flexible and precise irrigation platform to improve farm scale … · precise irrigation (pi)...

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Flexible and PrecIse IrriGation

PlAtform to Improve FaRm Scale

Water PrOductivity

Figaro WP8

Deliverable 8.2 “On-farm field test calibration”

Deliverable 8.3 “On-farm precise irrigation platform

validation results”

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THIS PAGE HAS BEEN INTENTIONALLY LEFT BLANK

Revision History

Report Version V 1.0.1

Due Date 29.2.2016

Dissemination Level PU

Author(s) A. Battilani, M.N. Andersen, F. Plauborg,

G. Sylaios, I. Tsakmakis, T. Ramos, L.

Simionesei , F. Martinez, M.A. Jimenez

Bello, A. Gips.

Deliverable Lead Contractor Orev

Responsible Person Raz Chen

Contact for query [email protected]

Summary

Foreword ........................................................................................................................................................................... 5

Introduction ...................................................................................................................................................................... 5

Materials and methods ..................................................................................................................................................... 6

PI models calibration and validation on Maize and Tomato in Italy ........................................................................... 8

Preface ........................................................................................................................................................................... 8

Priorities ........................................................................................................................................................................ 9

Limitations ..................................................................................................................................................................... 9

Objectives ....................................................................................................................................................................... 9

Materials and Methods .................................................................................................................................................. 9 Brief description of Irriframe model ........................................................................................................................ 12

Results .......................................................................................................................................................................... 14 Soil volumetric water content .................................................................................................................................. 14 Canopy Cover Measurements .................................................................................................................................. 19 Dry matter Yield ...................................................................................................................................................... 21

PI models calibration and validation on Potato in Denmark ...................................................................................... 22

Foreword ...................................................................................................................................................................... 22

Priorities ...................................................................................................................................................................... 22

Objectives ..................................................................................................................................................................... 22

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Materials and methods ................................................................................................................................................ 22

Results .......................................................................................................................................................................... 24 AquaCrop testing ..................................................................................................................................................... 24 Canopy Cover Measurements .................................................................................................................................. 27 Soil water content .................................................................................................................................................... 27

Dry Biomass ................................................................................................................................................................. 28

Yield.............................................................................................................................................................................. 29 Sensor guided split N application ............................................................................................................................ 29 Daisy modelling results for potatoes in Denmark. ................................................................................................... 30

PI models calibration and validation on Cotton in Greece ......................................................................................... 41

Preface ......................................................................................................................................................................... 41

Method ......................................................................................................................................................................... 41 Experimental Field Planning ..................................................................................................................................... 41 Cotton File Calibration procedure ............................................................................................................................ 43 Field Monitoring ...................................................................................................................................................... 47

Canopy Cover Measurements .................................................................................................................................. 47 Dry aboveground Biomass ....................................................................................................................................... 48 Soil Water Content ................................................................................................................................................... 48 Plant Growth Regulators (PGRs) ............................................................................................................................. 51 Seed Cotton Yield .................................................................................................................................................... 51

PI models calibration and validation on Maize in Portugal ........................................................................................ 51

Introduction ................................................................................................................................................................. 51

Material and methods .................................................................................................................................................. 52 Field location ........................................................................................................................................................... 52 Equipments .............................................................................................................................................................. 52 Sensors calibration ................................................................................................................................................... 53

Results .......................................................................................................................................................................... 54 Canopy Cover ........................................................................................................................................................... 54 Soil water storage ..................................................................................................................................................... 55 Above ground dry biomass ...................................................................................................................................... 57

PI models calibration and validation on Citrus in Spain ............................................................................................ 58

Description of the experimental fields in details ........................................................................................................ 58

Priorities ...................................................................................................................................................................... 59

Limitations ................................................................................................................................................................... 60

Objectives ..................................................................................................................................................................... 60

Material and methods .................................................................................................................................................. 60

Results .......................................................................................................................................................................... 62 Year 2014 ................................................................................................................................................................. 62 Yield and citrus composition ................................................................................................................................... 63 Year 2015 ................................................................................................................................................................. 65 Yield and citrus composition ................................................................................................................................... 65 Water saving results ................................................................................................................................................. 66

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Energy management ................................................................................................................................................. 67

PI test on Maize in Bulgaria........................................................................................................................................... 67

Introduction ................................................................................................................................................................. 67

Objectives ..................................................................................................................................................................... 68

Priorities ...................................................................................................................................................................... 68

Limitations ................................................................................................................................................................... 68

Methods and materials ................................................................................................................................................ 69

Results .......................................................................................................................................................................... 72 Moisture sensing ...................................................................................................................................................... 73 Yield......................................................................................................................................................................... 74

PI test on Cotton in Israel .............................................................................................................................................. 75

Introduction ................................................................................................................................................................. 75

Objectives ..................................................................................................................................................................... 76

Priorities ...................................................................................................................................................................... 76

Limitations ................................................................................................................................................................... 76

Materials and methods ................................................................................................................................................ 76

Results .......................................................................................................................................................................... 78

Summary and conclusions.............................................................................................................................................. 94

Literature cited ............................................................................................................................................................. 100

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Foreword

The present report combines results from the activities carried out in task 8.2 “Precise Irrigation

platform component calibration” and task 8.3 “Precise Irrigation platform component validation and

set-up of demonstration field”.

The original task 8.2 setup has slightly changed during the project. The foreseen calibration of the

precise irrigation (PI) models in year 2 was done in parallel with the calibration in year 3. The

modification required by Aquacrop, the reference model embedded into the Figaro PI platform,

required great efforts by FAO. The new model’ functions were tested using historical data provided

by Figaro’ partners and data collected in the first year. Thus, 8.2 and 8.3 activities were carried out

in parallel comparing Aquacrop calibrated outputs with those provide by other models (Daisy,

Irriframe, Mohid Land, IVIA Citrus, etc) already locally calibrated and widely applied.

The platform set-up tested in year 1 has been upgraded as forecast, mainly in its key components:

the Aquacrop model (FAO and Technion), the uManage interface and structure (Netafim) and the

Aquasafe shell (Hydromod).

The PI platform set-up, calibrated and validated, will be utilized for the last year on-farm

demonstration field and open days as well (WP9 and WP10). Data collected all over the extended

experimental period, and not only in year 3, are used to assess specific performance indicators

(WP7 link).

During the last year (2016) the on-farm test will be utilized mainly as demonstration field to show

the applicability and the results of the FIGARO platform. Policy and decision makers, farmer’s

organisation and association responsible and to the society as a whole will be invited to open field

days and workshops (WP9 and 10 link). Figaro PI operability and outcomes coherence with the res

Introduction

Today, there is no more doubt regarding the effectiveness of crop growth simulation models as tools

for evaluating effects of water deficits to optimize water use under limited conditions to enhance

sustainability and profitability of crop production. Simulation models are also considered as useful

tools for improving farm level water management and optimizing water use efficiency in water

scarce areas.

However, during the last five decades crop growth modeling has been evolving along with the

progress of computer technology. The changing goals, target users, and policies have influenced the

evolution of the modeling efforts over the years. Models targets slowly changed from strictly

scientific insight at leaf and plant scale to practical applications and impact analysis of management

practices (Sinclair and Seligman, 2000; Boote et al., 2003). Sufficiently precise simulation of plant

physiological processes and crop growth and development has progressively become necessary for

whatever management model. This progress imposed different structures regarding the levels of

complexity, the processes addressed and their functionality, the selection of algorithms and model

crop-growth modules, and input requirements (Bouman et al., 1996; Monteith, 1996; Fischer et al.,

2000; Hammer et al., 2002). At the core of any model, there is a set of algorithms that estimates the

production rate of biomass from the captured resources such as carbon dioxide, solar radiation, and

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water (Azam-Ali et al., 1994). Accordingly, three main models type (Steduto, 2003) are

distinguished: i) carbon-driven; ii) radiation-driven; iii) water-driven. Water-driven crop growth

modules are based on a water productivity (WP) parameter linearly correlating biomass growth rate

with transpiration (de Wit, 1958; Hanks, 1983; Tanner and Sinclair, 1983; Hsiao and Bradford,

1983; Steduto, 1996; Steduto and Albrizio, 2005). This approach results in a less complex model

structure and lower number of input parameters (Steduto et al., 2007, 2009). One of the major

advantages of water-driven models is in the opportunity to normalize the WP parameter for climate

and therefore having a greater applicability in space and time (Steduto and Albrizio, 2005; Hsiao et

al., 2007; Steduto et al., 2007).

Not many management models belongs to this group of models: only one of the two growth

modules of CropSyst, and AquaCrop, the model developed recently by FAO (Steduto et al., 2009;

Raes et al., 2009). Both of them need to be further adapted to daily management uses.

AquaCrop can be utilised by an ample range of users as farmers, practitioners, water managers, etc.

(Steduto et al., 2009). The model focuses on water input as the most limiting factor of crop growth,

especially in arid and semiarid regions where water stress varies in intensity, duration, and time of

occurrence (Hsiao, 1973; Bradford and Hsiao, 1982). The model has been tested and calibrated for

humid and sub-humid climate as well (Battilani and Letterio, 2015).

However, as Sinclair and Seligman explained (1996), no one universal model can exist in the field

of agricultural science and it is necessary to adapt system definition, simulated processes and model

formalizations to specific environments or to new problems (technical, environmental, genetic,

etc.). In this work, the performance of AquaCrop is compared with that of other locally well-

established models. Therefore, calibration for local climatic, soil and crop conditions is required,

when possible using as minimum data as possible.

In this report, three of the main principles underpinning the data selection approach implemented by

the Global Yield Gap Atlas (The Global Yield Gap and Water Productivity Atlas, 2015) were

followed: i) preference for using measured instead of estimated or interpolated data; ii)

transparency, reproducibility, and consistency in data selection; iii) use of local expertise to collect

and corroborate data inputs ensuring agronomic relevance.

The objective of this Figaro WP8 tasks was therefore to calibrate the FAO water productivity model

AquaCrop for various crops and validate its performance under contrasting environmental

conditions observed in the humid, sub-humid and semi-arid climate covered by the Figaro’ field

experiments.

Materials and methods Calibration and validation of Figaro platform components took place from 2013-2015 at eleven

sites in seven countries including Denmark, Italy, Greece, Bulgaria, Israel (two sites), Portugal and

Spain. Fields from a range of climactic and soil conditions tested the Figaro components on six

different crops: potato, maize, tomato, cotton, citrus, and grapes. Field test sites included academic

experimental sites as well as two commercial sites (table 1).

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Partner Country Crop Model(s) Other Parameters Sci/Com

Aarhus

University

Denmark Potato AquaCrop

Daisy

Nitrogen fertigation Sci

CER Italy Maize AquaCrop

Irriframe

Irrigation Strategies,

Management zones

Sci

CER Italy Tomato AquaCrop

Irriframe

Irrigation Strategies,

Management zones

Sci

DUTH Greece Cotton AquaCrop Sci

IST Portugal Maize AquaCrop

MohidLand

Sci

UPV Spain Citrus IVIA model Energy consumption Sci

UPV Spain Grape IVIA model Energy consumption Sci

Netafim Bulgaria Corn Com

Netafim Israel

(Upper

Galilee)

Cotton AquaCrop Com

Netafim Israel

(Gvaot

Hachoresh)

Citrus Com

The AquaCrop guideline papers recommendation for evaluation of the quality of the AquaCrop

calibration and validation were followed.

In order to evaluate the association between predicted and observed values and models outputs

accuracy and performances, summary and difference measures were calculated. Summary measures

include the index of agreement (d) that is a dimensionless indicator valued as a descriptive

parameter of model performance (Willmott, 1981; Willmott, et al., 2012) (equation 1). The more (d)

approaches 1, the more accurate model outputs are (0 ≤ d ≤ 1). While summary measures describe

the quality of simulation, difference measures try to locate and quantify errors. The normalized root

mean square error (NRMSE) has been used for comparative purposes indicating the magnitude of

the average error (Loague and Green, 1991) (equation 2). Model outputs accuracy has been tested

against the observed value calculating the model efficiency (E) (Nash and Sutcliffe, 1970) (equation

3). Nash–Sutcliffe efficiency can range from −∞ to 1. An efficiency of E = 1 corresponds to a

perfect match of modeled with observed data. An efficiency of E = 0 indicates that the model

predictions are as accurate as the mean of the observed data. An efficiency of E < 0 indicates that

observed mean is a better predictor than the model.

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Equation 1: Index of agreement

Equation 2: Normalized root mean square error

Equation 2: Model efficiency

Where: n = Number of observations; Pi = Predicted variable; Oi = Observed variable; Pm= mean

of Predicted variable; Om = Mean of observed variable.

PI models calibration and validation on Maize and Tomato in Italy

Preface

Modern agriculture is making use of crop/soil/water simulation models basically as tools for

research to analyse and organize knowledge gained in field experimentation. However, there is an

urgent need to make use of models also as tools for decision-making and technology-transfer.

Robust and locally calibrated models can be utilised to investigate a large number of water

management strategies extrapolating to alternative climate scenarios, cropping cycles and locations.

Despite some limitations and uncertainty, the modelling approach allows quantification of temporal

and spatial variability that would not be possible using traditional methodologies. Therefore,

modelling represents an effective way to assimilate different components of a cropping system,

analysing data, and finally address field operation. Models are not anymore simple mechanisms to

analyse information but instruments able to capitalise the scientific community knowledge helping

in the everyday field management for water stress mitigation purposes, food security – i.e. through

yield forecasting -, resource management and environmental issues.

AquaCrop is a crop water productivity model developed by FAO to provide an easy-to-use

modelling tool to an ample range of users, from farmers and agricultural consultants to water

managers and policy makers (Steduto et al., 2009). The model structure has been designed in order

to make it applicable across diverse locations, climate and seasons. To reach that goal AquaCrop

differentiates conservative (fixed) and non-conservative (case-specific) parameters. Conservative

parameters do not change with geographical location, crop cultivar, management practices or time,

100)(1

5.02

n

OP

ONRMSE

ii

m

2

2

)(

)(1

mi

ii

OO

OPEF

2

2

)(

)(1

mimi

ii

OOOP

OPd

9

and are meant to be determined with data from favourable and non-limiting conditions, but they

remain applicable for stress conditions via the modulation of their stress response functions. In fact,

it is expected that this simple structure and reduced number of parameters could facilitate model

calibration and utilization for different crops and under different management strategies (Steduto et

al., 2009; Raes et al., 2009).

Although proved flexible and precise, AquaCrop application in the Po Valley sub-humid climate

(Emilia-Romagna, North Italy) without local calibration gave poor results (Battilani et al., 2015).

Therefore, the FAO model has been calibrated on historical datasets and then validated using

datasets and then validated with data from irrigation experiments carried out in the frame of the

FIGARO EU project from 2013 to 2015.

Priorities

The overall purposes of the model calibration and validation is to evaluate the relative effect of

these essential components of the PI Platform, and compare the outputs of two different model

structures namely the water driven model Aquacrop and the soil/crop water balance DSS Irriframe.

Thereby the targets of the modelling exercise can be summarised as follows:

1) to assess the correctness of models estimating crop growth and yield;

2) to assess model capability to manage irrigation with different methods and applying deficit

strategies;

3) to determine the impact of field sub-management zones on the model behaviour and

reliability

Limitations

The main difficulties encountered relate to not pristine climatic condition.

The summer 2014 was particularly and unusually rainy. The number of drip irrigation was actually

limited and confined to the crop early stages. Afterward few interventions were necessary, mainly

to supply nutrients via fertigation.

At the end of June 2015 the experiments were ravaged by an unprecedented hail event

(20/06/2015). Both maize and tomato were sowed/transplanted again. The very late season re-start

obliged to change maize variety and FAO class with evident influence on the model calibration

parameters. Processing tomato was affected by the late transplant and by the very poor plantlet

conditions.

Objectives

The main objective is to analyse the performance of AquaCrop under different irrigation strategies

affecting the plant water status during the crop cycles and the final productivity.

In parallel the performance obtained with a local developed and calibrated irrigation Decision

Support System (DSS), IRRIFRAME, that could be implemented into the FIGARO platform in the

next future.

Materials and Methods

Aquacrop model has been calibrated making use of data collected in field experiments carried out

from 2013 to 2015. Each comprehensive dataset includes data on soil, climate, soil water content

measurements, cob/fruits weights and plant biomass, the latter recorded at least in correspondence

of critical plant phenological. All the data needed to calculate ET0 by a Pennman-Montheith-Allen

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equation were available at hourly step, as well as rainfall measurements. Processing tomato and

maize were chosen for the model calibration and validation due to its importance in the area.

In parallel the crop water balance DSS IRRIFRAME, developed locally and widely utilised in the

Emilia Romagna Region as well as all over Italy, has be tested for estimated soil volumetric water

content and compared with Aquacrop.

The experiments were carried out on silty-clay soils, typical of the Po valley low land (Table 1).

Those

soils are deep and without noticeable skeleton (> 2 mm).

Table 1. Main soil physical and chemical characteristics

An extended, shallow water table is usually present during the growing season at a depth ranging

from -0.60 m to -1.5/-1.8 m; until the beginning of fruit setting capillary rise could be significant in

terms of crop evapotranspiration replenishment or even harmful in rainy seasons.

The climate in the test area is sub-humid, but in the last decade water deficit in summer almost

doubled due to changes in rainfall distribution patterns, with an increase of heavy rainfall events

alternated with long periods of drought. The yearly rainfall total amount (750-850 mm) has not

changed, but its effectiveness satisfying crop water requirements had decreased significantly.

Irrigation methods in tomato field experiments were both sprinkler, by reel-machine boom, and drip

irrigated. Deficit irrigation concept has been applied calculating water regimes.

Deficit irrigation strategies were applied, namely regulated deficit of irrigation (RDI) and partial

rootzone drying (PRD). However models are validated only against RDI being not able to simulate

PRD.

