1. Understanding & Managing Agricultural Risk Caused by Climate Variability in the Southeast USA Keith T. Ingram

2. Climate Consortium Southeast Climate Consortium   University of Florida JW Jones, CW Fraisse, P Hildebrand, S Jagtap, KT Ingram (SECC Coordinator)  Florida State University JJ O’Brien, D Zierden, JG Bellow, T LaRow, RS Ajaymohan, R Pryor, M Griffin, P Leftwich, J Brolley  University of Miami D Letson, F Miralles-Wilhelm, G Podestá, N Breuer, K Broad, V Cabrera, R Garcia  University of Georgia G Hoogenboom, D Stooksbury, A Garcia y Garcia, L Guerra, J Paz  Auburn University LU Hatch, J Novak, M Master  University of Alabama—Huntsville J Christy, R McNider   University of Florida JW Jones, CW Fraisse, P Hildebrand, S Jagtap, KT Ingram (SECC Coordinator)  Florida State University JJ O’Brien, D Zierden, JG Bellow, T LaRow, RS Ajaymohan, R Pryor, M Griffin, P Leftwich, J Brolley  University of Miami D Letson, F Miralles-Wilhelm, G Podestá, N Breuer, K Broad, V Cabrera, R Garcia  University of Georgia G Hoogenboom, D Stooksbury, A Garcia y Garcia, L Guerra, J Paz  Auburn University LU Hatch, J Novak, M Master  University of Alabama—Huntsville J Christy, R McNider

3. Agricultural Risk  Probability of an undesirable outcome in an agricultural enterprise. Yield loss Yield loss Low profit or economic loss Low profit or economic loss Environmental damage Environmental damage  Agricultural outcomes are inherently uncertain. Whether we express risk in terms of losses or desirable outcomes we must emphasize probabilities.  Probability of an undesirable outcome in an agricultural enterprise. Yield loss Yield loss Low profit or economic loss Low profit or economic loss Environmental damage Environmental damage  Agricultural outcomes are inherently uncertain. Whether we express risk in terms of losses or desirable outcomes we must emphasize probabilities.

4. Guiding questions  Is it possible to forecast climate in the Southeast USA?  How much of the variability in crop yields is associated with predictable climate variability?  Can climate forecasts be used to help producers reduce risks?  Would a climate risk management information system be useful?  What are the research and extension needs for greater beneficial impacts of climate information use?

5. Shifts in Precipitation Probabilities by ENSO Phase Frequency distributionProbability of excedence

6. SepOct MM per month El NiñoLa NiñaNeutral Peanut Field Corn Wheat

7. Shifts in Freeze Probabilities

8. Guiding questions   Is it possible to forecast climate in the Southeast USA?  How much of the variability in crop yields is associated with predictable climate variability?  Can climate forecasts be used to help producers reduce risks?  Would a climate risk management information system be useful?  What are the research and extension needs for greater beneficial impacts of climate information use?

9. Fresh Vegetables: Winter Tomato Yields (1929-95)  Yields suppressed during El Niño

10. Historical Yields: Field Corn, FL  Yields higher if preceding ENSO phase was La Niña.  Similar results for cotton and other crops.  Yields higher if preceding ENSO phase was La Niña.  Similar results for cotton and other crops.

11. Florida Citrus

12. Guiding questions  Is it possible to forecast climate in the Southeast USA?  How much of the variability in crop yields is associated with predictable climate variability?  Can climate forecasts be used to help producers reduce risk?  Would a climate risk management information system be useful in the Southeast?  What are the research and extension needs for greater beneficial impacts of climate information use?  Is it possible to forecast climate in the Southeast USA?  How much of the variability in crop yields is associated with predictable climate variability?  Can climate forecasts be used to help producers reduce risk?  Would a climate risk management information system be useful in the Southeast?  What are the research and extension needs for greater beneficial impacts of climate information use?

13. Survey by multidisciplinary semi-structured discussions  1999: Assessed farmer interest and potential use of in climate forecasts in north central FL.   1999: Assessed extension interest in climate forecasts in 41 FL counties.  2000: Assessed the potential use of climate forecasts by livestock producers in north central FL.  2001: Further studied potential use of climate forecasts by ranchers.  2003/2004: Evaluate AgClimate prototypes.  1999: Assessed farmer interest and potential use of in climate forecasts in north central FL.   1999: Assessed extension interest in climate forecasts in 41 FL counties.  2000: Assessed the potential use of climate forecasts by livestock producers in north central FL.  2001: Further studied potential use of climate forecasts by ranchers.  2003/2004: Evaluate AgClimate prototypes.

14.  Used historical weather data, categorized by ENSO phase, to determine if knowledge of climate forecast would:  Reduce risk  Increase yield  Increase profit  Protect environment  Some variables tested:  Crop mix, variety  Planting date  Fertilizer applications  Drainage, irrigation  Stocking rate  Estimated probabilities of benefits  Used historical weather data, categorized by ENSO phase, to determine if knowledge of climate forecast would:  Reduce risk  Increase yield  Increase profit  Protect environment  Some variables tested:  Crop mix, variety  Planting date  Fertilizer applications  Drainage, irrigation  Stocking rate  Estimated probabilities of benefits Used simulation models to analyze crop responses to climate

