.28)

0.11 (0.06 – 0.16)

0.8 0.12

Clay loam

0.34 (0.30 – 0.37)

0.18 (0.15 – 0.21)

1.2 0.16

As given in Table 3.8, sands have less water storage capacity than clays but

most of it is available to plants. Therefore, low but frequent irrigation

should be more effective for sandy soils and vice versa for clays in terms of

water availability to plants.

3.2 The Overall Approach

While the development of a biophysical model for a given area is a complex

and time consuming process; its repeated application to test new scenarios

and interpretation of results requires even more time and expertise.

Furthermore, usually individual models are developed to address specific

aspects of a given area. For example, a groundwater model only simulates

groundwater movement and does not account for any crop-water

interactions. A separate crop-water model is needed to understand crop

water use; another one to simulate water movement in unsaturated zone,

another one to account for energy inputs and so on. Although there are some

modelling platforms available that can integrate all such processes, they are

complex, time consuming and sometimes area specific. The main focus of

this PhD research is to addresses this issue by devising an integrated

framework as an alternative approach to biophysical models. This

framework involves development of a simple yet dynamic node-link model

that is based on general principles and mathematical relationships that are

derived, and lumped to certain extent, from application of complex

biophysical models previously developed for the area. For example, if

groundwater model developed for an area indicates that 20% of its recharge

116

is contributed from surface irrigation for a range of irrigation application

rates then we do not have to run that groundwater model again and again to

find recharge for different crops in the same or similar areas.

Figure 3.14 provides an overview of different factors and processes

involved in understanding the water, energy, and greenhouse gas emissions

nexus. The first step in development of the framework is to gain an insight

of biophysical models for the study area and derive mathematical

relationships among various variables by observing behaviour and response

of key output variables to possible changes in input variables. An important

aspect that requires careful consideration in developing robust integrated

framework is to identify and incorporate any feedback mechanisms that

control interplay and non-linearity among various components and explain

the dynamic behaviour of a system as a whole. For example, Khan et al.

(2009c) described the interaction between evapotranspiration (ET) and

capillary rise for shallow watertable situation: the larger the ET, the larger

the capillary rise, then the larger the soil water content and the water stress

coefficient, which in turn increases ET, completing the positive feedback

loop. However, this mechanism cannot be explained if watertable

information from groundwater model is not taken into account. After

studying and analysing various models and their results for the study area,

an integrated node-link model is developed in this study that links data and

processes including climate, soil, crop water use, irrigation application

(methods and rates), irrigation scheduling, irrigation water supply system,

soil-water movement and groundwater response. Each simulation period

covers one year from July 1st to June 30th with a daily time step. However,

both the simulation time and computation time step can be varied relatively

easily. Each node is a irrigation supply point to the adjacent farm. The

model is developed in the development environment software called

VenSim (Ventana Systems, 2004). The model is relatively simple to use and

dynamic in nature where users can vary any parameter during run-time and

see the model response instantaneously both visually and numerically.

117

Figure 3.14: An inventory of factors involved in water and energy consumption and greenhouse gas emissions in irrigation supply systems: open channel network (left), pressurized pipe network (right) (Variables in dotted box are optional).

The overall goal of this research is to understand the water-energy nexus

and find an optimum match between water saved and energy used, as shown

Total number, volume and

timing of water

Type of irrigation supply system

Gravity based open Pressurized supply pipe

Conveyance & delivery losses

Channel seepage

Channel bed material

Evaporation loss

Climate conditions

Total irrigation demand

Total number, volume and

timing of water orders

Farm crop & soil

Farm irrigation method

On-farm storage

On-farm pumping

Total energy consumed

Energy consumed in construction

Channel length

Channel capacity

constraint

End-of-channel outflow

Rain rejections

Channel pre-filling

Conveyance & delivery losses

Pipe leakage

Age of the fittings

Pipe system capacity

Total irrigation

Farm crop & soil

Farm pressure irrigation system

Total irrigation volume

delivered

Total irrigation demand

Energy consumed

in pumping

Number of pumps in operation

Energy loss in pipe friction

Energy loss due to

Pipe network characteristics

Total energy

consumed

Minimum pressure

head requirement

Steady flow rate

Climate conditio

Greenhouse gas emissions

Greenhouse gas emissions

118

hypothetically in Figure 3.15, within the environmental and economic

constraints for four irrigation systems including flood, furrow, sprinkler and

drip. The approach is similar to Humphreys et al., (2005) where these

irrigation systems were compared side-by-side in terms of net irrigation

water use, net water productivity and yield.

Figure 3.15: Hypothetical curves of water savings and associated energy use

3.2.1 Application of System Dynamics Approach

System dynamics is a system modelling technique. A system level holistic

approach is required to understand the complex interactions amongst use of

water saving irrigation solutions, energy consumption and associated

greenhouse gas emissions and economic rationalization. To achieve

robustness in an integrated model it is vital to ensure that all interactions and

feedback mechanisms are well-understood. The system dynamics approach

will help us conceptualise discover and explain the underlying feedback

mechanisms at the scale of an irrigation scheme in this research. The use of

this approach is elaborated more in the later parts of the thesis.

119

3.3 Node-link model Development

It is possible to measure water savings and energy use of different irrigation

methods by conducting a comprehensive survey of the farms practicing

those irrigation methods and collecting extensive data. But those farms may

be operating at sub-optimal level and therefore, a daily simulation model is

considered a more appropriate, rapid and versatile tool that can be used to

explore maximum water and energy savings potential of different irrigation

supply and application methods. The case study area modelled in this

research consists of 13 horticulture farms (Table 3.5) covering an area of

about 291 hectares in MIA. Each farm grows a single crop only. All farms

are connected to a common water source which is located roughly at the

middle of upper side of the area. Water used to be conveyed under gravity to

the farms via a main earthen open channel which splits into two branch

channels. Those channel structures still exit but now water is conveyed to

these farms via pressurised pipes which are buried parallel to those open

channels and connected to a large water pumping station. A schematic of the

modelled case study area is given in Figure 3.16. The total length of the

distribution channels and also the adjacent irrigation pipes is 4,074 metres.

It is a branched water distribution system supplying water to farms in a line

and is best represented by a nodal network as used by Xevi and Khan

(2005). The node-link model was developed in system dynamics

environment using Vensim software (Ventana Systems Inc., 2004). Vensim

provides very basic building blocks, logical tools and mathematical

functions that can be used to model inter-linked processes and feedback

loops to develop a dynamic model and provides greater flexibility and

portability.

120

Figure 3.16: Schematic of farm nodes and supply channels/pipes (in parenthesis: channel/pipe length in metres)

In the node-link model, a node represented by Ni is created at each point on

the supply channels where an inlet structure is located for farm ‘i’. While a

farm may have more than one inlet, they are represented by only one node

in the model for computation simplicity. The model executes on daily time

steps and all calculations are carried out at the start of the day. Hence the

value of most of the variables for current day depends on previous day

calculations. The whole system can be driven in real-time by water demand

where each farm acts as a demand unit. For the real-time case, the water

demand of a farm is less than or equal to the calculated crop water

requirement depending on the availability of irrigation water which

sometimes may be limited due to constraints on capacity of the conveyance

system. For the on-farm water storage case, the temporal pattern of water

demand and supply will change. More details on schema of irrigation

N2

N3

N4 N5 N6

N7

N8

N9

N10 N11 N12 N13

WaterSource

N1a

N7a

(273) (552)(291)

(102)

(446) (150)

(368)

(100)

(585)

(508)

(271)

(324) (4) (94)

121

scheduling will follow in latter sections. The model structure is designed in

a way that it can easily be configured to model any crop on any soil type,

irrigation systems and irrigation supply networks. Nodes and links are the

building blocks of a nodal network. Further details about the nodes and links

of the developed model are given in the following sections.

Characteristics of a Node

Each inlet point from where water is supplied to a farm along the supply

channel is designated as a node, Ni, where ‘i’ represents the farm ID. The

farm ID is a number given to each farm for this research as listed in Table

3.5. The real farm numbers are not disclosed. At a given node all outflows

are balanced by inflow. The total flow demand at a given node is sum of

flow demands of all downstream nodes and is given by Equation 3.1:

∑ Equation 3.1

Where,

‘n’ represents the total number of nodes downstream of current node

At node, N2, which is a branching node (see Figure 3.16); the total flow is

the sum of the total flow demands on both the left and right branch.

Similarly, the total hydraulic head at a given node is the sum of hydraulic

heads required at the downstream nodes including head losses. When it is a

pressurised piped network then hydraulic head at a point also includes

pressure head. Other characteristics of a node include its elevation and

chainage. The termination node at each branch of the supply system

accumulates any surplus or shortfalls in daily irrigation supply.

Characteristics of a Link

The model considers each farm as one lumped demand unit. Each demand

unit (farm) is hydraulically connected to the supply system at a node. Each

node is hydraulically connected at its upstream and downstream node (if

any). The hydraulic connection between two nodes is called a ‘link’. The

length of each link is given in Figure 3.16. For the open channel supply

system, a link has the characteristics of an open channel while for a piped

122

supply system a link has the characteristics of a pressurised pipe. The

hydraulics of the two types of links is incorporated in the model. The

slope/grade of each link is determined by elevation and chainage difference

between its nodes. A link representing an open channel may have seepage

and evaporation losses. For a pipe link such losses are assumed to be zero.

Flow through a link is limited by its flow capacity.

3.3.1 Modules of the Developed Node-link model

The node-link model developed using Vensim software consists of the

following computational modules.

Crop water demand module

Calculates daily crop evapotranspiration for normal and water

stressed conditions. It is also capable of calculating crop irrigation

use under different irrigation application systems which includes

drip, sprinkler, furrow, and flood irrigation.

Irrigation application system module

The furrow system consists of 1.0m wide furrows with 0.5m on each

side of the tree or vine. The sprinkler system used in this study is

described as a non-overlapping under-canopy irrigation sprinkler

system. The drip system is a surface drip system with one drip line on

each side of a tree or vine. The variation in applied irrigation depth due

to non-uniform distribution, pressure variation, and irrigation time are

ignored for the sake of simplicity in modelling these systems in this

thesis.

Irrigation supply network (conveyance) module

This module can be configured to model either the open channel

irrigation supply system or the pressurised pipe irrigation system.

Energy use module

123

This module computes energy used in pumping and delivering the

irrigation water to the farms via a piped system. It also computes energy

used in operating the pressurised irrigation application systems

including sprinkler or drip. A separate spreadsheet model was developed

to account for other energy inputs in the annual crop production cycle.

Greenhouse gas emissions accounting (separate spreadsheet model)

Greenhouse gas emissions resulting from various energy inputs are

accounted in a separate spreadsheet model which is linked with the

energy accounting spreadsheet model.

Crop yield module

The crop yield module estimates reduction in crop yield resulting from

water shortages. The main reason of shortage of irrigation water is the

capacity constraint of the irrigation delivery system.

Economics module

The economics module is also a spreadsheet model, which gets input

data from the other modules. It includes crop annual budgets, financial

analysis of investment in water saving technologies and calculation of

indicators like water productivity.

Integration module (System dynamics module)

The integration module links all abovementioned modules. It targets to

identify feedback loops based on output of these modules. The purpose

of the integration module is to develop a water-energy policy framework

for irrigation systems based on interactions between different aspects of

the system considered in this thesis.

124

Figure 3.17: Flowchart of interaction among different modules of the node-link model

Each computational module is set up for a specific output that depends on

input from another module. The interaction among various modules of the

node-link model is graphically depicted by arrows in Figure 3.17.

3.3.1.1 Crop water demand module for Calculation of Crop

Evapotranspiration (ETc) for Various Irrigation Techniques

To accurately estimate energy use by a given irrigation method it is

important to first make an accurate estimation of water supplied to the crops

using that irrigation method. Furthermore, water application by different

irrigation methods needs to be estimated to compare water savings for a

given crop. This section corresponds to “crop water demand” module in

Figure 3.17. It explains the procedure adopted in the model for daily

calculation of evapotranspiration for crops irrigated with flood, furrow,

sprinkler or drip irrigation systems. The developed model has flexibility to

be configured to almost any irrigation system. The crop evapotranspiration

Irrigation supply system

Crop water demand

Crop yield

Field irrigation application system

Energy use

GHG emissions

Economic analysis

System dynamics

Water diverted

Water delivered

Start

125

is computed by the model for each crop grown on the 13 farms in the case

study area.

Evapotranspiration consists of two components; evaporation from the

wetted area of soil and the crop transpiration, a process by which water

acquired via root systems is lost through the leaves of a plant. Soil

evaporation is controlled by the amount of solar energy absorbed by the soil

surface, which depends on canopy cover of the crop and the soil moisture

level, which is maximum following rain or irrigation application. Each crop

has different rate and amount of evapotranspiration depending on the crop’s

physiological characteristics, development stage and on climate and the soil

type. To calculate crop evapotranspiration (ETc), the first step is to calculate

reference crop evapotranspiration (ETo) for the given area. For the study

area of this research, the daily ETo values were directly downloaded from

SILO website (http://www.longpaddock.qld.gov.au/silo/) for Griffith

CSIRO weather station (station ID 75174). The ETo calculation is based on

the FAO Penman-Monteith method (Allen et al., 1998). FAO Penman-

Monteith is the preferred method for ETo calculation as it closely

approximates reference grass ETo and explicitly incorporates both

physiological and aerodynamic parameters that control evapotranspiration.

The ETc for each crop is then calculated by (Equation 3.2):

Equation 3.2

Where, Kc is referred to as “crop coefficient”.

Determining Crop Coefficient (Kc)

In the Penman-Monteith method (Ellen et al., 1998) most of the effects of

the various weather conditions are incorporated into the ETo estimate.

Therefore, as ETo represents an index of climatic water demand, Kc varies

predominately with the specific crop characteristics and only to a limited

extent with climate. This enables the transfer of standard values for Kc

between locations and between climates. This has been a primary reason for

the global acceptance and usefulness of the crop coefficient approach and

126

the Kc factors developed in past studies. The Kc in Equation 3.2 predicts

ETc under standard conditions where no limitations are placed on crop

growth or evapotranspiration due to water stress, crop density, disease, or

salinity pressures.

Using Dual Crop Coefficient (Kc = Kcb + Ke) for Different Irrigation

Methods

Evapotranspiration comprises of two phenomena: transpiration from plants

and evaporation from soil. The single crop coefficient Kc, combines the

effect of crop transpiration and soil evaporation into a single time and space

averaged value. The amount of crop evapotranspiration obtained from

multiplication of the single crop coefficient and the reference crop

evapotranspiration for a given crop assumes soil evaporation from the entire

crop area. This is a more valid assumption for high plant density crops, for

example; cereal crops including wheat, rice and corn or pastures but not

fully applicable for estimation of evapotranspiration for horticultural crops.

The soil evaporation component of evapotranspiration is regulated by the

extent of wetted soil area and the uncovered (bare) soil area. Soil wetting

events like irrigation or rainfall affect the value of the crop coefficient due

to varying rates of evaporation from soil surface on a day-to-day basis. The

single mean crop coefficient value does not account for these varying

evapotranspiration rates resulting from wetting events. This research

postulates that aforementioned discrepancy in single crop coefficient is

more observable for the horticultural crops especially when irrigated with

high efficiency irrigation system and computed daily. Therefore, contrary to

the traditional single crop coefficient approach, this research adopts a dual

crop coefficients approach for estimation of crop evapotranspiration for

various irrigation application methods used for irrigating horticulture crops.

Each irrigation method has different frequency and different rates of

irrigation application which results in different extent of soil wetting (partial

wetting) under and around the plants’ canopy. Therefore, it was preferred to

use a dual crop coefficient that separates the effect of soil evaporation and

127

crop transpiration. The two coefficients are: the basal crop coefficient (Kcb)

which represents plant transpiration and the soil water evaporation

coefficient (Ke) which represents evaporation from the top soil surface. The

dual crop coefficient approach is relatively complex and computation

intensive but more precise for daily estimation of evapotranspiration in

horticultural crops. A similar approach was adopted by Johnson et al.,

(2004) for different irrigation systems for peach orchards. Similarly, Allen

and Robison (2007) revised ET estimations for 125 weather stations in

Idaho by employing the dual crop coefficient procedure and the ASCE

standardized Penman-Monteith method as the preferred method for water

transfer and administration by using dual crop coefficient method as

summarized by Allen et al., (2005) from Allen et al, (1998). For the dual

crop coefficient approach the Equation 3.2 for ETc is revised as Equation

3.3:

Equation 3.3

As indicated by Allen et al., (1998), the value of Ke is large following a rain

event or irrigation but the sum of Ke and Kcb can never exceed maximum

value of Kcmax which is determined by the energy available for

evapotranspiration at the soil surface. The value of Ke even drops to zero

when no further water can evaporate from the soil surface. The value of Ke

depends on remaining evaporable water content in the soil profile

represented by soil evaporation reduction factor, Kr, and hence a daily

continuous soil water balance computation is made for each farm in the

model developed for this research. This also involves estimation of deep

percolation which occurs when rainfall or irrigation is in excess of

prevailing soil moisture depletion. Apart from the soil evaporation reduction

factor (Kr), the other factor that impacts Ke is few, which is defined as the

extent to which soil surface of a given crop area is both wet and exposed to

sunlight and air ventilation. Mathematically, the value of Ke is determined

by Equation 3.4:

, Equation 3.4

128

Where,

Kr is soil evaporation reduction coefficient dependent on the

cumulative depth of water depleted (evaporated) from the top soil

(dimensionless),

few represents the fraction of the soil that is both exposed and wetted,

i.e., the fraction of soil surface from which most evaporation occurs

and,

Kcmax is the maximum value (upper limit) of Kc for a given growth

stage.

Equation 3.4 indicates that value of evaporation coefficient, Ke, depends on

two factors; amount of remaining water in top soil that can evaporate, and

the extent of surface area of top soil with evaporable water. The values of

Kcb for initial, middle and final growth stages of the crops used in this study

were taken from Table 17 in Allen et al., (1998). The Kcb values were

assigned to months according to local crop calendar based on Meyer (1996)

and are given in Table 3.9. Due to their deciduous nature there is a greater

variation in Kcb values for stonefruit. The values of Kcbmid and Kcbend in

Table 3.9 were corrected for local climatic conditions as analysed by Pereira

et al., (1999) for minimum relative humidity and average wind speed

differing from 45% and 2 m/s, respectively, using the following formula

(Equation 3.5) in the model.

0.04 2 0.004 45.

Equation 3.5

Where,

Kcb (Table) the value for Kcb mid or Kcb end (if ≥ 0.45) taken from

Table 17 in Allen et al., (1998),

u2 the mean value for daily wind speed at 2 m height over grass

during the mid or late season growth stage (m s-1) for 1 m s-1 ≤ u2 ≤

6 m s-1,

129

RHmin the mean value for daily minimum relative humidity during

the mid- or late season growth stage (%) for 20% ≤ RHmin ≤ 80%,

h the mean plant height during the mid or late season stage (m).

Table 3.9: Monthly basal crop coefficients (Kcb) for modelled horticultural crops (Allen et al., 1998)

Crops Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

Vines 0.15 0.15 0.15 0.65 0.65 0.65 0.65 0.65 0.65 0.40 0.40 0.40

Citrus 0.65 0.65 0.65 0.60 0.60 0.60 0.60 0.60 0.65 0.65 0.65 0.65

Stonefruit 0.35 0.35 0.60 0.60 0.60 0.85 0.85 0.85 0.85 0.85 0.60 0.35

The value of upper limits on evapotranspiration, the Kcmax, used in Equation

3.4 is given by Equation 3.6 (adopted from Allen et al. 1998):

0.04 2 0.004 45.

,

0.05 Equation 3.6

Equation 3.6 ensures that Kcmax is always greater or equal to the sum of

Kcb and 0.05.

Following irrigation application or a rain event the evaporation from the soil

surface is unrestricted hence Kr is set to 1 for such events in the model. As

the soil surface dries, Kr becomes less than one and soil evaporation begins

to reduce. Kr becomes zero when no water is left for evaporation in the

upper soil layer. For the latter case, Kr in the model is determined by

Equation 3.7:

, , Equation 3.7

Where,

De,i-1 is the cumulative depth of evaporation (depletion) from the soil

surface layer at the end of day i-1 (the previous day) (mm)

TEW is total evaporable water. It is the maximum cumulative depth

of evaporation (depletion) from the soil surface layer (mm),

130

REW is the readily evaporable water as a fraction of TEW. It is the

cumulative depth of soil-water evaporation when depletion = REW

(mm)

TEW is the amount of water that can be depleted by evaporation during a

complete wetting to halfway between complete drying, and was estimated

by Equation 3.8:

1000 0.5 Equation 3.8

Where,

θfc is the soil water content at field capacity (m3/m3)

θwp is the soil water content at wilting point (m3/m3)

Ze is the effective depth of the surface soil layer that is subject to

drying by way of evaporation (m)

The values of the abovementioned soil-water parameters for the soil types

used in this study are given in Table 3.10 (Hornbuckle and Christen, 1999;

Allen et al. 1998). The value of Kr remains unchanged from 1 until

cumulative depth of evaporation (De) exceeds REW. In the model De gets

adjusted for rainfall or irrigation and even reduced to zero when rainfall or

irrigation application depth is greater than or equal to De.

Table 3.10: Soil water characteristics used in calculation of soil evaporation reduction coefficient, Kr Soil type

(texture)

θfc

(m3/m3)

θwp

(m3/m3)

Ze

(m)

TEW

(mm)

REW

(mm)

Sandy loam 0.23 0.11 0.15 26.25 13.65

(52%)

Clay loam 0.34 0.18 0.10 25.00 9.5 (38%)

Referring back to Equation 3.4, the value of few, the fraction of the soil that

is both exposed and wetted, the second factor which determines value of Ke,

is calculated as follows.

Where the complete soil surface is wetted, as by precipitation or centre

pivots or flood irrigation, the fraction of soil surface from which most

131

evaporation occurs, few, is defined as (1- fc), where fc is the average fraction

of soil surface covered by vegetation and (1-fc) is essentially the

approximate fraction of soil surface that is effectively exposed to sun light.

However, for irrigation systems where only a fraction of the ground surface

is wetted like drip systems, few is limited to fw, the fraction of the soil

surface wetted by applied irrigation water. Therefore, in the model

developed, few, is calculated as Equation 3.9:

1 , Equation 3.9

The model developed for this study can simulate soil-water evaporation

from both partially wetting and fully wetting irrigation methods for crops

with different canopy covers. The values of fc the crop cover fraction for the

modelled citrus/stonefruit crops is computed using the steps listed in Figure

3.18. The average number of trees per hectare and the average canopy radius

were computed from collected data. The total canopy area per hectare for

wine grape was computed from the number of rows per hectare, number of

plants in a row, the distance between two plants in a row and average height

and width of the foliage. The values of fc computed this way for the three

crops are given in Table 3.11 and were used in the model. The soil wetted

area for different irrigation methods and crops was estimated through model

calibration and is explained under that section.

132

Figure 3.18: Steps involved in calculation of crop cover fraction for citrus and stonefruit Table 3.11: Values of wetted and vegetative covered soil fraction for irrigation methods and crops modelled in this study Crop cover fraction

fc Wetted soil surface fraction fw Comments

Citrus 0.69 Flood All crops 1 Whole field is wetted Stonefruit 0.62 Furrow

Vines 0.5

Citrus and Stonefruit

0.8 Wide bed

Sprinkler Vine 0.7 Narrow bed Drip Citrus 0.58 Stonefruit 0.56 Vine 0.5

Micro

Sprinkler

Citrus 0.72 Stonefruit 0.69 Vine 0.55

Sprinkler 0.9 Drip

Citrus 0.59 0.9 x *Canopy cover/10000

Stonefruit 0.63 0.9 x *Canopy cover/10000

Vines 0.6 1.0 x *Canopy cover/10000

* Total area (m2) covered by plants canopy/vegetative cover per hectare

Average no. of trees per hectare (n)

Average radius of tree canopy

(r)

Canopy area of a single tree, a = 3.14 * r2

Canopy cover of all trees in one hectare,

A = a * n

fc = A/10000

Area of circle

133

Adjusting ETc for Water Stress

The daily ETc value calculated by means of the above explained procedure

and using the dual crop coefficient (Kcb + Ke) is for standard field

conditions which include; no water stress, no salinity presence and no plant

diseases. The ETc is adjusted (ETc_adj) if necessary to non-standard field

conditions, where any environmental condition or characteristic is known to

have an impact on, or to limit, ETc. This research has considered the effect

of water stress which is one of such nonstandard field conditions. When the

soil is wet after rainfall or irrigation, the water in the rootzone has a high

potential energy and is easily taken up by the plant roots. As the soil dries,

the water has a low potential energy and is strongly bound by capillary and

absorptive forces to the soil matrix, and is less easily extracted by the crop.

When the potential energy of the soil water drops below a threshold value,

the crop is said to be water stressed (Allen et al., 1998) and crop

evapotranspiration rate is affected. To incorporate the impact of water stress

on the crop evapotranspiration in the model, the value of basal crop

coefficient, Kcb, is corrected via the introduction of water stress coefficient,

Ks, in the equation for adjusted evapotranspiration, ETc_adj, as given in

Equation 3.10 (Allen et al., 1998 and Khan et al., 2009c):

_ Equation 3.10

As indicated above the water stress coefficient (dimensionless), Ks, impacts

only crop transpiration. Value of Ks varies between 0 and 1. For soil water

stress conditions, Ks is always less than 1. Where there is no soil water

stress, Ks is equal to 1. Since different soils have different water holding

capacity and different crops root stock have different water extraction

potential, the numerical value of Ks depends both on soil texture and crop

type (effective rootzone depth) as depicted by the following equations.

, Equation 3.11

Where,

TAW is total available soil water in root zone (mm),

134

Dr,i-1 is the root zone depletion on previous day (mm) and,

RAW is the readily available soil water in the effective rootzone

(mm)

The variables given in Equation 3.11 are computed as follows.

Calculation of TAW and RAW

After heavy rainfall or irrigation, the soil water drains due to gravity until

field capacity is reached. As the soil water content is reduced due to plant

uptake and soil evaporation a point is reached when no more water can be

extracted by plant roots, called wilting point. The total available water

(TAW) can be defined with respect to total water storage capacity

accumulated over the effective root depth. Hence, TAW is the total amount

of soil water that a crop can extract from its root zone. The value of TAW

depends on soil type as well as the crop grown on it and is given by

Equation 3.12:

1000 Equation 3.12

Where,

TAW is total available soil water in root zone (mm),

θfc is the soil water content at field capacity [m3 m-3],

θwp is the soil water content at wilting point [m3 m-3],

Zr is the effective rooting depth of a given crop (m)

The values of θfc, θwp used in the model for sandy loam and clay loam soils

of the case study are given in Table 3.8 above. Plant root system extracts

water from different depths depending on crop type, irrigation application

depth, irrigation frequency and weather conditions. Therefore, an effective

root zone depth, Zr, is used for each crop. In this study, at initial model

development stage, the effective rooting depth is taken as half of the total

depth of root zone. The initial values of Zr for the modelled horticultural

crops as based on Allen et al., (1998) are given in Table 3.12. The amount

of water that can be held by a given soil type and thus available to plants

135

varies with depth of effective root zone, Zr. Thus irrigation should be aimed

at the effective root zone (Ramsay, 2007). The current study investigates use

of different irrigation application systems; each one of which has different

depth of irrigation. Therefore, value of Zr was carefully determined through

model calibration using parameter optimisation technique (Table 3.13) to

minimise leaching of irrigation water from the effective root zone that

affects efficiency of an irrigation system.

Practically, not all of the TAW is readily extractable by the plants to meet

their transpiration demand. Instead, as the soil water content decreases as a

result of ongoing plant water uptake, water becomes more strongly bound to

the soil matrix and is more difficult to extract. When the soil water content

drops below a threshold value, soil water can no longer be transported

quickly enough from soil towards the roots to respond to the transpiration

demand and the crop begins to experience stress and ETc rate begins to

decrease. The fraction of TAW that a given crop can extract from the root

zone without suffering water stress is referred to as the readily available soil

water, RAW, and is given as Equation 3.13:

Equation 3.13

Where,

p, is the depletion fraction (0 – 1)

The value of depletion fraction (p), which is the fraction of total available

water that can be depleted from the root zone before moisture stress occurs,

differs from crop to crop. The values used in this study are reported by

Allen et al., (1998) and given in Table 3.12 and are valid for ETc rate of up

to 5mm/day. Both the effective root zone depth (Zr) and depletion fraction

(p), are vital factors in determining how much water should be applied and

when. The root system of a plant trains itself to concentrate in the regularly

wetted area due to irrigation application and extract water as required.

Different irrigation methods have different extent of wetted area. For

example for flood irrigation 100% of the crop area is wetted but for drip

136

irrigation only 30% to 60% of the crop area is wetted depending on distance

between the drip lines. Therefore, to be able to determine crop water

requirement under different irrigation methods, it is necessary to adjust the

RAW of the soil for the actual wetted area as in Equation 3.14.

Equation 3.14

Table 3.12: Effective root zone and depletion fraction values used for the case study area Crop Root depth, Z

(m)

Effective root

depth, Zr (m)

Depletion

fraction, p*

Citrus and

stonefruit 1.2 0.6 0.5

Vines 1 0.5 0.45

* Source: Allen et al., (1998)

Replacing RAW by pTAW in Equation 3.11, the Ks is given by Equation

3.15:

, Equation 3.15

In the model the value of Ks remains equal to 1 unless root zone depletion,

Dr, exceeds RAW for a given crop.

The summary of abovementioned procedure for calculation of daily crop

evapotranspiration (ETc) in the node-link model using dual crop coefficient

approach is depicted in the flowchart shown in Figure 3.19.

137

Figure 3.19: Steps involved in calculation of ETc using dual crop coefficient as implemented in the model

A “causes tree” diagram for ETc adj for Farm No 6 as implemented in the

Vensim node-link model is shown in Figure 3.20, as an example of the

model structure. The “causes tree” lists the chain of the variables involved

in the computation of a given variable. The causal connections between the

model variables are defined by mathematical expressions by using Equation

Editor feature of the Vensim model development environment (Ventana

Systems, 2004).

Read-in reference ETo value for a given day, i

Read-in value for Kcb for that day, i

Adjust Kcb for local climatic conditions, if needed

Determine Ke for soil surface evaporation

Calculate crop evapotranspiration for day i ETc = (Kcb + Ke)ETo

Adjust Kcb for water stress (if any)

Compute Kcmax

Adjust Ke using Kr and few factors

Next day (i = i+1)

138

Figure 3.20: Causes tree for ETc adjusted for water stress for Farm No. 6

Rootzone Water Balance

In the crop water demand module, daily root zone water balance

computation is required to keep continuous track of crop water use and to

compute irrigation requirement. The water content in root zone is expressed

in terms of root zone depletion (Dr). Root zone can be conceptualised as a

container with fixed boundaries. A schematic of the root zone and the water

balance components are shown in Figure 3.21. Evapotranspiration is usually

the largest component of root-zone water balance. All water balance

components are expressed in terms of water depth in mm. In this study the

capillary rise due to shallow water table is ignored.

ETc_adj_6

ETo

Ev6(ETo)

Ke6

Kcb_citrus

Ks_6

RZ_depletion_6

p_Citrus

RAW_clay_loam_citrus

TAW_clay_loam_citrus

139

Figure 3.21: Schematic of root zone with water balance components (Adapted from Allen et al. (1998).

The root zone depletion (Dr,i) for a given day (i) is computed by using

Equation 3.16. The root zone depletion can be a negative number if depth of

irrigation applied or rainfall is more than antecedent soil-water depletion.

, , , , Equation 3.16

Where,

Dr,i-1 is previous day depletion, Ii is applied irrigation depth and Peff,i is

effective rainfall. It was assumed that deep percolation and runoff occurs

only if irrigation depth and/or effective rainfall exceeded current soil-water

depletion level. The deep percolation was taken as 50% of the excess water

depth for clay loam and 70% for sandy loam soils. Any residual amount

after deep percolation is classified as surface runoff in the model. However,

the model does not account for any runoff or deep percolation that can result

from rainfall. In practice the deep percolation may start soon after the

irrigation is applied and well before the rootzone is saturated, depending on

irrigation rate, irrigation duration and soil texture. In practice, the soil

moisture level is monitored by using soil moisture sensors. Growers develop

140

irrigation scheduling program through careful monitoring of crop water use,

soil moisture levels and crop appearance. The rootzone water balance

modelling is just a scientific approach to represent this irrigation scheduling

program.

Calibration of the Crop Water Demand Module

The crop water demand module was calibrated for drip irrigation installed

on all 13 farms using parameter optimisation capability available in Vensim

modelling environment. The actual annual irrigation volume applied per

hectare was calculated from available data on drip irrigation annual use for

these farms for the years 2003-04 to 2008-09. The model was calibrated for

drip irrigation for the first three years (2003-04 to 2005-06) and validated

for the last three years (2006-07 to 2008-09). The calibration parameters for

the three crops include effective rootzone depth (Zr), depletion fraction (p),

wetted area (due to irrigation) and irrigation adjustment factor (fadj) as given

in Table 3.13. The range of values for Zr and p for different crops given in

Table 3.13 is based on the two soil types represented in the model.

Similarly, the range of values for wetted area is based on number of fruit

trees per hectare. The wetted area is rounded to the nearest hundred for

simplicity. The depletion fraction value varies with crop type and the daily

ETc rate. Shallow rooted plants have depletion fraction as low as 0.3 at high

ETc rates and for deep rooted plants as high as 0.7 at low ETc rates.

Irrigation is applied when soil-water depletion reaches the level which is

equal to the deficit factor (fdeficit) times RAW. A value of deficit factor

greater than 1 represents deficit irrigation practice. Since more frequent

irrigations are undertaken with drip irrigation systems, the soil-water deficit

factor value was set to 0.5 for the calibration model.

Box 3.1: Pseudo code for “when and how much” to irrigate

141

The irrigation adjustment factor (fadj) is defined as the fraction of current

soil-water depletion by which the applied irrigation is increased to cover the

non-consumptive (e.g. deep percolation loss) use of irrigation water by the

plants. Therefore, the greater the value of fadj, the lower the irrigation

application efficiency of the irrigation system. The process of “when and

how much” to irrigate as implemented in the model is described by pseudo

code in Box 3.1. This code is processed for each simulation day for each

crop.

Figure 3.22: Vensim screen for setting optimisation parameters including optimisation decision variables

For each day and for each of the three modeled crops:

IF:

Rootzone depletion >= fdeficit x RAW

THEN:

Irrigation demand = Rootzone depletion x (1 + fdeficit)

ELSE:

Irrigation demand = 0

142

The objective of the model calibration was to adjust the model parameters in

order to minimise the difference between the modelled annual crop water

use per hectare and the actual annual crop water use per hectare for citrus,

stone fruit and wine grapes. Since no day-to-day irrigation data was

available, only the total annual crop water use data was compared. In the

model the total annual crop water use is calculated after the last time step

i.e. last simulation day, of a given model iteration. Vensim Optimisation

module was used to formulate a parameter optimisation problem. The

optimiser is based on Powell optimization algorithm (Powell 1978; Powell

and Yuan 1991). Powell optimization algorithm is a robust direction-set

search method. A set of directions are defined; the method moves along one

direction until a minimum is reached, then from there it moves along the

next direction until a minimum is reached, and so on, cycling through the

whole set of directions until the fit statistic is minimized for a particular

iteration. This parameter optimisation module randomly assigns each

designated calibration parameter a value between its specified range (refer

to Table 3.13) and then executes the developed Vensim crop water demand

model with the selected set of values of the calibration parameters and then

compares the modelled total crop water use to the actual total crop water use

for each of the three modelled crops. If the difference between the model

and actual value is reduced as compared to the previous iteration, it marks

that solution as the “best so far” solution otherwise the solution is discarded.

Also a penalty is imposed on the objective function (fitness value) if the

difference between modelled and actual crop water use exceeds a specified

threshold (1 ML in this case). In this way the optimisation module

completes a single iteration.

The optimisation problem is setup in such a way that it keeps on iterating in

search for the best solution and need to be manually interrupted to stop it. In

the current model the optimisation stops when there is no further

improvement noted in the objective function value, also referred to as the

‘fitness value’ (Equation 3.17) for a significant number of iterations, which

marks the achievement of an optimum solution. The number of iterations to

143

realise the optimum fitness value increases with the total number of decision

variables and their specified range of possible values. The current model

usually reached its optimum value after 25,000 simulations/iterations. The

objective function used in the developed optimisation model for the

parameters calibration is expressed in Equation 3.17. The optimisation setup

screen where optimisation parameters including ranges of decision variables

are defined is shown in Figure 3.22. It also includes other information like

optimisation search technique (Powell in this case), maximum number of

search iterations etcetera. The whole optimisation process as discussed

above is summarised in a detailed flowchart in Figure 3.23.

The calibrated values of the model parameters for each year are given in

Table 3.13. The average values of these calibration parameters were entered

into the final calibrated model. The comparison of actual irrigation rate and

that computed by the calibrated model for each year run is given in Table

3.14. The optimisation process successfully reproduced observed irrigation

application rates.

∑ Equation 3.17

Where,

i = 1 represents citrus, 2 represents stone fruit and 3 represents wine grapes.

In the Vensim model it is implemented as shown in Figure 3.24 below. The

negative sign indicates it is a minimisation optimisation problem.

144

Figure 3.23: Flowchart of parameter optimisation process as setup in Vensim optimisation framework

Last time step of current

simulation?

Define objective (fitness) function

Specify decision variables

Refer to Equation

YY

Refer to Table

Randomly select a solution for simulation, i

Difference bet. actual & modeled irrigation rates >

threshold?

Compute fitness penalty

Refer to Box 3.2

Compute fitness function value

Current fitness value less than

previous iteration?

Discard solution

Write solution to memory as “so far best”

Next simulation i = i+1

Compute difference bet. actual & modeled irrigation

Fitness penalty = 0

Refer to Box 3.3

145

Figure 3.24: Setup screen for the objective function definition in Vensim

Box 3.2: Pseudo code for penalty on fitness value

Box 3.3: Pseudo code for selection process of the best solution with

optimum fitness value

At the final time step of model iteration, for each of the three modeled crops: IF:

Abs(actual irrigation rate – modeled irrigation rate + Fitness penalty) > Fitness value of previous iteration

THEN: Start a new iteration with a new set of decision variables

ELSE: Write current set of decision variables and fitness value as the best solution achieved so far

For each of the three modeled crops: IF:

Abs(actual irrigation rate – modeled irrigation rate) > 1 (ML/ha) THEN:

Fitness Penalty = 100 ELSE:

Fitness Penalty = 0

146

Table 3.13: Calibration variables and their calibrated model values for years 2003-04, 2004-05 and 2005-06

Variable Range 2003-

04

2004-

05

2005-

06Average

Zr_citrus (m) 0.33 –

1.0 0.72 0.71 0.80 0.74

p_citrus 0.4 –

0.6 0.50 0.40 0.52 0.47

Wetted_area_citrus (m2/ha) 4000 –

7000 6200 5900 6000 6000

Zr_stone_fruit (m) 0.38 –

1.13 0.44 0.47 0.46 0.46

p_ stone_fruit 0.4 –

0.6 0.52 0.48 0.40 0.47

Wetted_area_stone_fruit

(m2/ha)

4000 –

7000 5300 5000 5000 5100

Zr_vines (m) 0.38 –

1.13 0.88 1.04 1.06 0.99

p_vines 0.35 –

0.55 0.41 0.49 0.45 0.45

Wetted_area_vines (m2/ha) 4000 –

6000 5100 5100 5000 5100

Irrigation adjustment factor 0.02 –

0.2 0.024 0.038 0.069 0.040

Fitness value (difference

between actual and optimised

solution)

0.0019 0.0012 0.0022

Table 3.14: Comparison of irrigation application rates (ML/ha) between the actual and the calibrated model

Year Citrus Stone

fruit Vines

2003-

04

Model 5.4 5.6 4.09

Data 5.4 5.6 4.1

2004- Model 5.50 5.50 4.199

147

05 Data 5.5 5.5 4.2

2005-

06 Model 5.499 5.700 4.801

Data 5.5 5.7 4.8

The soil-water availability parameters are calculated using the calibrated

data on effective rootzone depth and the depletion fraction for the modelled

crops and the soils and given in Table 3.15.

Table 3.15: Soil-water availability parameters using calibrated model data for the three crops

Clay Loam Sandy Loam

TAW

(mm)

RAW

(mm)

TAW

(mm)

RAW

(mm)

Citrus 118.4 55.6 88.8 41.7

Stone fruit 73.6 34.6 NA*- NA*-

Wine

grape

158.4 71.3 118.8 53.5

NA* not applicable as model only includes stone fruit on clay loam

Once the model was calibrated for crop water use, it was used for simulating

various scenarios which are discussed in the following chapters of this

thesis.

Validation of the Crop Water Use Module

The crop water use module of the node-link model computes daily irrigation

requirement for each crop on each of the modelled 13 farms for a specified

irrigation application method. Irrigation application is driven by soil water

depletion from the effective rootzone. The soil water depletion is computed

on a daily time step by the rootzone water balance approach. Rootzone

water balance components including loss of water due to soil evaporation,

crop transpiration, deep percolation, effective rainfall and irrigation depth

(when applied) are accounted for in the calculation of daily soil moisture

depletion. Irrigation is applied when soil moisture depletion is equal to the

readily available moisture times the ‘deficit factor’.

148

In contrast to traditional fixed-interval irrigation scheduling models, the

developed model is essentially a demand based irrigation model with

variable irrigation intervals. During hot and dry seasons the soil water

depletes faster and crop irrigation demand is higher and more frequent than

relatively cold and wet periods. This approach more closely represents

actual irrigation practices in the study area. Also the model assumes a non-

deficit irrigation practice. In this case the depth of each irrigation application

is such that the current depletion is reduced to zero and thus effectively

bring soil moisture back to the readily available moisture level provided that

no incidents of constraints on water delivery system are experienced. A

value of the deficit factor greater than 1 in the model gives effect to deficit

irrigation practice which reduces the irrigation application depth as well as

the irrigation application frequency and in this case both the crop

transpiration (Ks value becomes less than 1) and soil evaporation (Kr

becomes less than 1) rate begin to reduce and are limited by available soil

water once the soil water depletion exceeds readily available moisture level.

Table 3.16: Model validation by comparing actual and modelled drip irrigation application rates (ML/ha) for horticultural crops on 13 farms in the study area (Figure in brackets is total number of irrigation days)

Crop 2006-07 2007-08 2008-09 Average (ML/ha)

Citrus (ML/ha) Data 6.0 4.9 5.0 5.30

Model 6.01 (212) 5.55 (198) 5.43 (153) 5.66

Stonefruit (ML/ha) Data 6.0 5.0 5.7 5.57

Model 5.97 (159) 5.53 (159) 5.50 (158) 5.66

Vines (ML/ha) Data 4.5 4.0 4.1 4.2

Model 4.59 (81) 4.10 (59) 4.19 (60) 4.29

The validation of the calibrated crop irrigation demand model was carried

out for three years from 2006-07 to 2008-09. Table 3.16 presents actual and

modelled water use in megalitres per hectare for citrus, stonefruit and vines

for the three years from 2006-07 to 2008-09 with all modelled farms

irrigated with drip irrigation system. There is a reasonable agreement

between the actual and modelled irrigation application rates using the

calibrated model, especially between three-year modelled and field data

149

averages. The values of water use for each crop are averaged for all the

farms growing that crop. The model does not consider physical capacity

constraints on irrigation supply and generally suggests higher irrigation rates

than the actual ones. The actual irrigation rates are lower due to channel

capacity constraints during peak irrigation demand times or due to deficit

irrigation.

The number in brackets in Table 3.16 represents the total number of days

the irrigation was turned on for that crop. The average irrigation days for

citrus are higher than the other crops due to the fact that citrus are non-

deciduous and evapotranspiration continues through all seasons. In contrast,

both the stone fruits and wine grapes are deciduous plants and lose their

foliage in autumn.

The other reason for least number of irrigation days for wine grapes is the

fact that those farms are dominated by clay loam soils which exhibit a wider

range of soil moisture retention capacity than sandy clay soils of the other

farms. Moreover, some wine grape farms practice regulated deficit irrigation

which is not implemented in the developed model. As indicated in Table

3.6, 2006-07 was the driest year of the modelled period which resulted in

the highest number of irrigation days and highest irrigation application rates

for all crops. Another reason for lower water use on these perennial crops in

the MIA is the fact that water traded out of the area doubled in 2007-08 (MI

AR 2008) as compared to the previous year (about 100.8 GL in 2007-08 as

compared to 51.2 GL in 2006-07) owing to high water prices in the water

trade market due to continued drought conditions throughout the Murray-

Darling Basin since 2002-03. A more detailed account of the water being

traded out of MIA is given by ACIL Tasman (2009).

Moreover, the following assumptions that were made in the developed crop

water use model should be noted while comparing actual and modelled

water use for each crop.

The model assumes the same irrigation scheduling rules for all farms

for a given crop regardless of soil type.

150

The water use of a crop grown on different soil types across the

modelled farms was averaged regardless of soil type.

Once started, an irrigation event may last for at least 24 hours.

The average irrigation application rate (ML/ha) for over 900 horticulture

farms in MIA is listed in Table 3.17 from 2004-05 to 2008-09. Although for

horticultural crops, the difference in crop variety, irrigation system type,

irrigation management (such as regulated deficit irrigation in vines) and the

age of plantings makes an average water use figure not so representative;

there is a reasonable agreement between average water use figures for the

whole MIA as reported in Table 3.17 and the modelled ones for the case

study area within MIA as given in Table 3.16. The reported water use by

“other fruits” in Table 3.17 includes all fruits grown in MIA except for

citrus and vine and is not limited to stonefruit only. Therefore it is not

entirely comparable to modelled water use for stonefruit.

Table 3.17 also indicates that an additional 2,445 ha have been converted

into vineyards from 2004 to 2009. This was mainly due to the reason that

vines were favoured crops under tight water availability and the ever-

increasing demand for Australian wine locally and overseas.

Table 3.17: Reported water use (ML/ha) for fruit and vines (Figures in braces are total crop area in ha) (Sources: MIA 2005, 2006, 2007, 2008, 2009).

Crop 2004-

05

2005-

06

2006-

07

2007-

08

2008-

09

Average

(ML/ha)

Citrus

(ML/ha)

6.1

(8364)

5.5

(8423)

5.7

(8434)

5.0

(8357)

5.4

(8216) 5.5

*Other

fruit

(ML/ha)

3.8

(1881)

3.9

1953)

4.1

(2197)

3.5

(2546)

4.6

(2538) 4.0

Vines

(ML/ha)

5.5

(16798)

5.1

(17151)

4.5

(18160)

3.9

(18866)

4.0

(19243)4.6

151

It can be concluded from the above discussion that the developed irrigation

water use model is valid for the study area and reasonably extendable to

model irrigation water use by the whole horticultural area of MIA.

To explore the full extent of water and energy linkages, all scenarios

modelled and discussed in the following chapters are modelled with non-

deficit irrigation unless otherwise stated.

3.3.1.2 Irrigation supply network (conveyance) module

All farms are connected to a common water source which is located roughly

at the upper midpoint of the area. Water can be conveyed under gravity to

the farms via a main earthen open channel which splits into two branch

channels. Those channel structures still exist but now water is conveyed to

these farms via pressurised pipes which are buried parallel to those open

channels and connected to a large water pumping station. A schematic of the

modelled irrigation supply system is given in Figure 3.16. The total length

of distribution channels is 4,069 metres. The total length of the irrigation

pipes is almost the same with an extra 5m of suction side pipe. The node-

link model is capable of turning on either of the two irrigation supply

systems. The left branch channel supplies irrigation water to four farms with

a total irrigated area of 126.58 hectares and the right branch channel which

is longer in length and serves a total irrigated area of 110.13 hectares. A

layout of different model components and variables as developed using

Vensim dynamic model development environment is given in Figure 3.25.

This module also includes the option of designing on-farm storages.

152

Figure 3.25: User interface of the developed dynamic model in Vensim model development environment

Modelling of Open Channel Supply System

Some physical features of the open channel system in the case study area are

given in Table 3.18. All open channels are unlined earthen channels and

roughly with trapezoidal cross sections. Since the channel length is not large

and travel time is too short, no flow routing was implemented in modelling

the open channel flow.

Table 3.18: Physical features of the open channel system in the case study area Item Value Value

Total length of open

channels (m) 4,069

Length of main supply

channel (m) 825

Length of left (top)

branch channel (m) 989

Ph1

PumpEfficiency

N2

N3

N4

N5

N6

N7

N8

N9

N10 N11 N12N13

ETc_adj_3

ETc_adj_4

ETc_adj_5

ETc_adj_6

ETc_adj_7

ETc_adj_8

ETc_adj_9

ETc_adj_10

ETc_adj_11

ETc_adj_12

ETc_adj_13

TotalDynamic

Head

Reservoir

N1

Total_DutyFlow_Rate

TotalPumpPower

PumpDischarge

No._of_Pumps

O3vel

O4vel

O5vel

O6vel

O7vel

O8vel

O9vel

O10velO11vel O12vel

O13vel

Rn1

Rn2

Rn3

Rn4

Rn5

Rn6

Rn7

Rn8

Rn9

Rn10

Rn11 Rn12

Rn13

f1

f2

f3

f4

f5

f6

f7

f8

f9

f10

f11f12

f13

Hf1

Hf2

Hf3

Hf4

Hf5

Hf6

Hf7

Hf8

Hf9

Hf10

Hf11

Hf12 Hf13

Ph2H2

Ph3

H3

H4Ph4

H5Ph5

H6Ph6

H7

Ph7

H8

Ph8

H9

Ph9

H10

Ph10

H11

Ph11

H12

Ph12

H13Ph13

H1

N1a

ETc_adj_1a

O1avel

Ph1a

Hf1a

f1a

Rn1a

H1a

N7a

ETc_adj_7a

Hf7a

f7aPh7a

Rn7a

O7avel

H7a

CumulativeEnergy_Use

CumulativeIrrigation_Volume

I1a

I10

I11

I12

I13

I3

I4

I5

I6

I7

I7a

I8

I9

RZ_depletion1a

BrakePower

Motor_Efficiency

Total_Dynamic_Head

200

150

100

50

01 53 105 157 209 261 313 365

Time (Day)

Total_Dynamic_Head : WithIrrRateDrip m

RZ_depletion_6

Ks_6

RZ_depletion_3

RZ_depletion_7

RZ_depletion_8

RZ_depletion10

RZ_depletion11

ETc6

RZ_depletion_5

Ks_5

ETc1a Ks_1a

ks_3ETc3

RZ_depletion12Ks_12

ETc12

RZ_depletion13Ks_13

ETc13

Ks_7ETc7

ETc8

ETc5

Ks_10

ETc10 Ks_11

ETc11

RZ_depletion_4

Ks_4ETc4

RZ_depletion7a

Ks_7aETc7a

RZ_depletion_9

Ks_9

ETc9

Ks_8

RZD_6

DP_6

RZD_5

RZD_4

RZD_3

RZD_1a

RZD_7

RZD_7a

RZD_8

RZD_9

RZD_10

RZD_11

RZD_12

RZD_13

DP_5

DP_1a

DP_12

DP_13

DP_3

DP_7

DP_10

DP_11

DP_4

DP_7a

DP_8

DP_9

Ev6

Ev5

Ev1a

Ev3

Ev7

Ev10

Ev11

Ev4

Ev7a

Ev8

Ev9

Ev12

Ev13

Cum_evap_lossCum_DP_loss

153

Length of right

(bottom) branch

channel (m)

2,255

Average slope of main

channel (m/m) 0.0003

Average slope of left

(top) branch channel

(m/m)

0.001

Average slope of right

(bottom) branch

channel (m/m)

0.001

Manning’s roughness

coefficient (n) value 0.03

Dimensions of main

channel

Bottom width (m) 1.5

Side slope 2:1

Maximum depth (m) 1

Dimensions of branch

channels

Bottom width (m) 1.25

Side slope 2:1

Maximum depth (m) 0.75

Since flow is not routed through the open channels, the model only

calculates the maximum flow capacity of each channel to limit the

maximum irrigation flow rate. The open channel maximum flow capacity is

calculated using the Manning’s formula for each of the branch channels as

given in Equation 3.18.

⁄ ⁄ 86.4 Equation 3.18

Where,

154

A, is the cross section area of the trapezoidal channel,

n, is the Manning’s coefficient (0.03 for earthen channel with some

vegetation),

R, is the hydraulic radius,

S, is the average slope of the channel and 86.4 is the conversion factor from

m3/sec to ML/day.

The maximum flow capacities for the three channels as calculated from

using Equation 3.18 are given in Table 3.19.

Table 3.19: Maximum flow capacities of the open channels in the case study area Channel Name Maximum flow capacity (ML/day)

Main channel 93.55

Left (top) branch channel 90.58

Right (bottom) branch channel 110.9

Calculation of Open Channel Flow Losses

Unlined open channels are prone to two water loss processes including

evaporation from channel water surface and the seepage from bottom and

banks of the channel. Obviously these losses take place only when there is

water in the channel. During the irrigation season the supply channels are

pre-filled and then continuously kept at those levels to maintain some level

of supply reliability. This leads to a large proportion of diverted irrigation

water being lost. The formula for computing daily evaporation loss in the

model is given in Equation 3.19 and that for daily seepage loss is given in

Equation 3.20.

, , 10 , 0, , Equation 3.19

Where,

Eloss,i,j, is the evaporation loss (ML/day) from reach ‘j’ of the open channel

on day ‘i’,

Ei, is the pan evaporation rate (mm/day),

Lj, is the length of reach ‘j’ (m),

155

T, is the top width of the open channel (m), and

Qi,j is the flow rate (ML/day) on day ‘i’ of the reach ‘j’ of the open channel.

The maximum possible seepage from a given reach of the channel is limited

to the flow rate in that reach.

, , 10 , 0, , Equation 3.20

Where,

Sloss,i,j, is the seepage loss (ML/day) from reach ‘j’ of the open channel on

day ‘i’,

Si, is the average seepage rate on day ‘i’ (mm/day), and b, is the bottom

width of the open channel (m).

The maximum volume of channel seepage loss or evaporation loss is also

limited by the flow volume in the channel. Both the evaporation and the

seepage loss are set to zero if the channel flow is zero. Seepage loss and

evaporation loss from all reaches is summed up to estimate the total

conveyance loss.

The flow volume in the supply channels is driven by irrigation demand for

each node (farm) which is computed by Irrigation Demand Module. The

daily flow rate at any node ‘n’ is represented by Qn and is given by Equation

3.21.

, , ,

Equation 3.21

Where,

Qn-1 is the flow rate at the previous node,

Dn-1 is the irrigation demand at the previous node,

Sloss,n-1 to n and Eloss,n-1 to n is seepage and evaporation loss from the channel

reach from the previous node (n-1) to the current node (n), respectively. As

an example, to illustrate how the flow at a node is computed, a “causes tree”

156

for flow at Node 9 is shown in Figure 3.26. The variable names in Figure

3.26 are self-explanatory to some extent. For example, A8, is the total area

of Farm No.8; D8, is the total irrigation demand for a given day; E, is the

pan evaporation rate; L9, is the length of channel reach between Farm 8 and

Farm 9; T, is channel top width; A, is cross sectional area of the channel;

EvLoss9, is the evaporation loss from channel reach; and SpLoss8, is the

seepage loss from the channel reach.

Figure 3.26: Causes Tree for flow volume at Node 9 of the open channel supply system

Q9

D8

RZ_depletion_8

A8

Deficit_factor

Irr_Adjustment_Factor

RAW_sandy_loam_vine

Wetted_area_vine

EvLoss9

(D8)

E

(L9)

(Q8)

T

FlowCapR

A

n

R

SlopeR

Q8

D7a

EvLoss8

(FlowCapR)

Q7a

SpLoss8

SpLoss9

b

(D8)

L9

(Q8)

SeepageRate

157

Modelling of Pressurised Pipe Supply System

The layout of the pipe network is the same as shown in Figure 3.16. There is

a single pumping station with several pumps that run in parallel as per the

required duty flow to supply pressurised water to the main pipe. Like open

channel system in the case study area, the pipes are configured into main,

left pipeline and right pipeline. To supply required flow volumes with

appropriate hydrodynamic pressure heads the pipe sizes are gradually

decreased from 0.5 m diameter suction pipe at pumping station, 0.45m

diameter delivery (main) pipe to 0.2 m diameter tail-end pipe. Due to

topographic and other physical constraints, all pipes are not installed at the

same elevation and the model takes into account the elevation difference of

the pipes. All pipes are made of modified Poly Vinyl Chloride (mPVC)

which is a suitable material for pipes manufactured for pressure applications

including water supply and has a long service life of up to 100 years

(WSAA 2009).

Table 3.20 lists the main characteristics of the pipe system. Other

components of the pipe system including elbows, pressure regulators and

flow valves are installed as required and incorporated in the developed pipe

supply model.

In contrast to the open channel irrigation supply system, the flow losses in

the pipe supply system are assumed to be zero.

