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Climate Information and Agricultural Risk Management

Climate Information and Agricultural Risk Management. T. A. Crane *, C. Roncoli*, N. E. Breuer+, J. O. Paz*, K. T. Ingram#, K. Broad+, G. Hoogenboom* * University of Georgia, + University of Miami, # University of Florida . A Systemic Approach to Understanding Farmers’ Decision-Making.

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Climate Information and Agricultural Risk Management

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  1. Climate Information and Agricultural Risk Management T. A. Crane*, C. Roncoli*, N. E. Breuer+, J. O. Paz*, K. T. Ingram#, K. Broad+, G. Hoogenboom* * University of Georgia, + University of Miami, # University of Florida A Systemic Approach to Understanding Farmers’ Decision-Making

  2. Outline • Research methods and setting • Interactions with weather and climate information systems • Potential adaptive strategies • Challenges to forecast use • Farmers’ suggestions for usability

  3. Methods and Research Setting • Sample: 38 farmers • Sites: 21 counties in South Georgia • Methods: Semi-structured interviews • Weather and climate information systems • Climate variability and risk mgmt. strategies • Potential adaptations

  4. Methods and Research Setting • Sample: 38 farmers • Sites: 21 counties in South Georgia • Methods: Semi-structured interviews • Mixed production systems • Avg. 2 per operation

  5. Weather & Climate Info Sources • Daily use, often accessed multiple times • Spraying • Planting • Irrigation • Confidence low beyond 3-5 days • Wives & children are often internet users; information gateways

  6. Weather & Climate Info Sources • Passive exposure to climate forecasts • 90-day forecasts not used in agric. decisions • “Conversation piece” • “Peace of mind” • Collective credibility

  7. Adaptive Management Options • Cropping strategy • Corn or cotton ? • Dry land corn ? • Soil : crop : forecast ? • Forward contracts ? • Planting schedule • Dry year pine planting ? • Late frost risk ?

  8. Adaptive Management Options Forecast Use: Irwin County Spring 2006 forecast for summer drought  widespread shift from long- to short-cycle peanut variety

  9. Non-Climate Variables as Management Drivers • Agronomic requirements • Commodity prices • Insurance constraints • Input prices • Credit options • Policy environment • Price supports • Trade policies • Immigration laws Relative uncertainty of forecasts compared to non-climate variables = competition as mgmt. driver

  10. Challenges to Farmers’ Use of Forecasts • Discrepancy in scales of forecasts & decisions • Temporal • Spatial • Inexperience with climate forecasts • Unawareness of potential • Skepticism of accuracy • Discrepancy in understandings of key concepts • Probability • Accuracy

  11. Challenges to Farmers’ Use of Forecasts • Difficulty in processing additional information • Time • Mental energy • Inflexibility of highly-capitalized operations • Indebtedness • Infrastructural investments • Large acreage • Potential for actors to leverage info over farmers • Lenders • Insurers • Brokers

  12. Facilitating Appropriate Use of Climate-Based DSS • Create recognizable identity for DSS • “Show the people behind it” • Association with land-grant university • Communication • Use lay-users’ language • “Show you understand what it means to be a farmer” • Layer information for different users • Cultivate habitual reference to site • Regular outreach • Keep information updated

  13. Facilitating Appropriate Use of Climate-Based DSS • Enable users to evaluate forecasts • Publish forecast history • Publish forecast performance records • Explain probability upfront • Integrate users’ feedback into product development and assessment

  14. Questions? www.agclimate.org http://secc.coaps.fsu.edu/ This research was supported by funding from NOAA USDA-RMA USDA-CSREES

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