From the second year (2014) drip irrigated experimental fields were split into sub-management

zones according with changes in soil hydrologic parameters. The zones are identified as 1Q, 4Q and

Central (CEN).

A detailed description of the field experiments carried out by CER can be found in the FIGARO

WP8 CER Field Experiment Protocols.

A comprehensive AquaCrop calibration and validation has been done on 8 independent historical

datasets. The model was calibrated for the following outputs: soil-water content, total yield, harvest

index, total biomass and its partition in above ground vegetation and fruit (Battilani et al., 2015).

The FIGARO field experiments are utilised to further improve the AquaCrop local validation.

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The WP8 field experiments combined in a Technical Itinerary framework (TI) advanced irrigation

technologies and strategies, DSS and monitoring technologies (soil and plant sensors, etc) forming

the FIGARO Precise Irrigation Platform.

The aim of the TI is to organise the existing knowledge assessed as applicable in the pilot area to

produce flexible, crop tailored, precise irrigation scheduling at field and farm level. In FIGARO, TI

criteria and results are transferred into and managed by an ITC platform (WP6). That approach will

offer to the field site partners an easy to use, integrated precise irrigation management platform,

designed to deal with the most common constraint limiting scientific knowledge and technological

transfer. The platform could be managed without particular IT or modelling skills. The

experimental design consists at suitable sites of Technical Itineraries (TI) (Sebillotte, 1978; Dumas,

1990; Meynard et al., 1996: Qualitom EU project, 2000, FertOrgaNic EU project, 2007), with a

traditional management control compared with the chosen WP6 platform. The plot size will be large

enough to allow application of normal on-farm crop husbandry practices.

The results of these experiments were analysed and organised in datasets then used to validate

AquaCrop and Irriframe models outputs.

In order to evaluate the association between predicted and observed values and models outputs

accuracy and performances, summary and difference measures were calculated. Summary measures

include the index of agreement (d) that is a dimensionless indicator valued as a descriptive

parameter of model performance (Willmott, 1981; Willmott, et al., 2012). The more (d) approaches

1, the more accurate model outputs are (0 ≤ d ≤ 1). While summary measures describe the quality of

simulation, difference measures try to locate and quantify errors. The normalized root mean square

error (NRMSE) has been used for comparative purposes indicating the magnitude of the average

error (Loague and Green, 1991). Model outputs accuracy has been tested against the observed value

calculating the model efficiency (E) (Nash and Sutcliffe, 1970). Nash–Sutcliffe efficiency can range

from −∞ to 1. An efficiency of E = 1 corresponds to a perfect match of modeled with observed data.

An efficiency of E = 0 indicates that the model predictions are as accurate as the mean of the

observed data. An efficiency of E < 0 indicates that observed mean is a better predictor than the

model.

Table 2. Validation parameters used for each year of the experiment.

Parameter 2013

(Validation)

2014

(Validation)

2015

(Validation)

Canopy Cover (%) YES YES YES

Dry above ground biomass

during cultivation period

(tn/ha)

NO NO NO

Soil Water Content (mm) YES YES YES


on harvesting (tn/ha) NO NO NO

Roots depth expansion (cm) NO NO NO

Dry Yield (tn/ha) YES YES YES

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A try-and-error approach aimed to minimize NRMSE has been applied to identify the best fitting of

the parameters under calibration. The calibrated parameters are reported in table 3.

Table 3. Description of the calibrated, estimated or measured parameters utilised to calibrate

AquaCrop.

Note: Calibration values are reported in the “Cal.” column; the “default” column shows the original

AquaCrop parameters; the “Mod.” Column indicate whether the calibrated parameter result from try

and error calibration process or from CER’ datasets.

Brief description of Irriframe model

Irriframe is an expert system for Irrigation Scheduling, developed by the CER implementing the

results of more than 50 years of research on plant/water relation and sustainable irrigation

management. The project was supported and co-funded by the Emilia-Romagna Region with the

aim to progressively reduce water use for irrigation. Irriframe is among the tools provided to the

farmers in the frame of Emilia-Romagna Regional Action Plan for Rural Development 2007-2013,

recently confirmed . The service is freely available on Internet and provides an ‘irrigation advice’

for the main water demanding crops, combining several data sources: meteorological data from

ARPA-ER (Regional Environment Protection Agency- Department of Agro-Meteorology); soil data

from the regional “Hydro-Geologic and Seismic Service”; crop parameters as defined by the CER,

including the application of the most effective irrigation strategy for every crop considered. The

crop water balance is calculated at daily step and at field scale according to the geographical

position (GIS) and to the crop characteristic, simulated or inputted by the farmer. The service

provides the users with the optimal irrigation volume and timing, via web or mobile phone text

message. The expert system has been setting to reach the highest production while saving water.

Cal. Default Mod. Description

0.1 0.15 Cal. Soil water depletion factor for canopy expansion (p-exp) - Upper threshold

0.6 0.55 Cal. Soil water depletion factor for canopy expansion (p-exp) - Lower threshold

0.5 3 Cal. Shape factor for water stress coefficient for canopy expansion

0.55 0.5 Cal. Soil water depletion fraction for stomata control (p - sto) - Upper threshold

0.6 0.7 Cal. Soil water depletion factor for canopy senescence (p - sen) - Upper threshold

4 3 Cal. Shape factor for water stress coefficient for canopy senescence

0.15 0.3 Est. Minimum effective rooting depth (m)

0.76 1 Meas. Maximum effective rooting depth (m)

50 60 Est. Effect of canopy cover in reducing soil evaporation in late season stage

22 20 Est. Soil surface covered by an individual seedling at 90 % emergence (cm2)

35,70 33.33 Meas. Number of plants per hectare (*103)

0.120 0.123 Cal. Canopy growth coefficient (CGC; fraction soil cover per day)

0.68 0.75 Meas. Maximum canopy cover (CCx) in fraction soil cover

4 4 Est. Calendar Days: from transplanting to recovered transplant

52 55 Est. Calendar Days: from transplanting to maximum rooting depth

85 91 Est. Calendar Days: from transplanting to start senescence

103 110 Est. Calendar Days: from transplanting to maturity

32 34 Est. Calendar Days: from transplanting to flowering

39 42 Est. Length of the flowering stage (days)

50 58 Est. Building up of Harvest Index starting at flowering (days)

19 18 Cal. Water Prod. normalized for ETo and CO2 (WP*)(g m-2

)

64 63 Meas. Reference Harvest Index (HIo) (%)

12 15 Cal. Allowable maximum increase (%) of specified HI

13

Since 2009 it implements economic calculation of the irrigation profitability, providing farmers

with further information other than optimal irrigation volume and time, assessing the economic

benefit related to the next irrigation.

Climatic and meteorological data are gathered on daily basis on the web-DB server from several

acquisition and elaboration systems. Irrigation scheduling is determined applying a mathematical

model based on daily water balance of soil-plant-atmosphere system. The software is composed by

three main modules: Web application, External data importation module, SMS module. The

external data importation module accepts a XML file as input data. The system returns a XML file

with the irrigation scheduling results as output. The application uses a relational database that

includes more than 70 tables. The database is organised in informational areas (Table 4).

Table 4. Description of database settings.

The GIS maps are managed on a Geodatabase. The polygon identifiers of the maps are dynamically

linked to the information stored in the database (e.g. meteorological and water table stations, soil

units).

Users can access to the service in different way and, from the web interface, they can totally interact

with the system and parameters.

The processes simulated in IRRIFRAME model (Figure 2) can be assembled in four groups:

• Water dynamics in soil: hourly calculation of soil-water content is carried out considering

three soil surface layer, rooted layer, bottom layer. According to Driessen, the amount of water

which moves between the layers of the soil profile is the water that exceeds single layer water

storage.

• Crop growth: plant phenology and root system development are simulated.

• Crop water requirements: crop evapotranspiration is calculated for standard and not-standard

condition using a single coefficient approach, according to Doorenbos and Pruitt.

• Shallow water table contribution: capillary rise is calculated as fraction of Etc by empirical

functions developed by CER.

The irrigation method is taken into account by the model, thus irrigation depth and timing are

tailored on irrigation technology.

Knowledge base area Area information User and farm description GIS data

Model configuration parameters Meteorological data Detailed crop description Soil map

Model and crop parameters (Kc,

crop stages..)

Water table depth data

User data:

Water table depth data

Start and stop crop date

Shallow

groundwater map

Lookup lists (managed crops,

irrigation systems..)

Soil information

User information

(registered users only)

14

Figure 1: Schema of soil-water IRRIFRAME model

Results

Soil volumetric water content

Previous Aquacrop validation shown an overestimation of soil water content. The soil water content

estimation error it is probably a twofold effect: hydrological components, i.e. capillary rise

contribution from the shallow water table, are not correctly estimated for the considered field sites

conditions; plant evapotranspiration could be underestimated, thus leading to an overestimation of

the residual soil water content. AquaCrop poor prediction of ET in the case of tomato grown with

deficit irrigation has been reported by Katerij (2013).

Aquacrop 2013:

The model performed better in sprinkler than in drip irrigated maize. The overall model agreement

with measured data resulted similar (d= 0.88 vs 0.81 with sprinkler and drip respectively), as also

small differences in NMRSE confirm. However, the model efficiency was by far higher with

sprinkler irrigation (EF= 0.66) when compared with drip irrigation (EF= 0.27).

Vice versa, Aquacrop performed better with drip irrigation on processing tomato. The agreement

index was respectively of 0.98 and 0.91 with drip and sprinkler, with small NMRSE differences

following the same trend. Model efficiency was of 0.93 with drip and of 0.75 with sprinkler.

More in general Aquacrop did not over or under estimated the soil water volumetric content but

resulted slowly reacting and with a not adequate amplitude and intensity.

AQUACROP IRRIFRAME AQUACROP IRRIFRAME

MAIZE DRIP 2013 MAIZE DRIP 2013 Tomato DRIP 2013 Tomato DRIP 2013

ave 30.21058 ave 30.21058 ave 24.85757 ave 24.85757

NRMSE 0.053528 NRMSE 0.067959 NRMSE 0.064821 NRMSE 0.080395

EF 0.270561 EF -0.17578 EF 0.929618 EF 0.891735

d 0.810744 d 0.76438 d 0.980344 d 0.968472

MAIZE SPR. 2013 MAIZE SPR. 2013 Tomato SPR. 2013 Tomato SPR. 2013

ave 30.329 ave 30.329 ave 25.75625 ave 25.75625


EF 0.659882 EF 0.457929 EF 0.753707 EF 0.623014

d 0.877839 d 0.861704 d 0.911531 d 0.920713

15

Figure 2: Measured and model simulated soil water volumetric content - 2013

Irriframe 2013:

The model performances with drip irrigated maize indicate that Irriframe outputs were not fitting

the measured data, as also figure 2 shows. Irriframe was either under or over estimating soil water

content all over the season.

Irriframe output with sprinkler irrigated maize were similar, although agreement and efficiency

indicators looks better than with drip irrigation. The reason has to seek into the constant under-

estimation of soil water content that made constant the error polarity.

As for Aquacrop, Irriframe was able to better simulate the soil water content in processing tomato,

with satisfactory results. The agreement indexes were higher 0.9 with both the irrigation systems.

Model efficiency was not satisfactory with sprinkler irrigation (EF=0.62). Irriframe underestimated

soil water content in the tomato early stages with drip irrigation while overestimated during

ripening stage with sprinkler.

AQUACROP IRRIFRAME

TOMATO SPRINKLER TOMATO SPRINKLER

ave 23.820763 ave 23.820763

NRMSE 0.1059771 NRMSE 0.2879349

EF 0.5921783 EF -0.30142

d 0.8612281 d 0.9290014

AQUACROP IRRIFRAME

MAIZE SPRINKLER MAIZE SPRINKLER

ave 24.41331 ave 24.37207

NRMSE 0.190162 NRMSE 0.139506

EF 0.003248 EF 0.596905

d 0.76778 d 0.88691

16

Figure 3: Measured and model simulated soil water volumetric content – Sprinkler 2014

The field experiment in 2014 was split into sub-management zone with slightly different soil

structure and hydraulic parameters. Irrigation management showed little changes in term of

irrigation timing and no appreciable variations of irrigation volumes.

Aquacrop Sprinkler 2014:

The model gave poor performances on maize underestimating soil water content all over the growth

cycle unless for a rainy period from the third decade of June until the first of July, when the model

output were higher than the measured soil water content.

Aquacrop simulations on processing tomato were close to the measured data, as the model

calculated agreement and efficiency indicate. In the initial growth stage the model were slightly

underestimating soil water content.

Irriframe Sprinkler 2014:

Irriframe on maize showed the tendency to overestimate the soil water content. Notwithstanding,

the model show satisfactory agreement (d=0.89) although the efficiency was of only 0.60.

The model on processing tomato captured variation in soil water content, reacting correctly to

irrigation and rainfall, but it systematically overestimated the water availability. Therefore, the

agreement within simulated and measured data were high (d= 0.93) while the model efficiency was

very low (EF= -0.3).

Aquacrop Drip 2014:

Maize: the agreement between measured and simulated data was in general satisfactory.

Differences between sub-management zones were barely noticeable. The model efficiency was very

low, mainly because it overestimated soil water content in three periods with high rainfall frequency

and amount. Moreover in the late season the error became systemic.

Processing Tomato: Although still overestimating the rainfall impact on soil water content in some

periods, Aquacrop behaved well, fitting most of the time the measured trends.

Irriframe Drip 2014:

The model performances were unsatisfactory with both crops and irrespectively to sub-management

zone. Irriframe in that particular year either under or overestimated soil water content.

17

Figure 4: Measured and model simulated soil water volumetric content – Drip 2014


MAIZE DRIP 1Q MAIZE DRIP 1Q TOMATO DRIP 1Q TOMATO DRIP 1Q

ave 22.25069 ave 22.25069 ave 28.88231 ave 28.88231


EF -0.76304 EF -1.08942 EF 0.243874 EF -1.16785

d 0.481972 d 0.493015 d 0.738707 d 0.222396


ave 21.18225 ave 21.17187 ave 29.30229 ave 29.30229


EF -0.02926 EF 0.242431 EF -0.03111 EF -0.7617

d 0.759354 d 0.665333 d 0.753914 d 0.2876

MAIZE DRIP CEN MAIZE DRIP CEN TOMATO DRIP CEN TOMATO DRIP CEN

ave 18.62382 ave 18.62382 ave 27.62824 ave 27.62824


EF -0.6418 EF -2.1824 EF 0.607596 EF -1.34261

d 0.55292 d 0.590857 d 0.910995 d 0.537252

18

Figure 5: Measured and model simulated soil water volumetric content – Sprinkler 2015

Aquacrop & Irriframe Sprinkler 2015:

Both the model did not performed well. The particular 2015 situation put too much stress on several

models modules with clear effect on the crop water uptake and eventually on the soil water content

estimation. Aquacrop resulted more affected by that, while Irriframe with its simplified crop

module seems more robust and better performing especially in the late season after a sufficient

warm-up period.


MAIZE SPRINKLER MAIZE SPRINKLER TOMATO SPRINKLER TOMATO SPRINKLER

ave 26.52934 ave 26.82536 ave 37.581835 ave 37.581835


EF -21.0193 EF -1.02215 EF -5.693642 EF -0.806985

d 0.307076 d 0.662004 d 0.4477572 d 0.3042533



ave 36.84156 ave 36.84156 ave 27.37995 ave 27.0662


EF -1.37504 EF -0.80146 EF -3.96135 EF -0.51317

d 0.613043 d 0.607331 d 0.497013 d 0.650674


ave 30.91405 ave 30.91405 ave 25.04943 ave 24.59931


EF -5.43287 EF 0.236732 EF -7.80959 EF -10.9046

d 0.381511 d 0.711406 d 0.443782 d 0.336997

MAIZE DRIP CEN MAIZE DRIP CEN TOMATO DRIP CEN TOMATO DRIP CEN

ave 34.67327 ave 34.67327 ave 28.18243 ave 27.80321


EF 0.176572 EF 0.058468 EF -6.94202 EF -0.08022

d 0.811273 d 0.783147 d 0.361112 d 0.82377

19

Figure 6: Measured and model simulated soil water volumetric content – Drip 2015

Aquacrop & Irriframe Drip 2015:

Simulations done for drip irrigated maize and tomatoes in 2015 gave similar results than those

carried out with sprinkler irrigation. Both model were able to follow the overall soil water trend

until mid august, and in some cases till mid September. In the late season models show a strong

deviation from themeasured soil water content. This effect was more marked for Aquacrop.

Canopy Cover Measurements

Canopy cover simulationin Maize give good agreement with measured data mainly from the

beginning of the rapid growth phase (from about 9 leaves) until the end of female flowering. The

model, although calibrated over historical datasets collected in the same area, was not able to deal

with the early growth stage (from emergence to 7/9 leaves) and in the grain ripening phase. This

affected as well the calculation of soil water content reported before.

20

MAIZE DRIP 1Q MAIZE DRIP 4Q

ave 71.6014 ave 72.58793

NRMSE 0.3502 NRMSE 0.294688

EF 0.455619 EF 0.588146

d 0.864712 d 0.900964

MAIZE SPRINKLER

ave 72.01689

NRMSE 0.533625

EF -0.18881

d 0.723438

21

Aquacrop simulated processing tomato canopy cover captured well the measured trends. However,

simulation tend to underestimate the effective canopy cover as indicated by the NMRSE values and

the gap between model agreement (d) and efficiency (EF).

Dry matter Yield

Aquacrop performances simulating dry yield at harvest were excellent when considering the crop

average over years and treatment (d= 0.99, EF= 0.99). However, breaking down to treatment and

sub-management zone per year it become evident that the model were sometime overestimating and

sometime underestimating, with a clear influence of the year irrespectively of the treatment.

