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Modeling Yield Risk Under Technological Change: Dynamic Yield Distributions and the U.S. Crop Insurance Program

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  • Zhu, Ying
  • Goodwin, Barry K.
  • Ghosh, Sujit K.

Abstract

The objective of this study is to evaluate the risk associated with major agricultural commodity yields in the United States. We are particularly concerned with the nonstationary nature of the yield distribution, which arises primarily as a result of technological progress and changing environmental conditions over time. In contrast to common two-stage methods, we propose an alternative parametric model that allows the moments of yield distributions to change with time. Several model selection techniques suggest the proposed time-varying model outperforms more conventional models in terms of in-sample goodness-of-fit, out-of-sample predictive power, and the prediction accuracy of insurance premium rates.

Suggested Citation

  • Zhu, Ying & Goodwin, Barry K. & Ghosh, Sujit K., 2011. "Modeling Yield Risk Under Technological Change: Dynamic Yield Distributions and the U.S. Crop Insurance Program," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 36(1), pages 1-19, April.
    • Handle: RePEc:ags:jlaare:105549
    DOI: 10.22004/ag.econ.105549
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    Cited by:

    1. Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    2. Wyatt Thompson & Joe Dewbre & Patrick Westfhoff & Kateryna Schroeder & Simone Pieralli & Ignacio Perez Dominguez, 2017. "Introducing medium-and long-term productivity responses in Aglink-Cosimo," JRC Research Reports JRC105738, Joint Research Centre.
    3. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    4. Shen, Zhiwei, 2016. "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wageningen, The Netherlands 249984, European Association of Agricultural Economists.
    5. Barry K. Goodwin & Nicholas E. Piggott, 2020. "Has Technology Increased Agricultural Yield Risk? Evidence from the Crop Insurance Biotech Endorsement," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(5), pages 1578-1597, October.
    6. Xiaotao Li & Jinzheng Ren & Beibei Niu & Haiping Wu, 2020. "Grain Area Yield Index Insurance Ratemaking Based on Time–Space Risk Adjustment in China," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    7. Agarwal, Sandip Kumar, 2017. "Subjective beliefs and decision making under uncertainty in the field," ISU General Staff Papers 201701010800006248, Iowa State University, Department of Economics.
    8. Ramsey, Ford, 2014. "An Application of Kernel Density Estimation via Diffusion to Group Yield Insurance," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170173, Agricultural and Applied Economics Association.
    9. Yong Liu & A. Ford Ramsey, 2023. "Incorporating historical weather information in crop insurance rating," American Journal of Agricultural Economics, John Wiley & Sons, vol. 105(2), pages 546-575, March.
    10. Diao Panpan & Zhang Zhonggen, 2015. "Premium Rate Design and Risk Regionalization for the Policy-Based Wheat Insurance of Henan Province in China," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 9(2), pages 203-229, July.
    11. Poudel, Mahadeb Prasad & Chen, Shwu-En & Ghimire, Raju, 2013. "Rice Yield Distribution and Risk Assessment in South Asian Countries: A Statistical Investigation," International Journal of Agricultural Management and Development (IJAMAD), Iranian Association of Agricultural Economics, vol. 3(1), March.
    12. Yong Liu & Alan P. Ker, 2021. "Simultaneous borrowing of information across space and time for pricing insurance contracts: An application to rating crop insurance policies," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(1), pages 231-257, March.
    13. A. Ford Ramsey & Barry K. Goodwin, 2019. "Value-at-Risk and Models of Dependence in the U.S. Federal Crop Insurance Program," JRFM, MDPI, vol. 12(2), pages 1-21, April.
    14. Ghahremanzadeh, Mohammad & Mohammadrezaei, Rassul & Dashti, Ghader & Ainollahi, Moharram, 2018. "Designing a whole-farm revenue insurance for agricultural crops in Zanjan province of Iran," Economia Agraria y Recursos Naturales, Spanish Association of Agricultural Economists, vol. 17(02), January.
    15. Park, Eunchun & Harri, Ardian & Coble, Keith H., 2022. "Estimating Crop Yield Densities for Counties with Missing Data," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 47(3), September.

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