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Modeling Texas Dryland Cotton Yields, With Application to Crop Insurance Actuarial Rating

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  • Chen, Shu-Ling
  • Miranda, Mario J.

Abstract

Texas dryland upland cotton yields have historically exhibited greater variation and more distributional irregularities than the yields of other crops, raising concerns that conventional parametric distribution models may generate biased or otherwise inaccurate crop insurance premium rate estimates. Here, we formulate and estimate regime-switching models for Texas dryland cotton yields in which the distribution of yield is conditioned on local drought conditions. Our results indicate that drought-conditioned regime-switching models provide a better fit to Texas county-level dryland cotton yields than conventional parametric distribution models. They do not, however, generate significantly different Group Risk Plan crop insurance premium rate estimates.

Suggested Citation

  • Chen, Shu-Ling & Miranda, Mario J., 2008. "Modeling Texas Dryland Cotton Yields, With Application to Crop Insurance Actuarial Rating," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(1), pages 239-252, April.
    • Handle: RePEc:cup:jagaec:v:40:y:2008:i:01:p:239-252_02
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    References listed on IDEAS

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    1. Jerry R. Skees & J. Roy Black & Barry J. Barnett, 1997. "Designing and Rating an Area Yield Crop Insurance Contract," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 430-438.
    2. Bruce J. Sherrick & Fabio C. Zanini & Gary D. Schnitkey & Scott H. Irwin, 2004. "Crop Insurance Valuation under Alternative Yield Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 406-419.
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    5. Octavio A. Ramirez & Sukant Misra & James Field, 2003. "Crop-Yield Distributions Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 108-120.
    6. Vitor A. Ozaki & Sujit K. Ghosh & Barry K. Goodwin & Ricardo Shirota, 2008. "Spatio-Temporal Modeling of Agricultural Yield Data with an Application to Pricing Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 951-961.
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    8. Barry K. Goodwin & Monte L. Vandeveer & John L. Deal, 2004. "An Empirical Analysis of Acreage Effects of Participation in the Federal Crop Insurance Program," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1058-1077.
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    Cited by:

    1. Hennessy, David A., 2009. "Crop Yield Skewness and the Normal Distribution," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 34(1), pages 1-19, April.
    2. Rejesus, Roderick M. & Marra, Michele C. & Roberts, Roland K. & English, Burton C. & Larson, James A. & Paxton, Kenneth W, 2013. "Changes in Producers’ Perceptions of Within-Field Yield Variability after Adoption of Cotton Yield Monitors," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 45(2), pages 1-18, May.
    3. Christopher N. Boyer & B. Wade Brorsen & Emmanuel Tumusiime, 2015. "Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 1-10, January.

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    More about this item

    JEL classification:

    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • Q14 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Finance
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

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