TOMATO DRIP RDI1 TOMATO DRIP 4Q

ave 44.1 ave 42.1

NRMSE 0.299857 NRMSE 0.334251

EF 0.708204 EF 0.717151

d 0.91936 d 0.917034

TOMATO DRIP 4Q

ave 42.1

NRMSE 0.334251

EF 0.717151

d 0.917034

0 5 10 15 20 25

MAIZE-2013-DRIP

MAIZE-2013-DRIP

MAIZE-2013-SPRINKLER


MAIZE-2014-RDI1

MAIZE-2014-RDI4


MAIZE-2015-RDI1

MAIZE-2015-RDI1

MAIZE-2015-RDI4

MAIZE-2015-RDI4

MAIZE-2015-RDIcen

MAIZE-2015-RDIcen



MAIZE

OBS SIM

MAIZE

ave 9.5

NRMSE 0.211664

EF 0.998925

d 0.999729

TOMATO

ave 3.7

NRMSE 0.317882

EF 0.999248

d 0.999812

22

PI models calibration and validation on Potato in Denmark

Foreword

The following paragraphs briefly describe the results obtained in the FIGARO field experiments

during 2014 and 2015 at AU Denmark with respect to the Precise Irrigation platform component

calibration and validation including the AquaCrop model.

Priorities

Gun Irrigation (GI) systems are commonly used in potato production in Denmark, but are

considered to be susceptible to wind and evaporation losses (Kendy et al., 2006; Bavi et al., 2009),

often in the range of 10 – 20 % (Aslyng, 1978). Additionally, GI can also result in a non-uniform

soil wetting pattern across the hilly potato field (Starr et al., 2005), as water tends to run down the

hills especially on sandy soils. Nitrogen (N) is commonly applied as one dose at planting leaving it

susceptible to leaching. To reduce ground and surface water pollution with N, ceilings on fertilizer

application have been imposed in Denmark which are below the economic optimum, and make it

very important to the farmers to avoid losses.

Objectives

Irrigation and especially drip-irrigation enable the farmers to split their N-fertilizer in several doses.

This is likely to reduce leaching losses and should enable modelling and/or measuring crop N-

demand during the season and adjusting rates accordingly, as climatic factors and soil N-

mineralization affect the growth. Therefore we investigated precise and reliable irrigation and N-

dosing procedures, which should feed results into the FIGARO Platform for both water and

nitrogen management.

Materials and methods

In 2014 two field experiments with table potatoes cv. Sava were conducted at AU-Jyndevad

(54o53'60'', 9

o07'30'', Fig. 1), according to the Figaro field experiment protocol v1.1 May 2014. The

soil is a coarse-textured melt-water sand and contains 76 % coarse sand (0.2-2.0 mm); 15 % fine

sand (0.02-0.2 mm); 4 % silt (0.002-0.02 mm) and 3 % clay (<0.002 mm). In the top layer (0-20

cm) the organic matter content is about 3 %. The soil is classified as an Orthic Haplohumod

(Nielsen and Møberg, 1985) with a plant available water capacity of about 67 mm in the root-zone

usually reaching to not more than 60 cm depth. The dry bulk density is about 1.55 g/cm3 for both

the plough layer and the subsoil (Hansen et al., 1986).

Figure 1. Experiment 1 and 2: field coordinates in 2014

Mother tubers were planted on 10th April and shoots emerged 8th May, 2014. The tubers were laid

in rows 0.75 m apart and at 0.27 m distance within the row.

The two field experiments were repeated in 2015 with the same layout in a field just beside the one

used in 2014. Details of this are described in the Figaro field experiment protocol v1.0, 5 June 2015.

23

Experiment 1.

Three experimental treatments with 4 replicates were applied in a block design with net-plot size of

3.00 x 6.48 m:

1. GI-Figaro: Gun-irrigation scheduled with the Aquacrop model at 25 mm deficit and

fertilized with 120 N, 30 P and 180 K (kg/ha) at planting – beyond state of the art

2. GI-Daisy: Gun-irrigation scheduled with the Daisy model at 25 mm deficit and fertilized

with 120 N, 30 P and 180 K (kg/ha) at planting – state of the art

3. DF-Daisy: Drip-fertigation scheduled with Daisy every second day and fertilized with 36 N,

30 P and 180 K (kg/ha) as basic dressing and supplemental N in doses of 20 kg/ha whenever Daisy

indicated critical low N-content in plant dry matter. Covered with tents during gun-irrigation.

Experiment 2.

The 14 drip fertigated experimental treatments with 4 replicates were placed in a randomized

complete block design with a plot size of 12 x 8.1 m. Basic fertilizer dressing of 30 P and 180 K

was given at planting (kg/ha).

1. I0N0: No irrigation, no N

2. I0N3: No irrigation, 140 kg N/ha total; 42 N given a basic dressing at planting

3. IdN3: Deficit irrigation with 80 % of the irrigation amount given to I1 at the tuber initiation

stage and 60% of the irrigation amount given to I1 from tuber bulking to harvest. 140 N in total; 42

N given a basic dressing.

4. IdNd1: Deficit irrigation with 80 % of the irrigation amount given to I1 at the tuber initiation

stage and 60% of the irrigation amount given to I1 from tuber bulking to harvest. Dynamic N

application determined by Daisy real-time modelling; 42 N given a basic dressing.

5. I1N0: Drip-irrigation every second day scheduled according to TDR-measurements (0.9FC-

SWC), no N.


SWC), 60 N in total given as fertigation in 20 kg/ha rates every week starting from 49 DAP; 42 N

given a basic dressing.










10. I1Nd1: Drip-irrigation every second day scheduled according to TDR-measurements

(0.9FC-SWC), Dynamic N application determined by Daisy real-time modeling and given in 20

kg/ha rates every week starting from 49 DAP; 42 N given a basic dressing.

24

11. I1Nd2: Drip-irrigation every second day scheduled according to TDR-measurements

(0.9FC-SWC), Dynamic N application determined by AgroSens/RVI measurements and given in 20

kg/ha rates every week starting from 49 DAP; 42 N given a basic dressing.

12. I1Norg: Drip-irrigation every second day scheduled according to TDR-measurements

(0.9FC-SWC), Dynamic N application determined by AgroSens/RVI measurements and given in 20

kg/ha rates every week starting from 49 DAP; 42 N given a basic dressing in pig slurry.

13. IFigaroN3: Drip-irrigation every second day scheduled according to Aquacrop, Dynamic N

application determined by Daisy real-time modeling and given in 20 kg/ha rates every week starting

from 49 DAP; 42 N given a basic dressing.

14. IDaisyNDaisy: Drip-irrigation every second day scheduled according to Daisy, Dynamic N

application determined by Daisy real-time modeling and given in 20 kg/ha rates every week starting

from 49 DAP; 42 N given as basic dressing.

Results

AquaCrop testing

AquaCrop was tested using data from the potato experiment in 2014:

Treatment 1, I1N3: fully irrigated, fully fertilised was used for a state of the art calibration with

adjustment of first phenology parameters, canopy cover, soil water content, dry matter production

during the season and final dry matter yield.

Treatments selected for verification were:

Treatment 2, IdN3: deficit irrigated and fully fertilised

Treatment 3, I0N3: not irrigated, fully fertilised

Treatment 4, IaN3: fully fertilised and irrigated based on guidance from AquaCrop before the in-

depth calibration.


Parameter 2012-13

(Validation)

2013-14

(Calibration)

2014-15

(Validation)


Dry above and below ground biomass

during cultivation period (tn/ha) YES YES YES


Dry below ground biomass

on harvesting (tn/ha) YES YES YES

The new found AquaCrop parameters, which are applicable for potatoes in a temperate region is

given in Table 2 below (marked in bold), both in days format and in GDD format. The GDD format

25

is the crop parameter file to be used in different years, as using crop development based on fixed

days will fail in many years.

Table 2. Calibrated core AquaCrop model parameters shown in bold compared to the original

Origin

al

Aquac

rop

Calibrated

Final, days

Calibra

ted

GDD

based AquaCrop Version 4 (June 2012)

1.1 1.2 1.2

Crop coefficient when canopy is complete but prior to

senescence (KcTr,x)

0.15 0.15 0.15

Decline of crop coefficient (%/day) as a result of ageing,

nitrogen deficiency, etc.

0.3 0.3 0.3 Minimum effective rooting depth (m)

1.5 0.5 0.5 Maximum effective rooting depth (m)

15 15 15 Shape factor describing root zone expansion

0.016 0.048 0.048

Maximum root water extraction (m3water/m3soil.day) in top

quarter of root zone

0.004 0.012 0.012

Maximum root water extraction (m3water/m3soil.day) in

bottom quarter of root zone

60 60 60

Effect of canopy cover in reducing soil evaporation in late

season stage

10 10 10

Soil surface covered by an individual seedling at 90 %

emergence (cm2)

49382 49382 49382 Number of plants per hectare

0.188

96 0.25 0.25

Canopy growth coefficient (CGC): Increase in canopy cover

(fraction soil cover per day)

-9 -9 -9

Maximum decrease of Canopy Growth Coefficient in and

between seasons

-9 -9 -9

Number of seasons at which maximum decrease of Canopy

Growth Coefficient is reached

-9 -9 -9 Shape factor for decrease Canopy Growth Coefficient

0.92 0.9 0.9 Maximum canopy cover (CCx) in fraction soil cover

0.018

84 0.13 0.13

Canopy decline coefficient (CDC): Decrease in canopy cover

(in fraction per day)

22 35 35 Calendar Days: from sowing to emergence

70 75 75 Calendar Days: from sowing to maximum rooting depth

26

110 88 88 Calendar Days: from sowing to start senescence

135 110 110 Calendar Days: from sowing to maturity (length of crop cycle)

75 54 54 Calendar Days: from sowing to start of yield formation

0 0 0 Length of the flowering stage (days)

0 0 0 Crop determinancy unlinked with flowering

-9 -9 -9 Excess of potential fruits

55 56 56

Building up of Harvest Index starting at root/tuber enlargement

(days)

17 18 18

Water Productivity normalized for ETo and CO2 (WP*)

(gram/m2)

100 100 100

Water Productivity normalized for ETo and CO2 during yield

formation (as % WP*)

50 50 50

Crop performance under elevated atmospheric CO2

concentration (%)

80 84 84 Reference Harvest Index (HIo) (%)

2 2 2

Possible increase (%) of HI due to water stress before start of

yield formation

-9 -9 -9

No impact on HI of restricted vegetative growth during yield

formation

10 10 10

Coefficient describing negative impact on HI of stomatal

closure during yield formation

5 5 5 Allowable maximum increase (%) of specified HI

-9 -9 279 GDDays: from sowing to emergence

-9 -9 795 GDDays: from sowing to maximum rooting depth

-9 -9 975 GDDays: from sowing to start senescence

-9 -9 1359 GDDays: from sowing to maturity (length of crop cycle)

-9 -9 510 GDDays: from sowing to start tuber formation

-9 -9 0 Length of the flowering stage (growing degree days)

-9 -9

0.0190

4

CGC for GGDays: Increase in canopy cover (in fraction soil

cover per growing-degree day)

-9 -9

0.0071

3

CDC for GGDays: Decrease in canopy cover (in fraction per

growing-degree day)

-9 -9 848 GDDays: building-up of Harvest Index during yield formation

27

Several essential variables have been compared: Soil water content in the 0.6m root zone, canopy

cover, samples of total biomass during the season, final total dry matter and final tuber dry matter

yield. The results for 2014 are presented in the following section.


The model, once calibrated was able to describe the temporal development in canopy cover very

well with normalized root mean square error (NRMSE) values around 0.15.

I1N3 IdN3 I0N3 IaN3

NRMSE 0.158351 0.14218 0.188974 0.150987

EF 0.933217 0.94482 0.881839 0.928667

D 0.985842 0.988102 0.975425 0.985214

Soil water content

Calibration NRSME was around 0.1 and similar for adequately irrigated crops but increased in un-

irrigated (I0N3) where the model tended to overestimate the soil water content.

28

I1N3 IdN3 I0N3 IaN3

NRMSE 0.10663 0.098306 0.193833 0.124566

EF 0.381037 0.444246 0.485287 0.308959

D 0.795491 0.846192 0.856608 0.788563

Dry Biomass

Similarly, the NRMSE for biomass growth was around 0.1 apart from the unirrigated treatment

where it increased to 0.21 due to higher growth than predicted. This could perhaps indicate that

water was more available than indicated by the Aquacrop model.

I1N3 IdN3 I0N3 IaN3

NRMSE 0.072882 0.144118 0.210154 0.107387

EF 0.962761 0.854051 0.627595 0.925912

d 0.990879 0.962702 0.910837 0.980642

29

Yield

In general both tuber and total dry matter yield were simulated with good precision with NRMSE

values of 0.02 – 0.03.

Sensor guided split N application

More precise methods are needed to detect crop N status and guide in-season N application. We

developed a baseline approach (Zhou et al., 2016) using the integral information of ratio vegetation

index (RVI) and leaf area index (LAI). First the tuber yield response function to N application rate

was determined and next the relationship between RVI and LAI in optimally N fertilized

treatments. The different N treatments during two seasons 2013 and 2014 were used, having

fertilizer application range of 0-180 kg N/ha. RVI and LAI from the 180 kg N/ha treatments near to

the economic optimum of 187 kg N/ha was used to derive the baseline. RVI and LAI had a high

(R2=0.97) correlation and was best fitted with 2nd order polynomial function, which was largely

independent of season. The treatments where N fertigation was stopped before reaching 180 kg

N/ha started to deviate from the 95 % confidence interval of the baseline about 10 days after their

N-fertigation was stopped. This corresponded to 10-20 kg/ha difference in total plant N uptake

between reference and the N deprived treatments. Therefore better tools are now available to guide

supplementary N-fertilization in potatoes. These will be helpful in regional potato production for

diagnosis of N status, and allow discrimination between situations of sub-optimal and optimal N

supply. As split N can be applied late in the season in combination with irrigation the method is to a

large extent able to into account N-mineralization from soil organic matter, which often supply a

major part of the crop’s N-uptake and constitute the single largest uncertainty with respect to N

losses.

In the figure below, the confidence interval around the RVI/LAI baseline is shown with stippled

lines, while the points are measured in the 100 kg N/ha treatment. Once fertigation was stopped, the

points (in red) fall below the lower confidence limit indicating lack of nitrogen.

NRMSE 0.017262174

EF 0.876434152

d 0.967493801

NRMSE 0.026404412

EF 0.854395867

D 0.951898762

30

Fig. 2. The RVI/LAI baseline (fully drawn line) and it’s 95% confidence interval (stippled lines)

describing the relation between RVI and LAI in potatoes fertilized to their economic optimum.

Points are from the 100 kg N/ha treatment which deviated (shown in red) at the end of the season in

both 2013 (a) and 2014 (b and c).

There was furthermore good correspondence between the time that Daisy indicated nitrogen stress

and the time when RVI/LAI measurements deviated from the baseline in the different treatments.

Daisy modelling results for potatoes in Denmark.

Daisy has been calibrated using the same state of the art procedures and the same treatment (I1N3)

as for the calibration of AquaCrop, however for the LAI calibration was used as well the IfN4 (full

irrigated and 180 kg N/ha).

The results for the treatment I1N3 in 2014 are:

32

Daisy validation using complementary 2014 data

In the following are shown validation results with comparison to the treatment, IdaisyNdaisy, which

is drip irrigated and drip fertigated based on Daisy recommendation in 2014.

34

Further results with comparison to the treatment, I0N0, which is NOT irrigated and N fertilised

2014

35

Finally the comparison measured and Daisy simulated harvested tuber dry matter yield and N-

uptake in tubers at harvest (dry matter of tubers) yield 2014 from 7 treatments:

36

Daisy validation using 2013 data

Similar good results were found for the validation year 2013

First shown is the treatment where drip irrigation and fertigation was guided by Daisy simulation

38

The next results show deficit irrigation, but fully fertilised, IdN3

40

In the figures are shown comparisons between measured and Daisy simulated tuber dry matter yield

(t/ha) and N-uptake in tubers (kg N/ha) at harvest in 2013 from nine treatments:

41

PI models calibration and validation on Cotton in Greece

Preface

This document consists a brief description of the procedures followed for the calibration and

validation of the AquaCrop cotton file in Greece, the methods used for field measurements, as well

as the issues faced during the three years of FIGARO experimental implementation.

Method

Experimental Field Planning

The experiment was conducted in a 2 ha field at Magico Village (41.046oN; 24.892

oE, 13 m

altitude) approximately 12 km from the city of Xanthi in Northern Greece. The field has a

trapezoidal plan view, with parallel sides of 103 m and 96 m, respectively, and non-parallel sides of

199 m and 200.7 m.

The first year of the experiment (2012-13) and in order to investigate the differences between drip

and canon irrigation systems, as well as to highlight the increased water use efficiency of precise

and deficit irrigation over empirical farmer’s irrigation the field was divided into 6 sub plots (Figure

1). Deficit irrigation plots were defined to investigate the impact of irrigating 25% less water than

the amounts provided to the Figaro experimental plots.

Figure 1. Planning the field experiment.

Nevertheless, restrictions related with canon irrigation radius hampered the implementation of

initial planning and in fact the sub plots 4 and 5 were irrigated with the same amounts of water

(which was determined by the FIGARO DSS engine). Additionally, the water amounts applied by

the canon during irrigation events were almost standard and couldn’t be regulated. The irrigation

events were started roughly at 20:00 pm and ended almost at 12:00 next day. The amount of water

irrigated during these events depended mainly to the groundwater level and consequently to the

Daisy wrongly

calculates N

leaching in gun

irrigated treatment

42

available flow rate. This restriction added one more obstacle to the FIGARO irrigation scheduling

implementation.

