Characteristics of Climate Forecast Quality: implications for Economic Value to Midwestern Corn Producers

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  • 1 Department of agricultural Economics, Texas A&M University, College Station, Texas
  • | 2 Department of agricultural Economics, Oklahoma State University, Stillwater, Oklahoma
  • | 3 Department of agricultural Economics, University of Illinois, Urbana, Illinois
  • | 4 Cooperative Institute for Mesoscale Meteorological Studies and School of Meteorology, University of Oklahoma, Norman, Oklahoma
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Abstract

Using a conceptual forecasting format that is similar to some in current operational use, trade-offs between climate forecast quality and economic value are examined from the perspective of the forecast user. Various scenarios for climate forecast quality are applied to corn production in east-central Illinois. A stochastic dynamic programming model is used to obtain the expected value of the various scenarios. As anticipated, the results demonstrate that the entire structure of the forecast format interacts to determine the economic value of that system. Additional results indicate two possible preferred directions for research concerning climate forecasting and economic applications such as corn production in Illinois. First, increasing forecast quality by decreasing the error between the observed condition and the forecast condition may be preferred to increasing quality by increasing the number of predictions in the correct category. Second, corn producers may prefer research to increase the quality of forecasts for “poorer” climatic conditions over research directed toward increasing the quality of forecasts for “good” conditions.

Abstract

Using a conceptual forecasting format that is similar to some in current operational use, trade-offs between climate forecast quality and economic value are examined from the perspective of the forecast user. Various scenarios for climate forecast quality are applied to corn production in east-central Illinois. A stochastic dynamic programming model is used to obtain the expected value of the various scenarios. As anticipated, the results demonstrate that the entire structure of the forecast format interacts to determine the economic value of that system. Additional results indicate two possible preferred directions for research concerning climate forecasting and economic applications such as corn production in Illinois. First, increasing forecast quality by decreasing the error between the observed condition and the forecast condition may be preferred to increasing quality by increasing the number of predictions in the correct category. Second, corn producers may prefer research to increase the quality of forecasts for “poorer” climatic conditions over research directed toward increasing the quality of forecasts for “good” conditions.

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