landscape level hydrological modeling & farm-scale modeling

Landscape level hydrological modeling

&

Farm-scale modelingFred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012

Landscape level hydrological modeling

Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012

Andes • Ganges • Limpopo • Mekong • Nile • Volta

Study objectivesModeling hydrological dynamics to quantify water fluxes for achieving optimal crop‐livestock productivity

o Assess sub‐basin scale water balance thresholds at target sites

o Develop water allocations framework in target sites

o Recommend best‐fit integrated rainwater management strategies that maximize productivity


Study sites

Landscape hydrological modeling:

o Conduct sub‐basin water balance thresholds

o Develop a water allocations framework in target sites

o Assess water productivity in specific crop‐livestock systems


• Baseline characterization has been conducted in target sites at the household level

• Tools: and

• SWAT hydrological modeling is physically based– Weather, soil properties, topography, vegetation,

and land management practices data sets

• DEM:– Used at 90 m resolution– Watershed delineation; Stream network

Methods


6

Crop water use trends in Golinga

Data Source: Ministry of Food and Agriculture, GhanaProduction estimates and Regional Crop Acreage data for 1992 to 2010 ‐ Complemented and verified with V2 Household survey data


7

Water, crops and livestock distribution for Golinga

Source: Processed from FAO Geo‐portal data‐Not checked against V2 HH data

Source: Ramankutty et al, 2000Processed from Global Croplands database;Complemented with Ghana MoFA Data and V2 Household data


8

Water Balance Components for Golinga

0

200

400

600

800

1000

120019

8019

8119

8219

8319

8419

8519

8619

8719

8819

8919

9019

9119

9219

9319

9419

9519

9619

9719

9819

9920

0020

0120

0220

0320

0420

0520

0620

0720

0820

0920

1020

1120

1220

1320

14

Rainfall (m

m) a

nd Discharge (m

m)

Rainfall (mm) Surface Water Discharge (mm) Groundwater Discharge (mm)

Percolation (mm) Evapotranspiration (mm)

Warm‐up Calibration ValidationSim

ulated


Milestones:• Cropping density and livestock distribution ascertained for all study

sites; Water balance thresholds calculated for all study sites• Currently developing crop‐livestock water productivity maps for all

target sites• Landscape outputs from water allocations and water balance will

complement farm‐level flows analysis

Conclusion• Hydrological analysis indicated that reservoirs play a critical role in

maintaining storage and reducing surface runoff losses at sub‐basin scale

Conclusion

Farm-scale modeling

Fred Kizito, Katrien Descheemaeker, Sabine Douxchamps 3 / 7 / 2012


ObjectivesIdentify and evaluate promising interventions for improved farm productivity• Extrapolating field results in space and time• Aggregate field level outputs to farm level• Scenario analysis: exploring options• Risk analysis• Tradeoff analysis (tradeoffs in resource allocation)• Identifying issues for further (field) research• Discussion and decision support tool: informing the

innovation platform


NPKNPK

NPK

Options

Giller et al. 2010


NUANCES-FARMSIM: farm-scale modeling approach

Tittonell et al. (2007) Fld Crops Res. 100, 348-368; Rufino et al. (2007) Livestock Sci. 112, 273-287; Chikowo et al. (2008) Ag. Syst. 97, 151-166; Tittonell et al. (2009) Ag. Syst. 101, 1-19; van Wijk et al. (2009) Ag. Syst. 102, 89-101; Tittonell et al. (2010) E. J Agron.32, 10-21.


APSIM (Agricultural Production Systems sIMulator)


In-house feeding

Grazing

Feed gap

Constraint analysisExample of feedbase in villages around Golinga reservoir


Baseline situation• 1.5 ha farm• household of 8 people• crops: millet, sorghum and cowpea intercropped• no crop residue stored for cattle• 3 breeding cows, sells at 4-5 years, herd of 8-10

Scenario Analysis

Adapted from McDonald (2010)


Baseline

Animals sold (10y) 5‐6

Animals on hand 12‐13

Forage deficit 7000

Wet season labour +50

Cattle revenue 34000

Gross Margin* 515000

Cash balance ‐3000

* - including home consumption

Scenario Analysis



Baseline Manure (4 t/ha)

Animals sold (10y) 5‐6 6‐7

Animals on hand 12‐13 13

Forage deficit 7000 6000

Wet season labour +50 +20

Cattle revenue 34000 37000

Gross Margin 515000 637000

Cash balance ‐3000 109000

Scenario Analysis




Crop residue harvesting

Animals sold (10y) 5‐6 6‐7 7‐8

Animals on hand 12‐13 13 13

Forage deficit 7000 6000 3000

Wet season labour +50 +20 +10

Cattle revenue 34000 37000 41000

Gross Margin 515000 637000 671000

Cash balance ‐3000 109000 140000

Scenario Analysis




Crop residue harvesting

Sell cow, buy 10 sheep &

fatten

Calves sold (10y) 5‐6 6‐7 7‐8 6‐7

Cattle on hand 12‐13 13 13 9‐10

Forage deficit 7000 6000 3000 4400

Wet season labour +50 +20 +10 +50

Livestock revenue 34000 37000 41000 96000

Gross Margin 515000 637000 671000 739000

Cash balance ‐3000 109000 140000 205000

Scenario Analysis



Discussion support tool Learning tool

Scenario Analysis



Simulation experiment


Lessons:- Fertilizer increases average yield, but also production risk- Information on risk is useful for insurance providers (partner in the IPs?)- Water and nutrient use efficiency are interlinked

Simulation experiment


Understanding resource allocation decisions

Resources are finite; directing them to one objective will penalize other objectives

• Labor: weeding vs. marketing produce• Cash: fertilizers vs. hiring labor for weeding • Crop residues: soil organic matter vs. livestock feeding

Tradeoff analysis


concentrates

ferti

lizer

Tradeoff analysis


concentrates

ferti

lizer

Lessons:- Tradeoff analysis helps us in systems understanding- Linked with understanding of socio-institutional settings (e.g. market) and farmers’

objectives, this can be used to design well-adapted interventions

Tradeoff analysis


Farm systems models are useful tools for research to - Understand complex farm dynamics, including farmer

decision making- Identify topics for further (field) research

for development through - Assisting in the development of adapted interventions- Generation of information for discussion support (in IPs)

! Need for high quality input data

Conclusions


Merci pour votre attention!Thanks for your attention!

landscape level hydrological modeling & farm-scale modeling

Technology

scale modelingfred kizito

katrien descheemaeker

andesgangeslimpopomekongnilevolta

information

4t ha

farm

syst

ips