Additionally the drip irrigation system was installed in the field in mid-July, as farmers use to apply

cultivator (Figure 2) in the field at the first week of July, to loose the soil and remove the weeds.

Consequently, until mid-July all sub plots were irrigated the same amounts of water by canon.

Figure 2. Cultivator applied in the field in mid-July.

The required water amounts, in the first season, were pumped mainly from a nearby drilling. Due to

significant fall of the groundwater level in late July, the farmer pumped water from a stream, acting

as the wastewater recipient from a nearby wastewater treatment plant, roughly 4 km away from our

field. Stream’s water was used only for canon irrigation. Canon’s nozzle changed from 16 mm to 20

mm at this point and the canon’s irrigation radius as well as the amount of water applied at each

irrigation event were increased substantially.

Only three irrigation events took place during the second year of the experiment (2013-14), due to

heavy and frequent rainfalls occurring during the cultivation period. The same restrictions regarding

canon and drip irrigation systems were faced again. Only drilling water was used.

During the third year (2014-15) a new issue occurred. Farmer decided to irrigate his sub plots

according to FIGARO DSS. Consequently, the field was divided into three treatments. One irrigated

with canon based on FIGARO DSS (initial sub plots 4, 5 and 6); one irrigated with drip based on

FIGARO DSS (initial plots 2 and 3) and one irrigated with drip and 25% less water than the

recommended by FIGARO DSS (initial plot 1). Only drilling water was used for irrigation.

43

Taking into consideration farmer’s decision to irrigate according to FIGARO suggestions, the final

year of the experiment we will divide the field in only 2 sub plots. One plot will be irrigated with

drip and one with canon. The irrigation scheduling in both plots will be based in FIGARO DSS

engine.

Despite the changes in the initial program, the first year results underlined the fact that farmers in

Greece over-irrigate their cotton crops, as they exercise empirical agriculture without the use of any

scientific instruments (soil moisture sensors, hydrometers) or relative software to improve their

scheduling. Some farmers use the regional weather prediction information which is only qualitative

in terms of precipitation. Additionally, in most cases, they lack knowledge of the cotton growth

cycle and the water requirements of each growing phase. As a result of over irrigation and the lack

of knowledge they have to use plant growth regulators (Pix) in order to halt the vegetative growth

of the cotton plants, increasing therefore the cultivation cost, while at the same time they failed to

achieve the optimum yield.

Cotton File Calibration procedure

There are three main publications in literature regarding the calibration and validation of cotton

crop files in AquaCrop (García et al., 2009; Farahani et al., 2009; Hussein et al., 2011). During

these experiments, researchers calibrated the crop file using field results from one season

experiment and then validated it by applying it to at least two other data sets of different cultivation

years. Model file was validated in terms of the following parameters: Green Canopy Cover (CC),

dry aboveground Biomass (dB), Soil Water Content balance (SWC), crop evapotranspiration (ETc)

and final Seed Cotton Yield (SCY).

Table 1 shows the validation parameters utilized during our three year experiment. Crop file

calibration, in our experiment, was based in the results of the last year (2014-15) and more specific

to the dataset derived from Drip Irrigated Plot 2. Selection of the last year as calibration year was

based on the following facts:

a) this season’s data appeared more consistent and robust than previous seasons, and

b) our canopy cover measurement method was significantly improved.

Additionally, the favor of drip irrigation treatment over canon irrigation resulted in the poor

correlation between simulated (by AquaCrop) and measured (by Diviner 2000 sensor) water

balance terms in the canon irrigated plots. That was attributed, mainly, to the significant differences

between the water amounts measured by the hydrometer (installed between the drilling and the

canon system) and the amounts finally reaching the field (net irrigation), due to a) the great losses

of water related to the system function, and b) the ununiformed distribution of water (which was a

core assumption) to the field. In order to determine system losses, as well as the water distribution

pattern, our goal for the upcoming season is to set up an experiment based on water collection catch

cans.

44


Parameter 2012-13

(Validation)

2013-14

(Validation)

2014-15

(Calibration)



during cultivation period

(tn/ha)

NO NO YES



on harvesting (tn/ha) NO YES YES

Roots depth expansion (cm) NO YES YES

Dry Seed Cotton Yield (tn/ha) YES YES YES

During the calibration procedure, researchers, parameterized a substantial number of core

parameters from original model cotton crop file, either based on their observations in the field or by

the trial and error approach method. The AquaCrop model utilizes a number of basic parameters

such as CC, dry aboveground biomass (dB) and final yield (SCY), which are highly depended to

these core model parameters. The modification of a core parameter has a direct effect on one of the

model’s basic parameters (CC, dB, SCY) and an indirect effect to the others. For instance a change

to the Canopy Decline Coefficient (CDC) will have a direct effect to the CC evolution and an

indirect effect to dB, and consequently, to the SCY. In an attempt to help future researchers during

their calibration procedure we created Table 2 which shows the core model parameters and their

impact on CC, dB, and SCY. The Soil Salinity and Fertility Stresses were not considered.

Table 4. Core AquaCrop model parameters and their impact on CC, dB and SCY.

Core Parameter Affecting CC

Evolution

Affecting dB

Formation

Affecting

SCY

Days to emergence (d) Yes No No

Days to maximum CC (d) Yes No No

Days to senescence (d) Yes No No

Days to maturity (d) Yes No No

Canopy Decline Coefficient (%) Yes No No

Maximum Canopy Cover (%) Yes No No

Days to maximum root depth (d) Yes No No

45

Maximum root depth (m) Yes No No

Initial Depth (Zr) from which

seeds/seedlings could abstract water (m) Yes No No

Root expansion shape factor Yes No No

Water stress expansion upper limit (pupper) Yes No No

Water stress expansion lower limit (plower) Yes No No

Water stress shape factor Yes No No

Stomatal closure upper limit (pupper) Yes No No

Stomatal closure shape factor Yes No No

Early senescence upper limit (pupper) Yes No No

Early senescence shape factor Yes No No

Crop Water Productivity (WP)

normalized for climate and CO2 (g/m2)

No Yes No

Reference Harvest Index No No Yes

Water stress before flowering positive

effect in HI No No Yes

Water stress during flowering causes

failure to pollination (pupper) No No Yes

Water stress affecting leaf expansion a

(positive effect in HI) No No Yes

Water stress affecting stomatal closure b

(negative effect in HI) No No Yes

Mid - season Crop Coefficient (KcTr,X) No Yes No

Days to flowering (d) No No Yes

Days of flowering (d) No No Yes

Initially, our calibrated file, was validated by the other two Drip irrigated plots (Plot1 and Plot3) for

the year 2015 and successively with the data sets of the years 2014 and 2015. Although in previous

studies the reference treatment, against which calibration was performed, was a fully irrigated plot

(Irrigation = ETc), in our case we attempted a calibration approach to a deficit irrigated Plot.

According to AquaCrop estimations, the ratio between maximum cotton evapotranspiration and the

actual cotton evapotranspiration, during 2014-15 cultivation period, was roughly 0.70 (30% less

water than full irrigation, 70% Deficit). The original values of cotton crop file core parameters as

well as the values proposed from previous studies and those derived from our experiment are shown

in table 3. To our knowledge there are no publications regarding the calibration of AquaCrop’s

cotton file in Greece.

46

Table 5. Values of Cotton file parameters proposed by Aquacrop, previous studies and present

experimental study

Parameter

Original

Aquacrop

cotton file

Garcia et

al.

2009

Farahani et

al.

2009

Hussein et

al.

2011

FIGARO

DUTH

CGC (%) 7.6 9 10.5 7.2 7.8

CDC (%) 2.9 4.3 6.5 3 4.6

To emergence (d) 14 10 7 7 9

To Maximum CC (d) 112 93 113 104

To Senescence (d) 144 107 113 123 110

To Maturity (d) 174 169 147/133 169/149 163

To Flowering (d) 64 64 65 65 64

Duration of flowering (d) 52 40 40 40

Maximum roots depth (m) 2 1.3 1.3 1.3

To max roots depth (d) 98 113 113 106

Zr (m) 0.3 0.15 0.15 0.15

Root expansion shape

factor 1.5 1.2 1.2 1

Water stress expansion

pupper 0.2 0.27 0.25 0.25 0.25

Water stress expansion

plower 0.7 0.64 0.7 0.7 0.7

Water stress shape factor 3 2.3 4 4 4

Stomatal closure upper

limit (pupper) 0.65 0.5 0.55 0.55 0.55

Stomatal closure shape

factor 2.5 1 0 0 0

Early senescence upper

limit (pupper) 0.75 0.7 0.75 0.75 0.75

Early senescence shape

factor 2.5 1.5 1 1 1

Water stress before

flowering positive effect in

HI

5 0 0 5

47

Water stress affecting leaf

expansion a (positive effect

in HI)

2 1.5 1.5 2

Water stress affecting

stomatal closure b (negative

effect in HI)

10 6 2 8

Reference HI (%) 35 35 30 30 35

WP* (g/m2) 15 15 18.8 15.8 17

Field Monitoring


During the three years of experimentation, the CC was determined by taking photos of plants at

different incidents of the plant growth cycle, using a mobile phone. The method was modified

between the cultivation periods to improve its robustness and reliability. A brief description of the

method during the third year is listed below:

3 constant points in each plot were chosen at the beginning of the growing season,

Two black cardboards, 0.5 m2 each, were placed at each side of the cotton rows,

The photos were taken at a distance approximately 2 m above ground,

3 photos were taken from each point and the average CC-value was estimated,

Each plot’s CC was calculated by averaging the means of the three examination points

The CC was determined by image processing, initially using Photoshop and finally using

Gimp

The advantages of the method are:

Photos can be taken fast and easy

no advanced equipment is required as the photos can be taken with just a mobile phone

while disadvantages are related only to the time consuming image processing procedure.

During the experiment, modifications and improvements in the method were made, in order to

overcome difficulties, in terms of photographing and image processing. For instance during the first

two years, photos were taken from a different distance (1 m above ground). As a result, when the

plants reached 0.9 m in height, we were unable to continue obtaining images. Thus, during the third

year this value increased to roughly 2 m. Table 3 shows the successive improvements, in some

aspects of the method, among the cultivation periods. It is noteworthy to mention that the CC

measurements should cover the whole plant growth cycle. This way both the canopy growth and the

canopy decline AquaCrop coefficients could be correctly calibrated and the validated.

48

Table 6. Improvements in the CC determination method among the three years of the experiment

2012-13 2013-14 2015-16

Use of cardboards No No Yes

Photo taking height (m) 1 1 2

Taking Photos from a random point in the plot Yes No No

Taking Photos from a constant reference point No Yes Yes

Number of constant points per plot 0 1 3

Taking photos during hole cultivation period No No Yes

Dry aboveground Biomass

The dB, during the cultivation period, was determined only during the last year (2014-15). A brief

description of the method is listed below:

plants in the vicinity of the points used as CC measurement constant points, were collected

(2 plants approximately once per week),

these plants were cut in pieces, dried at 65 oC for two days and then their dry weight was

measured,

the dry weight was divided by two (the number of samples) and then multiplied by the

sampling point’s corresponding plant density.

It is noteworthy that the sampling of non-representative plants may result to significant

discrepancies between measured and simulated dB.

Soil Water Content

Decagon 5 TM sensors and Sentek’s Diviner 2000 probe were used for soil water and temperature

measurements. Both gauges belong to the FDR capacitance sensors sub-category.

Ten Decagon sensors and 2 data loggers were installed in the field. The first two years these sensors

were scattered in the field and placed at 30 cm depth. Each sensor was connected via a cable with a

data logger (5 sensors at each logger) which transfers telemetrically the recorded data to the internet

every 15 minutes (Figure 3). In the third year, these sensors were split into two groups (5 sensors in

each group) and then placed at two points at different depths (90, 70, 50, 40, 20 and 10 cm from soil

surface).

49

Figure 3. Soil moisture and temperature wireless sensors interconnection and data transfer system.

In parallel, Sentek’s Diviner 2000 portable probe was used. This capacitance sensor can take soil

moisture profile measurements up to 1 m depth. The gauge consists of a metal rod and a display

unit (data logger). The sensor is installed at the bottom side of the rod, while the upper edge of the

rod connects, via a cable, the rod with the logger (Figure 4). The gauge is inserted to pre-installed in

the field plastic PVC tubes. It measures the soil water content at 10 cm normal intervals up to 100

cm, as it is descended inside the tube and as it is ascended from it. During the first year, 9 PVC

tubes were installed in the field, while during the successive tow experimental periods 12 tubes

were placed. The raw value measured from the Diviner 2000 is the sensor’s frequency response in

soil (Fs). Nevertheless, different sensors have slightly different responses when measuring a

particular standard, for instance water (Sentek Pty Ltd, 2009). Therefore, the output raw count of

Diviner is the Normalized Scaled Frequency, a magnitude which represents the ratio of sensor’s

frequency responses in soil (Fs) compared with sensor’s responses in air (Fa) and in water (Fw)

(Sentek Pty Ltd, 2009)

( )

( )

A S

A W

F FSF

F F

50

Figure 4. Graphical illustration of Diviner 2000 (Sentek Pty Ltd, 2009)

The function of capacitance sensors has been described in detail in many studies (Dean et al., 1987;

Evett and Steiner, 1995). The charging time of an electromagnetic field is related to the capacitance

of the soil, which is related to the permittivity of the medium (Mittelbach et al., 2012). The charging

time is strongly dependent on the permittivity of the vicinal medium (soil, water, air) that surrounds

the tube. Due to the significantly higher value of the water’s dielectric constant compared to these

of air and soil, substantial changes in measurements are mainly attributed to fluctuations in soil

moisture.

For calibration purposes, soil samples were also collected from the soil at the vicinity of the Diviner

tubes and the Decagon sensors and the volumetric water content (θv) was determined via the

equation:

( )V b

Mw Md

Md

(17)

where Mw is the weight of wet soil and Md is the weight of dry soil. And ρb is the bulk density

(mass soil / volume soil).

The point measurements at 30 cm of Decagon sensors during the first two years were not used for

AquaCrop soil water content validation, as the model utilizes a soil-water soil profile. This type of

installation gives information related to soil moisture variability in x and y axes, but the AquaCrop

model does the assumption that this type of fluctuations is negligible.

Regarding the measurement quality, Decagon sensors gave poor results even after calibration. On

the other hand, Diviner 2000 readings improved significantly after calibration (pseudo R2 = 0.80).

In previous studies researchers reported R2 values above 0.9, but these experiments were conducted

at a controlled lab environment or in fields without a cultivar.

51

Plant Growth Regulators (PGRs)

The use of PGRs is a common practice for farmers in Greece. During our experiment our farmer

utilized two different PGRs. The first one named Pix (Bayer), was used to halt the vegetative

growth in the end of July, while the second one, Ethephon, used to promote cotton maturity (applied

in the first week of October). The impact of the PGRs in the CC and Biomass development is

unknown and thus the use of the Calibrated cotton file in crops cultivated without PGRs is to be

discussed.

Seed Cotton Yield

Cotton is usually harvested in Greece in mid - October. The mostly common harvesting method is

the mechanical cotton picker, while there are few cases where the cotton is still harvested by hand.

The cotton pickers harvest the seed cotton when approximately 80 % of the cotton balls are ready to

be harvested (first hand) and for a second time roughly 10-15 days after the first picking (second

hand).

In the first year of the experiment we let the cotton picker to harvest the seed cotton yield of a plot

and then we unloaded the yield to a tractor trailer. The tractor moved the trailer to a scale and the

yield wet weight was measured. This procedure was applied to all plots. A second hand picking

followed after 15 days.

Due to the intense rainfall events in October 2014, the harvest of cotton for 2013-14 period was

impossible. An indicative sample was gathered in mid - December 2014, in order to estimate the

seed cotton production. For this purpose 3 points from each plot were harvested by hand (1 m of

cotton plants from each point). The losses in the yield due to the exposure of the mature plants to

the intense rainfalls in the interval between mid - October (the normal cotton harvesting period in

Greece) and December, are unknown. Although the estimated seed cotton yield was integrated in

the final cotton calibration - validation file, the CC evolution and soil water balance are the

validation parameters which should be taken under consideration for 2013-14 period, as any

correlations between the estimated and predicted yield are in some extend meaningless.

In the last year a different approach was followed: During the harvesting day and before the cotton

picker entered the field, we cut the plants which we were photographed for the CC determination.

These plants were moved to the laboratory, where the seed cotton yield gathered by hand was

estimated. Then the plants were cut into pieces and both the plants and the seed cotton were placed

in an over at 65 oC. After two days the dry weight of biomass and yield were measured and the

proper calculations in order to determine mean plot biomass and seed cotton production were

performed.

PI models calibration and validation on Maize in Portugal

Introduction

The Sorraia valley irrigation district, located in southern Portugal, is part of one of the most

important agricultural areas in the country. Maize (Zea mays L.) is a leading crop in the region,

occupying 25.6 to 44.9% of the total area irrigated during 2004-2014 (ARBVS, 2015). Water

scarcity and inappropriate land management practices have been some of the most critical

constraints to the sustainable production of maize and other agricultural crops. Thus, over the last

decade many researchers have focused on studying soil water flow and solute transport processes in

the Sorraia valley region in order to improve irrigation water management and optimize water use

and water productivity. Some of the most relevant studies included the relationships between maize

52

production and irrigation strategies (Paredes et al., 2014a), evaluation of economic impacts of

various irrigation management strategies (Paredes et al., 2014b), and the assessment of nutrient

(Cameira et al., 2003, 2007) and pesticide (Azevedo et al., 2000a, 200b) fate while considering

farmer’s usual agricultural practices. However, as agriculture uses 75% of the available water

resources in the region, the Portuguese government has defined maximum threshold limits for

irrigation depending on the crop type and irrigation system used. There is thus the need for

promoting and adopting new irrigation practices in order to improve irrigation water efficiency and

reduce agricultural water consumption. The FIGARO DSS can have here an important role in

achieving such task by providing an “easy-to-use” service with optimized irrigation schedules for

the Sorraia Valley farmers.

