Crop Simulation Modeling

Gerrit HoogenboomDirector AgWeatherNet &

Professor of AgrometeorologyWashington State University, Prosser,

Washington, USA

Caribbean Agro-meteorological Initiative (CAMI)Conference

Breaking New Ground in the Caribbean: Weather and Climate Serving Agriculture

Knutsford Hotel, Kingston, JamaicaNovember 5-6, 2012

AgWeatherNet

Crop Modeling

Decision Support System for Agrotechnology Transfer (DSSAT)

Introduction to agricultural systems

Introduction to crop modeling

Model evaluation and experimental data

Example applications

Climate change

Climate variability

Information delivery

Final comments

Crop Modeling Training WorkshopJanuary, 2012 @ CIMH, Barbados

DSSAT Training WorkshopMay, 2012 @ University of Georgia, Griffin,

Georgia, USA

What is Agriculture?• Food (for human consumption)

• Feed (for livestock consumption)

• Fiber (for clothing and other uses)

• Fuel (for energy)

• Flowers (horticulture and green industry)

• [Forestry]

Agriculture

• The agricultural system is a complex system that includes many interactions between biotic and abiotic factors

Agriculture• Abiotic factors = Non-Living

– Weather/climate

– Soil properties

– Crop management• Crop and variety selection• Planting date and spacing• Inputs, including irrigation and

fertilizer

Agriculture• Biotic factors

– Pests and diseases

– Weeds

– Soil fauna

Agriculture

• Socio-economic factors– Prices of grain and byproducts– Input and labor costs– Policies– Cultural settings– Human decision making

• Environmental constraints– Pollution– Natural resources

Agriculture

• The agricultural system is a complex system that includes many interactions between biotic and abiotic factors

Management– Some of these factors can be modified by

farmer interactions and intervention, while others are controlled by nature.

Systems Approach

• Traditional agronomic approach:– Experimental trial and error

• Systems Approach– Computer models

– Experimental data

• Understand Predict Control & Manage– (H. Nix, 1983)

Application/Analysis

Control/Management/

Decision SupportDesignResearch

Model Development

Increased Understanding

Model

Test Predictions

Prediction

Research for Understanding

Problem Solving

Systems Approach

What is a model ?

• A model is a mathematical representation of a real world system.

• The use of models is very common in many disciplines, including the airplane industry, automobile industry, civil eng., industrial eng., chemical engineering, etc.

• The use of models in agricultural sciences traditionally has not been very common.

Simple Model

• Air temperature

==>Vegetative and reproductive development

• Solar radiation

==>Photosynthesis and biomass growth

Development * Biomass = Yield

Simple Model

• Yield = f (Development, Biomass)

• Development = f (Environment, Genetics)

• Biomass = f (Environment, Genetics)

• Environment = f (Weather, Soil)

• Other factors:– management

– stress (biotic and abiotic)

Crop Simulation Models

• Crop simulation models integrate the current state-of-the art scientific knowledge from many different disciplines, including crop physiology, plant breeding, agronomy, agrometeorology, soil physics, soil chemistry, soil fertility, plant pathology, entomology, economics and many others.

Agricultural Models

• Crop simulation models in general calculate or predict crop growth and yield as a function of:– Genetics– Weather conditions– Soil conditions– Crop management

Soil Conditions Weather data

Model Model

Simulation Simulation

Crop Management Genetics

GrowthGrowth DevelopmentDevelopment

YieldYield

Soil Conditions Weather data

Model Model

Simulation Simulation

Crop Management Genetics

GrowthGrowth DevelopmentDevelopment

YieldYield

Net IncomeNet IncomePollutionPollution Resource UseResource Use

Crop Simulation Models

Four levels or phases (School of De Wit)

LEVEL 1

• Potential Production– Solar radiation and temperature as input

– Simulate growth and development

– Plant carbon balance (photosynthesis, respiration, partitioning)