Table 3.20: Main characteristics of the pipe system

Item Value Value Total length of pipe system (m)

4,074

Length of suction pipe 5 Length of main supply pipe (m)

825

Length of left (top) pipeline (m)

989

Length of right (bottom) pipeline (m)

2,255

3.3.1.3 Energy and GHG Emissions Computation Module

158

Energy used in irrigation is one of the major energy inputs for crop

production. The energy computation module calculates the energy used in

moving irrigation water from water source to the farm outlet and the energy

required to operate the pressurised irrigation application system on the farm

which may be a drip/trickle irrigation system or a sprinkler irrigation

system. The energy consumed in other farming operations during the crop

life cycle is computed separately and not by this module. The pressure

requirement and irrigation application rate of the high efficiency irrigation

systems depends on their type and configuration. Consequently, the energy

consumed by each irrigation system varies. For the current case study, the

total energy requirement can be split into two components; (i) energy use by

irrigation supply/conveyance system and (ii) energy exploited by on-farm

irrigation system. The computation procedure for these two energy

components is discussed in detail below. It should be noted here that in

addition to energy use in irrigation, energy is also consumed directly or

indirectly in many other farming operations to grow crops including

cultivation, labour, spraying, fertigation, harvesting etcetera. The Energy

Computation Module in the model only estimates energy employed in

irrigation operations. Other energy inputs are discussed separately where

needed.

Total Energy Head Calculations

The layout of the piped irrigation supply system is shown in Figure 3.16.

Both branches of the pipeline are connected to the combined pumping

station via the main pipe. The computation of the total energy head (H)

required at the pumping station begins from both terminal nodes (final

outlet) of the two branch pipelines to the confluence node and then to the

main inlet node (pump water delivery node). The pressure head required at

the terminal node (last farm outlet on each pipe branch) is kept constant.

The value of the required constant pressure depends on the type of the

irrigation system (pressurised or gravity based) to be operated as indicated

in Table 3.21. The total energy head (Hn) required at the terminal node, i.e.

159

Node 6 or Node 13 (n = 6 or 3), is given by Equation 3.22. All terms in

Equation 3.22 are expressed in metres.

Equation 3.22

Where,

, is the pressure head required at farm outlet to operate the pressurised

irrigation system and is user input to the model,

, is the velocity head of the flow at farm outlet and is dependent on

current irrigation demand of that farm, and

, is the elevation difference between the supply pipe and the farm outlet.

A positive value of Zo shows that the farm outlet is higher than the supply

pipe and vice versa.

Table 3.21: Indicative pressure head requirement at each farm outlet Farm irrigation method Drip Sprinkler Furrow Flood

Outlet pressure head (m) 30 45 0 0

The energy head required at a given farm outlet node (n) other than the

terminal node is given by Equation 3.23 or Equation 3.24. A typical

schematic of the supply pipe and outlet pipe to farms is given in Figure

3.27. It is essentially a branched pipe situation where each outlet pipe is a

branch from the main supply pipe. Bernoulli’s energy equation was applied

between point A and C (Equation 3.23) and between point A and B

(Equation 3.24).

Equation 3.23

: Equation 3.24

Where,

, is the total energy head at node n (farm outlet B is located at node n)

160

, is the total energy head at next downstream node, n+1 (farm outlet D

is located at node n+1),

, is the head loss due to internal pipe friction from node n to node n+1,

, is the head loss due to sudden reduction (if any) in pipe size from

node n to node n+1. The value of the contraction loss coefficient depends

on the ratio between the sizes of the two pipes. Table 3.22 indicates pipe

diameters where such contraction occurs and the corresponding values

based on Giles et al., (1993) as used in the model in Equation 3.23. The term

in Table 3.22 is the ratio between the diameters of the larger and the

smaller pipes.

, is the head loss when flow enters into the smaller diameter pipe from

the larger diameter supply pipe as shown in Figure 3.27. The coefficient ,

is the entrance loss coefficient and set to a usual value of 0.5 in the model.

The diameter of each farm outlet pipe is 0.2 m. The flow at a given farm

outlet can be regulated by a discharge value and can be completely shut

down.

Figure 3.27: Schematic of supply pipe with outlet pipes to two farms

Supply pipe

.A

.C

.B

Farm outlet

Farm outletn

n+1.D

161

Table 3.22: Pipe size variations and the corresponding sudden contraction loss coefficient Cc values

Diameter of

larger pipe

dn

Diameter of

smaller pipe

dn+1

dn/dn+1

Sudden Contraction

Loss Coefficient

Cc

0.45 0.375 1.2 0.08

0.45 0.25 1.8 0.34

0.375 0.25 1.5 0.22

0.25 0.2 1.25 0.1

As mentioned above the outlet velocity at each farm outlet is known because

it is equal to the irrigation demand of that farm; even if the irrigation

demand is restricted due to supply constraints, the outlet velocity is adjusted

automatically in the model. Continuity equation is applied to calculate flow

velocity in the pipe. For example to find flow velocity at point A in Figure

3.27, continuity equation is applied between point A and point B and point

C as give in Equation 3.25.

:

∴ Equation 3.25

In the model the head loss due to pipe friction ( is computed for each

pipe element using the Darcy-Weisbach formula as given in Equation 3.26.

Equation 3.26

Where,

, is the friction factor,

, and represent length (m) and diameter of the pipe (m), respectively,

, is the flow velocity through the pipe (m/s).

The value of the head loss due to pipe friction, , depends on size of the

pipe and the velocity of flow. The friction factor, , depends on the nature

162

of flow and pipe internal surface. The nature of flow i.e. either laminar or

turbulent, is determined by the Reynold’s Number (Rn) for a given pipe size,

flow velocity and the fluid type. The modified PVC pipes modelled in this

study are relatively hydraulically smooth. For flows with Rn less than 2000,

is computed by using Equation 3.27 (Daugherty et al., 1985).

2000 Equation 3.27

For transitional and turbulent pipe flows, is computed by using Equation

3.28 (Blasius, 1913).

.. 2000 Equation 3.28

To compute total energy/power (ETotal) required to pump surface water and

supply to the irrigation network when required, the total dynamic head

(TDH) and duty flow rate of the system is calculated. The total dynamic

head is the total equivalent height that a fluid is to be pumped, taking into

account all friction losses in the pipe. In other words, TDH is the work done

by the pump(s) per unit volume of fluid. The TDH of the supply network in

the model is computed simply by adding all pressure head, velocity head,

elevation head and equivalent head loss due to friction and minor losses,

from tail end of the pipeline to the pumping station. The duty flow rate (QT)

is computed by simply adding flow (m3/s) required at each farm outlet. The

total pump power required to deliver total duty flow rate against TDH (m) is

given by Equation 3.29.

Equation 3.29

Where,

, is the total power in kilo watts (KW) that needs to be rendered to the

pump to deliver flow at the required pressure. It is the actual power required

to be transferred from the motor to the shaft of the pump and therefore also

referred to as “Shaft Pump Power”,

163

and , are the fluid density (1000 kg/m3 for water at normal temperature)

and gravity (9.81 m/s2), respectively,

, is the pump efficiency. As the pump efficiency decreases, greater shaft

power is required to run the pump to deliver the same flow.

Since the pump is driven by an electric motor, the efficiency of the motor

also needs to be incorporated to calculate the total power required from the

electricity grid. The power delivered by the shaft of the electric motor to

drive the pump is called “Brake Power” and is computed by dividing the

Shaft Power by the efficiency of the motor as given by Equation 3.30. The

total energy cost of pumping is computed using Brake Power (Etotal).

Equation 3.30

Where,

, is the electric motor efficiency in converting electric power to

mechanical power. Henceforth the brake power (Etotal) will be referred to as

energy use in operating the irrigation supply system in this thesis.

For the large communal irrigation pumping system, it is not possible for a

single pump to supply the required flow demand. Therefore, in this case

study there are a number of identical centrifugal pumps that are arranged in

parallel configuration at the pumping station. An electronic pump control

system turns on or off the pumps as required depending on the duty flow

rate. In the developed model, the number of the identical pumps turned on at

a time and operating in parallel is computed by dividing the total duty flow

rate by the rated flow rate of a pump as given in Equation 3.31.

Equation 3.31

Where, q is the flow rate of a pump when operating at peak efficiency.

Although theoretically flow is doubled when an identical pump is turned on

in parallel, in practice, both the flow rate and head are increased. The model

conducts all abovementioned calculations each time step which is 1 day.

164

A number of code excerpts from Vensim for selected variables are given in

Appendix A.

Accounting Energy Use and Greenhouse Gas Emissions

All processes and practices in the crop production cycle from land

preparation to harvesting involve energy exploitation in one form or the

other. All energy inputs, excluding solar energy, have associated greenhouse

gas emissions when exploited. Energy used in irrigation, depending on the

irrigation delivery and application method, usually accounts for a major

portion of all energy inputs. A detailed flowchart of steps involved in

accounting for energy use and associated carbon emissions in irrigated crop

production cycle is given in Figure 3.28. As suggested by Barber (2004) and

Dovring (1985) it is important to establish the limits and boundary of

analysis of a study to make it comparable to other studies. The energy inputs

and resulting greenhouse gas emissions were considered for each of the 13

farms in the case study area. Energy and greenhouse gas emissions were

accounted for each of the three horticultural crops grown in the case study

area. A structured approach was adopted based on previous studies (Hatirli

et al., 2006; Ozkan et al., 2004; Ozkan et al., 2007; Yaldiz et al., 1993;

Koctürk and Engindeniz, 2009) to account for energy inputs and energy

outputs for a complete one year cycle of these crops. In this context, the

energy inputs were categorised into two groups as explained in the

following.

Direct Energy Inputs

Direct energy inputs are those products which are consumed on-farm in

operations like irrigation management, irrigation water pumping,

cultivation, harvesting, post-harvest processing, food production, storage

and the transport of agricultural inputs and outputs. Solar energy is the

biggest direct energy input but it is not considered in this analysis because

of zero cost. Direct energy inputs primarily include various primary and

secondary fuel sources that are used to operate farm machinery and

165

irrigation pumps. A list of major direct energy inputs considered in this

study includes the following:

Electricity,

Fuel (diesel, petrol, gas) and lubricants,

Machinery use,

Irrigation,

Human labour,

Farmyard manure

Indirect Energy Inputs

Indirect energy inputs are in the form of sequestered energy in fertilizers,

herbicides, pesticides, and insecticides. Indirect energy consumption refers

to energy inputs in manufacturing the equipment and other goods and

services that are used on-farm (Pimental, 1992). This includes energy used

in production of fertilisers, tractors, agrochemicals, irrigation equipment and

harvesting equipment. Other indirect energy inputs include seeds, and

energy used in installation of water supply infrastructure and construction of

on-farm storages.

Different inputs in farm operations contain different levels of intrinsic

energy. For that reason, all forms of direct and indirect energy inputs as

used on the horticulture farms in the case study area were converted into a

common equivalent energy unit of kilo-watt-hour (KWh) to account for

total energy use and to make them comparable based on the current

literature and given in Table 3.23. To compute the total use for a given

energy input, the actual input quantity is multiplied with its equivalent

energy unit.

The developed node-link model only computes energy consumed in

irrigation water pumping and that used in operating the pressurised

irrigation systems. A separate spreadsheet model was developed for energy

and greenhouse gas emissions accounting.

166

Table 3.23: Energy equivalent values for different farm inputs and outputs

Input Sub-type Unit Equivalent energy (KWh/unit)

Reference or source

Human labour

Hour 0.64 Ozkan et al., 2004; Hatirli et al., 2006; FAO, 2000

Diesel Litre 10.73 Dept. Climate Change & Energy Efficiency, 2010

Lubricants Litre 10.78

Farm machinery (tractor)

Hour 161.38 Falivene, 2003

Electricity KWh 1.0 Fertilizer Nitrogen Kg 18.38 Hatirli et al., 2006 Phosphorous Kg 3.46 Hatirli et al., 2006 Potassium Kg 3.10 Hatirli et al., 2006

Farmyard manure

Tons 84.26 Hatirli et al., 2006; Canakci et al., 2005

Chemicals Herbicide Kg 66.72 FAO, 2000 (Falivene, 2003 reported 33.36)

Fungicide Kg 28.58 FAO, 2000 Pesticide Kg 55.60 FAO, 2000 Irrigation water

m3 0.18 Ozkan et al., 2004

Yield (output)

Orange Kg 0.53 Ozkan et al., 2004

Stone fruit (peach)

Kg 0.61 Johansson & Liljequist, 2009

Grapes Kg 3.28 Ozkan et al., 2007; Singh, 2002

As shown in Table 3.23, the energy sequestered in one kilogram of grapes is

significantly higher than that of peach or citrus due to high sugars and

carbohydrates in grapes.

167

Figure 3.28: Flowchart of steps to account for energy use, productivity indicators as well as carbon footprint of energy use in irrigation and crop production

The mechanical energy used on the farm mainly includes a tractor which

consumes diesel and lubricating oil (other tractor oil inputs are ignored for

their negligible magnitude). An 86 HP tractor which is commonly used in

MIA consumes diesel at the rate of 15 l/h and 10 litres of engine oil is

Identify energy use related processes in crop life cycle within study area

(including irrigation)

Identify energy inputs in each process

List all direct energy inputs

List all indirect energy inputs

Convert all energy input types to a single equivalent

(sequestered) energy unit

Convert each energy input to equivalent

CO2 emission

Compute total energy input for all

considered processes

Compute total equivalent CO2

emissions

Compute total system output (i.e. yield)

Compute carbon footprint of system output (CO2

emissions per unit output)

Compute energy footprint of system output (KW

consumed per unit output)

Repeat abovementioned steps for different scenarios (combinations of irrigation methods and conveyance systems)

Convert total system output into equivalent energy

Compute other relevant energy use and carbon emission indicators

Compare those scenarios

168

replaced every 250 hours of running time (i.e. 0.04 l/h). Therefore, the

energy input in one hour of tractor operations is equal to 161.38 KWh. The

energy consumption in practices like soil preparation, growing cover crops,

harvesting the cover crop etcetera, are not considered in this study.

Figure 3.28 provides a generic procedure to quantify energy and carbon

footprints of crop production for a given scenario. The right hand side boxes

describe steps to quantify energy footprint of crop production expressed as

KW consumed in producing a unit output. The left had side boxed describe

steps to compute various quantities required to quantify

carbon/environmental footprint of crop production expressed as CO2

emissions exhausted to produce a unit output. Other relevant energy and

CO2 emissions indicators as defined in Chapter 2 are also computed using

the computed values for different steps/boxes in Figure 3.28.

Calculating CO2 equivalent emissions

Agriculture produces greenhouse emissions in Australia at 15.7% of the net

national emissions in 2005 (AGO, 2007). Here the term 'agriculture' is

generic to cover agricultural, livestock, forestry and fishery activities.

Agriculture is the single dominant source of methane and nitrous oxide

emissions. However, methane and nitrous oxide are mainly associated with

livestock, rice cultivation, and field burning of agricultural residuals

etcetera. Carbon dioxide equivalents (CO2-e) is a unit of measurement that

allows the effect of different greenhouse gases and other factors to be

compared using CO2 as a standard unit for reference. The emissions of

different greenhouse gases can be aggregated by converting them to carbon

dioxide equivalents. The conversion is done by multiplying the mass of

emissions by the appropriate global warming potentials (GWPs). GWPs

represent the relative warming effect of a unit mass of the gas when

compared with the same mass of CO2 over a specific period (IPCC, 2001;

OECD, 2001).

This research does not examine the CO2 mitigation function that agriculture

provides in the forms of carbon storage in forestry/trees or carbon

169

sequestration in soil. The focus of this research is limited to irrigation

systems and production of the horticultural crop rather than the whole

agriculture sector. The energy inputs relevant to this study are listed in

Table 3.23. The conversion factors for equivalent CO2 emissions expressed

as kilogram of CO2-e per KWh of energy contents for the inputs related to

this study are given in Table 3.24. A similar approach for computation of

CO2 equivalent emissions was employed by Wells, (2001); Dept. CC&EE,

(2010); and Barber, (2004). Those conversion factors were applied to

calculate and compare the carbon footprint of irrigation conveyance

systems, irrigation application methods and the horticultural crops

production. The conversion factors for one kilowatt of electricity purchased

from the national grid are different for different States connected to the grid

due to varying transmission losses. For example the electricity purchased

from NSW/ACT has a conversion factor of 0.9. For this study the CO2

emissions associated with energy inputs including human labour and

machinery, are also considered. However, the CO2 equivalent emissions

from the consumption of fuel by farm machinery are considered while any

heat radiated by their use is not considered. It should also be noted that for

the sake of convenience in aggregating the CO2 equivalent emissions, the

CO2 equivalent factor for inputs like fertilizers is expressed in units of

KgCO2-e/KWh (i.e. CO2-e emissions per KWh of sequestered energy)

instead of KgCO2-e/Kg. Human work hour is computed to be equivalent to

GHG emissions of 0.426 KgCO2-e based on the assumption that human

energy is sourced from meat intake. For example, the production of 1 kg of

beef, results in GHG emission with global warming potential of 36.4

KgCO2-e (Ogino et al., 2007). The CO2-equivalent emissions from use of a

tractor are computed for the diesel and oil consumed by the tractor engine.

The emissions from an 86 HP tractor as mentioned above are estimated to

be 40.22 KgCO2-e per hour of operation.

170

Table 3.24: CO2 equivalent emissions factors for various farm inputs

Input Sub-type CO2-e emissions (KgCO2-e/KWh of input energy)

Source reference

Diesel 0.249

Dept. Climate Change & Energy Efficiency, 2010

Petrol 0.240 Electricity NSW/ACT 0.90 Victoria 1.23 Queensland 0.89 South Australia 0.72 South West

Interconnected System in Western Australia

0.82

Tasmania 0.32 Northern Territory 0.68 Fertilizer Nitrogen 0.18 Barber, 2004;

Nevison et al., 1996

Phosphorous 0.216

Barber, 2004 Potassium 0.216 Chemicals Herbicides 0.216 Fungicides 0.216 Insecticides 0.216 Human 0.426 Computed based

on: Ogino et al., 2007

3.3.1.4 Crop Yield Module

Crop yield is affected by water shortage/deficit which can be due to limited

water availability, capacity constraints or inadequate irrigation scheduling.

Crop-water production functions are developed for a given crop from the

field data on water use and the yield obtained. No data covering a sufficient

range of water and crop was available for any crops in the case study area

for this purpose. Some crop-water production functions for other regions of

Australia were available. For example, Figure 3.29 shows a production

function for citrus crops from monitoring sites in South Australia. The line

in Figure 3.29 is statistically fitted and represents the boundary of dataset

171

and approximates the potential yield at a given depth of applied water

(Skewes, 2010). In the absence of yield-water use data for the study area, a

simpler and linear crop-water production function developed by Doorenbos

and Kassam, (1979) was used for this study. The FAO crop-water

production function (Equation 3.32) predicts the reduction in crop yield

when crop stress is caused by a shortage of soil water.

Figure 3.29: Plot between applied water (including rainfall) and yield for citrus crops in South Australia (Source: Skewes, 2010)

1 1

: 1 1 Equation 3.32

Where,

, is the actual crop yield (t/ha),

, is maximum expected or agronomically attainable crop yield under no

water stress (t/ha),

, is the adjusted evapotranspiration for water deficit. It is calculated

by the crop water demand module of the developed node-link model.

172

, is the crop evapotranspiration for standard conditions, i.e. no water

stress,

1 , is the relative yield decrease due to water shortage,

1 , is water stress or relative evapotranspiration deficit. In this

study, the magnitude of water deficit refers to the deficit in relation to crop

water requirements over the total growing period of the crop.

K , is the yield response factor. It is the relative decrease in yield per unit

relative water deficit. Different crops have different Ky value. The values of

K for the whole growing season and for the three crops used in the

model are given in Table 3.25. The Ky values in Table 3.25 are based on

Doorenbos and Kassam, (1979) and Stewart et al. (1977). The value of

in Table 3.25 are the reported maximum yields (Khan et al., 2005a; Hope

and Wright, 2003; Singh et al., 2005). The agronomic potential yield may be

even higher than these values. Higher values of Ky indicate that the crop

yield is more sensitive to water deficiency than that of lower Ky values. In

other words if Ky < 1 then the decrease in yield is proportionally less with

the increase in water deficit and while the decrease in yield is proportionally

greater with the increase in water deficit for crops with Ky > 1. Crops like

citrus have a Ky value greater than 1 while wine grapes have Ky less than 1

as shown in Table 3.25.

Table 3.25: and values for the modelled crops

Crop Citrus Stone fruit Wine grape

K * 1.3 1.0 0.85

**(t/ha) 50 20 25

*Source: Doorenbos and Kassam, (1979). **Source: Khan et al., (2005)

The crop water demand model performs daily calculation of and water

stress coefficient (K ) for computing adjusted where,

. The daily values of and are aggregated by

the Crop Yield Module at each time step and the actual yield ( ) is

173

estimated after the final time step of the model using Equation 3.32. At each

computation time step of the model and for each crop, a composite value of

water stress coefficient (Ks) for adjusting ETc in Equation 3.32 is computed

by multiplying water stress coefficient values for the individual farms

growing the same crop.

3.3.1.5 Economic Analysis Module

The economic analysis module covers the financial aspects of different

scenarios. Financial analysis is required to determine whether increased

water or energy efficiency and yield are financially beneficial to farmers in

the long term as recommended by O’Neill et al. (2006). It includes the

analysis of the capital and running costs of different irrigation application

systems and irrigation infrastructure over their working life. The economic

analysis module is developed in MS Excel spreadsheet but it extensively

utilises various outputs of the nodal network modules. The methodology of

economic analysis is similar to others including Giddings, (2005); Giddings,

(2004); Falivene, (2003); and Sing et al. (2005). It performs the following

analyses:

Annual farm budget of each modelled crop by incorporating variable

costs,

Cost-benefit analysis of water saving options including energy costs

involved,

Financial analysis of different capital investment scenarios,

Calculation of economic performance indicators.

The methodology of the economic analysis is presented in more detail in the

relevant chapter in the later part of this thesis.

3.3.1.6 Integration Module

The integration module primarily presents all computed indicators for water,

energy, greenhouse emissions and economics. It provides a holistic

overview of different aspects of any scenario being considered. It also

applies a system dynamics approach to identify feedback loops between

174

different model variables within the boundaries of the system under

consideration. The feedback loops identified by this approach are shown in

Figure 3.30. These feedback loops can be quantified using model outputs

and some external data.

There are six positive feedback loops identified in Figure 3.30. For example,

the feedback loop shown in orange colour can be described as follows:

Greater the ‘energy use’, higher will be the ‘water savings’ which will result

in further ‘capital investment’ to convert more area to ‘pressurised

irrigation’. This will result into even higher ‘energy use’. This completes a

positive feedback loop starting from ‘energy use’. Other feedback loops can

be explained in similar fashion.

Figure 3.30: Feedback loops identified and quantified through integration of modelled variables

3.4 Node-link model Mass Balance Test

To test the robustness of the developed model and for the proof of concept

the model was executed in flood irrigation mode with the open channel

supply system for the year 2007-08. This model run was made under no

constraints on supply capacity. Therefore, total irrigation supply should be

IrrigationEfficiency

GroundwaterRecharge

WatertableRise

WaterSaving

CapitalInvestment

PressurisedIrrigation

Salinity

EnvironmentalBenifits

EnergyUse

+

+

-

+

+

- +

+

GHGEmissions

+

+

-

+

+

Water Traded toEnvironment

+

-

+

ProductionCost+

Yields

+

+

175

equal to total demand if the conveyance system is 100% efficient, otherwise

the total conveyance loss should make up the difference between demand

and supply. The mass balance components as computed by the model are

given in Table 3.26. The total conveyance loss which is the sum of the

channel seepage and channel evaporation was calculated by the model as

16.42 ML for the whole irrigation season in the modelled case study area.

The difference between total irrigation demand and total irrigation water

supply is calculated to be 17 ML. Thus the percentage mass balance error as

computed by Equation 3.33 is 0.03% (0.58ML). Such a minuscule error in

mass balance could be due to the rounding-off phenomenon in the model

computations.

∗ 100 Equation 3.33

Table 3.26: Mass balance components as computed by model run for 2007-08

Total

volume

diverted

(ML)

I

Total

volume

delivered

(supply)

(ML)

II

Total

irrigation

requirement

(demand)

(ML)

III

Demand –

Supply

(ML)

IV

Conveyance

losses (seepage

+ evaporation)

(ML)

V

% Mass

balance

error

VI = (IV –

V) / IIx100

2,302 2,285 2,302 17.0 16.42 0.03

3.5 Demand-based verses fixed interval scheduling for different

irrigation methods

The level of soil moisture content available to plants in the rootzone is the

key factor that triggers the need for moisture replenishment by irrigation in a

demand-based irrigation application system. The rootzone moisture

depletion is regulated by climatic factors and plant growth stage. Rootzone

soil moisture content depletion can be monitored by different methods

including moisture probes, rootzone water balance etcetera. Then irrigation

is applied when a certain level of rootzone moisture depletion is reached.

176

This implies more frequent irrigation during hot and dry seasons than the

wet and cooler seasons. This approach of irrigation management is called

demand-based irrigation scheduling. On the other hand, the fixed interval

irrigation or supply based scheduling does not involve any complicated

equipment and does not require any soil moisture monitoring. This

traditional method of irrigation scheduling is adopted due to channel

capacity constraints where the supply channel is not big enough to serve all

users at a time or due to the absence of any environmental impact

considerations or any water restrictions. The demand based irrigation

scheduling is an advanced method of irrigation that supposedly results in

minimum groundwater accessions and minimum runoff (return flow) from

irrigation areas (Khan et al., 2004). Demand-based irrigation is discussed in

detail in Section 3.6. A separate model is developed for the supply based

irrigation scheduling where both the irrigation application rate and irrigation

interval are fixed for each crop. The optimisation framework on Vensim was

used to optimise both the irrigation interval and irrigation rate. The supply

based irrigation approach does not take into account any climatic influences

on crop water demand and presents more risk, especially in case of

horticultural crops. However, it is included in this research to compare and

justify any cost and benefits associated with demand-based hi-tech irrigation

approaches. The procedure for supply-based irrigation as implemented in

the developed node-link model is shown in Figure 3.31. It also includes

calculation of number of days (d) of continuous water stress (cumulative

ETc readily available moisture) as well as any water lost due to excess

irrigation.

3.6 Calculating water and energy efficiency and productivity

indicators

The water and energy efficiency and productivity indicators were computed

for the farming operations for the whole case study area. The definitions of

these well-established and commonly used indicators are given in Table

3.27 and are based on Khan et al., (2009); Koctürk and Engindeniz, (2009),

177

Pereira (2006), Pereira (2007) and others. Water accounting involves

estimation of water use and losses to compute water productivity indicators.

According to Molden et al., (2003), water accounting can be applied at all

scales of interest, and requires the definition of a domain bounded in three-

dimensional space and time. For example, at the field scale, this could be

from the top of the plant canopy to the bottom of the root zone, bounded by

the edges of the field, over a growing season.

Figure 3.31: Flowchart of supply based irrigation strategy as implemented in the node-link model (n=days since start of simulation, d=days since crop gone in water stress)

The task in water accounting is to estimate the flows across the boundaries

of the domain during the specified time period. At the field scale, water

enters the domain by rain, by subsurface flows and, when irrigation is

available, through irrigation supplies. Water is depleted by the processes of

Apply irrigation on day n = 1

No. of days since last irrigation, n

n = specified irrigation interval?

Apply irrigation (I) & set n = 0

Supply available?

ETc for day n

Cumulative ETc = Cumulative ETc + ETc

Cumulative ETc = Cumulative ETc – I

AND: d = 0

IF: Cumulative ETc > RAW

THEN: Cumulative ETc = RAW

AND: d = d+1

IF: Cumulative ETc < I

THEN:

Loss = Loss + I - Cumulative ETc

n = n + 1

178

growing plants: transpiration and evaporation. The remainder flows out of

the domain as surface runoff or subsurface flows or is retained as soil-

moisture storage. In estimating irrigation water productivity, we are

interested in water inflows (rain plus irrigation) and water depletion

(evaporation and transpiration) as shown in Figure 3.32. At irrigation

system scale, as in the case study area of this research, the water losses due

to channel seepage, channel evaporation or pipe leakage are also considered.

A similar approach for segregation of water accounting components is

adopted in this study. These indicators are computed for each scenario

considered in this study where applicable to capture the water and energy

use footprints.

Figure 3.32: Water use accounting components at field scale (Adapted from Molden et al., 2003).

Table 3.27: Indicators of water and energy use efficiency and productivity

Indicator Unit Definition Description

Energy

efficiency Ratio

The ratio of

total energy

output to total

energy input

Water

productivity Kg/m3

Yield of

marketable

produce per unit

Rain

Irrigation

Out

flow

Percolation

Runoff

Evaporation

Transpiration

179

of water used.

Energy

productivity Kg/kWh

Yield of

marketable

produce per unit

of energy input.

Specific

energy kWh/kg

Energy input

per unit of

marketable

yield.

Water and

energy

productivity

Kg/

m3kWh

Yield per unit

of energy and

water inputs. It

captures the

effect of these

inputs on yield.

Lower values

may indicate

lower efficiency

and higher

environmental

footprint.

Net energy

return KWh/ha

Absolute

difference

between energy

output and

energy input

Water

energy ratio Ratio

The ratio of

energy input

from irrigation

to total energy

input. Higher

ratio may imply

180

higher water

footprint.

3.7 Structure of the Thesis Report

In order to assist readers, the major topics and key scenarios discussed in

this thesis are summarised in Table 3.28.

Table 3.28: Summary of key topics of the thesis

Serial No.

Title Brief description

4.2 Scenario 1 - Flood irrigation with open channel supply system

Water and energy analysis of demand-based open channel flood irrigation.

4.3 Scenario 2 - Furrow irrigation with open channel supply system

Water and energy analysis of demand-based open channel furrow irrigation.

4.4 Scenario 3 - Flood irrigation with pipe supply system

Water and energy analysis of demand-based piped supply flood irrigation.

4.5 Scenario 4 - Furrow irrigation with pipe supply system

Water and energy analysis of demand-based piped supply furrow irrigation.

4.6 Scenario 5 - Sprinkler irrigation with pipe supply system

Water and energy analysis of demand-based piped supply sprinkler irrigation.

4.7 Scenario 6 – Drip irrigation with pipe supply system

Water and energy analysis of demand-based piped supply drip irrigation.

4.8 Comparison of the demand-based irrigation scenarios

Comparison of water, energy and GHG emissions of above listed scenarios.

5.1.1 Scenario 1: Flood irrigation supplied with an open channel system

Water and energy analysis of supply-based flood irrigation supplied with an open channel system

5.1.2 Scenario 2: Furrow irrigation supplied with an open channel system

Water and energy analysis of supply-based furrow irrigation supplied with an open channel system.

181

5.1.3 Scenario 3: Sprinkler irrigation system connected with communal piped supply

Water and energy analysis of supply-based sprinkler irrigation connected with piped supply system.

5.1.4 Scenario 4: Drip irrigation system connected with communal piped supply

Water and energy analysis of supply-based drip irrigation connected with piped supply system.

5.2 Modifications made in the node-link model

Details of the modifications made in the node-link model to change it from demand-based to supply-based model.

5.4 Water use and yield comparison of supply-based and demand-based irrigation

Water use and yield comparison of corresponding scenarios in Chapter 4 and Chapter 5.

5.5 Energy and GHG emissions for the supply-based scenarios

Accounting of energy and GHG emissions for the supply-based scenarios.

5.7 On-farm storages: water-energy analysis

Water and energy analysis of using on-farm storages and comparison with piped supply system.

6.3 Up-scaling the model results using mosaic approach

Up-scaling the model water and energy results using mosaic approach based on representative area.

6.4 Estimating and mapping water and energy savings for MIA – using GIS-Based distributed approach

Estimating and mapping water and energy use/savings for whole MIA – using GIS-Based distributed processing.

7.3 Capital cost for conversion to pressurized irrigation system

Capital cost for conversion to pressurized irrigation systems including pipe network and sprinkler and drip irrigation systems.

7.4 Economic analysis of conversion to sprinkler or drip system for citrus

Full economic analysis of conversion to sprinkler or drip system and piped supply network for citrus.

7.5 Economic analysis of conversion to sprinkler or drip

Full economic analysis of conversion to sprinkler or drip

182

system for wine grapes system and piped supply network for wine grapes.

8.1 Understanding and representing the dynamics of the system

A holistic view of the system using system dynamics approach.

3.8 Chapter Summary

This chapter starts with an introduction of the Murrumbidgee River basin

which is the geographic focus in this thesis. It is then followed by the

description of the salient features of the Murrumbidgee Irrigation area

(MIA) which is the specific case study area with particular spotlight on

irrigated horticulture. After introducing the study area and the rationale for

choosing it for this purpose, the case study site located in MIA is described

for the purpose of a test case for development of a node-link model. A major

part of this chapter is dedicated to the explanation of the methodology used

for the node-link model development. The mathematical equations, data and

procedures followed in each module of the model are explained in detail.

The results of these modules are validated against observed data.

This chapter also explains the methodology used for energy input / output

accounting and greenhouse gas emissions estimation for various irrigation

methods, crops and irrigation strategies. Finally, various commonly known

indicators for water and energy efficiency and productivity are explained in

their mathematical forms. These indicators are the most useful and valid

tools to test the effectiveness of improving water and energy systems.

183

Chapter 4: Water and Energy Nexus for Demand Based

Irrigation Methods and Conveyance Systems

Water resources in the sub-catchments of Murray-Darling Basin are in

instances either fully and in some catchments over-allocated for

consumptive use to the detriment of the environment. For example, it is

estimated that in New South Wales, licences and water allocations equal 120

per cent of total available water resources (Melville and Broughton 2004).

To ensure that we have enough water for irrigation development, the water

we have should be used more efficiently at both farm and catchment scales.

Water can be saved through better management of its delivery and

application (Khan et al., 2004, Khan et al., 2005b). The focus of this chapter

is to investigate how water losses in irrigation can be minimized by looking

at both the delivery and the application sides. However, the water savings

are realized only at the expense of high inputs and potentially contribute to

other aspects of environmental deterioration. This chapter puts forward the

argument that unless the energy requirement aspects are equally considered,

the improvement in irrigation efficiency is a partial solution for minimizing

the environmental footprint of consumptive use of water and to tackle water

shortage.

4.1 Rationale of this chapter

The efficiency in transport of water from its source to the farm is referred to

as conveyance efficiency. The efficiency in application of water in the field

is called irrigation application efficiency. Irrigation conveyance losses

which impact upon conveyance efficiency can be caused by evaporation,

seepage, leakage and operational losses but by far the greatest losses are to

seepage (Meyer, 2005). Seepage and leakage from water supply channels

contribute substantially to ground water accessions creating salinity

concerns from rising groundwater which is mostly highly saline. Average

weighted conveyance losses for 46 irrigation districts nationally was

184

reported as 27.8% where Murrumbidgee irrigation region sits at 22.3% for

the year 1999-2000 (Marsden Jacob Associates, 2003).

Figure 4.1 shows the ten year accounts of seepage and evaporation losses

from the open channel system of Murrumbidgee Irrigation Area (MIA)

(Murrumbidgee Irrigation, 2008). It is evident from Figure 4.1 that

conveyance losses fluctuate with seasonal climate conditions and supply and

diversion volume. Another important operational factor that causes high

conveyance losses in MIA is the fact that all open channels are constantly

kept filled with water during the irrigation season to maintain the reliability

and timeliness of irrigation supply. The conveyance losses can be entirely

eliminated by replacement with piped system. As identified previously, two

systems of irrigation water conveyance or delivery are considered in this

research including:

Open channel system

Pressurized pipe supply system

Figure 4.1: Seepage and evaporation losses from channel system of Murrumbidgee Irrigation Area (MIA)

The “high-tech” irrigation application systems like high pressure subsurface

drip irrigation system, sprinkler system etcetera are remarkably water

1

10

100

10

20

30

40

50

60

70

80

90

100

1998/99 1999/00 2000/01 2001/02 2002/03 2003/04 2004/05 2005/06 2006/07 2007/08

%

Water Loss (G

L)

Year

Seepage from channels (GL)

Evaporation from channels (GL)

Total conveyance losses

Type of year (% dry)

Loss as % of gross diversions (%)

185

efficient and effective for high value horticulture crops. Cummins (1998)

ranked horticulture second after rice, almost a decade ago, for potential

water savings of up to 150 GL through adoption of irrigation application

technology in the Murray-Darling Basin. The pressurized irrigation systems

including sprinkler and drip irrigation improved water application efficiency

80% to 95% (Biswas et al., 2005) by reducing water losses through deep

drainage and runoff. Literature indicates that up to 4 ML/ha can be realized

in water savings by drip irrigation and is being rapidly rolled out in

horticulture areas. This chapter looks at water and energy aspects of four

irrigation application systems including:

Flood irrigation,

Furrow irrigation,

Low head fixed sprinkler irrigation (we will refer to it simply as

sprinkler irrigation from here on), and

Drip irrigation also called trickle irrigation

Although, only horticultural crops are studied in this research, the

methodology and framework is generic for use with any crop.

The developed and calibrated node-link model which is described in

Chapter 3 can be set up to simulate water delivery by either of the above

mentioned two conveyance systems to meet the modelled irrigation demand

of each of the 13 farms of the case study area (refer to Chapter 3 for details

about the case study area) irrigated with any of the four irrigation

application methods listed above. This model is purposefully developed as a

combination of both the crop water use model and the irrigation supply

system model so that it presents a full picture and holistic information on the

system and processes being modelled. Table 4.1 provides further details on

the modelled crops. Since the irrigated area and number of farms for each of

the three crops are not the same, the water use and energy inputs are finally

reported on the basis of unit irrigated area, i.e. per hectare. This approach

186

encapsulates the heterogeneity which is mainly introduced by different soil

types of the irrigated farms.

Table 4.1: Details about the crops in the modelled case study area Citrus Stone fruit Wine grape

Crop area (ha) 244.03 24.34 22.6

No. of farms 9 2 2

Based on the practicable combinations, a total of six scenarios are modelled

and discussed in this chapter for the same case study area. For each

scenario, irrigation demand, total energy use and total greenhouse gas

emissions are calculated and then compared across all the scenarios. The

physical layout of the modelled case study area located in MIA is described

in Chapter 3. Some of the data used for the modelled case study area, for

example; wetted surface area for flood irrigation, is from other farms that

are located in other similar areas of MIA. The six scenarios discussed and

compared in this chapter are listed as below:

Scenario 1: Flood irrigation with open channel supply system

Scenario 2: Furrow irrigation with open channel supply system

Scenario 3: Flood irrigation with pipe supply system

Scenario 4: Furrow irrigation with pipe channel supply system

Scenario 5: Sprinkler irrigation system with pipe supply system

Scenario 6: Drip irrigation system with pipe supply system

Energy consumed in irrigation water supply and irrigation application is

expected to change significantly from one scenario to another. All other

energy inputs are anticipated not to vary much between the scenarios.

However, all energy inputs are accounted to the extent possible in order to

estimate total energy consumption and greenhouse gas emissions for each of

the six scenarios. Also the energy consumed in preparation of land,

excavation of channels, installation of pipes etcetera is not taken into

account for these scenarios as most of these inputs are one off events for

permanent plantings. The two additional possible scenarios which are not

187

discussed in this chapter are sprinkler and drip irrigation system supplied

with open channel irrigation supply system. To put these two scenarios into

operation, on-farm storage of irrigation water is required due to the

operating nature of the two scenarios. The on-farm storage option is

examined in later chapters of this thesis.

The irrigation scheduling adopted in each of the aforementioned six

scenarios is demand-based zero-deficit irrigation. In this irrigation

scheduling approach crop evapotranspiration is continuously monitored and

water is applied to fully compensate for evapotranspiration when soil-water

passes a certain level of depletion. The amount of irrigation applied is equal

to the water lost due to evapotranspiration and there is no deliberate water

deficit in this irrigation practice except for the potential irrigation shortage

due to limited capacity of the irrigation delivery system during peak

demand. The only other restriction incorporated in the model is that length

of an irrigation event is no longer than one day for any farm, that is; there is

a minimum of one day gap between two irrigation events for a given farm.

The demand-based irrigation principle is followed in this chapter on the

grounds that this restrictions-free approach enables comparability among

different irrigation application techniques as well as irrigation delivery

systems.

Modelling of the irrigation application methods/techniques in this research

is based on the assumption that different irrigation methods create different

extent of wetted soil area around a plant and that the amount of irrigation

required is dependent on the desired wetted area, among other parameters

like effective rooting depth, depletion fraction, soil type etcetera. Thus the

amount of available irrigation water is different for different irrigation

methods. This approach is similar to the one followed by Allen et al.,

(1998); Moreshet et al., (1983); Bielorai (1982); Karmeli and Keller (1974);

Kriedemann and Goodwin (2003). Table 4.2 lists the values of wetted soil

area per hectare as used in modelling of the four irrigation methods for the

three crops in this study. For sprinkler and drip irrigation, the wetted area

188

depends on distance between the emitters or sprinkler heads as well as the

distance between the drip lines or laterals. The sprinkler system used in this

study is described as the non-overlapping under canopy irrigation sprinkler

system. The wetted area values given in Table 4.2 are based on collected

data or model calibration and are similar to the ones reported in Helen

(2007); http://www.irrigationcalculator.com/ ; TDPIWE (2001); Moreshet et

al., (1983); Bielorai (1982). The wetted area for flood irrigation is taken as

10,000 m2/ha as the irrigation water covers the whole field.

Table 4.2: Wetted area (m2/ha) for the modelled irrigation methods and the crops Citrus Stone fruit Wine grape

Flood 10,000 10,000 10,000

Furrow 8,400 7,140 7,140

Sprinkler 7,800 6,630 6,630

Drip 6,000 5,100 5,100

As mentioned in Chapter 3 the node-link model executes for maximum of

one year simulation period using daily time steps. Therefore, a typical year

needs to be chosen for hydro-climatic data to model the six scenarios. Based

on the available data, 2006-07 (1 July 2006 to 30 June 2007) was selected.

This year represents the driest and the hottest conditions among the

available data with an average annual rainfall of just 186.6 mm (less than

half of the long term average rainfall), average daily maximum temperature

of 25.5 oC and average daily potential evapotranspiration (ETo) of 4.3

mm/day. These conditions should lead to the highest irrigation demand by

crops and should test the limits of the system under consideration and hence

used in these scenarios.

4.2 Scenario 1 - Flood irrigation with open channel supply

system

This scenario involves all three crops on the modelled 13 farms being flood

irrigated. All irrigation water is supplied by unlined earthen open channels

from a single source. Scenario 1 is regarded as a base case or a reference

scenario and, from time to time, other scenarios have been compared against

189

this scenario. For the farms with large irrigated area, the daily irrigation

supply may be higher than the maximum daily flow capacity of 13 ML/day

(Murray Irrigation Limited, 2010) of the flow measuring device, the

dethridge wheel. Such farms are usually supplied with two or more farm

inlets with dethridge wheels installed on each inlet. However, each farm is

considered to have only one irrigation inlet in the node-link model.

4.2.1 Irrigation demand versus irrigation delivery

Total irrigation demand here refers to the sum of the calculated irrigation

water requirement from all farms at the system supply point for a given day.

Irrigation demand does not include any transmission/conveyance losses due

to seepage and evaporation. The seepage and evaporation losses depend

upon the actual supply of irrigation water in the channels. Since the actual

supply is unknown at the time of order placement for a given day, the

transmission losses cannot be predicted and as a result the order cannot be

adjusted to compensate for transmission losses (unless we assume a constant

transmission loss rate). Therefore daily supply is always slightly less than

the daily demand as shown in Figure 4.2 and the cumulative shortage in

irrigation supply increases steadily. However, the sudden jumps in

cumulative shortage are not due to the transmission losses. These jumps in

cumulative shortage are rather due to the channel flow capacity constraint.

There have been at least five occurrences when the irrigation demand could

not be met due to insufficient capacity of the delivery channel which is

capped at 79.18 ML/day. The cumulative shortage in supply in this scenario

was 81.77 ML. The cumulative irrigation demand over the whole year was

3,682 ML and the cumulative irrigation supply to the farms was 3,600 ML.

190

Figure 4.2: Comparison of daily irrigation demand and supply and cumulative shortage in irrigation supply (ML shown on y-axis) for Scenario 1

4.2.2 Estimation of water losses

The losses accounted by the node-link model include channel seepage,

channel evaporation, and deep percolation and the surface runoff from the

paddocks. The cumulative losses are shown in Figure 4.3. The deep

percolation losses are the highest at 448.85 ML followed by surface runoff

(or surface drainage) of 296.68 ML and then relatively smaller transmission

losses of 18.74 ML. The total non-productive losses (sum of deep

percolation, surface runoff and transmission losses) of irrigation water

amounts to 764.27 ML which is 21.2% of the total irrigation supply. The

total on-farm water losses (sum of deep percolation and surface runoff) are

745.53 ML which is roughly 2.56 ML/ha of the overall cropped area. The

transmission/conveyance losses occur at the rate of 4.6 ML per kilometre of

the open channel. The transmission losses should be subtracted from the

total irrigation supply shortage calculated above to get the actual supply

shortage due to the channel capacity constraint which then amounts to 63.03

ML. In practice the transmission losses may also include losses from

intermediate storage, channel leakages and water thefts.

120

90

60

30

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

(Days, 1 = 1 Jul)

Total_Demand : Scenario 1_Flood_with_OpenCh MLTotal_Supplied : Scenario 1_Flood_with_OpenCh MLCumulative_Shortage : Scenario 1_Flood_with_OpenCh ML

191

Figure 4.3: Cumulative irrigation water losses (ML shown on y-axis) for Scenario 1

4.2.3 Effect on crop yield

The crop yield can be affected by water shortage/deficit due to limited water

availability resulting from capacity constraints or inadequate irrigation

scheduling. In the current scenario only channel capacity constraint is

considered. The possible reduction in crop yield was calculated for the water

deficit over the whole growing season for the current scenario i.e. at the end

of the last simulation step of the model. Figure 4.4 shows the cumulative

plots of ETc and adjusted ETc for the three modelled crops. In this sub-

section the ETc only refers to transpiration part of crop evapotranspiration as

the yield is related to transpiration processes only. It shows that the ETc for

wine grapes is relatively less affected by the water shortage due to higher

water holding capacity of the clay loam soils of the wine grape farms.

Extremely low infiltration capacity (4mm/hr) is recorded from the soil

between the tree rows, due to repeated compaction occurring over many

years. Ponding sometimes remains for up to 2 days, resulting in temporary

waterlogging. Water stress (leaf curling) is evident 7-10 days after watering

using flood irrigation. These two factors contribute to the slower fruit

460

345

230

115

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

(Days, 1 = 1 Jul)

Cum_DP_loss : Scenario 1_Flood_with_OpenCh MLCum_RO_loss : Scenario 1_Flood_with_OpenCh MLCumulative_Conv_Loss : Scenario 1_Flood_with_OpenCh ML

192

growth and decreased yield recorded in flood irrigated trees (Loveys et al.,

1999).

Figure 4.4: Normal and water deficit affected cumulative evapotranspiration (mm shown on y-axis) for the three crops for Scenario 1

The effect on crop yield and other related variables as output by model are

reported in Table 4.3. The biggest impact is predicted on the citrus crop with

1.54 t/ha reduction in yield resulting from 23 mm reduction in ETc and least

impact on wine grape yield with a reduction of just 0.09 t/ha resulting from

4 mm reduction in ETc over the whole year. The soil water stress in this

scenario is neither very high nor prolonged; therefore, the impact on crop

yield is not significant. However, severe water shortage can result in

detrimental effects on crop yield (Doorenbos and Kassam, 1979).

Table 4.3: Effect of water deficit due to channel capacity constraint on ETc (transpiration only) and crop yield Variable Citrus Stone fruit Wine grape Cumulative ETc without water deficit for whole year (mm)

963 1,118 852

Cumulative ETc adjusted for water deficit (ETc adj) for whole year (mm)

940 1,100 848

Yield without water deficit (Ym) (t/ha) 50 20 25 Yield with water deficit (Ya) (t/ha) 48.46 19.64 24.91

1,200

900

600

300

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

(Days, 1 = 1 Jul)

ETc_citrus : Scenario 1_Flood_with_OpenCh mmETc_adj_citrus : Scenario 1_Flood_with_OpenCh mmETc_stonefruit : Scenario 1_Flood_with_OpenCh mmETc_adj_stonefruit : Scenario 1_Flood_with_OpenCh mmETc_vine : Scenario 1_Flood_with_OpenCh mmETc_adj_vine : Scenario 1_Flood_with_OpenCh mm

193

4.2.4 Irrigation Application Rate

The amount of irrigation applied to the same crop grown on different farms

is aggregated and then divided by the total irrigated area of that crop within

the domain of the model, to find the irrigation application per hectare of that

crop; also referred to as “irrigation application rate”. The irrigation

application rate for a crop as computed by this process is an average

modelled figure regardless of the soil type of the individual farms. The

irrigation application rates for the three crops as computed by the node-link

model for Scenario 1 are given in Table 4.4. The modelled values of the

irrigation application rate are very similar to those reported by Khan et al.,

(2005); Khan and Abbas (2007); Loveys et al., (1999); Giddings (2005); and

Giddings, (2004). Table 4.4 also shows that on average there are potential

water savings of at least 3.72 ML/ha that can be achieved by minimizing

soil evaporation, deep percolation and surface runoff, by adopting water

saving practices and technology. The total irrigation water use combined for

the three crops in the case study area for Scenario 1 is 3,600 ML as

estimated by the model.

Table 4.4: Average irrigation application rate for the three crops for the modelled Scenario 1

Citrus

Stone fruit

Wine grape

Average

Irrigation application rate (ML/ha)

12.38 13.38 11.13 12.29

Total soil evaporation component of ETc (ML)

317.37 28.43 22.62 368.42 (total)

Soil evaporation component of ETc (ML/ha)

1.30 1.17 1.00 1.16

Deep percolation (ML/ha) * * * 1.54

Surface runoff or drainage (ML/ha)

* * * 1.02

* Model only computes overall losses in deep percolation and surface runoff

194

4.2.5 Accounting for Energy Use and GHG Emissions in Crop

Production for Scenario 1

In this scenario (Scenario 1), irrigation supply is made via open channels.

The water flow is under the force of gravity in open channels and no water

pumping is required to move the water through the supply channels to the

farms. All the irrigation water is from surface water source i.e. the

Murrumbidgee River, therefore; the energy use for irrigation supply is zero.

For flood irrigation, the water advances under gravity on the field and no

energy is used. However, some human hours are expended in operating the

farm water inlet structures and water metering devices e.g. dethridge wheel.

It also involves regular trips by channel operators on four-wheel drive

vehicles to the field channels to monitor the water supply and the channel

operations. The human hours spent by channel operators and the fuel

consumed by the vehicles are taken as energy inputs to the crop production

system. The labour hours and diesel under irrigation energy inputs include

average hours spent by channel operators and the fuel consumed by their

vehicles, respectively.

Table 4.5 lists data related to channel operators who manage irrigation

orders for citrus, stone fruit and wine grape farms of the case study area.

Based on this data and energy conversion factors for diesel consumption and

human hours given in Chapter 3, the equivalent energy consumed in channel

operations management amounts to 13.0 KWh/ha for citrus, 37.9 KWh/ha

for stone fruit and 22.8 KWh/ha for wine grape crop. These calculations are

based on the assumption that channel operators make dedicated trips to the

irrigation supply outlets half the number of irrigation events.

For this scenario (Scenario 1), the number of irrigation applications as

estimated by the model is 97, 27, and 15 for citrus, stone fruit and wine

grapes, respectively, as given in Table 4.5.

195

Table 4.5: Estimated time and fuel expended by channel operators to manage the irrigation orders for the farms in the case study area in a year

Item Citrus

Stone fruit

Wine grape

Total number of irrigation events/days

97 27 15

Total number of random trips by channel operators (assumed)

49 14 8

Distance travelled in each trip (Km) 50 50 50 Total crop area (ha) 244.3 24.3 22.6 Time expended (1 human hour per trip) (h/ha)

49/244.3 = 0.2

14/24.3 = 0.6

8/22.6 = 0.4

Total diesel consumed at the rate of 12 l/100km (litre)

294 84 48

Diesel consumed per ha (l/ha) 294/244.3 = 1.2

84/24.3 = 3.5

48/22.6 = 2.1

4.2.5.1 Energy inputs and GHG emissions for citrus

A detailed inventory of input and output energy for flood irrigated citrus

crops with open channel supply system is given in Table 4.6. The labour

hours under “irrigation” in Table 4.6 includes time spent by irrigators on the

farm to manage irrigation as well as time expended by channel operators.

The rates of different energy inputs are either based on data collection,

personal communications or publications by local agencies including NSW

Department of Primary Industries; Falivene (2003); Giddings (2005);

Giddings (2004); Crean et al., (2004). Each form of input energy is

converted into equivalent energy in kilowatt hours using the conversion

factors given in Chapter 3. The conversion factors given in Chapter 3 for N,

P and K are for conversion of pure N, P, and K fertilizers to equivalent

energy. The commonly used fertilizers only contain a fraction of one or two

of these non-organic nutrients as given in Table 4.7 (Bright, 2005).

Therefore, the actual amount of the main nutrient(s) for each fertilizer was

first computed from the given rate of that fertilizer application and then

converted into equivalent energy. For example, 2.17 kg of Urea fertilizer

contains only 1 kg of nitrogen (N) and thus equivalent to 18.38 KWh of

energy (1kg N = 18.38 KWh). The economic cost of energy inputs is not

196

analysed in this section. The labour hours under tractor refers to driver’s

time. The labour hours under harvesting include time expended in picking

and packaging.

Table 4.6: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of citrus crop for Scenario 1

Input Quantity used per hectare

Equivalent energy (KWh/ha)

GHG emissions (KgCO2-e/ha)

Irrigation

Electricity (KWh/ha)

Not applicable

Labour (hr/ha)

55.2 35.33 15.05

Diesel (l/ha) (Vehicle)

1.2 12.88 3.21

Fertilizer (kg/ha)

Urea 267 2261 407.07 DAP 117 471 87.68 Potash 100 127 27.44 Chemicals (kg/ha)

Herbicide 2.5 167 36.07 Fungicide 5.3 152 32.83 Pesticide 0.14 8 1.73 Tractor (hr/ha)

Fertilizer application

2.0 323 80.43

Chemicals spray

17.0 2743 683.00

Bin placement

1.0 161 40.09

Sod mowing 3.0 484 120.52 Labour 23 15 9.80 Manual Pruning (hr/ha)

42 27 17.90

Harvesting

197

Labour (h/ha)

400 256 109.06

Tractor (h/ha)

4 646 160.86

Total energy input (kWh/ha) 7,889.21 Output (kg/ha)

Citrus 35,000 18,550

Total GHG emissions (KgCO2-e/ha) 1,832.74

As per the information given in Table 4.6, the total energy input from all

considered sources to the flood irrigated citrus with open channel irrigation

supply system is aggregated to 7,889.21 KWh/ha and the total output energy

sequestered in citrus yield at the rate of 35 t/ha is 18,550 KWh/ha. Similarly

the total GHG emissions are estimated to be 1,832.74 Kg of CO2-

equivalent.

Table 4.7: Nutrient contents in major fertilizers and their application rates to supply 1kg of N, P or K

Fertilizer Nutrient content Kg of fertilizer needed to supply 1kg of N, P or

K Urea 46% N 2.17 kg

Ammonium nitrate 34% N 2.94 kg

Di ammonium phosphate (DAP)

18% N 20% P

5.55 kg for N 5.00 kg for P

Single Superphosphate 8.8% P 11.36 kg Double Superphosphate

17% P 5.88 kg

Potassium sulphate 41% K 2.44 kg

4.2.5.2 Energy inputs and GHG emissions for stone fruit

A detailed inventory of input and output energy for flood irrigated stone

fruit (peach mainly) with open channel supply system is given in Table 4.8.

The rates of different energy inputs are either based on data collection,

personal communications or publications by local agencies including

Department of Primary Industries NSW, Department of Primary Industries

Victoria; Falivene (2003); Giddings (2005); Giddings (2004); Bright

(2005); Crean et al., (2004);

http://new.dpi.vic.gov.au/agriculture/horticulture accessed in April, 2011).

198

As detailed in Table 4.8, the total energy input from all considered sources

to the flood irrigated stone fruit (peach) with open channel irrigation supply

system is aggregated to 7,195.27 KWh/ha and the total output energy

sequestered in yield of peach crop at the rate of 18 t/ha is 10,980 KWh/ha.

Similarly the total GHG emissions are estimated to be 1,634.52 Kg of CO2-

equivalent.

Table 4.8: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of stone fruit crop for Scenario 1




Irrigation


Not applicable

Labour (hr/ha)

55.6 35.58 15.16


3.5 37.56 9.35

Fertilizer (kg/ha)

N 141 2591.58 466.48 P 21 72.66 15.69 K 75 232.5 50.22 Chemicals (kg/ha)

Herbicide 3 200.16 43.23 Fungicide 5 142.9 30.86 Pesticide 0.1 5.57 1.20 Tractor (hr/ha)


3 484.18 120.65

Chemicals spray

11 1775.18 442.37

Bin placement

0.75 121.04 30.16

Sod mowing 3 484.18 120.65 Labour 17.75 11.36 4.84 Manual 125 80 34.08

199

Pruning and Thinning (hr/ha) Harvesting

Labour (h/ha)

178 113.92 48.53

Tractor (h/ha)

5 806.90 201.05

Total energy input (kWh/ha) 7,195.27 Output (kg/ha) Peach 18,000 10,980 Total GHG emissions (KgCO2-e/ha) 1,634.52

4.2.5.3 Energy inputs and GHG emissions for wine grapes

A detailed inventory of input and output energy for flood irrigated wine

grape crop with open channel supply system is given in Table 4.9. The rates

of different energy inputs are either based on data collection, personal

communications or publications by local agencies including NSW

Department of Primary Industries; Falivene (2003); Giddings (2004); Crean

et al., (2004); http://new.dpi.vic.gov.au/agriculture/horticulture (assessed in

April 2011).