Material and methods

Field location

This document describes the field monitoring carried out in the Portuguese site during 2014 and

2015, and the main results obtained with the modelling tools AQUACROP and MOHID-Land.

The field experiment was conducted at Herdade do Zambujeiro, Barrosa, Portugal. The area is

located in the Sorraia valley; one of the most important agricultural regions in the country.

Three experimental plots were defined based on the type of irrigation method available and the

configuration of irrigation sectors. Each plot was then monitored at two locations.

Table 1. Location of the experimental plots.

Plot Latitude

(º)

Longitude

(º)

Irrigation method

P1 38.96660 -8.74630 Stationary sprinklers

P2 38.96633 -8.74640 Stationary sprinklers

P3 38.96431 -8.74734 Linear pivot

Equipments

The equipments installed for monitoring automatically soil water content, groundwater table depth,

and irrigation depths were the following:

Adcon SM1 for soil moisture measurement at every 0.10 m up to 0.60 cm depth (0-100%,

precision reading accuracy ±2%);

ECH2O-5 for soil moisture measurement at 0.10, 0.30, and 0.50 cm depth (0-100%,

precision reading accuracy ±3-4%);

Soil Moisture Equip. Corp. TDR-Trase sensor for soil moisture measurement at 0.10, 0.30,

and 0.50 cm depth (0-100%, precision reading accuracy ±1%);

Adcon LEV1 Level sensor for groundwater level measurement (0-10 m);

RG1 Rain Gauge for irrigation depth measurement (0-10 m; precision reading accuracy

3%).

53

Sensors calibration

Soil moisture sensors from Adcon and Decagon were calibrated by comparing the measured values

with those taken with TDR Trase probes (Soil Moisture Equip. Corp.). It was impossible to find a

simple unique equation set for calibrating the Adcon probe since the range of measured values

varied between years. The following relationships were found:

Adcon SM1 Soil Moisture Sensor:

Plot 1, year 2014

10 cm y = 1.813 x – 51.493 (R2=0.88; n=6)

30 cm y = 0.404 x + 20.925 (R2=0.47; n=6)

50 cm y = 2.796 x – 126.38 (R2=0.53; n=11)

Plot 1, year 2015

10 cm y = 1.823 x – 67.989 (R2=0.92; n=6)

30 cm y = 1.954 x – 77.019 (R2=0.87; n=6)

50 cm y = 2.796 x – 126.38 (R2=0.53; n=11)

Plot 2, year 2014

10 cm y = 0.800 x – 9.046 (R2=0.63; n=4)

30 cm y = 1.293 x – 12.404 (R2=0.51; n=6)

50 cm y = 3.402 x – 172.36 (R2=0.98; n=5)

Plot 2, year 2015

10 cm y = 1.433 x – 48.638 (R2=0.86; n=5)

30 cm y = 0.969 x – 19.737 (R2=0.93; n=6)

50 cm y = 3.402 x – 172.36 (R2=0.98; n=5)

ECH2O-5:

All data y = 1.4529 x – 9.6597 (R2=0.62; n=66)

5. Crop growth

The following table shows maize growth dates during both experimental years.

Stage 2014 2015

Sowing 24 May 16 Apr

Emergence 2 Jun 25 Apr

Maximum canopy cover 24 Jul 15 Jun

Flowering 27 Jul 19 Jun

Start of canopy 13 Sep 6 Aug

54

senescence

Maturity 10 Oct 2 Sep

Leaf area index (LAI), crop height, and above ground dry biomass were monitored every 15 days

during maize growing period. Length (L) and width (W) of crop leafs were measured on 3 random

plants grown in each experimental plot. These dimensions were then related to previously calibrated

LAI values with the following equation (Ramos et al., 2012):

n

1i

WL7586.0LAI

(1)

where LAI are the values measured using a LI-COR area meter (Model LI-3100C, LI-COR

Environmental and Biotechnology Research Systems, Lincoln, NE) and n is the number of green

leafs on each measured plant. LAI values were then used to compute the corresponding canopy

cover (CC) values in order to parameterize crop growth (maximum canopy cover, CCx; canopy

growth coefficient, CGC; and canopy decline coefficient, CDC) in AQUACROP. The following

exponential time decay function was used (Hsiao et al., 2009):

2.1LAI6.0exp1005.1CC (2)

Crop height and the above ground dry biomass was also determined on the same 3 random plants.

Stems, leaves, and grain were separated and oven-dried at 70 ºC to constant weight. Total dry

biomass was then determined as the sum of stems, leaves, and grain dry biomasses. Maize yield was

measured at the end of each crop season.

Results

The following section presents the modelling results obtained for Plot 1. This plot was selected for

showing the performance of the FIGARO platform during the final year of the Project. Models were

calibrated during 2014 and validated during 2015.

Canopy Cover

Calibration

Validation

0

20

40

60

80

100

21/05/14 30/06/14 09/08/14 18/09/14

Can

op

y co

ver

(%)

0

20

40

60

80

100

26/04/15 05/06/15 15/07/15 24/08/15 03/10/15

Can

op

y co

ver

(%)

55

Fig. 1. Canopy cover simulated with Aquacrop (–) and MOHID-Land (–) in Plots 1. Vertical bars

correspond to the standard deviation of measured data.

Goodness-of-fit tests between canopy cover simulations and measured data:

AQUACROP MOHID-Land

Calibration Validation Calibration Validation

R2 0.91 0.95 0.89 0.93

ME (%) 5.07 4.30 7.48 6.60

MAE (%) 6.01 5.36 8.14 7.05

RMSE (%) 14.48 11.20 17.02 14.25

NRMSE 0.20 0.15 0.23 0.19

Pbias (%) 6.84 5.66 10.08 8.69

ME 0.84 0.90 0.78 0.84

IA 0.97 0.98 0.95 0.97

R2, coefficient of determination; ME, mean error; MAE, mean absolute error; RMSE, root mean

square error; NMRE, normalized root mean square error; Pbias, percent bias; ME, model efficiency

(Nash and Sutcliffe, 1970); IA, index of agreement (Wilmott, 1981).

Soil water storage

Calibration

Validation

0

50

100

150

200

250

21/05/14 20/06/14 20/07/14 19/08/14 18/09/14 18/10/14

soil

wat

er

sto

rage

(mm

)

56

Fig. 2. Soil water storage simulated with Aquacrop (–) and MOHID-Land (–) in Plots 1. Vertical

bars correspond to the standard deviation of measured data.

Goodness-of-fit tests between soil storage simulations and measured data:

AQUACROP MOHID-Land


R2 0.87 0.40 0.77 0.41

ME (mm) -5.01 -1.39 -2.77 4.36

MAE (mm) 9.93 7.39 5.88 7.63

RMSE

(mm)

12.29 24.80 7.92 9.37

NRMSE 0.07 0.05 0.04 0.05

Pbias (%)

-2.81

-0.76

-1.55

2.36

ME 0.35 0.23 0.73 0.08

IA 0.90 0.79 0.92 0.75




0

50

100

150

200

250

26/04/15 26/05/15 25/06/15 25/07/15 24/08/15 23/09/15

soil

wat

er

sto

rage

(mm

)

57

Above ground dry biomass

Calibration

Validation

Fig. 3. Above ground dry biomass simulated with Aquacrop (–) and MOHID-Land (–) in Plots 1.

Vertical bars correspond to the standard deviation of measured data.

Goodness-of-fit tests between above ground dry biomass simulations and measured data:

AQUACROP MOHID-Land


R2 0.94 0.97 0.94 0.93

ME (%) 1.45 2.38 2.96 2.54

MAE (%) 2.59 2.40 3.09 3.33

RMSE (%) 3.83 3.72 5.13 4.62

NRMSE 0.25 0.22 0.33 0.28

Pbias (%) 9.44 14.31 19.20 15.27

ME 0.93 0.93 0.87 0.89

IA 0.98 0.98 0.96 0.97




In terms of equipment, it was impossible to find a simple unique equation for calibrating the Adcon

probe since the range of measured values varied between years. This makes the future results

obtained with sensor questionable.

0

10

20

30

40

21/05/14 30/06/14 09/08/14 18/09/14

Dry

bio

mas

s (t

on

/ha)

0

10

20

30

40

26/04/15 05/06/15 15/07/15 24/08/15 03/10/15

Dry

bio

mas

s (t

on

/ha)

58

PI models calibration and validation on Citrus in Spain

Description of the experimental fields in details

The total irrigated area in the pilot site of is 180 ha composed of 500 plots. The average plot area is

3598 m2. The irrigation network (called Realon) has 62 multi-outlet hydrants and a total of 342

intakes. The network topology is branched. A multi-outlet hydrant has several intakes, a common

solution adopted by engineers for network design when plot size is small. In this way, network pipe

lengths are shorter and more economic. As a result, users connect their drip irrigation subunits to

the water supply system through water

intakes. The average hydrant elevation is

90.8 m and it ranges from 111.5 m to 79 m.

The total delivery network length is 14426 m.

Fig 1 Picassent water users association

Water is stored in a pond fed by a canal. Its elevation was 114.4 m and it was 3 m above the

pumping station. The system regulation is carried out by three equal vertical multistage pumps

powered by an engine of 45 KW. Two of them are Fixed Speed Pump and the other one is a

Variable Speed Pump (VSP). All users are charged according to their water consumption with a

fixed price per m3. Collective fertilization is performed for all users. The cropped area is composed

of orchards and the predominant crop is citrus (95 %). The complete area is drip irrigated.

Pumps are monitored on real time with energy analysers. There are 24 moisture probes

disseminated thorough the WUA and one cosmic ray probe. Crop water requirements are estimated

by several methods.

Plot Area (m2) Crop Treatment

Realón

Camí Torrent

Test site

59

Three different treatments were carried out in seven plots. In two of them, irrigation scheduling has

been performed by means of Crop Land Modeling (CLM). Three plots were selected to apply a new

treatment called FIGARO. The other two plots have been used as farmer reference (FR).

Fig 2 Different tested irrigation treatments

In the Table 1 are shown plot features. Every row is colored depending on the irrigation treatment

and it is related with plots in the image.

Priorities

Priorities were to set irrigation scheduling with the aim of saving water without decreasing yield.

Stem water potential was measured in selected trees in order to guarantee the stress level was below

the damage yield level.

Moreover we developed a methodology that aimed to schedule the irrigation network supplying the

right amount of water guaranteeing the minimum energy consumption

01.17.05 3288 Clementina Fina FIGARO

01.09.02 5692 Clemenules FIGARO

01.20.07 16501 Navelina CLM

01.09.08 12376 Hernandina CLM

01.20.01 4502 Clemenules CLM

01.08.01 15280 Hernandina FR

01.09.07 10584 Orogrande FR

60

Limitations

The main limitation was the orchards were commercials, which mean that farmers had to trust in the

irrigation requirements that were scheduled. If they disagree with the recommendations they had the

chance to modify the irrigation times set by themselves.

Moreover there is not any tested crop model that simulates yield for tree crops. Then the used model

was only focused on water requirements. Final yield cannot be assumed as an objective.

Regarding the energy model, due to we did not have full control of the network, we have only

compared calibrated actual scenarios with those carried out by the water user association.

Objectives

Saving water applying the right amount of water without decreasing yield by the use of a FAO

model that relates crop coefficient with ground cover , moisture soil sensor and climate weather.

Scheduling the irrigation network by realtime calibrated models guaranteeing the estimated crop

water requirements.

Material and methods

Crop water requirements

The FIGARO treatment is based on the Castel method for calculating water requirements. For this,

the Instituto Valenciano de Investigaciones Agrarias (IVIA) provide a weekly irrigation time for

each plot by means of the irrigation web service (http://riegos.ivia.es/calculo-de-necesidades-de-

riego). Additionally, these recommendations were fitted for the UPVLC team considering values of

stem potential measurements, soil water content and weather forecast. Strategy details can be found

in the previous document FIGARO WP8 Protocol 2014 Template.

The aim of the FIGARO strategy is minimizing water consumption without neglecting plant water

status, so irrigation times usually were adjusted below or above the optimum recommended by

IVIA irrigation service.

In the plot 01.17.05, two treatments were performed to asses this new approach. The plot was

divided in four blocks where six trees of each block were assessed during the irrigation period. In

two blocks, the irrigation was increased a 33% from the FIGARO recommendations with the aim of

having a group of control trees. The plot 01.17.05 was divided as showed in Figure 1 where red

blocks are the over irrigated trees.

Figure 1: Irrigation treatments in 01.17.05 plot.

http://riegos.ivia.es/calculo-de-necesidades-de-riego

http://riegos.ivia.es/calculo-de-necesidades-de-riego

61

Weather conditions

Weather conditions were measured with an automated meteorological station.

Soil water content

There was one FDR probe in each plot with sensors at 10, 30 and 50 (root-zone) and 70 cm

(drainage sensor). In the FIGARO plots, every week the soil water content (ϴ) was assessed by

means of the following graphics.

Figure 2: Soil water content measured by means of FDR probes.

Three features of the ϴ graphics were weekly assessed to modify irrigation time: variations in the

drainage sensor (percolation), variations in the root profile (10, 30 and 50 cm) and moisture level’s

slope in the root profile (accumulation or reduction). The goal was maintaining in the root-zone a

constant margin of soil water content without percolation.

Plant water status

Midday stem potential (Ψstem) is considered the benchmarking measurement of plant water status.

Ψstem shouldn’t go below -13 to -15 bars for avoiding yield and quality problems. These

determinations were performed with a pressure chamber on two bag covered leaves from five

(twelve in the 01.17.05) representative trees per plot at midday intervals from June to October.

Measurements in plots 01.17.05 and 01.09.02 were done weekly because Ψstem was used every week

to schedule irrigation. However, the other plots were measured every two weeks. The seasonal

evolution of Ψstem shows that citrus water status was clearly affected by the differential irrigation

treatments.

62

Results

Year 2014

Figure 3: Seasonal variation of midday stem water potential (Ψstem) in the different irrigation

treatments in 2014. Rainfall rates are also shown.

Figure 3 shows there were some problems with 01.09.02 (FIGARO) and 01.09.08 (CLM) because

Ψstem reaches values higher tahn-15 bars most of the time. Technicians from the WUA were

suggested that users don’t manage properly the irrigation network at plot level and probably water

applied was lower than the recommended and registered every week. Therefore, these plots are not

considered in the results assessment of the study this year. Trees irrigated with FR

recommendations are always under the stress limit.

Figure 4 shows Ψstem in the two treatments carried out in the 01.17.05 plot. Both treatments are

above the minimum stress threshold. There aren’t significant differences between treatments, so

FIGARO strategy doesn’t generate water stress in plants.

63

Figure 4: Seasonal variation of midday stem water potential (Ψstem) in 01.17.05 plot and error bars.

Yield and citrus composition

The two treatments carried out in the 01.17.05 plot for assessing FIGARO strategy, were evaluated

in terms of yield and citrus composition. The statistical design was a randomized complete block

with two replicates per treatment. Each experimental unit had six trees. Analysis of variance was

performed using Statgraphics Plus.

In the table 2 are shown yield results in terms of fruits per tree, fruits weight per tree and average

fruit weight. There are not significant differences between treatments.

Strategy Fruits /tree Fruits weight / tree Average fruit weight

- - kg gr

133% 341,0 a 32,8 a 103,1 a

100% 334,8 a 27,1 a 87,3 a

Table 2: Yield results in the 01.17.05 plot.

An average sample of 80 fruits per tree was collected for assessing fruit size. The following tables

and figure shows that there is a significant difference between treatments, but in both cases the ratio

D/H is near from the optimum for harvesting that is 1,20.

64

133% 100%

Table 3: Fruit size results in the 01.17.05 plot.

Finally, coloration index (ICC), total acidity (g/L), total

soluble solids (°Brix) and maturity index (E/A) were

determined for a 10 fruit sample from the two

treatments. These determinations were carried out in the

expected date of harvesting. In the table 4 shows that

irrigation strategies did not produce any differences in citrus composition at harvest. This implies

that the FIGARO irrigation treatment was not severe enough to modify citrus composition.

Strategy ICC Total acidity Sugars E/A

- - g/L º Brix -

133% 12,0 a 0,67 a 11,930 a 18,4 a

100% 12,4 a 0,76 a 12,19 a 16,0 a

Optimum 13,9 ↓ 0,82 ↓ 11,25 ↑ 13,7 ↑

Table 4: Citrus compositions results in the 01.17.05 plot

Strategy Tree Samples D H D/H

- - - cm cm -

133% 31 78 56,2 48,0 1,17

133% 32 84 58,3 48,8 1,21

133% 33 70 64,9 56,1 1,16

100% 34 81 54,5 50,5 1,09

100% 35 80 51,0 43,4 1,18

100% 36 81 56,8 50,1 1,14

133% 37 87 51,0 44,8 1,14

133% 38 80 58,7 50,5 1,18

133% 39 81 57,7 48,6 1,19

100% 40 78 54,8 46,8 1,18

100% 41 83 54,1 46,3 1,18

100% 42 83 54,1 46,1 1,18

Strategy D/H

- -

133% 1,18 a

100% 1,16 b

p-Value 0,0146

D/H

65

Year 2015

Figure 4: Seasonal variation of midday stem water potential (Ψstem) in 01.17.05 plot and error bars

originating from the x-axis.

Yield and citrus composition

The two treatments carried out in the 01.17.05 plot for assessing FIGARO strategy were evaluated

in terms of yield and citrus composition. The statistical design was randomized complete block with

two replicates per treatment. Each experimental unit had six trees. Analysis of variance was

performed using Statgraphics Plus.

