Overview of Crop Models Gerrit Hoogenboom Director Ag
Overview of Crop Models Gerrit Hoogenboom Director, Ag. Weather. Net & 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 • • • Feed (for livestock consumption) Fiber (for clothing and other uses) Fuel (for energy) Flowers (horticulture and green industry) [Forestry]
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] 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 Ø 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
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 Experimental. Data and Simulations n Model credibility and evaluation n Data needs: n. Weather and soil data n. Crop Management n. Specific cultivar information n. Observations (yield and components, dates, etc. )
Simulated and Measured Soybean 8000 6000 4000 2000 0 175 Yield 200 225 250 Day of Year 275 Grain - IRRIGATED Total Crop - NOT IRRIGATED Grain - NOT IRRIGATED 300
Observed and simulated soybean yield as a function of seasonal average rainfall (Georgia yield trials)
Observed and simulated soybean yield as a function of average max temperature (Georgia yield trials)
Modeling Limitations? Agricultural Production Model • • Intercropping Economics Food quality Human decisions Complexity • Potential production • Water-limited production • Nitrogen-limited production • Nutrient-limited production • Pest-limited production • Other factors Real World
Crop Model Concepts Production situation defining factors: CO 2 1 potential 2 attainable Yield increasing measures Radiation Temperature Crop characteristics -physiology, phenology -canopy architecture limiting factors: a: Water b: Nutrients - nitrogen - phosphorous 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 • • • 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) Aqua. Crop (FAO) HERMES & MONIKA, ZALF, Leibniz, Germany
Some Major Multi-Modeling Efforts • MACSUR – Modeling European Agriculture with Climate Change for Food Security • Ag. MIP – Agricultural Model Intercomparison and Improvement Project
Science Approach Track 1: Develop and Test Agricultural Systems Models Track 2: Conduct Multi-Model Assessments 28 Update from Rosenzweig et al. , 2013 Ag. For. Met
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 • Ag. MIP – Agricultural Model Intercomparison and Improvement Project Crops Number of Crops that have Ag. MIP crop-specific teams # of Models # of People Comment 28 51 Advanced, testing Temp Maize 23 38 Advanced, testing CO 2 Rice 14 26 Advanced, testing CO 2/Temp Potato 10 28 First evaluations Sugarcane 4 12 First evaluations Grain Sorghum 5 9 Forming, sorghum/millet Peanut 4 7 Forming, Singh Canola ? ? Forming, Wang Rangeland/Pasture ? ? First evaluations? , Sousanna Bioenergy crops ? ? Forming, Kakani/Le. Bauer Wheat
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 • • 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 Ag. MIP)
Water Conflict in the Southeast: GA – FL - AL
Climate in the Southeast: How do farmers make decisions? Agro. Climate – Southeast Climate Consortium
Capacity Building & Training DSSAT 2015 @ University of Georgia DSSAT 2015 @ ICRISAT
Modeling & Simulation Social scientists/agronomists/atmospheric scientists & engineers Current Weather Prediction Climate Forecast Climate Change Crop/Livestock/Pest/Disease/Economic Modeling Planting Flowering Harvest Maturity Information delivery to stakeholders
Resources • • Gerrit. Hoogenboom@wsu. edu www. Gerrit. Hoogenboom. com www. DSSAT. net www. Ag. MIP. org
Thank you
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