Table 4.9: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of wine grape crop for Scenario 1




Irrigation


Not applicable

Labour (hr/ha)

55.4 35.46 15.10


2.1 22.53 5.61

Fertilizer (kg/ha)

N 115 2113.7 380.47 P 25.5 88.23 19.06 K - Chemicals

200

(kg/ha) Herbicide 2 133.44 28.82 Fungicide 4 114.32 24.69 Pesticide 1 55.60 12.01 Tractor (hr/ha)


4 645.52 160.88

Chemicals spray

10.5 1694.49 422.31

Sod mowing 5 806.90 201.10 Labour 21.5 13.76 5.86 Manual Pruning and Thinning (hr/ha)

50 32 13.63

Harvesting (mechanical)

Labour (h/ha)

6 3.84 1.64

Tractor or harvester (h/ha)

6 968.28 241.32

Total energy input (kWh/ha) 6,728.07 Output (kg/ha) Grapes 20,000 65,600 Total GHG emissions (KgCO2-e/ha) 1,532.5


to the flood irrigated wine grapes with open channel irrigation supply


sequestered in total yield of wine grapes crop at the rate of 20 t/ha is 65,600

KWh/ha. Similarly the total GHG emissions are estimated to be 1,532.5 Kg

of CO2-equivalent.

4.3 Scenario 2 - Furrow irrigation with open channel supply

system

The furrow system is a common irrigation application technology in

horticulture farms. In the horticultural areas of MIA around 92 per cent of

citrus and 85 per cent of wine grape farms were adopting furrows as

201

reported by Kemp and Hafi (2001). The recent uptake of pressure irrigation

system especially the drip irrigation has altered this distribution. In Scenario

2, we assume all 13 farms in the case study area are irrigated with broad

furrow system with furrows as wide as two meters and with relatively

narrow ridges. The water is conveyed to the farms through the same open

channel system as described previously. Water is siphoned from the supply

channels to the head of each furrow. Each siphon needs to be manually

primed which involve human labour for each irrigation event.


Total daily irrigation demand and total daily actual supply time series are

shown in Figure 4.5. The irrigation demand computed by the model does

not include any transmission/conveyance losses due to seepage and

evaporation. Therefore daily supply is always slightly less than the daily

demand as shown in Figure 4.5. Hence the cumulative shortage in irrigation

supply increases steadily. Unlike Scenario 1, there are no abrupt jumps in

cumulative shortage as demand does not reach the channel flow capacity

constraint which is capped at 79.18 ML/day for the system under

consideration. The total daily irrigation demand is peaked at 78.57 ML/day.

The cumulative shortage in supply in this scenario was 18.75 ML. The

cumulative irrigation demand over the whole year was 2,848 ML and the

cumulative irrigation supply to the farms was 2,830 ML.

202

Figure 4.5: Comparison of daily irrigation demand and supply and cumulative shortage in irrigation supply (ML shown on y-axis) for Scenario 2

4.3.2 Water losses estimation

The losses accounted by the node-link model include channel seepage,

channel evaporation, deep percolation and the surface runoff. The

cumulative losses are shown in Figure 4.6. The deep percolation losses are

the highest at 273.14 ML followed by surface runoff (or surface drainage) of

184.57 ML and relatively smaller transmission losses of just 18.75 ML. The

total non-productive losses (sum of deep percolation, surface runoff and

transmission losses) of irrigation water amounts to 476.46 ML which is

16.8% of the total irrigation supply at the end of a one-year simulation. The

total on-farm water losses (sum of deep percolation and surface runoff) are

457.71 ML which is roughly 1.57 ML/ha for the overall cropped area. In

practice the transmission losses may also include losses from intermediate

storage, channel leakages and water thefts.

90

67.5

45

22.5

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

(Days, 1 = 1 Jul)

Total_Demand : Scenario 2_Furrow_with_OpenCh MLTotal_Supplied : Scenario 2_Furrow_with_OpenCh MLCumulative_Shortage : Scenario 2_Furrow_with_OpenCh ML

203

Figure 4.6: Cumulative irrigation water losses (ML shown on y-axis) for Scenario 2


The model predicts no significant reduction in crop evapotranspiration due

to absence of any water stress and hence no reduction in yield of the three

crops. Crops usually start showing the signs of water stress if the span of

irrigation absence goes over several days after it is due. Although the model

predicts no reduction in yield, practically in field situations water is

sometimes not applied on time due to lack of soil-water monitoring and the

yield may actually decrease in such situations.

4.3.4 Irrigation application rate

The total amount of irrigation applied per hectare of a given crop over the

complete growing cycle is referred to as the “irrigation application rate”. It

is an average modelled number for a given crop for all farms regardless of

the soil type of the individual farms. However, the model takes into account

soil type in determining irrigation amount and timing for individual farms.

The irrigation application rates for the three crops as computed by the node-

link model for Scenario 2 are given in Table 4.10. The modelled values of

the irrigation application rate are very similar to those reported by Khan et

350

262.5

175

87.5

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

(Days, 1 = 1 Jul)

Cum_DP_loss : Scenario 2_Furrow_with_OpenCh MLCum_RO_loss : Scenario 2_Furrow_with_OpenCh MLCumulative_Conv_Loss : Scenario 2_Furrow_with_OpenCh ML

204

al., (2005); Khan and Abbas (2007); Giddings (2005); and Giddings, (2004).

Table 4.10 also shows that on average there are potential water savings of at

least 2.47 ML/ha that can be achieved by minimizing non-consumptive

water use that occurs via soil evaporation, deep percolation and surface

runoff, by adopting water saving practices and technology. The average

irrigation application rate is also reduced by 3.53 ML/ha as compared to

flood irrigation in Scenario 1. The total irrigation water use combined for

the three crops in the case study area for a complete one-year period for

Scenario 2 is 2,830 ML as estimated by the model, which is 770 ML less

than that of Scenario 1 indicating water savings of 21.4% as compared to

Scenario 1.

Table 4.10: Average irrigation application rates for the three crops for the modelled Scenario 2

CitrusStone fruit

Wine grape

Average


10.03 8.87 7.38 8.76

Total soil evaporation component of ETc (ML)

269.38 21.33 16.60 307.31 (total)


1.10 0.88 0.73 0.90

Deep percolation (ML/ha) * * * 0.94 Surface runoff or drainage (ML/ha)

* * * 0.63


4.3.5 Accounting for energy use and GHG emissions in crop

production for Scenario 2

In Scenario 2, irrigation supply is made via open channels similar to

Scenario 1. The water flows under gravity in open channels from source to

the supply points and no water pumping is required to move the water

through the supply channels to the farms. Moreover, all the irrigation water

is sourced from the surface water source i.e. the Murrumbidgee River, and

no groundwater pumping is made to irrigate the crops. Therefore, the energy

use for irrigation supply is zero. For the broad furrow irrigation, the water

205

advances under gravity through the furrow and no energy is used. However,

some human hours are expended in handling and priming of the siphons

which are laid at the head of each furrow and operating the water metering

devices e.g. dethridge wheel. It also involves regular trips by channel

operators on four-wheel drive vehicles to the field channels to monitor the

water supply and the channel operations. The procedure followed for

calculating energy expended by channel operators is discussed in Scenario

1. The human hours spent by channel operators and the fuel consumed by

the vehicles are taken as energy inputs to the crop production system.

Table 4.11 lists data related to channel operators who manage irrigation

orders for citrus, stone fruit and wine grape farms of the case study area.

Based on this data and energy conversion factors for diesel consumption and

human hours given in Chapter 3, the equivalent energy consumed in

channel operations amounts to 8.7 KWh/ha for citrus, 58.5 KWh/ha for

stone fruit and 29.3 KWh/ha for wine grape crop. These calculations are

based on the assumption that channel operators make dedicated trips to the

irrigation supply outlets half the number of irrigation events. For this

scenario (Scenario 2), the number of irrigation applications as estimated by

the model is 65, 44, and 20 for citrus, stone fruit and wine grapes,

respectively, as given in Table 4.11.

Table 4.11: Estimated time and fuel expended by channel operators to the manage irrigation orders for the farms in the case study area in a year

Item Citrus Stone fruit

Wine grape

Total number of irrigation events/days 65 44 20 Total number of random trips by channel operators (assumed)

33 22 10

Distance travelled in each trip (Km) 50 50 50 Total crop area (ha) 244.3 24.3 22.6 Time expended (1 human hour per trip) (h/ha)

33/244.3 = 0.14

22/24.3 = 0.91

10/22.6 = 0.44

Total diesel consumed at the rate of 12 l/100km (litre)

198 132 60

Diesel consumed per ha (l/ha) 198/244.3 = 0.8

132/24.3 = 5.4

60/22.6 = 2.7

206

A detailed inventory of input and output energy for furrow irrigated citrus

crop with open channel supply system is given in Table 4.12. The labour

hours under “irrigation” in Table 4.12 also includes time spent by irrigators

on the farm to manage irrigation, for example priming of siphons in this

case, as well as the time expended by channel operators. The number of

labour hours spent in managing irrigation application depends on the

number of irrigation application events. The rates of different energy inputs

are either based on data collection, personal communications or publications

by local agencies including NSW Department of Primary Industries;

Falivene (2003); Giddings (2005); Giddings (2004); Crean et al., (2004).

Each type of input energy is converted into the equivalent energy

sequestered in that input and expressed as kilowatt hours using the

conversion factors given in Chapter 3.



to the furrow irrigated citrus with open channel irrigation supply system is

aggregated to 7,794.6 KWh/ha and the total output energy sequestered in the

resulting citrus yield at the rate of 40 t/ha is 21,200 KWh/ha. Similarly the

total GHG emissions are estimated to be 1,820.66 Kg of CO2-equivalent.

The increase in citrus yield by 5 t/ha as compared to Scenario 1 is due to the

reason that with furrow irrigation the soil-water stress probably is relatively

to a lesser extent due to more frequent irrigations and also that more

nutrients are available to plants for uptake as lesser fertilizer, especially

urea, is lost due to reduced leaching or surface runoff.





Irrigation


Not applicable

207

Labour (hr/ha)

65.14 41.69 17.76


0.8 8.58 2.14

Fertilizer (kg/ha)

Urea 260 2202.2 396.40 DAP 110 440.4 95.13 Potash 95 120.7 26.07 Chemicals (kg/ha)

Herbicide 2.5 166.8 36.03 Fungicide 5.3 151.5 32.72 Pesticide 0.14 7.8 1.68 Tractor (hr/ha)


2.0 322.8 80.44

Chemicals spray

17.0 2743.5 683.7

Bin placement

1.0 161.4 40.22

Sod mowing 3.0 484.1 120.7 Labour 23 14.7 6.27 Manual Pruning (hr/ha)

42 26.9 11.46

Harvesting

Labour (h/ha)

400 256 109.06

Tractor (h/ha)

4 645.5 160.88


Citrus 40,000 21,200.0



208

A detailed inventory of input and output energy for furrow irrigated stone

fruit (peach mainly) with open channel supply system is given in Table

4.13. The rates of different energy inputs are either based on data collection,

personal communications or publications by local agencies including

Department of Primary Industries NSW, Department of Primary Industries

Victoria; Falivene (2003); Giddings (2005); Giddings (2004); Bright

(2005); Crean et al., (2004);

http://new.dpi.vic.gov.au/agriculture/horticulture accessed in April, 2011).


to the furrow irrigated stone fruit (peach) with open channel irrigation

supply system is aggregated to 7,409.28 KWh/ha and the total output energy

sequestered in yield of peach crop at the rate of 19 t/ha is 11,590 KWh/ha.

Similarly the total GHG emissions are estimated to be 1,692.41 Kg of CO2-

equivalent.





Irrigation


Not applicable

Labour (hr/ha)

60.91 38.98 16.61


5.4 157.94 39.33

Fertilizer (kg/ha)


Herbicide 3 200.16 43.23 Fungicide 5 142.90 30.87 Pesticide 0.1 5.56 1.19

209

Tractor (hr/ha)


3 484.14 120.66

Chemicals spray

11 1775.18 442.42

Bin placement

0.75 121.04 30.17


125 80.00 34.08

Harvesting

Labour (h/ha)

180 115.20 49.08

Tractor (h/ha)

5.5 887.59 221.21



A detailed inventory of input and output energy for furrow irrigated wine

grape crop with open channel supply system is given in Table 4.14. The

rates of different energy inputs per hectare are either based on data

collection, personal communications or literature including publications by

local agencies including NSW Department of Primary Industries; Falivene

(2003); Giddings (2004); Crean et al., (2004);

http://new.dpi.vic.gov.au/agriculture/horticulture (accessed in April 2011).

Table 4.14: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of wine grapes crop for Scenario 2




Irrigation

210


Not applicable

Labour (hr/ha)

60.44 38.68 16.48


2.7 28.97 7.21

Fertilizer (kg/ha)

N 110 2021.80 363.92 P 22 76.12 16.44 K - - Chemicals (kg/ha)

Herbicide 2 133.44 28.82 Fungicide 4 114.32 24.69 Pesticide 1 55.60 12.01 Tractor (hr/ha)


4 645.52 160.88

Chemicals spray

10.5 1694.49 422.31


50 32.00 13.63


Labour (h/ha)

6 3.84 1.64


6 968.28 241.32



to the furrow irrigated wine grapes with open channel irrigation supply


211

sequestered in total yield of wine grapes crop at the rate of 22 t/ha is 72,160

KWh/ha. Correspondingly, the total GHG emissions are estimated to be

1,515.77 Kg of CO2-equivalent. The increase in yield can be attributed to

more water availability to plants owing to the reduced irrigation water losses

in seepage and runoff as well as relatively more fertilizer uptake by plants as

compared to flood irrigation.

4.4 Scenario 3 - Flood irrigation with pipe supply system

This scenario is similar to Scenario 1 except that the irrigation water is

delivered through pipe from its source to the farm inlets. The pipe flow

model used to simulate this scenario is described in Chapter 3. Unlike drip

or sprinkler irrigation, there is no minimum hydrodynamic pressure head

requirement at the delivery outlets to flood irrigate and the water is

delivered from the pipe outlet to the farm main inlet channel under the

atmospheric pressure. However, energy is still required to move water

through the pipe, against the pipe friction, and in some sections against the

slope, by use of pumps. Except for the addition of energy required for

irrigation pumping, all other energy inputs are same as for Scenario 1 as

given in Table 4.7 to Table 4.9 for citrus, stone fruit and wine grapes,

respectively.

4.4.1 Optimization of pipe diameters and why

The Bernoulli’s Energy Equation for pipe flow indicates that hydrodynamic

pressure decreases as velocity increases in pipe flow. The very low

hydrodynamic pressure inside pipe can damage the pipe. Therefore, there is

a limit on maximum permissible velocity for pipe flows to avoid very hight

pressures from occurring, especially in PVC pipes. The maximum

permissible velocity of flow in PVC pipes as recommended by the

American Society for Testing and Materials (ASTM, 2006) Schedule 80 and

other relevant literature is up to 3 m/s. Another factor that needs significant

consideration is the head loss ( in pipe flow which is given by Darcy-

Weisbach Formula (Equation 4.1). As given in Equation 4.1, the head loss is

212

proportional to the squared velocity. Therefore, to reduce head/energy loss

though the pipe, the flow velocity should be carefully set.

Equation 4.1

Where,

, is a dimensionless coefficient called the Darcy friction factor,

, and represent length (m) and internal diameter of the pipe (m),

respectively,

, is the flow velocity through the pipe (m/s).

For the current scenario of supplying irrigation water to flood irrigated

farms of the case study area, the irrigation demand can be as high as 80

ML/day for flood irrigation. To supply irrigation water at this flow rate

through pipe system would result in very high flow velocity, potentially up

to 8 m/s, which is not safe as per abovementioned reasons as well as result

in high consumption of pumping energy. Therefore, to model this scenario,

the first step is to increase the pipe(s) diameter(s) where appropriate to

facilitate high flow rates without occurrence of very high flow velocities. To

achieve this, an optimization module was setup within the Vensim node-link

model. Vensim optimization module is based on Powell’s search algorithm

as explained in Chapter 3. The optimization module was set up with the

defined objective to find the diameters of different pipe sections (links) such

that the maximum velocity of flow through the corresponding flow outlet

for each pipe link does not exceed a magnitude of 3 m/s. The diameter of

each outlet pipe is selected as 5 cm less than the diameter of its supply pipe.

The pipe diameters achieved through the optimization process and the

original ones for the node-link model are given in Table 4.15.

Table 4.15: Original and optimized diameters for supply pipe network

Link ID (node x to node y)

Original diameter (mm)

Optimized diameter (mm)

1a – junction 450 450

213

junction – 3 375 435

3 – 4 375 385

4 – 5 250 315

5 – 6 250 340

Junction – 7 250 350

7 – 7a 250 330

7a – 8 250 275

8 – 9 250 275

9 – 10 250 330

10 – 11 250 275

11 – 12 250 285

12 – 13 250 285

4.4.2 Irrigation supply, losses and irrigation application rates

The node-link model with optimized pipe diameters as described above was

used to simulate water supply through pipes and the flood irrigation of the

three horticultural crops in the case study area. Owing to the replacement of

open channels with pipes for irrigation supply, the water losses from

channel water evaporation and seepage through the unlined channel is

eliminated. Thus water losses of 19 ML are avoided over the whole

irrigation period for this scenario. This results in irrigation supply to the

farms being increased by 19 ML as compared to the Scenario 1.

Table 4.16: Comparison of losses and irrigation application rates for Scenario 3 and Scenario 1

Scenarios Field losses (ML) Irrigation application rate

(ML/ha) Percolation Runoff Citrus Stone fruit Wine grapes

Scenario 3 460.37 304.12 12.43 13.60 11.21 Scenario 1 448.85 296.68 12.38 13.38 11.13

The increased supply of irrigation water also exaggerated other processes

like deep percolation, surface runoff and the average irrigation application

rate. A comparison of Scenario 3 and Scenario 1 for these parameters is

given in Table 4.16. The field losses increased from 745.53 ML to 764.49

214

ML. Similarly, the average irrigation application rate increased from 12.3

ML/ha for Scenario 1 to 12.4 to ML/ha for Scenario 3.



Since the irrigation application method and the total water use does not

change, the magnitude of energy inputs for the three crops in Scenario 3 are

also assumed to be the same as that of Scenario 1. The only difference is

that energy is also required for pumping operations to move water from

source to the farm outlets through the pipe system for Scenario 3 as

compared to zero energy requirement flow under gravity through open

channels in case of Scenario 1. For the current scenario, the pressure head

requirement at each outlet was set to zero. Hence the energy required to

pressurize water to operate pressure irrigation system in the field is saved.

However, still a great deal of pumping energy is required to move water

through the pipe against the flow resistance due to pipe friction, elevation

differences and flow momentum requirement.

Figure 4.7: Daily number of parallel pumps turned on to supply irrigation water for Scenario 3

.

0 01

Equation 4.2

Where,

No. of active pumps

12

8

4

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

Day (1 = 1st July)

"No._of_Pumps" : Scenario 3_Flood_with_Pipe

215

, is the rated flow capacity of a single pump operating at maximum

efficiency,

, is the instantaneous duty flow rate depending on the irrigation demand

for the whole irrigation system for a given day. The ratio is rounded up or

rounded down to a whole number as appropriate by the model.

The node-link model determines the number of active pumps on a given

irrigation day by using Equation 4.2. The node-link model tweaked for

simulating this particular scenario indicates that up to 11 pumps installed at

a pumping station near the water source, each with a peak discharge rate of

0.08 m3/s, are simultaneously operated in parallel configuration to supply

irrigation water as shown in Figure 4.7. In the parallel configuration of the

pumps the flow rate is added up and hence pumps are turned on or turned

off by the electronic control system software depending on the current duty

flow. The model reports the maximum and average pumping system duty

flow rates of 0.916 m3/s and 0.115 m3/s, respectively.

The model computes that a total of 481.2 MWh (megawatt-hour) of

electrical energy is consumed by the electrical motors to drive the pumps to

supply irrigation water with a total volume of 3,619 ML to the three crops

during one complete year of simulation. The energy consumed in irrigating

individual crops is assumed to be proportional to the irrigation volume

applied to that crop. The total energy consumption is divided among the

three crops based on their proportional water use as reported by the model

and is given in Table 4.17.

Table 4.17: Irrigation water applied and corresponding energy consumption in irrigation pumping for Scenario 3

Total Citrus Stone fruit Wine grapeIrrigation (ML) 3,619.0 3,034.0 331.2 253.5 Pumping energy (MWh) 481.2 350.6 38.3 29.3 Pumping energy (KWh/ha) 1653.8 1436.7 1573.5 1296.5

All energy inputs for each of the three crops for Scenario 3 are assumed to

be similar in magnitude as that of Scenario 1 with the exclusion of energy

consumption in the form of electricity which is used in pumping the

216

irrigation water at the pumping station. The electricity consumed in

pumping irrigation water depends on a range of factors including flow rate,

flow volume, type of pump, pump efficiency, electric motor efficiency, pipe

size and pipe material. All these factors are incorporated in the node-link

model for accurate estimation of energy consumed by the pumping system.

The theoretical background and the governing equations that are

implemented in the energy module of the developed node-link model to

compute energy consumption in irrigation pumping on a daily basis are

discussed in Chapter 3 in greater detail. In the current model, a value of 70%

for the centrifugal pumps and a value of 80% for the electric motors were

used based on the specifications for the installed equipment.

The modelled energy/electricity use for irrigation pumping, other energy

inputs and corresponding greenhouse gas emissions in the form of

equivalent carbon dioxide emissions on a per hectare basis are given in

Table 4.18. The electricity energy consumption was converted in to

equivalent kilograms of carbon dioxide emissions per kilowatt of electricity

using the conversion factor 0.9 for electricity generated within NSW.

Among the three crops, stone fruit requires the highest amount of pumping

energy per hectare followed by citrus and wine grapes. However, citrus

stays at the top when the total energy requirement per hectare from all

inputs is compared for the three crops. Similarly, the total greenhouse gas

emissions per hectare of crop associated with the energy inputs are also

highest for citrus crop followed by stone fruit and wine grapes.

Table 4.18: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of citrus, stone fruit and wine grapes for Scenario 3

Citrus Stone fruit

Wine grape

Energy input excluding electricity for pumping (KWh/ha)

7,889.21 7,195.27 6,728.07

Electricity consumed in irrigation pumping (KWh/ha)

1,436.7 1,573.5 1,296.5

Total energy input (KWh/ha) 9,325.91 8,768.77 8,024.57 Total energy sequestered in yield (KWh/ha)

18,550 10,980 65,600

217

GHG emissions, excluding electricity for pumping (KgCO2-e/ha)

1,832.74 1,634.52 1,532.50

GHG emissions for electricity consumed in irrigation pumping (KgCO2-e/ha)

1,293.03 1,416.15 1,166.85

Total GHG emissions (KgCO2-e/ha) 3,125.77 3,050.67 2,699.35

The total GHG emissions from electricity consumption are as high as 70%

to 87% of the GHG emissions from energy used excluding electricity by

each crop. This indicates that the electricity consumed by irrigation delivery

and application systems is likely to result in a profound environmental

footprint. Therefore, one of the prime questions addressed in this research is

how to design and operate irrigation system that is both environmentally and

economically balanced.

4.5 Scenario 4 - Furrow irrigation with pipe supply system

This scenario is similar to Scenario 2 except that the irrigation water is

delivered under pressure through pipe from the water source to the farm

inlets. The pipe flow model used to simulate this scenario is described in

Chapter 3. Unlike flood irrigation, water is not delivered under atmospheric

pressure; instead, a minimum pressure head of 3 m is maintained at the

supply pipe outlets (farm inlets) by installing pressure regulating valves at

each outlet. Each supply pipe outlet is connected with on-farm riser pipes

which deliver water at the top end of each furrow through taps. The use of

pressurized riser pipes and taps eliminates the need for priming the siphons.

It also improves the water application rate. Hence under this scenario energy

is required to pressurize and move water through the pipe against the pipe

friction, and in some sections against the slope, by the pumps. Except for

the addition of energy required for irrigation pumping and delivery, all other

energy inputs are assumed to be the same as for Scenario 2. The only

modification made in the energy inputs for Scenario 2 is that the human

labour energy for irrigation is reduced to half due to elimination of the need

for priming of siphons.

4.5.1 Optimization of pipe diameters

218

The magnitude of flow velocity through a pipe is inversely proportional to

the diameter of the pipe. Therefore, to avoid flow velocity to exceed the

limit of 3 m/s the pipe diameter should be increased. The same optimization

module which is set up for Scenario 3 for the same reasons was executed to

find the optimum diameter of the flow pipes for the furrow irrigation case.

The optimization results are shown in Table 4.19. The average diameter of

the pipe system is increased by 15 mm as compared to Scenario 3.

Table 4.19: Original and optimized diameters for supply pipe network for Scenario 4

Link ID (node x to node y)

Original diameter (mm)

Optimized diameter (mm)

1a – junction 450 450 junction – 3 375 440

3 – 4 375 430 4 – 5 250 350 5 – 6 250 350

Junction – 7 250 340 7 – 7a 250 340 7a – 8 250 300 8 – 9 250 275 9 – 10 250 265 10 – 11 250 310 11 – 12 250 305 12 – 13 250 265

4.5.2 Irrigation supply, losses and irrigation application rates

The node-link model with optimized pipe diameters as described above was

used to simulate water supply to three furrow irrigated horticultural crop

types in the case study area. Owing to the replacement of open channels

with pipes for irrigation supply, the water losses from channel evaporation

and seepage are eliminated. This results in conveyance loss savings of 4.6

ML/km of irrigation supply distance or 18.75 ML over the whole irrigation

period for this scenario. Out of these 18.75 ML about 11 ML are offset by

the increased irrigation supply as compared to Scenario 2 which used open

channel supply system. The field losses and irrigation application rates are

also driven up due to enhanced supply of irrigation water as shown in Table

4.20. The field losses (deep percolation plus surface runoff) increased from

219

457.71 ML to 470.37 ML. Similarly, the average irrigation application rate

increased slightly from 8.76 ML/ha for Scenario 2 to 8.88 ML/ha for

Scenario 4.

Table 4.20: Comparison of losses and irrigation application rates for Scenario 4 and Scenario 2

Scenarios Field losses (ML) Irrigation application rate

(ML/ha) Deep

percolation Runoff Citrus Stone fruit Wine grapes

Scenario 4 281.05 189.32 10.04 9.12 7.47 Scenario 2 273.14 184.57 10.03 8.87 7.38



Since the irrigation application method and the total water use do not

change significantly from Scenario 2, the magnitude of energy inputs for the

three crops in Scenario 4 are also assumed to be the same as that of Scenario

2. The only modification made in the energy inputs for Scenario 2 is that the

human labour energy for irrigation is reduced to half due to elimination of

the need for priming of siphons to each furrow. However, the energy

consumption (especially diesel) in irrigation monitoring trips by channel

operators is not changed from Scenario 2. In addition, a significant amount

of energy is consumed in running pumps to move water from its source to

the farm outlets through the pipe system for Scenario 4. Also energy is

required to generate the pressure head of 3 m for the current scenario at each

pipe outlet. Hence the total energy required for Scenario 4 should be higher

than that of Scenario 2. However, when compared with Scenario 3 (flood

irrigation with piped supply), the optimized diameter of supply pipes is

relatively increased (340 mm versus 333 mm) while the total flow volume

pumped for irrigation is decreased (2841 GL versus 3619 GL). These two

factors contribute to a remarkable decrease in pumping energy consumption

for the current scenario.


irrigation day depending on the duty flow rate. The node-link model

220

tweaked for simulating this particular scenario indicates that as high as 11

pumps installed at a pumping station near the surface water source, each

with a peak discharge rate of 0.08 m3/s, are simultaneously operated in

parallel configuration to supply irrigation water. The model reports the

maximum and average duty flow rates of 0.916 m3/s and 0.093 m3/s,

respectively, for the communal pumping system. The model computes that a

total of 388.9 MWh of electrical energy is consumed by the electrical

motors to drive the pumps to supply irrigation water with a total supply

volume of 2,841 ML to the three crops during one complete year of

simulation. The total energy consumption is divided among the three crops

based on their proportional water use of the total irrigation volume as given

in Table 4.21. The pumping energy consumed per hectare of a crop is given

by dividing the total pumping energy consumption of that crop by its total

area in hectares.


Total Citrus Stone fruit Wine grape Irrigation (ML) 2,841.0 2,450.0 222.1 168.8 Pumping energy (MWh) 388.9 335.4 30.4 23.1 Pumping energy (KWh/ha) 1336.6 1374.4 1249.0 1022.1

The modelled use of energy/electricity for irrigation pumping, other energy

inputs and the corresponding greenhouse gas emissions in the form of

equivalent carbon dioxide emissions on per hectare crop area basis are given

in Table 4.22 for the three crops for the current scenario.

Among the three crops, citrus requires the highest amount of pumping

energy on a per hectare basis followed by stone fruit and wine grapes. Citrus

is also highest when the total energy requirement per hectare from all energy

inputs is compared for the three crops. Similarly, the total greenhouse gas

emissions per hectare of crop associated with the energy inputs are also

highest for citrus crop followed by stone fruit and wine grapes.

221

Table 4.22: Accounts for energy inputs and corresponding greenhouse gas emissions on per hectare basis in the yearly production cycle of citrus, stone fruit and wine grapes for Scenario 4

Citrus Stone fruit

Wine grape

Energy input excluding electricity for pumping (KWh/ha)

7,762.1 7,388.9 6,623.7


1374.4 1249.0 1022.1


21,200 11,590 72,160


1,806.8 1,683.7 1,507.1


1,237.0 1,124.1 919.9


The line to line comparison of Table 4.18 for Scenario 3 (flood irrigation

with piped supply) and Table 4.22 for Scenario 4 (furrow irrigation with

piped supply) indicates that the two cases are not much different from each

other in terms of total energy use and total greenhouse gas emissions for

each crop. However, Scenario 4 performs marginally better, chiefly due to

the reduced volume of total irrigation water pumped as compared to

Scenario 3. However, Scenario 4 can perform even better if the minimum

pressure head of 3m is not to be maintained at each outlet.

4.6 Scenario 5 - Sprinkler irrigation with pipe supply system

Scenario 5 refers to a fixed-head sprinkler irrigation system connected with

a pressurized pipe water supply system from a common water source to the

farm outlet. The system is assumed to be installed on each of the 13 farms

of the case study area. Unlike flood or furrow irrigation, the sprinkler

system requires a minimum pressure at each sprinkler head to operate. An

operating pressure less than the minimum required pressure will result in

lesser coverage of irrigation area and higher drainage loss; similarly, a very

high operating pressure may result in mist formation by sprinkler heads and

hence less distribution uniformity and greater evaporation losses. The

222

sprinkler system simulated under Scenario 5 works on a commonly used

operating pressure of 35 PSI or 25 m water head. That’s why; the node-link

model of the pipe supply system is setup with a fixed pressure head of 25 m

at each supply node (farm inlet).

The pressure required to be produced by the pump(s) at the pumping station

is much higher than the abovementioned fixed required pressure at each

outlet. This is due to the fact that as water moves through a pipe it loses

pressure due to a phenomenon called "friction loss" as explained in Chapter

3. The amount of friction loss is determined by the type of pipe, the

diameter of the pipe, the amount/speed of water flowing through the pipe,

and the length of the pipe. These factors are plugged into the Williams-

Hazen formula that is implemented in the node-link model to calculate the

friction loss in terms of meters of water head loss. In addition to friction

loss, pressure is also lost as water passes through a valve, pipe bend or

change in pipe diameter. These losses are termed as minor losses. The total

friction loss and minor losses are added to the fixed pressure head at each

farm inlet required to operate the sprinkler system, to get the total operating

pressure head required at the pump outflow pipe.


The daily total irrigation demand and daily total actual supply time series

are shown in Figure 4.8. The irrigation demand computed by the crop ET

model and the daily irrigation volume supplied via supply pipe by the

pumping system are same as shown in Figure 4.8. The cumulative shortage

in irrigation supply remains zero throughout the simulation period; hence

the condition for a demand-based irrigation system is fulfilled. The total

daily irrigation demand is peaked at 48 ML/day as compared to 78.57

ML/day for furrow irrigation under Scenario 2. The cumulative irrigation

demand and the cumulative irrigation supply over the whole year remain

2,312 ML.

223

Figure 4.8: Comparison of daily irrigation demand and supply and cumulative shortage in irrigation supply (ML/day on Y-axis) for Scenario 5


Since this scenario consists of the piped irrigation supply system, no

channel seepage and channel evaporation loss occurs. The field losses

include deep percolation and the surface runoff. The cumulative deep

percolation losses at the end of the simulation are the highest at 47.19 ML

followed by the cumulative surface runoff (or surface drainage) of 30.66

ML. The total non-productive losses (sum of deep percolation and surface

runoff losses) of irrigation water amounts to 77.85 ML which is 3.4 per cent

of the total irrigation supply at the end of a one-year simulation and roughly

0.27 ML of water loss per hectare for the overall irrigated area of the case

study. The irrigation water loss rate for the current sprinkler irrigation

system is significantly lesser than the flood (Scenario 1) and furrow

(Scenario 2) irrigation system. There are two main reasons for lower losses

under the current scenario, firstly, the more precise and adequate application

of irrigation water where and when needed and elimination of transmission

losses due to piped supply.

60

40

20

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

(Days, 1 = 1 Jul)

Total_Demand : Scenario 5_Sprinkler_with_Pipe ML/DayTotal_Supplied : Scenario 5_Sprinkler_with_Pipe ML/DayCumulative_Shortage : Scenario 5_Sprinkler_with_Pipe ML/Day

224


The model shows that the demand-based irrigation supply system under

Scenario 5 does not encounter any supply constraints. Therefore, there is no

reduction in crop evapotranspiration due to the absence of any water stress

and hence no reduction in yield of the three crops. Instead, the timely and

precise application of irrigation results in an increase in yield as compared

to flood and furrow irrigation.


The “irrigation application rate” is computed from the modelled irrigation

amount expressed as the number of mega-litres of water applied per hectare

for a given crop averaged for all farms with that crop regardless of the soil

type of the individual farms. Nonetheless, the model takes into account soil

type in determining irrigation amount and irrigation timing for individual

farms using a soil-water balance approach. The irrigation application rates

for the three crops as computed by the node-link model for Scenario 5 are

given in Table 4.23. The modelled values of the irrigation application rate

are very similar to those reported by Khan et al., (2005); Khan and Abbas

(2007); Giddings (2005); and Giddings, (2004). The analysis of information

given in Table 4.23 shows that on average there are potential water savings

of at least 1.35 ML/ha that can be achieved by minimizing irrigation water

loss in the form of soil evaporation, deep percolation and surface runoff, by

adopting water saving practices and technology. The average irrigation

application rate is also reduced by 4.72 ML/ha and 1.19 ML/ha as compared

to flood irrigation (Scenario 1) and furrow irrigation (Scenario 2),

respectively. The total irrigation water use combined for the three crops in

the case study area for a complete one-year cycle for Scenario 5 is 2,312

ML as estimated by the model, which is 1,307 ML less than that of Scenario

1 and 518 ML less than that of Scenario 2 indicating the water savings

potential of improved irrigation technology and supply system. Sprinkler

systems are considered to be more water efficient than furrow irrigation

because irrigation can be matched to crop requirements better than with

225

furrow systems with less water wasted to drainage. Sprinkler systems

involve less maintenance and labour costs but have high pumping costs due

to high pumping pressure requirements. They also provide better frost

control in grapes. However, wetting patterns of irrigation by sprinklers are

distorted especially during windy conditions (Crean et al., 2004).


CitrusStone fruit

Wine grape

Average


8.10 8.20 6.40 7.57

Cumulative soil evaporation component of ETc (ML)

272.44 23.73 16.96 313.13 (total)


1.12 0.97 0.75 1.08

Deep percolation (ML/ha) * * * 0.16 Surface runoff or drainage (ML/ha)

* * * 0.11




Unlike Scenario 2, irrigation supply is made via pipes from a central

pumping station at certain fixed pressure for Scenario 5. The water flows

under hydraulic pressure through irrigation supply pipes from source to the

supply points and hence water pumping is required to move the water

through the supply channels to the farms. The pipe flow rate is varied by

increasing/decreasing the number of pumps depending upon the irrigation

demand for a given day, however, the pressure head at each pipe outlet

(farm inlet) remains almost constant by use of pressure regulating valves.

Moreover, all the irrigation water is sourced from the surface water source

i.e. the Murrumbidgee River, and no groundwater pumping is made to

irrigate the crops. Therefore; the energy use for irrigation supply has to be

incorporated in the energy use analysis. For the current scenario, the

sprinkler heads and lines are fixed and no energy is used in rolling laterals.

226

However, some human hours are expended in inspecting and maintenance

of the sprinkler system at each farm. It also involves random trips by

irrigation operators/inspectors on four-wheel drive vehicles to inspect and

monitor the piped water supply for any leakages or unauthorized access.

Given that there is relatively lesser need for monitoring, both the human

hours spent by irrigation inspectors and the fuel consumed by the vehicles

are assumed to be halved as compared to what is reported in Table 4.11

(Scenario 2) for citrus, stone fruit and wine grape farms of the case study

area. Based on this data and energy conversion factors for diesel

consumption and human hours given in Chapter 3, the equivalent energy

consumed in irrigation monitoring operations amounts to 4.4 KWh/ha for

citrus, 29.25 KWh/ha for stone fruit and 14.7 KWh/ha for wine grape crop.


A detailed inventory of energy inputs and energy outputs for sprinkler

irrigated citrus crop with pipe supply system is given in Table 4.24. The

labour hours under “irrigation” in Table 4.24 also includes time spent by

irrigators on the farm to manage irrigation, for example monitoring and

maintenance of sprinkler heads in this case, as well as the time expended by

irrigation inspectors. The number of labour hours spent in managing

irrigation application depends on the number and duration of the irrigation

application events. Similar to the preceding scenarios, the amounts of

different energy inputs are either based on data collection, personal

communications or publications by local agencies including NSW


Giddings (2004); Crean et al., (2004).

Each type of input energy is converted into the equivalent energy

sequestered in that input and expressed as kilowatt hours using the

conversion factors given in Chapter 3. With sprinkler systems, through

fertigation, fertilizers dissolved in the irrigation water can be applied almost

direct to the bulk of rootzone, providing more efficient uptake of nutrients

by trees. This allows easier, controlled, more effective and more precise

227

application of fertilizers especially Urea which can quickly leach out of the

root zone due to its high solubility. Therefore, the quantity of the fertilizers

used for Scenario 5 is much less than that of Scenario 1 (flood) or Scenario

2 (furrow) as given in Table 4.24. Fertigation also eliminates the need for

use of a tractor to spread fertilizer in the field. These factors contribute in a

decrease in both the direct (diesel, labour) and indirect (fertilizer) energy

inputs. The fertilizers are usually dissolved in water with the ratio of 1:5, i.e.

100 kg of fertilizer in 500 litres of water (Giddings, 2004). For the current

scenario, the amount of fertilizer applied is reduced by 20% when compared

with Scenario 2 for each of the three crops. Conversely, the use of fungicide

is increased by 5% as there are greater levels of disease infection,

particularly downy and powdery mildew, under sprinkler irrigation.

As detailed in Table 4.24, the total energy input from all considered energy

sources (excluding electricity consumption in irrigation pumping) to the

sprinkler irrigated citrus farms connected with piped irrigation supply


sequestered in the resulting citrus yield at the rate of 44 t/ha is 23,320

KWh/ha.

Similarly the total GHG emissions for citrus farming operations excluding

irrigation pumping are estimated to be 1,639.05 Kg of CO2-equivalent. The

increase in citrus yield by 4 t/ha as compared to Scenario 2 is due to the

reason that with sprinkler irrigation system the irrigation application can be

matched to crop requirements better than with furrow systems with less

water wasted to drainage and also that more nutrients are available to plants

for uptake because fertilizer is applied through fertigation, thus eliminating

fertilizer wastage, especially urea, due to reduced leaching or surface runoff.

228





Irrigation


Refer to Table 4.27

Labour (hr/ha)

51.07 32.68 13.92


0.4 4.29 1.84

Fertilizer (kg/ha)

Urea 208 1761.8 317.12

DAP 88 352.0 76.03

Potash 76 95.6 20.65 Chemicals (kg/ha)

Herbicide 2.5 166.8 36.03

Fungicide 5.6 160.1 34.58

Pesticide 0.14 7.8 1.68 Tractor (hr/ha)

Chemicals spray

17.0 2743.5 683.7

Bin placement

1.0 161.4 40.22

Sod mowing 3.0 484.1 120.7

Labour 21 13.4 5.73 Manual Pruning (hr/ha)

42 26.9 11.46

Harvesting

Labour (h/ha)

420 268.8 114.51

Tractor (h/ha)

4 645.5 160.88


Citrus 44,000 23,320.0


229


A detailed account of input (excluding electricity consumption in irrigation

pumping) and output energy (yield) on per hectare basis for sprinkler

irrigated stone fruit (peach mainly) with pressurized pipe supply system is

given in Table 4.25. The assumptions for estimation of labour hours and

diesel consumption for irrigation operators are same as those described for

citrus above. As detailed in Table 4.25, the total sum of energy input from

all considered sources to the sprinkler irrigated stone fruit (peach) with

piped irrigation supply system is aggregated to 6,192.37 KWh/ha and the

total output energy sequestered in yield of peach crop at the rate of 21 t/ha is

12,810 KWh/ha. Similarly the total GHG emissions for peach farming

operations excluding irrigation pumping are estimated to be 1,430.47 Kg of

CO2-equivalent per hectare of the crop.





Irrigation


Refer to Table 4.27

Labour (hr/ha)

48.46 31.01 13.21


2.7 28.97 7.21

Fertilizer (kg/ha)


Herbicide 3 200.16 43.23 Fungicide 5.25 150.05 32.41

230

Pesticide 0.1 5.56 1.19 Tractor (hr/ha)


0 0.0 0.0

Chemicals spray

11 1775.18 442.42

Bin placement

0.75 121.04 30.17


125 80.00 34.08

Harvesting

Labour (h/ha)

200 128.00 54.53

Tractor (h/ha)

6.0 968.28 241.32



Accounts of input energy (excluding electricity consumption in irrigation

pumping) and output energy on a per hectare basis for sprinkler irrigated

wine grape farms connected with central pipe supply system is given in

Table 4.26. The fertilizer application rates are estimated from Giddings

(2004) based on 65% fertilizer use efficiency.

Table 4.26: Accounts for Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of wine grapes crop for Scenario 5




Irrigation


Refer to Table 4.27

Labour 51.22 32.78 13.96

231

(hr/ha)


1.35 14.49 3.61

Fertilizer (kg/ha)


Herbicide 2 133.44 28.82 Fungicide 4.5 128.61 27.78 Pesticide 1 55.60 12.01 Tractor (hr/ha)


0 0 0

Chemicals spray

10.5 1694.49 422.31


50 32.00 13.63


Labour (h/ha)

6 3.84 1.64


6 968.28 241.32



(excluding electricity consumption in irrigation pumping) to the sprinkler

irrigated wine grape farms connected with pressurized pipe supply system is

aggregated to 5,618.65 KWh/ha and the total output energy sequestered in

total yield of wine grapes crop harvested at the rate of 23 t/ha is 75,440

232

KWh/ha. Correspondingly, the total GHG emissions resulting from this

energy use are estimated to be 1,294.28 Kg of CO2-equivalent. The

improvement in yield can be attributed to timely water availability to plants,

reduced irrigation water losses in seepage and runoff as well as relatively

more fertilizer uptake by plants through fertigation as compared to the

furrow or flood irrigation.

4.6.5.4 Energy use and GHG emissions in irrigation pumping for the

three crops

The total energy use for irrigation pumping is distributed among the three

crops proportional to their irrigation volume as given in Table 4.27. The

sprinkler system operating pressure head of 25 m for Scenario 5 is much

greater than the previously discussed pipe supply scenarios. Therefore, the

energy required to drive pumps to generate the sprinkler operating pressure

head of 25 m at each pipe outlet should be considerably higher than

previous scenarios. However, the comparison of the total irrigation volume

applied (refer to Table 4.17, Table 4.21 and Table 4.27) shows that the total

irrigation volume pumped for Scenario 5 is up to 36% lesser than the

previous scenarios. Therefore, the pumping energy requirement is offset by

a certain amount due to a reduction in the total volume of irrigation water to

be pumped as shown in Table 4.27.


Total Citrus Stone fruit Wine grape

Irrigation Volume (ML) 2,312.0 1,976.6 199.6 136.5

Pumping energy (MWh) 430.6 368.1 37.2 25.4

Pumping energy (KWh/ha) 1479.9 1508.4 1528.3 1123.9


irrigation day depending on the duty flow rate. The node-link model

tweaked for simulating this particular scenario indicates that as high as 7

pumps installed at a pumping station near the surface water source, each

with a peak discharge rate of 0.08 m3/s, are simultaneously operated in

parallel configuration to supply irrigation water as shown in time series

233

plots of number of active pumps in Figure 4.9. The model reports the

maximum and average duty flow rates of 0.56 m3/s and 0.073 m3/s,

respectively, for the communal pumping system.

The model computes that a total of 430.6 MWh of electrical energy is

consumed by the electrical motors to drive the pumps to supply irrigation

water with a total supply volume of 2,312 ML to the three crops using

sprinkler system during one complete year of simulation. The total energy

consumption is divided among the three crops based on their proportional

water use of the total irrigation volume as given in Table 4.27. The pumping

energy consumed per hectare of a crop is given by dividing the total

pumping energy consumption of that crop by its total area in hectares and is

almost unchanged for the three crops.

Figure 4.9: Time series of the daily number of pumps turned on in parallel configuration to supply irrigation water for Scenario 5

The modelled use of energy/electricity for irrigation pumping, other energy

inputs and the corresponding greenhouse gas emissions in the form of

equivalent carbon dioxide emissions on per hectare crop area basis are

summarized in Table 4.28 for the three crops for the current scenario.

Similar to the previous piped irrigation supply scenarios, citrus production

stays at the top when the total energy use per hectare from all energy inputs

including electricity consumption for irrigation pumping is compared for the

three crops. Similarly, the total greenhouse gas emissions per hectare of

crop associated with the energy inputs are also highest for citrus crop

followed by stone fruit and wine grapes. Another vital observation to note in

9

6

3

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

Days (1 = 1st July)

"No._of_Pumps" : Scenario 5_Sprinkler_with_Pipe

234

Table 4.28 is the fact that the greenhouse gas emissions from the single

energy input for irrigation pumping operation are almost equal (17% to 22%

less) in magnitude to the total greenhouse gas emissions from all other

energy inputs for each of the three crops. This signifies the link between the

irrigation modernization and its environmental footprint that possibly

contributes toward exacerbation of phenomenon of climate change.

Table 4.28: Accounts for energy inputs and corresponding greenhouse gas emissions on per hectare basis in the yearly production cycle of citrus, stone fruit and wine grapes for Scenario 5

Citrus Stone fruit

Wine grape

Total energy input excluding electricity for pumping (KWh/ha)

6,924.7 6,192.4 5,618.7


1508.4 1528.3 1123.9


23,320.0 12,810 75,440


1,639.1 1,430.5 1,294.3


1,357.6 1,375.5 1,011.5


The rates of energy (electricity) consumed per hectare of the three crops for

irrigation pumping using a communal pumping station are reasonably

comparable to what is reported in Giddings (2004) and Giddings (2005).

Other than a difference in pump size, pumping efficiency etcetera,

additional energy is required to pump water from the off-farm central

location (communal pumping station) as compared to the on-farm pumping.

4.7 Scenario 6 – Drip irrigation with pipe supply system

Scenario 6 refers to the surface drip/trickle irrigation system connected with

a pressurized pipe water supply system from a common water source to the

farm outlet. The common pipe supply network provides for the minimum

hydraulic pressure required for operating drippers on each drip line in the

field. This scenario closely replicates the current field setup of the 13 farms

235

of the case study area. The drip system for each farm as simulated under

Scenario 6 works on a commonly used operating pressure of 45 PSI or 32 m

water head. Therefore, the node-link model of the pipe supply system is

setup with a fixed pressure head of 32 m at each supply node (farm inlet).

Due to their differing approach for application of irrigation to the trees, the

operating pressure for drip system is higher than that of sprinkler system;

however, the rate of irrigation application volume is the other way around

(i.e. lesser).

The pressure required to be produced by the pump(s) at the pumping station

has to be much higher than the abovementioned fixed required pressure at

each outlet. This is due to "friction loss" as explained in Chapter 3 and

Scenario 5. The total friction loss and minor losses are added to the fixed

pressure head to get the total operating pressure head required at the

pump(s) outflow pipe.


The daily total irrigation demand and daily total actual supply time series

for the case study area are shown in Figure 4.10. The irrigation demand

computed by the crop ET model and the daily irrigation volume supplied via

supply pipe by the operation of the pumping system are the same as shown

in Figure 4.10. Therefore, the cumulative shortage in irrigation supply

remains zero throughout the simulation period; hence the condition for a

demand-based irrigation system is fulfilled. The total daily irrigation

demand is peaked at 28.4 ML/day as compared to 48 ML/day for sprinkler

irrigation under Scenario 5. The cumulative irrigation demand and the

cumulative irrigation supply over the whole year remain 1,789 ML as

compared to 2,312 ML for the sprinkler system under Scenario 5.

236

Figure 4.10: Comparison of daily irrigation demand and supply and cumulative shortage in irrigation supply (ML/day on Y-axis) for Scenario 6


No channel seepage and channel evaporation loss occurs for this scenario as

it consists of the piped irrigation supply system. The field losses include

deep percolation and the surface runoff. The cumulative deep percolation

losses over the simulation period are higher than the cumulative surface

runoff as 34.7 ML and 22.0 ML, respectively. The total non-productive

losses (sum of deep percolation and surface runoff losses) of irrigation water

amounts to 56.7 ML which is 3.2 per cent of the total irrigation supply at the

end of a one-year simulation and roughly one-fifth (0.19 ML) of each mega-

litre of irrigation water applied per hectare is lost for the irrigated area of the

case study. The irrigation water loss rate for the drip irrigation system is

significantly lesser than all other irrigation systems discussed so far. There

are three main reasons for lower losses under the current scenario, firstly;

the more precise and controlled application of irrigation water where needed

and when needed, secondly; lesser field irrigation evaporation losses due to

smaller wetted area and thirdly; the elimination of transmission losses due to

piped supply. However, the frequency of irrigation for drip irrigation system

30

20

10

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

(Days, 1 = 1 Jul)

Total_Demand : Scenario 6_Drip_with_Pipe ML/DayTotal_Supplied : Scenario 6_Drip_with_Pipe ML/DayCumulative_Shortage : Scenario 6_Drip_with_Pipe ML/Day

237

is higher than other irrigation methods due to least rootzone storage due to

least size of the bulk of wetted region around the trees. For example, the

total number of irrigation days for Scenario 6 is 307 days as compared to

176 days for Scenario 5 (sprinkler system).


The model shows that the demand-based irrigation supply system under

Scenario 6 does not encounter any supply constraint. Therefore, there is no

reduction in crop evapotranspiration due to the absence of any water stress

and hence no reduction in yield of the three crops. The drip irrigation

system has the advantage of the timely and precise application of irrigation

which results in an increase in yield as compared to flood and furrow

irrigation. As mentioned by Dasberg (1995) and supported by field trials by

others the drip system helps manipulate irrigation application during water

stress sensitive periods such as during the crop growth cycle. This helps

control quantity as well as quality of yield.


The developed model takes into account soil type in determining the

irrigation demand and irrigation timing for individual farms using a soil-

water balance approach. However, the irrigation application rate is defined

as the number of megalitres of water applied per hectare for a given crop

averaged for all farms with that crop regardless of the soil type of the

individual farms. The irrigation application rates for the three crops as

computed by the node-link model for the current scenario are given in Table

4.29. The irrigation application rate computed by the model are very similar

to those reported by Khan et al., (2005); Khan and Abbas (2007); Giddings

(2005); and Giddings, (2004) and other published literature. The analysis of

information given in Table 4.29 shows that on average there is potential for

further water savings of at least 0.97 ML/ha which, can be achieved by

minimizing irrigation water loss in the form of soil evaporation, deep

percolation and surface runoff, by efficient management of the irrigation

system and by adopting water saving practices and technology, for example,

238

replacing surface drip system with subsurface drip system. The average

modelled irrigation application rate is also the lowest and application

efficiency highest among the scenarios discussed so far. The total irrigation

water use combined for the three crops in the case study area for a complete

one-year cycle for Scenario 6 is 1,789 ML as estimated by the model, which

is 523 ML less than that of Scenario 5 indicating the water savings potential

of improved irrigation technology and supply system. In drip irrigation

system coupled with piped supply, less water is pumped but more efficiently

applied to the plants. The drip irrigation systems are potentially the most

water efficient than any other irrigation options, if managed properly, by

minimizing water loss through deep drainage, surface runoff and

evaporation from the soil surface. The drip systems may involve more

maintenance requirement than gravity based irrigation systems however; the

maintenance can also be automated to some extent for drip system.


CitrusStone fruit

Wine grape

Average


6.26 6.30 4.77 5.78

Cumulative soil evaporation component of ETc (ML)

221.89 19.65 13.82 255.38 (total)


0.91 0.81 0.61 0.78

Deep percolation (ML/ha) - - - 0.12 Surface runoff or drainage (ML/ha)

- - - 0.07



In this section different direct and indirect energy inputs and their

greenhouse gas emissions are discussed for Scenario 6. Similar to the

sprinkler irrigation system discussed in Scenario 5, irrigation supply is made

via pipes from a central pumping station at certain fixed pressure for

Scenario 6. The water is pumped and moved under hydraulic pressure

239

through irrigation supply pipes from the source (pumping station), to the

supply points i.e. the farm irrigation system inlets. The pipe flow rate is

varied by increasing/decreasing the number of pumps depending upon the

irrigation demand for a given day, however, the pressure head at each pipe

outlet (farm inlet) is kept almost constant by use of pressure regulating

valves to operate the drip irrigation system on each farm. Since irrigation

pumping is an energy intensive operation; the energy use for irrigation

supply has to be incorporated in the energy use analysis. The drip lines,

once installed on either side of the tree line, are fixed and no energy is

required to roll the drip lines. However, some human hours are expended in

inspecting and maintenance of the drippers and drip lines at each farm. It

also involves random trips by irrigation operators/inspectors on four-wheel

drive vehicles to inspect and monitor the piped water supply for any

leakages or unauthorized access. Given that there is relatively lesser need

for monitoring, both the human hours spent by irrigation inspectors and the

fuel consumed by their vehicles are assumed to be halved as compared to

what is reported in Table 4.11 (Scenario 2) for citrus, stone fruit and wine

grape farms of the case study area. Based on this data and energy conversion

factors for diesel consumption and human hours given in Chapter 3, the

equivalent energy consumed in monitoring operations amounts to 4.4

KWh/ha for citrus, 29.25 KWh/ha for stone fruit and 14.7 KWh/ha for wine

grape crop.

The energy consumed in monitoring operations also includes periodic trips

by the technical personnel to check pumping system at the pumping station

for any potential faults and scheduled maintenance.


Energy inputs, energy outputs and corresponding greenhouse gas emissions

per hectare of drip irrigated citrus crop with pipe supply system are given in

Table 4.30. The labour hours under “irrigation” in Table 4.30 also includes

time spent by irrigators on the farm to manage irrigation, for example

monitoring and servicing of drippers in this case, as well as the time

240

expended by irrigation inspectors. Similar to the preceding scenarios, the

rates of direct and indirect energy inputs are either based on data collection,

personal communications or publications by local agencies including NSW


Giddings (2004); and Crean et al., (2004). The fertilizer application rates are

relatively conservative to those of Scenario 5 (Sprinkler system) owing to

improved fertilizer use efficiency for suitably managed drip irrigation

system.

In order to compute the total energy use, each type of input energy is

essentially converted into the equivalent energy sequestered in that input

and expressed as kilowatt hours using the conversion factors given in

Chapter 3. With drip irrigation system, through fertigation, continuous small

applications of soluble nutrients are made which overcome the fertilizer loss

through runoff or leaching problems, save labour, reduce compaction in the

field, result in the fertilizer being placed around the plant roots uniformly

and allow for rapid uptake of nutrients by the plant. This allows easier,

controlled, more effective and more precise application of fertilizers

especially Urea which can quickly leach out of the root zone due to its high

solubility. Therefore, quantity of the fertilizers used for Scenario 6 is much

less than that of Scenario 1 (flood) or Scenario 2 (furrow) as given in Table

4.30. Fertigation also eliminates the use of a tractor to spread fertilizer in the

field. These factors contribute in a decrease in both the direct (diesel,

labour) and the indirect (fertilizer) energy inputs. The fertilizers are usually

dissolved in water with the ratio of 1:5, i.e. 100 kg of fertilizer in 500 litres

of water (Giddings, 2004) and applied during irrigation using methods like

suction injection, pressure differential injection or pump injection (NSW

DPI, 2000) and (Treeby et al., 2011).