In the table 2 are shown yield results in terms of average fruit weight. There are not significant

differences between treatments.

Strategy Average fruit weight

- gr

133% 49.14 a

100% 47.64 a

Table 2: Yield results in the 01.17.05 plot.

An average sample of 8 fruits per tree was collected for assessing fruit size. The following tables

and figure shows that there is not significant differences between treatments, but in both cases the

ratio D/H is near from the optimum for harvesting that is 1,20.

Strategy Tree D H D/H

- - mm mm -

133% 31 42.9 41.5 1.0

66

Table 3: Fruit size results in the 01.17.05 plot.

Finally, coloration index (ICC) and maturity index (E/A) were determined in a sample of 8 fruits

from the two treatments. These determinations were carried out in the expected date of harvesting.

In the table 4 shows that irrigation strategies did not produce any differences in citrus composition

at harvest. This implies that the FIGARO irrigation treatment was not severe enough to modify

citrus composition.

Strategy ICC E/A

- - -

133% 9.21 a 14.19 a

100% 9.15 a 13.86 a

Optimum 13,9 ↓ 13,7 ↑

Table 4: Citrus compositions results in the 01.17.05 plot

Despite the fruit quality obtained in the plot conducted by FIGARO irrigation strategy, the owner

couldn’t harvest due to the current low prices of the citrus.

Water saving results

FIGARO adopted strategies savings ranged from 10 % to 45 % compared to farmer strategies with

an 20% average saving

133% 32 45.6 38.9 1.2

133% 33 45.4 42.9 1.1

100% 34 45.3 41.5 1.1

100% 35 44.9 43.2 1.0

100% 36 46.4 40.8 1.1

133% 37 46.0 40.5 1.1

133% 38 46.8 44.0 1.1

133% 39 41.9 38.9 1.1

100% 40 44.4 40.5 1.1

100% 41 47.1 41.7 1.1

100% 42 45.1 41.3 1.1

Strategy D/H

- -

133% 1,09 a

100% 1,09 a

67

Energy management

With the aim of reducing energy consumption and improve water use in pressurised irrigation

systems, the methodology for grouping intakes of pressurised irrigation networks into sectors to

minimize energy consumption developed by Jimenez Bello et al. (2010a) was modified to allow

irrigation intakes to operate the scheduled time for each one instead of operating in restricted

irrigation periods of the same length. Moreover a method was developed to detect the maximum

number of intakes that can operate without extra energy in the case the source has enough head to at

least feed some of them.

Fig 3 Procedure for setting the daily irrigation scheduling Energy savings

A saving of 36.3 % was achieved, by increasing the total volume supplied by gravity, by

decreasing the injection pump head and by improving the pump performance. Therefore all intakes

operate just the strict irrigation time at the minimum required pressure

PI test on Maize in Bulgaria

Introduction

Increasing demand for food and livestock feed, coupled with changes in climate variables and

competition for water with urban and other sectors will most likely increase competing demands on

freshwater resources. Increasing competition for limited freshwater supplies is already evident in

major irrigated cropping systems of the world. Thus, increasing the water productivity in irrigated

agriculture will continue to be a vital goal in sustaining the balance between supply and demand of

food and fiber production.

68

One of the options to increase the water productivity is by using drip irrigation, Irrigating corn with

a drip system is more expensive than other irrigation methods (center pivot, irrigation gun,

sprinklers or flooding). In order to justify the cost of the drip system for growing corn, it is

necessary to increase the yield and quality.

The Key factors in order to achieve this goals are: Hybrid selection, planting date, Fertility,

Irrigation, Plant density and Row spacing.

Objectives

The targets of this study were:

1. Examine the influence of twin-row configuration (2 double rows instead of 2 single rows) in

order to increase the availability of Sunlight energy, Water and Nutrients to each plant in the

plot.

2. Examine this configuration with of 2 soil textures (light and heavy).

3. Examine the suitability of Aqua-Crop under uManage to provide irrigation scheduling

recommendations in real time.

Priorities

Increase water productivity is on highest priority while using AquaCrop for auto –optimize the

process is the main project priority

Limitations

Usage of real world sensors and filtering wrong data of climate and soil moisture.

Integration irrigation and entering rain events into the system in real time.

Calibration of the AquaCrop model to the local conditions is another limitation.

69

Methods and materials

Location

Table 1: Treatments

Treatment Shift Valve

number

Lateral distance

(m)

Irrigation method

1 1 2 1.40 local

2 1 1 1.40 Control (Aqua-

crop)

1 6 2 1.40 local

2 6 1 1.40 Control (Aqua-

crop)

The laterals are DripNet PC 16mm every 0,6m; 1.00l/h.

Table 2: Variety

Variety FAO

P0216 480

P1114 590

P1535 660

P1921 710

Seedling date: 06/05/2015, Harvesting date: 27/11/2015, total growing season: 205 days.

42012'20.26" N

24048'41.42" E

70

Stand: 110,000 seeds/ha,

Fertigation: 60kg/ha of H3PO4 (P- 16 kg/ha), application time: 22 May up to 1 of June.

488kg/ha Urea (224kg of N/ha), application time: 15 of June up to 30 of July.

Plant Protection

Herbicides - application of Laudis (Bayer), 2 liter/Ha was done prior to seedling stage.

Climate

Figure 1: Evaporation and Precipitation (mm), month's 04.06-26.11.2015

Table 3: Evaporation and Precipitation Data, 05 - 10/2015

Month Decade Average

Evaporation

(mm/day)

Evaporation

(mm)

Precipitation

(mm)

Evaporation

-

Precipitation

May 1 5.1 50.6 24.8 -25.8

2 4.5 45.0 26.2 -18.8

3 5.3 57.9 3.2 -54.7

June 1 4.5 45.1 6.6 -38.5

2 6.0 60.2 29.4 -30.8

3 5.4 53.6 19.4 -34.2

July 1 5.8 57.9 0.2 -57.7

2 5.9 59.1 0.0 -59.1

3 6.0 65.6 4.2 -61.4

August 1 5.6 55.7 3.2 -52.5

2 5.0 49.8 35.8 -14.0

71

3 4.4 48.5 15.0 -33.5

September 1 3.9 39.4 1.6 -37.8

2 3.4 34.4 39.4 5.0

3 2.5 25.4 19.8 -5.6

October 1 2.2 22.4 10.6 -11.8

2 1.5 14.7 11.2 -3.5

3 1.5 16.5 6.4 -10.1

Total 801.8 257.0 -544.8

Deficit water budget.

Figure 2: Average, Min and Max Temp, months 05-11.2015

The average Minimum temperatures for May, June, July, August, September and October were: 12,

15, 18, 15 and 100C respectively. The average Maximum temperatures for May, June, July, August,

September and October was: 25, 27, 32, 30, 25 and 200C respectively.

Figure 3: GDD Accumulation, months 05-11.2015

72

Accumulation of 900 GDD represents the Tasseling stage (last week of July), 1660 GDD represents

physiological maturity (middle of September)

Table 4: Accumulation of growing degree days and phenology stage according to days from sowing

GDD Development stage DAP Date

0 Sowing 0 06.05.15

70 VE - Emergence 7 13.05.15

114 V1- First Collared leaf 11 17.05.15

161 V2 - Second Collared

leaf

15 21.05.15

208 V3 - Third Collared leaf 20 26.05.15

256 V4 - Forth Collared leaf 25 31.05.15

303 V5 - Fifth Collared leaf 30 05.06.15

893 VT - Tasseling 79 24.07.15

913 R1 - Silking 80 25.07.15

1063 R2 - Blister 89 03.08.15

1213 R3 - Milk 99 13.08.15

1363 R4 - Dough 110 24.08.15

1513 R5 - Dent 121 04.09.15

1663 R6 – Physiological

Maturity

135 18.09.15

Results

Table 5: Irrigation Data (mm) according to the shift, 2015

Date Shift 1 Shift 6

Irrigation

(mm)

Interval

(days)

Irrigation

(mm)

Interval

(days)

1-10/07 46.6 2 48.8 2

10-20/07 64.2 1.7 66.3 1.7

20-31/07 37.1 3.7 56.3 1.8

1-10/08 60.5 1.7 58.7 1.7

10-20/08 44.2 2.0 37.0 1.7

Total 252.6 267.1

Shift 1 - Total of 27 irrigation events, average quantity per irrigation event is 9.4 mm.

73

Shift 6 - Total of 23 irrigation events, average quantity per irrigation event is 11.6 mm.

Moisture sensing

Figure 4: Shift 1, soil moisture tension (0.3, 0.6 and 0.9 m depth), and irrigation events.

The soil moisture tension of the different soil layers represent the water extraction pattern by the

roots, during most of the growing season the top soil layer (0.3 m) supply most of the water demand

of the crop, only from late July the deeper soil layers participate on the water budget of the crop.

Figure 5: Shift 3, soil moisture tension (0.3, 0.6 and 0.9 m depth), and irrigation events.

The water consumption trend is similar to the data presented from shift 1, the increasing

consumption from 0.6 m depth from 23/07/15 until the end of the growing season is more

pronounced on shift 3

74

Yield

Table 6: Grain Yield according to varieties

Variety FAO Grain Yield

(Ton/Ha), 15%

Moisture

Moisture at

harvesting

(%)

P0216 480 13,57 13,4

P1114 590 14,56 14,6

P1535 660 15,09 16,8

P1921 710 12,98 18,0

Average yield for all the varieties 14.1 ton/Ha

Figure 6: The Influence of drip lateral distance from the corn row on the Grain Yield (Ton/Ha).

*In – row distance from the drip lateral is 15 cm, **Out - row distance from the drip lateral is 25

cm.

In the more sandy soil, the yield was affected by the row distance from the drip lateral, the yield

difference was 34% in favor of the "in rows" (shorter distance), and the total average yield was the

same for the two soil type.

75

Figure 7: Simulation of Aqua-Crop, data 2015, irrigation regime – shift 1.

The data base for this simulation represents the climate of 2015, crop seedling date and density, soil

type and the irrigation regime.

PI test on Cotton in Israel

Introduction

Citrus is an important crop in Israel and the Or variety is the major constituent of the citrus export

today. The planting area is approximately 4800 Ha (25% of the total citrus area in Israel). The

export income is around 105 million euro (50% of the total income from citrus export in 2013).

Although this variety represents a large market share, irrigation and nutrition knowledge still are

lacking.

The irrigation requirements of citrus vary with climatic conditions and variety (Fares and Alva,

1999). Less rainfall during the growing season usually results in higher irrigation requirements.

Other factors like soil water availability, rooting depth, management of irrigation and the portion of

the field that is irrigated with micro-irrigation are important in determining the amount of irrigation

needed.

Micro-irrigation (drip and micro-sprinklers) are the predominant methods of irrigation for citrus in

Israel. With fertigation, micro-irrigation systems can also provide an economic method of applying

fertilizer and other agricultural chemicals on a timely basis. However, micro-irrigation

Systems require a higher level of management expertise than other irrigation methods. Micro-

irrigation systems are more complex, require greater filtration and water treatment, and typically

have high maintenance costs compared to other types of irrigation.

Irrigations generally must be scheduled more frequently with micro-irrigation systems, since they

reach only a fraction of the root zone as compared to other types of systems. Proper management of

76

micro-irrigation systems demand the use of automation and central control systems together with on

line evapotranspiration (ET) calculations from nearby weather stations allowing a precise irrigation

scheduling.

Objectives

The aim of this observation is to establish a proper irrigation scheduling and management practice

for “Or” variety under Mediterranean conditions.

Priorities

Irrigations generally must be scheduled more frequently with micro-irrigation systems, since they

reach only a fraction of the root zone as compared to other types of systems. Proper management of

micro-irrigation systems demand the use of automation and central control systems together with on

line evapotranspiration (ET) calculations from nearby weather stations allowing a precise irrigation

scheduling.

Limitations

The irrigation requirements of citrus vary with climatic conditions and variety (Fares and Alva,

1999). Less rainfall during the growing season usually results in higher irrigation requirements.

Other factors like soil water availability, rooting depth, management of irrigation and the portion of

the field that is irrigated with micro-irrigation are important in determining the amount of irrigation

needed.

Materials and methods

Table 1: General knowledge

Plot Treatment Area (Ha) Irrigation

equipment

Planting

Year

Or 05 west Commercial 4.0 1.6/30*1 2005

Or 05 east Semi-

Automation

1.4 1.6/30*1 2005

Or BY

south

Commercial 2.9 1.6/30*2 2005

Or BY

north

Semi-

Automation

2.1 1.6/30*2 2005

Both irrigation systems are Surface drip irrigation. Or 06 was converted on 2014 from subsurface to

surface drip irrigation.

Or 05 divided into two sub-plots: East and West, Or 06 divided to two sub-plots: North and South.

Commercial irrigation refer to the local custom of the irrigation, Semi-Automation refer to

irrigation activation by constant irrigation quantity (determine by ETo*Kc), and monitoring this

regime by soil moisture sensors.

Irrigation monitoring:

Each plot consists of 3 tensiometers stations, each station with 3 tensiometers:

1. Between two drippers, 0.05 m from the drip lateral (Horizontal), depth 0.3 m.

77



The idea is to monitor the wetted area, were most of the active root zone is functioning.

Nutrition monitoring:

1. Each soil moisture station gets 2 extractors (Between two drippers, 0.10 m from the drip lateral

(Horizontal), depth 0.2 m and 0.4 m.

2. Soil sampling and analysis.

3. Leaf analysis.

Figure 1: Orthophoto of the two Or plots: A) Or 05, B) Or BY.

Table 2: Monthly Rain quantities for Season 2014-2015 compare to Average Rain distribution,

1999-2015

Year Year

14-15

Average

(1999-

2015)

Stdev

(1999-

2015)

Stdev/Average

(%) Month

September 2 3.4 6.4 189

October 27.9 35.5 46.0 129

November 141.1 67.1 58.3 87

December 28.3 115.1 62.3 54

January 195.8 146.6 84.3 58

February 161.9 93.6 53.4 57

March 23 29.4 24.4 83

April 66.7 9.3 19.3 207

May 0.6 2.4 6.7 276

Total 647.3 502.4 113.3 23

32023'27.16" N

34056'46.33" E

32022'56.76" N

34055'59.91" E

A B

78

The average annual rain amount is 502 mm, duration of the rain season is around 6 months

(October-March), and the peak quantities are in November- February. The rain quantities in 2014-

2015 were above the average rain quantities.

Table 3: Average ETo (mm/decade) for years 1999-2015, Recommended Kc and Calculated ETC.

Month Decade ETo Kc ETc Month Decad

e

ETo Kc ETc

January 1 1.7 - - July 1 6.9 0.55 3.80

2 2.2 - - 2 6.8 0.55 3.74

3 2.2 - - 3 6.8 0.55 3.74

February 1 2.3 - - August 1 6.7 0.57 3.82

2 2.5 - - 2 6.4 0.57 3.65

3 2.7 - - 3 6.2 0.57 3.53

March 1 3.3 - - September 1 6.2 0.62 3.84

2 3.3 - - 2 5.6 0.62 3.47

3 4.1 - - 3 4.6 0.62 2.85

April 1 4.3 0.33 1.42 October 1 4.7 0.70 3.29

2 4.8 0.33 1.58 2 4.0 0.70 3.29

3 5.5 0.33 1.81 3 3.6 0.70 2.52

May 1 5.9 0.38 2.24 November 1 3.4 0.70 2.38

2 6.0 0.38 2.28 2 2.6 0.70 1.82

3 6.2 0.38 2.36 3 2.6 0.70 1.82

June 1 6.5 0.50 3.25 December 1 2.3 - -

2 6.7 0.50 3.35 2 1.9 - -

3 6.8 0.50 3.40 3 2.0 - -

Total average water consumption per season is around 705 mm.

Results

Table 4: Plot 05, Soil analysis: 28.05.2015.

Treatment Commercial Semi-Automation

Parameter units Depth (m) Depth (m)

0.0-0.3 0.3-0.6 0.6-0.9 0.0-0.3 0.3-0.6 0.6-0.9

79

SP % 34.5 32.7 37.1 29.7 32.9 30

pH - 7.0 8.0 7.0 8.0 7.0 7.0

EC dS/m 0.7 0.78 0.8 0.58 0.55 0.92

Cl mg/l 75 124 99 78 53 124

Na mg/l 67 113 90 97 60 145

Ca+Mg meq/l 3.4 2.0 4.0 1.4 2.7 2.2

N-NO3 mg/l 10 3.6 3.3 6.7 8.0 12.5

P mg/Kg 20.2 10.4 12.0 14.7 19.2 25.6

K mg/l 24.0 12.0 20.0 4.0 12.0 16.0

B mg/l 0.2 0.1 0.2 0.1 0.11 0.13

Soil Texture: Depth 0-0.3 m – 88.8% sand, 4.6% silt, 6.6% clay, Sand. Depth 0.3-0.6 m – 76.8%

sand, 2.6% silt, 20.6% clay, Sandy clay. Depth 0.6-0.9 m – 48.8% sand, 36.6% silt, 14.6% clay,

Loam. EC: Low - Normal, Cl: Low, P: Medium, K: High for the Commercial plot, Low-Medium

for the Semi-Automation plot, pH: Normal, and B: Low.

