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S. T. Sonka
,
S.A. Changnon Jr.
, and
S. L. Hofing

The paper presents an analysis of climate prediction needs and uses within six important subsegments of the agribusiness sector. Results are based on a mail survey of 114 managers. Although nearly 70% of the respondents indicated some use of climate predictions in the last year, only 1 in 8 of the respondents used that information in a specific decision. Lack of sufficient accuracy and prediction lead time were identified as two important impediments to current use of climate predictions. Estimates of necessary accuracy levels and lead time are reported both for the group average and by segments of need. Recommendations are offered regarding research needs to enhance climate prediction and activities of the government and the private sector to improve use of climate predictions.

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James W. Mjelde
,
Derrell S. Peel
,
Steven T. Sonka
, and
Peter J. Lamb

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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