Transcript Gerrit Hoogenboom - Overview of Crop Models

Overview of Crop Models
Gerrit Hoogenboom
Director, AgWeatherNet &
Professor of Agrometeorology
Washington State University, USA
Food – Energy – Water
Coupling of Economic Models with Agronomic, Hydrologic, and Bioenergy
Models for Sustainable Food, Energy, and Water Systems
Iowa State University, Ames, Iowa
October 12 – 13, 2015
What is Agriculture?
• Food (for human consumption)
– Crops
– Meat, dairy products, eggs, etc.
– Aquaculture
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Feed (for livestock consumption)
Fiber (for clothing and other uses)
Fuel (for energy)
Flowers (horticulture and green industry)
[Forestry]
What is Agriculture?
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Food (for human consumption)
Feed (for livestock consumption)
Fiber (for clothing and other uses)
Fuel (for energy)
Flowers (horticulture and green industry)
[Forestry]
Bioplastics
Pharmaceuticals
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
• Weather
– Rainfall/Precipitation
– Temperature
– Solar radiation
– Relative humidity
– Dewpoint
– Soil temperature
– Soil moisture
– Atmospheric pressure
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
Why Models?
• Traditional agronomic approach:
– Experimental trial and error
Why Models?
• Traditional agronomic approach:
– Experimental trial and error
• Systems Approach
– Computer models
– Experimental data
• Understand  Predict Control & Manage
– (H. Nix, 1983)
•  Options for adaptive management and
risk reduction
Systems Approach
Problem Solving
Research for
Understanding
Model
Development
Research
Increased
Understanding
Model
Control/
Management/
Decision Support
Design
Prediction
Test Predictions
Application/
Analysis
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.
What is a crop model?
• 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, and
many others.
Crop Models
• Based on the understanding of interaction of plant
genetics, soil, weather, and crop management
o Morphological and phenological development
o Photosynthesis, and growth and maintenance
respiration
o Partitioning of biomass to leaves, stems, roots, and
reproductive structures
o Remobilization & senescence
o Soil water flow
o Evaporation and transpiration & root water uptake
o Soil and plant nutrient processes
o Stress effects on development and growth processes
Genetic Information
Daylength
Temperature
Crop
Development
Photoperoid
Response
Thermal Time
Transplanting
Shock
Growth
Stage
Partitioning
Rules
Plant N
Root Water
Uptake
Plant N
Status
Transpiration
Urea
Evaporation
Oxidized
Soil Zone
Floodwater
ET
Aquatic Photosynthetic
Activity
Runoff
loss
Available
P
Urea
Urea Hydrolysis Rate
Temp.
Org. C
pH
Inhibitors
Diffusion
Percolation
Urea Hydrolysis
Rate
Diffusion
Percolation
Straw N
Denitrification
Loss
NH3
Loss
NO3
NH+
4
NO3
NH3 Soln
Floodwater
{ pH
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& Temp.
NH+
Soln
4
Runoff
loss
Diffusion
Percolation
Runoff
loss
Diffusion
Percolation
Nh4+ Soil
Urea
Straw
Redistribution
Rate
Plant
Water
Status
Grain N
Tops N
Precipitation
Irrigation
Bund Height
Water Table Depth
Grain Yield
Crop Growth
Processes
Leaf Growth
Stem Growth
Root Growth
Panicle Growth
Grain Growth
Root N
Solar Radiation
Temperature
Albedo
CO2
Nitrification Rate
NO-3
pH
Temp.
Water Filled
Porosity
Inhibitors
Diffusion
Percolation
NH+
4 Soln
Adsorption/desorption
{CEC }
Diffusion
Reduced
Soil Layers
Adsorption/
desorption
NH+
4 Soln
Urea
Urea Hydrolysis
Rate
Diffusion
Percolation
NH+
4 Soln
Mineralization/
Immobilization
C/N
Water
Temp.
Lignin/N
Fresh Organic Matter
Leaching Loss
Diffusion
Percolation
Leaching Loss
Root Residue
Stable Organic Matter
Crop Residue
Soil Organic C
Total Soil N
NO-3
Water and
N Uptake
Denitrification
Rate
Soluble C
Temp.
Water filled
Porosity
pH
Crop Models
• Crop simulation models in general calculate
or predict crop growth, development, and
yield as a function of:
– Genetics
– Weather conditions
– Soil conditions
– Crop management
Soil Conditions
Weather data
Model
Crop Management
Genetics
Simulation
Growth
Development
Yield
Soil Conditions
Weather data
Model
Crop Management
Genetics
Simulation
Growth
Development
Yield
Pollution
Net Income
Resource Use
Linkage Between ExperimentalData
and Simulations
Model credibility and evaluation
 Data needs:
Weather and soil data
Crop Management
Specific cultivar information
Observations (yield and components, dates, etc.)