Level 2

Water-Limited Production

– Potential production +– Precipitation and irrigation as input

– Soil profile water holding characteristics

– Plant water balance (transpiration, water uptake)

– Soil water balance (evaporation, infiltration, runoff, flow, drainage)

Level 3

Nitrogen-Limited Production

– Water-limited production +– Nitrogen fertilizer applications as input

– Soil nitrogen conditions

– Plant nitrogen balance (uptake, fixation, mobilization)

– Soil nitrogen balance (mineralization, immobilization, nitrification, denitrification)

Level 4

Nutrient-Limited Production

– Nitrogen-limited production +– Fertilizer applications as input

– Soil nutrient conditions

– Plant nutrient balance (uptake, mobilization)

– Soil nutrient balance

• Phosphorus, potassium, other minerals

Level 4

Pest-Limited Production

– Nitrogen-limited production +– Pest inputs - scouting report

– Dynamic pest simulation

• Insects, diseases, weeds

Agricultural Production• Potential production

• Water-limited production

• Nitrogen-limited production

• Nutrient-limited production

• Pest-limited production

• Other factors• Extreme weather events• Salinity

Model

Real World

Com

plexity

1

2

3 actual

attainable

potential

Yield increasingmeasures

Yield protecting measures

defining factors:

reducing factors:

limiting factors:

CO2

RadiationTemperatureCrop characteristics-physiology, phenology-canopy architecture

a: Waterb: Nutrients- nitrogen- phosphorous

WeedsPestsDiseasesPollutants

1500 10,0005000 20,000 Production level (kg ha-1)

Production situation

Crop Model Concepts

Source: World Food Production: Biophysical Factors of Agricultural Production, 1992.

Crop Simulation Models• Require information (Inputs)

– Field and soil characteristics– Weather (daily)– Cultivar characteristics– Management

• Model calibration for local variety

• Model evaluation with independent data set

• Can be used to perform “what-if” experiments

What is a minimum data set?

• Computer models require a set of input data to be able to operate.

• Different models require different sets of input data.

• Define a minimum set of data that:– Can be relatively easily collected under field

conditions– Provides reasonable answers

Soil Conditions • Weather data

Model Model

• Simulation• Simulation

Crop Management • Genetics

• Growth• Growth • Development• Development

• Yield• Yield

Inputs

Outputs = Measurements

Linkage Between Data and Simulations

Model credibility and evaluation Input data needs:

Weather and soil dataCrop ManagementSpecific crop and cultivar informationEconomic data

• Yield

0

2000

4000

6000

8000 D

ry W

eig

ht

(kg/h

a)

175 200 225 250 275 300 Day of Year

Grain - Irrigated Total Crop - Irrigated

Total Crop - Not IrrigatedGrain - Not Irrigated

Simulated and Measured, Soybean

Gainesville, FL1978

Observed Yield vs. Rainfall (mm/d)

0

500

1000

1500

2000

2500

3000

3500

4000

0 2 4 6 8

Rainfall (mm/d)

Yie

ld (

kg/h

a)

Simulated Yield vs. Rainfall (mm/d)

0

500

1000

1500

2000

2500

3000

3500

4000

0 2 4 6 8

Rainfall (mm/d)

Yie

ld (

kg/h

a)

Observed and simulated soybean yield as a function of seasonal average

rainfall (Georgia yield trials)

Observed Yields

0500

1000150020002500300035004000

25 27 29 31 33

Max Temp Average (C)

Yie

ld (

kg/h

a)

Simulated Yields

0500

100015002000

2500300035004000

25 27 29 31 33

Max Temp Average (C)

Yie

ld (

kg/h

a)

Observed and simulated soybean yield as a function of average max

temperature (Georgia yield trials)

Applications• Diagnose problems (Yield Gap Analysis)

• Precision agriculture– Diagnose factors causing yield variations– Prescribe spatially variable management