15. RMSE fitting = 167 kg/ha Crop Models Simulate Yearly Yield Variations due to Climate

16. Expected Value of Climate Forecast Use  Relatively high probabilities of benefits (60 – 80%)  Expected benefits vary with crop, time of year, and location  Can reduce, but not eliminate climate risks  Probable benefits include:  Higher yields  Greater profit  Less nutrient loss and groundwater contamination  Relatively high probabilities of benefits (60 – 80%)  Expected benefits vary with crop, time of year, and location  Can reduce, but not eliminate climate risks  Probable benefits include:  Higher yields  Greater profit  Less nutrient loss and groundwater contamination

17. Guiding questions   Is it possible to forecast climate in the Southeast USA?   How much of the variability in crop yields is associated with predictable climate variability?   Can climate forecasts be used to help producers reduce risk?   Would a climate risk management information system be useful?   What are the research and extension needs for greater beneficial impacts of climate information use?   Is it possible to forecast climate in the Southeast USA?   How much of the variability in crop yields is associated with predictable climate variability?   Can climate forecasts be used to help producers reduce risk?   Would a climate risk management information system be useful?   What are the research and extension needs for greater beneficial impacts of climate information use?

18. AgClimate: Risk Information and Decision Support System Extension Partnership USDA Cooperation Climate Information Agricultural Commodity Risks Crop Forecast Outlook Forest Fire Risk Extension Partnership USDA Cooperation Climate Information Agricultural Commodity Risks Crop Forecast Outlook Forest Fire Risk

19. AgClimate: Forecasts are downscaled to county level  Farmers and decision makers can obtain climate information at the local level.  County level climate information and forecasts are available based on nearest climate station.  Cooperating with FAWN in Florida, AEMN in Georgia, State Climatologists in Florida, Georgia, and Alabama  Farmers and decision makers can obtain climate information at the local level.  County level climate information and forecasts are available based on nearest climate station.  Cooperating with FAWN in Florida, AEMN in Georgia, State Climatologists in Florida, Georgia, and Alabama

20. Guiding questions   Is it possible to forecast climate in the Southeast USA?   How much of the variability in crop yields is associated with predictable climate variability?   Can climate forecasts be used to help producers reduce risk?   Would a climate risk management information system be useful?   What are the research and extension needs for greater beneficial impacts of climate information use?   Is it possible to forecast climate in the Southeast USA?   How much of the variability in crop yields is associated with predictable climate variability?   Can climate forecasts be used to help producers reduce risk?   Would a climate risk management information system be useful?   What are the research and extension needs for greater beneficial impacts of climate information use?

21. Current Research and Extension Questions  Can climate forecast skill be improved?  What are the skill levels of regional forecasts such as drought, crop yield, and water demand that are produced from climate forecasts?  What additional climate information is needed by growers, Extension?  Can climate information help growers with crop insurance decisions?  Can agricultural Best Management Practices (BMPs) be improved by using climate forecasts?  Can we make AgClimate more sustainable and dynamic?  Can climate forecast skill be improved?  What are the skill levels of regional forecasts such as drought, crop yield, and water demand that are produced from climate forecasts?  What additional climate information is needed by growers, Extension?  Can climate information help growers with crop insurance decisions?  Can agricultural Best Management Practices (BMPs) be improved by using climate forecasts?  Can we make AgClimate more sustainable and dynamic?

22. Experimental rainfall forecast, Feb 2005

23. Experimental climate forecasts show great promise

24. Simulated crop yields based on experimental forecasts

25. How do we develop a sustainable system for AgClimate forecasts? “FAWN” Weather Station Agronomic Database Soils, Crop Varieties Management SECC Historical Climate Database Weather Models Data QC Ag & Water Model Inputs Model Interface Models Ag & Water Model Outputs AgClimate Database Climate & Yield Forecasts Web Server Output Formatting “FAWN” Weather Station Agronomic Database Soils, Crop Varieties Management SECC Historical Climate Database Weather Models Data QC Ag & Water Model Inputs Model Interface Models Ag & Water Model Outputs AgClimate Database Climate & Yield Forecasts Web Server Output Formatting Fraisse et al.

26. Blueprint for a climate information system (Adapted from: Letson, 2004 who adapapted from Sarewitz et al., 2000.) RESEARCH Information generation EXTENSION: Communication, evaluation, and comprehension of information OPERATIONS Implementation of information system STAKEHOLDERS Use of information ?

27. Landgrant university model Ag Producers Research Education Extension

28. Operational entities for traditional agricultural research products  New varieties  New nutrient management technology  New pest management technology  Knowledge  Climate information and forecasts  New varieties  New nutrient management technology  New pest management technology  Knowledge  Climate information and forecasts  Seed companies and certification boards  Fertilizer companies  Chemical companies  Extension, publishers, farmers  ? ? ?  Seed companies and certification boards  Fertilizer companies  Chemical companies  Extension, publishers, farmers  ? ? ?

29. Integrated Research and Extension Approach New Knowledge New Methods Decision makers Climate Information & Decision Support System SECC Extension Services Climate offices (Federal, State) Sector researchers Adapted from JW Jones, 2005

30. Summary & Conclusions  Extension agents and farmers want and ask for AgClimate products.  Such requests often arise when researchers cannot meet user expectations for operational production.  The private company that did the web programming for AgClimate would like to market the design.  Potential operational entities will need resources to maintain and update databases.  For some products the best operational entity is not clear.  Extension agents and farmers want and ask for AgClimate products.  Such requests often arise when researchers cannot meet user expectations for operational production.  The private company that did the web programming for AgClimate would like to market the design.  Potential operational entities will need resources to maintain and update databases.  For some products the best operational entity is not clear.

31. http://secc.coaps.fsu.edu/