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Climate Data and Crop Modeling Joel Paz, Gerrit Hoogenboom, Axel Garcia y Garcia, Larry Guerra, Clyde Fraisse and James W. Jones The University of Georgia.

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Presentation on theme: "Climate Data and Crop Modeling Joel Paz, Gerrit Hoogenboom, Axel Garcia y Garcia, Larry Guerra, Clyde Fraisse and James W. Jones The University of Georgia."— Presentation transcript:

1 Climate Data and Crop Modeling Joel Paz, Gerrit Hoogenboom, Axel Garcia y Garcia, Larry Guerra, Clyde Fraisse and James W. Jones The University of Georgia and the University of Florida Southeast Climate Consortium

2 Agriculture The agricultural system is a complex system that includes many interactions between biotic and abiotic factors Some of these factors can be modified by actions and interventions of producers, while others are controlled by nature.

3 Agriculture Abiotic factors Weather/climate Soil properties Crop management Crop and variety selection Planting date and spacing Inputs, including irrigation and fertilizer Biotic factors Pests and diseases Weeds Soil fauna

4 Systems Approach Understand the complete soil-plant-atmosphere system. To use computer models and data in agricultural sciences in contrast to a traditional agronomic approach (trial and error) Understanding  Prediction  Control & Manage

5 Agricultural Models There are many agricultural models that predict other aspects of the agricultural production system besides yield. These models can range from very simple degree-day or chilling hours calculations to very complex models that predict pest and disease interactions with plants.

6 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, pathology, entomology, economics and many others. Crop Simulation Models

7 Soil parametersWeather data Model Simulation Management dataGenetic coefficients Growth Development Yield Crop Simulation Model

8 Decision Support System for Agrotechnology Transfer A single software package that facilitates the application of crop simulation models in research, teaching, outreach, service and decision making. Crop simulation models CERES, CROPGRO, SUBSTOR, CANEGRO, CROPSIM, AROID, OILCROP, and others

9 Simulation – Growth and Development Photosynthesis Maintenance and growth respiration Partitioning of biomass to leaves, petioles, roots, pods/ears, seeds/grains, tubers, etc. Senescence Vegetative development Vegetative stages, leaf area, SLA, plant height and width Reproductive development Germination, emergence, anthesis, first pod/ear, first seed/grain, physiological and harvest maturity

10 Water balance Potential ET Soil evaporation Plant transpiration Root water uptake Runoff Drainage Soil water flow Nitrogen balance Mineralization of crop residues/organic matter Immobilization Nitrification/ denitrification Nitrate and urea movement Nitrate leaching Nitrogen uptake (Nitrogen fixation) Simulation – Water and Nitrogen Balance

11 Crop Simulation Models Key to success : evaluation with experimental and on-farm data

12 Model Evaluation : Peanut (2003) Baker County – Field 3Mitchell County – Field 2Mitchell County – Field 1

13 Model Evaluation : Peanut (2004)

14 Irrigated and rainfed cotton at Stripling Irrigation Research Park (Camilla, GA) 2004 growing season Crop data collected every two weeks Rainfed Plot Irrigated Plot Model Evaluation : Cotton

15 Model Evaluation : Cotton (2004) IrrigatedRainfed Cotton variety DP555 BG/RR

16 Model Applications Research applications Policy applications Management applications Climate change and climate variability

17 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

18 Analyzing Long-Term Historical Yield Data USDA-NASS yield data for Burke County, GA from 1934 to 2003. The CSM-CROPGRO-Peanut was used to simulate peanut yield for the 70 years. Burke N Climatological period (1934 – 2003) ENSO Phases (JMA index) El Niño (14 years) La Niña (16 years) Neutral (40 years)

19 Observed and Simulated Yield: Burke County Observed and Simulated historical yield data expressed as standard deviation from the average of the ENSO phases Neutral [r = 0.90* (  = 0.05)] La Niña r = 0.94* (  = 0.05) El Niño r = 0.91* (  = 0.05)

20 Irrigation Water Use Use CSM-CROPGRO-Peanut model to simulate yield and irrigation amount Several peanut counties in AL, FL and GA Different planting dates ENSO phases Examine seasonal irrigation amount

21 Irrigation Water Use

22 Crop Modeling and Climate Data Based on the historical ENSO phases, the outputs of the crop simulation models can be grouped by EL Niño, La Niña and neutral events. Following the update of the climate forecasts for the upcoming season by the climatologists, the associated yield predictions (and other model output variables) can be provided to farmers, including various management and cultivar selection.

23 Agclimate website ( AgClimate Tools Forecasts Crops Forestry Pasture Livestock

24 DSSAT Training Program (May 15-May 24, 2006) Assessing Crop Production, Nutrient Management, Climatic Risk and Environmental Sustainability with Simulation Models Contact: Dr. Gerrit Hoogenboom

25 Thank you!

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