Understanding & Managing Agricultural Risk Caused by Climate Variability in the Southeast USA

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

2. 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

3. Agricultural Risk • Probability of an undesirable outcome in an agricultural enterprise. • Yield loss • Low profit or economic loss • 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 distribution Probability of excedence

6. Tifton, Georgia Monthly Mean Rainfall by ENSO Phase 160 Wheat 140 120 100 80 60 Field Corn 40 20 Peanut 0 Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug MM per month El Niño La Niña Neutral Sep Oct

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.

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?

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.

14. Used simulation models to analyze crop responses to climate • 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

15. Crop Models Simulate YearlyYield Variations due to Climate RMSEfitting = 167 kg/ha

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

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?

18. AgClimate: Risk Information and Decision Support System • 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

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?

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?

22. Experimental rainfall forecast, Feb 2005

23. Experimental climate forecasts show great promise

24. Simulated crop yields based on experimental forecasts

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

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

27. Landgrant university model Education Ag Producers Research Extension

28. 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 ? ? ? Operational entities for traditional agricultural research products

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

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