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Simulated and Measured Soybean
8000
6000
4000
2000
0
175
Yield
200
225
250
Day of Year
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Grain - IRRIGATED
Total Crop - IRRIGATED
Total Crop - NOT IRRIGATED
Grain - NOT IRRIGATED
300
Observed and simulated soybean yield as a
function of seasonal average rainfall (Georgia
yield trials)
Simulated Yield vs. Rainfall (mm/d)
4000
4000
3500
3500
3000
3000
Yield (kg/ha)
Yield (kg/ha)
Observed Yield vs. Rainfall (mm/d)
2500
2000
1500
2500
2000
1500
1000
1000
500
500
0
0
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2
4
Rainfall (mm/d)
6
8
0
2
4
Rainfall (mm/d)
6
8
Observed and simulated soybean yield as a
function of average max temperature
(Georgia yield trials)
Simulated Yields
4000
3500
3000
2500
2000
1500
1000
500
0
Yield (kg/ha)
Yield (kg/ha)
Observed Yields
25
27
29
31
Max Temp Average (C)
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4000
3500
3000
2500
2000
1500
1000
500
0
25
27
29
31
Max Temp Average (C)
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Modeling Limitations?
Agricultural Production
Model
Potential production
Water-limited production
Nitrogen-limited production
Nutrient-limited production
Pest-limited production
Other factors
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Intercropping
Economics
Food quality
Human decisions
Complexity
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Real World
Crop Model Concepts
Production
situation
defining factors: CO2
Radiation
Temperature
Crop characteristics
-physiology, phenology
-canopy architecture
1 potential
limiting factors: a: Water
2 attainable
b: Nutrients
- nitrogen
- phosphorous
Yield increasing
measures
reducing factors: Weeds
3 actual
Pests
Diseases
Pollutants
Yield protecting measures
1500
5000
10,000
20,000
Production level (kg ha-1)
Source: World Food Production: Biophysical Factors of Agricultural Production, 1992.
Some Major Crop Modeling Efforts
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APSRU (CSIRO, Australia)
STICS (France)
SUCROS, LINTUL, etc. (Wageningen Univ, the Netherlands)
WOFOST (Alterra & WU, the Netherlands)
DSSAT (USA, Canada, others …)
EPIC/APEX (USDA, Temple, Texas; J. Williams)
CROPSYST (Washington State University; C. Stockle et al.)
RZWQM (USDA-ARS, Fort Collins, Colorado)
INFOCROP (India)
AquaCrop (FAO)
HERMES & MONIKA, ZALF, Leibniz, Germany
Some Major Multi-Modeling Efforts
• MACSUR
– Modeling European Agriculture with Climate Change for Food
Security
• AgMIP
– Agricultural Model Intercomparison and Improvement Project
Science Approach
Track 1: Develop and Test Agricultural Systems Models
Track 2: Conduct Multi-Model Assessments
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Update from Rosenzweig et al., 2013 AgForMet
Teams, Linkages and Outcomes
Climate Team
Crop Modeling Team
Information
Technology
Team
Economics Team
Improvements and
Intercomparisons
• Crop models
• Agricultural economic models
• Scenario construction
• Aggregation methodologies
Assessments
• Regional
• Global
• Crop-specific
Capacity Building
and Decision Making
• Regional expertise
• Adaptation strategies
• Technology exchange
Links to CCAFS, Global Yield Gap Atlas, Global Futures, MACSUR, et al. 29
Rosenzweig et al., 2013
Some Major Multi-Modeling Efforts
• AgMIP
– Agricultural Model Intercomparison and Improvement Project
Number of Crops that have AgMIP crop-specific teams
Crops
# of Models
# of People
Comment
Wheat
28
51
Advanced, testing Temp
Maize
23
38
Advanced, testing CO2
Rice
14
26
Advanced, testing CO2/Temp
Potato
10
28
First evaluations
Sugarcane
4
12
First evaluations
Grain Sorghum
5
9
Forming, sorghum/millet
Peanut
4
7
Forming, Singh
Canola
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Forming, Wang
Rangeland/Pasture
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First evaluations?, Sousanna
Bioenergy crops
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Forming, Kakani/LeBauer
Risk Analysis (What If ?)
Crop Model Applications
• Diagnose problems (Yield Gap Analysis)
• Precision agriculture
– Diagnose factors causing yield variations
– Prescribe spatially variable management
• Water and irrigation management
• Soil fertility management
• Plant breeding and Genotype * Environment
interactions (“virtual” crop models)
• Gene-based modeling
• Yield prediction for crop management
Crop Model Applications
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Climate variability & risk management
Climate change impacts & adaptation
Soil carbon sequestration
Land use change analysis
Targeting aid (Early Warning)
Yield forecasting
Biofuel production
Risk insurance (rainfall)
Policy Brief (source AgMIP)
Water Conflict in the Southeast: GA – FL - AL
Climate in the Southeast:
How do farmers make decisions?
AgroClimate – Southeast Climate Consortium
Capacity Building & Training
DSSAT 2015 @ University of Georgia
DSSAT 2015 @ ICRISAT
Modeling & Simulation
Social scientists/agronomists/atmospheric scientists &
engineers
Current Weather
Weather Prediction
Climate Forecast
Climate Change
Crop/Livestock/Pest/Disease/Economic Modeling
Planting
Flowering
Harvest Maturity
Information delivery to stakeholders
Resources
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[email protected]
www.GerritHoogenboom.com
www.DSSAT.net
www.AgMIP.org
Thank you