crop simulation modeling
DESCRIPTION
Crop Simulation Modeling. Gerrit Hoogenboom Director AgWeatherNet & Professor of Agrometeorology Washington State University, Prosser, Washington, USA. Caribbean Agro-meteorological Initiative (CAMI) Conference Breaking New Ground in the Caribbean: Weather and Climate Serving Agriculture - PowerPoint PPT PresentationTRANSCRIPT
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