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
![Page 1: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/1.jpg)
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
![Page 2: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/2.jpg)
AgWeatherNet
![Page 3: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/3.jpg)
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
![Page 4: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/4.jpg)
Crop Modeling Training WorkshopJanuary, 2012 @ CIMH, Barbados
![Page 5: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/5.jpg)
DSSAT Training WorkshopMay, 2012 @ University of Georgia, Griffin,
Georgia, USA
![Page 6: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/6.jpg)
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]
![Page 7: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/7.jpg)
Agriculture
• The agricultural system is a complex system that includes many interactions between biotic and abiotic factors
![Page 8: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/8.jpg)
Agriculture• Abiotic factors = Non-Living
– Weather/climate
– Soil properties
– Crop management• Crop and variety selection• Planting date and spacing• Inputs, including irrigation and
fertilizer
![Page 9: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/9.jpg)
Agriculture• Biotic factors
– Pests and diseases
– Weeds
– Soil fauna
![Page 10: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/10.jpg)
Agriculture
• Socio-economic factors– Prices of grain and byproducts– Input and labor costs– Policies– Cultural settings– Human decision making
• Environmental constraints– Pollution– Natural resources
![Page 11: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/11.jpg)
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.
![Page 12: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/12.jpg)
Systems Approach
• Traditional agronomic approach:– Experimental trial and error
• Systems Approach– Computer models
– Experimental data
• Understand Predict Control & Manage– (H. Nix, 1983)
![Page 13: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/13.jpg)
Application/Analysis
Control/Management/
Decision SupportDesignResearch
Model Development
Increased Understanding
Model
Test Predictions
Prediction
Research for Understanding
Problem Solving
Systems Approach
![Page 14: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/14.jpg)
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.
![Page 15: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/15.jpg)
Simple Model
• Air temperature
==>Vegetative and reproductive development
• Solar radiation
==>Photosynthesis and biomass growth
Development * Biomass = Yield
![Page 16: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/16.jpg)
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)
![Page 17: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/17.jpg)
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.
![Page 18: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/18.jpg)
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
![Page 19: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/19.jpg)
Soil Conditions Weather data
Model Model
Simulation Simulation
Crop Management Genetics
GrowthGrowth DevelopmentDevelopment
YieldYield
![Page 20: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/20.jpg)
Soil Conditions Weather data
Model Model
Simulation Simulation
Crop Management Genetics
GrowthGrowth DevelopmentDevelopment
YieldYield
Net IncomeNet IncomePollutionPollution Resource UseResource Use
![Page 21: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/21.jpg)
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)
![Page 22: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/22.jpg)
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)
![Page 23: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/23.jpg)
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)
![Page 24: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/24.jpg)
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
![Page 25: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/25.jpg)
Level 4
Pest-Limited Production
– Nitrogen-limited production +– Pest inputs - scouting report
– Dynamic pest simulation
• Insects, diseases, weeds
![Page 26: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/26.jpg)
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
![Page 27: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/27.jpg)
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.
![Page 28: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/28.jpg)
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
![Page 29: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/29.jpg)
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
![Page 30: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/30.jpg)
Soil Conditions • Weather data
Model Model
• Simulation• Simulation
Crop Management • Genetics
• Growth• Growth • Development• Development
• Yield• Yield
Inputs
Outputs = Measurements
![Page 31: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/31.jpg)
Linkage Between Data and Simulations
Model credibility and evaluation Input data needs:
Weather and soil dataCrop ManagementSpecific crop and cultivar informationEconomic data
![Page 32: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/32.jpg)
• 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
![Page 33: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/33.jpg)
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)
![Page 34: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/34.jpg)
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)
![Page 35: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/35.jpg)
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
![Page 36: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/36.jpg)
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
![Page 37: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/37.jpg)
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
![Page 38: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/38.jpg)
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
![Page 39: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/39.jpg)
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
![Page 40: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/40.jpg)
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.
![Page 41: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/41.jpg)
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.
![Page 42: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/42.jpg)
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
![Page 43: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/43.jpg)
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
![Page 44: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/44.jpg)
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
![Page 45: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/45.jpg)
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
![Page 46: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/46.jpg)
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
![Page 47: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/47.jpg)
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.
![Page 48: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/48.jpg)
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
![Page 49: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/49.jpg)
Model Sites for the InternationalClimate Change Study
![Page 50: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/50.jpg)
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
![Page 51: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/51.jpg)
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
![Page 52: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/52.jpg)
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
![Page 53: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/53.jpg)
Agriculture and Climate ChangeImpact and Adaptation
Camilla, Mitchell County, Georgia
Maximum and Minimum Temperature
Precipitation
![Page 54: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/54.jpg)
Maize Yield (kg/ha) Mitchell County, Georgia
4 varieties, 3 soils, rainfed and irrigatedLong-term historical weather data
![Page 55: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/55.jpg)
Maize Yield (kg/ha)
Mitchell County, Georgia4 varieties, 3 soils, rainfed and irrigated
Historical weather
GCM-ModifiedCSIROMK2, Scenario IS92a, 2010-
2039
![Page 56: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/56.jpg)
Climate in the southeastern USA
Why should farmers care?
![Page 57: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/57.jpg)
• 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
![Page 58: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/58.jpg)
Simulations: Cotton Yield Variety “DP555 BG/RR”
9 planting dates, rainfed vs irrigated38 – 107 years of daily historical weather data
![Page 59: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/59.jpg)
-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
![Page 60: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/60.jpg)
Optimizing Planting Date and Nitrogen Fertilizer Corn Grown in Camilla, Georgia; 45 Years of Weather (1951-95)
From F. S. Royce
![Page 61: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/61.jpg)
Climate in the SoutheastHow do farmers make decisions?
![Page 62: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/62.jpg)
Farmer Joe’s Questions
El NiñoLa Niña
![Page 63: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/63.jpg)
Management Decisions
• Crop selection
• Variety selection
• Planting dates
• Acreage allocation
• Irrigation
• Pest management
• Amount and type of crop insurance
![Page 64: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/64.jpg)
WWW.AGROCLIMATE.ORG
![Page 65: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/65.jpg)
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
![Page 66: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/66.jpg)
Georgia
Crop Simulations: Research Analysis
![Page 67: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/67.jpg)
Crop Simulations: AgroClimateExtension, Producers and Consultants
![Page 68: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/68.jpg)
AgroClimate Tools
![Page 69: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/69.jpg)
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?
![Page 70: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/70.jpg)
Agricultural Production& Modeling
• Potential production
• Water-limited production
• Nitrogen-limited production
• Nutrient-limited production
• Pest-limited production
• Other factors
Model
Real World
Com
plexity
![Page 71: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/71.jpg)
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
![Page 72: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/72.jpg)
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
![Page 73: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/73.jpg)
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
![Page 74: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/74.jpg)
![Page 75: Crop Simulation Modeling](https://reader037.vdocuments.us/reader037/viewer/2022102404/56814352550346895dafce20/html5/thumbnails/75.jpg)
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