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Imperfect forecasts and decision making in agriculture

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  • Kusunose, Yoko
  • Mahmood, Rezaul
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.

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

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    Cited by:

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    6. Chimaliro, Aubrey Victor, 2018. "Analysis of main determinants of soya bean price volatility in Malawi," Research Theses 334743, Collaborative Masters Program in Agricultural and Applied Economics.
    7. Pereira, L.S. & Paredes, P. & Jovanovic, N., 2020. "Soil water balance models for determining crop water and irrigation requirements and irrigation scheduling focusing on the FAO56 method and the dual Kc approach," Agricultural Water Management, Elsevier, vol. 241(C).
    8. Gitonga, Zachary M. & Visser, Martine & Mulwa, Chalmers, 2020. "Can climate information salvage livelihoods in arid and semiarid lands? An evaluation of access, use and impact in Namibia," World Development Perspectives, Elsevier, vol. 20(C).
    9. Ingrid Nesheim & Line Barkved & Neha Bharti, 2017. "What Is the Role of Agro-Met Information Services in Farmer Decision-Making? Uptake and Decision-Making Context among Farmers within Three Case Study Villages in Maharashtra, India," Agriculture, MDPI, vol. 7(8), pages 1-16, August.
    10. Madende, Primrose & Grové, Bennie, 2019. "Risk efficiency of optimal water allocation within a single- and multi-stage decision-making framework," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 59(1), August.
    11. Ali Akbar Barati & Hossein Azadi & Milad Dehghani Pour & Philippe Lebailly & Mostafa Qafori, 2019. "Determining Key Agricultural Strategic Factors Using AHP-MICMAC," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    12. Mitter, Hermine & Schmid, Erwin, 2019. "Computing the economic value of climate information for water stress management exemplified by crop production in Austria," Agricultural Water Management, Elsevier, vol. 221(C), pages 430-448.

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