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Crop Yield Skewness under the Law of Minimum Technology

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Abstract

A large empirical literature exists seeking to identify crop yield distributions. Consensus has not yet formed. This is in part because of data aggregation problems but also in part because no satisfactory motivation has been forwarded in favor of any distribution, including the normal. This article explores the foundations of crop yield distributions for the Law of the Minimum, or weakest-link, resource constraint technology. It is shown that heterogeneity in resource availabilities can increase expected yield. The role of stochastic dependence is studied for the technology. With independent, identical, uniform resource availability distributions the yield skew is positive, whereas it is negative whenever the distributions are normal. Simulations show how asymmetries in resource availabilities determine skewness. Extreme value theory is used to suggest a negative yield skew whenever production is in a tightly controlled environment so that the left tails of resource availability distributions are thin.

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  • David A. Hennessy, 2007. "Crop Yield Skewness under the Law of Minimum Technology," Center for Agricultural and Rural Development (CARD) Publications 07-wp451, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    • Handle: RePEc:ias:cpaper:07-wp451
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    1. is not listed on IDEAS
    2. Joseph Cooper & A. Nam Tran & Steven Wallander, 2017. "Testing for Specification Bias with a Flexible Fourier Transform Model for Crop Yields," American Journal of Agricultural Economics, John Wiley & Sons, vol. 99(3), pages 800-817, April.
    3. Xiaodong Du & David A. Hennessy & Cindy L. Yu, 2012. "Testing Day's Conjecture that More Nitrogen Decreases Crop Yield Skewness," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(1), pages 225-237.
    4. Daniel Schuurman & Alan Ker, 2025. "Heterogeneity, climate change, and crop yield distributions: Solvency implications for publicly subsidized crop insurance programs," American Journal of Agricultural Economics, John Wiley & Sons, vol. 107(1), pages 248-268, January.
    5. Jesse Tack & David Ubilava, 2013. "The effect of El Niño Southern Oscillation on U.S. corn production and downside risk," Climatic Change, Springer, vol. 121(4), pages 689-700, December.
    6. Curtis J. McKnight & Grant Hauer & Marty Luckert & Feng Qiu, 2024. "Bioenergy feedstock supply from wheat straw: A farm level model incorporating trade‐offs in crop choices, disease risk, and soil fertility," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 72(3), pages 285-307, September.
    7. Martinet, Vincent, 2014. "The economics of the Food versus Biodiversity debate," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182800, European Association of Agricultural Economists.
    8. Jesse B. Tack & David Ubilava, 2015. "Climate and agricultural risk: measuring the effect of ENSO on U.S. crop insurance," Agricultural Economics, International Association of Agricultural Economists, vol. 46(2), pages 245-257, March.
    9. Huang, Pei & McCarl, Bruce A., 2014. "Estimating Decadal Climate Variability Effects on Crop Yields: A Bayesian Hierarchical Approach," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169828, Agricultural and Applied Economics Association.
    10. repec:isu:genstf:201101010800002976 is not listed on IDEAS
    11. David A. Hennessy, 2009. "Crop Yield Skewness Under Law of the Minimum Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(1), pages 197-208.
    12. Buchholz, Matthias & Musshoff, Oliver, 2014. "The role of weather derivatives and portfolio effects in agricultural water management," Agricultural Water Management, Elsevier, vol. 146(C), pages 34-44.
    13. Arora, Gaurav & Agarwal, Sandip K., 2020. "Agricultural input use and index insurance adoption: Concept and evidence," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304508, Agricultural and Applied Economics Association.
    14. Vincent Martinet, 2012. "Effect of soil heterogeneity on the welfare economics of biofuel policies," Working Papers 2012/01, INRA, Economie Publique.
    15. Hennessy, David A., 2009. "Crop Yield Skewness and the Normal Distribution," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 34(01), pages 1-19, April.
    16. Xiaodong Du & Cindy L. Yu & David A. Hennessy & Ruiqing Miao, 2015. "Geography of crop yield skewness," Agricultural Economics, International Association of Agricultural Economists, vol. 46(4), pages 463-473, July.
    17. Alan D. Hutson & Albert Vexler, 2018. "A Cautionary Note on Beta Families of Distributions and the Aliases Within," The American Statistician, Taylor & Francis Journals, vol. 72(2), pages 121-129, April.
    18. Vincent Martinet, 2010. "Soil heterogeneity, agricultural supply and land-use change: an application to biofuels production," Working Papers 2010/05, INRA, Economie Publique.
    19. Paulson, Nicholas D. & Babcock, Bruce A., 2010. "Readdressing the Fertilizer Problem," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 35(3), pages 1-17, December.
    20. Danso, G.K. & Jeffrey, S.R. & Dridi, C. & Veeman, T., 2021. "Modeling irrigation technology adoption and crop choices: Gains from water trading with farmer heterogeneity in Southern Alberta, Canada," Agricultural Water Management, Elsevier, vol. 253(C).
    21. 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.
    22. Jesse Tack & Ardian Harri & Keith Coble, 2012. "More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1037-1054.

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    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services

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