1. 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

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?

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?

4. The agricultural system is a complex system that includes many interactions between biotic and abiotic factors Agriculture?

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

6. Weather – Rainfall/Precipitation – Temperature – Solar radiation – Relative humidity – Dewpoint – Soil temperature – Soil moisture – Atmospheric pressure Agriculture

7. Biotic factors – Pests and diseases – Weeds – Soil fauna Agriculture

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

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

10. Traditional agronomic approach: – Experimental trial and error Why Models?

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?

12. Application/ Analysis Control/ Management/ Decision Support Design Research Model Development Increased Understanding Model Test Predictions Prediction Research for Understanding Problem Solving Systems Approach

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?

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?

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

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

18. Soil ConditionsWeather data Model Simulation Crop Management Genetics Growth Development Yield

19. Soil ConditionsWeather data Model Simulation Crop Management Genetics Growth Development Yield Net Income Pollution Resource Use

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.)

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

22. Observed and simulated soybean yield as a function of seasonal average rainfall (Georgia yield trials)

23. Observed and simulated soybean yield as a function of average max temperature (Georgia yield trials)

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?

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.

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

27. Some Major Multi-Modeling Efforts MACSUR – Modeling European Agriculture with Climate Change for Food Security AgMIP – Agricultural Model Intercomparison and Improvement Project

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

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.

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

31. Risk Analysis (What If ?)

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

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

34. Policy Brief (source AgMIP)

35. Water Conflict in the Southeast: GA – FL - AL

36. Climate in the Southeast: How do farmers make decisions? AgroClimate – Southeast Climate Consortium

37. DSSAT 2015 @ University of Georgia DSSAT 2015 @ ICRISAT Capacity Building & Training

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

39. Resources Gerrit.Hoogenboom@wsu.edu www.GerritHoogenboom.com www.DSSAT.net www.AgMIP.org

40. Thank you