As given in Table 4.30, the total energy use from all considered energy

inputs (excluding electricity consumption in irrigation pumping) to the drip

irrigated citrus farms connected with piped irrigation supply system is

aggregated to 6,897.1 KWh per hectare and the total output energy

241

sequestered in the resulting citrus yield at the rate of 48 t/ha is 25,440 KWh

per ha. Similarly the total GHG emissions from energy use for citrus

farming operations excluding irrigation pumping are estimated to be

1,637.75 Kg of CO2-equivalent emissions per hectare.

Table 4.30: Accounts for energy inputs and GHG emissions on per hectare basis in the yearly production cycle of citrus crop for Scenario 6 Input Quantity

used per hectare



Irrigation Electricity

(KWh/ha) Refer to Table 4.33

Labour (hr/ha)

42.07 27.33 11.64


0.4 4.29 1.84

Fertilizer (kg/ha)

Urea 200 1694.0 304.92 DAP 80 320.3 69.18 Potash 70 88.93 19.21 Chemicals (kg/ha)

Herbicide 2.5 166.8 36.03 Fungicide 5.6 160.1 34.58 Pesticide 0.14 7.8 1.68 Tractor (hr/ha)

Chemicals spray

17.0 2743.5 683.7

Bin placement

1.0 161.4 40.22

Sod mowing

3.0 484.1 120.7

Labour 21 13.4 5.73 Manual Pruning (hr/ha)

42 26.9 11.46

Harvesting

242

Labour (h/ha)

425 272.0 115.87

Tractor (h/ha)

4.5 726.21 180.99


Citrus 48,000 25,440.0



A detailed account of inputs (excluding electricity consumption in irrigation

pumping) and output energy (yield) on a per hectare basis for drip irrigated

stone fruit (peach mainly) with pressurized pipe supply system is given in

Table 4.31. The energy input rates may differ slightly from farm to farm, for

example, the fertilizer application rate and timing depends on management

skills of individual farmers and monitoring the need of nutrients by the

crops. Therefore, the figures given in Table 4.31 represent average energy

inputs per hectare of the crop. The total sum of energy inputs from all

considered sources to the drip irrigated stone fruit (peach) with piped

irrigation supply system on per hectare basis amounts to 6,000.91 KWh/ha

and the total output energy sequestered in yield of peach crop at a

production rate of 25 t/ha is 15,250 KWh/ha. Similarly the total GHG

emissions for peach growing operations excluding irrigation pumping are

estimated to be 1,396.68 Kg of CO2-equivalent per hectare of crop.

Table 4.31: Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of stone fruit crop for Scenario 6




Irrigation


Refer to Table 4.33

Labour (hr/ha)

40.46 25.89 11.03


2.7 28.97 7.21

243

Fertilizer (kg/ha)

N 100 1838 330.84 P 25 86.5 18.68 K 27 83.7 18.08 Chemicals (kg/ha)

Herbicide 3 200.16 43.23 Fungicide 5.25 150.05 32.41 Pesticide 0.1 5.56 1.19 Tractor (hr/ha)


0 0.0 0.0

Chemicals spray

11 1775.18 442.42

Bin placement

0.75 121.04 30.17


130 83.2 35.44

Harvesting

Labour (h/ha)

220 140.8 59.98

Tractor (h/ha)

6.0 968.28 241.32

Total energy input (kWh/ha) 6,000.91 Output (kg/ha) Peach 25,000 15,250.0 Total GHG emissions (KgCO2-e/ha) 1,396.68


The factsheet of input energy (excluding electricity consumption in

irrigation pumping) and output energy on a per hectare basis for drip

irrigated wine grape farms connected with the centrally pumped pipe supply

system is given in Table 4.32. The fertilizer application rates for wine

grapes are referenced from Giddings (2004) based on 75% fertilizer use

efficiency (nutrient removal efficiency). For example, if 63 kg of nitrogen

244

are taken up by one hectare of crop at harvest then nitrogen will be applied

at the rate of 84 kg/ha.

Table 4.32: Energy inputs and GHG emissions on per hectare basis in the yearly production cycle of wine grapes crop for Scenario 6




Irrigation


Refer to Table 4.33

Labour (hr/ha)

38.22 24.46 10.42


1.35 14.49 3.61

Fertilizer (kg/ha)


Herbicide 2 133.44 28.82 Fungicide 4.5 128.61 27.78 Pesticide 1 55.60 12.01 Tractor (hr/ha)


0 0 0

Chemicals spray

10.5 1694.49 422.31


50 32.00 13.63


Labour (h/ha)

6.5 4.16 1.77

245


6 968.28 241.32


According to the data given in Table 4.32, the total energy input from all

considered sources (excluding electricity consumption in irrigation

pumping) to the drip irrigated wine grapes farms operated with pressurized

pipe supply system is aggregated to 5,739.53 KWh/ha and the total output

energy sequestered in total yield of wine grapes crop harvested at the rate of

26 t/ha is 85,280 KWh/ha. Correspondingly, the total GHG emissions

resulting from this energy use are estimated to be 1,314.73 Kg of CO2-

equivalent emissions. The improvement in yield can be attributed to

efficient irrigation management, timely water availability to plants, reduced

irrigation water losses in seepage and runoff as well as relatively higher

fertilizer use and more fertilizer uptake by plants through fertigation as

compared to the furrow or flood irrigation.

4.7.5.4 Energy use and GHG emissions in irrigation pumping for the

three crops

The total energy use for irrigation pumping is distributed among the three

crops proportional to their irrigation volume as given in Table 4.33. The

drip/trickle system for Scenario 6 operates at a pressure head of 32 m which

is much higher than the previously discussed pipe supply scenarios.

Therefore, the energy required to drive pumps to generate the drip system

operating pressure head of 32 m at each pipe outlet should be considerably

higher than previous scenarios. However, the comparison of the total

irrigation volume applied (refer to Table 4.17, Table 4.21, Table 4.27 and

Table 4.33) shows that the total irrigation volume pumped for Scenario 6 is

up to 51% lesser than the previous scenarios. Therefore, the additional

pumping energy required to generate high operating pressures is offset by a

246

certain extent due to a reduction in the total volume of irrigation to be

pumped as shown in Table 4.33.


Total Citrus Stone fruit Wine grape

Irrigation (ML) 1,789 1,500.4 149.7 139.0

Pumping energy (MWh) 352.3 295.5 29.5 27.4

Pumping energy (KWh/ha) 1,210.8 1,210.9 1,212.0 1,212.4


irrigation day depending on the duty flow rate. The duty flow rate depends

on the number of farms irrigating that particular day. The model indicates

that up to 4 pumps installed in-parallel at a pumping station near the surface

water source, each with a peak discharge rate of 0.08 m3/s, are

simultaneously operated to supply irrigation water as evident from time

series plot of the number of active pumps in Figure 4.11. The model reports

the maximum and average duty flow rates of 0.33 m3/s and 0.057 m3/s,

respectively, for the communal pumping system. As expected the duty flow

rate for drip irrigation system is the lowest among all irrigation methods

discussed in this chapter and as a consequence have the lowest consumption

of electricity for irrigation pumping.

The model computes that a total of 352.3 MWh of electrical energy is

consumed by the electrical motors to drive the pumps to supply irrigation

water with a total supply volume of 2,312 ML to the three crops over a total

area of 290.97 hectares using drip system during one complete year of

simulation. Data collected from relevant irrigation provider, the actual

energy consumed for communal irrigation pumping to be 307 MWh in the

case study area. The actual energy consumption is slightly less than the

model estimate as, in practice, the system is not run as a complete demand

based drip irrigation system and actual pumped irrigation volume is less

than what is estimated by the demand-based model in this chapter. Water

and energy links for a supply based irrigation system is discussed in the next

chapter.

247

The total energy consumption in irrigation pumping is divided among the

three individual crops based on their proportional water use of the total

irrigation volume as given in Table 4.33, with a major portion of energy

used for citrus production due to a larger area of plantings and irrigation

volume used compared to the other two crops in the case study. The

pumping energy consumed per hectare of a crop is given by dividing the

total pumping energy consumption of that crop by its total area in hectares.

The pumping energy consumed per hectare of the three crops is almost the

same for the three crops.

Figure 4.11: Daily number of pumps turned on in parallel configuration to supply irrigation water for Scenario 6

The modelled energy/electricity use for irrigation pumping and other energy

inputs for crop production and their corresponding greenhouse gas

emissions in the form of equivalent carbon dioxide emissions on a per

hectare crop area basis are given in Table 4.34 for the three crops for the

current scenario.

Contrary to the previous piped irrigation supply scenarios, the amount of

consumed pumping energy per hectare is almost the same for each of the

three crops for drip irrigation system. However, citrus ranks at the top when

the total energy requirement per hectare from all energy inputs is compared

for the three crops. Similarly, the total greenhouse gas emissions per hectare

of crop associated with the energy inputs are also highest for citrus crop

followed by stone fruit and wine grapes. It should be noted in Table 4.34

that the greenhouse gas emissions from the single energy input for irrigation

No. of active pumps

6

4

2

01 27 53 79 105 131 157 183 209 235 261 287 313 339 365

Days (1 = 1st July)

"No._of_Pumps" : Scenario 6_Drip_with_Pipe

248

pumping operations are 17% to 34% less than the total greenhouse gas

emissions from all other energy inputs for each of the three crops. This

signifies the link between irrigation modernization and its environmental

footprint that possibly contributes toward exacerbation of phenomenon of

climate change. However, the greenhouse gas emissions from drip irrigation

system in Scenario 6 are much less than those for the sprinkler system in

Scenario 5.

Table 4.34: Energy inputs and corresponding greenhouse gas emissions on per hectare basis in the production cycle of citrus, stone fruit and wine grapes for Scenario 6

Citrus Stone fruit

Wine grape

Total energy input excluding electricity for pumping (KWh/ha)

6,897.1 6,000.9 5,739.5


1,210.9 1,212.0 1,212.4


25,440.0 15,250.0 85,280


1,637.8 1,396.7 1,314.7


1,089.8 1,090.8 1,091.2


4.8 Comparison of the demand-based irrigation scenarios

This section draws comparisons among the abovementioned six irrigation

scenarios with respect to water, energy and greenhouse gas emissions. A

range of established indicators and key variables are calculated and

discussed in this section to cover different aspects and viewpoints for the

simulated scenarios. All six scenarios consist of the same crops with the

same representative irrigated area and wit the only difference from one

scenario to the other being the irrigation method and/or irrigation supply

system (open channel or pipes). Therefore, the indicators discussed here

reflect purely the water and energy aspects of irrigation methods and

irrigation conveyance systems than the crop themselves.

249

4.8.1 Comparison of water and energy use rates

The irrigation application rates (ML/ha) to the three selected crops in the

case study area, for each of the six scenarios, are shown in Figure 4.12.

Similarly the energy use rate per hectare (kWh/ha) of the three crops can be

compared from Figure 4.13. Since each scenario is simulated as a demand-

based irrigation system, the irrigation rates tend to be higher than fixed

interval irrigation scheduling systems. However, as mentioned earlier,

Scenario 1 is not fully simulated as a demand-based system because of the

capacity constraint of the open channel supply system. That is why; the

irrigation rate for Scenario 3 is rather higher than Scenario 1 despite water

savings from conveyance losses.

Figure 4.12: Irrigation application rates (ML/ha) for each crop for the six scenarios

The comparison of irrigation rates for the given six scenarios in Figure 4.12

is in fact comparison of water savings from different irrigation application

methods as well as those resulting from open channel to piped supply

system. Based on the results shown in Figure 4.12, drip irrigation connected

with pressurized pipe supply system offers the highest water savings.

It should be clarified that there is only a slight difference in the rates of

energy use rate for flood irrigation based system (Scenario 1) compared to

the drip irrigation based system (Scenario 6). But in the context of energy

use for irrigation pumping alone, Scenario 6 involves as much as 1,212

250

kWh/ha of energy while energy use for irrigation pumping for Scenario 1 is

effectively zero. A similar magnitude of total energy use for both scenarios

is the fact that the energy inputs in the form other than irrigation pumping

are significantly lesser for Scenario 6 than that of Scenario 1. However,

these energy savings are off-set by the energy used for irrigation pumping

for Scenario 6.

Figure 4.13: Energy use per hectare (KWh/ha) for each crop for the six scenarios

From the comparison of Figure 4.12 and Figure 4.13, it is suggested that

both the energy and the water saving aspects of conversion from open

channel to the piped supply system should be duly considered side-by-side.

The water and energy analysis of using on-farm storages are carried out in a

later chapter of this thesis.

4.8.2 Comparison of efficiency and productivity indicators for

water and energy

Efficiency and productivity indices or indicators are a well-adopted

approach to compare different scenarios which deal with similar problems

and also to compare scenarios against some standard or acceptable

benchmark values of those indices. The domain of the current discussion

encompasses water and energy linkages in different irrigation supply and

application systems for a given study area. These indices/indicators are

251

widely used in scientific and research discussions about water-energy nexus

in irrigation and crop production systems. Their theory is discussed in

Chapter 3.

4.8.2.1 Comparison of irrigation efficiency

The definition of “irrigation efficiency” as endorsed by the Irrigation

Association of Australia is based on an approach suggested by the

International Commission on Irrigation and Drainage (ICID) as per the

paper by Bos et al., (1993). One of the essential elements of this approach is

that it tracks and accounts for water use from the point of supply all the way

through to the crop and provides the following (Equation 4.3) overall

definition of “irrigation project efficiency”. This definition is suitable for all

irrigation systems at an irrigation case study/scheme/district level and

above.

Equation

4.3

Another definition of irrigation efficiency which is closest to the above

equation is given by Israelsen (1932) as “the ratio of irrigation water

transpired by the crops of an irrigation farm or project during their growth

period to the water diverted from a river or other natural source into the

farm or project canal or canals during the same period of time.” It is usually

expressed in percentage terms.

The term defined in Equation 4.3 is further broken up into sub-components

including conveyance efficiency, distribution efficiency, and field

application efficiency. For this study, however, more emphasis is given to

the irrigation project efficiency as the focus is to compare water and energy

use at the representative case study scale rather than the individual farms

scales.

Despite two soil types with somewhat different irrigation thresholds a single

value of irrigation efficiency is computed for the case study area for

simplicity. High levels of irrigation efficiency translate into lower operating

252

costs and energy use, improved production per megalitre of water used and

improved environmental management. A comparison of computed irrigation

efficiency at the case study area level for the six scenarios is given in Table

4.35. The total transpiration is from the three crops in the case study area

and similarly total irrigation supplied refers to water volume extracted from

the communal water source over the course of one year. The transpiration

depth is duly converted to water volume by multiplying with the product of

irrigation wetted area (m2/ha) and the crop area. The conveyance losses

which include channel seepage and channel evaporation (open channel

supply only); field losses including evaporation from wet soil surface;

surface runoff and deep percolation constitute the difference between

irrigation supply and total transpiration volume.

It is evident from Table 4.35 that pressurized irrigation i.e. sprinkler and

drip system with a piped supply system result in the highest irrigation

efficiency. As discussed for the individual scenarios earlier, the sprinkler or

the drip system has lower water supply requirements due to minimal field

losses, zero conveyance losses and precise and controlled application of

irrigation water to the plants. It can also be concluded from Table 4.35 that

the only water savings from conversion of open channels (Scenarios 1 and

2) to a piped supply (Scenarios 3 and 4) for flood and furrow irrigation

systems leads to savings through reductions in conveyance losses. Since the

magnitude of conveyance losses is much lesser than the field losses, as long

as the irrigation efficiency is concerned, there is no significant improvement

in it for the case study area with these two irrigation systems even if the

piped supply is used. However, this may not be the case for large irrigation

areas with vast network of open channels where conveyance losses can be

significant. Therefore, Scenario 3 and Scenario 4 do not make any

improvement as far as irrigation efficiency is concerned, but result in

conveyance loss savings of 4.6 ML/km of supply channel. It is also evident

from Table 4.35 that there is more efficient water use by plants under

sprinkler and drip irrigation systems and hence relative improvements in

yields. Moreover, in terms of field losses, the extent of wetted area is a key

253

determinant. The larger the wetted area, the larger will be the field losses.

Hence the field losses for flood and furrow irrigation are much higher than

those of sprinkler and drip systems. The irrigation efficiency ranges from

76.1% (flood irrigation) to 92.6% (drip irrigation) as given in Table 4.35 for

the six scenarios. These irrigation efficiency values are much high than what

is attainable with conventional systems and can be attributed to the demand-

based irrigation strategy. The irrigation amount and irrigation interval are

varied as per field conditions under demand-based irrigation to minimise

water losses and achieve maximum irrigation efficiency.

254

Table 4.35: Computed overall/project level irrigation efficiency for the six scenarios

Scenario Total Transpiration for

Each Crop (mm)

Irrigation Wetted Area

(m2/ha)

Total Transpiration Volume

for Each Crop (ML)

Total

Transpiration

over Project

Area (ML)

Irrigation

Supplied

(ML)

Irrigation

Efficiency

(%)

Citrus Stone

Fruit Vine Citrus

Stone

Fruit Vine Citrus

Stone

Fruit Vine

S1 940.35 1100 848.55 10000 10000 10000 2294.7 267.7 191.8 2754.2 3619 76.1

S2 938.37 1098 847.86 8400 7140 7140 1923.5 190.8 136.8 2251.2 2949 76.3

S3 939.74 1100 848.55 10000 10000 10000 2293.2 267.7 191.8 2752.8 3600 76.5

S4 938.39 1098 847.86 8400 7140 7140 1923.6 190.8 136.8 2251.2 2930 76.8

S5 963.03 1118 851.82 7800 6630 6630 1833.1 180.4 127.6 2141.1 2312 92.6

S6 963.03 1118 851.82 6000 5100 5100 1410.0 138.8 98.2 1647.0 1789 92.1

255

4.8.2.2 Comparison of water productivity

Water productivity refers to the ratio between marketable produce/yield and

total irrigation water applied and is expressed as kg/m3. It measures the

productive performance of irrigated agriculture. The water productivity

indicators for the three crops calculated from the results for the six scenarios

are given in Table 4.36. The last column in Table 4.36 is just an average of

the values for the three crops to get an overall indicator for a given scenario.

Table 4.36: Water productivity (kg/m3) indicators for the six scenarios

Scenario No.

Citrus (kg/m3)

Stone fruit (kg/m3)

Wine grape (kg/m3)

Average (kg/m3)

1 2.83 1.35 1.80 1.99 2 3.99 2.14 2.98 3.04 3 2.82 1.32 1.78 1.97 4 3.98 2.08 2.95 3.00 5 5.43 2.56 3.59 3.86 6 7.67 3.97 5.45 5.70

The comparison of water productivities given in Table 4.36 manifests that

Scenario 6 which represents drip irrigation installed on all farms and

connected with a communal pipe supply system has the highest water

productivity for each of the three modelled crops. Significant improvement

in water use efficiency for drip irrigation as compared to the other scenarios

is the main contributing factor to the highest water productivity than the

fruit yield which just marginally improves among these scenarios.

Moreover, the productive use of 1 cubic meter of irrigation water for drip

irrigated crop is significantly higher than that of flood irrigation, mainly due

to the fact that a given quantity of irrigation is applied more frequently in

smaller amounts for drip irrigation and hence more water remains available

to the plants for a longer time than that of flood irrigated crops.

4.8.2.3 Comparison of energy productivity

Energy productivity in agriculture refers to the quantity of marketable yield

per unit of input energy and is expressed as kg/kWh. It includes all direct

and indirect energy inputs which are regularly applied. Optimum energy use

is vital for agricultural production systems (Ommani, 2011). Energy

256

productivity reflects the performance of an agricultural production system

(irrigated horticulture in current case) especially when total energy use is a

particular concern and reflects the utilization of energy by a given

agricultural system. The higher the energy productivity of a system, the

greater the production per unit of energy input.

The energy productivity values are given in Table 4.37 and are calculated

from simulated results discussed earlier for the six scenarios. Drip irrigation

is usually considered an energy intensive system mainly due to the high

energy requirements for pumping the irrigation water. However, a

significant portion of the required pumping energy to operate drip system is

offset by energy savings from reduced volumes of water required, reduced

application of fertilizers and other chemicals and of course yield

improvements due to proper and timely irrigation management as compared

to other irrigation systems. Therefore, drip system (Scenario 6) exhibits the

highest energy productivity among all scenarios.

Table 4.37: Energy productivity (kg/kWh) indicators for the six scenarios

Scenario No.

Citrus (kg/kWh)

Stone fruit (kg/kWh)

Wine grape (kg/kWh)

Average (kg/kWh)

1 4.44 2.50 2.97 3.30

2 5.13 2.56 3.32 3.67

3 3.75 2.05 2.49 2.76

4 4.38 2.20 2.88 3.15

5 5.22 2.72 3.41 3.78

6 5.92 3.47 3.74 4.38

The comparisons of energy productivity and water productivity given in

Table 4.37 for each crop for Scenario 1 with Scenario 3 and that of Scenario

2 with Scenario 4 indicate that despite some water savings by conversion

from open channel to pipes to supply water to the flood or furrow irrigation

system, the additional energy required for pumping water through piped

supply is huge and results in significant reduction in energy productivity

with relatively small increases in water savings from channel seepage and

257

channel evaporation. Therefore, Scenario 3 and Scenario 4 will have to be

rejected as viable irrigation practices based on their low energy productivity.

4.8.2.4 Comparison of energy efficiency

Energy efficiency of the agricultural production system can be defined as

the ratio of total energy output from agricultural produce to the total energy

input to engender that produce. For the current study, the energy inputs

include fertilizers, chemicals, pruning, thinning, fruit picking, use of

machinery and labour and electricity for irrigation pumping for three

horticulture crops. Energy from the sun is also a major input which is

usually not considered in energy analysis for crop production as it is not

purchased. The only output energy accounted in this analysis is in the form

of fruit yield. Energy sequestered in the remaining biomass e.g. trunk,

branches, leaves and fruit waste are not considered. As explained by the

laws of thermodynamics, the useful energy extracted from an energy store

(fruit yield in this case) is always less than the energy put into that energy

store. It means energy efficiency of a production system can never be

greater than unity. However, we do not consider free energy inputs like solar

in this analysis and hence the energy efficiency of each scenario is expected

to be greater than unity.

Crop production is an energy sequestration process, mainly through

photosynthesis, and therefore energy efficiency of a given crop should be at

least higher than unity. The energy efficiency indicators as computed for the

three crops grown on the farms in the case study area for each of the six

scenarios are given in Table 4.38. It is evident from energy efficiency values

in Table 3.38 that conversion from open channel to piped supply for gravity

irrigation systems namely flood and furrow, does not improve energy

efficiency of the selected crops as long as the irrigation demand is fully met

by either supply system. Therefore, Scenario 3 and Scenario 4 are

inefficiently high energy demanding options given the use of piped supply

which has to be pumped. However, there could be other justifiable reasons

for this form of conversion, such as where conveyance losses are high,

258

limited and untimely supply of irrigation water and inaccurate metering of

farm water use. All these issues are addressed by use of piped supply.

Table 4.38: Energy efficiency (kWh/kWh) indicators for the six scenarios

Scenario No.

Citrus (kWh/kWh)

Stone fruit (kWh/kWh)

Wine grape (kWh/kWh)

Average (kWh/kWh)

1 2.35 1.53 9.75 4.54

2 2.72 1.56 10.88 5.05

3 1.99 1.25 8.17 3.80

4 2.32 1.34 9.44 4.37

5 2.77 1.66 11.19 5.21

6 3.14 2.11 12.27 5.84

The energy efficiency of sprinkler and drip irrigation systems, both

connected with pressurized piped supply systems, is quite comparable.

However, the drip system performs best due to relatively low energy inputs

and marginally higher or equal yield for each crop as compared to the

sprinkler system. Wine grapes contain the highest amount of energy in the

fruit and therefore highest energy efficiency among all three selected crops.

However, energy efficiency of grain crops is usually higher than horticulture

as reported by Khan et al., (2009).

4.8.2.5 Comparison of specific energy

The specific energy of an agricultural production system can be defined as

the total energy input per unit of marketable yield and is expressed as

kWh/kg. It is essentially the reciprocal of energy productivity. As

mentioned earlier, the total input energy for specific energy estimation does

not include free solar energy. The specific energy is the amount of energy

used in different forms through different processes to produce a unit of

marketable yield rather than actual energy that is ultimately sequestered in

the yield. Specific energy calculated for the six scenarios in the current

study are given in Table 4.39. The lower the value of specific energy of an

agricultural production system the more efficient that system is in producing

259

that output as is the case for the pressurized pipe driven drip system

represented by Scenario 6.

Table 4.39: Specific energy (kWh/kg) indicators for the six scenarios Scenario

No.

Citrus

(kWh/kg)

Stone fruit

(kWh/kg)

Wine grape

(kWh/kg)

Average

(kWh/kg)

1 0.23 0.40 0.34 0.32

2 0.19 0.39 0.30 0.29

3 0.27 0.49 0.40 0.36

4 0.23 0.45 0.35 0.32

5 0.19 0.37 0.29 0.26

6 0.17 0.29 0.27 0.23

4.8.2.6 Comparison of water – energy productivity

Water-energy productivity refers to yield per unit of energy and water inputs

and expressed as g/m3/kWh. This indicator captures the effect of these major

inputs on yield. Lower values of water-energy productivity may indicate

lower efficiency and higher environmental footprint of the system under

consideration.

Table 4.40: Water – energy productivity (g/m3/kWh) indicators for the six scenarios Scenario

No.

Citrus

(g/m3/kWh)

Stone fruit

(g/m3/kWh)

Wine grape

(g/m3/kWh)

Average

(g/m3/kWh)

1 0.36 0.19 0.27 0.27

2 0.51 0.29 0.45 0.42

3 0.30 0.15 0.22 0.23

4 0.44 0.24 0.39 0.35

5 0.64 0.33 0.53 0.50

6 0.95 0.55 0.78 0.76

Water-energy productivity as computed for the six simulated scenarios is

given in Table 4.40. The values are expressed as g/m3/kWh in Table 4.40.

The water-energy productivity of Scenario 6 is the highest amongst all

scenarios. The relatively higher magnitude of this indicator for Scenario 6

indicates that Scenario 6 has the highest yield and the lowest energy

260

footprint of both water use and energy input. Citrus especially outperforms

the other two crops.

4.8.2.7 Comparison of water – energy ratio

The water – energy ratio is the ratio of energy input from irrigation to total energy

input. It is the fraction of the total input energy that is expended in irrigation

operations. A higher ratio may imply higher input energy for irrigation and thus

higher environmental footprint of irrigation. Each irrigation method involves use of

energy in different forms including human labour, machinery and fuels

(diesel/electricity etc.). The modern irrigation technologies including sprinkler,

centre pivot and drip systems are more energy intensive methods of irrigation

which require significant amount of direct energy for pumping operations as

compared to the conventional gravity based irrigation methods. Water energy ratios

for the three selected crops for each of the six scenarios are given in Table 4.41

where the last column represents average values for the overall case study area.

The water energy ratios reconfirm that the irrigation systems which require

pumping of water have conspicuously higher energy and thus exhibit higher energy

footprint. The water energy ratio for the drip system is marginally lower than that

of the sprinkler system mainly due to lower volumes of irrigation pumping for the

former.

Table 4.41: Water – energy ratio (kWh/kWh) for the six scenarios Scenario

No.

Citrus

(kWh/kWh)

Stone fruit

(kWh/kWh)

Wine grape

(kWh/kWh)

Average

(kWh/kWh)

1 0.01 0.01 0.01 0.01

2 0.01 0.01 0.01 0.01

3 0.16 0.19 0.17 0.17

4 0.16 0.16 0.14 0.15

5 0.18 0.21 0.17 0.19

6 0.15 0.18 0.18 0.17

4.8.3 Comparison of greenhouse gas emissions for modelled

scenarios

During the process of energy conversion and energy consumption, different

greenhouse gases are emitted. Similarly, greenhouse gases are emitted

261

during the crop production process as a result of the use of different forms

of energy as discussed previously. Generally, higher energy and water use is

linked with higher greenhouse gas emissions. The rate of greenhouse gas

emissions produced from expending of different energy sources is different.

Nevertheless, the water and energy indicators discussed in the previous

section are directly linked with greenhouse gas emission rates and hence can

be used as a surrogate indicator for the environmental footprint of water and

energy use.

Figure 4.14: Total greenhouse gas emissions per hectare (kg-CO2e) of each crop for the six scenarios (line graph shows GHG emissions from irrigation only and not other factors of crop production)

The total greenhouse gas emissions from irrigation and non-irrigation

energy inputs on a per hectare basis for each crop in the case study area are

plotted in Figure 4.14. The line graphs show GHG emissions from energy

inputs for irrigation operations only (water supply and delivery) and exclude

other production factors. For Scenario 1 and Scenario 2, the emissions rates

are lower as there is no irrigation water pumping involved. The sprinkler

and drip systems operate under high pressure which expends high pumping

energy and are thus categorized as high environmental footprint options.

262

4.9 Sensitivity analysis

Sensitivity analysis of selected modelled scenarios is carried out to

understand how a particular output variable responds to the variation in the

selected inputs within a specific range. The sensitivity analysis can be uni-

variate or multivariate. In this study only a uni-variate approach was taken,

where sensitivity of a given output is gauged against variability/uncertainty

of a single input variable at a time. The sprinkler and drip system as

discussed before involve some prominent characteristics including

significant amounts of energy use in irrigation pumping, GHG emissions,

water savings, yield improvement and increasing rate of technology

adoption. Therefore, sensitivity analysis of key variables mainly “total

energy use” is carried out in this study for sprinkler (Scenario 5) and drip

systems (Scenario 6). Since all scenarios discussed in this chapter are

demand-based irrigation system, crop water shortages are assumed to be

non-existent for each scenario and therefore total water use remains

unchanged negating the need for sensitivity analysis of water use.

4.9.1 Sensitivity of energy use in irrigation

Energy consumed in irrigation pumping constitutes the single major

component of total energy inputs in crop production with pressurized

irrigation systems. Therefore, sensitivity analyses of energy use for

irrigation pumping each for sprinkler (Scenario 5) and drip system (Scenario

6) are carried out to determine how energy use responds to variation in

different factors. For example, the delivery pressure head at the irrigation

outlets (sprinkler heads for sprinkler system and drippers for drip system) is

assumed to be constant and a single value is used for the entire simulation of

a given irrigation method. However, in the field situation, despite

installation of pressure compensating devices, the delivery pressure is likely

to vary within a certain range around its mean value and less likely to take

extreme values. Therefore, normal distribution of probability of delivery

pressure head values was assumed to capture this uncertainly. Then the

original irrigation simulation model developed in Vensim for each of these

263

two irrigation systems scenarios was setup to execute in sensitivity analysis

mode. For each scenario, the sensitivity module randomly generates the

irrigation delivery pressure head within 10% of the original model values

using “normal distribution” for up to 500 iterations of the model to compute

the consequent energy required for irrigation pumping for the whole case

study area. The cumulative probability of normal distribution functions used

for varying the delivery pressure in the model for the sprinkler and drip

system scenarios are plotted in Figure 4.15.

Figure 4.15: Cumulative probability distribution plots for the delivery pressure head for sprinkler (left) and drip system (right)

Figure 4.16: Sensitivity of cumulative energy use (kWh) for sprinkler irrigation pumping to ±10% change in delivery pressure head (m)

0.0

0.2

0.4

0.6

0.8

1.0

22.5 23.5 24.5 25.5 26.5 27.5Cumulative probability distribution

Delivery pressure head (m)

0

0.2

0.4

0.6

0.8

1

28.8 30.4 32.0 33.6 35.2

Cumulative probability distribution

Delivery pressure head (m)

Sensitivity_Sprinkler_System_Delivery_Pressure50% 75% 95% 100%

Cumulative_Energy_Use600,000

480,000

360,000

240,000

120,000

01 92 183 274 365

Time (Day)

264

The sensitivity plots of cumulative energy use as generated by the model for

sprinkler system are given in Figure 4.16 and those for drip system are

given in Figure 4.17 for 50%, 75%, 95% and 100% confidence bounds. For

example, as shown in Figure 4.16, there is 95% reliability (95% confidence

bound) that change in the delivery pressure by ±10% is likely to change the

cumulative energy use in irrigation between 403 MWh and 459 MWh.

Similarly, for drip irrigation system as shown in Figure 4.17, there is 95%

reliability that the cumulative energy use is likely to change between 325

MWh and 380 MWh when delivery pressure is changed by ±10% of its

current value. The energy use for both the sprinkler system and drip system

seems to be equally sensitive to irrigation delivery pressure head.

Figure 4.17: Sensitivity of cumulative energy use (kWh) for drip irrigation pumping to ±10% change in delivery pressure head (m)

Sensitivity of cumulative energy use was also tested for soil moisture deficit

factors for sprinkler and drip systems. Deficit factor refers to the point the

readily available soil moisture is depleted and as a result irrigation is

applied. In practice, it is likely that irrigation application is not strictly

adhered to due to operational constraints. Therefore, sensitivity of total

energy use is determined against the normal distributed (Figure 4.18) soil

moisture deficit factor. It should be noted that the deficit factor is different

Sensitivity_Drip_System_Delivery_Pressure50% 75% 95% 100%


320,000

240,000

160,000

80,000

01 92 183 274 365

Time (Day)

265

from deficit irrigation. Deficit factor is a point on the soil-water depletion

curve to trigger irrigation while deficit irrigation refers to the practice of

irrigating lesser than actual crop water requirement.

Figure 4.18: Cumulative probability distribution plots for the irrigation deficit factor for sprinkler (left) and drip system (right)

Figure 4.19: Sensitivity of cumulative energy use (kWh) for sprinkler irrigation pumping to ±50% change in deficit factor

For normal model runs a value of 0.5 was used for the soil moisture deficit

factor. The higher the deficit factor the lower the irrigation frequency and

vice versa. Since the developed model implements a demand-based

irrigation system, lower irrigation frequency implies a high rate (more

0.00

0.20

0.40

0.60

0.80

1.00

0.25 0.35 0.45 0.55 0.65 0.75


Irrigation deficit factor

0.00

0.20

0.40

0.60

0.80

1.00

0.25 0.35 0.45 0.55 0.65 0.75


Irrigation deficit factor

Sensitivity_Sprinkler_System_Deficit_Factor50% 75% 95% 100%


480,000

360,000

240,000

120,000

01 92 183 274 365

Time (Day)

266

volume) of irrigation pumping and thus higher energy use, and conversely

for the lower value of the deficit factor. The wider band of the upper bound

of the sensitivity plots in Figure 4.19 and Figure 4.20 signify the same fact

that energy use for pressurized irrigation, for 95% confidence bounds as an

example, is more sensitive to increasing value of deficit factor than that of a

decreasing one. Therefore, one has to find a balance between the energy

consumption and the irrigation frequency. Also the wider band of energy

use for sprinkler system than that of drip system indicates that the energy

use by sprinkler system is more sensitive to deficit factor than the drip

system. This is mainly due to higher irrigation application rates for the

sprinkler system.

Figure 4.20: Sensitivity of cumulative energy use (kWh) for drip irrigation pumping to ±50% change in deficit factor

4.10 Chapter summary

This chapter focused on exploring the water and energy nexus of demand-

based irrigation systems connected with open channel or pressurized pipe

supply. The demand-based irrigation system refers to the irrigation

infrastructure and the management practices which ensure almost zero water

stress (applied irrigation equal to evapotranspiration and other losses) during

Sensitivity_Drip_System_Deficit_Factor50% 75% 95% 100%


320,000

240,000

160,000

80,000

01 92 183 274 365

Time (Day)

267

the whole production cycle of the crops grown in the study area. Demand-

based irrigation is explored in this chapter on the grounds that this

restriction-free approach enables comparability among different irrigation

application techniques as well as irrigation delivery systems. Six scenarios

with various combinations of flood, furrow, sprinkler and drip systems with

open channel or pressurized piped supply are discussed for three selected

crops including citrus, stone fruit and wine grapes for the case study area

described in Chapter 3. Detailed analysis of primary inputs including

various forms of energy and irrigation water etc. and outputs; yield and

greenhouse gas emissions etc. for each of the three crops for the case study

area was carried out for each scenario. The amounts of various forms of

energy inputs are based on local sources, personal communications and

some international literature like FAO (2000). Water and energy use in

irrigation supply is simulated using the developed node-link model which is

described in Chapter 3. In these scenarios it is assumed that no water is

stored on-farm. The use of on-farm storages alters the irrigation

management practices, infrastructure and other inputs/outputs and will be

discussed separately in forthcoming chapters.

Among the scenarios analysed, Scenario 3 and Scenario 4 which represent

flood and furrow irrigation supplied with piped supply, respectively, are

highly unlikely to be practiced in this particular case study area however

have been included for comparative purposes. The piped irrigation supply

system is highly suitable to areas where conveyance losses are significantly

high. Among the scenarios discussed, drip irrigation (Scenario 6) used the

least amount of irrigation, achieved the highest irrigation efficiency, highest

water and energy productivity and least greenhouse gas emissions.

However, it involves advanced irrigation management expertise and highest

capital cost which will be discussed in a separate chapter. As expected the

irrigation supply system for drip and sprinkler systems are more energy

intensive, however, their performance justifies their use. The increased

environmental footprint due to higher energy consumption for irrigation

268

pumping may be offset by energy savings from other inputs like fertilizers

etc.

Sensitivity analysis of Scenario 5 and Scenario 6 for energy use in irrigation

only was conducted using the Vensim based node-link model. Sensitivity of

irrigation energy use to delivery pressure head to each irrigation farm inlet

and to irrigation deficit factor was carried out. There is almost similar

response of irrigation energy use to variability in irrigation delivery pressure

head for both the sprinkler and drip system connected with same size

pressurized pipe supply system. For deficit factor, however, the irrigation

energy use for sprinkler system is more sensitive as compared to that of the

drip system, particularly to higher ranges of irrigation deficit. Water and

energy nexus for supply-based (fixed irrigation scheduling) irrigation is

discussed in the next chapter.

269

Chapter 5: Water and Energy Nexus for Supply Based

Irrigation Methods and Conveyance Systems

Chapter 4 described demand-based irrigation system which requires modern

technology and expertise to fully implement it in field situations. The

purpose of the current chapter is to explore the water, energy and

greenhouse gas emissions interplay for the more traditional and widely

practiced irrigation approach; the supply-based irrigation system, for the

same setting of 13 farms in the case study area as described in Chapter 3. In

supply-based irrigation system the irrigation application is influenced by the

size of irrigated area and the availability (both volume and timing) of

irrigation water. A supply-based irrigation system may be constrained by

factors including limited capacity of the irrigation water conveyance

infrastructure, scarcity of water for irrigation and lack of capital investment.

The supply-based irrigation system is relatively simple and normally

implements a fixed interval irrigation application schedule. This may result

in occasional over irrigation or under irrigation as crops require different

amounts of water at different growth stages.

5.1 Description of modelled scenarios

A total of four supply-based irrigation scenarios are analysed in this chapter

as described below.

5.1.1 Scenario 1: Flood irrigation supplied with an open channel

system

All farms are flood irrigated under gravity through an open channel network

which draws water from a common source. The irrigation orders are placed

in a fixed roster among the farms on the channel network.

5.1.2 Scenario 2: Furrow irrigation supplied with an open

channel system

270

All farms are furrow irrigated through an open channel network which

draws water from a common source. The furrows are as wide as two meters

with relatively narrow ridges.

5.1.3 Scenario 3: Sprinkler irrigation system connected with

communal piped supply

This system consists of a piped supply network with outlets to each farm.

Water is conveyed to each farm under certain pressure through a pipe by a

large pumping station located at the communal water source. No on-farm

pumps are used and the sprinkler system is operated by the energy head

rendered by the piped supply system. The irrigation orders are placed in a

fixed roster among the farms. The energy consumption of the pumping

system increases significantly due to the energy dynamics of piped flow if

all orders are supplied on the same day.

5.1.4 Scenario 4: Drip irrigation system connected with


This system is same as that of Scenario 3 except that the on-farm irrigation

application system is replaced with a drip system. The system normally is

operated under a higher hydraulic pressure than that of Scenario 3.

5.2 Modifications made in the node-link model

It is important to understand changes in the model’s internal structure and

various computation algorithms before setting it to determine the water and

energy nexus under supply-based irrigation. The configuration of the node-

link model to represent actual layout remains identical to that used in

Chapter 4. However, the flows are no longer driven by irrigation demand.

Instead, a fixed amount of irrigation is applied at a fixed minimum interval

to each farm. The irrigation supply can be factored down if it exceeds the

system conveyance capacity or if constrained by water availability. The

major changes made in the model are explained below.

5.2.1 Modifications in crop water use module

271

The crop water use module in the supply-based irrigation node-link model

still computes daily water balance of the rootzone for each crop to determine

soil-water depletion using the dual crop coefficient approach. However, in

contrast to the demand-based system, the volume of irrigation requirement

and the timing of irrigation are not based on magnitude of soil-water

depletion. The procedure for calculation of field losses, i.e. soil surface

evaporation, deep percolation and surface runoff remains the same. Also no

changes are made in computation methods for water stress coefficient (Ks),

soil evaporation reduction coefficient (Kr) and estimation of impacts on crop

yield. The module is enabled to simulate any of the four irrigation

application methods which are flood, furrow, sprinkler, and drip irrigation.

The model uses data for the year (2006-07) for reference evapotranspiration

(ETo), wind speed, rainfall and evaporation rate identical to that used for the

demand-based system.

5.2.2 Modifications in irrigation supply/conveyance module

The irrigation supply module can be setup to simulate either unlined open

channels or pressurized pipes connected with a communal pumping station

with water supply nodes at each farm outlet. The model is written in a way

that a specified volume of irrigation water up to the pre-defined flow

capacity of the system can be delivered to each farm at intervals selected by

the user. The irrigation application rate to each farm is also defined by the

user and is fixed at a given number of litres per second per hectare

depending on crop type.

272

Figure 5.1: Process of triggering irrigation application events for a given irrigation method

In practice, irrigation is not necessarily applied at pre-determined intervals

throughout the growing season. The irrigation intervals can be altered due to

unexpected rains, heat waves or crop growth stages based on experience and

local knowledge. To mimic this practice in the model, therefore, the

irrigation application is linked with two mutually inclusive triggers; first

trigger being that the number of days since last irrigation have exceeded the

irrigation interval and the second trigger being that soil-water depletion have

exceeded a pre-defined level (called deficit factor). No irrigation is applied

unless these two triggers are met at the same time. The use of these two

mutually inclusive triggers ensures that over irrigation remains to a

minimum level. For example, if there has been rain in recent days then soil-

I: Select irrigation interval (days)

F: Select soil-water deficit factor (fraction)

L: No. of days since last irrigation

L > I?

D > RAW x F?

D: soil-water depletion at the end of day, J

(mm)

Irrigate at pre-defined rate (l/s/ha)

J= J + 1

L = 0

L = L + 1

RAW: readily available water, J: iteration counter (in days)

273

water depletion will be reduced due to improved moisture in the soil and

there will be no irrigation even if the irrigation interval has passed. This

scheme of ‘if and when to turn irrigation supply on’ is shown in the

flowchart in Figure 5.1. Both, the irrigation interval and the value of deficit

factor (F) are chosen in accordance with the irrigation method. Once

triggered, an irrigation event lasts for 24 hours. Hence, if the irrigation

application rate is set at 1.0 l/sec/ha then total irrigation applied in a 24-hour

irrigation event would be 0.0864 ML/ha. A value of 1.0 for the deficit factor

is set for flood and furrow irrigation, and a value of 0.5 for the sprinkler and

drip system identical to that for demand-based irrigation.

5.2.3 Modifications in irrigation application rate and irrigation

interval

As described above, the model implements a fixed amount of irrigation that

is applied each time an irrigation event is triggered for a given crop for the

supply-based irrigation scheme. Fixing the volume of the irrigation

application simplifies irrigation management by removing the need for hi-

tech instruments for continuous monitoring of soil moisture to find out the

exact amount of irrigation demand. The second step taken to simplify

irrigation management for the supply-based system is the fixing length of

the irrigation interval for a given crop. The length of irrigation interval

depends on rotational availability of irrigation water, irrigation application

method, and the seasonal weather conditions.

The irrigation intervals in terms of minimum number of days used in the

model for the four scenarios are given in Table 5.1. The model may increase

the irrigation intervals depending on soil-moisture levels which are related

to weather conditions including recent events of rainfall or heat waves that

may occur within the simulation period.

274

Table 5.1: Irrigation intervals used in the model for the four supply-based irrigation scenarios

Scenario Description Irrigation

Interval (days)

Scenario 1 Flood irrigation supplied with an open channel system

10

Scenario 2 Furrow irrigation supplied with an open channel system

10

Scenario 3 Sprinkler irrigation system connected with communal piped supply

7

Scenario 4 Drip irrigation system connected with communal piped supply

7

5.3 Determining irrigation application rate

For a supply-based irrigation management system, whether it is an open

channel system or piped supply, the total delivery capacity of the supply

system is shared among the irrigators situated along the supply path. For

modelling purposes the irrigation share of each farm in the case study area is

assumed to be based on crop type and the area irrigated for that crop on the

farm. The irrigation application rate is expressed as litres/sec/ha and is kept

fixed/constant for a given crop. For example, assume an irrigation

application rate fixed at 1.2 litres/sec/ha for citrus crop planted over an area

of 5 hectares. The total irrigation share for that citrus farm would be 0.518

ML/day for a 24 hour irrigation application event, provided that the total

irrigation volume does not exceed the delivery capacity of the supply

system. The water delivery capacity of the open channel system is 70

ML/day and that of the pressurized pipe system is 38 ML/day for sprinkler

irrigation and 25 ML/day for drip irrigation as specified in the model.

Before carrying out water and energy analysis, the model should be setup to

accurately simulate each supply-based scenario. An optimization module

was setup (Figure 5.2) that estimates irrigation application rate for each

irrigation event for each of the three crops under each of the four scenarios.

The optimization process as described in Chapter 3 is based on Powell

optimization algorithm (Powell 1978; Powell and Yuan 1991). When the

irrigation rate is too low, soil moisture is depleted and as a result crop

275

evapotranspiration is reduced which impacts upon crop yield. For a supply-

based irrigation system, the irrigation delivery capacity is shared among the

users in proportions of their irrigation areas. However, a more sensible

approach is to select an irrigation application rate based on crop type. To

find an optimum value, the daily irrigation application rates are varied

within specified ranges for each iterative simulation and resulting reduction

or increase in basal crop evapotranspiration (ETcb) is noted. The change in

ETcb of a given crop is used as a proxy of the resulting change in the crop

yield. If ETcb of a given crop is reduced more than a specified threshold then

a positive penalty is applied on the objective function which the

optimization module aims to minimise. Similarly, a penalty on the objective

function is applied when total irrigation exceeds system capacity for certain

iteration of the optimisation simulation. As a result of the penalties the

magnitude of the objective function is increased from the current optimum

value which triggers a new iteration with a new random solution. The

process continues until no penalty is triggered.

Figure 5.2: Layout of the module for optimization of the irrigation application rate for each crop

This optimization process is repeated for each scenario (irrigation method).

The optimization results for irrigation application rates under the

abovementioned constraints for the four scenarios are shown in a stock plot

276

in Figure 5.3. The horizontal lines show the optimum values while the

maximum and minimum range of decision variables is shown by vertical

lines for each crop under each scenario in Figure 5.3. The irrigation

application rates for flood irrigated crops (Scenario 1) are relatively higher

owing to larger delivery capacity of the open channel system as well as

relatively higher irrigation requirements. The irrigation supply rate for drip

irrigated crops is found to be the lowest due to relatively lower irrigation

requirements and limited discharge capacity of the pipes and pumps. Figure

5.3 also shows that for citrus, the optimum irrigation supply rate is quite

variable within the feasible range for each scenario, which suggests that

citrus yield is relatively more sensitive to the type of irrigation technology

used. For, stone fruit and wine grapes, the optimum values are found to be at

or near the top end of the feasible range for each scenario, which suggests

that probably these two crops are probably less sensitive to irrigation supply

and application technology used. For wine grapes for example, there will be

no significant change in yield as compared to corresponding demand-based

scenarios if the irrigation method changes from flood to drip systems.

However, more water will be saved by using drip system.

5.4 Water use and yield comparison of supply-based and

demand-based irrigation

The node-link model was executed using the optimized irrigation

application rates shown in Figure 5.3 for each scenario. This section is

aimed at comparison of model outputs including irrigation volume applied,

water losses and crop yields etcetera for each of the four modelled

scenarios; both for supply-based (this chapter) and corresponding demand-

based irrigation (from Chapter 4) for the case study area. The total irrigated

area of the 13 case study farms for citrus, stone fruit and wine grapes is

244.03 ha, 24.34 ha and 22.6 ha, respectively.

277

Figure 5.3: Maximum-minimum range and the optimized rates of irrigation for the three crops under the four scenarios

5.4.1 Comparison of total irrigation water use

The comparisons of total water use for the supply-based irrigation scenarios

(Chapter 5) and that for the corresponding demand-based scenarios (Chapter

4) are given in Table 5.2. The figures in parentheses represent per cent

reduction in irrigation use as compared to the demand-based irrigation.

Flood irrigation under supply-based arrangements has the biggest reduction

(50%) while the sprinkler system bears the least reduction (36%) in

irrigation use as compared to corresponding demand-based scenarios. The

huge reduction in irrigation use for flood irrigation is due to both the long

irrigation interval and limited flow capacity of the supply channels. For drip

system the reduction of 42% results from limited supply capacity of the

system and the fixed irrigation interval. The long irrigation intervals and

low capacity supply systems are consequences of constrained and infrequent

availability of irrigation water which is the main motive of adopting a

supply based irrigation strategy.

278

Table 5.2: Comparison of total irrigation water use (ML) between supply-based and demand-based irrigation scenarios

Irrigation regime

Scenario 1 Scenario 2

Scenario 3 (Scenario 5 for demand

based)


based) Demand

based 3,600 2,830 2,312 1,789

Supply based 1,795 (50%) 1,740 (39%) 1,489 (36%) 1,035 (42%)

5.4.2 Comparison of net irrigation rate

Table 5.3 provides a further detailed comparison of net irrigation rate

(ML/ha) applied to each crop over the whole growing cycle both for

demand-based and supply-based scenarios. The comparison of net irrigation

rates given in Table 5.3 reveals that flood irrigation, which used the highest

amount of irrigation water under demand-based case (Chapter 4, Scenario

1), is the worst impacted scenario in terms of reduction in irrigation rate for

supply-based irrigation strategy (Chapter 5, Scenario 1).

Table 5.3: Net irrigation rate (ML/ha) for three crops for demand-based and supply-based scenarios

Irrigation regime

Crop Scenario 1 Scenario 2

Scenario 3 (Scenario

5 for demand based)


6 for demand based)

Demand based

Citrus 12.38 10.03 8.1 6.26 Stone fruit

13.38 8.87 8.20 6.30

Wine grapes

11.13 7.38 6.40 4.77

Supply based

Citrus 6.22 5.68 4.87 3.18 Stone fruit

6.31 8.91 7.23 6.61

Wine grapes

5.44 6.05 5.52 4.42

5.4.3 Comparison of crop yield

Comparison of the reduction in irrigation water use alone is not that

meaningful unless the effects on crop yield are analysed simultaneously.

279

Moreover, the supply-based irrigation system is more likely to have long

dry spells therefore the crop yield should be affected as given in Table 5.4.

For supply-based systems the yield of citrus crop is reduced by 49 per cent

for flood to 66 per cent for drip irrigation. The yields of stone fruit and wine

grapes are not reduced by similar magnitude. Hence, it can be concluded

that citrus crops are more sensitive to irrigation management strategies as

compared to stone fruit and wine grapes. This also raises the question of

return on capital investment for hi-tech (drip) irrigation systems under

supply based scheduling, particularly for irrigated citrus areas, where water

supply is as low as 58% of crop water demand.

Table 5.4: Comparison modelled crop yield (t/ha) between supply-based and demand-based irrigation systems

Irrigation regime

Crop Scenario

1 Scenario

2

Scenario 3 (Scenario 5

for demand based)


6 for demand based)

Demand based

Citrus 35 40 44 48 Stone fruit

18 19 21 25

Wine grapes

20 22 23 26

Supply based

Citrus 18.0 (49%)

22.8 (43%)

20.8 (53%) 16.5 (66%)

Stone fruit

8.8 (51%)16.4

(14%) 16.7 (21%) 21.1 (16%)

Wine grapes

13.9 (30%)

21.3 (3%) 21.0 (9%) 24.2 (7.1%)

5.4.4 Comparison of water losses

The comparison of different types of water losses is a key component of

analysis of the demand-based and supply-based irrigation strategies. Table

5.5 lists different water loss components which are outputs of the developed

model for each scenario. When compared with demand-based scenarios,

there is a drastic reduction (up to 93%) in deep percolation and surface

runoff for the supply based scenarios due to reduction in irrigation volumes.

However, the total soil evaporation reduction is only limited to 9% for the

280

supply-based scenarios mainly due to the fact that the soil wetted area is

kept identical for both irrigation strategies. It is also evident from Table 5.5

that total water losses for high water use scenarios (flood and furrow) are

more sensitive to irrigation management strategy than the low water use

(sprinkler and drip) scenarios. It can also be concluded that the supply-based

irrigation strategy has effectively worked in reducing deep percolation and

surface runoff for the flood irrigation scenario.

The conveyance loss from open channels is reduced from 18.7 ML for

demand-based to just 6.8 ML for the supply-based irrigation, mainly due to

reduced number of irrigation days for the latter system. The model assumes

that the supply channels are filled with water at the time of irrigation supply

and hence the conveyance loss takes place only when irrigation is being

supplied. The conveyance loss would be of significantly higher magnitude if

supply channels remain pre-filled during the irrigation season.

Table 5.5: Comparison of total water losses (ML) for supply-based and demand-based irrigation scenarios

Irrigation regime Loss Type Scenario

1 Scenario

2


based)


based)

Demand based

Conveyance loss 18.74 18.75 0.0 0.0

Soil evaporation

368.43 307.31 313.14 255.38

Deep percolation

448.85 273.14 47.19 34.7

Surface runoff 296.68 184.57 30.66 22.0

Total water Loss 1132.7 783.77 390.99 312.08

Supply based

Conveyance loss 6.84 6.69 0.0 0.0

Soil evaporation

376.35 309.50 300.29 231.54

Deep percolation

33.07 48.70 39.58 40.18

Surface runoff 21.69 38.70 28.53 31.49

Total Water Loss

437.95 403.59 368.4 303.21

281

The comparison of yields given in Table 5.4 and total water losses given in

Table 5.5 for each scenario for the two irrigation management strategies

indicates that there is negative feedback or inverse relationships between the

two variables. The supply based irrigation strategy results in more water

savings but at the expense of crop yield and vice versa for the demand based

case. This brings in the need for an intermediary approach that optimizes the

two quantities. This observation will be further explored in a separate

chapter on system dynamics.

5.5 Energy and GHG emissions for the supply-based scenarios

This section provides a detailed analysis of energy use and GHG emissions

for each of the scenarios being discussed in this chapter and also provides

their comparison with corresponding scenarios discussed in Chapter 4 on

demand-based irrigation.

5.5.1 Comparison of energy use and energy output

It can be envisaged that energy use is proportional to irrigation water use for

any crop production. The yield of a fruit crop also increases up to a certain

limit with increasing irrigation volume, which implies that more energy has

to be expended in pruning the trees and harvesting the produce. Similarly,

more fertilizer can be usefully applied and expended with more and frequent

irrigation. Likewise, the energy use in irrigation pumping for pressurized

irrigation systems is increased with increased volume of irrigation. The

major difference between demand-based and supply-based irrigation is that

a higher and more frequent volume of water is applied in the former case.

Due to the unavailability of full data on energy inputs in production of

individual crops for the supply-based irrigation scenarios discussed in this

chapter, it is assumed that the energy use (excluding energy consumed in

irrigation pumping where applicable) for the production of three crops for a

given scenario is factored from the energy used in corresponding demand-

based scenario by the ratio of the water use volume for the supply-based and

demand-based irrigation for that scenario. The node-link model is able to

282

simulate total energy consumed in pumping irrigation water for the piped

supply systems (Scenario 3 and Scenario 4). No energy is used in pumping

water for open channel supply system that feeds water to gravity based

irrigation (flood and furrow system) as no pumping is involved.

Based on the abovementioned assumptions and discussion, the total direct

and indirect energy inputs in crop production i.e. excluding irrigation

pumping, for the four scenarios for both irrigation strategies is given in

Table 5.6. The energy use for each crop under the supply based scenario is

computed from that of demand-based scenarios using the formula given in

Equation 5.1.

Equation 5.1

Where,

= energy use for a given crop under given supply-based scenario

(kWh/ha)

= energy use for a given crop under given demand-based scenario

(kWh/ha)

= total water use for a given crop under given supply-based scenario

(ML/ha)

= total water use for a given crop under given demand-based scenario

(ML/ha)

Similar to the approach followed for demand-based scenarios, the modelled

total energy consumption for irrigation pumping for each supply-based

scenario is distributed among the three crops in proportion to their irrigation

volume. The resulting pumping energy is converted to kWh/ha by dividing

it by the crop irrigated area and is reported in Table 5.6. The energy use for

the three crops under demand-based scenarios is also included in Table 5.6

for quick comparisons.