Table 5: Commercial irrigation scheduling, Plot 05 West (Commercial), 2015

Month Interval Water

quantity

per

application

Daily

water

application

Number

of

irrigations

Total

water

application

ETo

Month

Crop

Factor

Days mm mm - mm mm -

January - 5.0 - 1 5.0 48.0 -

February - 6.2 - 2 12.3 60.7 -

March - 6.5 - 1 6.5 96.7 -

April 21 12.0 0.8 2 24.0 107.6 0.22

May 4.4 7.1 1.6 7 50.0 142.4 0.35

June 2.5 4.5 1.8 12 54.0 168.5 0.32

July 2.4 5.9 2.5 13 76.8 192.9 0.40

August 2.0 6.6 3.2 15 98.4 162.6 0.61

September 2.0 7.3 3.6 15 109.1 134.1 0.81

October 3.4 5.3 1.5 9 47.8 95.0 0.50

November 7.5 6.0 0.8 4 23.9 62.3 0.38

December - - - - - 41.5 -

80

Total - - - 81 507.8 1312.3 -

Average 6.6 - - - - -

The peak demand months are June- September; the maximum Kc was 0.81 on September.

Table 6: Semi-Automation irrigation scheduling, Plot 05 East (Semi-Automation), 2015.


quantity

per

application

Daily

water

application

Number

of

irrigations

Total

water

application

ETo

Month

Crop

Factor


January - 5.0 - 1 5.0 48.0 -

February - 8.6 - 2 17.2 60.7 -

March - 6.2 - 1 6.2 96.7 -

April 21 12.0 0.8 2 24.1 107.6 0.22

May 5.2 6.9 1.3 6 41.1 142.4 0.29

June 2.5 4.6 1.9 12 55.5 168.5 0.33

July 2.4 6.0 2.5 13 77.4 192.9 0.40

August 1.9 6.6 3.4 16 105.6 162.6 0.79

September 2.0 7.1 3.6 15 106.5 134.1 0.79

October 3.9 4.8 1.2 8 38.5 95.0 0.41

November 6.0 4.0 0.7 5 20.1 62.3 0.32

December - - - - - 41.5 -

Total - - 81 497.2 1312.3 -

Average - 6.5 - - - - -

The peak demand months are June- September; the maximum Kc was 0.79 on September

Figure 2: Min (A) and Max (B) Tension readings (kPa) and irrigation quantity (mm) for Or 05 east

(Semi-Automation).

A

81

0.3 m1 – depth 0.3 m, close to the drip lateral, 0.3 m2 - depth 0.3 m, far from the drip lateral ,0.6 m

– depth 0.6 m, close to the drip lateral.

The Main root zone located in the soil surface (0-0.3 m) represents most of the tree extraction

capability.

The Higher tension for the 3 soil moisture sensors locations is between March to April, the sensors

are reaching 70 kPa that represents water deficit of 60-70% for this soil type, this water extraction

pattern is mostly based on the rain harvest during the fall and the winter time.

It's interesting to look on the suction patterns of the sensor located far from the lateral (0.3 m2) in a

timeline during the year. The higher tension on the spring and the early summer may represent a

favorable conditions for roots development and water extraction by the root (high water content,

low EC and moderate soil temperature), later on after the intensive irrigation season start (July), the

electric conductivity on the sensor location increased because of inadequate water quantity per

irrigation and low water quality creating conditions that eliminate water extraction from this area

(low water content and high salinity), this low extraction is represent by the low tension of the soil

moisture sensor.

Similar soil moisture extraction pattern we are getting for the deep moisture sensor (0.6 m), the

difference between the two sensors is the air content in the soil on the volume that they represent (as

we are moving to deep layers, the oxygen content is decreasing).

The sensor located close to the drip lateral (0.3 m1) represent the soil volume that supply most of

the water consumption during the intensive irrigation season (June-September).

The question regarding this sensor is when to open the water? The answer consist of 2 parts: 1. the

tension threshold 2. The measuring time during the day.

The tension threshold is 25 kPa (40% water depletion), the second issue is more complicated, we

can open the water straight after the sensor reached this tension (normally noon time) or we can

wait until the soil water potential is reaching steady state (normally in the early morning) and only

then to open the water (if the tension reach the threshold).

B

b

82

Figure 3: Min (a) and Max (b) Tension readings (kPa) and irrigation quantity (mm) for Or 05 west

(Commercial).

When we compare between the 2 curves of Figure 2 (b and a), a represent the minimum tension

values (early morning) and b represent the tension values normally in mid-day. We can see clearly

that during June-September there is tremendous difference between the min and max values,

however from October this differences are reducing and we get the same trend with min and the

max values.

0.3 m1 – depth 0.3 m, close to the drip lateral, 0.3 m2 - depth 0.3 m, far away from the drip lateral

,0.6 m – depth 0.6 m, close to the drip lateral.

During August to October the soil moisture tension was low-moderate on the active root zone.

Since October the tension of the sensor close to the drip lateral increased representing higher water

consumption by the tree (fruit enlargement), there is also increasing consumption from depth of 0.6

m.

A

B

83

Table 7: Plot 05 fertilizer type and quantity 2014.

Month Fertilizer Kg/Ha Liter/Ha

A.N N

April Uran

32%

40 95

May Uran

32%

20 47

June Uran

32%

20 47

July Uran

32%

20 47

Total - 100 236

Uran. 32% is a liquid fertilizer

Table 8: Leaf Analysis 2010-2015, Or (05)

Year 2010 2011 2012 2013 2014 2015

Elemen

t

East Wes

t

East Wes

t

East Wes

t

N (%) 2.37 2.36 2.14 2.25 2.09 2.42 2.43 1.69 2.21

P (%) 0.10

3

0.09

7

0.11

6

0.09 0.10

4

0.11

6

0.13

6

0.12 0.12

K (%) 0.79 0.66 0.88 0.64 0.51 0.76 0.78 1.30 1.15

Mg (%) 0.28 0.21 0.23 0.21 0.22 0.27 0.26 0.26 0.27

Ca (%) - - - - - 5.58 4.20 3.37 3.23

Na (%) - - - - - 0.07 - 0.07 0.07

Cl (%) - - - - - 0.13 - 0.06 0.11

B(ppm) - - - - - 43.8 - 42.2 48.7

Fe

(ppm)

- - - - - 163.

8

128 204 214

Zn

(ppm)

- - - - - 20.8 16.4 14.8 16.2

Mn

(ppm)

- - - - - 56.4 47.4 46 56.2

84

Cu

(ppm)

- - - - - 44.6 40.2 69.8 82.4

Recommended values: N (%): 1.75-2.3, P (%): 0.08-0.13, K (%): 0.5-0.9, Mg (%): 0.28-0.31.

Zn (mg/kg): >30, Mn (mg/kg) : >30

Table 9: Water Analysis 2015, Or 5

Month 01 02 03 04 05 06 07 08 09 10 Ave.

Parameter Unit

s

B mg/l 0.11 0.11 0.11 0.11 0.11 0.13 0.12 0.16 0.18 0.13 0.13

Ca mg/l 84 84 80 84 83 84 76 74 70 77 80

Cl mg/l 157 165 160 160 169 173 179 178 156 145 164

COD mg/l 19 18 15 14 17 18 17 16 13 14 16

DO mg/l 7.1 8 10 4.5 6.2 8.5 5.5 4.5 4.0 2.5 6.0

EC dS/m 1.17 1.20 1.20 1.20 1.19 1.21 1.21 1.16 1.03 1.04 1.16

K mg/l 19 18.9 19 20 21.6 21.5 21.5 19.8 16.3 16.6 19.4

Mg mg/l 27.2 27.8 26.2 27.2 27.3 27.4 27.0 24.3 21.7 20.7 25.7

Na mg/l 120 122 117 127 128 132 132 137 112 106 123

NH4 mg/l 0.04 0.11 0.04 0.06 0.09 0.07 0.45 0.66 0.51 0.55 0.26

NKJT mg/l 1.67 1.48 2.04 1.59 1.64 1.64 1.85 2.45 1.9 2.32 1.86

NO2 mg/l 0.99 0.24 0.17 0.27 0.48 0.61 1.73 0.86 0.52 1.35 0.72

NO3 mg/l 35 37 35 26 17 15 7 6 4 10 19

Total N mg/l 10 10.1 10.1 7.65 5.7 5.27 3.97 4.1 2.98 5.02 6.5

pH - 7.3 6.9 8.2 8.6 9.1 9.1 9.1 9.1 8.9 8.4 8.5

PTOTAL mg/l 6.4 7.3 6.3 5.3 3.4 2.9 2.3 2.5 1.8 4.2 4.2

SAR - 2.9 2.9 2.9 3.1 3.1 3.2 3.3 3.5 3.0 2.8 3.1

Table 10: Water Nutrient Contribution 2015, Or 5, Commercial plot

Months Water

quantity

(mm)

NO3

(mg/l)

P

(mg/l)

K

(mg/l)

NO3

(Kg)

P

(Kg)

K

(Kg)

January 5.0 35 6.4 19 0.175 0.032 0.095

February 12.3 37 7.3 18.9 0.455 0.09 0.232

85

March 6.5 35 6.3 19 0.228 0.041 0.124

April 24.0 26 5.3 20 0.408 0.127 0.480

May 50.0 17 3.4 21.6 0.850 0.170 1.08

June 54.0 15 2.9 21.5 0.810 0.184 1.16

July 76.8 7 2.3 21.5 0.538 0.177 1.65

August 98.4 6 2.5 19.8 0.590 0.246 1.95

September 109.1 4 1.8 16.3 0.436 0.196 1.78

October 47.8 10 4.2 16.6 0.478 0.201 0.793

November 23.9 19 4.2 19.4 0.454 0.100 0.464

December - - - - - - -

Total - - - - 5.422 1.564 9.81

When data was missing, we use the average values: total N from NO3 is 12.3 Kg/Ha, P: 15.6 Kg/Ha

and K: 98.1 Kg/Ha.

86

Figure 4: Plot 05, Extractor Soil solution analysis (NO3, Cl and EC), 2015, Commercial (A-0.2 m,

B- 0.4 m), and Semi - Automation (C-0.4 m)

The NO3 concentration pattern during the season represent the fertigation procedure of the farmer,

During April until July the NO3 concentration are relatively high representing nitrogen application

with liquid nitrogen fertilizer starting on April ending on July, from July the NO3 concentration in

the soil solution is diminishing either by consumption and by leaching by the irrigation water.

We can see clearly the close relationship between the Cl concentration and the electric conductivity

in the soil solution, The Cl is the major anion that influence the electric conductivity especially

from August (when the nitrogen application stop) until the rain season.

87

Figure 5: Dendro-meter daily Max, Min readings and Irrigation events (mm) for Or 05 west

(Commercial).

The tree trunk diameter increased during September until mid-October, and then remained

relatively unchanged for the rest of the time, when the irrigation interval was short, it was hard to

distinguish the influence of the irrigation event on the growing curve, but from mid – October, we

can see respond of the irrigation events on the growing curve (more clearly with Min values).

Table 11: Plot BY, Soil analysis: 28.05.2015.

Treatment Commercial Semi-Automation

Parameter units Depth (m) Depth (m)

0.0-0.3 0.3-0.6 0.6-0.9 0.0-0.3 0.3-0.6 0.6-0.9

SP % 68.2 64.4 65.3 64.2 68.0 67.1

pH - 8.0 8.0 8.0 7.6 7.6 7.6

EC dS/m 1.6 1.1 1.0 2.0 1.3 1.1

Cl mg/l 82 67 92 96 85 124

Na mg/l 124 115 131 160 138 145

Ca+Mg meq/l 9.6 5.6 4.0 12.4 5.4 4.2

N-NO3 mg/l 106 49 17.1 167 62.8 19.1

P mg/Kg 47 31 10 44.5 31 11.2

K mg/l 23 23 12 28 25 12

B mg/l 0.2 0.1 0.1 0.1 0.09 0.1

SAR - 2.4 3.0 4.0 2.8 3.7 4.3

Soil Texture Commercial: depth 0-0.3 m – 58.8% sand, 28.6% silt, 12.6% clay, Sandy loam.

88

Depth 0.3-0.6 m – 54.8% sand, 32.6% silt, 12.6% clay, Sandy loam. Depth 0.6-0.9 m – 48.8% sand,

38.6% silt, 12.6% clay, Loam. Depth 0.9-1.2 m – 58.8% sand, 29.6% silt, 11.6% clay, Sandy loam.

Soil Texture Semi-Automation: Depth 0-0.3 m – 56.8% sand, 32.6% silt, 10.6% clay, Sandy loam.

Depth 0.3-0.6 m – 52.8% sand, 34.6% silt, 12.6% clay, Sandy loam. Depth 0.6-0.9 m – 48.8% sand,

36.6% silt, 14.6% clay, Loam. Depth 0.9-1.2 m – 56.8% sand, 30.6% silt, 12.6% clay, Sandy loam.

EC: Medium, pH: Normal, Cl: Low, Na: Medium, Ca+Mg: High, N-NO3: High, P: High, K: High,

B: Low, SAR: Low

Table 12: commercial irrigation scheduling, Plot BY South (Commercial), 2015


quantity

per

application

Daily

water

application

Number

of

irrigations

Total

water

application

ETo

Month

Crop

Factor


January - - - - - 48.0 -

February - 8 - 1 8.0 60.7 -

March - 5.7 0.6 3 17.1 96.7 -

April 21 17.6 1.2 2 35.1 107.6 0.33

May 7.1 7.1 1.6 7 49.4 142.4 0.35

June 5.1 5.1 1.2 7 35.5 168.5 0.21

July 3.3 8.5 2.5 9 76.1 192.9 0.39

August 3.4 10.9 3.2 9 98.8 162.6 0.61

September 2.0 7.4 3.7 15 111.6 134.1 0.83

October 2.6 5.3 2.0 12 63.4 95.0 0.67

November 7.5 6.3 0.8 4 25.3 62.3 0.41

December - - - - - 41.5 -

Total - - - 69 520.3 1312.3 -

Average - 8.2 - - - - -

The peak demand months are July- October; the maximum Kc was 0.83 on September

89

Table 13: Semi-Automation irrigation scheduling, Plot BY North (Semi-Automation), 2015


quantity

per

application

Daily

water

application

Number

of

irrigations

Total

water

application

ETo

Month

Crop

Factor


January - - - - 48.0 -

February - 8 - 1 8 60.7 -

March - 5.7 - 3 17.1 96.7 -

April 15 12.6 0.8 2 25.1 107.6 0.23

May 4.4 6.5 1.5 7 45.5 142.4 0.32

June 4.3 5.4 1.3 7 38.1 168.5 0.23

July 3.4 8.4 2.4 9 75.7 192.9 0.39

August 3.4 11.0 3.2 9 99.0 162.6 0.61

September 2.0 7.8 3.9 15 117.5 134.1 0.88

October 2.6 5.5 2.1 12 65.5 95.0 0.69

November 7.5 6.3 0.8 4 25.3 62.3 0.41

December - - - - - 41.5 -

Total - - - 69 516.8 1312.3 -

Average - 7.7 - - - - -

The peak demand months are July- September; the maximum Kc was 0.88 on September.

Table 14: Plot BY fertilizer type and quantity 2014.

Month Fertilizer Kg/Ha Liter/Ha

A.N N

April A.N.

21%

50 186

May A.N.

21%

40 149

June A.N.

21%

30 112

July A.N.

21%

30 112

Total - 150 559

A.N. 21% is a liquid fertilizer.

90

Figure 6: Min (a) and Max (b) Tension readings (KPa) and irrigation quantity (mm) for Or BY

North (Semi-Automation)

0.3 m1 – depth 0.3 m, close to the drip lateral, 0.3 m2 - depth 0.3 m, far away from the drip lateral

Table 15: Leaf Analysis 2010-2015, or (BY)

Year 2010 2011 2012 2013 2014 2015

Element North South North South North South

N (%) 2.1 2.62 2.1 2.62 2.72 2.43 2.25 1.68 1.94

P (%) 0.09

3

0.10

7

0.10

7

0.10

8

0.12

4

0.14

0

0.12

8

0.12 0.12

K (%) 0.44 0.51 0.54 0.43 0.57 0.59 0.66 0.59 0.76

Mg (%) 0.26 0.32 0.26 0.27 0.25 0.27 0.24 0.28 0.31

Ca (%) - - - 4.35 3.98 4.53 3.95 3.47 4.10

Na (%) - - - 0.08 0.04 0.08 0.08 0.08

91

B

(ppm)

- - - 51.2 71.2 50.0 42.5 65.5

Cl (%) - - - 0.09 0.06 0.15 0.11 0.12

Fe

(ppm)

- - - 173.

4

186.

8

90.8 118 114 178

Zn

(ppm)

- - - 21.6 18.0 17.0 16.8 17.2 21.4

Mn

(ppm)

- - - 82.4 71.6 53.6 58.0 56.4 64.6

Cu

(ppm)

- - - - - 28.4 28.2 37.2 102

Recommended values: N (%): 1.75-2.3, P (%): 0.08-0.13, K (%): 0.5-0.9, Mg (%): 0.28-0.31.

Zn (mg/kg): >30, Mn (mg/kg) : >30.

Figure 7: Min (a) and Max (b) Tension readings (KPa) and irrigation quantity (mm) Or BY South

(Commercial)

0.3 m1 – depth 0.3 m, close to the drip lateral, 0.3 m2 - depth 0.3 m, out of the drip lateral ,0.6 m –

depth 0.6 m, close to the drip lateral.

92

The soil moisture tension pattern looks similar to the plot Or 05, unfortunately we don't have the

tension measurement before the end of May. During the intensive irrigation season (June-October

the tension on the main root zone (0.3-0.4 depth) was between 10-20 kPa representing water

depletion of no more than 20%. On the edges of the intensive irrigation season (early spring and

mid-late fall), we clearly define more water extraction from deep soil layers and also relatively far

away from the drip lateral.

Figure 8: Dendrometer daily max readings, Tensiometers readings and irrigation events (mm) for

Or BY, Commercial plot, 2015

The diameter of the trunk stop to grow on the of October.

Table 16: Water Analysis 2015, or (BY)

Month 01 02 03 04 05 06 07 08 09 10 Ave.