• Irrigation management

• Water use projection

• Soil fertility management

• Plant breeding and Genotype * Environment interactions

• Yield prediction for crop management

Applications• Adaptive management using climate forecasts

• Climate variability

• Climate change

• Soil carbon sequestration

• Environmental impact

• Land use change analysis

• Targeting aid (Early Warning)

• Biofuel production

Model CalibrationPeanut, variety “Georgia Green”Statewide variety trials• “Best” variety trials selected

- Irrigated

- Very high yields

- No reported pest and

disease pressure

- No reported water stress

• Selected variety trials

Plains: 1995, 1996, 2001

Tifton: 1994 & Midville: 1996

Tifton

MidvillePlains

Georgia Peanut Variety TrialsModel calibration

4000

4500

5000

5500

4000 4500 5000 5500

Simulated seed yield (kg ha-1)

Mea

sure

d s

eed

yie

ld (

kg h

a-1

) 1:1 line

Measured

RMSE = 78 kg ha-1

Field 3

0

1000

2000

3000

4000

5000

6000

7000

8000

20 40 60 80 100 120 140

Days after Planting

RMSE = 974.9d = 0.95

Baker County Field 3Field 1

0

1000

2000

3000

4000

5000

6000

7000

8000

20 40 60 80 100 120 140

Days after Planting

kg

dm

ha-1

SimulatedMeasured

RMSE = 264.8d = 0.996

Mitchell County Field 1

CASE STUDY: Off-season Maize in Brazil

During the last decade maize has become one of the most important alternative crops for the Fall–Winter growing season (off-season) in several regions of Brazil.

PROBLEMS:

Insufficient and variable precipitation during Fall-Winter months.

Water deficits, sub-optimum temperatures and solar radiation are also common during the Fall–Winter growing season, causing a reduction in potential yield.

Background informationPlanting can be delayed when available soil water is

insufficient to establish a crop or due to a previously late-harvested crop.

A delayed planting date increases the risk of damage due to frosts during anthesis and grain filling.

There is a lack of technical information on the impact of variable weather conditions on yield.

TOOLS

Many of the decision support systems can assess the long-term impact of climate and associated yield.

Three experiments with four maize hybrids were conducted at the University of Sao Paulo, in Piracicaba, Brazil.

- One in 2001 under irrigated conditions,

- Two in 2002, one under rainfed and one under irrigated conditions.

The hybrids used were: AG9010, (very short season), DAS CO32 and Exceler (short season), and DKB 333B (normal season).

Irrigated experiment Rainfed experiment

Results

Observed and simulated LAI and biomass for four hybrids grown under irrigated conditions in 2002

EXCELER

Days after planting

0 20 40 60 80 100 120 140 160 180

LAI

(m2 m

-2)

0

1

2

3

4

Bio

mas

s (k

g ha

-1)

0

2000

4000

6000

8000

10000

12000

14000

16000

LAId = 0.97RMSE = 20.8%

Biomassd = 0.88RMSE = 23.6%

Days after planting

0 20 40 60 80 100 120 140 160 180

LAI

(m2 m

-2)

0

1

2

3

4

Bio

mas

s (k

g ha

-1)

0

2000

4000

6000

8000

10000

12000

14000

16000

LAId=0.98RMSE = 15.8%

Biomassd=0.88RMSE = 24.8%

DAS CO32

DKB 333B

0 20 40 60 80 100 120 140 160 180

LAI

(m2 m

-2)

0

1

2

3

4

Bio

mas

s (k

g ha

-1)

0

2000

4000

6000

8000

10000

12000

14000

16000

LAId = 0.99RMSE = 10.4%

Biomassd = 0.88RMSE = 24.8%

AG9010

0 20 40 60 80 100 120 140 160 180

LAI

(m2 m

-2)

0

1

2

3

4

Bio

mas

s (k

g ha

-1)

0

2000

4000

6000

8000

10000

12000

14000

16000

LAId = 0.96RMSE = 24.2%

Biomassd = 0.80RMSE = 32.9%

CSM-CERES-Maize evaluation

Simulated vs. observed yield for four hybrids grown under irrigated and rainfed conditions in 2002

