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Imperfect forecasts and decision making in agriculture


  • Kusunose, Yoko
  • Mahmood, Rezaul


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.

Suggested Citation

  • Kusunose, Yoko & Mahmood, Rezaul, 2016. "Imperfect forecasts and decision making in agriculture," Agricultural Systems, Elsevier, vol. 146(C), pages 103-110.
  • Handle: RePEc:eee:agisys:v:146:y:2016:i:c:p:103-110
    DOI: 10.1016/j.agsy.2016.04.006

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    References listed on IDEAS

    1. Miguel A. Carriquiry & Daniel E. Osgood, 2012. "Index Insurance, Probabilistic Climate Forecasts, and Production," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(1), pages 287-300, March.
    2. James W. Mjelde & Harvey S.J. Hill & John F. Griffiths, 1998. "A Review of Current Evidence on Climate Forecasts and Their Economic Effects in Agriculture," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(5), pages 1089-1095.
    3. James W. Mjelde & Steven T. Sonka & Bruce L. Dixon & Peter J. Lamb, 1988. "Valuing Forecast Characteristics in a Dynamic Agricultural Production System," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 70(3), pages 674-684.
    4. Richard M. Adams & Kelly J. Bryant & Bruce A. Mccarl & David M. Legler & James O'Brien & Andrew Solow & Rodney Weiher, 1995. "Value Of Improved Long-Range Weather Information," Contemporary Economic Policy, Western Economic Association International, vol. 13(3), pages 10-19, July.
    5. Messina, C. D. & Hansen, J. W. & Hall, A. J., 1999. "Land allocation conditioned on El Nino-Southern Oscillation phases in the Pampas of Argentina," Agricultural Systems, Elsevier, vol. 60(3), pages 197-212, June.
    6. Cabrera, Victor E. & Letson, David & Podesta, Guillermo, 2007. "The value of climate information when farm programs matter," Agricultural Systems, Elsevier, vol. 93(1-3), pages 25-42, March.
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    Cited by:

    1. Santeramo, Fabio Gaetano & Lamonaca, Emilia & Contò, Francesco & Stasi, Antonio & Nardone, Gianluca, 2017. "Drivers of grain price volatility: a cursory critical review," MPRA Paper 79427, University Library of Munich, Germany.
    2. repec:gam:jagris:v:7:y:2017:i:8:p:70-:d:107906 is not listed on IDEAS


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