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Comparisons between Subjective and Statistical Forecasts of Crop Yields

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  • James W. Pease
  • Ernest W. Wade
  • Jerry S. Skees
  • Chandra M. Shrestha

Abstract

This research highlights differences between farm-level prescriptive models that define optimal actions and descriptive models that simulate actual behavior. Statistical forecasting procedures of varying complexity are applied to farm-level yield series that have different length and composition. The forecasts are then compared with farmers' subjective expectations. Comparisons of data-based and subjective expectations are also made across crops. Although results are not completely consistent between corn and soybeans, there are indications that operators use simple cognitive procedures to estimate expected yields and other decisions. Crop insurance purchases are affected by differences between how farmers and policymakers formulate yield expectations.

Suggested Citation

  • James W. Pease & Ernest W. Wade & Jerry S. Skees & Chandra M. Shrestha, 1993. "Comparisons between Subjective and Statistical Forecasts of Crop Yields," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 15(2), pages 339-350.
    • Handle: RePEc:oup:revage:v:15:y:1993:i:2:p:339-350.
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    File URL: http://hdl.handle.net/10.2307/1349453
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    Citations

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    Cited by:

    1. Lin Liu & Bruno Basso, 2020. "Linking field survey with crop modeling to forecast maize yield in smallholder farmers’ fields in Tanzania," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(3), pages 537-548, June.
    2. Sherrick, Bruce J., 2001. "The Accuracy Of Producer Expectations: Evidence And Implications For Insurance Valuation," 2001 Regional Committee NC-221, October 1-2, 2001, McLean, Virginia 132390, Regional Research Committee NC-1014: Agricultural and Rural Finance Markets in Transition.
    3. Bulut, Harun & Collins, Keith J., 2013. "Political Economy of Crop Insurance Risk Subsidies under Imperfect Information," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150577, Agricultural and Applied Economics Association.
    4. Bregaglio, Simone & Ginaldi, Fabrizio & Raparelli, Elisabetta & Fila, Gianni & Bajocco, Sofia, 2023. "Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets," Agricultural Systems, Elsevier, vol. 209(C).
    5. Umarov, Alisher & Sherrick, Bruce J., 2005. "Farmers' Subjective Yield Distributions: Calibration and Implications for Crop Insurance Valuation," 2005 Annual meeting, July 24-27, Providence, RI 19396, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    6. Hong Fu & Yuehua Zhang & Yinuo An & Li Zhou & Yanling Peng & Rong Kong & Calum G. Turvey, 2022. "Subjective and objective risk perceptions and the willingness to pay for agricultural insurance: evidence from an in-the-field choice experiment in rural China," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 47(1), pages 98-121, March.
    7. Sherrick, Bruce J., 2002. "The Accuracy Of Producers' Probability Beliefs: Evidence And Implications For Insurance Valuation," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(1), pages 1-17, July.

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