283

Table 5.6: Energy use for the three crops under demand-based scenarios and the computed energy use for the corresponding supply based scenarios

Irrigation regime

Crop Energy

form

Scenario 1

(kWh/ha)

Scenario 2

(kWh/ha)


5 for demand based)

(kWh/ha)


6 for demand based)

(kWh/ha)

Demand based

Citrus

Crop production

7,889.2 7,794.6 6,924.7 6,897.1

Irrigation pumping

0.0 0.0 1508.4 1,210.9

Stone fruit

Crop production

7,195.3 7,409.3 6,192.4 6,000.9

Irrigation pumping

0.0 0.0 1528.3 1,212.0

Wine grapes

Crop production

6,728.1 6,632.4 5,618.7 5,739.5

Irrigation pumping

0.0 0.0 1123.9 1,212.4

Total

energy use

21,812.6 21,836.3 22,896.4 22,272.8

Supply based

Citrus

Crop production

3,963.7 4,414.1 4,163.4 3,503.6

Irrigation pumping

0.0 0.0 1204.8 863.8

Stone fruit

Crop production

3,393.3 7,409.3 5,459.9 6,296.2

Irrigation pumping

0.0 0.0 1,788.6 1,795.5

Wine grapes

Crop production

3,288.5 5,437.1 4,846.1 5,318.4

Irrigation pumping

0.0 0.0 1,365.6 1,200.6

Total

energy use

10,645.5 17,260.5 18,828.4 18,978.1

284

The overall high energy intensity (kWh/ha) of demand-based irrigation

scenarios for crop production (excludes irrigation) is clearly evident from

Table 5.6 where Scenario 5 the sprinkler system connected with piped

irrigation supply sits at the top of the ladder with energy intensity of 22,896

kWh/ha of irrigated area. However, for the supply-based irrigation strategy,

Scenario 4, the drip system requires marginally higher amounts of overall

energy than the scenario with sprinkler system. It is also evident from Table

5.6 that the electrical energy consumed in pumping irrigation water for the

supply-based scenarios (sprinkler and drip only) is not necessarily lower

than corresponding demand-based scenarios despite the fact that lesser total

water volume has to be pumped in the former case. The reason for this

observation is evidenced from Figure 5.4. It is a hydraulically proven fact

that the dynamic head/energy required for pumping water through a pipe is

proportional to the squared magnitude of flow velocity as depicted by

Bernoulli’s energy equation (Daugherty et. al., 1985). In other words, if

flow rate through the pipe is doubled, the energy required to move the water

will increase by four fold, keeping other parameters constant.

Figure 5.4 shows that the occurrence of higher duty flow rates for supply

based scenarios are much more frequent than that of demand-based

scenarios, which is what is expected for a supply-based irrigation strategy

i.e. apply larger amounts of irrigation at longer intervals. The use of higher

flow rates, though not much often, results in higher head losses and hence

higher pumping energy consumption for supply-based scenarios. This is the

explanation for higher pumping energy consumption for supply-based

scenarios discussed in the current study. These results indicate that water

use and enormity of energy inputs and energy outputs in irrigated crops

(horticultural crops in this case study) are fundamentally and seamlessly

intertwined.

285

Figure 5.4: Percentage exceedance plots of total duty flow for pumps for demand-based and supply based irrigation (top plot: drip system, bottom plot: sprinkler system)

The total energy output, expressed as kWh/ha, for a given scenario is

calculated by converting yield of each crop into the equivalent energy

quantity using the conversion factors given in Chapter 3 and then summing

up the equivalent energy of the three crops. The total energy output

expressed as equivalent kilowatt hours for each crop under each scenario is

listed in Table 5.7. The energy sequestered in crop output for demand-based

irrigation regimes is higher than that of supply-based irrigation regimes

owing to the higher yield for the former.

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0 20 40 60 80 100

Duty flow rate (m

3 /s)

% exceedance

Scenario 6 (demand‐based drip) Scenario 4 (supply‐based drip)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0 20 40 60 80 100

Duty flow rate (m

3 /s)

% exceedance

Scenario 5 (demand‐based sprinkler) Scenario 3 (supply‐based sprinkler)

286

Table 5.7: Total equivalent energy output (kWh/ha) from each crop for supply-based and demand-based irrigation scenarios

Irrigation regime

Crop Scenario

1 (kWh/ha)

Scenario 2

(kWh/ha)


based) (kWh/ha)


based) (kWh/ha)

Demand based

Citrus 18,550 21,200 23,320 25,440 Stone fruit

10,980 11,590 12,810 15,250

Wine grapes

65,600 72,160 75,440 85,280

Total 95,130 104,950 111,570 125,970

Supply based

Citrus 9,540 12,084 11,024 8,745 Stone fruit

5,368 10,004 10,187 12,871

Wine grapes

45,592 69,864 68,880 79,376

Total 60,500 91,952 90,091 100,992

5.5.2 Energy efficiency and energy productivity indicators

The energy efficiency and productivity indicators are defined in Chapter 3

and discussed in detail in Chapter 4 for demand-based irrigation. Therefore,

the mathematical equations representing those indicators are not repeated

for the current chapter.

The indicators given in Table 5.8 are computed using modelled results for

supply-based scenarios. The energy efficiency of gravity irrigation scenarios

is higher than that of pressurized irrigation as net use of energy is higher in

the latter case due to extra energy required for irrigation water pumping

through the supply pipes despite application of lesser volume of irrigation

than the gravity based scenarios. Among the pressurized irrigation

scenarios, the drip system is more energy efficient than the sprinkler system.

This is mainly due to higher irrigation efficiency of the drip system than that

of the sprinkler system connected with a similar pressurized pipe supply

system.

287

Table 5.8: Energy indicators for supply-based irrigation scenarios

Indicator Crop Scenario

1 Scenario

2 Scenario

3 Scenario

4

Energy efficiency

(kWh/kWh)

Citrus 2.40 2.74 2.05 2.00

Stone fruit

1.58 1.35 1.41 1.59

Wine grapes

13.86 12.85 11.10 12.18

Average 5.95 5.65 4.85 5.26

Energy productivity (kg/kWh)

Citrus 4.54 5.17 3.87 3.78

Stone fruit

2.59 2.21 2.30 2.61

Wine grapes

4.23 3.92 3.38 3.71

Average 3.79 3.77 3.18 3.37

Specific energy

(kWh/kg)

Citrus 0.22 0.19 0.26 0.26

Stone fruit

0.39 0.45 0.43 0.38

Wine grapes

0.24 0.26 0.30 0.27

Average 0.28 0.30 0.33 0.30

Water – energy

productivity (g/m3/kWh)

Citrus 0.73 0.91 0.80 1.19

Stone fruit

0.41 0.25 0.32 0.39

Wine grapes

0.78 0.65 0.61 0.84

Average 0.64 0.60 0.58 0.81

Water – energy ratio (kWh/kWh)

Citrus 0 0 0.22 0.20

Stone fruit

0 0 0.25 0.22

Wine grapes

0 0 0.22 0.18

Average 0 0 0.23 0.20

288

Similarly, the energy productivity of the pressurized irrigation systems

(Scenario 3 and Scenario 4) is less than that of the gravity based irrigation

systems (Scenario 1 and Scenario 2) due the reasons explained above. As

given in Table 5.8, more energy is required to produce a kilogram of

produce using the pressurized irrigation systems than that of gravity based

irrigation systems. Again, the drip irrigation scenario generally used less

energy to produce a unit output among the pressurized irrigation systems.

So far we have compared irrigation systems based on energy input and

energy output only. This approach, which is solely energy based, ranked

gravity based system to be more favoured than the pressurized/modern

irrigation systems. However, the picture is not complete unless water use is

also compared for the four supply-based scenarios. The “water-energy

productivity” and “energy ratio” are two indicators which represent a

holistic and system wide perspective by considering both water and energy.

The cropping scenario using a drip irrigation system demonstrates the

highest value of 0.81 g/m3/kWh for the water-energy productivity indicator

which shows that more is produced from a given amount of water and

energy input for the drip system as compared to the other scenarios. It is

also revealed from Table 5.8 (energy ratio) that the energy consumed in

irrigation operations for crops with drip system ranges from 18 % to 22 %

of total input energy as compared to 22 % to 25 % of total input energy for

the sprinkler system. Therefore, holistically the horticulture crops irrigated

with a drip irrigation system which is connected with pressurized pipe

supply operated under a supply-based irrigation strategy have the least water

and energy footprint among the scenarios discussed in this chapter.

The water and energy indicators for the demand-based scenarios of Chapter

4 are also revisited here for comparison. The overall energy efficiency of

pressurized irrigation scenarios is marginally higher than that of gravity

irrigation due to the fact that, despite no pumping operations, the total use of

energy is higher in the latter case due to extra energy input in the form of

higher fertilizer application rates etcetera required to overcome nutrient

losses through leaching and surface runoff.

289

The comparison of energy efficiency for pressurized irrigation scenarios for

demand-based (Chapter 4) and supply-based (Chapter 5) settings leads to

some interesting findings. The energy efficiency of the supply-based

settings is lower than that of demand-based ones despite lesser irrigation

pumping and hence lesser energy input for the former case. This is in fact a

result of significant reduction (up to 66%) in yield (energy output) for the

pressurized irrigation scenarios for the supply-based irrigation regime.

However, for the holistic overview, the combined water and energy

productivity indicator, the water-energy productivity, should be compared.

The values of this indicator are higher for supply-based scenarios than that

of demand-based scenarios. This discussion concludes that supply-based

scenarios are less favored when only energy aspects are considered. But a

contrasting conclusion is reached when a holistic approach is adopted and

both the water and energy aspects are analysed. Similarly, the energy ratio,

which refers to the fraction of total input energy that is expended in irrigation

operations, is higher for the supply-based pressurized irrigation than that of

corresponding demand-based ones. The reason for this unexpected behavior can be

explained by plots of duty flow rates in Figure 5.3 and Figure 5.4, which indicate

that supply-based irrigation systems frequently involve significantly higher flow

rates through the pipe network thus consuming higher energy in irrigation pumping

operations.

Looking at the water-energy productivity indicator values for individual crops in

Table 5.8, it is evident that citrus under a drip irrigation system has the least water

and energy footprint and highest water-energy productivity; followed by wine

grapes and then stone fruit. Wine grapes under a drip system have the least water-

energy ratio followed by citrus and stone fruit, indicating that irrigation pumping

for wine grapes results in the lowest water and energy footprint while, in contrast,

stone fruit irrigation both under drip and sprinkler systems results in higher water

and energy impacts.

5.5.3 Comparison of greenhouse gas emissions

For supply-based scenarios, greenhouse gas emissions (GHG) data for

individual energy inputs for each crop is not available. The only information

290

which is modelled energy use in irrigation pumping (where applicable) is

available. Therefore, it is assumed that the GHG emissions, expressed as

kilograms of equivalent CO2 per hectare (kgCO2-eq/ha), resulting from

energy use in crop production for the scenarios discussed in this chapter, are

a factor of the ratio of the energy use for the supply-based and demand-

based irrigation scenarios. The resulting GHG emissions values for each

crop under each scenario are given in Table 5.9. The GHG emission values

are calculated from data in Table 5.6 and the GHG emission values

mentioned in Chapter 4 for Scenarios 1, 2, 5 and 6. The GHG emissions

values for the demand-based scenarios from Chapter 4 are also given in

Table 5.9 for ready reference.

Table 5.9: Greenhouse gas emissions rates (kgCO2-Eq/ha) for the three crops under demand-based and supply-based (computed) scenarios

Irrigation regime

Crop GHG

emissions from

Scenario 1

(kgCO2-Eq/ha)

Scenario 2

(kgCO2-Eq/ha)

Scenario 3

(Scenario 5 for

demand based)

(kgCO2-Eq/ha)

Scenario 4

(Scenario 6 for

demand based)

(kgCO2-Eq/ha)

Demand based

Citrus

Crop productio

n 1832.74 1820.66 2996.7 2727.6

Irrigation pumping

0 0 1373.36 1103.28

Stone fruit

Crop productio

n 1634.52 1667.51 2806.00 2487.5

Irrigation pumping

0 0 1395.92 1109.04

Wine grape

s

Crop productio

n 1532.5 1515.77 2305.8 2405.9

Irrigation pumping

0 0 1029.07 1105.23

Supply based

Citrus Crop

productio920.8 1031.0 1801.7 1385.6

291

n Irrigation pumping

0 0 1084.3 777.4

Stone fruit

Crop productio

n 770.8 1667.5 2474.1 2609.9

Irrigation pumping

0 0 1609.7 1616.0

Wine grape

s

Crop productio

n 749.0 1242.6 1988.7 2229.4

Irrigation pumping

0 0 1229.0 1080.5

Based on data in Table 5.9, it would be right to say that GHG emissions

from pressurized irrigation scenarios are higher than the gravity based

system mainly due to the fact that pressurized irrigation systems are more

energy intensive. Among the pressurized irrigation supply-based scenarios,

citrus has the least carbon footprint both from crop production and irrigation

operations.

5.6 Sensitivity analysis of pressurized irrigation scenarios

Sensitivity analysis is an effective tool to gauge the response of the

dependent variables of a developed model to a change in an independent

variable within a specified feasible range. It also provides modellers a lead

to identifying the most prominent variables in models that involve many

independent variables. The sensitivity of water use, pumping energy use and

crop yield to irrigation interval for sprinkler and drip system for a supply-

based model developed in Vensim is presented in this section. The reason

for choosing only pressurized irrigation scenarios is that they seem to be

more responsive in terms of changes to water and energy footprints.

5.6.1 Sensitivity of irrigation supply, pumping energy and yield

to irrigation interval

The reason for choosing irrigation interval is the observation that it is an

operational variable that seems to be varying in the model as well as in the

292

field both for supply-based and demand-based irrigation regimes and seems

to have significant impact on water use, energy use and yield. The irrigation

interval is varied by ±3 days from its default value of 7 days with the change

of ±1 day. For this purpose, “vector” distribution in Vensim’s sensitivity

analysis module is selected. The model selects a value of irrigation interval

and keeps it constant for the one complete simulation.

The sensitivity plots of cumulative irrigation supply (ML) to the irrigation

interval for sprinkler and drip irrigation can be compared from Figure 5.5.

The blue line shows the cumulative irrigation for the default value of

irrigation interval which is 7 days. A common observation from the two

plots is that irrigation supply response is not linearly proportional to

increase or decrease in irrigation interval from its default value. The

response to decrease in irrigation interval is more prominent than the

increase. This indicates that lower irrigation intervals are more suited to the

crop-soil combination of this study area. Moreover, irrigation use is almost

doubled for drip system and less than double for sprinkler system when the

irrigation interval is reduced from 10 days to 3 days. This shows that drip

irrigation system required more frequent irrigation. This is mainly due to

lower soil-water storage for drip irrigated crops than that of the sprinkler

system.

293

Figure 5.5: Sensitivity graphs of cumulative irrigation supply (ML) for sprinkler (top) and drip (bottom) systems to irrigation interval (days)

The sensitivity plots of cumulative irrigation pumping energy use (kWh) to

the irrigation interval for sprinkler and drip irrigation are given in Figure

5.6. The blue line shows the cumulative pumping energy use for the default

value of irrigation interval which is 4 days. The general trends for sensitivity

of energy use and irrigation use are the same. However, the range of

variation of pumping energy for sprinkler system is wider than that of drip

system. The pumping energy use for sprinkler system is increased by

213,427 kWh in response to an increase of 1017 ML in irrigation use (i.e.

210 kWh/ML increase in irrigation volume) while the pumping energy use

for the drip system is increased by 201,945 kWh in response to an increase

of 782 ML in irrigation use (i.e. 258 kWh/ML increase in irrigation

Sprinkler-supply-based50% 75% 95% 100%

Cumulative_Irrigation_Supplied4,000

3,200

2,400

1,600

800

01 92 183 274 365

Time (Day)

Drip-supply-based-sen50% 75% 95% 100%


1,600

1,200

800

400

01 92 183 274 365

Time (Day)

294

volume). The reason is most likely due to a higher operating hydraulic

pressure head (32 m) being maintained in the case of drip irrigation system

as compared to that of sprinkler system (25 m).

Figure 5.6: Sensitivity graphs of cumulative pumping energy use (kWh) for sprinkler (top) and drip (bottom) to irrigation interval (days)

Sensitivity graphs of yield (t/ha) of citrus (first row), stone fruit (second

row) and wine grapes (third row) for sprinkler system and drip system to

irrigation interval are given in Figure 5.7. The blue line on each plot shows

the yield of default irrigation interval of seven days. These plots show that

the yield of citrus crops is more sensitive to irrigation interval; hence to total

irrigation volume, and water shortage events, while wine grapes are

comparatively least sensitive.

295

Figure 5.7: Sensitivity graphs of yield (t/ha) for sprinkler (left) and drip (right) to irrigation interval (days) for the three crops

5.6.2 Sensitivity of crop yield and energy use to irrigation water

use

This sub-section explores sensitivity of energy use in irrigation pumping

and that of crop yield to various levels of irrigation water use on a per

hectare basis. This analysis is indirectly covered in the previous sub-section

as level of water use is linked with length of irrigation interval, but here it is

aimed to explore it in further detail.

To get a variety of model responses the irrigation interval (proxy of total

water use) is varied by a wider range of ±5 days from its default value of 7

days with the change of ±1 day, which indirectly changes the volume of

irrigation water pumped and the corresponding energy consumed. A new

model run is performed for each value of the irrigation interval. Hence a

296

total of ten supply-based model runs are performed for each of the drip and

the sprinkler systems. Then the model results are post-processed to compute

energy use in kWh/ML for each of the three crops. The model outputs water

use in ML/ha and yield in t/ha for each crop. The total water use increases

with decreasing irrigation interval and vice versa and so does the water use

per hectare.

Although, it is obvious that energy use and yield will increase with increase

in water use within a certain range, this analysis focuses on determining the

nature of this relationship as it can be a steep/flat linear or non-linear

relationship.

5.6.2.1 For drip irrigation system

Figure 5.8 shows the scatter plots and the polynomial models fitted between

water use rates (ML/ha) and the corresponding yield in tons/ha for each of

the three crops. A second degree polynomial fits well to the scatter data. It is

evident from Figure 5.8 that for wine grapes and stonefruit, there is no

further improvement in yield for irrigation rates higher than 5.9 ML/ha and

9.75 ML/ha, respectively. In case of citrus, it can be said that the crop yield

responds almost linearly for the given range of irrigation application rates.

However, similar to other crops, this response is likely to diminish for

higher irrigation application rates. Hence, it can be concluded that a higher

gain in yield can be achieved with a little increase in irrigation use for the

lower range of irrigation application rates. A similar approach is adopted by

Khan et al. (2005a) and Khan and Abbas (2007) to optimize crop yield and

water use in the Murrumbidgee Irrigation area.

297

Figure 5.8: Sensitivity of crop yield to irrigation water use for the three modelled crops with drip system

The modelled irrigation system is based on use of a communal line for

irrigation water supply; it does not have separate supply lines for each crop,

therefore the model is only capable of computing overall daily energy use in

irrigation pumping for the modelled area and cannot track down energy use

in irrigation pumping for supply to individual farms or crops. The energy

use per unit irrigated area (kWh/ha) is calculated by dividing the cumulative

y = 0.078x2 + 9.240x ‐ 13.607R² = 1.000

0.0

10.0

20.0

30.0

40.0

50.0

60.0

0 1 2 3 4 5 6 7

Crop

Yield (t/ha)

Water use (ML/ha)

Sensitivity of yield to water use (Citrus)

y = ‐1.020x2 + 12.368x ‐ 11.358R² = 0.982

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0 1 2 3 4 5 6 7

Crop

Yield (t/ha)

Water use (ML/ha)

Sensitivity of yield to water use (Wine Grapes)

y = ‐0.310x2 + 6.251x ‐ 6.424R² = 0.988

0.0

5.0

10.0

15.0

20.0

25.0

30.0

0 2 4 6 8 10 12

Crop

Yield (t/ha)

Water use (ML/ha)

Sensitivity of yield to water use (Stonefruits)

298

energy use at system level (model output) by the total modelled irrigated

area with the assumption that energy use to pump a unit volume of water is

always the same regardless of which crop is irrigated with that water. This

assumption is valid only if the pipe supply network is not very large and the

peak irrigation demand for one crop is not significantly higher than the other

crops.

Figure 5.9 shows the scatter plot between modelled irrigation pumping

energy use (kWh/ha) and the water use (ML/ha) for a range of irrigation

intervals. The energy use varies from 600 kWh/ha to 1545 kWh/ha when

irrigation rate is varied from 2 ML/ha to 7 ML/ha. A second degree

polynomial fits best to the formulate relation between the two variables. It

should be noted that the plot in Figure 5.9 is based on total water and energy

use at a modelled system level and the relationship for individual crops may

vary from the one presented here, however the general trend should not vary

much.

Figure 5.9: Sensitivity of irrigation pumping energy consumption (kWh/ha) to irrigation water use (ML/ha) for the three modelled crops with drip system

5.6.2.2 For sprinkler irrigation system

The same node-link model with the irrigation system changed to a sprinkler

system was executed with the irrigation interval varied between default

y = ‐26.912x2 + 456.056x ‐ 288.288R² = 0.994

0.00

200.00

400.00

600.00

800.00

1000.00

1200.00

1400.00

1600.00

1800.00

0.00 1.00 2.00 3.00 4.00 5.00 6.00 7.00 8.00

Energy Use (kW

h/ha)

Water Use (ML/ha)

299

irrigation intervals of 7 ± 5 days. For each run, the irrigation interval was

increased or decreased by 1 day to impose an irrigation interval of a

maximum of 12 days and minimum of 2 days. The modelled irrigation use

(ML/ha), crop yield (t/ha) and pumping energy use (kWh/ha) were recorded

for each run. The scatter plots between different levels of modelled water

use (ML/ha) and corresponding yield for sprinkler system are shown in

Figure 5.10. The response of yield to a change in irrigation rate is very

similar to the ones for the drip system i.e. the marginal increase in crop

yield diminishes with increasing amount of water application. The water use

rate for sprinkler system varies over a wider range than that of drip system

due to higher irrigation demand to replenish a larger wetted area as

compared to drip irrigation.

Table 5.10: Comparison of drip and sprinkler system in terms of yield response to water use

Indicator Variable Irrigation methods

Citrus Wine grapes

Stone fruit

Mean

Water use (ML/ha)

Drip 3.61 4.68 7.16 Sprinkler 5.37 5.45 7.59

Yield (t/ha) Drip 20.88 22.98 20.55 Sprinkler 24.42 20.30 16.46

Standard deviation

Water use (ML/ha)

Drip 1.49 1.13 2.59 Sprinkler 1.81 1.04 2.32

Yield (t/ha) Drip 14.72 3.32 4.23 Sprinkler 13.12 2.89 4.06

The comparison of drip irrigation and sprinkler irrigation systems in terms

of the response of yield to change in water use is given in Table 5.10. The

table lists the mean value and the standard deviation from mean of water use

rate (ML/ha) and the crop yield (t/ha) for the three crops under the two

irrigation systems for all irrigation intervals. As indicated by the mean and

standard deviation values given in Table 5.10, the citrus yield under a drip

system seems to be more responsive to the change in water application rate

than that of wine grapes and stonefruit. Similarly, the change in water use

by stone fruit under drip and that by citrus under sprinkler system is more

prominent than their counterparts.

300

Figure 5.10: Sensitivity of crop yield to irrigation water use for the three modelled crops with sprinkler system

The sprinkler system requires a higher volume of water to be pumped as

compared to the drip system and therefore it consumes more pumping

energy than that of the drip system. The scatter plot between water use rate

(ML/ha) and total energy consumed (kWh/ha) in pumping that volume of

y = 0.023x2 + 7.006x ‐ 13.911R² = 1.000

0.0

10.0

20.0

30.0

40.0

50.0

0 1 2 3 4 5 6 7 8 9

Crop Yield (t/ha)

Water use (ML/ha)

Sensitivity of yield to water use (Citrus)

y = ‐0.417x2 + 7.181x ‐ 6.025R² = 0.985

10.0

12.0

14.0

16.0

18.0

20.0

22.0

24.0

0 1 2 3 4 5 6 7

Crop Yield (t/ha)

Water use (ML/ha)

Sensitivity of yield to water use (Wine Grapes)

y = ‐0.211x2 + 5.011x ‐ 8.359R² = 0.995

0.0

5.0

10.0

15.0

20.0

25.0

0 2 4 6 8 10 12

Crop Yield (t/ha)

Water use (ML/ha)

Sensitivity of yield to water use (Stonefruits)

301

water is shown in Figure 5.11. The relationship between energy use and

irrigation application rate is non-linear in nature. Other than total irrigation

volume pumped over the entire irrigation season, the energy use in

pressurized irrigation supply system also happens to be highly sensitive to

the instantaneous flow rate through the pipes. This conclusion is supported

by the higher end of the plot in Figure 5.11 where energy use corresponding

to irrigation interval of two days (1508 kWh/ha) is lower than that of

irrigation interval of three days (1,749 kWh/ha) despite a higher volume of

irrigation pumped in the former case. A closer look at irrigation supply flow

rate time series indicates that the supply system runs at peak supply level for

a total of two times for the irrigation interval of two days, while it peaks for

sixteen times when executed with the irrigation interval of three days. The

more frequent occurrence of higher flow rate results in higher energy use for

the irrigation interval of three days. The analysis indicates that we can save

up to 14% of energy consumed in irrigation pumping by supplying a little

bit extra irrigation volume (just 0.3 ML/ha in this case by reducing irrigation

interval from 3 days to 2 days) for frequent wetting of the soil to reduce

occurrence of peak irrigation demands.

Figure 5.11: Sensitivity of irrigation pumping energy consumption to irrigation water use for the three modelled crops with sprinkler system

y = ‐33.513x2 + 564.155x ‐ 708.684R² = 0.956

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7 8 9

Energy Use (kWh/ha)

Water Use (ML/ha)

302

5.7 On-farm storages: water-energy analysis

In the past, the average water allocation in the Murrumbidgee Irrigation

Area (MIA) has been as high as 116% (Khan et al., 2005a). Such a high

level of irrigation allocation will not be available in future due to the

increasing competition for water, climate change and the continual water

reforms allocating more water for the environment which is backed by the

Murray-Darling Basin Draft Plan that has proposed a further 320 GL

reduction in consumptive use of water in the Murrumbidgee valley (MDBA

2012). The average general security allocation for irrigators in the

Murrumbidgee is more likely to be around 80%. On-farm storages can help

in irrigation drainage reuse to supplement irrigation supply. More

importantly, the on-farm storages can be an integral part of irrigation

infrastructure and play a vital role in day-to-day management of irrigation

supply. This aspect of on-farm storages is discussed in detail in this section

only for pressurized irrigation systems i.e. sprinkler and drip system.

5.7.1 Function of on-farm storages

Irrigation interval is a vital parameter for irrigation management. Both crop

growth and yield are affected by the interval between the irrigations and the

amount of irrigation application. The Vensim model developed for the

supply-based irrigation strategy discussed in previous sections assumed a

fixed irrigation interval of seven days both for sprinkler and drip irrigation

systems of the case study area. It is evident from the model results that crops

experience occasional water stress under a supply-based irrigation strategy

with an irrigation interval of seven days. As a result the crop yield is

decreased; especially for citrus under drip irrigation with a reduction of 66

per cent and for wine grapes a reduction of 7.1 per cent is predicted by the

model as given in Table 5.4. Similar magnitudes of yield reduction are

predicted for sprinkler system. To overcome water stress the most effective

solution is to irrigate more often i.e. to reduce the irrigation interval.

However, there are operational constraints to supply water that are often due

to; a) limited capacity of supply systems, b) rules for placing water orders,

303

c) timely availability of irrigation water and, d) last but not least the energy

required to operate the pumps for pressurized delivery through the main

supply line to each farm. Almost each of these issues can be either removed

or minimized by storing water on farm in purpose built on-farm storages on

each farm. Irrigators can construct on-farm private storages to store their

allocated water for the season at the start of the irrigation season when there

is not much load on the overall irrigation supply system. Once stored on-

farm, water can be pumped from on-farm storages at any time at any

interval thus providing irrigators a secure and timely supply of their

irrigation water.

5.7.2 Incorporating on-farm storages into supply-based model

The supply-based node-link model built earlier was modified to replace

pressurized pipe supply from the common water source with open channels

and to incorporate on-farm storages at each node (supply point/ farm inlet)

and to implement water filling and releasing rules for those storages. The

capacity of each on-farm storage is fixed for a given irrigation system (drip

or sprinkler). Each farm inlet is equipped with a dethridge wheel which can

draw water from the supply channel at a maximum flow capacity of 12

ML/day. Therefore, the inflow to a given on-farm storage is the minimum of

the available channel flow rate and the farm inlet capacity which is 12

ML/day as expressed by Equation 5.2, unless the storage becomes full or the

supply ceases.

, _ _ , 12 , , 12 , 0

Equation 5.2

Where,

, is inflow to the given on-farm storage for current day, j;

is storage volume of the given on-farm storage at the end of previous day,

i;

is the prevailing flow rate upstream of the given on-farm storage and;

304

is the maximum storage capacity of the given on-farm storage.

It is assumed that all on-farm storages are filled from the available water

allocation to each farm during the first month of the irrigation season. In the

model this is achieved by running the open channel supply system at full

capacity from 1st July to 31st of July. The reason for filling of on-farm

storages at the start of irrigation season is that the demand is quite low

during the first month and hence places no extra pressure on supply system

or other water users. The water stored in a given on-farm storage remains

there unless consumed by irrigation or lost through evaporation. The

maximum depth of water in each on-farm storage is assumed to be fixed at 3

meters. The daily evaporation loss from each storage is computed by

multiplying storage surface area with daily evaporation rate. Seepage loss

from storage is assumed to be negligible (Kemp and Hafi, 2001) as these

storages are likely to be located on land with soils of low permeability.

Rainfall runoff is not included as its contribution to water balance of on-

farm storage is negligible. The irrigation drainage (return flows) is also not

included in the water balance analysis as all surface drainage flows are

separately collected by drainage channels. Storage volume in a given

storage at the end of each current day is given by Equation 5.3.

, Equation 5.3

Where,

is the storage volume in a given storage at the end of current day, j;

is the volume of irrigation supplied from given storage by the end of

current day, and;

is the volume of water loss through evaporation from a given storage by

the end of current day.

5.7.3 Comparison of with and without on-farm storage scenarios

As mentioned previously, the on-farm storage option is only tested for

pressurized irrigation scenarios. Scenario 3 (sprinkler system) and Scenario

305

4 (drip system) which are discussed in previous sections represent supply-

based scenarios with irrigation interval of 7 days with no on-farm storage

and with pumping from a single location of shared water source. The

original supply-based model is re-run with irrigation interval of 4 days for

these scenarios which are renamed as “Scenario 3-four-days-interval” and

“Scenario 4-four-days-interval”. These two scenarios are used as baseline

and results saved to compare with two new corresponding scenarios which

are based on a modified model with on-farm storage option turned on for

each farm and irrigation pumping taking place at individual farm and energy

use recorded for each farm. These scenarios are referred to as “Scenario 3-

on-farm” or “Scenario 3 with on-farm-storage” and “Scenario 4-on-farm” or

“Scenario 4 with on-farm-storage”. Both of these scenarios implement on-

farm storage procedure as described above. The 4-day irrigation interval is

chosen because the model suggests that when irrigated at this interval the

number of water stress days and the yield is virtually unaffected. Using a

higher irrigation interval would require higher irrigation rate and hence a

higher storage capacity of a given on-farm reservoir will be required. This

will lead to higher capital cost and higher losses from the on-farm storages.

Moreover, with on-farm storages filled it is operationally possible to irrigate

at this short interval. The flowchart of steps followed to execute these two

on-farm storage scenarios for comparison with their corresponding baseline

scenarios is given in Figure 5.12.

306

Figure 5.12: Flowchart of steps to execute supply-based model with on-farm storages

The only difference between the previously developed supply-based model

and the new supply-based with on-farm storage model is that the common

supply pipeline is replaced with open channel for the latter and each farm is

irrigated from its on-farm storage; and the combined pumping station is

replaced by individual pumps that draw water from on-farm storage for each

farm. As mentioned in Figure 5.12, the original supply-based model is

executed with an irrigation interval of 4 days to determine the capacity of

the on-farm storage so that the same irrigation application rate can be

maintained from those storages. Once the storage capacity is determined, the

new model is run; on-farm storages filled as much as possible by 31st of July

Run the previous supply-based model with 4 days

irrigation interval

Find Irrigation Rate (ML/d) from model output for each crop

Select sprinkler or drip system to run model for

For each farm: Irrigation rate x crop area = on-farm storage size (Smax)

Run the new model with same irrigation interval of 4 days

For each farm: Enter values of on-farm storage size in the new

model with on-farm storage option

Compare water losses, energy use etc. with baseline scenario

Select the second irrigation system

307

and irrigation water pumped as per irrigation interval. The model computes

water losses from evaporation from on-farm storages and open channel. The

energy consumed in on-farm pumping for each farm is also computed and

aggregated. The water losses and energy consumption are then compared

with the baseline scenario as explained in the following.

5.7.3.1 Comparison of with and without on-farm storage scenarios for

sprinkler system

The descriptions of “Scenario 3-four-days-interval” and “Scenario 3-with-

on-farm-storage” are given in the previous section. Both scenarios are

supply-based and both model sprinkler systems. In this section results from

these two scenarios are compared. The irrigation application rates for each

crop from the original supply-based model under the “Scenario 3-four-days-

interval” are given in Table 5.11. The irrigation application rate for each

farm is multiplied with its irrigated area to get the initial estimate of

capacity of on-farm storage as given in Table 5.11. The final capacity of

each on-farm storage; which caters for irrigation supply and evaporation

from the storage, is determined once the evaporation amount for the storage

is known from the initial model run.

Table 5.11: Irrigation rates for 4-day irrigation interval and on-farm storage capacity for each farm with sprinkler system

Farm No.

Farm ID

Irrigation Rate (ML/ha)

Irrigated Area (ha)

On-farm Storage Capacity (ML)

1a A 7.29 54.26 396 3 B 7.29 35.4 258 4 C 7.29 28.18 205 5 D 7.29 35.3 257 6 E 7.29 27.7 202 7 F 7.29 28.76 210 7a G 7.29 11.24 82 8 H 6.47 10.17 66 9 I 6.47 12.43 80 10 J 7.29 6.87 50 11 K 7.29 16.32 119 12 L 9.97 4.57 46

308

13 M 9.97 19.77 197

The storage capacities values for the on-farm storage from Table 5.11 are

entered into the new supply-based model which implements on-farm storage

to execute the model for the corresponding scenario; “Scenario 3-with-on-

farm-storage”. Initially the model was run with storage filling period of one

month. It was found that most of the storages did not reach their capacity by

the end of the filling period and more over the storage capacity needed to be

increased to cater for evaporation losses as mentioned previously. To cope

with the first issue the filling period for on-farm storages for this scenario

was increased to one and a half months (46 days). To handle the second

issue, the model is setup in a way that it does not restrict daily irrigation

supply even if storage is emptied for a given farm. This results in a negative

storage level or storage deficit which is an accumulation of evaporation loss

from a given storage. The total evaporation loss from each storage and

storage deficit at the end of the simulation are given in Table 5.12. These

two quantities should match each other to achieve the same amount of

irrigation supply as that of the baseline scenario which is “Scenario 3-four-

days-interval”. If the magnitude of storage deficit at the end of simulation is

higher than the total evaporation loss from a given storage then either that

storage was not completely filled at the end of the filling period or its

capacity needs to be increased. The evaporation loss from a given storage

should be added to the storage size to get the final capacity of that storage as

given in the last column of Table 5.12. In fact with the increased storage the

actual evaporation loss would be even higher than estimated here.

Table 5.12: Computation of final capacity of each on-farm storage for sprinkler system

Farm No.

Farm ID

On-farm Storage

Capacity -Initial (ML)

On-farm Storage at the end of simulation

Total evaporation loss from on-farm storage

(ML)

On-farm Storage

Capacity - Final (ML)

1a A 396 -94.5 103.6 499.6 3 B 258 -59.2 69.9 327.9 4 C 205 -50.3 53.4 258.4

309

5 D 257 -62.7 67.1 324.1 6 E 202 -48.5 51.8 253.8 7 F 210 -59.0 54.3 264.3 7a G 82 -20.0 21.4 103.4 8 H 66 -18.0 17.4 83.4 9 I 80 -22.1 21.1 101.1 10 J 50 -12.3 12.9 62.9 11 K 119 -28.0 31.3 150.3 12 L 46 -11.1 12.4 58.4 13 M 197 -63.2 42.5 239.5

It should be noted from Table 5.12 that the storage deficit is about 21 ML

higher than evaporation loss for the Farm M. From the time series of storage

level for this farm, it was found that the maximum storage reached during

filling was 175.7 ML which is roughly 21 ML less than the desired level.

This results in a storage deficit of 21 ML for this storage at the end of

simulation period.

Water loss and energy use are the two key quantities being compared

against with and without on-farm storage scenarios for sprinkler system as

given in Table 5.13.

Table 5.13: Key variables for with and without on-farm storage scenarios for sprinkler system

Variable Scenario 3-four-days-

interval (baseline i.e. no on-farm storages)

Scenario 3-with-on-farm-storage

Total evaporation loss from on-farm storages (ML)

0.0 559.3

Total conveyance loss (ML) 0.0 5.2 Total water use (ML) 2,166 2,730.5 Total energy consumed in irrigation pumping (kWh)

502,111 352,710

As given in Table 5.13 additional water loss of 559.3 ML occurs from on-

farm storages through evaporation. The conveyance loss for the baseline is

zero due to piped supply line from the communal water source to each farm

inlet. The conveyance loss from open channels for the on-farm storage

scenario is just 5.2 ML. In total, an additional amount of around 564.5 ML

of water needs to be supplied for the on-farm storage scenario. On one hand

310

these losses seem to be quite high while on the other hand irrigators enjoy

the secured and timely supply of irrigation water. With either of the two

scenarios, irrigators also have to bear their share of operational water losses

which mainly include seepage and evaporation loss from main supply

channels that occur at irrigation scheme scale and are socialized among the

water users. Irrigators which have on-farm storages could be considered

partially or fully exempted from sharing those bulk losses.

On the energy consumption front, things are quite favorable for the on-farm

scenario. The numbers reported in Table 5.13 indicate that the total energy

consumed in irrigation pumping is around 352.7 MWh as compared to 502

MWh for the baseline scenario. Hence, apparently some energy savings of

149.3 MWh are achieved by irrigating through pumping from individual on-

farm storages. The pumping energy consumed in the baseline scenario is

significantly high due to pumping through a fairly large supply pipeline

exceeding four kilometres in length. It should be noted that the energy

consumed in construction of on-farm storages and that in installation of

communal pipe network are not included in this analysis.

From the discussion given above it can be concluded that energy savings of

around 0.26 megawatt hours can be achieved by losing one megalitre of

water through on-farm storages and a relatively secured and on-time supply

of irrigation water as compared to the baseline scenario with the case of

sprinkler irrigation system. It should also be noted that operation and

maintenance costs and capital investment on individual pump stations on

farm is the responsibility of the farm operators. While with the communal

pumping arrangements (baseline scenario) the operation, maintenance and

capital costs are the responsibility of the irrigation provider and the relevant

fixed and variable costs are socialized among the users. Before making any

final decision the pros and cons of both options should be considered

carefully.

5.7.3.2 Comparison of with and without on-farm storage scenarios for

drip system

311

The on-farm storage with drip irrigation system is referred to as “Scenario 4

with on-farm-storage”. The model configuration is the same as Scenario 3

with on-farm storage except that the irrigation system is changed to drip and

the initial storage capacity of the each on-farm storage is different. The

baseline scenario for the current case is referred to as “Scenario 4-four-days-

interval”. The modelling steps are same as those explained for the sprinkler

system. First, we run the baseline scenario on the original supply-based

model with irrigation interval of 4 days. The baseline run is similar to the

Scenario 4 (please see Table 5.1 for description) except that the irrigation

interval is reduced from 7 days to 4 days. The irrigation application rates for

each crop as computed by the baseline model are used to get an initial

estimate of capacities of on-farm storages as given in Table 5.14. The

storage capacities are generally lower than that of “Scenario 3 with-on-farm-

storage” as irrigation application rates for drip system are significantly

lower than that of sprinkler system.

Table 5.14: Irrigation rates for 4-day irrigation interval and on-farm storage capacity for each farm with drip system

Farm No.

Farm ID

Irrigation Rate (ML/ha)

Irrigated Area (ha)

On-farm Storage Capacity (ML)

1a A 4.77 54.26 259 3 B 4.77 35.4 169 4 C 4.77 28.18 134 5 D 4.77 35.3 168 6 E 4.77 27.7 132 7 F 4.77 28.76 137 7a G 4.77 11.24 54 8 H 5.90 10.17 60 9 I 5.90 12.43 73 10 J 4.77 6.87 33 11 K 4.77 16.32 78 12 L 10.33 4.57 47 13 M 10.33 19.77 204

The new model with on-farm storages is configured for drip system and the

maximum storage sizes entered and the model is executed for the scenario

labelled as “Scenario 4-with-on-farm-storage”. The cumulative evaporation

312

loss and final desired capacity of each on-farm storage are computed by the

model as given in Table 5.15. Since the storage size is smaller to that of the

sprinkler scenario, the on-farm storage filling period is reduced from 46

days to 31 days for this scenario. The storage deficit for a couple of farms is

significantly higher than the evaporation loss as shown in Table 5.15. This

is due to incomplete filling of the storage by the end of the filling period.

This situation can be avoided by adjusting the filling schedule of the

individual farms. The final size of the on-farm storages is also given in

Table 5.15 which is not affected by any storage deficit.

Table 5.15: Computation of final capacity of each on-farm storage for drip system

Farm No.

Farm ID

On-farm Storage

Capacity -Initial (ML)

On-farm Storage at the end of simulation

Total evaporation loss from on-farm storage

(ML)

On-farm Storage

Capacity - Final (ML)

1a A 259 -65.1 67.2 326 3 B 169 -42.8 43.9 213 4 C 134 -34.1 35.0 169 5 D 168 -42.6 43.8 212 6 E 132 -34.3 33.1 165 7 F 137 -38.2 34.7 172 7a G 54 -13.4 14.2 68 8 H 60 -16.3 15.5 76 9 I 73 -25.5 15.2 88 10 J 33 -9.9 7.2 40 11 K 78 -23.0 17.4 95 12 L 47 -14.7 11.0 58 13 M 204 -106.9 20.8 225

Water losses and energy use are the two key quantities to be compared for

with and without on-farm storage scenarios for the drip system as given in

Table 5.16.

A total of 359 ML or 19% of the total irrigation supplied is lost through

surface evaporation from the on-farm storages.

313

Table 5.16: Key variables for with and without on-farm storage scenarios for drip system

Variable Scenario 4-four-days-

interval (baseline i.e. no on-farm storages)

Scenario 4-with-on-farm-storage

Total evaporation loss from on-farm storages (ML)

0.0 359.0

Total conveyance loss (ML) 0.0 3.3 Total water use (ML) 1,548 1,910.3 Total energy consumed in irrigation pumping (kWh)

412,166 423,994

The total energy consumed in irrigation pumping is estimated to be almost

the same for with and without on-farm storages, which is an interesting

finding. It can be inferred that the total energy losses in moving water

though the common pipeline and those from individual pumping system is

almost of similar magnitude, but in fact a bit higher for the latter. Therefore,

as far as energy use in irrigation pumping is concerned, the use of individual

pumps on each farm or common pressurized supply from a single location

does not make any difference. However, the former incurs more water

losses due to continued evaporation from the private storages while the

latter one involves more risk of affecting all users if the system is down due

to faults or maintenance.

5.7.3.3 Pros and cons of using on-farm storages versus communal

pumping for pressurized irrigation

The pressurized irrigation systems require pressurized supply of water to

operate. There are two options to make pressurized supply available. One is

to use large pumps at the irrigation water source which pump water into a

common supply pipeline with outlets to each farm. The other option is to

convey water through open channels to the farm and store it on private on-

farm storages and then pump from there to the field irrigation system. The

use of on-farm storages is modelled and discussed in detail in this chapter

for pressurized irrigation systems. Some general benefits and deficiencies of

the two options are identified in Table 5.17.

314

Table 5.17: Comparison of use of on-farm storages and the common piped supply

Pumping from on-farm storages Communal pumping stations

supplying via common pipeline

Pros: Pros:

Just-in-time supply of irrigation

Reliable supply ensured

No fixed charges for the infrastructure

Opportunity to pump during off-peak times to lower electricity costs

System maintenance can be schedule to avoid any crop losses

Relatively simple design

Capital investment not very high as compared to communal pumping.

The energy consumed per ML pumped is relatively low

Individual users not responsible for day-to-day operations and maintenance

The whole system can be automated to supply individual water orders

No conveyance losses

Electricity prices can be negotiated with provider

Measuring and monitoring can be automated.

The system can be automatically controlled and adjusted to required load.

Cons: Cons:

High evaporation loss from on-farm storages

Conveyance losses may occur during water movement from supply source to the individual farms

Irrigator to bear all evaporation loss from his/her on-farm storage.

Operation and maintenance

Fixed charges to be paid by all users

Opportunity to pump during off-peak times to lower electricity costs is not guarantied

Risk of crop losses due to system failures or unscheduled maintenance

Need to maintain usage

315

responsibility of individual irrigators

High capital cost to construct on-farm storage

accounts for all users

A complicated system design.

Very high initial capital cost

The system is not easily extendable

The energy consumption per ML pumped is quite high

5.8 Chapter summary

In this chapter we have focused on water and energy aspects of supply-

based irrigation management system which includes irrigation practices and

physical equipment and structures etcetera. In doing so, a system-wide

approach is adopted looking beyond the paddock scale. This approach

involves considering transmission of irrigation water from source to the

farm inlet, operating rules and the processes associated with infrastructure

from irrigation source to farm inlet and then from farm inlet to the on-farm

structures (e.g. on-farm storages) and on-farm irrigation application

techniques. A node-link model for the whole system was developed using

Vensim for a case study area consisting of 13 farms as discussed in Chapter

3. The model consists of different modules specific to different components

of the system. For example, the “Crop Water Use Module”, computes daily

farm level water balance for each crop (citrus, wine grapes and stone fruit)

irrigated with specified irrigation application methods at irrigation intervals

specified by the user. This module is capable of modelling four irrigation

techniques namely; flood, furrow, sprinkler, and drip system. The irrigation

interval is fixed at 10 days for flood and furrow irrigation and as 7 days for

the pressurized irrigation systems. The “irrigation supply” module simulates

water movement and water losses through the supply infrastructure whether

it is open channel or pressurized pipes. The “energy” module computes

energy consumption in irrigation pumping for pressurized supply whether it

316

is through the main supply pipe connected with a common source or from

the on-farm storages with individual pumps. The direct and indirect energy

inputs other than electricity used in irrigation pumping for each crop are

scaled from demand-based irrigated crops given in Chapter 4 in the ratio of

water use in supply-based and demand-based irrigation strategies. The

analyses are initially focused on a total of four scenarios which are listed

below to explore the water, energy and greenhouse gas emissions interplay

for the supply-based irrigation system which is a more traditional and

widely practiced irrigation approach.

Scenario 1: Flood irrigation supplied with an open channel system

Scenario 2: Furrow irrigation supplied with an open channel system

Scenario 3: Sprinkler irrigation system connected with communal

piped supply

Scenario 4: Drip irrigation system connected with communal piped

supply

Scenario 1 and Scenario 2 are gravity based irrigation systems connected to

open channel supply system. The irrigation systems for Scenario 3 and

Scenario 4 are operated under high pressure by a series of pumps connected

to a common supply pipeline. The water use, water losses, and energy

consumption for each of the abovementioned scenarios are different from

each other. Based on irrigated area, irrigation water supply capacity, crop

type, soil type and irrigation application method, an optimized rate of

irrigation application that has minimum impact on crop yield was found by

setting up and executing the optimization module of the node-link model for

each crop under each of the four scenarios listed above. The resulting

optimized irrigation application rates (l/ses/ha) are given in Figure 5.3. Each

crop whenever irrigated is supplied water at this rate. Hence the amount of

irrigation supply per irrigation event is fixed for the whole irrigation season.

A range of water and energy indicators were computed for each scenario

most of which are given in Table 5.8.

317

It has been noticed that the irrigation interval of 7 to 10 days for the supply-

based irrigation scenarios has a significant impact on crop yield (up to 66%

reduction) when compared with those for demand-based scenarios.

Therefore, a sensitivity analysis of crop yield, energy use and water use

against the irrigation interval for the pressurized irrigation scenarios

(Scenario 3 and Scenario 4) only was conducted using the Sensitivity

Analysis module of Vensim. It was found from the sensitivity analysis that

all those variables, especially crop yield, were highly sensitive to the

variation in irrigation interval. From this analysis an irrigation interval of 4

days was found to be appropriate for the operation of the case study area

under supply-based irrigation settings. However, given the constraints on

supply capacity and water ordering rules, it is likely impossible to achieve

an irrigation interval of 4 days. Therefore, the option of using on-farm

storage for each farm was explored only for sprinkler and drip system.

Hence the necessary modifications were done in the model to implement on-

farm storage option. The modified model was executed for two scenarios

called; “Scenario 3 with on-farm-storage” which is for sprinkler system and

“Scenario 4 with on-farm-storage” which is for drip system. A baseline

scenario using the original supply-based model using irrigation interval of 4

days with communal piped supply was also added corresponding to each of

the two on-farm storage scenarios to compare against the on-farm storage

scenarios.

5.8.1 Summary of the key variables

A summary table (Table 5.18) which lists some key variables which help

understand the water-energy nexus for all the scenarios modelled in this

chapter is given below. Most of the data condensed in Table 5.18 is given in

relevant sections of Chapter 5. It should be noted that the highest amount of

energy (502.1 MWh) in irrigation pumping is consumed by sprinkler system

with a four day irrigation interval followed by drip irrigation with on-farm

storage (424 MWh). Also the least amount of irrigation water (1035 ML) is

used by drip system connected with a pressurized common pipeline

318

Table 5.18: Summary of important variables for all scenarios modelled in Chapter 5 under supply-based irrigation strategy

Scenario

Name Scenario description

Total

water use

(ML)

Trans-

mission

loss (ML)

Total on-

field

losses

(ML)

Total loss

from on-

farm storage

(ML)

Operat-ing

pressure

head (m)

Total energy

use for

pump-ing

(MWh)

Total energy

input for crop

produc-tion

(MWh)

Total energy

use at system

level (MWh)

Scenario 1 Flood irrigation supplied

with an open channel

system

1,795 6.8 431.1 NA 0.0 NA 1124.2 1124.2

Scenario 2 Furrow irrigation supplied

with an open channel

system

1,740 6.7 396.9 NA 3.0 NA 1380.4 1380.4

Scenario 3 Sprinkler irrigation system

connected with a


1,489 0.0 368.4 NA 25 368.4 1258.4 1626.8

Scenario 4 Drip irrigation system

connected with a


1,035 0.0 303.2 NA 32 281.6 1128.4 1410.0

319

Scenario 3–

four day

(baseline)

Same as Scenario 3 but

irrigation interval set to 4

days

2,166 0.0 435.9 NA 25 502.1 1258.4 1760.5

Scenario 3–

on-farm-

storage

Same as “Scenario 3-four

day” but irrigation water

pumped from individual

on-farm storages

2,730.5 5.2 435.9 559.3 25 352.7 1258.4 1611.1

Scenario 4–

four day

(baseline)

Same as Scenario 4 but

irrigation interval set to 4

days

1,548 0.0 419.5 NA 32 412.2 1128.4 1540.6

Scenario 4–

on-farm-

storage

Same as “Scenario 4-four

day” but irrigation water

pumped from individual

on-farm storages

1,910.3 3.3 419.5 359.0 32 424.0 1128.4 1552.4

320

5.8.2 Pros and cons of demand-based versus supply-based

irrigation strategy

A number of pros and cons and differences are identified by looking at

model results for demand-based scenarios discussed in Chapter 4 and those

for supply-based scenarios in Chapter 5. A comparison of general positives

and negatives of the two irrigation management strategies is given in Table

5.19. They are mainly discussed in the context of horticultural crops which

are modelled in this study.

Table 5.19: Comparison of demand-based and supply-based irrigation strategies (the “high” or “low” refers to comparison with each other)

Item Demand-based

irrigation Supply-based

irrigation

Water availability Abundant in quantity

Limited in quantity

Timing of water availability Just-in-time Fixed irrigation interval

Irrigation rate per hectare High Low

Irrigation frequency Variable as per demand

Fixed irrigation interval

Chances of over/under irrigation

Minimum High

Crop yield High Low Use of soil moisture monitoring equipment

Required Optional

Energy consumption for irrigation pumping (for pressurized irrigation systems)

High Low

Energy inputs for crop production

High Low

Risk to crop yield Low High

On-farm water losses High (especially for gravity based irrigation)

Low (especially for gravity based irrigation)

Best suited to: Pressurized irrigation systems

All type of irrigation methods

321

Based on the above discussion, it can be concluded that both supply-based

and demand-based irrigation approaches have relative advantages over each

other. The selection of which approach to implement depends on the

circumstances. The demand-based system seems to be more effective for

pressurized irrigation (drip or sprinkler) systems while the supply-based

system is more suited to gravity based irrigation methods.

323

Chapter 6: Up-scaling Water and Energy Linkages from

Case Study to Irrigation Scheme Level

In the previous chapters we developed, tested and applied a framework to

model the water use, crop yield and energy use, especially the pumping

energy consumption; for a case study area of about 291 hectares in the

Murrumbidgee Irrigation Area (MIA). That modelling framework covered

both demand-based and supply-based irrigation strategies for a range of

irrigation application techniques and irrigation water supply methods as

discussed in the previous chapters. In the current chapter, the models are

based on the same framework but with different datasets to serve the

purpose of estimating water and energy use at the irrigation system/scheme

level.

The irrigation strategy in MIA, the study area, is dominated by a supply-

based approach. Apart from significant water losses, the major drawback of

the supply-based irrigation approach is the reduction in crop productivity

due to inappropriate timing as well as inaccurate magnitude of irrigation

supplied. The main focus of this PhD research is to explore the demand-

based irrigation approach and to estimate the water and energy footprint of

this approach which essentially involves pressurized delivery of irrigation

water from a commonly located source to the farms through pipes and then

applied to the crops using pressurized irrigation system like sprinkler or drip

system. The previous chapters only dealt with modelling of the two

approaches at a case study scale. This chapter is about up-scaling the results

to the entire horticultural area of the MIA. Up-scaling of only the demand-

based irrigation system is considered in this chapter.

6.1 Prerequisites for up-scaling demand-based irrigation system

When we look at up-scaling water and energy relationships from a field

scale (tens of hectares) to an irrigation system/scheme (thousands of

hectares) in the context of soil-water-crop interactions, the spatial variability

in the following variables should be taken into account.

324

Soil type,

Rate of evapotranspiration,

Rate of soil surface evaporation,

Size and length of irrigation supply pipes,

Pressure head requirement at supply nodes,

Flow rate requirement at supply nodes,

Energy loss in pipe system,

Rate of direct and indirect energy inputs

The abovementioned variables which can be grouped into uncontrolled (soil

type) and controlled (all others in the list) may change with change in size of

the spatial coverage. The soil type is an independent (naturally occurring)

variable that cannot be manipulated and that most prominently varies

spatially. All other variables are directly or indirectly dependent on soil type

and the crop/irrigation management practices. Therefore, the node-link

model developed for the case study area as described in the previous

chapters is re-configured for various soil types to get corresponding water-

energy relationships.

325

Figure 6.1: Map showing horticultural farm boundaries and their soil textural classes in the Murrumbidgee Irrigation Area

6.1.1 Data preparation and approach for up-scaling

In this chapter all land use, water use data and climatic data is taken for the

year 2007-08. The reason to select a different dataset as compared to that

used in the previous two chapters is to test the robustness of the developed

modelling framework. As discussed above soil is the basic data item for up-

scaling processes. There are fifteen USDA/FAO based textural classes of

the soils of the farms where horticultural crops are planted in the whole

irrigated area of the Murrumbidgee Irrigation Scheme as shown in the soils

spatial map in Figure 6.1. The map in Figure 6.1 is the final product of

intersection and union of different GIS layers of soils and farm data which

was carried out using a GIS tool. The map shows that most of the

horticultural farms have light clay or medium clay soils. The USDA/FAO

326

system of soil textural classification is recommended by Minasny and

McBratney (2001) and others for Australian soils. These textural classes are

combined into five broad soil groups based on their physical characteristics

as detailed by Hornbuckle and Christen (1999) after their review of 78

publications on properties of soils in the Murrumbidgee catchment. The five

broad soil groups include:

Clays which were further sub-divided into self-mulching and non

self-mulching clays;

Red-brown earths (RBE), which were further sub-divided into four

sub-plasticity classes;

Transitional red-brown earths;

Sands over clays, and;

Deep sandy soils.