Parameter Unit

s

B mg/l 0.09 0.12 0.11 0.11 0.13 0.14 - 0.16 0.14 0.09 0.11

Ca mg/l 74.4 90 85 82 94 90 78 81 95 90 85.5

Cl mg/l 131 175 159 149 186 186 179 180 178 175 169

COD mg/l 20 25 27 25 26 21 20 25 24 20 23.7

DO mg/l 7 8 6 6.5 5 10 7.5 5.5 8.6 3.5 7.1

EC dS/m 1.09 1.40 1.36 1.09 1.33 1.28 1.15 1.22 1.29 1.27 1.24

K mg/l 18.6 23.1 22.1 18.7 25.2 24.4 25.8 23.9 23 22.6 22.8

Mg mg/l 26.3 35.3 29.2 15.8 23.1 19.8 17.7 21.5 25.4 26.1 23.8

Na mg/l 99 137 132 105 141 140 139 139 140 135 130

NH4 mg/l 10.5 8.9 17.6 5.3 3.6 2.2 0.96 4.1 4.1 1.6 6.4

NKJT mg/l 9.6 8.4 16.2 6.3 5.8 5.1 5.2 6.2 5.3 3.8 7.6

NO2 mg/l 0.29 0.95 1.67 0.9 0.05 0.91 0.76 0.7 1.8 0.4 0.9

NO3 mg/l 28 27 14 16 11 26 19 25 27 29 21.4

93

Total N mg/l 16.1 14.9 19.9 10.2 8.4 11.3 9.8 12.1 12.0 10.6 12.8

pH - 6.9 7.15 7.12 7.6 7.7 8.5 9.1 8.3 8.2 8.5 7.8

PTOTAL mg/l 9.3 9 7 4.7 21.3 7.8 4.8 19 13 12.5 10.7

SAR - 2.5 3.09 3.1 2.8 3.4 3.5 3.7 3.5 3.3 3.2 3.2

Table 17: Water Nutrient Contribution s 2015, Or BY, Commercial plot

Months Water

quantity

(mm)

NO3

(mg/l)

P

(mg/l)

K

(mg/l)

NO3

(Kg)

P

(Kg)

K

(Kg)

January - 28 9.3 18.6 - - -

February 8.0 27 9 23.1 0.216 0.072 0.185

March 17.1 14 7 22.1 0.239 0.120 0.378

April 35.1 16 4.7 18.7 0.562 0.165 0.656

May 49.4 11 21.3 25.2 0.543 1.05 1.25

June 35.5 26 7.8 24.4 0.923 0.277 0.866

July 76.1 19 4.8 25.8 1.45 0.365 1.96

August 98.8 25 19 23.9 2.47 1.88 2.36

September 111.6 27 13 23 3.01 1.45 2.57

October 63.4 29 12.5 22.6 1.84 0.793 1.43

November 25.3 21.4 10.7 22.8 0.541 0.271 0.577

December - - - - - - -

Total - - - - 11.79 6.44 12.23

When data was missing, we use the average values: total N from NO3 is 26.8 Kg/Ha, P: 64.4 Kg/Ha

and K: 122.3 Kg/Ha.

94

Figure 9: Plot BY, Extractor Soil solution analysis (NO3, Cl and EC), 2015, Semi - Automation (A-

0.2 m, B- 0.4 m),

The NO3 concentration was relatively high on July and August in the soil solution on both depth

(0.2 and 0.4 m), this may represent the nitrogen application until July and also can be the result of

wrong irrigation regime because also the Cl concentration and the electric conductivity increase.

Red arrows may represent days of water stress, the blue arrow represent the turning point,

Summary and conclusions

CER Conclusions

The models were calibrated and validated on independent datasets and then applied in the FIGARO

experiments to check the validation reliability.

Both models resulted not able to cope with the uncommon growing seasons 2014 and 2015. The

models did not catch the plant resilience and capability to adapt modifying its own morphology and

growth patterns. Moreover, the calibration appears not only crop dependent but even influenced by

the varietal characteristics.

However, results obtained in 2013 indicate that it’s possible to use Aquacrop to support water

management in maize and processing tomato.

AU Discussion and Conclusion

A calibration of the Aquacrop model was performed based on the fully fertilized and irrigated

treatment in a field experiment on potatoes performed during 2013-2015. Phenological development

was better described using a growing degree day (GDD) approach than when development was

calculated based on days after emergence. Validation of the parameter set showed that water

balance and growth in treatments with near optimal N and water supply could be described with the

same precision as the calibration data while deviations in a non-irrigated treatment were higher.

Yield response curves to N fertilization were derived and the economic optimum determined to be

near 180 kg N/ha of fertilizer N. A relation between RVI and LAI at this fertilization level was

shown to be a sensitive measure of crops’ N-sufficiency and could be used for scheduling split N-

application in potato. AquaCrop was well calibrated and validated. The calibration based on the full

irrigated treatment in 2014 showed good statistical performance with ranges of

0.02-0.16 for NRMSE, 0.85-0.96 for EF, and 0.95-0.99 for d for plant parts and

95

For soil water the NRMSE, EF and d were 0.11, 0.38, and 0.80, respectively.

The validation showed ranges of

0.02-0.21 for NRMSE, 0.78-0.95 for E, and 0.95-1.00 for plant parts

For soil water the NRMSE, EF and d were 0.10-0.28, -1.88-0.81 and 0.53-0.94, respectively.

The modelling errors were small for crop parameters, although with a tendency to under simulation

in deficit and non-irrigated treatments. It may be possible to reduce these errors with further

calibration of model parameters or by changing some functionality in the stress process description

of the model

For soil water the comparisons were satisfactory. Mostly differences were caused by spatial

variability in the measurements e.g. the level of field capacity. Another noise element is the timing

of the daily measured and modelled data. Soil water content is typically measured just before

irrigation, but the modelled soil water content represents the status same day at midnight. Other

errors may be caused in the difficulties of comparing a 1D model with a 2-3D soil water content

measurement.

Conclusion for Daisy modelling of potatoes:

The Daisy model showed after calibration very good performance in calculating dry matter and N

uptake during the season and final harvested N and dry matter and as well soil water content was

predicted very good.

DUTH Conclusions

The Laboratory of Ecological Engineering and Technology (Department of Environmental

Engineering, Democritus University of Thrace, Greece) as partner of the FIGARO program carried

out an experiment in twenty acres cotton cultivation land.

Cotton cultivation was selected because it is a water consuming crop and at the same time it is of

great economic importance for Greece. Indeed the cotton crop in Greece:

- covers about 13% of the total arable land

- production is an important source of income for about 80.000-100.000 Greek families

- employs more than 150,000 working people in different production and processing stages

- is an important source of foreign currency for the national economy, as the largest amount of

its production is exported.

Furthermore, precipitation is very low during the cotton growing period in Greece (May to

October), so that irrigation is absolutely needed for productive growth.

The platform utilized a meteorological prognostic model for evapotranspiration and precipitation

prediction as well as a crop growth model. The test was implemented in six experimental plots and

a different irrigation strategy was applied in each plot.

The experiment results showed that scheduling the irrigation events is feasible in terms of time and

amount and that drip irrigation, especially the Figaro precision irrigation platform, contributes

significantly to reducing the amount of irrigation water.

96

Indeed, the measurement results highlighted that drip irrigation greatly contributes to increasing

efficiency of use of water, while it further improves the performance and productivity of the system

in combination with the Figaro precision irrigation platform.

Furthermore, results demonstrated that empirical management of farmers based on practice

achieved the same crop yield output as that of precision irrigation, consuming, however more

irrigation water. Drip irrigation contributed significantly to the raise of water productivity and in

combination with precision irrigation the system’s efficiency and productivity are enhanced.

In particular, at farm scale level using drip irrigation, approximately 4.4 tons of crop per hectare

were produced under empirical management and precision irrigation practice, with the latter using

20% less water. Water productivity was thus improved from 0.77 kg yield per m3 under empirical

farmer management to 0.85 kg of yield per m3 though the Figaro platform.

Considering that in the region of Anatoliki Makedonia, Thraki about 50,000 hectares of cotton are

cultivated every season, the water saving through Figaro Platform of 1,000 m3 per hectare could

lead towards overall water saving of 50,000,000 m³ per growing season. This water corresponds to

that extracted for irrigation by 850 drillings operating in the area, thus minimizing farmers’ costs

and improving environmental sustainability of agriculture.

IST Conclusions

The AquaCrop model was well calibrated and validated. The calibration based on the full irrigated

treatment in 2014 showed good statistical performance of the canopy cover and dry biomass

simulations with NRMSE, ME and IA ranging between 0.20-0.25, 0.84-0.93, and 0.97-0.98,

respectively. For the water content simulations, the adjustment between observed and fitted values

produced a NRMSE, ME and IA of 0.07, 0.35, and 0.90, respectively.

Likewise, the MOHID-Land model showed to be properly calibrated and validated, with the

NRMSE, ME and IA values ranging between 0.23-0.0.33, 0.78-0.89, and 0.95-0.97 for crop growth,

and 0.04-0.05, 0.08-0.73, 0.75-0.92 for water content simulations, respectively.

The AQUACROP model, due to the empirical nature of the capillary rise equation used, needed to

be locally calibrated in order to properly simulate soil water storage and crop growth. This was even

more relevant in the presence of a shallow water table since the calibrated parameters obtained in

the FIGARO experimental plot for simulating capillary rise in AQUACROP will only be useful for

running the model in other plots with similar conditions. On the other hand, the MOHID-model

main constraint was the need for more detailed description of the soil hydraulic properties in order

to solve the Richards equation. Although this information can be measured with cost or obtained by

indirect methods, it still is difficult to obtain in many regions of the world.

UPV Conclusion

The adopted strategy to schedule irrigation with the aim of saving water is based on the FAO

methodology that assumes a crop coefficient to correct the reference evapotranspiration. Moreover

soil sensors are used to perform water balances. In order to guarantee plant stress is over the limit

threshold, stem water potential was measured.

97

Average water savings compared to farm orchards were 20 %. No meaningful differences in yield

amount and quality were found. That means that the application of the tested approach in FIGARO

platform was successful.

This water saving can be assumed as an energy saving due to 20% less energy is used.

The application of the designed methodology for scheduling in pressurized irrigation networks

meant a decreasing of 36.3 % in specific consumption (kWh m3) for the case study.

NETAFIM Bulgaria Conclusion

1. The Aqua-Crop under the Figaro Platform was supposed to provide on line data regarding

irrigation scheduling recommendations based on the local metrological station data and the

climate prediction. Since we didn't receive the climate prediction, the integration of this

factor into the Figaro Platform and the Aqua-Crop model didn't work out, and in fact we

used the metrological data and the moisture sensor readings to determine the irrigation

scheduling in real time.

2. The plot water balance consists of 3 main components: ETp (potential ETo), Rain and

irrigation quantity. The figures for these segments during May-October where: ETp = 800

mm, Rain = 260 mm, irrigation quantity = 260 mm. These figures represent 0.65 return of

the water consumption.

3. We start the growing season with full water capacity (F.C.), for the first growing stages

(emergence to 5th

leaf), there was enough water in the soil profile. The first irrigation started

in July, applying 150 mm of water that covered the water consumption for July but didn't

cover the water deficit of 200 mm from the beginning of the season.

4. During most of July and August, the rain events were rare, and with small quantities per

event, the combination of soil water deficit with not enough rain and irrigation to

compensate for this deficit, create unfavorable conditions for the plant during the

reproduction stage. This stress condition was attribute to high soil moisture tension on the

top soil (where most of the roots system).

5. There were 2 options to reduce the stress conditions: a) Earlier irrigation, start in the middle

of June instead of the beginning of July; the problem with this solution was 50 mm rain

during the second half of June that created a temporary saturation condition. B) Irrigation

start at the beginning of July but instead of 10-11 mm per irrigation event to increase the

quantity to 16-18 mm per irrigation event (45% more water = around 125 mm).

6. The distance of the corn row from the drip lateral can play a major rule on the final yield. At

the beginning of the season, when the rain quantities are small, the emergence and the

vegetation stages depend on the water distribution from the drip.

7. Uneven water distribution may affect a) the emergence percent and also can create un-even

stand development (yield variability). b). during the reproduction stage, uneven water

distribution may affect the pollination and the grain filling. The longer the distance of the

plant root from the water source, the higher the energy the plant had to invest in order to

overcome the increased resistance to water flow in the roots as a result of the longer

hydraulic path.

98

8. Within a plot, plants can compensate to some extent for uneven water distribution by

producing bigger cobs and heavier grain, probably because the plants close to the water

source can grow faster, creating bigger canopy, and increased their radiation absorbance

efficiency.

9. The yield difference between rows as a function of their distance from the water source was

much more pronounced on light soil compare to heavy soil.

10. Twin rows with one lateral for 4 rows could work on medium-heavy soil, but may create a

problem on light soil.

11. The Aqua-Crop simulation overestimates the grain yield by 3 ton/Ha compared to the actual

results.

NETAFIM Israel Conclusion

1. Total irrigation amount - The average water application for the Or 05 plots was 508 mm, and

for the Or BY 518 mm. In irrigation trial conducted in this region (Zeta 2008-2013); the

average annual water application was around 550-600 mm. So the irrigation amounts in this

observation were a little below the average, this may be related to the rainy season of 2014-

15 (28% above the annual average).

2. Number of irrigations and Water quantity per Irrigation - Or 05: commercial: 81 irrigations,

average of 6.6 mm per irrigation, Semi Automation: 81 irrigations, average of 6.5 mm per

irrigation. Or BY: 69 irrigations, average of 8.2 mm per irrigation, Semi Automation: 69

irrigations, average of 7.7 mm per irrigation.

3. Soil Moisture Sensors – each day we evaluate the Max and Min values of the soil water

tension, the Max value reflect on average the mid-day tension (12-14:00pm,peak demand),

while the Min value represent early morning tension (07-08:00am, steady state water

potential in the soil). We can use soil moisture sensors to monitor the irrigation regime, the

question is, what is the threshold to activate the irrigation and when to measure it? Are we

referring to the Max value (mid-day) or we refer to early morning reading when the soil

water potential is in steady state?

The threshold value can be determined according to the soil type, the volume of the root zone and

the growing stage. The reference time can be determined mostly by the growing stage, meaning in

critical stages like flowering and setting, we can use mid-day values, on vegetation stage without

sensitive process we may sue the early morning detection.

4. Soil Water tension pattern during the year – Increasing demand from April until the end of

June, the starting point in April represent the rain harvest from fall to early spring, on April

we get the main flush of flowering and fruits setting together with new vegetation and root

develop , explaining the big tension fluctuation on the max and the min daily values, from

July the tension is stabilize, with very low Min tension values until mid of October, when

again the tension increased until harvest in January/February.

5. The volume of the wetted area or the active root zone – we install one of the moisture sensors

at distance of 0.25-0.3 m from the drip lateral (depth 0.3 m), and another one, close to the

drip lateral (depth 0.6 m), this two sensors supposed to represent the lateral movement

99

(horizontal wetting front) and the vertical wetting front. during the intensive irrigation season

(June-September), most of the water extraction by the roots are from the soil volume on the

vicinity of the drip lateral, mainly because of better roots environment (soil moisture and

salinity), but on the early-mid spring the water extraction is also far away from the lateral

representing extraction of rain water.

The fact that during the main irrigation months the water extraction is restricted to a small soil

volume is mainly because of high intensity irrigation with small irrigation quantity per irrigation,

it's looks like the tree can handle with this mainly in the growing period between June and October,

but in October the tension of the soil solution increased (fruit growth), and then maybe it's demand

a change in the water regime (continue frequent irrigation) until the main rain season start

(December).

6. Influence of dripper discharge on the water distribution in the soil – the trend today of most

drip irrigation manufactures is to reduce the water flow of the dripper (below 1 l/H), however

there is inconsequent results from different locations regarding the relationship between

discharge and water movement in the soil, some of the researchers claim that reducing the

emitter discharge increased the vertical movement of the water compare to higher flows that

encourage the lateral movement. On the other hand adjusting the application rate to the water

intake rate of the plant by reducing the hourly discharge and increasing the irrigation time

have the potential for increasing the water and nutrient use efficiency.

7. Using Dendrometer for determination of the water status of the tree – Dendrometer is

measuring the growth rate of the trunk and the trunk diameter fluctuations during the day.

We get a good suitability between the Max and Min values of the dendrometer and the soil

moisture sensors, in respond to water shortage in the soil, this suitability was maintain until

mid of October, from mid - October to harvesting the dendrometer stop to give indication of

the water status of the tree, maybe because of changing trend from vegetation to fruit growth.

8. Automate irrigation scheduling – In season 2015, we were supposed to start working with

full automation irrigation by the next procedure:

a. Calculating the daily ETo by the local metrological station

b. Multiply the ETo by the relevant Kc = ETc daily.

c. Determine the soil volume that the tree is using to extract the water on the main

irrigation season (June-September), by using soil moisture sensors: the first one close to

the drip lateral below the tree canopy, depth: 0.3 m. The second close to the drip lateral

but depth 0.6 m and the third 0.25 cm from the lateral, depth 0.3 m.

d. This sensors configuration was multiply by 3 (3 sensors stations). We determine the

tension threshold according to the soil type and the phenology of the tree.

e. Each day the irrigation controller was checking the soil moisture tension values on a

specific time (07:00 in the morning), this values come from the soil moisture sensor

located close to the lateral, depth 0.3 m. in order to activate the irrigation, 2 of 3 sensors

located in this position were supposed to reach the planned threshold.

f. If the irrigation controller get positive input from 2 of the sensors then he open the water

and apply the ETc that was accumulated from the last irrigation.

100

g. Most of the season this plan didn't work because of technical problems (connections

between the irrigation controller, the metrological station and the sensors interface), only

on the end of September this technical problem was solved and we start working with

this procedure.

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