Responding to stakeholder's demands for climate information: From research to applications in Florida

Abstract Previous research shows that Florida's climate and agricultural production are influenced by the El Nino-Southern Oscillation, suggesting that farmers and ranchers might use new methods of climate forecasting to modify management, increase profits and reduce economic risks. The purposes of this paper are to describe the framework used by a Florida Consortium (FC) of researchers to assess the potential use of climate forecasts in agricultural decision-making and to summarize what was learned in the research process. The framework includes components for generation, communication and use of climate information as well as an implementation and evaluation component. Results showed that winter months are affected most by ENSO phase (higher rainfall and lower temperatures in El Nino years and the opposite during La Nina years). Yields of most crops were significantly associated with ENSO phase as were prices of some commodities. Through various mechanisms of interacting with farmers, ranchers, and extension faculty, we learned that interest in climate forecasts varied widely from highly optimistic to skeptical, and that these clients had good ideas of how to vary management if they have good forecasts. Case studies aimed at understanding potential value and risks associated with use of climate forecasts were conducted for winter fresh market tomato, cow-calf operations, and peanut production. Analytical results, confirmed by interactions with clients, showed significant value in using climate forecasts to alter specific decisions. Risks of using climate information varied among commodities, with considerable risk found in tomato due to the strong link between production and price. Perhaps the most important lesson learned was the importance of engaging trusted advisors in research and outreach efforts. A major output of the project was the close cooperation established between the FC and the Florida Cooperative Extension Service. Prospects for sustaining a climate information program in Florida are high due to joint research and extension initiatives.

[1]  Graeme L. Hammer,et al.  Advances in application of climate prediction in agriculture , 2001 .

[2]  Gerrit Hoogenboom,et al.  Simulation of Crop Growth: CROPGRO Model , 2018, Agricultural Systems modeting and Simulation.

[3]  James Hansen,et al.  Land allocation conditioned on El Niño-Southern Oscillation phases in the Pampas of Argentina☆ , 1999 .

[4]  James W. Jones,et al.  El Niño-Southern Oscillation effects on peanut yield and nitrogen leaching , 2002 .

[5]  Chester F. Ropelewski,et al.  Quantifying Southern Oscillation-Precipitation Relationships , 1996 .

[6]  Chester F. Ropelewski,et al.  North American Precipitation and Temperature Patterns Associated with the El Niño/Southern Oscillation (ENSO) , 1986 .

[7]  Balaji Rajagopalan,et al.  The role of ENSO in determining climate and maize yield variability in the U.S. cornbelt , 1999 .

[8]  C. Rosenzweig.  Maize suffers a sea-change , 1994, Nature.

[9]  James W. Jones,et al.  El Niño–Southern Oscillation Impacts on Winter Vegetable Production in Florida* , 1999 .

[10]  Holger Meinke,et al.  Increasing profits and reducing risks in crop production using participatory systems simulation approaches , 2001 .

[11]  J. O'Brien,et al.  Marginal Probabilities of the Extremes of ENSO Events for Temperature and Precipitation in the Southeastern United States , 1994 .

[12]  R. L. McCown,et al.  Advances in farming systems analysis and intervention , 2001 .

[13]  Mojib Latif,et al.  A review of ENSO prediction studies , 1994 .

[14]  James W. Jones,et al.  Potential benefits of climate forecasting to agriculture , 2000 .

[15]  Steven T. Sonka,et al.  Valuing Climate Forecast Information , 1987 .

[16]  N. Nicholls.  Impact of the southern oscillation on australian crops , 1985 .

[17]  Aul Handler.  USA corn yields, the El Niñmo and agricultural drought: 1867–1988 , 1990 .

[18]  R. Bruce Curry,et al.  Agricultural Systems modeting and Simulation , 1997 .

[19]  S. L. Hofing,et al.  How Agribusiness Uses Climate Predictions: Implications for Climate Research and Provision of Predictions , 1992 .

[20]  Christian L. Keppenne,et al.  An ENSO Signal in Soybean Futures Prices , 1995 .

[21]  J. Rogers.  Precipitation Variability over the Caribbean and Tropical Americas Associated with the Southern Oscillation , 1988 .

[22]  Mojib Latif,et al.  A review of the predictability and prediction of ENSO , 1998 .

[23]  Steven T. Sonka,et al.  Economic use of weather and climate information: Concepts and an agricultural example , 1986 .

[24]  Bruce A. McCarl,et al.  VALUE OF IMPROVED LONG‐RANGE WEATHER INFORMATION , 1995 .

[25]  J. Martsolf.  Relationship Between Florida Freeze Incidence and Sea Surface Temperature in the Tropical Pacific , 2001 .

[26]  Holger Meinke,et al.  Climatic risk to peanut production: a simulation study for Northern Australia , 1995 .

[27]  C. Messina,et al.  Associations between Grain Crop Yields in Central-Eastern Argentina and El Nino-Southern Oscillation , 1999 .

[28]  M. Khandekar,et al.  The impact of large-scale atmospheric circulations and anomalies on Indian monsoon droughts and floods and on world grain yields—a statistical analysis , 1992 .

[29]  N. Nicholls,et al.  Applications of seasonal climate forecasting in agricultural and natural ecosystems : the Australian experience , 2000 .

[30]  Gidon Eshel,et al.  Forecasting Zimbabwean maize yield using eastern equatorial Pacific sea surface temperature , 1994, Nature.

[31]  S. H. Franchito,et al.  Interannual variations of rainfall and corn yields in Northeast Brazil , 1997 .

[32]  R. E. Carlson,et al.  Midwestern Corn Yield and Weather in Relation to Extremes of the Southern Oscillation , 1996 .