Simulated yield (kg ha-1)

3500 4000 4500 5000 5500 6000

Obs

erv

ed

yie

ld (

kg h

a-1)

3500

4000

4500

5000

5500

6000

CSM-CERES-Maize evaluation

Simulated yield for different planting dates under rainfed and irrigated conditions

DAS CO32- Irrigated conditions

Planting date

Feb-01 Feb-15 Mar-01 Mar-15 Apr-01 Apr-15

Yie

ld (

kg h

a-1)

0

2000

4000

6000

8000

DAS CO32- Rainfed conditions

Yie

ld (

kg h

a-1)

0

2000

4000

6000

8000

Planting date evaluation

Average forecasted yield and standard deviation for 2002 as a function of the forecast date and observed yield (kg ha−1) for the four hybrids.

a) AG9010

Forecast date

Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01

Yie

ld (

kg h

a-1)

0

1000

2000

3000

4000

5000

6000

7000

Simulated yieldObserved yield

b) DKB 333B

Forecast date

Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01

Yie

ld (

kg h

a-1

)

1000

3000

5000

7000

0

2000

4000

6000

Simulated yield Observed yield

c) DAS CO32

Forecast date

Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01

Yie

ld (

kg h

a-1)

0

1000

2000

3000

4000

5000

6000

7000

Simulated yieldObserved yield

d) Exceler

Forecast date

Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01

Yie

ld (

kg h

a-1

)

0

1000

2000

3000

4000

5000

6000

7000

Simulated yieldObserved yield

Yield Forecast

ConclusionsThe CSM-CERES-Maize model was able to accurately simulate phenology and yield for four hybrids grown off-season in a subtropical environment in Brazil.

In general, total biomass and LAI were also reasonably well simulated.

For both rainfed and irrigated cropping systems, average yield decreased with later planting dates.

This study also showed that the CSM-CERES-Maize model can be a promising tool for yield forecasting for maize hybrids, grown off-season in Piracicaba, SP, Brazil, as an accurate yield forecast was obtained at approximately 45 days prior to harvest.

Climate Change and Climate Variability

The impact of climate change and climate variability on agricultural production and the potential for mitigation and adaptation

• Issues can only be studied with simulation models

• “What-If” type of scenarios

Model Sites for the InternationalClimate Change Study

T+2 T+4

16

12

8

4

0

-4

-8

Yield Change, %

Wheat Rice Soybean Maize

Aggregated DSSAT Crop Model Yield Changesfor +2 oC and +4 oC Temperature Increase

CURRENT PRODUCTION CHANGE IN SIMULATED YIELD --------------------------------------------------------------- --------------------------------------------

Yield Area Production Total GISS GFDL UKMO t ha-1 Mha Mt % % % %

Australia 1.38 11,546 15,574 3.2 -18 -16 -14Brazil 1.31 2,788 3,625 0.8 -51 -38 -53Canada 1.88 11,365 21,412 4.4 -12 -10 -38China 2.53 29,092 73,527 15.3 -5 -12 -17Egypt 3.79 572 2,166 0.4 -36 -28 -54 France 5.93 4,636 27,485 5.7 -12 -28 -23India 1.74 22,876 39,703 8.2 -32 -38 -56Japan 3.25 237 772 0.2 -18 -21 -40Pakistan 1.73 7,478 12,918 2.7 -57 -29 -73Uruguay 2.15 91 195 0.0 -41 -48 -50Former USSR winter 2.46 18,988 46,959 9.7 -3 -17 -22 spring 1.14 36,647 41,959 8.7 -12 -25 -48USA 2.72 26,595 64,390 13.4 -21 -23 -33

WORLD 2.09 231,000 482,000 72.7 -16 -22 -33

Current production and changes in simulated wheat yields under GCM 2 x CO2 climate change

scenarios

International Climate Change Study Results Summary

• Crop yields in mid- and high-latitude regions are less adversely affected than yields in low-latitude regions