The widely used farm scale water and salt balance model called

SWAGMAN Farm (Salt Water and Groundwater MANagement), which is

an irrigation farm management tool developed by CSIRO Land and Water

(Khan et al., 2000; Khan et al., 2001; Edraki et al., 2003) , also uses the

same five soil groups in the Murrumbidgee catchment. Since there is only

minor variation in soil types of horticultural areas in MIA and also to reduce

the size and number of similar model results it was decided to group the soil

types given in Figure 6.1 into the abovementioned distinct five soil groups

(Hornbuckle and Christen, 1999). Soil-water properties (water content at

field capacity and at wilting point) for each USDA soil textural class were

compared with those of the five soil groups to assign it to the most

appropriate group. The final grouping hence achieved is given in Table 6.1.

Both Hornbuckle and Christen (1999) and SWAGMAN Farm follow the

same soil groups with the exception that the latter subdivides “clays” into

“self-mulching” and “non-self mulching” clays and also combines “sands-

over-clays” and “deep sandy soils” into simply “sandy soils” as given in

327

Table 6.1. The soil-water properties of the five soil groups are quoted from

SWAGMAN Farm model and Hornbuckle and Christen (1999). The soil-

water properties of the USDA soil textural classes were sourced from Rawls

et al., (1982) and Allen et al, (1998).

Figure 6.2: Map of the five soil groups in the MIA horticultural area

The features of the five soil groups in the MIA horticultural area which are

based on the combinations of USDA soil classes as given in Table 6.1 are

mapped in Figure 6.2. It is worth noting that horticulture farms with similar

soil groups are generally located as clusters.

Table 6.1: Soil groups and their equivalent USDA soil types

Hornbuckle & Christen

SWAGMAN Farm

Field Capacity (cm3/cm3)

Wilting Point

(cm3/cm3)

Equiv. USDA Soil Type

Clays - self mulching,

Self-mulching clay

0.38 0.25 Clay loam, Silty clay

328

non-self mulching

Non self-mulching clay

0.38 0.23 Light clay, Light medium clay

Transitional red-brown earths


0.42 0.3 Medium clay, Heavy clay

Red-brown earths

Red-brown earths

0.35 0.22 Silt loam, Silt clay loam, Sandy clay

Sands over clay

Sandy soil 0.28 0.2

Loam, Sandy loam, Fine sandy loam, Loamy sand, Sandy clay loam, Sand

Deep sandy soils

6.1.2 Limitations regarding up-scaling water and energy use

As mentioned earlier the main objective of this chapter is to estimate water

and energy use by horticulture over the whole MIA irrigated area.

Generally, the up-scaling procedure involves determination of properties for

a unit area of a given soil type which are then linearly extrapolated to the

entire area. This approach is quite suitable for properties like water use rate,

water savings or crop yield which are mainly dependent on a single

parameter i.e. soil type.

In the current study we have also included a rather more complex variable;

the energy use in irrigation pumping and application. In fact, the case of

energy use for pressurized irrigation pumping is totally different from other

quantities. It cannot be linearly extrapolated from unit area to the larger area

of interest. The reason behind this up-scaling limitation on pumping energy

use is the physical reality that it is non-linearly related to the size of the

system (pipe lengths and diameters), pipe gradient, and to the flow rate

inside an irrigation supply system. This limitation is demonstrated by

running the supply-based node-link model with piped irrigation supply and

with total irrigated area increased to different levels and then noting the total

329

energy consumption to irrigate that area with drip irrigation system while

keeping the size of the pipe supply network unchanged. The results are

summarized in Table 6.2. It is evident from Table 6.2 that the energy use is

increased by 75 per cent with 50 per cent increase in irrigated area and by

177 per cent with 100 per cent increase in irrigated area. Furthermore,

Figure 6.3 shows that water use is increased and decreased by the same

proportions (equal distance from either side of blue line) as that of the

irrigated area. On the other hand, the increase in cumulative pumping

energy consumption is significantly higher than its decrease for the ±50%

variation in irrigated area, as shown in Figure 6.4. The water and energy

sensitivity plots given in Figure 6.3 and Figure 6.4, respectively, are based

on 500 simulation runs for each case.

Figure 6.3: Sensitivity of cumulative water use (ML) when the irrigated area is uniformly varied by ±50% for the node-link model with a drip system (blue line refers to the baseline value)

Drip_demand_based_sensitivity_run50% 75% 95% 100%


3,200

2,400

1,600

800

01 92 183 274 365

Time (Day)

330

Figure 6.4: Sensitivity of cumulative energy use (KWh) when the irrigated area is uniformly varied by ±50% for the node-link model with a drip system (blue line refers to the baseline value)

Table 6.2: Comparison of increase in pumping energy use with increase in total irrigated area for a supply-based drip irrigation strategy

Area Case Total pumping energy

consumed (kWh) Change in energy use

(±% of baseline) Baseline area 321,362 0 Area increased by 50 per cent

563,833 +75%

Area decreased by 50 per cent

600,285 142,689

-56%

Area increased by 100 per cent

892,944 +177%

The above discussion concludes that it will be highly inaccurate to compute

energy use at unit area (1 hectare) and uniformly upscale it to the entire

area. Similarly, it is inappropriate to extend the model domain to cover the

entire horticultural area of MIA by a single connected network of supply

pipes as it is practically impossible. To address this limitation the concept of

a “representative unit” was introduced and the whole MIA horticulture area

is assumed to be a mosaic of representative units. A representative unit here

is defined as a grouping of 300 hectares consisting of ten irrigated farms all

having the same size (30 ha), the same soil type/soil group and growing the

three horticultural crops distributed in the same proportion as to that of the

Drip_demand_based_sensitivity_run50% 75% 95% 100%


480,000

360,000

240,000

120,000

01 92 183 274 365

Time (Day)

331

three crops distribution with that soil type over the entire MIA horticultural

area. The node-link model was setup with the representative unit and run

with each set of soil types and the values of the parameters to be up-scaled

were recorded.

As shown in Figure 6.1 and Figure 6.2, most of the farms with the same soil

type/group are co-located therefore, the assumption of using the same soil

type for all farms in the representative unit is considered to be an

appropriate one. To find the relative distribution of the crop area for the

representative unit the attributes data of the GIS map in Figure 6.1 was

analysed. The attributes include soil type, crop name and crop area for each

horticultural farm in MIA as per year 2007-08. Table 6.3 provides a

summary of the analysis of this attribute data. It gives the area (both

hectares and percentage) of each soil type in MIA’s horticultural zones. The

table also provides the area of each of the three horticultural crops as

percentages of the total area of a given soil type. For example, Table 6.3

shows that citrus are grown at 42 per cent of the area under clay loam.

Similarly, for wine grapes and stone fruits (all other fruits) grown on clay

loam, the percentage area is 54% and 4%, respectively. It should be noted in

Table 6.3 that about 84% of the soils of horticultural farms are some sort of

clayey soils. Medium clay is the most common (34%) soil type followed by

light clay (18%) and light medium clay (11%) and then all others in the

MIA horticultural soils. This further supports the assumption of using the

same soil type for all ten farms of the representative unit.

Table 6.3: Distribution of different soil classes in MIA horticultural farms and percentage distribution of area of each horticultural crop for each soil type

Soil Class Area (ha)

Area as %age of total area

Citrus area (%)

Wine grapes

area (%)

Stone fruit area (%)

Clay loam 1374 5 42 54 4 Fine sandy loam

173 1 18 81 1

Heavy clay 2717 9 30 66 4 Light clay 5286 18 21 77 2 Light 3109 11 21 74 5

332

medium clay Loam 89 0 76 24 0 Loamy sand 57 0 98 2 0 Medium clay

9740 34 23 71 7

Sand 74 0 100 0 0 Sandy clay 923 3 74 23 3 Sandy clay loam

293 1 69 31 0

Sandy loam 256 1 28 71 1 Silt clay 2579 9 39 36 25 Silt clay loam

1894 7 35 64 1

Silt loam 407 1 31 69 0 Total 28970 100

Given these 15 soil types, the node-link model would have to run for times

respectively for each irrigation method thus generating excessive

information. Since we have already sorted those 15 soil types into five soil

groups as given in Table 6.1, only five model runs were produced i.e. one

set of results for each soil group. As shown in Table 6.4, 43 per cent of the

horticultural area in MIA is transitional red-brown earths followed by non

self-mulching clays at 29 per cent. The least area is covered with sandy soils

at 3 per cent.

Table 6.4: Distribution of different soil groups in MIA horticultural farms and percentage distribution of area of each horticultural crop for each soil group

Soil Group Area (ha)

Area as %age of total area

Citrus area (%)

Wine grapes

area (%)

Stone fruit area (%)

Self-mulching clay

3953 14 40 42 18


8395 29 21 76 3


12457 43 24 69 6

Red-brown earths

3224 11 46 53 1

Sandy soils 941 3 58 46 1

333

6.2 Node-link model run for representative area unit

As mentioned earlier the node-link model is based on the same framework

which is discussed in previous chapters. The physical layout of the irrigation

supply system which is a branched piped supply system as represented by

the model is also unchanged. Running the node-link model at the

representative area scale to determine water-energy use is the first step. For

each of the two irrigation methods (i.e. sprinkler and drip), five model runs

(instead of 15 runs) were repeated constituted by one model run for each of

the five soils groups using the relative proportions of the three horticultural

crop areas as given in Table 6.4 over the representative unit. For a given

irrigation method, one model run differs from the other only by its soil type.

Since the total area of the representative unit (300 ha) is almost same as the

one used for the case study (290.97 ha) in the previous chapters, no change

in length, diameters or material of the supply pipes was assumed for the

model runs discussed here.

Table 6.5: Distribution of number of farms in the representative unit for each model run using a given soil group and crop area (in parentheses, ha)

Soil Group No. of citrus

farms No. of wine

grapes farms No. of stone fruit farms

Self-mulching clay 4

(120) 4

(120) 2

(60)


2 (60)

8 (240)

0 (0)


2 (60)

7 (210)

1 (30)

Red-brown earths 5

(150) 5

(150) 0

(0)

Sandy soils 6

(180) 4

(120) 0

(0)

The number of 30 ha farms each growing one of the three crops for each soil

type model run using the representative area are given in Table 6.5. The

number of farms is rounded up to the closest whole number; therefore, there

are no stonefruit farms on some of the soil group due to their very small area

as compared to the other two crops. The total irrigated area for each soil

334

group is summed to be 300 hectares. A depletion factor of 80% was used for

all model runs i.e. an irrigation order is placed and irrigation delivered when

and if soil-water is depleted more than 80% of the readily available soil

moisture for the given soil group to a given farm.

6.3 Up-scaling the model results using mosaic approach

The node-link model set up as per the abovementioned configuration was

run five times; one run for each of the five soil groups under sprinkler

irrigation system with piped supply and then same number of model runs

were repeated for the drip irrigation system. Since it is a demand-based

model, both the irrigation quantity and irrigation delivery are regulated by

the crop-water demand by keeping a continuous account of the soil-water

depletion. It also assumes no constraints on availability of irrigation water.

6.3.1 Water and energy use at representative area unit scale

First, the model computes the water use per hectare for each crop and the

total water use for the entire model area i.e. 300 hectares and total energy

consumed in pumping and delivering this water. Then the modelled water

use and energy use are up-scaled to the entire horticultural area of MIA. The

results of the first step i.e. water and energy uses for the representative unit

for the sprinkler irrigation case are given in Table 6.6 and those for the drip

irrigation are given in Table 6.7. The energy use reported in Table 6.6 and

Table 6.7 is the total energy consumed in irrigation pumping for the

sprinkler and drip irrigation systems, respectively for the modelled

representative area and can be converted into values for individual crops

using the water use proportions method applied in previous chapters. It is

worth noting from the these two tables for sprinkler and drip system that

energy use for irrigation pumping and delivery for sandy soils is not highest

among the five soil groups despite the fact that the water volume pumped is

the highest for the sandy soils. The most commanding reason for this is that

sandy soils have the lowest water holding capacity among the five soil

groups and hence water is depleted relatively quickly in sandy soils thus

requiring more frequent irrigation but in lesser quantity.

335

As mentioned in the beginning of this chapter, energy use for irrigation

pumping is very sensitive to instantaneous flow rates through the supply

pipes. Therefore, less energy is consumed in irrigating sandy soils due to

smaller flow rates required. As expected, the total water use and energy use

are higher for the sprinkler irrigation system than that of the drip irrigation

system.

336

Table 6.6: Water and pumping energy uses for different soil groups with sprinkler irrigation system as computed by the demand-based node-link model for the representative unit area (300 ha) for 2007-08

Water use (ML/ha) Total Water Use for Representative Unit (ML/300ha)Total water

use (ML) Total pumping

energy Use (kWh) Crop/Soil Citrus Wine

Grapes Stonefruit Citrus Wine Grapes Stonefruit

Self-mulching clay 7.24 5.48 7.23 869 658 434 1,960 552,351


7.13 5.42 0 428 1,301 0 1,729 904,805

Transitional Red-brown Earths

7.41 5.5 7.44 445 1,155 223 1,823 711,978

Red-brown Earths 7.24 5.48 0 1,086 822 0 1,908 909,144

Sandy soil 7.39 5.49 0 1,330 659 0 1,989 557,622

337

Table 6.7: Water and pumping energy uses for different soil groups with drip irrigation system as computed by the demand-based node-link model for the representative unit area (300 ha) for 2007-08

Water use (ML/ha) Total Water Use for Representative Unit (ML/300ha)Total water

use (ML) Total pumping

energy Use (kWh) Crop/Soil Citrus Wine

Grapes Stonefruit Citrus Wine Grapes Stonefruit

Self-mulching clay 5.6 4.13 5.69 672 496 341 1,509 381,251


5.63 4.2 0 338 1,008 0 1,346 598,013


5.52 4.17 5.69 331 876 171 1,378 421,804

Red-brown Earths 5.6 4.13 0 840 620 0 1,460 526,189

Sandy soil 5.71 4.25 0 1,028 510 0 1,538 377,131

338

6.3.2 Water and energy use at MIA scale

The next step is to up-scale modelled water use from the representative unit

to the whole horticultural area of MIA. The procedure for up-scaling water

use is depicted by Equation 6.1. The water use amount computed by using

Equation 6.1 for each of the five soil groups is added up to get the total

water use for the entire MIA horticultural area irrigated with a given

irrigation method. The same procedure is followed for the second irrigation

method. In Equation 6.1, only the quantity is computed by the model.

Equation 6.1

Where,

i is one of the five soil groups listed in Table 6.5; is the total water use

for the MIA horticultural area with the soil group i; is the total MIA

horticultural area with the soil group i; is the modelled total water for the

representative unit area which has soil group i; and is the size of the

representative unit used in the model i.e. 300 hectares.

The total horticultural area in MIA is 28,970 ha while that of the model is

300 ha. Therefore, at least a total of 97 pumping stations will have to be

established to cover the entire MIA horticultural area with each station

servicing its command area of 300 hectares.

A formula similar to Equation 6.1 is applied for each soil type to up-scale

the modelled energy use in pumping irrigation water and in operating the

pressurized irrigation delivery systems from water source to each farm, for

the entire horticultural area of MIA. The formula used for energy up-scaling

is given in Equation 6.2. The requirement for the mosaic approach for

estimating pumping energy at the MIA scale by using the representative unit

area has been mentioned in the previous section. The sole purpose of this

approach is to avoid over-estimates of pumping energy consumption.

Equation 6.2

Where,

339

is the total pumping energy used in the MIA horticultural area with the

soil group i; and is the modelled energy consumed in irrigation pumping

for the representative unit area with the same soil group i.

The data for the variables used in Equation 6.1 and the total up-scaled water

use estimated for each soil group with horticultural crops in MIA are given

in Table 6.8. The modelled water use given in Table 6.8 is the output of the

model as mentioned above for each soil group with all farms irrigated with

sprinkler system as well as for the case of all farms irrigated with drip

system for the representative unit area of 300 ha. There are zero conveyance

losses as water is transmitted through pipes and the only losses constituting

the water use are on-farm water losses.

The up-scaled water use for each soil group is the result of Equation 6.1.

The total water use for each irrigation method, which is assumed to be

applied to the entire horticultural area of MIA (28,970 ha), is the sum of that

of the five soil types and is given on the last row of Table 6.8. The total

water use at MIA horticultural area scale is 23% higher for sprinkler system

than that of the drip system. As derived from Table 6.8, regardless of crop

type, the water use per hectare for sprinkler system is 6.1 ML/ha and that for

drip system is 4.7 ML/ha for the overall horticultural area of MIA. Hence,

water savings of 1.4 ML/ha/year can be achieved by converting all the MIA

horticultural area from sprinkler to drip system (both connected with

pressurized irrigation supply system from the water source).

To estimate the potential water savings, the node-link model was also run

with flood irrigation system for each soil type. The water use estimates for

the modelled area and the up-scaled values for the entire MIA under flood

irrigation are given in Table 6.8. It is evident from these results that the total

water use for MIA horticultural area with flood irrigation is 1.7 times higher

and 2.2 times higher than that for the sprinkler and drip irrigation systems,

respectively.

340

Table 6.8: Water use for sprinkler and drip irrigation system for five soil groups at model scale and at the scale of the MIA irrigated horticulture area

Soil Group

Total Area in MIA (Ai)

(ha)

Modelled Water Use (Xi) with Sprinkler

System (ML/300h

a)

Up-scaled Water

Use (W) with

Sprinkler System in MIA (ML)

Modelled Water Use (Xi) with

Drip System

(ML/300ha)

Up-scaled Water

Use (W) with Drip

System in MIA (ML)

Modelled Water Use (Xi) with

Flood System

(ML/300ha)

Up-scaled Water

Use (W) with

Flood in MIA (ML)

Self-mulching

clay 3,953 1,959 25,812 1,509 19,883 3,191 42,046

Non self-mulching

clay 8,395 1,729 48,385 1,347 37,695 3,001 83,981


12,457

1,823 75,698 1,377 57,178 3,051 126,68

9

Red-brown Earths

3,224 1,907 20,491 1,459 15,677 3,132 33,654

Sandy soil 941 1,989 6,241 1,538 4,826 3,103 9,737

Total 28,97

0 176,628

135,260

296,10

7

The total energy consumed to pump water at the communal pumping station

and then conveying it to individual farms via pressurized pipes for the

representative area and for the whole MIA area with horticultural crops, for

each of the five soil groups over the whole year, is given in Table 6.9.

Table 6.9: Energy use in irrigation pumping and conveying for sprinkler and drip irrigation system for five soil groups at model scale and at the scale of the MIA irrigated horticulture area

Soil Group

Total Area in

MIA (Ai) (ha)

Modelled Energy Use

(Yi) with Sprinkler

System

(kWh/300ha)

Up-scaled Energy Use

(E) with Sprinkler System in

MIA (kWh)

Modelled Energy Use

(Yi) with Drip System

(kWh/300ha)

Up-scaled Energy Use

(E) with Drip System

in MIA (kWh)

341

Self-mulching

clay 3,953 552,351 7,277,961 381,251 5,023,490

Non self-mulching

clay 8,395 904,805 25,320,365 598,013 16,734,995

Transitional Red-brown

Earths 12,457 711,978 29,563,937 421,804 17,514,849

Red-brown Earths

3,224 909,144 9,769,055 526,189 5,654,076

Sandy soil 941 557,622 1,749,818 377,131 1,183,437

Total 28,970 73.681x106 46.111x106

It can be computed from Table 6.9 that on average the energy use rate for

sprinkler is estimated to be 2543 kWh/ha and that for the drip system is

1592 kWh/ha for the entire horticultural area in MIA. This includes both the

energy use in pumping the irrigation water from the water source and in

delivering it to the irrigation equipment on each farm at a required minimum

pressure. Assuming the price of electricity at $0.15 per kilowatt-hour, the

energy cost will be $382/ha and $239/ha for sprinkler and drip based

system, respectively.

It should be noted that the water and energy use reported here is for a

perfect and ideal demand-based irrigation system where irrigation is

assumed to be applied as soon as the soil water depletion exceeds 80%.

Once started, the irrigation is not stopped until soil-water depletion is

reduced back to zero. In practice, the amount and timing of irrigation may

vary due to supply and delivery constraints and management choices and

thus the actual rate of water and energy use may be lower than what is

estimated by the model here. For example, wine grapes are sometimes kept

under prolonged soil-water depleted conditions to manipulate the taste and

quality of the produce.

342

6.3.3 Water and energy use under different climatic conditions

The water use and energy consumption estimates given in Table 6.8 and

Table 6.9, respectively, are based on agro-climatic data for the year 2007-08

which is closest to the average climatic conditions for the available data

period from 2003-04 to 2008-09. The water and energy figures reported in

those two tables may vacillate depending on the prevailing climatic

conditions. To capture the likely range of variation in water and energy use

with different climatic conditions, the model detailed above with same

model runs listed above and with same land use settings were repeated for

the year 2005-06; a relatively wetter year, and the year 2006-07; a relatively

drier year, and the average climatic conditions for the last decade. The daily

climatic data used includes potential evapotranspiration rate, evaporation

rate, rainfall, relative humidity and wind speed. The water use and energy

use for horticultural areas at the scale of MIA which are up-scaled from the

model results for those climatic conditions are given in Figure 6.5. The

“medium” in Figure 6.5 refers to the year 2007-08 and the “average”

represents the average climatic data for the period from 2003-04 to 2008-09.

Figure 6.5: Water use (ML) and energy use (kWh) up-scaled from the model results for the whole MIA horticulture area for different climatic conditions

It is concluded from Figure 6.5 that between wet and dry conditions, the

total water use varies by almost 25 GL and 16 GL for sprinkler and drip

systems, respectively; which is translated into 0.86 ML/ha for sprinkler and

30,000,000

40,000,000

50,000,000

60,000,000

70,000,000

80,000,000

90,000,000

50,000

70,000

90,000

110,000

130,000

150,000

170,000

190,000

210,000

Wet Medium Dry Average

Total pumping energy use (kWh)

Total w

ater use (ML)

Average climatic conditions

Total water use sprinkler Total water use drip Total energy use sprinkler Total energy use drip

343

0.55 ML/ha for the drip system. Similarly, the respective total energy use

varies by almost 8,000 MWh (276 KWh/ha) and 4,600 MWh (158.8

KWh/ha) for the same wet and dry conditions. Thus the range of variability

of water use and energy use is wider for the sprinkler system than that of the

drip system between dry and wet climatic conditions.

6.4 Estimating and mapping water and energy savings for MIA –

using GIS-Based distributed approach

The method used for up-scaling water and energy use in the

abovementioned tables follows a mosaic approach; where the whole MIA

horticultural area is divided into area units. The size of all area units is kept

equal and each area unit has only one soil group. Then water and energy use

for a similar area unit (referred to as “representative unit” here) is computed

by the model for each soil group. Then the same modelled values of water

and energy use are allocated to all area units with corresponding soil groups.

This approach is a lump sum approach which involves some rounding-off of

the crop areas at the model level as the total number and size of the farms in

the representative unit to be modelled is fixed to 10 and 30 hectares,

respectively, for each soil group. Although this approach is less time

consuming, the major drawback of this approach is that water and energy

use cannot be mapped for individual farms as this approach does not

consider geographic location of the area units.

In this section, we will discuss a potentially more accurate approach for up-

scaling water and energy use. This is a distributed approach where the

modelled values of water and energy use are allocated to individual farms

for individual crops as per their soil groups. The model values come from

the model for the same representative unit used in the first approach. Since

each farm is allocated water and energy use as per its crop and soil group,

this approach is considered a relatively more accurate way of up-scaling.

Also with the geographic boundary of each farm known, it is possible to

allocate any number of attributes to individual farms and then map them.

Using this approach the potential savings in water use and energy use are

344

estimated by comparing both the sprinkler and drip system against the flood

irrigation system on a farm-by-farm basis.

Data for the average climatic conditions was used to estimate potential

savings. The same node-link model with piped irrigation supply was used

for the three irrigation systems to be compared. For flood irrigation the

required delivery pressure head at each farm inlet was set to zero. This

should result in reduced total dynamic head which in turn should reduce the

rate of pumping energy consumption. The water use rates (ML/ha) and total

water use (ML/300ha) for each of the three crops for each soil group as

output by the model for flood, sprinkler and drip irrigation are given in

Table 6.10, Table 6.11, and Table 6.12, respectively. The water use is

computed by the model as a direct output. However, as explained in the

chapter on methodology previously, the model only computes overall

energy use for irrigation pumping regardless of individual crops for each

soil group. With some post-processing in MS Excel, the energy use per

hectare of each crop is computed with the assumption that pumping energy

use for each crop is proportional to the volume of irrigation applied to that

crop. Hence, the energy use for each crop (KWh/ha) is given by the

Equation 6.3.

Equation 6.3

Where,

(ML) is modelled total water use for a given crop, c;

(ML) is the total modelled water use for all crops in the model domain;

(KWh) is the total modelled energy use in irrigation pumping and;

(ha) is the total irrigated area of the crop, c, in the model.

345

Table 6.10: Water and Energy use for flood irrigation at the model scale for each crop for average climatic conditions

Total Water Use for 300ha unit Water use (ML/ha) Energy use (kWh/300ha) Energy use (KWh/ha)

Crop/Soil Group Citrus Wine Grapes

Stone fruit Citrus

Wine Grapes

Stone fruit All crops Citrus

Wine Grapes

Stone fruit

Self-mulching clay 1358 1273 736 11.32 10.61 12.26 1068000 3590 3365 3889

Non self-mulching clay 652 2441 0 10.87 10.17 0 1455000 5113 4784 0

Transitional Red-brown Earths 659 2165 367 10.99 10.31 12.24 1544000 5316 4988 5921

Red-brown Earths 1697 1556 0 11.31 10.37 0 1744000 6065 5561 0

Sandy soil 1973 1207 0 10.96 10.06 0 943572 3252 2985 0

346

Table 6.11: Water and Energy use for sprinkler system at the model scale for each crop for average climatic conditions



Stone fruit Citrus

Wine Grapes


Wine Grapes

Stone fruit




Red-brown Earths 1094 825 0 7.29 5.5 0 759827 2887 2178 0

Sandy soil 1312 672 0 7.29 5.6 0 559267 2055 1578 0

Table 6.12: Water and Energy use for drip system at the model scale for each crop for average climatic conditions

347



Stone fruit Citrus

Wine Grapes


Wine Grapes

Stone fruit




Red-brown Earths 839 656 0 5.59 4.37 0 474585 1776 1388 0

Sandy soil 1017 529 0 5.65 4.41 0 370778 1355 1058 0

348

The resultant of the first three variables on the right hand side of Equation

6.3 is the total energy use for a given crop. Dividing it by the area of that

crop in the model gives energy use per unit area (KWh/ha) of that crop.

The energy use rates for irrigation pumping for each crop for each soil

group as computed by the above given method are also given in Table 6.10

to Table 6.12.

The water use rates and energy use rates for each crop for each

corresponding soil group from direct and indirect model outputs were

entered as attributes of each horticultural farm in the GIS database of the

MIA. The water use rate (ML/ha) averaged (depending upon the number of

crops in each farm) over the crops in each farm for each soil group as

mapped for the flood, sprinkler and drip system are given in Figure 6.6,

Figure 6.7 and Figure 6.8, respectively. Each polygon in these maps

represents a farm boundary. There can be more than one crop grown in a

single farm. As shown in Figure 6.8, drip irrigation has the widest range of

water use rates among the three irrigation methods which indicates that

water use/demand for drip irrigation is relatively more sensitive to the soil

type. It may also suggest that irrigation application rate and duration for drip

irrigation in particular should be matched with the soil type. For example,

shorter application rates are suggested for sandy soils and longer ones for

the clayey soils (Beckingham et. al., 2004). The water use maps also show

that majority of the farms have low to medium water usage and only a few

isolated farms have water use rates at the higher end of the spectrum.

349

Figure 6.6: Map of water use rate (ML/ha) for each horticultural farm in MIA for flood irrigation

Figure 6.7: Map of water use rate (ML/ha) for each horticultural farm in MIA for sprinkler irrigation

350

Figure 6.8: Map of water use rate (ML/ha) for each horticultural farm in MIA for drip irrigation

Similarly, the maps of water savings are also prepared for the pressurized

irrigation systems (sprinkler and drip irrigation system) in the horticultural

area of MIA. The water savings are computed by comparing the water use

for the two pressurized irrigation systems against that of the flood irrigation

system from the model results of each soil group run. The water savings rate

as ML/ha for each farm are entered as attributes in the GIS database and

then mapped as shown in Figure 6.9 (sprinkler vs. flood) and Figure 6.10

(drip vs. flood). For drip irrigation the water savings range from 4.5 ML/ha

to 6.5 ML/ha and for sprinkler irrigation the water savings range from 3.7

ML/ha to 5.2 ML/ha. Hence, the water savings achievable by converting

from flood to drip irrigation are higher than that of sprinkler system, as

expected.

351

Figure 6.9: Map of water savings (ML/ha) for each horticultural farm in MIA for sprinkler irrigation

Figure 6.10: Map of water savings (ML/ha) for each horticultural farm in MIA for drip irrigation

352

6.5 Estimating water and energy use at different levels of

technology adoption

Table 6.13 lists the total as well as per hectare based values of water and

energy use for sprinkler and drip systems for average climatic conditions for

the entire MIA horticultural area as derived from data behind plots given in

Figure 6.5. It is strikingly notable that operating costs including energy use

and corresponding energy cost is very high when either sprinkler or drip

irrigation demand-based systems with piped supply is rolled out to 100 per

cent of the MIA horticultural area. Therefore, it is probably unlikely that the

proposed system will be adopted by the whole area, as uptake is dependent

upon economic viability. Hence, various levels of system adoption were

analysed to get an idea of likely water and energy use for each case. An

adoption level of 25%, 50%, 75% and 100% of the total MIA horticultural

area was assumed for each of the irrigation systems for the average climatic

conditions. The results for the modelled representative unit area scale with

each soil group are already known. For various levels of system adoption,

each soil group was reduced by that percentage and then Equation 6.1 and

Equation 6.2 were applied to the model level water and energy use to

compute the water and energy use for different adoption levels in the MIA.

The model results indicate that a total irrigation time of up to 45 hours/ha

and 61 hours/ha are expended per season for sprinkler and drip irrigation

systems, respectively. Hence, based on the total energy use values given in

Table 6.13 and the total irrigation time, the corresponding energy use

expressed in mega-watts (MW = 106 watts) will be 1,657 MW/year and 744

MW/year for sprinkler and drip irrigation systems, respectively.

To place this in context, the total electricity generation capacity of the

Snowy Hydro electricity scheme, the only renewable energy generator in the

region, is 3750 MW per year (Jaques, 2005). Hence, if the whole of MIA

horticultural area is converted to sprinkler system, an additional 50% of

Snowy hydro generation capacity is required to supply these energy needs.

However, for drip irrigation, at 100% adoption in MIA horticultural area

353

only another 20% of the generation capacity of Snowy Hydro would be

required.

Table 6.13: Total and unit area based water and energy use for sprinkler and drip systems for average climatic conditions for MIA horticultural area

Quantity Water use –

sprinkler (ML)

Water use – drip (ML)

Energy use – sprinkler (KWh)

Energy use – drip (KWh)

Total 178,230 137,493 74,576,543 45,400,122 Per

hectare basis

6.2 4.7 2574 1567

The water and energy use for each case of adoption level (% of total MIA

horticultural area) for both of the irrigation systems are plotted in Figure

6.11. The plots given in Figure 6.11 represent adoption of either of the two

irrigation systems. For example, point ‘A’ corresponding to 50% adoption

level refers to that half of the MIA horticultural area is installed with

pressurized pipe based drip system and other half remains unchanged. If the

other half of the MIA horticultural area adopts sprinkler system then the

total water use will be the sum of values at point ‘A’ and point ‘B’ in Figure

6.11. It should also be noted that the slope of the plot of water use and

energy use for each of the irrigation system is different from one another

which refers to the uniqueness of water or energy use patterns for each

irrigation system.

354

Figure 6.11: Total water use (ML) and total energy use (MWh) for the two irrigation systems at various level of roll out in MIA horticultural area

6.6 Chapter Summary

In this chapter we have basically discussed two different approaches of up-

scaling modelled water and energy use to the scale of entire horticultural

area of the MIA. Both approaches are based on the principle that water and

energy use is different for different crops on different soil types. The first

approach is relatively a lump sum method where the whole MIA

horticultural area is divided into mosaics of the fixed size which are referred

to as “representative unit area” in this chapter. Water and energy use is

modelled for the representative unit area. The relative area of each crop and

soil type in the representative area is kept the same as that of the actual MIA

horticultural area. This is a quicker approach and does not require much

computation.

The second up-scaling approach is a GIS based distributed approach. In this

approach water and energy use is modelled at model scale for each crop

with each soil type. Then all GIS data consisting of soil types, crop types,

for each farm with its geographical location information for the entire

horticultural area of the MIA is loaded into ArcGIS. Then for each assumed

irrigation method, the modelled values of water use per hectare and energy

use per hectare are assigned to individual farms. Hence different maps of

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

200,000

20 30 40 50 60 70 80 90 100

Water use (M

L) & Energy use (M

Wh)

Percent of MIA horticultural area covered (%)

Total water use ‐ drip (ML) Total water use ‐ sprinkler (ML)

Total energy use ‐ drip (MWh) Total energy use ‐ sprinkler (MWh)

A

B

355

water use and energy use are achieved through processing by ArcGIS. The

total water and energy use for the entire up-scaled area is achieved by

processing the data in the attributes table.

A comparison summary of up-scaled water and energy use calculated by the

abovementioned two approaches is given in Table 6.14. The values reported

in Table 6.14 are based on the assumption that a given irrigation system is

adopted by the entire horticultural area of MIA. This shows that there is no

significant difference between the results of the two up-scaling approaches

and that either of the methods can be used for this purpose.

Table 6.14: Comparison of the two up-scaling methods for water and energy use over 28,970 ha area of MIA

Up-scaling method

Water use –

sprinkler (ML)

Water use – drip (ML)

Energy use – sprinkler

(KWh)

Energy use – drip (KWh)

Representative unit area 176,628 135,260 73,681,000 46,111,000 GIS-based 178,230 137,493 74,576,543 45,400,122

It is also noted from Figure 6.6 that the rates of increase of water/energy use

are not the same for the two pressurized irrigation systems. This refers to the

unique behaviour of water and energy consumption for the two irrigation

methods. It is also evident from the results that the energy use by sprinkler

system is considerably higher than that of drip system. It is estimated that if

the whole MIA horticultural area is converted to sprinkler system then the

increase in electricity demand equates to generation capacity equivalent to

approximately 50% of production generated through the Snowy Hydro

scheme. However, for drip irrigation, at 100% adoption in MIA horticultural

area, only an additional 20% of the generation capacity of the Snowy Hydro

scheme would be required. This huge energy requirement should be

considered in decision making on conversion of the entire MIA horticulture

area.

357

Chapter 7: Is Irrigation Conversion Worthwhile?

Previously in Chapter 4 we simulated and compared the results of water use,

energy use and crop yield for different irrigation methods with and without

pressurized-pipe delivery of irrigation water under a demand-based

irrigation strategy for three horticulture crops over a complete annual cycle.

The same analyses were repeated for the supply-based irrigation strategy in

Chapter 5. Comparison of the two irrigation strategies indicates that

although water and pumping energy consumption in case of supply-based

irrigation is less than that of demand-based irrigation, there are some

additional benefits attributable to the latter strategy. Demand-based

irrigation ensures timely and precise application of irrigation; better crop

yield; is less labour intensive and can be automated, especially for larger

farms.

The irrigation strategy in MIA, the study area, is dominated by a supply-

based approach. Apart from significant water losses, the major drawback of

the supply-based irrigation approach is the reduction in crop productivity

due to inappropriate timing as well as inaccurate magnitude of irrigation

supplied. The main focus of this PhD research is to explore the demand-

based irrigation approach and to estimate the water and energy footprint of

this approach which essentially involves pressurized delivery of irrigation

water from a centrally located source to the farms through pipes and then

applied to the crops using pressurized irrigation systems like sprinkler or

drip system. Hence, the current chapter is focused on demand-based

irrigation strategy and is based on facts and figures given in Chapter 4.

Analysis of various options is incomplete unless a proper financial analysis

is incorporated. In fact, implementation of a project of this nature, whether it

is a small or big project, is not justifiable unless it is tested for its financial

viability.

In this chapter, we focus on the financial/economic analysis of various

irrigation modernization options including conversion to drip or sprinkler

system from furrow irrigation and/or replacement of open-channels with

358

centralized pressurized-pipe irrigation supply. A comprehensive analysis by

Mays and Tung (1992) explaining the economics of hydrosystems was

extensively consulted to perform the widely used financial analysis called

benefit cost ratio (also called profitability index) and net present value

(NPV) approach. The net present value is defined as the difference between

the present value of cash inflows (returns) and the present value of cash

outflows (costs) and is widely used for analysing profitability of long-term

projects. The economic methods used in this analysis are also well

documented and applied by Khan at al. (2005a). Other literature related to

economics of irrigation methods includes: Singh et al., (2005); Giddings

(2004); Giddings and Deegenaars (2008); Cuykendall and White (1998);

Texas Cooperative Extension (2001); New Maxico State University (2000);

Malik and Luhach (2002).

7.1 Need for water saving irrigation technologies

The use of efficient and water saving irrigation technologies and methods is

vitally important for river basins where there are competing demands from

various users including irrigators, stock and domestic and the environment.

The latest and most relevant example is the Murray-Darling Basin Plan

which aims at achieving a sustainable balance between those competing

users. Such initiatives provide incentives for farmers to use hi-tech irrigation

technologies. The hi-tech irrigation methods can be more water efficient but

at the same time are likely to be more energy consuming and as a result

produce higher greenhouse gas emissions. The Australian government has

imposed a tax on GHG emissions, effective as of July 2012, which implies

that the operational costs of hi-tech irrigation systems is also likely to

increase. This begs the question – what returns can farmers get from selling

their saved water and from the potential of increased production, in order to

recover their capital costs and the increased operational costs. This creates

the need for conducting a comparative and holistic economic analysis of

different irrigation methods to economically justify investment in new

water-saving irrigation technologies and methods.

359

Gravity based irrigation methods including flood or furrow irrigation

methods have high rates of groundwater recharge, which over the long run

can cause watertable to rise which can result in rootzone salinity. Hi-tech

irrigation techniques, especially drip irrigation provide controlled

application of irrigation water and hence reduce groundwater recharge.

Moreover, the historical data shows prolonged periods of low water

availability, such as that experienced over the Millennium drought (1997 –

2009) which suggests advantages can be accrued from adopting more

efficient irrigation practices given the likelihood of continued climatic

changes. Hi-tech irrigation methods are more sustainable during such dry

periods to support crop production. Other factors that support use of water

saving irrigation technologies are explained below.

7.1.1 Water availability

The variability in irrigation water availability in MIA is depicted by the

reliability plots (i.e. per cent exceedance) of announced water allocation

levels (percentage of total water entitlements) in MIA as shown in Figure

7.1. It shows that there have been only 34% of times when irrigation water

allocation in MIA was announced to be higher than 50% and only 6% of

times the allocation was 100% of the entitlements from 1993-94 to 2009-10.

Figure 7.1: Per cent exceedance plot of announced allocation in MIA from 1993-94 to 2009-10

0

10

20

30

40

50

60

70

80

90

100

110

120

0 10 20 30 40 50 60 70 80 90 100

Announced Allo

cation

(%)

% Exceedance

Announced Allocation in MIA

360

Figure 7.1 shows general that based on historical data the announced

allocation is likely to be non-zero for 85% of times while only 6% of times

it is likely to be 100%. The water availability is highly variable and the

water shortage becomes more pronounced during dry years as in 2007-08

when allocation dropped to zero as shown in Figure 7.2. The announced

allocation data used in Figure 7.1 and Figure 7.2 was announced at various

dates and is sourced from New South Wales (NSW) Government Water

Information Website at http://waterinfo.nsw.gov.au/ac/allocation.shtml

accessed in April 2011.

Figure 7.2: Time series of announced allocation in MIA from 1993-94 to 2009-10

7.1.2 Water markets

Volumes of water allocated, diverted and traded in the southern Murray-

Darling system have varied greatly over the past ten years. Factors

contributing to such fluctuation have been a combination of policy choices,

natural circumstances and attitudinal shifts (Kaczan et al., 2011).

Widespread water trading is a relatively recent activity. Institutional reforms

over the past 20 years have been focused on creating water markets by

decoupling water and land property rights, and allowing water to flow from

0

20

40

60

80

100

120

01/1993 10/1995 07/1998 04/2001 01/2004 10/2006 07/2009

Announced Allo

cation

(%)

Date (mm/yyyy)

Announced Allocation in MIA

361

uses of low value to uses of high value with a minimum of transaction costs

(CoAG, 2004). This reform process is ongoing. By 2007-08, the value of

transactions in water markets was estimated at $1.68 billion in the southern

Murray-Darling system and over 98 per cent of water licenses in New South

Wales are now tradable (NWC, 2008).

The water trade market in the Murrumbidgee catchment has been very

active during the last four years. The average annual estimated market

turnover of water allocation trade in four years from 2007-08 to 2010-11 in

the Murrumbidgee valley remained at $152 million per annum as compared

to $283.7 million per annum for the whole state of NSW (NWC, 2011).

Furthermore, the rollout of the Australian Government’s Restoring the

Balance in the Murray–Darling Basin (‘buyback’) program

(http://www.environment.gov.au/water) to purchase water entitlements for

environmental flows has also provided irrigators incentive to adopt water

efficient irrigation methods and sell the water thus saved to maximize their

returns. Irrigators not only accept water trading: they are increasingly reliant

on it. Trading in both allocations and entitlements grew markedly over the

past five years. Over 30% of announced allocations and 10% of entitlements

on issue are traded in the southern Murray-Darling Basin in some years.

Water trading is an important tool for irrigators in making production,

investment, adjustment and risk management decisions. It is valuable in a

variety of seasonal conditions, not just as a reactive response to droughts

(NWC, 2012).

Figure 7.3 shows a scatter plot between announced water allocation and

monthly average trade price per ML for MIA from 1998-99 to 2010-11. The

scatter plot shows interactions between water availability and the price of

water in the market and that water trade price reaches peak when announced

allocation is low and vice versa.

362

Figure 7.3: Announced percentage allocation versus water trade price ($/ML) in market for MIA from 1998-99 to 2010-11

The interplay between water availability and water trade price is further

clarified in Figure 7.4. As the exceedance percentage (% of times a variable

is likely to exceed a value) is decreased, the level of allocation and trade

price that is likely to be exceeded is increased. For example, at 50% chance

of exceedance, the water trade price is likely to exceed $154/ML and that of

announced allocation is just 14 per cent of water entitlements in MIA.

However, the water trade price has been as high as 1062 $/ML during dry

periods and the water saved through hi-tech irrigation methods can be traded

in the market to recover capital and operating costs incurred in converting to

hi-tech irrigation. The average water trade price from 2005-06 to 2010-11 in

MIA remained at 271 $/ML.

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80 90 100

Monthly average price of trade ($/M

L)

Announced Allocation (%)

363

Figure 7.4: Per cent exceedance plots of water trade price ($/ML) and announced allocation (%) for MIA

The water trade price data used in Figure 7.3 and Figure 7.4 is compiled

from various sources including: Murrumbidgee Water Exchange (2011),

Kaczan et al., (2011), and Watermove (2011).

7.1.3 Crop yield improvement

Crop yield quality and yield improvement is another benefit that can be

achieved by using hi-tech irrigation methods. Applying the right amount of

water at the right time is a key factor that contributes to yield improvement.

Hi-tech irrigation systems provide such flexibility and accuracy to occur.

Table 7.1: Yield (t/ha) of citrus and wine grapes for various irrigation systems

Furrow Sprinkler Drip

Citrus

Mid-season orange 40 44 48

Navel 22 25 30 Valencia 25 30 35 Mandarin 20 22 25

Wine grapes 22 23 26

0

10

20

30

40

50

60

70

80

90

100

110

0

100

200

300

400

500

600

700

800

900

1000

1100

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Announced Allo

cation

(%)

Monthly Average

Trade

Price ($/M

L)

% Exceedance of Trade Price

Price Exceedance Allocation Exceedance

364

A comparison of yields of citrus and wine grapes for three irrigation

systems is given in Table 7.1. These yield values have been used in various

analyses in this thesis. The yield for citrus varies significantly depending on

the variety of a crop as given in Table 7.1. For water and energy analysis for

citrus in previous chapters, only the highest yield values are used.

Drivers for changing production practices to hi-tech include decline in

reliability and availability of irrigation allocation due to climate change,

high water buyback prices and high water trade prices supported by high

water trade demands during dry periods and finally, the potential

improvement in crop yield warrants the use of water efficient irrigation

technologies. This chapter is dedicated to the economic analysis of water

efficient irrigation technologies in the context of horticulture crops.

Irrigation water conveyance losses (seepage and evaporation) can also be

mitigated by using piped supply from canal/source to the individual farms.

7.2 Representative node-link model

The node-link model developed in Chapter 4 for the demand-based

irrigation system represents a horticultural area of around 300 hectares. The

integrated irrigation supply system (pumping station, pipe network, farm

delivery outlets, filtration system, computer control system etc.) for the case

study area is originally designed to irrigate a horticultural area of up to 550

hectares. However, only 13 farms with a total area of around 300 hectares

were connected to the system at the time of this study. To perform a credible

financial analysis of the system the whole 550 hectares were essentially

assumed connected and supplied with on-demand irrigation from the

integrated irrigation supply system. For this purpose, the node-link model

that was used in Chapter 4 was extended to represent the whole 550 hectares

consisting of citrus, stone fruit and wine grapes.

7.2.1 Modelled water and energy use

Table 7.2 summarizes the key results in terms of water use and energy use

for the node-link model representing the 550 hectare area for furrow,

365

sprinkler and drip irrigation systems. The furrow irrigation on each farm is

setup to be supplied with open channels while pressurized pipe supply from

a central pumping station to each farm is setup in the model for both

sprinkler and drip systems. The water and energy use results of the model

for an area of 550 hectares are summarized in Table 7.2 and will be used in

the benefit-cost analysis later on in this chapter.

Table 7.2: Node-link model output for a modelled area of 550 ha

Irrigation system

Crop

Total water used (ML)

Water use

(ML/ha)

Pumping energy use (KWh/ha)

Irrigation hours

(hr/ha)

Pumping energy

use (KW/ha)

Furrow Citrus 5459 9.93 0 40 0

Wine grapes

4084 7.43 0 40 0

Low-head sprinkler

Citrus 4451 8.09 1996 46 43.4

Wine grapes

3321 6.04 1489 44 33.8

Drip Citrus 3443 6.26 1664 60 27.7

Wine grapes

2621 4.77 1266 62 20.4

7.3 Capital cost for conversion to pressurized irrigation system

Conversion of an irrigation system requires high capital investment and

ongoing operating costs. Therefore, economic feasibility of such projects

has to be performed to justify the investment. As mentioned earlier in this

chapter, the financial profitability and economic viability of the conversion

from gravity based irrigation (furrow) to pressurized irrigation (sprinkler or

drip) is analysed by using benefit-cost analysis and net present value method

in the context of horticulture crops including citrus and wine grapes. The

analysis also includes replacement of open-channel supply system with

pressurized piped supply. Furrow irrigation is assumed as a

baseline/benchmark case in the economic analyses. This section only deals

366

with capital investment which is initially required for setting up an irrigation

system.

7.3.1 Assumptions for the economic analysis

A number of assumptions were made to carry out the economic analysis of

conversion from furrow to sprinkler or drip system. The assumptions which

are common between the three irrigation systems in the horticultural area of

MIA are listed in Table 7.3. The total area of the representative unit was

taken as 550 ha; however different cost items (capital or operational costs)

were computed on a per hectare basis.

Table 7.3: Assumed values of various parameters for economic analysis

Item Value Comments/source Irrigated crop area (ha) 550 Modelled area Water usage charges ($/ML) 8.67 MIA website Irrigation facilities charges ($/ML) 19.84 MIA website Landholding charges ($/ha) 3.48 MIA website Average temporary water trade price ($/ML)

271 Water trade data

Electricity charges – peak rate (c/KWh) 17 MIA per. comm. Electricity charges – off-peak rate (c/KWh)

9.89

Greenhouse Gas Emissions Tax ($/t) 23

KWh to Kg-CO2e Conversion Factor 0.9 Dept. CC & EE (2010)

Interest rate on loan (%) 10 Labour costs ($/hr) 20 Falivene (2003) Salvage/residual value (%) 20

The labour costs were indexed at the rate of 3% from their 2003 values. The

water charges and irrigation supply charges are assumed the same for all

three irrigation systems. The average water trade price in MIA is based on

the trade data plotted in Figure 7.3. The salvage value of the material and

equipment is assumed to be 20 per cent of the initial cost. The operational

life of most of the items is assumed to be 30 years to compute the annual

depreciation of assets. An interest rate of 10 per cent per annum is assumed

to compute annual interest on capital loan. The total annual ownership cost

367

on per hectare basis for a given irrigation system is equal to the sum of

annual depreciation and annual interest on capital.

7.3.2 Capital costs of the irrigation systems

The capital cost refers to the amount of money initially spent on material

and equipment (pumps, motors, control system, pipes, valves) and

installation of a given irrigation system. This also includes capital costs on

the pipe supply system (only applicable for sprinkler and drip system). The

capital costs required for furrow, sprinkler and drip systems are sourced

from Giddings (2004) and Grape and Wine research and Development

Corporation website at: www.gwrdc.com.au. Capital costs for the furrow

irrigation system are detailed in Table 7.4. It mainly includes PVC pipes

used to siphon water from the field channels. For furrow irrigation the total

initial capital cost is 2,200 $/ha and annual ownership cost of 277 $/ha/year

as given in Table 7.4.

Table 7.4: Capital cost for furrow irrigation system (baseline case)

Item

Initial capital

cost ($/ha)

Estimated life (yr)

Value at end of

period ($)

Annual depreciatio

n ($/yr)

Annual Interest

on capital ($/yr)

Total ownershi

p costs ($/yr/ha)

PVC mains

1,600 30 500 37 160 197

Installation

600 30 0 20 60 80

TOTAL 2,200 500 57 220 277

The capital cost of low-head sprinkler system converted into a per hectare

basis is detailed in Table 7.5. The capital cost items exclude pumps and

motors as they are included as separate items in Table 7.7 for the

pressurized pipe supply system. The major part of the capital cost is

expended on laterals, sprinkler heads and sub-mains, followed by the PVC

main pipeline. The total initial capital cost is 8,100 $/ha and annual

ownership cost of 1416 $/ha/year for the low-head sprinkler irrigation as

given in Table 7.5.

368

Table 7.5: Capital cost for conversion to low head sprinkler irrigation system

Item

Initial capital cost ($/ha)

Estimated life

(yr)

Value at end of period

($)

Annual depreciatio

n ($/yr)

Annual Interest

on capital ($/yr)

Total ownershi

p costs ($/yr/ha)

Filter 350 30 70 9 35 44PVC mains 2,450 30 490 65 245 310Laterals, sprinklers, sub-mains.

3,550 6 710 473 355 828

Installation 1,750 30 0 58 175 233TOTAL 8,100 1,270 606 810 1,416

The capital costs for setting up a typical drip irrigation system on a per

hectare basis are given in Table 7.6. The working life of all drip irrigation

system components except for laterals and sub-mains, which require

replacement every 10 years, is assumed to be 30 years. The total initial

capital cost is 7,100 $/ha and annual ownership cost of 1,068 $/ha/year for

the surface drip irrigation as given in Table 7.6. This indicates that an

amount of $1,068 per hectare will have to be paid each year just to keep the

system in place. The capital cost of the piped irrigation supply system

required to operate the drip system at a certain hydraulic pressure is

discussed in the next sub-section.

Table 7.6: Capital cost for conversion to drip irrigation system

Item


Estimated life

(yr)

Value at end of period

($)

Annual depreciatio

n ($/yr)

Annual Interest

on capital ($/yr)

Total ownershi

p costs ($/yr/ha)

Disc filter (including fertigation equipment)

2,100 30 420 56 210 266

PVC mains 750 30 150 20 75 95Laterals, sub-mains.

3,000 10 600 240 300 540

Installation 1,250 30 0 42 125 167TOTAL 7,100 1,170 358 710 1,068

369

The comparison of Table 7.5 and Table 7.6 shows that up to 9% less capital

investment is required for drip system than that of sprinkler system, noting

that the drip system is also usually more water efficient.

7.3.3 Capital costs of pressurized pipe irrigation supply system

The layout of the pressurized pipe irrigation supply system has already been

discussed in Chapter 3. It consists of a centrally located pumping station at

the water source (irrigation canal in this case) consisting of six pumps

installed in parallel configuration, pump electronic control system, filtration

system, main supply pipe and outlets from the main pipe to each farm. The

system is designed to irrigate an area of 550 ha. At the time of this study a

total of 13 farms with a combined area of 291 ha were connected to this

pressurized pipe supply system and the node-link model was setup to reflect

this configuration. For this economic analysis the nodal model is extended

to 550 ha to model water and energy use. The capital costs of various

components of the pressurized pipe irrigation supply system are listed in

Table 7.7 and are sourced from MIA through personal communication.

Table 7.7: Capital costs of pressurized irrigation supply system (Source: MIA per. com.)

Item

Total

initial

cost ($)


Estimated life (yr)

Value at end

of period

($)

Annual depreciation ($/yr)

Annual interest

on capital ($/yr)

Total ownershi

p costs ($/yr/ha)

Pumping station (6 pumps in parallel)

668777

1216 30 243 32 122 154

PVC Main reaches

458128

833 30 167 22 83 106

Fittings 9180

8 167 30 33 4 17 21

TOTAL 1218713

2216 443 59 222 281

The working life of the whole system is assumed to be 30 years. With 10%

interest on capital investment and 20% residual value, the total cost of the

370

system is 2,216 $/ha and 281 $/ha/yr as given in Table 7.7. The comparison

of capital costs of irrigation systems; sprinkler (Table 7.5) and drip (Table

7.6) systems with the capital cost of pressurized pipe supply system (Table

7.7) indicates that investment in installation of a piped irrigation supply

network is significantly less than that of the sprinkler/drip irrigation

installation on farm. However, the potential returns in terms of water

savings (seepage and evaporation savings) and operation and maintenance

costs achieved by piped supply are notably high.

7.4 Economic analysis of conversion to sprinkler or drip system

for citrus

This section deals with testing of the economic viability of the on-farm

irrigation upgrade which involves conversion from furrow irrigation to a

pressurized irrigation system, i.e. sprinkler or drip irrigation, along with

replacement of open channels with a pressurized pipe supply system, for

citrus farms.

To conduct an economic analysis, the total cost including initial investment

(capital cost), ongoing annual cost (operating costs) as well as the annual

returns/benefits must be known. The capital costs for each on-farm

irrigation system and the piped irrigation supply system are determined in

the previous section. The capital cost of the infrastructure i.e. irrigation

system and supply system is assumed to be constant for all horticultural

crops. The annual operating costs for the three irrigation systems are likely

to be different for different crops. The operating costs for the three irrigation

systems to produce citrus in MIA are given in the next sub-section. Like

capital costs, the operating cost values are taken from various sources.

Where applicable, the costs are indexed for 3% inflation to current year. The

rates of the cost items which are common to all irrigation systems are given

in Table 7.8. Appendix C and Appendix D provide details on cost of

fertilizers/chemicals and the operating cost of tractor, respectively, used in

economic analyses in this chapter.

371

Table 7.8: Values of common cost items for the three irrigation systems

Cost Item Sub-Item Units Rate Labour Manual labour $/hr 20

Crop harvest (contracted) $/t 60 Water charges Water usage charges $/ML 8.67

Irrigation facilities charges $/ML 19.84Landholding charges $/ha 3.48

Electricity charges Peak rate c/KWh 17

Off-peak rate c/KWh 10 Service charges $/ha/month 8.8

GHG emissions KWh to Kg-CO2e conversion

factor 0.90

GHG emissions tax $/t-CO2e 23 Machinery Tractor $/hr 35.20

The above mentioned rates of each item do not depend on irrigation method,

supply infrastructure or crop type. Electricity supply charges are assumed to

be included in “irrigation facilities charges”. The GHG emissions tax is

based on recently introduced legislation (Commonwealth of Australia,

2011) which imposes a tax of $23 per tonne of CO2-e emissions effective

from July 2012. The electricity consumption charges are generally low for

off-peak (usually night times) periods.

7.4.1 Operating costs for furrow irrigation with citrus

Table 7.9 lists unit cost of different operations and total annual cost per

hectare for growing citrus irrigated with furrow irrigation system. These

operations involve labour and/or materials input, individual input rates and

costs. Unit costs of these operations are given in Table 7.8. Most of the

input rates given in Table 7.9 are discussed in Chapter 4. Water usage

charges for citrus are based on an irrigation rate of 9.93 ML/ha for furrow

irrigation as given in Table 7.2. A total of 83 hours of labour are expended

in the production of a hectare of citrus using furrow irrigation.

