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A Relational Model for Predicting Farm-Level Crop Yield Distributions in the Absence of Farm-Level Data

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  • Porth, Lysa
  • Tan, Ken Seng
  • Zhu, Wenjun

Abstract

Individual farm-level expected yields serve as the foundation for crop insurance design and rating. Therefore, constructing a reasonable, accurate, and robust model for the farm-level loss distribution is essential. Unfortunately, farm-level yield data is often insufficient or unavailable in many regions to conduct sound statistical inference, especially in developing countries. This paper develops a new two-step relational model to predict farm-level crop yield distributions in the absence of farm yield losses, through "borrowing" information from a neighbouring country, where detailed farm-level yield experience is available. The first step of the relational model defines a similarity measure based on a Euclidean metric to select an optimal county, considering weather information, average farm size, county size and county-level yield volatility. The second step links the selected county with the county to be predicted through modeling the dependence structures between the farm-level and county-level yield losses. Detailed farm-level and county-level corn yield data in the U.S. and Canada are used to empirically examine the performance of the proposed relational model. The results show that the approach developed in this paper may be useful in improving yield forecasts and pricing in the case where farm-level data is limited or not available. Further, this approach may also help to address the issue of aggregation bias, when county-level data is used as a substitute for farm-level data, which tend to result in underestimating the predicted risk relative to the true risk.

Suggested Citation

  • Porth, Lysa & Tan, Ken Seng & Zhu, Wenjun, 2016. "A Relational Model for Predicting Farm-Level Crop Yield Distributions in the Absence of Farm-Level Data," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236278, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea16:236278
    DOI: 10.22004/ag.econ.236278
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    References listed on IDEAS

    as
    1. Lysa Porth & Wenjun Zhu & Ken Seng Tan, 2014. "A credibility-based Erlang mixture model for pricing crop reinsurance," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 74(2), pages 162-187, July.
    2. Gerlt, Scott & Thompson, Wyatt & Miller, Douglas, 2014. "Exploiting the Relationship between Farm-Level Yields and County-Level Yields for Applied Analysis," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 39(2), pages 1-18.
    3. Schnitkey, Gary, 2012. "Crop Insurance in 2012," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 2, July.
    4. Julia I. Borman & Barry K. Goodwin & Keith H. Coble & Thomas O. Knight & Rod Rejesus, 2013. "Accounting for short samples and heterogeneous experience in rating crop insurance," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 73(1), pages 88-101, May.
    5. Joshua D. Woodard & Alexander D. Pavlista & Gary D. Schnitkey & Paul A. Burgener & Kimberley A. Ward, 2012. "Government Insurance Program Design, Incentive Effects, and Technology Adoption: The Case of Skip-Row Crop Insurance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(4), pages 823-837.
    6. Jerry R. Skees & Michael R. Reed, 1986. "Rate Making for Farm-Level Crop Insurance: Implications for Adverse Selection," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(3), pages 653-659.
    7. Barry J. Barnett & Dmitry V. Vedenov, 2007. "Is There a Viable Market for Area-Based Crop Insurance?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(2), pages 508-519.
    8. Alan P. Ker & Keith Coble, 2003. "Modeling Conditional Yield Densities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 291-304.
    9. Coble, Keith H. & Barnett, Barry J., 2008. "Implications of Integrated Commodity Programs and Crop Insurance," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(2), pages 431-442, August.
    10. Lysa Porth & Ken Seng Tan & Chengguo Weng, 2013. "Optimal reinsurance analysis from a crop insurer's perspective," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 73(2), pages 310-328, July.
    11. Mario J. Miranda, 1991. "Area-Yield Crop Insurance Reconsidered," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(2), pages 233-242.
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    Keywords

    Crop Production/Industries; Research Methods/ Statistical Methods;

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