Crop Simulation Modeling

1. 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 Knutsford Hotel, Kingston, JamaicaNovember 5-6, 2012

2. AgWeatherNet

3. Crop ModelingDecision 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

4. Crop Modeling Training WorkshopJanuary, 2012 @ CIMH, Barbados

5. DSSAT Training WorkshopMay, 2012 @ University of Georgia, Griffin, Georgia, USA

6. 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]

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

8. Agriculture • Abiotic factors = Non-Living • Weather/climate • Soil properties • Crop management • Crop and variety selection • Planting date and spacing • Inputs, including irrigation and fertilizer

9. Agriculture • Biotic factors • Pests and diseases • Weeds • Soil fauna

10. Agriculture • Socio-economic factors • Prices of grain and byproducts • Input and labor costs • Policies • Cultural settings • Human decision making • Environmental constraints • Pollution • Natural resources

11. 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.

12. Systems Approach • Traditional agronomic approach: • Experimental trial and error • Systems Approach • Computer models • Experimental data • Understand  Predict Control & Manage • (H. Nix, 1983)

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

14. 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.

15. Simple Model • Air temperature ==>Vegetative and reproductive development • Solar radiation ==>Photosynthesis and biomass growth Development * Biomass = Yield

16. 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)

17. 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.

18. 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

19. Soil Conditions Weatherdata Model Crop Management Genetics Simulation Growth Development Yield

20. Soil Conditions Weatherdata Model Crop Management Genetics Simulation Growth Development Yield Pollution Net Income Resource Use

21. 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)

22. 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)

23. 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)

24. 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

25. Level 4 Pest-Limited Production • Nitrogen-limited production + • Pest inputs - scouting report • Dynamic pest simulation • Insects, diseases, weeds

26. Agricultural Production Model • Potential production • Water-limited production • Nitrogen-limited production • Nutrient-limited production • Pest-limited production • Other factors • Extreme weather events • Salinity Complexity Real World

27. Crop Model Concepts Production situation defining factors: CO2 Radiation Temperature Crop characteristics -physiology, phenology -canopy architecture 1 potential limiting factors: a: Water b: Nutrients - nitrogen - phosphorous 2 attainable Yield increasing measures reducing factors: Weeds Pests Diseases Pollutants 3 actual Yield protecting measures Production level (kg ha-1) 1500 5000 10,000 20,000 Source: World Food Production: Biophysical Factors of Agricultural Production, 1992.

28. 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

29. 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

30. Inputs Soil Conditions Weatherdata Model Crop Management Genetics Simulation Growth Development Yield Outputs = Measurements

31. Linkage Between Data and Simulations • Model credibility and evaluation • Input data needs: • Weather and soil data • Crop Management • Specific crop and cultivar information • Economic data

32. Gainesville, FL 1978 Yield

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

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

35. 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

36. 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

37. 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 Midville Plains Tifton

38. Georgia Peanut Variety TrialsModel calibration

39. Baker County Field 3 Mitchell County Field 1

40. 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.

41. Background information Planting 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.

42. 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

43. CSM-CERES-Maize evaluation Results Observed and simulated LAI and biomass for four hybrids grown under irrigated conditions in 2002

44. CSM-CERES-Maize evaluation Simulated vs. observed yield for four hybrids grown under irrigated and rainfed conditions in 2002

45. Planting date evaluation Simulated yield for different planting dates under rainfed and irrigated conditions

46. Yield Forecast 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.

47. Conclusions The 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.

48. 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

49. Model Sites for the International Climate Change Study

50. Aggregated DSSAT Crop Model Yield Changes for +2 oC and +4 oC Temperature Increase 16 12 8 Yield Change, % 4 0 -4 -8 T+2 T+4 Wheat Rice Soybean Maize