372

Table 7.9: Annual operating costs per hectare for citrus with furrow irrigation

Operation Labour

Materials ($/ha)

Total ($/ha)

Unit (hr/ha)

Total hours

Cost ($)

Furrow out 9.6 10 192 0 192Ripping 2 2 40 0 40Irrigation 6.5 7 130 0 130Manual pruning 42 42 840 0 840Fertilizer & pesticide application

23 23 460 614 1,074

Harvesting 2,400 2,400 Tractor 1,218 1,218 Power (incl. service charge)

0 0

Irrigation supply charges

200 200

Water usage charges 86 86 GHG emissions tax 42 42

TOTAL 83.1 83 1,662 4,518 6,222

It is worth noting that no energy/electricity is required to operate the

irrigation system except for a labour input of 6.5 hr/ha. Most of this labour

time is expended in priming of the siphons. Another major labour intensive

operation is pruning of the fruit trees followed by labour required for

application of fertilizers and other chemicals. The harvesting operation is

generally completed through contracted labour with some use of machinery

and a lump sum cost of harvesting is reported here. Tractor costs include use

of a tractor in different operations around the year. The operating cost also

includes $42/ha paid as annual carbon tax for the emissions generated by

cropping operations and energy inputs. As given in Table 7.9, the total

operating cost of citrus with furrow irrigation is 6,222 $/ha.

7.4.2 Operating costs for low head sprinkler irrigation with citrus

Table 7.10 lists unit costs of different operations and the total annual cost

per hectare for growing citrus irrigated with low head sprinkler irrigation

system. These operations involve labour and/or materials input. Individual

rates and costs of each item are given in Table 7.10 using the unit cost rates

given in Table 7.8. Most of the input rates given in Table 7.10 are discussed

373

in Chapter 4. A total of 68 hours of labour are expended in one production

cycle of a hectare of citrus using sprinkler irrigation which is 15 hours less

than that of citrus production using furrow irrigation. Hence, sprinkler

irrigation saves on labour costs of approximately $300/ha as compared to

furrow irrigation.

Table 7.10: Annual operating costs per hectare for citrus with low head sprinkler irrigation Operation Labour

Materials ($/ha)

Total ($/ha)

Unit

(hr/ha) Total (hr)

Cost ($)

Irrigation 5 5 100 0 100Mulching & mowing 3 3 60 0 60Fertilizer & pesticide application

18 18 360 567 927

Manual pruning 42 42 840 0 840

Harvesting 2,640 2,640

R&M irrigation system 50 50

Tractor 739 739 Power (incl. service charge)

375 375

Irrigation supply charges

164 164

Water usage charges 70 70

GHG emissions tax 79 79 TOTAL 68 68 1,360 4,606 6,045

As given in Table 7.10 electricity consumed in operating the pumps of the

integrated irrigation system to operate the sprinkler system amounts to

$375/ha. Harvesting is $240/ha higher than that of furrow system which is

mainly due to increased citrus yield as given in Table 7.1. The water usage

charges for sprinkler system are $16/ha less than that of furrow irrigation

due to the fact that the former used less water. The operating cost also

includes $79/ha paid as annual carbon tax; around half of which is attributed

to emissions from different farm operations and energy inputs and the other

half can be attributed to greenhouse gas emissions from electricity use for

pumping irrigation water. As given in Table 7.10, the total operating cost of

citrus with sprinkler irrigation is $6,045/ha.

7.4.3 Operating costs for surface drip irrigation with citrus

374

Table 7.11 lists the unit cost of different operations and total annual cost per

hectare for growing citrus irrigated with surface drip irrigation system.

These operations involve labour and/or materials input. The input rates and

costs of individual items are given in Table 7.11 which is computed using

the unit cost rates outlined in Table 7.8. Most of the input rates given in

Table 7.11 are discussed in Chapter 4. A total of 57 hours of labour are

expended in one production cycle of a hectare of citrus using drip irrigation

which is 26 hours less than that of citrus production using furrow irrigation

and 11 hours less than that of sprinkler irrigation. Hence, drip irrigation

saves labour costs equivalent to around $520/ha as compared to furrow

irrigation. It should also be noted that around 70% less labour is required to

operate the drip irrigation system when compared against furrow irrigation.

Table 7.11: Annual operating costs per hectare for citrus with surface drip irrigation system Operation Labour

Materials ($/ha)

Total ($/ha)

Unit (hrs/ha)

Total (hr)

Cost ($)

Irrigation 2 2 40 0 40Mulching & mowing 3 3 60 0 60Fertilizer & pesticide application 10 10 200 482 682 Manual pruning 42 42 840 0 840Harvesting 2,880 2,880R&M irrigation system 15 15Tractor 458 458Power (incl. service charge) 330 330 Irrigation supply charges 128 128 Water usage charges 54 54GHG emissions tax 72 72TOTAL 57 57 1,140 4,347 5,559

Electricity is consumed in operating the pumps of the integrated irrigation

system and to operate the drip system at the required hydraulic pressure.

The cost of energy consumed in pumping operations amounts to a rate of

$330/ha as given in Table 7.11, which is about 12% less than the cost of

pumping to operate sprinkler system. Harvesting is about 9% higher than

375

that of sprinkler system which is mainly due to increased citrus yield as

given in Table 7.1. The water usage charges for drip system are $16/ha less

than that of water usage charges for sprinkler system due to the fact that the

former uses less water (6.26 ML/ha vs. 8.09 ML/ha). The operating cost

also includes $72/ha paid as annual carbon tax; around half of which is

attributed to emissions from different farm operations and energy inputs and

the other half can be attributed to greenhouse gas emissions from electricity

use for pumping irrigation water. As given in Table 7.11, the total operating

cost of citrus production with drip irrigation is $5,559/ha which is $486/ha

less than that of sprinkler system and $663/ha less than the annual operating

cost of furrow irrigation. The main cause of higher operating costs for

furrow irrigation is higher labour and tractor use costs compared to the other

two systems. For example, in the case of drip irrigation a significant

reduction in labour and tractor use is achieved by automating the system and

by application of fertilizers through fertigation.

7.4.4 Financial benefits/returns from citrus with the three

irrigation systems

Both costs and benefits are required to be quantified to conduct the

economic/financial viability analysis. The costs have a further two

components; capital costs and operating costs. The capital costs of different

irrigation systems are discussed in Section 7.3 and the operating costs are

discussed in sub-section 7.4.1 to sub-section 7.4.3. The potential benefits or

returns are discussed in the current sub-section. Selling of the output/yield is

the regular source of financial return. Based on Falivene (2003); Khan, et al.

(2005a) and others the long-term average return at farm-gate from citrus is

$225/t. Other possible sources of financial returns are water trade, i.e. from

selling of any water savings achieved in the water market. The return from

water trade can also be considered as avoided cost (cost saving) by not

having to purchase that water from the market due to reduced water demand

by adoption of water efficient irrigation systems. It is also mentioned earlier

in the chapter that the long term average water trade price is $254/ML for

376

MIA valley. The potential financial benefits/returns from citrus irrigated

with the three irrigation systems are summarized in Table 7.12. Average

values of citrus yield figures given in Table 7.1 are used in Table 7.12.

Table 7.12: Annual financial returns per unit area per for the three irrigation systems growing citrus

Irrigation System

Source QuantityReturn ($/unit)

Return ($/ha)

Furrow Yield (T/ha) 26.75 225 6018.8 Water Saving 0 0 0

Total 6018.8

Sprinkler Yield (T/ha) 30.3 225.0 6806.3 Water Saving 1.8 254.0 465.6

Total 7271.8

Drip Yield (T/ha) 34.5 225.0 7762.5 Water Saving 3.7 254.0 931.2

Total 8693.7

7.4.5 Discounted payback period and financial viability of the

three irrigation systems for citrus

The irrigation systems namely furrow, sprinkler, and drip system; to be

analysed in this chapter have a working life of at least 30 years and impose

high initial investment costs, especially the sprinkler and drip systems.

Therefore the financial analyses of these systems are conducted based on the

concept of time value of money which essentially implies that for any given

amount, the value of future money is less than its present value. Therefore,

the future potential returns from the system are converted to their present

value (PV) using a discount rate of 10 per cent. The Net Present Value

(NPV) is defined as the difference between present value of cash

inflows/income/benefits (PVB) and the present value of cash outflows/costs,

including initial capital costs (PVC). The NPV approach is applied over the

life of the irrigation system(s) to determine the discounted payback period

(break even period on capital investment) and the overall value/profitability

of the system under consideration. The discounted payback period is defined

as the number of years by which the PVB equals the initial capital

investment.

377

The formula for NPV is given in Equation 7.1.

∑ Equation 7.1

Where,

n, is the total number of years of useful life of the system; Rt, is the net

return for a given year ‘t’, and r, is the discount/interest rate.

In the NPV calculation for the current study, the annual costs and annual

returns are assumed to be constant over an analysis period of 30 years for a

given irrigation system with a given horticultural crop. The NPV during

initial years is likely to be a negative value due to high initial costs but

improves each successive year. The number of years by which the NPV

becomes zero corresponds to the discounted pay-back period.

Another profitability indicator called benefit cost ratio (B-C ratio) is also

computed by dividing present value of benefits (PVB) by present value of

costs (PVC). A B-C ratio of greater than 1 indicates a profitable project.

A summary of initial investments, annual operating costs and annual returns

for the three irrigation systems with citrus is given in Table 7.13. It includes

figures on a per hectare basis as well as total for the case study area of 550

ha. The returns from water savings of 22 ML/year from channel seepage and

evaporation loss by piped supply system are also included in Table 7.13.

Table 7.13: Summary of initial and annual costs and annual returns for the three irrigation systems for citrus Furrow Item $/ha Total for 550 haInitial capital cost

Irrigation Supply System 0 0Irrigation System 2,200 1,210,000

Total initial cost 1,210,000Operating Costs Annual operating cost 6,222 3,421,975Total returns

Crop production 6,018.75 3,310,313

Water Trade 0 0 Total returns 3,310,313

Sprinkler Initial capital

378

cost

Irrigation Supply System 2,216 1,218,713Irrigation System 8,100 4,455,000


Crop production 6,806.25 3,743,438Water Trade 465.6 256,070Water saving by pipe supply 5,588

Total returns 4,005,096Drip Initial capital cost



Crop production 7,762.5 4,269,375Water Trade 931.2 512,140Water saving by pipe supply 5,588

Total returns 4,787,103

For furrow irrigation the return is sourced from yield only. The NPVs for

the furrow irrigation system with citrus over the case study area of 550 ha

for a yield rates of 26.75 t/ha and 28 t/ha are given in Figure 7.5. As shown

in Figure 7.5, the NPV is negative for the original yield rate of 26.75 t/ha,

indicating that crop production using furrow irrigation at this yield rate will

not be profitable over next 30 years. However, at a yield rate of 28 t/ha, the

NPV is $362,171 over 30 years with a breakeven occurring by the end of

year 13. It corresponds to a net present value of $12,072/year or a very

modest profit with present value of $22/ha/year. The PVC is $5.9525x106

while the PVB is computed to be $6.1699x106 resulting in a B-C ratio of

1.04 which indicates a marginally profitable enterprise.

379

Figure 7.5: Net present value plots of furrow irrigation with citrus over a period of 30 years

In the economic analysis of pressurized irrigation systems (sprinkler and

drip) given below, an additional capital cost as compared to the furrow

system is the investment of $1,218,713 made upfront to setup the integrated

piped supply system.

Considering furrow irrigation as a baseline case, the returns from sprinkler

irrigation system includes yield as well as market price of the water savings

as compared to the furrow irrigation. Figure 7.6 shows the NPV values of

sprinkler system which is connected with central integrated irrigation supply

system for irrigating citrus. The two plots correspond to different market

prices of water saved by conversion to sprinkler system from furrows.

‐1400000

‐1200000

‐1000000

‐800000

‐600000

‐400000

‐200000

0

200000

400000

600000

0 5 10 15 20 25 30

NPV ($)

Year

Net present value (@26.75 t/ha) Net present value (@29 t/ha)

380

Figure 7.6: Net present value plots of sprinkler irrigation with citrus connected with an integrated supply system over a period of 30 years

Figure 7.6 indicates that the sprinkler system has a NPV of $741,545 over

30 years with an average sale price of $254/ML for saved water and $225$/t

for citrus yield. This corresponds to NPV of $24,718/year or a NPV of profit

of $44.9/ha/year. The present value of profit is increased by 1.9 times as

compared to the furrow irrigated citrus. The NPV of sprinkler system is

increased to $1,227,102 which is an increase of 66% if the average trade

price of saved water is increased by $50/ML. However, the B-C ratio is

merely improved from 1.02 to 1.03 with $50/ML increase in trade price.

The payback period for sprinkler system and integrated supply system

combined is 18 years, reducing to 11 years if costs of an integrated supply

system are excluded. The payback period is reduced by 3 years if the market

price of water is increased by $50 for the 30 years.

‐7000

‐6000

‐5000

‐4000

‐3000

‐2000

‐1000

0

1000

2000

0 5 10 15 20 25 30

NPV ($'000)

Year

Net present value (@254 $/ML) Net present value (@304 $/ML)

381

Figure 7.7: Net present value plots of drip irrigation with citrus connected with an integrated supply system over a period of 30 years

Figure 7.7 shows the NPV values of the drip system which is connected

with a central integrated irrigation supply system for irrigating 550 ha area

of citrus. The two plots correspond to different market prices of the water

saved by conversion from furrows to the drip irrigation system. It indicates

that the drip system pays off its capital cost in 3 years as compared to 18

years for the sprinkler system. Unlike the sprinkler system, there is no

reduction in the payback period if market price of water traded is increased

by $50/ML.

For the drip system, the present value of annual costs (PVC) is $3.395x107

and the present value of total annual benefits (PVB) is $4.513x107 which

results in a B-C ratio of 1.33, an increase of 30% and 32% in B-C ratio as

compared to the sprinkler system and furrow system, respectively. From the

number given in Table 7.13, it is evident that total annual costs including

capital costs for drip is only 9% less than sprinkler system while the total

annual return is 20% higher for drip system as compared to the sprinkler

system.

The financial analyses given above indicate that a citrus crop irrigated with

a drip system which is connected with an integrated supply system is most

‐6000

‐4000

‐2000

0

2000

4000

6000

8000

10000

12000

14000

0 5 10 15 20 25 30

NPV ($'000)

Year

Net present value (@254 $/ML) Net present value (@304 $/ML)

382

financially viable as compared to citrus production with sprinkler and

furrow irrigation. The effects of variation in costs or benefits on overall

viability of the system are explored in detail later in the sensitivity analysis

section.

7.5 Economic analysis of conversion to sprinkler or drip system

for wine grapes

This section is a repeat of the data and analyses carried out in Section 7.4 for

growing wine grapes for the same irrigation systems which are furrow,

sprinkler and drip. The furrow system is supplied water via an open channel

and is taken as a baseline case. The sprinkler and drip systems at each farm

are connected with a central pumping station through a large supply pipe.

The data used for the economic analysis is taken from WGGA (2008);

Retallack, et. al., (2008); Khan et. al., (2003a) and Giddings (2004).

The capital costs of establishing the three irrigation systems and the piped

supply system are already given in Table 7.4, Table 7.5, Table 7.6 and Table

7.7. The annual operating costs of the three irrigation systems are briefly

discussed in the following subsections. The unit rates of the operating cost

items which are common to all irrigation systems are also given in Table

7.8. Such items include per hour cost of tractor use, water charges and

electricity charges etc. The operating cost also includes $42/ha paid as

annual carbon tax for the emissions generated by cropping operations and

energy inputs and a labour cost of $20/hour.

7.5.1 Operating costs for furrow irrigation with wine grapes

Table 7.14 lists unit cost of different operations and total annual cost per

hectare for growing wine grapes irrigated with a furrow irrigation system.

Water usage charges for wine grapes are based on irrigation rate of 7.43

ML/ha for furrow irrigation as given in Table 7.2. A total of 88 hours of

labour are expended in the production of a hectare of wine grapes using

furrow irrigation. The cost of labour is 50% of the total annual operating

cost as given in Table 7.14. Similarly, the total annual operating cost for

383

wine grapes irrigated with furrow irrigation is $3,522/ha. It is also notable

that the fertilizer input cost for wine grapes is almost 40% lower than that of

citrus with furrow irrigation. One of the reasons is that there is higher

irrigation application rate for citrus with furrow irrigation than that of wine

grapes which result in higher leaching of applied fertilizer.

Table 7.14: Annual operating costs per hectare for wine grapes with furrow irrigation

Operation Labour

Materials ($/ha)

Total ($/ha)

Unit (hr/ha)

Total hours

Cost ($)

Furrow out 9.6 10 192 0 192Ripping 2 2 40 0 40Irrigation 6 6 120 0 120Manual pruning 50 50 1,000 0 1,000Fertilizer & Pesticide application 14.5 15 290 380 670 Harvesting 6 6 120 0 120Tractor 1,130 1,130Power (incl. Service charge) 0 0 Irrigation supply charges 151 151Water Charges 64 64GHG Emissions tax 35 35

TOTAL 88.1 88 1,762 1,725 3,522

7.5.2 Operating costs for sprinkler irrigation with wine grapes

Table 7.15 lists unit costs of different operations and total annual costs per

hectare for growing wine grapes irrigated with low-head sprinkler irrigation

system. Water usage charges for wine grapes are based on irrigation rate of

6.04 ML/ha for sprinkler irrigation as given in Table 7.2. A total of 76 hours

of labour are expended in the production of a hectare of wine grapes using

sprinkler irrigation as compared to 88 hours for the furrow irrigation. As

given in Table 7.15, the total annual operating cost for wine grapes irrigated

with sprinkler irrigation is $3,419/ha which is around 3% less than that of

furrow irrigated wine grapes.

384

Table 7.15: Annual operating costs per hectare for wine grapes with low-head sprinkler irrigation system

Operation Labour

Materials ($/ha)

Total ($/ha)

Unit (hr/ha)

Total hours

Cost ($)

Irrigation 5 5 100 0 100Mulching & mowing 5 5 100 0 100Fertilizer & Pesticide application 10 10 200 567 767 Manual pruning 50 50 1,000 0 1,000 Harvesting 6 6 120 0 120R&M irrigation system 50 50Tractor 739 739Power (incl. Service charge) 307 307 Irrigation supply charges 123 123Water 52 52GHG Emissions tax 61 61TOTAL 76 76 1,520 1,839 3,419

7.5.3 Operating costs for drip irrigation with wine grapes

Table 7.16 lists unit costs of different operations/inputs and the total annual

cost per hectare for growing wine grapes irrigated with surface drip

irrigation system. Water usage charges for wine grapes are based on

irrigation rate of 4.77 ML/ha for drip irrigation as given in Table 7.2. A total

of 74 hours of labour are expended in the production and harvesting of one

hectare of wine grapes using sprinkler irrigation as compared to 88 hours

and 76 hours for furrow and sprinkler irrigation, respectively. As given in

Table 7.16, the total annual operating cost for wine grapes irrigated with

drip irrigation is $3,161/ha which is around 10.25% less than that of furrow

irrigated wine grapes and 7.5% lesser than that of wine grapes irrigated with

sprinkler irrigation. The water usage by one hectare of wine grapes using

drip irrigation is around 36% and 21% less than that of furrow and sprinkler

irrigation, respectively.

385

Table 7.16: Annual operating costs per hectare for wine grapes with surface drip irrigation system

Operation Labour

Materials ($/ha)

Total ($/ha)

Unit (hr/ha)

Total hours

Cost ($)

Irrigation 3 3 60 0 60Mulching & mowing 4 4 80 0 80Fertilizer & Pesticide application 10 10 200 482 682 Manual pruning 50 50 1,000 0 1,000Harvesting 6.5 7 130 0 130R&M irrigation system 15 15Tractor 722 722Power (incl. Service charge) 277 277 Irrigation supply charges 98 98Water 41 41GHG Emissions tax 56 56TOTAL 73.5 74 1,470 1,635 3,161

7.5.4 Financial benefits/returns from wine grapes irrigated with

the three irrigation systems

The aim of this analysis is to determine if the irrigation upgrade is

worthwhile for wine grapes. The financial analyses of growing wine grapes

with each of the three irrigation systems are conducted here on an annual

basis. Two items, the costs and benefits, are required to be quantified to

conduct the economic/financial viability analysis. The costs have further

two components; capital costs and operating costs. The capital costs of

different irrigation systems are discussed in Section 7.3 and the operating

costs are discussed in sub-section 7.5.1 to sub-section 7.5.3. The potential

benefits or returns are discussed in the current sub-section. Selling of the

output/yield is the major and regular source of financial return. Based on

WGGA (2008); Retallack, et. al., (2008); Khan, et al. (2005a) and others the

long-term average return at farm-gate from wine grapes is $400/t which is

around 44% higher return than citrus. Another possible source of financial

return is water trade, i.e. from selling of any water savings achieved by

irrigation upgrade in the water market.

386

The return from water trade can also be considered as avoided cost (cost

saving) by not having to purchase that water from the market due to reduced

water demand by adoption of water efficient irrigation system. It is also

mentioned earlier in the chapter that the long term average water trade price

is $254/ML for MIA valley.

The potential financial benefits/returns from wine grapes irrigated with each

of the three irrigation systems are summarized in Table 7.17. Average

values of wine grape yield figures given in Table 7.1 are used in Table 7.17.

The total return per hectare of wine grapes ranges from $8,800 for furrow to

$11,076 for drip irrigation. The higher return for drip is due to higher yield

than furrow irrigation and income from sale of saved water.

Table 7.17: Annual financial returns per unit area for the three irrigation systems growing wine grapes

Irrigation System

Source QuantityReturn ($/unit)

Return ($/ha)

Furrow Yield (T/ha) 22 400 8,800

Water Saving (ML/ha) 0 254 0 Total 8,800

Sprinkler Yield (T/ha) 23 400 9,200

Water Saving (ML/ha) 1.4 254.0 353 Total 9,553

Drip Yield (T/ha) 26 400 10,400

Water Saving (ML/ha) 2.7 254.0 676 Total 11,076

7.5.5 Discounted payback period and financial viability of the

three irrigation systems for growing wine grapes

The concepts of time value of money and the definitions of NPV, PVB,

PVC and B-C ratio have been discussed in Subsection 7.4.6. The same

analyses are conducted here for the three irrigation systems when growing

wine grapes over a period of 30 years.

A summary of initial investments, annual operating costs and annual returns

for the three irrigation systems with wine grapes is given in Table 7.18. It

includes figures on a per hectare basis as well as total for the case study area

of 550 ha. The returns from water savings of 22 ML/year from channel

387

seepage and evaporation loss by piped supply system are also included in

Table 7.18.

Table 7.18: Summary of initial and annual costs and annual returns for the three irrigation systems for wine grapes Furrow Item $/ha Total (550 ha)

Initial capital cost

Irrigation Supply System 0 0Irrigation System 277 152,166

Total initial cost 152,166 Operating Costs Annual operating cost 3,522 1,937,209

Total returns

Crop production 8,800 4,840,000

Water Trade 0 0 Total returns 4,840,000

Sprinkler Initial capital cost


Total initial cost 5,673,713 Operating Costs Annual operating cost 3,419 1,880,634

Total returns

Crop production 9,200 5,060,000Water Trade 353 194,183

Water saving by pipe supply 5,588 Total returns 5,259,771 Drip Initial capital cost


Total initial cost 5,123,713 Operating Costs Annual operating cost 3,161 1,738,586 Total returns

Crop production 10,400 5,720,000Water Trade 676 371,602

Water saving by pipe supply 5,588 Total returns 6,097,190

388

Figure 7.8 shows the plots of NPV values of the drip, sprinkler and furrow

irrigation systems over a period of 30 years. The drip and sprinkler systems

are connected with central integrated irrigation supply system for irrigating

550 ha of wine grapes. The capital cost of installing the integrated irrigation

supply system is also taken into account in the analyses. Figure 7.8 indicates

that NPV for both the drip system and sprinkler system turns positive at the

end of the second year of operation indicating that the system is paid off in

just two years as compared to the same irrigation system for citrus which

took up to 18 years to return a positive NPV. Unlike sprinkler and drip

systems the furrow irrigation with wine grapes returns a positive NPV at the

end of very first year of its operation. However, the furrow with citrus is

found not to be so profitable. Moreover, as shown in Figure 7.8, the NPV of

returns from drip exceeds that of furrow in first five years.

Figure 7.8: Net present value plots of drip, sprinkler and furrow irrigation with wine grapes connected (excluding furrow) with integrated supply system over a period of 30 years

‐10000

‐5000

0

5000

10000

15000

20000

25000

30000

35000

40000

0 5 10 15 20 25 30

NPV ($'000)

Year

Total NPV‐Drip ('000) Total NPV‐Sprinkler ('000) Total NPV‐Furrow ('000)

389

Table 7.19: Profitability indicators for the three irrigation systems irrigating wine grapes over the case study area of 550 ha

Irrigation System Indicator Value

Furrow PVB 8,321,202 PVC 3,339,279

B-C Ratio 2.49

Sprinkler PVB 9,042,896 PVC 3,558,445

B-C Ratio 2.54

Drip PVB 10,482,634 PVC 3,282,709

B-C Ratio 3.19 Values of the computed profitability indicators for the three irrigation

systems with wine grapes over the working life of 30 years are given in

Table 7.19. It indicates that all three irrigation systems with production of

wine grapes are highly profitable. The drip irrigation with wine grapes is the

most profitable among the three irrigation systems. When compared with

citrus irrigation, the wine grapes crop brings relatively higher returns for the

three irrigation systems.

From the analyses given in Section 7.4 and Section 7.5, it is evident that

conversion from furrow to drip system is the most worthwhile option for

both citrus and wine grapes; especially for the latter the B-C ratio being

higher than 3 indicates that the risk of financial loss is very low.

7.6 Sensitivity analysis

The economic analyses given in the above sections are based on the

assumption that the average value of operating costs (e.g. fertilizers,

electricity, labour etc.) and the average value of financial returns (e.g. sale

price of production, market price of water etc.) remains constant over the

entire analysis period of 30 years. This assumption may not remain valid if

there is a long-term shift in costs or returns. For example, there has been a

significant reduction in wine prices in Australia due to oversupply in the

market during the last 4 to 5 years. Such factors influence the long-term

financial viability of the system under consideration. To take into account

390

the effect of variation in the key variables of the financial analysis, the

sensitivity of the outcome is tested against those key variables.

In the context of this chapter, the sensitivity of financial viability of the

conversion of the irrigation system of the two crops is carried out here.

Table 7.20 lists changes in various costs and return items and their

corresponding effect on PVB, PVC and B-C ratio. It indicates that

profitability of all three irrigation systems are highly sensitive to labour

costs; where furrow irrigation is at the top of the sensitivity ladder due to

higher dependency on labour as compared to the more mechanized farming

using sprinkler or drip irrigation. The level of increase in costs for an

increase of 3 c/KWh (peak) to 5 c/KWh (off-peak) in electricity price and

doubling of the price of GHG emissions to 46 $/t CO2e is almost the same

among the three irrigation systems except that no electricity cost incurred

for furrow irrigation. On the benefits side, drip irrigation is more sensitive to

change in price of water trade and to change in the sale price of citrus than

those for sprinkler irrigated citrus. The movement in B-C ratio as a result of

change in sale price of citrus is highest among all variables.

Table 7.20: Sensitivity of present values of benefits, costs and B-C ratio to different cost and revenue items for citrus crop (–ve sign shows decrease with respect to original value)

Item Change in value

New value

Indicator Furrow Sprinkler Drip

Labour ($/hr)

5 25

PVB 0 0 0 PVC 392,893 321,501 269,493

Change in B-C Ratio

-0.07 -0.06 -0.06

Electricity (c/kWh)

Peak: 3 c/kWh,

Off-peak: 5 c/kWh

20 and 15

PVB 0 0 0

PVC 0 75,493 62,926 Change in B-C Ratio 0 -0.01 -0.02

GHG emission

price ($/T-CO2e)

23 46

PVB 0 0 0

PVC 39,604 74,713 68,188 Change in B-C Ratio

-0.02 -0.01 -0.01

391

Water usage

charges ($/ML)

3.33 12

PVB 0 0 0

PVC 31,255 25,483 19,712 Change in B-C Ratio

-0.005 -0.005 -0.005

Water trade price

($/ML) -54 200

PVB 0 -95,639 -189,235

PVC 0 0 0 B-C Ratio 0 -0.016 -0.034

Water trade price

($/ML) 46 300

PVB 0 81,470 161,201

PVC 0 0 0 Change in B-C Ratio

0 0.013 0.029

Citrus sale price ($/T)

-50 175

PVB -

1,323,828 -1,430,207 -1,631,145


-0.225 -0.237 -0.294

Citrus sale price ($/T)

50 275

PVB 1,323,82

8 1,430,207 1,631,145


0.225 0.237 0.294

Table 7.21 lists assumed changes in selected costs and return items and their

corresponding effect on PVB, PVC and B-C ratio. The response of the B-C

ratio to a $5 increase in labour cost is relatively higher for wine grapes than

citrus. This is due to the fact that labour cost is a major cost component for

wine grapes, mainly due to manual pruning and training of vines. The

marginal response to variation in other variables is similar to that of citrus

for the three irrigation systems.

Table 7.21: Sensitivity of present values of benefits, costs and B-C ratio to different cost and revenue items for wine grapes crop (–ve sign shows decrease with respect to original value)

Item Chang

e in value

New value

Indicator

Furrow Sprinkle

r Drip

Labour 5 25 PVB 0 0 0

392

($/hr) PVC 416,533 359,325 347,505

B-C Ratio

-0.276 -0.233 -0.306

Electricity (c/kWh)

Peak: 3 c/kWh,

Off-peak: 5 c/kWh

20 and 15

PVB 0 0 0

PVC 0 56,333 47,898

B-C Ratio

0 -0.01 -0.046

GHG emission

price ($/T-

CO2e)

23 46

PVB 0 0 0

PVC 32,971 57,295 53,365

B-C Ratio

-0.024 -0.04 -0.051

Water usage

charges ($/ML)

3.33 12

PVB 0 0 0

PVC 23,396 19,019 15,020

B-C Ratio

-0.017 -0.014 -0.015

Water trade price

($/ML) -54 200

PVB 0 -73,019 -137,867

PVC 0 0 0

B-C Ratio

0 -0.021 -0.042

Water trade price

($/ML) 46 300

PVB 0 62,201 117,442

PVC 0 0 0

B-C Ratio

0 0.017 0.036

Wine grapes

sale price ($/T)

-50 350

PVB -

1,040,150

-1,087,43

0

-1,229,268

PVC 0 0 0

B-C Ratio

-0.311 -0.306 -0.374

Wine grapes

sale price ($/T)

50 450

PVB 1,040,15

0 1,087,43

0 1,229,268

PVC 0 0 0

B-C Ratio

0.311 0.306 0.374

393

7.7 Chapter summary

In this chapter we analysed costs and benefits of individual irrigation

systems, item by item for the two crops in greater detail. Before doing a

proper financial analysis of the irrigation systems in question, we looked at

different factors that support the need for adoption of hi-tech irrigation

systems. These factors include planned cuts in irrigation water entitlements,

increasing pressure on farmers to realize irrigation savings, increasing price

of water in the water trade market and potential improvement in crop yield

by controlled irrigation. For example, in MIA, the case study area of this

research, the price of water temporarily traded in the open market exceeded

$1,100 during the drought of 2007-08 (Watermove, 2011). Data given in

Table 7.1 indicates that more than 25% improvement in citrus yield can be

obtained by converting from furrow to drip irrigation along with water

savings of more than 3.6 ML/ha.

To model the water and energy use and savings, the previously developed

node-link model was extended over a case study area of 550 ha. The model

computed water savings and energy consumption in irrigation pumping

while taking into account the operation of an integrated pump supply system

under a demand-based irrigation strategy. The node-link model results for

the three irrigation systems for each of the two crops are given in Table 7.2.

A 30 year working life of each irrigation system was assumed. To conduct

the financial viability analyses of the irrigation systems under consideration,

profitability indicators like present value of costs, present value of benefits,

benefit-cost ratio and payback period using net benefit approach were used.

All future costs and returns were discounted at the assumed interest rate of

10%. For these indicators, the inputs including capital investment, annual

operating costs, and annual benefits were prepared for each irrigation

system (including integrated piped irrigation supply system) for the two

crops. New cost items like tax on GHG emissions are also factored in. A

summary of key items related to the financial analyses conducted in this

chapter is given in Table 7.22.

394

Table 7.22: Summary of selected profitability indicators for the three irrigation systems

Indicator Crop Furrow Sprinkler Drip

Capital cost ($/yr/ha) 277 1,416 1,068

Operating cost ($/yr/ha) Citrus 6,222 6,045 5,559

Wine grapes 3,522 3,419 3,161

B-C ratio Citrus 1.01 1.14 1.48

Wine grapes 2.49 2.54 3.19

*Net payback period (yr) Citrus 30+ 18 3

Wine grapes 0 2 2

*Payback period also includes cost recovery of the integrated irrigation supply system

Conversion costs from furrow to sprinkler or drip irrigation systems is

$8,100/ha and $7,100/ha (Table 7.13), respectively. However, the

profitability indicators given in Table 7.22 indicate that conversion from

furrow to drip irrigation is likely to be more profitable and viable in the

long-term than that of conversion to sprinkler system. An important

assumption worth mentioning here is that average values of cost items and

those of returns are assumed to be unchanged over the analysis period of 30

years. To test the sensitivity of profitability indicators to assumed variation

in certain input variables, sensitivity analyses were carried out. The

sensitivity analyses indicate that profitability is highly sensitive to labour

cost, water trade price and crop revenues. It also indicates that due to high

profit margins the risk of unprofitability of drip irrigation is very low as

compared to the other two irrigation systems over the long-term.

As mentioned earlier, conversion to sprinkler irrigation for citrus is not very

economical due to a long payback period and non-attractive B-C ratios.

However, any government subsidies paid for conversion to sprinkler system

could make this an increasingly viable option. Also with the introduction of

the environment as a third user/competitor the future water trade prices may

395

be be higher than in the past which will make the conversion to water saving

irrigation technologies even more financially viable.

In the financial analyses separate annual operating costs were estimated for

each crop and each irrigation system due to varying levels of energy use and

other inputs. Also in this chapter the scenario of installing individual

irrigation pumping stations on each farm to operate their pressurized

irrigation systems is not analysed. The cost of individual pumping stations

could be even higher due to higher initial capital investments (e.g.

individual power supply poles and transformers) and higher operational

costs (e.g. higher electricity charges given as individual customer rates,

higher maintenance costs) which need to be investigated.

397

Chapter 8: Integrated Analysis, Discussion and Policy

Implications

Previous chapters discussed modelling and analysis of irrigation water,

energy and greenhouse gas emission linkages for two irrigation strategies,

namely demand-based and supply-based irrigation for three irrigation

methods including furrow, sprinkler and drip irrigation for the major

horticultural crops in the Murrumbidgee Irrigation Area (MIA). This study

has looked into benefits and energy implications of using a centralized piped

supply system to pump pressurized water from source to individual farms to

operate pressurized irrigation i.e. sprinkler and drip irrigation. The study

also analysed the water and energy use with regard to private on-farm

irrigation storages on individual farms and compared this option against the

centralized piped supply system. In the preceding chapter, a detailed

economic and financial analysis of conversion from furrow to pressurized

irrigation was conducted for selected options based on physical quantities

relating to water and energy use in irrigation of horticultural crops which

were determined through modelling.

This chapter is focused on bringing together the key learning from

interpretation of modelling results, sensitivity analyses and the economic

analyses by applying a system dynamics framework. It looks into

identifying inter-dependent variables and understanding dynamics of the

processes and exogenous factors that control those variables. The major

findings of this study are also summarized in this chapter.

8.1 Understanding and representing the dynamics of the system

To define a system we need to define its physical and conceptual

boundaries. For the purpose of this research the system under consideration

consists of the large irrigation area of MIA with particular focus on

horticulture, its crop growing and harvesting practices and irrigation

methods. The MIA is not a closed or isolated system because it is connected

to a bigger system. It responds to the quantity and seasonal availability of

398

the upstream water sources and has potential to impact/control downstream

users including river environment and consumptive users. The behavior of

the system is characterized by various practices (irrigation, pruning,

harvesting), processes (evapotranspiration, fruiting) and inputs and outputs

(mainly in the form of water and energy). All these characteristics of the

system have been in one way or the other considered and discussed in

previous chapters.

The aim of this chapter is to integrate and inter-relate what is found in

previous chapters to understand the overall dynamics of the system in a

holistic manner. In the following sub-sections we try to explore those

dynamic relationships and underlying feedback loops among the inter-

dependent variables. Vensim is a powerful tool to analyse the

interdependence and dynamics of the modelled variables. As an example of

integrated analysis, Appendix B shows a graphic view of the developed

Vensim model in “dynamic simulation” mode.

8.1.1 Water availability versus water saving feedback loop

Irrigation water availability can be affected by multiple factors including

system constraints, climate change, climate shift and/or changes in policy

settings such as changes to the limits on diversions from river system for

irrigation purposes. For the Murrumbidgee case study area irrigation

diversions may reduce by 320 GL if the new basin plan is implemented

(MDBA, 2010; MDBA 2012). Refer to the Figure 8.1 to understand this

feedback mechanism. It is demonstrated in water trade data discussed in

previous chapters that water trade price peaked during the driest years. It

exceeded the price mark of $1100/ML which was offered by downstream

water users in 2007-08 in the water trade market during the last drought

period in MDB. Hence the price of additional water purchased is determined

by the water trade markets driven by water availability. Hence a negative

causal relationship exists between water availability and market price of

water; i.e. the lower the availability of water, the higher the market price.

Furthermore, high water trade price leads to decisions of more investment in

399

water saving irrigation technologies in order to save irrigation water to sell

in the market or to save costs that would otherwise have been incurred to

meet irrigation demand; a positive causal feedback as represented

diagrammatically by a positive arrow in Figure 8.1.

A second positive causal feedback exists between adoption of irrigation

technology and the water savings achieved. The amount of water savings

achieved depends on the level of investment and the savings achieved per

dollar invested. In other words, highly efficient irrigation system may

require higher capital investment. For example, as mentioned in Section

7.3.2, the capital investment for drip irrigation is estimated as $7,100/ha as

compared to just $2,200/ha for less efficient furrow irrigation.

Figure 8.1: Water availability, investment and water savings negative feedback loop

Higher water savings, initiated by low water availability trigger investment

in water saving irrigation technologies, ultimately increasing water

availability. Hence a negative feedback loop exists between water

availability and the water savings as represented by the negative sign in red

colour inside the arrow representing the feedback loop.

8.1.2 Water savings versus energy use feedback loop

Exploring dynamic links between achieved water savings and increased

energy requirement is one of the major objectives of this thesis and is

discussed in detail in different parts of this thesis including Sections 4.8,

Water_Availability Water_Trade_Price

Investment_on_WaterSaving_Irrigation

-

+

Irrigation_WaterSavings

+

+ Water_savingsper_$_invested

Climate_change

Climate_shift

Change_inpolicy_settings

400

4.9, 5.5, 5.6, 5.7, 6.1, 6.3, 6.4, 6.5, 7.2 and 7.6 for various irrigation systems.

Model results summarized in Table 7.2 of Section 7.2 show that up to 3.7

ML/ha can be saved by drip irrigation when compared with furrow

irrigation for citrus. However, at the same time up to 1664 KWh/ha energy

is consumed just in pumping and pressurizing water for drip irrigation as

compared to zero energy requirements for furrow irrigation. Energy

consumption also results in greenhouse gas (GHG) emissions. For drip

system the GHG emissions only from additional energy consumption in

irrigation water pumping are estimated to be 1.5 CO2e t/ha for citrus which

requires payment of GHG emissions tax. Hence, as shown in Figure 8.2, the

higher the water savings, the greater the energy consumption which in turn

causes more emissions tax liability; hence capping and reducing the net

financial return from water savings. This completes a negative feedback

loop as shown in Figure 8.2.

Figure 8.2: Feedback loop between water savings and energy use

8.1.3 Water savings versus environmental benefits feedback

loop

Water savings achieved by adoption of more efficient irrigation

technologies decreases the need for drainage of saline water from irrigation

areas and increases the amount of fresh water available to the environment.


+

PumpingEnergy_Use

GHGEmissions_Tax

Net_Returnfrom_Saved

Water

+

+

-

- Energy_use_perML_savings

Emissions_perKWh_energy_use

401

Water buyback programs by the government sector also encourage more

water savings and return saved water to the environment. This will result in

more water in riparian systems and hence provide benefit to the ecosystem

as well as offset impacts of GHG emissions from pumping energy use. The

financial incentives from the government (e.g. subsidies on water saving

infrastructure) to make water available for the environment as well as the

long-term intrinsic benefits (e.g. avoidance of climate change etc.) from the

improved environment also encourage the adoption of water saving

irrigation technology. In other words, high water savings result in more

water available to environment. This in turn brings more environmental

benefits and can serve to increase investment/incentives from the

government which ultimately encourages more water savings. Hence, as

shown in Figure 8.3, a positive causal loop exists between water savings and

the environmental benefits.

Figure 8.3: Positive feedback loop between water savings and environmental benefits

8.1.4 Analysis of the feedback dynamics of the integrated

system

Figure 8.4 provides a holistic view of the overall system under consideration

and integration of feedback mechanisms which are discussed above. It is a

representation of how the system components namely water, energy, returns

and the environment are integrated and how they interact interdependently.


-

Water_forEnvironment

EnvironmentalBenefits

Long-termbenefits

++

+ +

Climate_changemitigation

Governmentsubsidies

Water_buyback_bygovernment

402

Figure 8.4: Representation of the integrated system and the constituent causal feedback loops

The variables shown in italics in Figure 8.4 represent the external factors

which impact a subset of or the entire system. It shows that water savings

are driven by water availability. However, the negative feedback loop

indicates that water savings are not always driven in one direction by water

availability. There can be a point when marginal increase in achieved water

savings becomes higher than the marginal decrease in water availability.

The second negative feedback loop between water savings and the energy

use indicates that the energy costs and associated GHG emissions cost also

limit water savings.

The third feedback loop is likely to be the driving force behind the adoption

of water saving irrigation technologies. There is a positive feedback

between water savings and the environmental benefits. The new knowledge

on importance of improving the environment by returning its share of the

water resource for long-term sustainability of the whole system is the main

spur for the need for defining sustainable diversions limits for consumptive

uses in the Murray-Darling Basin. Since there is no hard limit on share of

water for the environment, the need for water savings for the environment

will always be going in the positive direction. Hence, the positive feedback

Water_Availability Water_Trade_Price

Investment_on_WaterSaving_Irrigation

-

+


+

+ Water_savingsper_$_invested

Climate_change

Climate_shift

Change_inpolicy_settings

PumpingEnergy_Use

GHGEmissions_Tax

Net_Returnfrom_Saved

Water

+

+

-

- Energy_use_perML_savings

Emissions_perKWh_energy_use

Water_forEnvironment

EnvironmentalBenefits

Long-termbenefits

++

+ +

Climate_changemitigation

Governmentsubsidies

Water_buyback_bygovernment

403

causal loop between water savings and environmental returns. Due to the

location of MIA in the upper part of the basin, the water savings can

potentially serve a dual role; i.e. generating economic return from trading

saved water to downstream users as well as the associated environmental

benefits (lower salinity, benefits to flora and fauna) from increased flows in

the river.

8.2 Discussion on main findings and policy implications

The main conclusions derived from this study are directly applicable to the

MIA case study area with reference to horticulture crops; however, the

developed modelling approach is generic and therefore can also be applied

in other irrigated areas.

As mentioned earlier, this study is focused around the idea of irrigation

conversion from furrow to pressurized irrigation methods namely sprinkler

and drip irrigation for large irrigation areas. Irrigation demand and supply,

water savings, water trade price, gross margins, and energy use (particularly

energy use in irrigation pumping), are the typical variables modelled in this

study for horticultural crops.

The main findings of this research work and envisaged policy implications

are given below.

8.2.1 Modelling of water and energy for irrigation systems

In this research a node-link model is developed which computes irrigation

demand, irrigation supply, soil water balance, water stress affected crop

yield, conveyance losses (if applicable) and energy consumed in irrigation

pumping (if applicable) on a daily time step. One model run covers one

complete yearly cycle of crop production. The node-link model is developed

using Vensim software from scratch and is unique in its ability to simulate

both water use and pumping energy consumption at the same time on a daily

time step. The model is also configured for simulating any of the irrigation

systems including flood, furrow, sprinkler or drip irrigation. The model is

also capable of simulating either open channel supply system or pressurized

404

pipe irrigation supply system. It accounts for conveyance losses in open

channel and head losses in pipe system. It is developed as a generic tool and

can be applied to any irrigation area if data is available.

8.2.2 Water and energy nexus for irrigation strategy

In this research two irrigation strategies are explored, namely, demand-

based and supply based irrigation. Demand-based irrigation strategy

requires constant availability of water which is pumped to irrigate a crop

when needed. On the other hand supply-based irrigation is driven by water

availability and usually involves fixed-interval irrigation. Water can be

supplied through open channels for supply based irrigation. It is found that

demand-based irrigation consumes higher energy but at the same time

produces higher yields due to stress free plant water availability as

compared to supply based irrigation. For citrus under supply-based

irrigation the water use per hectare is as low as 46% of that of demand-

based irrigation but at the same time the yield is found to be as low as 66%

of that of drip irrigation method. Similar trends prevail for wine grapes

production.

Although it is evident that demand-based irrigation produces more yields, at

the same time the cost of energy and its environmental impacts should not

be ignored. Demand-based irrigation involves less labour and relies more on

technological advances. The decision on whether to invest in demand-based

irrigation is to a large degree influenced by policy and economic factors and

their relationships on water use, energy consumption and crop yield. This

study has investigated these relationships through sensitivity analyses and

detailed economic analyses.

8.2.3 Water and energy nexus for irrigation methods

One of the objectives of this research is to explore the water and energy

nexus for various irrigation systems and analyse the water and energy use

implications of irrigation system upgrades. It is noted in this modelling

study that there are significant variations in water use and energy

consumption among various irrigation methods for a given crop and given

405

irrigation strategy. Gravity-fed irrigation like flood and furrow has wetted

area of up to 100 per cent of the crop area. These methods also apply large

amount of irrigation in short time. Hence, the water loss in evaporation from

soil surface and deep percolation are very high for gravity-fed irrigation.

The results indicate that there is significant difference in water use rate

between gravity-fed and pressurized irrigation systems. For example, the

water application rate for flood and furrow irrigated citrus is 12 ML/ha and

10 ML/ha, respectively. On the other hand it is around 8 ML/ha and 6

ML/ha for sprinkler and drip irrigation, respectively, representing 50%

water savings with conversion from flood to drip irrigation. The

corresponding water savings for wine grapes are as high as 60%.

There is almost zero energy consumption using surface water for flood and

furrow irrigation. Groundwater pumping for irrigation is not considered in

this study as it does not occur in the study area. In contrast to gravity-fed

irrigation, the simulation of pressurized irrigation shows that although it

saves irrigation water yet requires more energy to operate pumps. Moreover,

the timely and precise application of irrigation water ensures higher yields

which improve both water productivity and energy productivity. For

example, the model results given in Chapter 4 show that water productivity

of drip irrigation is 5.7 kg/m3 as compared to just 1.99 kg/m3 for flood

irrigated horticultural crops. Furthermore, the energy productivity of drip

irrigation is 4.38 kg/kWh as compared to 3.30 kg/kWh for flood irrigation

of horticultural crops in the case study area. Other key water and energy

indicators are computed and discussed in Chapter 4 and Chapter 5. The

water and energy indicators computed and discussed in this thesis provide a

basis for making informed policy and investment decisions in relation to

irrigation conversion.

It is interpreted from the results in this study that the conversion to hi-tech

irrigation is economically and environmentally justifiable as long as the

increased energy cost and environmental impacts due to greenhouse gas

406

emissions are offset by increased yield, lesser accessions to the saline

groundwater and more water returned to the environment.

It is also noted that drip irrigation outperforms sprinkler irrigation both in

terms of water use and energy consumption for the horticulture crops under

both irrigation strategies. This assertion may not hold true for the sprinkler

system for irrigating broad acre crops as their irrigation application pattern

is totally different from horticulture.

8.2.4 Up-scaling modelled water and energy use

A node-link model is developed in this study to model water and energy

consumption in various irrigation systems. This model represents a case

study area of around 300 hectares and computes water and energy use at the

model scale. One of the objectives of this study is to examine water and

energy dynamics at the irrigation scheme scale, in this case the MIA.

Keeping all other parameters same, soil type is a major factor that controls

irrigation water requirement. The developed model computes irrigation

water use for the major soil groups for given horticulture crops in MIA.

Using the information of soils and the corresponding model output on water

use rate for each horticultural crop, it is an acceptable approach to linearly

up-scale water use to the entire MIA horticultural area. However, this linear

up-scaling approach is not valid for pumping energy use because the

pumping energy consumption is not a linear function of irrigated area

because of non-linear relationship between head losses and the flow volume

in pipes.

It is noted from the model runs completed in this study that the pumping

energy use almost doubles with 50% increase in irrigated area. To overcome

this issue, two up-scaling approaches are proposed as discussed in Chapter

6. The first approach is based on some relatively crude lumping assumptions

but still gives reasonably accurate results. The second approach is GIS

based and involves intensive processing at each farm scale and is relatively

more accurate. However, both up-scaling approaches are physically based

407

on soil data. The water and pumping energy use are up-scaled for each

irrigation system at various levels of adoption.

It is estimated that given 100% conversion of the MIA horticultural area of

28,970 ha to drip irrigation technology would result in around 137.49 GL of

water use per annum, while around 45,400 MWh of electricity would be

consumed in pumping that irrigation water over the year. For sprinkler

irrigation at 100% adoption level the total water and total energy use are

roughly 30% and 64% higher than that of drip irrigation, respectively. These

results again emphasize the point that drip irrigation outperforms sprinkler

irrigation both in terms of water savings and energy consumption for

horticultural crops.

8.2.5 Effectiveness of on-farm storages versus centralized

irrigation supply

All the observations and results discussed in the above sub-sections are for

the irrigation systems of each farm connected with a centrally located water

supply source, typically an irrigation canal or en-route storage. Private on-

farm storages are also widely used in the study area, especially at the farms

which use some sort of pressurized irrigation system. Therefore, in this

study we have also modelled and compared the effectiveness of the on-farm

storages in terms of water savings and pumping energy consumption in

Chapter 5. The major function of on-farm storages is to ensure the timely

supply of irrigation when needed and when the total irrigation demand

exceeds the capacity of the regular irrigation supply system.

It is evident from results in Chapter 5 that on-farm storages are less efficient

both in terms of water savings and energy consumption. For example in the

case of the drip irrigation scenario, the on-farm storage option shows

additional evaporation and seepage loss of 362 ML for the case study area.

Interestingly the pumping energy consumption of on-farm storages option is

negligibly higher than that of the centralized irrigation option. Hence,

significant water savings can be achieved by adopting centralized irrigation

supply system for drip irrigated farms.

408

For sprinkler irrigated farms, the water losses from on-farm storage option

are as high as 564 ML as compared to the centralized system. But the

energy consumption for on-farm storages is significantly lower than the

alternative option. However, it is estimated that for each 1 ML of water

savings, an additional 0.26 MWh energy are consumed by the centralized

pumping system. The market value of 1 ML of water is much higher than

that of 0.26 MWh of additional required energy. Moreover, this analysis

does not consider the fact that operation and running cost for the centralized

irrigation system are significantly lower than the individual pumping

stations on each farm. In totality, the centralized integrated irrigation supply

system is more effective than on-farm storages.

8.2.6 Long-term viability of irrigation conversion

It is hard to justify the conversion of gravity based irrigation system to one

of the pressurized systems if it does not payback capital costs within a

reasonable period and remains profitable in the long run. Therefore, the

economic viability of each conversion option is tested thoroughly in this

study. The analysis also includes the capital cost of installing the centralized

pumping station and the distribution pipe network with at least one outlet to

each farm. The economic analysis is conducted for three irrigation methods

(furrow, low head sprinkler and drip) in the case study area of 550 ha for

each citrus and wine grape production. The water use and energy

consumption rates are computed by the developed node-link model. The

reason for using the size of 550 ha of the study area is the fact that the

centralized/integrated irrigation supply system is designed to service this

much area. Therefore, it is imperative to analyse economic efficiency of this

system for the design area. Since, the economic analyses are based on the

model results and the data related to the case study area in MIA, the

conclusions are directly linked to the MIA. However, the methods applied

are applicable to any area and the general conclusions are likely to remain

unchanged.

409

On the cost side, capital costs and running costs are taken into account. On

the benefits side, returns from the sale of the raw product and from the

selling of the saved water in the water trade market, are considered. All

costs (including interest on initial capital investment and equipment

depreciation) and benefits (including yield sale and water traded out) are

converted into annual values. The payback period is computed by

comparing cumulative annual present value of benefits with the cumulative

annual present value of costs using an interest rate of 10%. The working life

of each irrigation technology is assumed to be 30 years. The results indicate

that the drip irrigation system with wine grapes has the least payback period

of 2 years followed by 3 years for the drip system with citrus. The sprinkler

system and furrow irrigation with citrus have payback periods of 18 years to

over 30 years, respectively. The reason for long payback period for furrow

irrigation is the fact that its annual operational costs are higher than the

annual returns. Similarly, the longer payback period for sprinkler system

owes to higher initial capital costs, higher energy costs and relatively lower

annual returns compared to drip system. It is noticed that the profitability

indicator; the benefit-cost ratio, for citrus crop is highly sensitive to the sale

price of the yield obtained, followed by the trade price of water, followed by

the labour cost which is followed by the energy/electricity price. For wine

grapes the benefit-cost ratio is most sensitive to the sale price of the yield,

followed by labour cost. Although, both citrus and wine grapes farms are

highly mechanized and automated, a significant cost of manual labour is

incurred in pruning and fruit harvesting as reported in Chapter 7.

410

Figure 8.5: Annual costs and returns for the three irrigation systems with citrus on per hectare basis (capital cost includes the cost of integrated irrigation supply system, except for furrow irrigation)

Figure 8.5 and Figure 8.6 summarize the annual costs and annual returns for

the three irrigation systems for citrus and wine grape, respectively, on a per

hectare basis. The capital cost also includes the annual cost incurred in the

installation of a centralized irrigation supply system. The annual operational

cost includes the cost of all inputs, equipment use, water charges (fixed and

usage based) and most importantly the cost of greenhouse gas emissions

(carbon tax).

A comparison of greenhouse gas (GHG) emission costs for various

irrigation methods for the two crops is given in Table 8.1 based on numbers

reported in previous chapters. The GHG emissions are accounted for all

energy inputs including fertilizer, pumping etc. The greenhouse gas

emissions cost, also referred to as carbon tax, is reported as the percentage

of the total annual operational cost on a per hectare basis. Drip system

operated by the centralized irrigation supply system for growing wine

grapes has the highest GHG emissions cost of roughly 2% of the annual

operational cost. The furrow irrigation has the least GHG emissions cost due

to absence of irrigation pumping.

411

Figure 8.6: Annual costs and returns for the three irrigation systems with wine grapes on a per hectare basis (capital cost includes cost of integrated irrigation supply system, except for furrow irrigation) Table 8.1: Greenhouse gas emissions cost as percentage of the total annual operational cost per hectare

Crop Furrow Sprinkler Drip

Citrus 0.67% 1.31% 1.30%

Wine grapes 0.99% 1.77% 1.79%

As mentioned earlier and also shown in Figure 8.5 and Figure 8.6,

conversion to sprinkler irrigation for citrus production is not a very

economical option due to long payback periods, high cost low return and

consequently non-attractive B-C ratio. However, any government subsidies

on conversion to sprinkler system could make it a viable option. Also, with

the introduction of the environment as a third user/competitor, the future

water trade prices are likely to be higher than in the past which will make

the conversion to water saving irrigation technologies an even more

financially attractive option.

8.2.7 View from system dynamics lens

A detailed system dynamic analysis of the system under consideration is

carried out at the start of this chapter. Partial dynamic analyses have also

been conducted at appropriate parts of this thesis. In a nutshell, the system

dynamics analysis suggests that at the system scale the need to achieve

maximum water savings is driven by the water availability which in turn is

412

driven by natural factors and policy shifts. It is evident from the analyses of

the identified feedback loops that there is a conflict between maximizing

water savings to support the environment and the negative environmental

impacts of the means adopted to achieve those water savings.

This study has to a greater extent quantified the interacting variables and

exogenous factors for various developed scenarios to help establish better

understanding of the underlying feedback mechanisms. It also shows that

the water and energy nexus is a complex structure to comprehend. The

analysis also suggests that water and energy nexus should be looked at a

wider scale to support any policy decision making. The analysis at irrigation

scheme scale as conducted in this study seems to be appropriate if not best

scale. It is not appropriate to make a policy decision just by looking at farm-

scale water and energy results. For example, the amount of irrigation

pumping energy required at farm scale seems relatively low. However, if a

decision is made to provide assistance to convert all farms in the irrigation

scheme to pressurized irrigation then the amount of total energy required

can be equivalent to half the generating capacity of the Snowy Hydro

Scheme as mentioned in Chapter 6. Installing a new coal fired power

generation plant to fulfil this additional energy requirement would not be an

environmentally sustainable solution, to say the least. Therefore,

consideration of the scale of problem and taking a holistic approach is very

important to reach an environmentally and economically optimum decision.

