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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 Slide 2 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? Slide 3 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 What is Agriculture? Slide 4 The agricultural system is a complex system that includes many interactions between biotic and abiotic factors Agriculture? Slide 5 Abiotic factors = Non-Living Weather/climate Soil properties Crop management Crop and variety selection Planting date and spacing Inputs, including irrigation and fertilizer Agriculture Slide 6 Weather Rainfall/Precipitation Temperature Solar radiation Relative humidity Dewpoint Soil temperature Soil moisture Atmospheric pressure Agriculture Slide 7 Biotic factors Pests and diseases Weeds Soil fauna Agriculture Slide 8 Socio-economic factors Prices of grain and byproducts Input and labor costs Policies Cultural settings Human decision making Environmental constraints Pollution Natural resources Agriculture Slide 9 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 Agriculture Slide 10 Traditional agronomic approach: Experimental trial and error Why Models? Slide 11 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 Why Models? Slide 12 Application/ Analysis Control/ Management/ Decision Support Design Research Model Development Increased Understanding Model Test Predictions Prediction Research for Understanding Problem Solving Systems Approach Slide 13 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 model? Slide 14 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. What is a crop model? Slide 15 Based on the understanding of interaction of plant genetics, soil, weather, and crop management oMorphological and phenological development oPhotosynthesis, and growth and maintenance respiration oPartitioning of biomass to leaves, stems, roots, and reproductive structures oRemobilization & senescence oSoil water flow oEvaporation and transpiration & root water uptake oSoil and plant nutrient processes oStress effects on development and growth processes Crop Models Slide 16 Slide 17 Crop simulation models in general calculate or predict crop growth, development, and yield as a function of: Genetics Weather conditions Soil conditions Crop management Crop Models Slide 18 Soil ConditionsWeather data Model Simulation Crop Management Genetics Growth Development Yield Slide 19 Soil ConditionsWeather data Model Simulation Crop Management Genetics Growth Development Yield Net Income Pollution Resource Use Slide 20 Linkage Between ExperimentalData and Simulations Model credibility and evaluation Model credibility and evaluation Data needs: Data needs: Weather and soil data Weather and soil data Crop Management Crop Management Specific cultivar information Specific cultivar information Observations (yield and components, dates, etc.) Observations (yield and components, dates, etc.) Slide 21 Yield 0 2000 4000 6000 8000 175200225250275300 Day of Year Grain - IRRIGATEDTotal Crop - IRRIGATED Total Crop - NOT IRRIGATEDGrain - NOT IRRIGATED Simulated and Measured Soybean Slide 22 Observed and simulated soybean yield as a function of seasonal average rainfall (Georgia yield trials) Slide 23 Observed and simulated soybean yield as a function of average max temperature (Georgia yield trials) Slide 24 Agricultural Production Potential production Water-limited production Nitrogen-limited production Nutrient-limited production Pest-limited production Other factors Intercropping Economics Food quality Human decisions Model Real World Complexity Modeling Limitations? Slide 25 1 2 3 actual attainable potential Yield increasing measures Yield protecting measures defining factors: reducing factors: limiting factors: CO 2 Radiation Temperature Crop characteristics -physiology, phenology -canopy architecture a: Water b: Nutrients - nitrogen - phosphorous Weeds Pests Diseases Pollutants 1500 10,000 5000 20,000 Production level (kg ha -1 ) Production situation Crop Model Concepts Source: World Food Production: Biophysical Factors of Agricultural Production, 1992. Slide 26 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) AquaCrop (FAO) HERMES & MONIKA, ZALF, Leibniz, Germany Slide 27 Some Major Multi-Modeling Efforts MACSUR Modeling European Agriculture with Climate Change for Food Security AgMIP Agricultural Model Intercomparison and Improvement Project Slide 28 Science Approach 28 Update from Rosenzweig et al., 2013 AgForMet Track 1: Develop and Test Agricultural Systems Models Track 2: Conduct Multi-Model Assessments Slide 29 Capacity Building and Decision Making Regional expertise Adaptation strategies Technology exchange Climate Team Crop Modeling Team Economics Team Information Technology Team Improvements and Intercomparisons Crop models Agricultural economic models Scenario construction Aggregation methodologies Assessments Regional Global Crop-specific Teams, Linkages and Outcomes Rosenzweig et al., 2013 29 Links to CCAFS, Global Yield Gap Atlas, Global Futures, MACSUR, et al. Slide 30 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 PeopleComment Wheat2851Advanced, testing Temp Maize2338Advanced, testing CO2 Rice1426Advanced, testing CO2/Temp Potato1028First evaluations Sugarcane412First evaluations Grain Sorghum59Forming, sorghum/millet Peanut47Forming, Singh Canola??Forming, Wang Rangeland/Pasture??First evaluations?, Sousanna Bioenergy crops??Forming, Kakani/LeBauer Slide 31 Risk Analysis (What If ?) Slide 32 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 Slide 33 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 ) Crop Model Applications Slide 34 Policy Brief (source AgMIP) Slide 35 Water Conflict in the Southeast: GA FL - AL Slide 36 Climate in the Southeast: How do farmers make decisions? AgroClimate Southeast Climate Consortium Slide 37 DSSAT 2015 @ University of Georgia DSSAT 2015 @ ICRISAT Capacity Building & Training Slide 38 Modeling & Simulation Current WeatherWeather PredictionClimate Forecast Crop/Livestock/Pest/Disease/Economic Modeling PlantingFloweringHarvest Maturity Information delivery to stakeholders Social scientists/agronomists/atmospheric scientists & engineers Climate Change Slide 39 Resources Gerrit.Hoogenboom@wsu.edu www.GerritHoogenboom.com www.DSSAT.net www.AgMIP.org Slide 40 Thank you