Abstract
The past few decades saw tremendous advances in weather and climate forecasting ability. These advances opened up the possibility of strategic adaptation of agricultural management in anticipation of weather and climate outcomes, resulting in a profusion of studies estimating the value of weather and climate forecasts. Estimated values from this literature were, in many cases, substantive, implying that farmers could significantly benefit from forecasts. Yet the response from farmers, it appears, was not commensurate with the values suggested by the studies. In this article we make the case that forecast quality, both real and perceived, may still pose a significant obstacle; despite recent gains in forecasting ability, forecasts-especially seasonal climate forecasts-are far from certain. Unless this uncertainty is explicitly and more realistically incorporated into models of forecast use, a gap will always exist between expectations of forecast use and actual forecast use by farmers. We conclude by establishing the need for 1) making imperfect forecasts a standard feature in models of forecast use and 2) informing these models with empirical research on farmer use of imperfect forecasts.
Original language | English |
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Pages (from-to) | 103-110 |
Number of pages | 8 |
Journal | Agricultural Systems |
Volume | 146 |
DOIs | |
State | Published - Jul 1 2016 |
Bibliographical note
Funding Information:This is publication No. 16-04-043 of the Kentucky Agricultural Experiment Station and is published with the approval of the Director. This work is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture , Hatch Project under 1006174 . Additionally, we wish to thank the editors and our three anonymous reviewers for their careful readings of prior drafts, and for their thoughtful comments and suggestions.
Publisher Copyright:
© 2016 Elsevier B.V.
Keywords
- Agricultural decision making
- Climate
- Forecast accuracy
- Imperfect information
- Weather
ASJC Scopus subject areas
- Animal Science and Zoology
- Agronomy and Crop Science