413

Chapter 9: Conclusions and the Way Forward

This doctoral research thesis is an attempt to analyse the complex nexus

between water and energy that exists in irrigated systems. The basic

hypothesis behind this research has been to identify and realize water and

energy savings it is critical to adopt a system level thinking to explore the

water-energy nexus. The system level thinking implies just not a larger

physical scale of the problem; it also refers to the brining more and more

inter-related variables and processes into consideration. Therefore, in this

research we have focused on a large irrigation scheme, the Murrumbidgee

Irrigation Area (MIA), and all possibly inter-related variables like water use,

energy consumption, environment, and last but not the least the economic

factors. However, before considering the whole irrigation scheme scale, a

smaller case study is analysed first. The water, energy, greenhouse gas

emissions and economic indicators are explored by developing a node-link

model and other methods at the case study scale. Then the results are up-

scaled and critically analysed at the irrigation scheme level.

The overarching objectives of this research are as follows:

1. To synthesise knowledge and future challenges related to energy and

water use efficiency in large irrigation areas.

2. To quantify spatio-temporal trends in energy and water use

efficiency in a major irrigation area using a node-link model.

3. To develop a hydrologic-economic dynamic system framework for

testing the economic viability and for estimating the environmental

footprint of farming operations by exploring system-wide linkages

among water use efficiency and associated costs, irrigation

management strategies, energy-yield relationships, energy

consumption and associated greenhouse gas emissions.

414

To achieve these objectives this research has addressed a number of

questions and drawn conclusions from the results/answers to these following

questions:

1. What are the missing/unknown links between water, energy and

environment which could play out as huge challenges with future

irrigated systems?

The literature review indicates that in the past most of the emphasis has

been given to improving water use efficiency in irrigated systems. Use of

modern irrigation systems has been accepted as the most effective solution

to achieving high irrigation efficiency. However, the literature concludes

that very little attention has been rendered to the estimation of increased

energy that is required to operate modern irrigation systems. The literature

review highlights the knowledge gap to properly understand water

efficiency and energy consumption nexus. To understand water-energy-

environment nexus this study has focused on the environmental impacts of

water diversion from rivers for irrigation, the potential impacts of climate

change on water resources and the greenhouse gas emissions from increased

energy consumption by modern irrigation systems in the MIA.

2. How can a biophysical tool help understand and quantify water-energy-

environment interactions in a large irrigation area?

This question is addressed by developing a node-link model of the

horticultural area of MIA. The developed node-link model has various

modules that compute irrigation demand and supply, irrigation management

strategy (demand-based or supply-based), irrigation supply system and yield

for a given crop and given irrigation method on a daily time step. At the

same time, the model keeps track of electricity consumption in pumping

irrigation water by computing energy head requirements to overcome head

losses in the irrigation supply system and to provide the required pressure

head at each farm inlet to operate hi-tech irrigation systems. The model is

developed to represent a case study area and then results are up-scaled using

415

appropriate up-scaling techniques over the entire horticultural area of the

MIA. The other direct and indirect energy inputs and greenhouse gas

emissions are also estimated for each scenario. Hence the developed model

and other biophysical data provide an adequate information-base to

understand and quantify the water-energy-environment nexus.

3. The third and the most comprehensive question is, “What is the nature

of linkages between water use, energy consumption and greenhouse gas

emissions from irrigation conversion for a large irrigation area, and

what approach should be taken to understand those linkages”?

The major part of this thesis is dedicated to finding an answer to this

question. Different scenarios representing different irrigation methods, crops

and irrigation strategies were modelled using the developed node-link

model. A holistic and system dynamics approach was adopted to

simultaneously monitor behaviour of key variables including irrigation rate,

water losses, water savings, energy consumption in pumping, and

corresponding greenhouse gas emissions. Furthermore, economic analysis

and sensitivity was also conducted for the most promising scenarios. All

variables related to the water, energy, greenhouse gas emissions and

profitability indicators were put into a matrix. This matrix was analysed

through a system dynamics lens to identify underlying feedback loops

between the inter-dependent variables. This analysis concludes that there is

a strong inter-dependence between water savings, energy consumption and

environmental implications and that no decision should be made based on

just one of these key variables. If we do so, we will never get an optimum

solution. The analysis of the feedback mechanisms also shows that the

whole water and energy initiative is mainly driven by water availability and

environmental considerations. The overall framework developed to analyse

the water-energy-environment nexus in this study is not area specific and in

fact can be applied to any large irrigation area to achieve similar objectives.

416

9.1 Major recommendations

This thesis research adds knowledge that helps understand the water-energy-

environment nexus and make decisions regarding conversion to modern

irrigation technology in a large horticultural area in Australia. However, the

developed framework is applicable to any area with surface irrigation.

This study suggests the following recommendations for the stakeholders in

the irrigation industry in general and horticultural production industry in

particular, in Australia:

9.1.1 Recommendations for policy makers

Collect as much relevant data as possible to cover the length, breadth

and depth of an irrigation conversion problem. A well considered

problem definition will help devise more effective solutions.

Always widen the scope of problem to a possible extent that defines a

comprehensive irrigation scheme conversion objective so that an

effective and fit-for-all decision can be made. For example, improved

infrastructure alone may provide maximum water savings but may have

adverse economic, energy related and/or environmental consequences in

the long run. These issues are highlighted in Chapter 4, Chapter 5,

Chapter 6 and Chapter 7.

This study does not take into account soil carbon sequestration in

agriculture as mentioned in equivalent GHG emissions calculations in

Chapter 4 and Chapter 5. Detailed policy should be developed to offset

the GHG emissions tax on agriculture with the amount of carbon

sequestered by the crops.

Take a holistic and long-term view to devise a possible solution. For

example, it is found in Chapter 6 that if a decision is made to convert the

whole of MIA horticultural area to sprinkler system, an additional 50%

of Snowy hydro generation capacity is required to supply these energy

needs. The energy required to supply 100 adoption of drip system in

417

MIA requires an additional 20% of Snowy hydro generation capacity.

This high requirement of energy should be considered in decision

making on conversion of the entire MIA horticulture area.

If possible, visit the area or talk to the local farmers before making any

decision to support a particular water saving initiative.

9.1.2 Recommendations for irrigators

Time has come to change community preferences to favour

improvements in irrigation efficiency to help the environment as a

legitimate stakeholder in the water industry.

Make informed decisions on acceptable and viable tradeoffs on water

use, energy consumption and achievable yield. The framework

developed in this thesis as well as sample results obtained using real

farming data can help inform these decisions.

Should not blindly follow others as the each farm may have different

circumstances. For example, converting to drip irrigation may not

necessarily be economic if your soil/crop/environment is able to achieve

comparable water usage by furrow irrigation system.

Undertake a proper biophysical analysis and long-term economic

analysis of all alternatives and choose the most optimum alternative.

Also manage the key variables by conducting a sensitivity analysis.

Demand-based irrigation is better suited to modern irrigation systems

and supply-based irrigation strategy is more appropriate for

conventional gravity-based irrigation methods.

Connect your farms with centralized irrigation supply system to operate

your sprinkler or drip system. With a nominal service fee it saves time,

labour cost as well as operation and maintenance costs. Energy cost is

also reduced by avoiding fees for installation of electricity supply

418

equipment (transformer etc.) at each farm. Moreover, the irrigation

company may negotiate price of the electricity with the provider.

In developed countries like Australia, farm labour availability is low and

labour costs are very high. There are up to 31% reduction in cost of

overall labour-based operations for horticulture production with drip

irrigation as compared to furrows. The labour saving for irrigation alone

is estimated to be 80% less than furrow irrigation. For sprinkler based

production the overall labour savings are 18% as compared to furrow

system and 48% labour savings in irrigation only.

Use of on-farm storages is not recommended as the irrigator has to bear

evaporation losses from the storage. Also the energy savings as

compared to the centralized irrigation supply system do not offset the

increased operation and maintenance costs.

Where possible, be prepared to adapt to the potential future challenges

such as climate change and policy reforms.

9.1.3 Recommendations for irrigation providers

Adopt appropriate measures to minimize water conveyance losses from

“hot-spots” by lining the channels or by replacing open channels with

pipes.

Water trade has now become a significant part of the water industry.

Therefore, work with irrigators and policy makers to facilitate water

trade in an open water market.

Work with policy makers and irrigators to achieve environmental and

economic objectives.

9.2 The Way Forward

This research study is conducted by adopting available methods and tools to

link water, energy and environment in horticultural areas; however, there is

scope for improvement though further work in the following areas:

419

Modifying the developed node-link model to perform continuous

simulation over multiple years.

Currently the model uses a fixed proportion of applied irrigation being

lost through deep drainage. Furthermore, it does not take into account

the effects of a raised watertable. A proper biophysical model could be

developed to achieve a dynamic relationship to quantify surface-

groundwater interactions in irrigated areas.

The developed node-link model does not simulate groundwater pumping

as it does not occur in the study area. However, the model can be

modified to include groundwater pumping and energy use in

groundwater pumping.

Application of the developed water-energy-environment analysis methodology at the river basin scale.

9.3 Changes in Developed Model for Application in Other Areas

The following major changes will have to be made to apply developed

node-link model to other areas.

Soil parameters as per new soil types being modelled.

Crop parameters for the new crops.

Number and/or size of pipe system.

The layout of the model components to represent physical system.

Simulation period.

421

References

Australian Bureau of Statistics (ABS). (2006). Water Account Australia

2004-05. Australian Bureau of Statistics, Canberra. ABS Catalogue

No. 4610.0. ISBN 0 642 47942 9.

Australian Bureau of Statistics (ABS) (2009). Murrumbidgee sustainable

yields region socioeconomic profile, report for the Murray–Darling

Basin Authority by the Australian Bureau of Statistics, Australian

Bureau of Agricultural and Resource Economics & Bureau of Rural

Sciences, Adelaide.

Australian Bureau of Statistics (ABS) (2011). Energy Account Australia


No. 4604.0.

Australian Bureau of Statistics (ABS). (2012). Water Account Australia


No. 4610.0.

ACIL Tasman, (2009). Regional economic effects of irrigation efficiency

projects. Report of case studies prepared for the Crane Group.

Available online at: http://www.pc.gov.au/ (accessed in May 2011)

Ahlfeld, D. P. and Laverty, M.M. (2011). Analytical solutions for

minimization of energy use for groundwater pumping, Water

Resour. Res., 47, W06508, doi:10.1029/2010WR009752.

Ahmad, A., Khan, S., and Rana, T. (2007). System Dynamics Approach for

Modelling Seasonality of River Flows. Proceedings (CD) of

MODSIM, International Congress on Modeling and Simulation, 10-

13 December 2007, Christchurch, New Zealand

Allen, R.G., Pereira L.S., Raes D, Smith M. (1998). Crop

Evapotranspiration: Guidelines for Computing Crop Water

Requirements. FAO Irrigation and Drainage Paper 56, Rome.

422

Available online at:

http://www.fao.org/docrep/X0490E/X0490E00.htm

Allen, R.G., L.S. Pereira, M. Smith , D.Raes , and J.L. Wright. (2005).

FAO-56 Dual Crop Coefficient Method for Estimating Evaporation

from Soil and Application Extensions. J. Irrig. and Drain. Engrg.,

ASCE 131(1):2-13.

Allen, R.G., and Robison, C.W., (2007). Evapotranspiration and

Consumptive Irrigation Water Requirements for Idaho. Research

Technical Completion Report submitted by University of Idaho.

Available online at: http://www.kimberly.uidaho.edu/ETIdaho/

Ahmad A. and Khan S. (2009). Comparison of Water and Energy

Productivities in Pressurized Irrigation Systems. Proceedings (CD)

of MODSIM, International Congress on Modelling and Simulation,

13-17 July 2009, Cairns, Australia. ISBN: 978-0-9758400-7-8

American Society for Testing and Materials (ASTM) (2006). Standard

Specification for Poly Vinyl Chloride (PVC) Plastic Pipe, Schedules

40, 80, and 120. ASTM International, West Conshohocken, PA,

2006, DOI: 10.1520/D1785-06, www.astm.org.

Australian Greenhouse Office (AGO), (2007). National Greenhouse Gas

Inventory 2005: Accounting for 108% target. ISBN: 978-1-921297-

22-9.

Australian National Committee on Irrigation and Drainage (ANCID)

(2000), Open Channel Seepage: Current Knowledge of Channel

Seepage Issues and Measurement in the Australian Rural Water

Industry. Prepared by Sinclair Knight Merz, August 2000.

Barber A. (2004). Seven Case Study Farms: Total Energy & Carbon

Indicators for New Zealand Arable & Outdoor Vegetable

Production, AgriLINK New Zealand Ltd.

423

Beckingham, C., Bright, J., Creecy, H., Moulds, G., Quirk, L., and Somers,

A. (2004) Irrigating grapevines with limited water supplies. NSW

Agriculture. ISBN: 0734715749

Bevan, G.S., and Wendy, W. (2009). The Carbon Footprint of Water. River

Network, Portland, OR 97204. May, 2009

Bielorai, H., (1982). The effect of partial wetting of the root zone on yield

and water use efficiency in a drip-and sprinkler-irrigated mature

grapefruit grove. Irr. Sci. Vol. 3, No. 2, p89-100. DOI:

10.1007/BF00264852

Biswas, T., Schrale, G., and Dore, D. (2005). Measuring the effects of

improving water use efficiency on root zone salinity. Land and

Water Australia - National Program for Sustainable Irrigation,

Research Bulletin 1.

Blackmore, D. (1995). Murray Darling Basin Commission: a case study in

integrated catchment management. Water Science and Technology

32, 15–25.

Blasius, H. (1913). Das Ahnlichkeitsgesetz bei Reibungsvorg¨angen in

Fl¨ussigkeiten. Forsch. Arb. Ing. 134.

Bos, M.G. (1997). Performance indicators for irrigation and drainage.

Irrigation and Drainage Systems, 11(2), 119–137.

Bos, M.G., Murray-Rust, D.H., Merrey, D.J., Johnson, H.G. and Sneller,

W.B. (1993). Methodologies for assessing performance of irrigation

and drainage management. Irrig. Drain. Syst. 7(4): 231-261.

Bright, J. (2005). Apple and pear nutrition. Department of Primary

Industries, NSW. PRIMEFACT 85. ISSN 1832-6668

Bureau of Rural Sciences (BRS) (2005) 2000 Land Use of the Murray-

Darling Basin, Version 2. Resource Identifier: DI01. Online digital

dataset and spatial data layer. File identifier:

http://adl.brs.gov.au/anrdl/metadata_files/pa_mdblur9abl_00711a04.xml

Accessed on December 8, 2010.

424

Canakci M., Topakci M., Akinci I., and Ozmerzi A. (2005). Energy use

pattern of some field crops and vegetable production: Case study for

Antalya Region, Turkey. Energy Conversion and Management, Vol.

46, pp. 655–666.

Chandrakanth, M.G., Arun, V. (1997). Externalities in groundwater

irrigation in hard rock areas. Indian Journal of Agricultural

Economics 52 (4), 761–771.

Checkland, P. (1981). Systems Thinking, Systems Practice. (Wiley) ISBN

0-471-27911-0

Conyers, M.K., Hume, I., Summerell, G., Slinger, D., Mitchell, M.,

Cawley, R. (2008). The ionic composition of the streams of the mid-

Murrumbidgee River: implications for the management of

downstream salinity. Agricultural Water Management 95, 598–606.

Cox, W. and Baxter, P. (1996). Setting the Cap: Report of the Independent

Audit Group. Murray-Darling Basin Ministerial Council. ISBN 1

875209 96 4.

Cummins, T. (1998). Developing implementation pathways for more

efficiency irrigation technology. Scoping study prepared for

Murray-Darling Basin Commission.

Cuykendall, H.C., and White, B.G. (1998). Economics of Drip Irrigation

for Apple Orchards in New York State. Department of Agricultural,

Resource, and Managerial Economics College of Agriculture and

Life Sciences Cornell University, Ithaca, New York 14853-7801

CoAG (2004). Intergovernmental Agreement on a National Water

Initiative. National Water Commission, accessed online 10th Apr,

09 at: http://www.nwc.gov.au/www/html/117-national-water-

initiative.asp

Commonwealth of Australia, (2011). The Clean Energy Act 2011.

Available at: http://www.comlaw.gov.au

425

Cooperative Research Centre for Viticulture (CRCV) (2005). Viti-Notes

2005. Cooperative Research Centre for Viticulture. Available online

at: www.crcv.com.au (assessed on October 2010).

Crean, J., Shaw, A., Singh, R.P., and Mullen, J.D. (2004). An Assessment

of the Economic, Environmental and Social Impacts of NSW

Agriculture’s Advisory Programs in Water Use Efficiency:

Economic Research Report No. 21. Department of Primary

Industries, NSW. ISBN 0-7347-1578-1

CSIRO (2008). Water availability in the Murrumbidgee. A report to the

Australian Government from the CSIRO Murray-Darling Basin

Sustainable Yields Project. CSIRO, Australia. 155pp.

CSIRO (2012). Climate and water availability in south-eastern Australia: A

synthesis of findings from Phase 2 of the South Eastern Australian

Climate Initiative (SEACI), CSIRO, Australia, Sep. 2012, 41 pp.

Dasberg, S. (1995). Drip and spray irrigation of citrus orchards in Israel. In:

Microirrigation for a changing world: Conserving

resources/preserving environment; proceedings of the Fifth

International Microirrigation Congress, April 2-6, 1995, Orlando,

Florida. pp 281 – 287.

Daugherty, R.L., Franzini, J.B., and Finnemore, E.J. (1985). Fluid

Mechanics with Engineering Applications, 8th ed. McGraw-Hill

Companies, pp-598. ISBN: 978-0070154414.

Davies, P.E., Harris, J.H., Hillman, T.J., and Walker, K.F. (2008). SRA

Report 1: A Report on the Ecological Health of Rivers in the

Murray–Darling Basin, 2004–2007. Prepared by the Independent

Sustainable Rivers Audit Group for the Murray–Darling Basin

Ministerial Council. MDBC Publication No. 16/08. ISBN 978 1 921

257 56 8

426

Department of Climate Change & Energy Efficiency (Dept. CC & EE).

(2010). National greenhouse accounts factors. ISBN: 978-1-921299-

04-9.

Department of Climate Change and Energy Efficiency (DCC&EE) (2012).

Australian National Greenhouse Accounts: National Inventory by

Economic Sector. Department of Climate Change and Energy

Efficiency, Canberra. ISSBN: 978-1-922003-23-2

Department of Primary Industries, Victoria (DPI Vic) website:

http://new.dpi.vic.gov.au/agriculture/horticulture. Accessed in April

2011.

Department of Primary Industries, New South Wales (2000). Horticultural

fertigation - techniques, equipment and management. AgNote 1-

009, 2nd edition. Available online at:

http://www.dpi.nsw.gov.au/agriculture/resources/water/irrigation/cr

ops/publications/fertigation

Doorenbos, J. and Pruitt, W.O. (1977). Crop and water requirements.

Irrigation and Drainage Paper 24: 79–82. Food and Agricultural

Organisation of the United Nations, Rome.

Doorenbos, J. and Kassam, A.H. (1979). Yield Response to Water. FAO

Irrigation and Drainage Paper No 33, FAO, Rome.

Dovring, F. (1985). Energy use is United States agriculture: a critique of

recent research. Energy in Agriculture, Vol. 4, 79-86.

Edraki, M., Smith, E., Humphreys, E., Khan, S., O’Connell, N., and Xevi,

E. (2003). Validation of SWAGMAN Farm and SWAGMAN

Destiny models. Technical Report No. 44/03, 2003, CSIRO Land

and Water, Griffith, NSW.

Fairweather, H., and Austin, N. (2003). Water Use Efficiency: an

Information Package Irrigation. Insights Number 5, Land and Water

Australia, Canberra. Product No. PR030566, ISBN 1 920860 09 6

427

Falivene, S., (2003). Farm Budget Handbook 2003; NSW Citrus. NSW

Dept. of Agriculture. ISSN 1328-4630.

Falivene, S., Giddings, J., Hardy, S., and Sanderson, G. (2006). Managing

citrus orchards with less water. PrimeFact 427. Department of

Primary Industries, NSW. ISSN 1832-6668.

Forrester, J.W. (1961). Industrial Dynamics. The MIT Press, Cambridge,

MA, 464pp

Forrester, J.W. (1995). The beginning of system dynamics. The McKinsey

Quarterly. ISSN: 0047-5394 4, 4 – 16.

Frazier, P., and Page, K. (2006). The effect of river regulation on floodplain

wetland inundation, Murrumbidgee River, Australia. Marine and

Freshwater Research, 2006, 57, 133 – 141.

Gerald, M.W. (2001). An Introduction to General Systems Thinking.

(Dorset House) ISBN 0-932633-49-8

Gerbens-Leenes, P.W., Hoekstra, A.Y. and Van der Meer, Th.H. (2008)

Water Footprint of Bio-energy and Other Primary Energy Carriers.

Value of Water Research Report Series No. 29. UNESCO-IHE

Institute for Water Education, Delft, the Netherlands.

Giddings, J. (2004). Drip Irrigation: a grapegrower’s guide, 3rd edition.

NSW Agriculture, NSW. ISBN: 0734713053.

Giddings, J. (2005). Drip Irrigation: a citrus grower’s guide. NSW

Agriculture, NSW. ISBN: 0734716362.

Giddings, J., and Deegenaars, A. (2008). Managing the conversion to drip

irrigation in vineyards. NSW Department of Primary Industries.

Giles, R.V., Evett, J.B., and Liu, C. (1993). Theory and problems of fluid

mechanics and hydraulics, 3rd ed. McGraw Hill, Inc. ISBN:

0070233160

428

Gleick P.H. (1993). Water and energy. In: Gleick PH (eds.), Water in crisis:

A guide to the world's fresh water resources. p 67-79 (Oxford

University Press, New York, Oxford).

Gleick P.H. (1994). Water and Energy. Annu. Rev. Energy Environ. Vol 19,

1994. p 267-99.

Goodwin, I. (1995). Irrigation of Vineyards: A Winegrape Grower’s Guide

to Irrigation Scheduling and Regulated Deficit Irrigation Institute of

Sustainable Irrigated Agriculture, Tatura Victoria. (Department of

Agriculture, Energy and Minerals, Agriculture Victoria, ISBN

073064160 0 (56 pp).

Graham, P.W., and Williams, D.J. (2005). Optimal technological choices in

meeting Australian energy policy goals, Energy Economics 25

(2003), 691–712.

Gutteridge, Haskins and Davey, Acil Australia; Australian Groundwater

Consultants (1990). A pipeline to the Sea: Pre-feasibility study. A

report prepared for Murray Darling Basin Commission Canberra.

Hafi, A., Kemp, A., and Alexander, F. (2001). Estimating the benefits of

improving water use efficiency – A case study of the Murrumbidgee

Irrigation Area, Australian Bureau of Agricultural and Resource

Economics (ABARE) report to the Land and Water Research and

Development Corporation,

Hatcho, N., and Sagardoy, J.A. (1993). Water distribution module of the

SIMIS program. In: Irrigation water delivery models, proceedings

of the FAO Expert Consultation, Rome, Italy, 4 – 7 October 1993.

Hatirli, S., Ozkan, B., & Fert, C. (2006). Energy inputs and crop yield

relationship in greenhouse tomato production. Renewable Energy,

31, 427-438.

Hendrickson, J. (1996). Energy Use in the U.S. Food System: A Summary

of Existing Research and Analysis. Madison, WI: Univ. of

Wisconsin, Centre for Integrated Agricultural Systems.

429

Heller, M.C. and Keoleian, G.A. (2000). Life Cycle Based Sustainability

Indicators for Assessment of the U.S. Food System. Ann Arbor, MI:

University of Michigan, Center for Sustainable Systems.

Hoekstra, A.Y. and Hung, P.Q. (2002). Virtual water trade: a quantification

of virtual water flows between nations in relation to international

crop trade. Value of Water Research Report Series, Vol. 11, 2002.

UNESCO-IHE, Delft, the Netherlands.

Hoffman, G,J. (1990). Leaching fraction and root zone salinity control. In

Agricultural Salinity Assessment and Management, ed. K.K.Tanji.

New York: American Society of Civil Engineers.

Horticulture Australia Limited (2010). Value of Horticulture, [online]

http://www.horticulture.com.au/areas_of_Investment/Environment/

Climate/value_horticulture.asp

Hope, M. and Wright, M. (2003). Murrumbidgee Irrigation Catchment

Profile, Water Use Efficiency Advisory Unit, NSW Agriculture,

Dubbo.

Hornbuckle, J.W., and Christen, E.W. (1999). Physical properties of soils

in the Murrumbidgee and Coleambally irrigation areas, CSIRO

Land and Water Technical Report No 17/99, Griffith,

http://www.clw.csiro.au/publications/technical99/tr17-99.pdf

Horst, L. (1995). The discrepancy between irrigation scheduling and actual

water distribution: An analysis and suggestions for possible

solutions. In: Irrigation Scheduling: From Theory to Practice –

Proceedings. Proceedings of the ICID/FAO Workshop on Irrigation

Scheduling Rome, Italy, 12-13 September 1995. ISBN 92-5-

103968-2.

Humphreys, L., Fawcett, B., O’Neill, C., and Muirhead, W. (2005). Maize

under sprinkler, drip and furrow irrigation; In: IREC Farmer’s

Newsletter, No. 170, Spring 2005.

430

Hussain, I., Mudasser, M., Hanjra, M.A., Amrasinghe, U., Molden, D.

(2004). Improving wheat productivity in Pakistan: econometric

analysis using panel data from Chaj in the upper Indus Basin. Water

International, 29, 189–200.

Illangasekare, T., Tyler, S.W., Clement, T.P., Villholth, K.G., Perera,

A.P.G.R.L., Obeysekera, J., Gunatilaka, A., Panabokke, C.R.,

Hyndman, D.W., Cunningham, K.J., Kaluarachchi, J.J., Yeh, W.W.-

G., van Genuchten, M.T., Jensen, K. (2006). Impacts of the 2004

tsunami on groundwater resources in Sri Lanka. Water Resources

Research 42 (4).

Irrigation Association of Australia (IAA) (1998). The definition of

irrigation efficiency as adopted by the Irrigation Association of

Australia. Journal of Irrigation Association of Australia, 13(1), 5–6.

IPCC, (2007). Climate change 2007: Synthesis report - An Assessment of

the Intergovernmental Panel on Climate Change.

IPCC, (2001). Climate Change 2001: Synthesis Report. A Contribution of

Working Groups I, II, and III to the Third Assessment Report of the

Intergovernmental Panel on Climate Change [Watson, R.T. and the

Core Writing Team (eds.)]. Cambridge University Press,

Cambridge, United Kingdom, and New York, NY, USA, 398 pp.

Israelsen, O.W. (1932). Irrigation Principles and Practices. Wiley and Sons,

New York. 411 pp.

Jackson, T.M. (2009). An Appraisal of the On-farm Water and Energy

Nexus in Irrigated Agriculture. Doctoral Thesis, Charles Sturt

University, Wagga Wagga, NSW, Australia.

Jaques, M.S. (2005). Snowy Hydro Limited; Briefing Paper to the National

Competition Council by Snowy Hydro. New South Wales.

Jensen, M.E., Burman, R.D., & Allen, R.G. (eds) (1990).

Evapotranspiration and Irrigation Water Requirements. ASCE

431

Manuals and Reports on Engineering Practice No. 70: 72–74.

ASCE, USA.

Johansson, K., and Liljequist, K. (2009). Can agriculture provide us with

both food and fuel? – A survey of present agricultural production.

Uppsala Universitet, Uppsala, Sweden

Johnson, S.R., Ayars, J., and Hsiao, T. (2004). Improving a Model for

Predicting Peach Tree Evapotranspiration. Acta Hort. (ISHS)

664:341-346.

Kaczan, D., Qureshi, M.E., and Connor, J. (2011). A Summary of Water

Trade and Price Data for the Southern Murray-Darling Basin.

CSIRO: Water for a Healthy Country National Research Flagship,

Canberra. 20 pp

Karmeli, D., and Keller, J. (1974). Trickle irrigation design parameters.

Transactions of ASAE, Vol. 17, No. 4, p678-684.

Kassam, A., and Smith, M. (2001). FAO Methodologies on Crop Water

Use and Crop Water Productivity. Paper No. CWP-M07: Expert

Meeting on Crop Water Productivity Rome, 3 to 5 December 2001.

Kemp, A., and Hafi, A. (2001). Benefits of increased irrigation efficiency in

the Murrumbidgee Irrigation Area. 45th international conference of

the Australian Agricultural and Resource Economics Society, 22-25

January, 2001, Adelaide.

Kennedy, J. (1973). MIA Mixed Farming, NSW Agriculture, Leeton, NSW.

Kenny, J.F., Barber, N.L., Hutson, S.S., Linsey, K.S., Lovelace, J.K., and

Maupin, M.A. (2009). Estimated use of water in the United States in

2005: U.S. Geological Survey Circular 1344, 52 p.

Khan, S. (2005). Rethinking rational solutions for irrigation salinity,

Australian Journal of Water Resources, Vol. 9, No. 2, 129 – 140.

Khan, S. (2006). Water reforms in the Murray Darling Basin: law and

policy challenges. Chapter 3 in Hydrology and Water Law —

432

Bridging the Gap, Wallace J, Woulters P (eds). IWA Publishing:

London; 60–78.

Khan, S. (2007). Equivalent Cropping Area and Whole Farm Water

Balance Approaches to Reduce Net Recharge to Shallow Saline

Groundwater from Rice Based Cropping Systems. Paddy and Water

Environment. 5(3)

Khan, S., Rana, T., Yuanli, C., Blackwell, J. (2006). Can irrigation be

sustainable? Agric Water Manage 80:87–99.

Khan, S, Xevi, E., O’Connell, N., Madden, J.C., and Zhou, F. (2000). A

farm scale hydrologic economic optimization model to manage

waterlogging and salinity in irrigation areas. Proceedings of the

Fourth Biennial Engineering Mathematics and Applications

Conference, EMAC 2000, RMIT University, Melbourne September,

2000. 179-182.

Khan, S., Wang, Z., O’Connell, N., Xevi, E., and Robinson, D. (2001).

Optimizing Agronomic Options at the Farm Scale. Rice CRC

Project Report No. 1201 by CSIRO Land and Water, Griffith, NSW.

Khan, S., Akbar, S., Rana, T., Abbas, A., Robinson, D., Dassanayake, D.,

Hirsi, I., Blackwell, J., Xevi, E., and Carmichael, A. (2005a).

Hydrologic Economic Ranking of Water Savings Options.

Murrumbidgee valley water efficiency feasibility project.

Consultancy report to Pratt Water Group. CSIRO, Griffith

Khan, S., Akbar, S., Rana, T., Abbas, A., Robinson, D., Paydar, Z.,

Dassanayke, D., Hirsi, I., Blackwell, J., Xevi, E., and Carmichael,

A. (2005b). Off-and-on farm savings of irrigation water.

Murrumbidgee Valley water efficiency feasibility project.Water for

a Healthy Country Flagship report. Canberra: CSIRO.

Khan, S. and Abbas, A. (2007). Upscaling water savings from farm to

irrigation system level using GIS-based agro-hydrological

modelling. Irrigation and Drainage, 56: 29–42. doi: 10.1002/ird.284

433

Khan S, Ahmad A, Malano M.H. (2009). Managing Irrigation Demand to

Improve Seasonality of River Flows. ICID Journal of Irrigation and

Drainage. Vol 58, pages 157–170 DOI: 10.1002/ird.405.

Khan, S., Hanjra, M.A. (2008). Sustainable land and water management

policies and practices: a pathway to environmental sustainability in

large irrigation systems. Land Degradation and Development 19,

469–487.

Khan, S., Rana, T., Hanjra, M.A. (2008a). A cross disciplinary framework

for linking farms with regional groundwater and salinity

management targets. Agricultural Water Management 95 (1), 35–

47.

Khan, S., and Hanjra, M.A. (2009). Footprints of water and energy inputs in

food production – Global perspectives. Food Policy, 34(2009), 130

– 140.

Khan, S., Khan, M.A., Hanjra, M.A., and Mu, J. (2009a). Pathways to

reduce the environmental footprints of water and energy inputs in

food production. Food Policy, Vol. 34, pp. 141–149

Khan, S., Rana, T., Hanjra, M.A., and Zirilli, J. (2009b). Water markets and

soil salinity nexus: Can minimum irrigation intensities address the

issue? Agricultural Water Management 96, 493–503.

Khan, S., Yufeng, L., Ahmad, A. (2009c). Analysing complex behaviour of

hydrological systems through a system dynamics approach.

Environmental Modelling & Software, Volume 24, Issue 12,

December 2009, Pages 1363-1372.

Khan, S., Rana, T., Beddek, R., Blackwell, J., Paydar, Z., Carroll, J. (2004).

Whole of Catchment Water and Salt Balance to Identify Potential

Water Saving Options in the Murrumbidgee Catchment. CSIRO

Land and Water, Griffith Laboratory, Australia. Consultancy Report

to Pratt Water Pty Ltd, Australia.

434

Khan, S., Rana, T., Carroll, J., Wang, B., and Best, L. (2004b). Managing

Climate, Irrigation and Ground Water Interactions using a

Numerical Model: A Case Study of the Murrumbidgee Irrigation

Area. CSIRO Land and Water Technical Report No. 13/04 March

2004.

Khan, S., Rana, T., Dassanayake, D., Abbas, A., Blackwell, J., Akbar, S.

and Gabriel, H.F. (2009b). Spatially distributed assessment of

channel seepage using geophysics and artificial intelligence.

Irrigation and Drainage, 58: 307–320. doi: 10.1002/ird.415

Khan, S., Rana, T., and Hanjra, M. (2010). A whole-of-the-catchment water

accounting framework to facilitate public–private investments: an

example from Australia. Water Policy 12 (2010) 336–356

Kijne, J.W. (1998). Yield response to moderately saline irrigation water:

implications for feasibility of management changes in irrigation

systems for salinity control. J. Applied Irrigation Sci. 33:249-277.

Kijne, J.W. (2006). Salinisation in irrigated agriculture in Pakistan:

Mistaken predictions. Water Policy, 8(4), 325–338.

Kijne, J.W., Prathapar, S.A., Wopereis, M.C.S., and Sahrawat, K.L. (1998).

How to manage salinity in irrigated lands: a selective review with

particular reference to irrigation in developing countries. SWIM

Paper 2. International Water Management Institute, Colombo, Sri

Lanka.

Koctürk, O.M., and Engindeniz, S. (2009). Energy and cost analysis of

sultana grape growing: A case study of Manisa, west Turkey.

African Journal of Agricultural Research Vol. 4 (10), pp. 938 – 943.

Kriedemann, P.E., and Goodwin, I. (2003). Regulated Deficit Irrigation and

Partial Rootzone Drying: An overview of principles and

applications. Land and Water Australia Product No. PR 020 382,

ISBN 0642 76089 6.

435

Kumar, M.D. (2005). Impact of electricity prices and volumetric water

allocation on energy and groundwater demand management:

analysis from Western India. Energy Policy 33 (1), 39–51.

Lal, R. (2004). Carbon sequestration in soils of central Asia. Land

Degradation and Development 15 (6), 563–572.

Leslie, D. (1992). Rice 2000: environmental policy paper, Dwyer Leslie Pty

Ltd, Canberra.

Loveday, J, Beatty, H.J. and Stewart, G.A. (1978). Data relating to some

irrigated soils of the M.I.A., New South Wales, and Correlations

between some of their Properties, Technical Memorandum 2/1978.

Loveys, B., Dry, P., Hutton, R., and Jerie, P. (1999). Improving the water

use efficiency of horticultural crops. Project report prepared for

Land and Water Australia under National Program for Irrigation

Research and Development.

Malik, D.P., and Luhach, M.S. (2002). Economic dimensions of drip

irrigation in context of fruit crops. International Workshop on

Economics of Water and Agriculture, Institute of Food, Agriculture

and Environmental Sciences, The Hebrew University, Jerusalem,

Rehovot, Isreal, December 18-20,2002.

Mandavia, A.B. (1999). Modernization of irrigation system operational

management by way of canal automation in India. Proceedings of

the Fifth International ITIS Network Meeting, Aurangabad,

Maharashtra, India, 28 - 30 October 1998. In FAO (1999),

Modernization of irrigation system operations. Bangkok, Thailand.

Marsden Jacob Associates, (2003). Improving Water-use Efficiency in

Irrigation Conveyance Systems: a Study of Investment Strategies.

For Land & Water Australia, 2003. Product Number PR030516.

ISBN: 0642 760 993

Marsh, M.D. (2008). The Water-Energy Nexus: A Comprehensive Analysis

in the Context of New South Wales. PhD thesis University of

436

Technology, Sydney, Australia. Available online at:

http://utsescholarship.lib.uts.edu.au/dspace/handle/2100/1075

(accessed December 2010).

Mays, L.W., and Tung, Y.K. (1992). Hydrosystems Engineering and

Management. McGraw-Hill, Inc., NY. ISBN: 0-07-041146-8

Meadows, D. (2008). Thinking in Systems - A primer (Earthscan) ISBN

978-1-84407-726-7.

Melville, F., and Broughton, P. (2004) Trading in water rights, water and

the Australian economy, Growth, 52, Committee for Economic

Development of Australia, Melbourne.

Merrey, D.J. (1997). Expanding the frontiers of irrigation research: Results

of research and development at the International Irrigation

Management Institute (IIMI), 1984 to 1995. Columbo, Sri Lanka:

IIMI. Xlii, 217p.

Meyer WS. (1996). Crop coefficients for selected crop based on lysimeter

studies. CSIRO Land and Water, Griffith Laboratory (unpublished).

Meyer, W.S. (2005). The Irrigation Industry in the Murray and

Murrumbidgee Basins. CRC for Irrigation Futures Technical Report

No. 03/05.

Minasny B., McBratney A.B. (2001). The Australian soil texture

Boomerang: A Comparison of the Australian and USDA/FAO Soil

Particle Size Classification Systems. Australian Journal of Soil

Research, 39:1443-1451.

Molden, D., Rust, H.M., Sakthivadivel, R. and Makin, I. (2003). A Water-

productivity framework for understanding and action. In: Water

Productivity in Agriculture: Limits and Opportunities for

Improvement; eds. Kijne, J.W., Barker, R., and Molden, D.

International Water Management Institute, Colombo, Sri Lanka.

437

Modi, V., McDade, S., Lallement, D., Saghir, J. (2007). Energy services for

the millennium development goals. Energy Sector Management

Assistance Programme, United Nations Development Programme,

UN Millennium Project, and World Bank, New York.

Moreshet, S., Cohen, Y., and Fuchs, M. (1983). Response of mature

‘Shamouti’ orange trees to irrigation of different soil volumes at

similar levels of available water. Irr. Sci., Vol. 3, No. 4, p223-236,

DOI: 10.1007/BF00272838

Murray-Darling Basin Commission (MDBC) (2006). The Pilot Interstate

Water Trading Project.

http://www.mdbc.gov.au/nrm/water_issues/water_trade/pilot_interst

ate_water_trading_project, Murray-Darling Basin Commission,

Australia.

Murray–Darling Basin Authority (MDBA) (2010). Guide to the proposed

Basin Plan: overview, Murray–Darling Basin Authority, Canberra.

ISBN978-1-921557-72-9

Murray–Darling Basin Authority (MDBA) (2012). Proposed basin plan – A

revised draft. Murray–Darling Basin Authority, Canberra. ISBN

(print): 978-1-922068-70-5

Murray Irrigation Limited, (2010). Customer information kit. Murray

Irrigation Limited, Deniliquin, NSW

Murrumbidgee Irrigation Annual Report (MI AR) (2008). Murrumbidgee

Irrigation Limited Annual Report, Griffith, NSW 2680, Australia.

Murrumbidgee Irrigation, (2008) Murrumbidgee Irrigation Licence

Compliance Report 2007/08 – September 2008.

Murrumbidgee Irrigation, (2009). Licence compliance report,

Murrumbidgee Irrigation Limited, Griffith NSW.

Murrumbidgee Water Exchange (2011). “History” Accessed on December

2011. https://murrumbidgeewater.com.au/history.html

438

National Academy of Sciences (NAS) (2010). Hidden Costs of Energy:

Unpriced Consequences of Energy Production and Use. The

National Academies Press, Washington, DC 20001. 506 pp. ISBN:

0-309-14640-2

National Water Commission (NWC) (2008). Australian Water Markets

Report 2007-2008. National Water Commission, Canberra.

National Water Commission (NWC) (2009). Water Dictionary. Available

online at:

http://dictionary.nwc.gov.au/water_dictionary/pdf/WaterDictionary.

pdf


Report 2009–10. December 2010, Canberra. Available online at:

http://www.nwc.gov.au/


Report 2010-2011. National Water Commission, Canberra. ISBN

978-1-921853-45-6

National Water Commission (NWC) (2012). Impacts of water trading in the

southern Murray–Darling Basin between 2006–07 and 2010–11,

NWC, Canberra. ISBN: 978-1-921853-63-0

National Water Commission (2012). Assessing water stress in Australian

catchments and aquifers, NWC, Canberra. ISBN: 978-1-921853-72-

2. Available at: http://nwc.gov.au/

Nevison C.D., Esser G., Holland, E.A. (1996). A Global-Model of

Changing N2O Emissions from Natural and Perturbed Soils.

Climatic Change, Vol. 32, Iss. 3, 327-378, 1996

New Maxico State University (2000). Drip Irrigation for Row Crops.

Cooperative Extension Service Circular 573, College of Agriculture

and Home Economics. Las Cruces, New Mexico

439

Northern Victoria Irrigation Renewal Project (NVIRP) website:

http://www.nvirp.com.au/

NSW Agriculture (1998). Murrumbidgee Irrigation Area and districts Land

and Water Management Plans. NSW Agriculture, Yanco, NSW

NSW Department of Industry and Investment (2011). Fertigation:

delivering fertiliser in the irrigation water. Primefact No. 1089

OECD, (2001). Environmental Indicators for Agriculture – Vol. 3: Methods

and Results, OECD, 2001, glossary, pages 389-391.

Ogino, A., Orito, H., Shimada, K. and Hirooka, H. (2007). Evaluating

environmental impacts of the Japanese beef cow–calf system by the

life cycle assessment method. Animal Science Journal, 2007

Ommani, A.R. (2011). Productivity of energy consumption in agricultural

productions: A case study of corn farmers of Ahwaz Township,

Iran. African Journal of Agricultural Research Vol. 6(13), pp. 2945-

2949.

Organization of Food and Agriculture (FAO) (1998). News & highlights.

International coalition focuses on research and technology to help

farmers in developing countries grow ‘‘more crop per drop’’.

Organization of Food and Agriculture (FAO), (2000). The energy and

agriculture nexus. Environment and Natural Resources Working

Paper No. 4. FAO, Rome, 2000.

Organization of Food and Agriculture (FAO) (2003). Statistical databases.

Organization of Food and Agriculture (FAO) (2007). Review of

agricultural water use per country.

www.fao.org/nr/water/aquastat/water_use/index.stm, AQUAST,

FAO, Rome.

O’Neill, C., Humphreys, L., Fawcett, B., and Katupitiya, A. (2006).

Subsurface drip superior to sprinkler and furrow – again! In: IREC

Farmers’ Newsletter, No. 173, Spring 2006.

440

Ozkan, B., Akcaoz , H., Karadeniz, F. (2004). Energy requirement and

economic analysis of citrus production in Turkey, Energy

Conversion and Management, 45(11/12), pp. 1821 – 1830.

Ozkan, B., Fert, C., and Karadeniz, C.F. (2007). Energy and cost analysis

for greenhouse and open-field grape production. Energy, Vol. 32,

Iss. 8, pp. 1500-1504.

Pendlebury, P. (1994). Evaluating Drainage Reduction Strategies using

BBSWAMP model, DWR Report to MIA LWMP, Leeton, NSW.

Pereira, L.S. (2006). Relating water productivity and crop

evapotranspiration. Options méditerranéennes, Series B "Studies

and Research”, No. 57. Proceedings of Amman WASAMED

Workshop. International Centre for Advanced Mediterranean

Agronomic Studies, Paris.

Pereira, L.S. (2007). Indicators to support developing a new paradigma for

irrigated agriculture. méditerranéennes, Series B "Studies and

Research”, No. 59. Proceedings of the 5th WASAMED Workshop,

International Centre for Advanced Mediterranean Agronomic

Studies, Paris.

Pereira, L.S, Perrier A., Allen R.G. (1999). Evapotranspiration: concepts

and future trends. Journal of Irrigation and Drainage Engineering-.

ASCE 125: 45–51

Pimentel, D. (1991). Ethanol fuels: Energy security, economics, and the

environment. Journal of Agricultural and Environmental Ethics 4:

1-13.

Pimentel, D. (1992). Energy inputs in production agriculture. Pages 13-29

in R.C. Fluck, ed. Energy in World Agriculture. Amsterdam:

Elsevier.

Pimentel, D. (1998). Energy and dollar costs of ethanol production with

corn. In Hubbert Center Newsletter, 1-2. #98/2. Golden, CO.: M.

441

King Hubbert Center for Petroleum Supply Studies, Colorado

School of Mines.

Pimentel, D. and Heichel, G. (1991). Energy efficiency and sustainability of

farming systems. Pages 113-123 in R. Lal and F.J. Pierce, eds. Soil

Management for Sustainability. Ankeny, Iowa: Soil and Water

Conservation Society.

Pimentel, D., Berger, B., Filiberto, D., Newton, M., Wolfe, B., Karabinakis,

E., Clark, S., Poon, E., Abbett, E., Nandagopal, S. (2004). Water

resources: agricultural and environmental issues. BioScience 54(10),

909–918.

Postel, S.L. (2000). Entering an era of water scarcity: the challenges ahead.

Ecological Applications 10 (4), 941–948.

Powell, M.J.D. (1978). A fast algorithm for nonlinearly constrained

optimization calculations. Numerical Analysis: Lecture Notes in

Mathematics, 1978, Volume 630/1978, 144-157, DOI:

10.1007/BFb0067703

Powell, M.J.D., and Yuan, Y. (1991). A trust region algorithm for equality

constrained optimization. Mathematical Programming, Vol. 49. Pp.

189-211, North Holland.

Pratt Water. (2004). The Business of Saving Water - Report of the

Murrumbidgee Valley Water Efficiency Project. Pratt Water Pty

Ltd, Australia. ISBN 0 975 725 610

Price, W. (1999). Guidelines for Rehabilitation and Modernization of

Irrigation Projects. International Commission on Irrigation and

Drainage (ICID). ISBN: 81-85068-71-2

Proust, K. (2003). Ignoring the signals: irrigation salinity in New South

Wales, Australia. Irrigation and Drainage 52, 39–49.

Ramsey, H. (2007). Citrus Irrigation. In FarmNote number 275.

Department of Agriculture and Food, Waroona, WA, Australia

442

Raupach, M.R., Marland, G., Ciais, P., Le Quere, C., Canadell, J.G.,

Klepper, G., Field, C.B. (2007). Global and regional drivers of

accelerating CO2 emissions. PNAS 104(24), 10288–10293.

Rawls, W.J., Brakensiek, D.L., and Saxton, K.E. (1982). Estimation of soil

water properties. Transactions of the ASCE, pp 1316 – 1328

Renault, D. and Vehmeyer, P.W. (1999). On reliability in irrigation service

preliminary concepts and application. Irrigation and Drainage

Systems 13: 75–103, 1999

Rhoades, J.D. and Loveday. (1990). Salinity in irrigated agriculture, In:

Americans Society of Civil Engineers. Irrigation of Agricultural

crops. (Eds.): B.A. Steward and D.R. Nilson. Am. Soc. Agron.

Mono., 30: 1089-1142.

Rushton, K.R. (1999). Groundwater aspects: losses are inevitable but re-use

is possible? Agric. Water Manage, 40: 111-116.

Sakthivadivel, R., Thiruvengadachari, S., and Amarasinghe, U.A. (1999).

Intervention analysis of an irrigation system using a structured

system concept. Proceedings of the Fifth International ITIS Network

Meeting, Aurangabad, Maharashtra, India, 28 - 30 October 1998. In

FAO (1999), Modernization of irrigation system operations.

Bangkok, Thailand.

Schnepf, R. (2004). Energy Use in Agriculture: Background and Issues.

Available on the:

http://www.nationalaglawcenter.org/assets/crs/RL32677.pdf

Simonovic, S.P. (2000). Tools for water management: one view of the

future. Water International 25, 76 – 88.

Singh, J.M. (2002). On farm energy use pattern in different cropping

systems in Haryana, India. Master of Science Thesis, International

Institute of Management, University of Flensburg, Germany. 106p.

443

Singh, R.P., Mullen, J.D., and Jayasuriya, R.T. (2005). Farming Systems in

the Murrumbidgee Irrigation Area in NSW, Economic Research

Report No. 10, NSW Department of Primary Industries, Yanco,

NSW. ISBN 0734713436.

Skewes, M. (2010). Adapting Permanent Horticulture to Cope with Water

Scarcity. 2010 Outlook Conference, Australian Bureau of

Agricultural and Resource Economics (ABARE), Canberra.

Smart, A., and Aspinall, A. (2009). Water and the electricity generation

industry, Waterlines report, National Water Commission, Canberra.

ISBN online: 978-1-921107-78-8.

Srinivasan, M.S., Schmidt, J., Poyck, S., and Hreinsson, E. (2011).

Irrigation Reliability under Climate Change Scenarios: A Modeling

Investigation in a River-Based Irrigation Scheme in New Zealand.

Journal of the American Water Resources Association.

Sterman, J.D. (2000). Business Dynamics: Systems Thinking and

Modelling for a Complex World. McGraw-Hill, NY, USA.

Stewart, J.I., Danielson, R,E., Hanks, RT, Jackson, E.B., Hagan, R.M.,

Pruitt, W.O., Franklin, W.T., Riley, J.P. (1977). Optimizing crop

production through control of water and salinity levels in the soil.

Utah Water Research Lab. PR. 151-1, Logan, Utah, 191 pp.

Stout, B.A. (1990). Handbook of Energy for World Agriculture London:

Elsevier Applied Science. London/New York

Talsma, T. (1963). The control of saline groundwater, Veenman & Zonen,

Wageningen.

Tasmanian Department of Primary Industries, Water and Environment

(TDPIWE), (2001). Irrigation Equipment and Techniques: Wise

Watering, Irrigation Management Course.

444

Taylor, J. K., and Hooper, P.D. (1938). A soil survey of the horticultural

soils in the Murrumbidgee. Irrigation Areas, New South Wales.

118ed. Melbourne: CSIRO; 1938 (Bulletin No. 118)

Texas Cooperative Extension (2001). Economics of Irrigation Systems.

Texas A & M University System. Article No. B-6113, 12/01.

Torcellini, P., Long, N., and Judkoff, R. (2003). Consumptive Water Use

for U.S. Power Production. National Renewable Energy Laboratory

Technical Report No. NREL/TP-550-33905, Colorado 80401-3393.

Treeby, M., Falivene, S., and Skewes, M. (2011). Fertigation: delivering

fertilizer in the irrigation water. PrimeFact 1089. ISSN 1832-6668

Van der Lely, A. (1993). Present and Future Salinity Conditions in the

MIA, Department of Water Resources, Technical Report 93/19

van der Lely, A. (1998). Soil salinity assessment and prediction model –

Review of methodology for irrigated areas in NSW. Department of

Land and Water Conservation Report for the CRC for the

Sustainable Rice Production, Leeton, NSW.

Ventana Systems Inc. (2004). Vensim 5 User’s Guide. Ventana Systems.

Harvard, Massachusetts, USA.

Vlek, P., Rodraguez-Kuhl, G., Sommer, R. (2004). Energy use and CO2

production in tropical agriculture and means and strategies for

reduction or mitigation. Environment, Development and

Sustainability 6 (1), 213–233.

Watermove (2011) ‘Price History for Trading Zone’, accessed online in

December 2011. https://www.watermove.com.au

Water Services Association of Australia (WSAA). (2009). Enviromain -

PVC – M pipes for Pressure Applications, DN100 – DN300,

AS/NZS 4765:2007. WSAA Product Appraisal Report PA 0814.

445

Wells, C. (2001). Total Energy Indicators of Agricultural Sustainability:

Dairy Farming Case Study. Ministry of Agriculture and Forestry,

Wellington, New Zealand.

Williams, A. (2007). Carbon Farming. Australian Boardcasting

Corporation, Sydney.

<http://www.abc.net.au/stateline/nsw/content/2006/s1844608.htm>.

Winnie, G.L., Arjen, H., Theo, van der M. (2008). The Water Footprint of

Energy Consumption: an Assessment of Water Requirements of

Primary Energy Carriers. Science & Technology Vision, Vol. 4, No.

5. p 38 – 42.

Wichelns, D. (2005). Economic analysis of integrated on-farm drainage

management. Irrigation and Drainage Systems 19, 161–177.

Wichelns, D., Cone, D., Stuhr, G. (2002). Evaluating the impact of

irrigation and drainage policies on agricultural sustainability.

Irrigation and Drainage Systems 16, 1–14.

Wine Grape Growers’ Australia (WGGA), (2008). Regional Vineyard

Benchmarking Report. By Scholefield Robinson Horticultural

Services Pty Ltd, June 2008.

Xevi, E., and Khan, S. (2005). Computer Software for Nodal Network

modelling, Simulation and Optimisation of Water Resources

Management. In Zerger, A. and Argent, R.M. (eds) MODSIM 2005

International Congress on Modelling and Simulation. Modelling and

Simulation Society of Australia and New Zealand, December 2005,

pp. 1992-1997. ISBN: 0-9758400-2-9.

http://www.mssanz.org.au/modsim05/papers/xevi.pdf

Yaldiz, O., Ozturk, H.H., Zeren, Y., Bascetincelik, A. (1993). Energy use in

field crops of Turkey, V International Congress of Agricultural

Machinery and Energy, 12–14, Kusadas.

447

Appendix A: Excerpts from Vensim code for

calculation of different model variables

A1: Calculation of Crop evapotranspiration for each time step

448

A2: Calculation of irrigation demand and supply at a given node

A3: Calculation of estimated crop yield affected by water stress

449

A4: Calculation of components of energy head

A5: Calculation of cumulative pumping energy consumption

450

Appendix B: A snapshot of developed Vensim model in dynamic simulation mode

451

Appendix C: Fertilizer and chemicals input costs

C1: Fertilizer and chemicals input costs ($/ha) for citrus and wine grapes for

the three irrigation systems.

Fertilizer or chemicals

name

Unit rate

Furrow Sprinkler Drip

Kg/ha $/ha Kg/ha $/ha Kg/ha $/ha

Urea ($/kg) $0.60 260 156 208 124.8 200 $120

DAP ($/kg) $0.55 110 60.5 88 48.4 80 44

Potash ($/kg) $1.00 95 95 76 76 70 $70

Herbicide ($/l) 65 100 92 80

Fungicide ($/kg)

$4.00 5.5 22 6.5 26 4.5 $18

Pesticide ($/l) 180 180 200 150

Total 613.5* 567.2 482

*For wine grapes, this figure is 380 $/ha

Source: Giddings (2004), Giddings (2005), Falivene (2003), Department of

Primary Industries, New South Wales (2000).

452

Appendix D: Tractor operating costs as per 2008

D1: Tractor operating costs as per 2008 (values of operating cost have been

indexed at 3% per annum rate)

Tractor Model New Holland T4030 (70 HP)

New Price: $70,000 Yearly work: 1000 hrs/yr

Trade in price:

45% of new $31,500 Age at trade in: 5000 hrs = 5

years

Interest rate: 10%

OVERHEAD COSTS

Item Cost per year Item Cost per hour

Depreciation 7,700.0 Depreciation 7.70

Interest 5,075.0 Interest 5.08

Insurance 508.0 Insurance 0.51

Tractor Overhead Costs $13.28

VARIABLE COSTS

Item No. Cost Use Variable costs summary

Diesel Fuel ($/L) 1.16

12

L/hrFUEL ($/h) 13.92

Engine oil ($/L) 4.70

9.5

L/600hrsOIL($/h) 0.07

Hydraulic oil ($/L) 4.18

45

L/1200hrs 0.16

Diff/Hub oil ($/L) 4.18

7.5

L/1200hrs 0.03

Coolant ($/L) 4

14

L/1200hrsCOOLANT ($/h) 0.05

Fuel filter ($/filter) 1 30

600

hrs/filterFILTERS ($/h) 0.05

Oil filter ($/filter) 1 10

600

hrs/filter 0.02

Hydraulic oil filter ($/filter)

1 60 600

hrs/filter 0.10

Air filter ($/filter) 1 150 1200 0.13

453

hrs/filter

Grease ($/kg) 11.15

50

hrs/0.25kgGREASE ($/h) 0.06

Rear tyres 2 1,000

3000

hrs/tyreTYRES ($/h) 0.67

Front tyres 2 500

3000

hrs/tyre 0.33

Battery ($/yr) 1 300

2000

hrs/battery

BATTERIES ($/h) 0.15

Maintenance labour ($/yr)

20 0.05

hrs

MAINTENANCE ($/h) 1.00

Repairs (% of tractor price/yr) 2% REPAIRS ($/h) 1.40

Tractor Variable Costs ($/h) 18.12

Total Tractor cost per Hour ($/h) 31.40

Total Tractor cost per Hour ($/h) adjusted for 3% inflation 35.2

Source: Giddings (2004), Giddings (2005), Falivene (2003)

a spatial dynamic framework to integrate regional water …

Documents