Improving Irrigation at the Farm Level: An overview of

the EU FP7 Project FIGARO

Raphael Linker

Faculty of Civil and Environmental Engineering

Technion – Israel Institute of Technology

EU FP7 Call for Proposals: Precision technologies to improve irrigation management and increase water productivity in major water-demanding crops in Europe

“(…) water is a limited resource (…). A wiser use of fresh water becomes now imperative. Irrigated agriculture is one of the major water-consuming sectors and as such, it provides good opportunities for substantial water savings.

The project's aim will be the optimisation of irrigation water use by improving the management of farm scale irrigation equipment - and water release scheduling – taking into account real time soil-water availability, local weather dynamics and crop specific physiological status and water needs. The successful proposal will exploit state-of-the-art techniques and technologies (…), models and devices to optimise irrigation water use at farm level. “

Background

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A. Battilani, FIGARO Consortium – WATEC 2013 (Tel Aviv 22th October 2013)

FIGARO System Architecture

Slide4

Beginning of season:

Decision support system engine

Slide5

Crop model

Expected weather

Water quotas

Soil data

Prices

Optimization procedure

Optimal irrigation schedule

During the season, whenever information becomes available:

Decision support system engine

Slide6

New information Expectations

Update of scheduling required?

Crop model

Expected weather

Water quotas

Soil data

Prices

Optimization procedure

Optimal irrigation schedule

If yes, repeat optimization

Developed by FAO to simulate crop development in response to various irrigation scenarios

Includes modeling of soil water content

Not too complex

Calibrated for many crops

Can be used to determine irrigation required in order to keep soil water content within user-specified boundaries

Default crop model - AquaCrop

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Flexible and precIse irriGation plAtform to improve faRm

scale water prOductivity Slide8 Total irrigation: 249mm

Flexible and precIse irriGation plAtform to improve faRm

scale water prOductivity Slide9 Total irrigation: 169mm

Assuming that the number of irrigation events for the whole season (N) has been set a priori, the basic questions are when to irrigate and how much to irrigate each time so that yield is maximized while the amount of irrigation water is minimized. Complex "Min-Max" problem

Optimal irrigation scheduling

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Alternative formulation: Consider that the desired yield is given a priori. In this case the basic questions are when and how much to irrigate so that the amount of irrigation water is minimized while still reaching the desired yield. Simpler minimization problem which can be solved repeatedly with increasing target yields

Optimal irrigation scheduling

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Step 1: Determine irrigation amounts assuming irrigation days are known

Step 2: Determine irrigation days assuming irrigation amounts are known

The whole procedure is repeated until convergence is achieved.

Genetic algorithms are used as optimization tool

Optimal irrigation scheduling

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Two-step approach:

AquaCrop model

Six years of climate data (rain range: 112-367mm)

Assume 8, 10, or 12 irrigation events per season

Optimization repeated six times with increasing yield target: 4.68 – 5.46 t/ha

Case study: Cotton in Northern Greece

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Typical results

Case study: Cotton in Northern Greece

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200 250 300 350 400 450 500 550 6003

3.5

4

4.5

5

5.5

6

Irrigation, mm

Yie

ld, t

/ha

Typical results

Case study: Cotton in Northern Greece

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Irrigation Irrigation

2004 (rain: 291mm) 2007 (rain: 154mm)

Yie

ld

Typical results

Case study: Cotton in Northern Greece

Slide16

Irrigation Irrigation

2005 (rain: 291mm) 2008 (rain: 122mm)

Yie

ld

Typical results

Case study: Cotton in Northern Greece

Slide17

Irrigation Irrigation

2006 (rain: 185mm) 2009 (rain: 274mm)

Yie

ld

Drawback of approach: Prohibitive computation time…

Case study: Cotton in Northern Greece

Slide18

During the season, whenever information becomes available:

Slide18

New information Expectations

Update of scheduling required?

Crop model Expected weather Water quotas

Soil data

Prices

Optimization procedure

Optimal irrigation schedule

If yes, repeat optimization

Drawback of approach: Prohibitive computation time…

Let’s have a look at soil water content immediately before and after irrigation according to optimal scheduling for yile(6 years, rain range: 112-367mm)

0 20 40 60 80 100 120 140 160-60

-40

-20

0

20

40

60

80

100

120

DAP

Deple

tion,

mm

empty symbol are when irrigation is triggered, filled symbols are after irrigation

So

il w

ate

r d

ep

leti

on

Time

Case study: Cotton in Northern Greece

Run full optimization with historical climate data and determine “trigger levels”

Sub-optimal irrigation scheduling

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Off-line

Run AquaCrop with these trigger levels and save corresponding irrigation schedule

On-line

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Run full optimization with historical climate data and determine “trigger levels”

Run AquaCrop with these trigger levels and save corresponding irrigation schedule

Use this irrigation schedule as starting point for optimization procedure

Sub-optimal irrigation scheduling

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Off-line

On-line

Sub-optimal, but very fast

Results:

Case study: Cotton in Northern Greece

Slide23

“True” optimum

Based on trigger levels

Sub-optimal schedule

Irrigation Irrigation

2004 2007

Yie

ld

Results:

Case study: Cotton in Northern Greece

Slide24

“True” optimum

Based on trigger levels

Sub-optimal schedule

Irrigation Irrigation

2005 2008

Yie

ld

Results:

Case study: Cotton in Northern Greece

Slide25

Average irrigation increase: ~10%

Average yield change <1%

“True” optimum

Based on trigger levels

Sub-optimal schedule

Irrigation Irrigation

2006 2009

Yie

ld

Test procedure with additional crops/soil types

Create database of “trigger levels” for each crop/yield/soil combination

Investigate sensitivity of whole procedure to accuracy of weather forecasts

Devise strategy for updating irrigation schedule during season

Next steps…

Slide26

Computing-time required for determining optimal irrigation scheduling is prohibitive for real-time applications

A sub-optimal approach suitable for real-time application has been devised

For the Case Study, applying this sub-optimal approach would result in significant water saving compared to common practice

Conclusions

Slide27

Thank you and thanks to all project participants!

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PT Technical University of Lisbon IL Netafim

I University of Bologna DK Aarhus University

I Consorzio di bonifica per il Canale Emiliano Romagnolo

NL Hydrologic Research

GR Democritus University of Thrace SP Polytechnic University of Valencia

GR Regional Union of Municipalities of Eastern Macedonia-Thrace

UK C-Tech Innovation

NL University of Twente UK Eden Irrigation

I Food and Agriculture Organization of the United Nations

PT Hidromod

IL Agora Partners DK AgroSensing

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