• Simple farm-level adaptations in the temperate regions can generally offset the detrimental effects of climate change

• Appropriate adaptations for tropical regions need to be developed and tested further, with particular emphasis on genetic resources and information provision

Agriculture and Climate ChangeImpact and Adaptation

Camilla, Mitchell County, Georgia

Maximum and Minimum Temperature

Precipitation

Maize Yield (kg/ha) Mitchell County, Georgia

4 varieties, 3 soils, rainfed and irrigatedLong-term historical weather data

Maize Yield (kg/ha)

Mitchell County, Georgia4 varieties, 3 soils, rainfed and irrigated

Historical weather

GCM-ModifiedCSIROMK2, Scenario IS92a, 2010-

2039

Climate in the southeastern USA

Why should farmers care?

• County level data• Long-term historical

weather data for each county.

• Three representative soil profiles for each county

• Crop management options:– Crop selection– Variety selection– Planting date– Irrigated versus rainfed– Fertilizer applications

– Prices and production costs

Spatial Crop Model ApplicationsAlabama, Florida and Georgia, USA

Simulations: Cotton Yield Variety “DP555 BG/RR”

9 planting dates, rainfed vs irrigated38 – 107 years of daily historical weather data

-150

-100

-50

0

50

100

150

Planting date

Rainfed

Yie

ld D

evia

tion

s fr

om N

eutr

al

-150

-100

-50

0

50

100

150

Irrigated

El Niño

La Niña

Optimizing Planting Date and Nitrogen Fertilizer Corn Grown in Camilla, Georgia; 45 Years of Weather (1951-95)

From F. S. Royce

Climate in the SoutheastHow do farmers make decisions?

Farmer Joe’s Questions

El NiñoLa Niña

Management Decisions

• Crop selection

• Variety selection

• Planting dates

• Acreage allocation

• Irrigation

• Pest management

• Amount and type of crop insurance

WWW.AGROCLIMATE.ORG

Historical weather data (1900-2005)

ENSO Phases

Planting dates

Soil types

Select AL, FL, GAcounties

Yield

Total amount of irrigation

No. of irrigationevents

CSM-CROPGROPeanut Model

April 16, 23May 1, 8, 15, 22, 29June 5, 12

Crop Simulations

Georgia

Crop Simulations: Research Analysis

Crop Simulations: AgroClimateExtension, Producers and Consultants

AgroClimate Tools

Interaction &

Participation

Forecasts,Climatology

Web-based DSSwww.AgroClimate.org

Climate-based Management

Options

Stand aloneDecision Aid

Tools

Needs for Specific Commodities

Crop Models & Climate-based Tools

Extension Agents& Specialists

Farmers/Growers

Climate in the Southeast: How do farmers make decisions?

Agricultural Production& Modeling

• Potential production

• Water-limited production

• Nitrogen-limited production

• Nutrient-limited production

• Pest-limited production

• Other factors

Model

Real World

Com

plexity

Crop Modeling – Fact or fiction?

Environment * Management * GenotypeEconomics

• Computer simulation model:

– “A mathematical representation of a real world system”

• Requires careful evaluation for local conditions

Crop Modeling – Fact or fiction?Environment * Management * Genotype

Economics• Prediction:

– Yield

– Resource use

– Environmental impact

– Net return

– Others

• Management decisions and explore “what-if” type questions

• Research design and analysis

• Policy and planning

Crop Modeling - CAMI Opportunities and Challenges

• Caribbean region

• Local infrastructure

• Complex terrain

• Complex agricultural systems

• “New” crops

• Weather variability

• Information delivery

• Opportunities for adaptation

• Farmer participation

Weather conditions and weather-based decision support tools

www.weather.wsu.edu

www.georgiaweather.net

Southeast climate information and tools: www.agroclimate.org

For crop model information: www.DSSAT.net

www.GerritHoogenboom.com

Gerrit.Hoogenboom@wsu.edu