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Crop Yield Skewness and the Normal Distribution

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  • Hennessy, David A.
Abstract
Empirical studies point to negative crop yield skewness, but the literature provides few clear insights as to why. This paper formalizes three points on the matter. Statistical laws on aggregates do not imply a normal distribution. Whenever the weather-conditioned mean yield has diminishing marginal product with respect to a weather-conditioning index, then there is a disposition toward negative yield skewness. This is because high marginal product in bad weather stretches out the yield distribution's left tail relative to that for weather. For disaggregated yields, unconditional skewness is decomposed into weather-conditioned skewness plus two other terms and each is studied in turn.

Suggested Citation

  • Hennessy, David A., 2012. "Crop Yield Skewness and the Normal Distribution," Staff General Research Papers Archive 35019, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:35019
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    References listed on IDEAS

    as
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    Cited by:

    1. Phoebe Koundouri & Nikolaos Kourogenis, 2011. "On the Distribution of Crop Yields: Does the Central Limit Theorem Apply?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(5), pages 1341-1357.
    2. Tack, Jesse, 2013. "A Nested Test for Common Yield Distributions with Applications to U.S. Corn," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 38(1), pages 1-14, April.
    3. 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.
    4. Yu, Tian, 2011. "Three essays on weather and crop yield," ISU General Staff Papers 201101010800002976, Iowa State University, Department of Economics.
    5. 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.
    6. Finger, Robert, 2012. "Biases in Farm-Level Yield Risk Analysis due to Data Aggregation," Journal of International Agricultural Trade and Development, Journal of International Agricultural Trade and Development, vol. 61(1).
    7. Santeramo, Fabio Gaetano & Maccarone, Irene, 2022. "Analisi storica delle rese agricole e la variabilità del clima: Analisi dei dati italiani sui cereali [Historical crop yields and climate variability: analysis of Italian cereal data]," MPRA Paper 114135, University Library of Munich, Germany, revised 04 Aug 2022.
    8. 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.
    9. 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.
    10. Danja Sonntag & Gudrun P. Kiesmüller, 2016. "The shape of the yield and its impact on inventory decisions," 4OR, Springer, vol. 14(4), pages 405-415, December.
    11. Woodard, Joshua D. & Chiu Verteramo, Leslie & Miller, Alyssa P., 2015. "Adaptation of U.S. Agricultural Production to Drought and Climate Change," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205903, Agricultural and Applied Economics Association.
    12. Finger, Robert, 2012. "Biases in Farm-Level Yield Risk Analysis due to Data Aggregation," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 61(01), pages 1-14, February.
    13. Hennessy, David A., 2011. "Modeling Stochastic Crop Yield Expectations with a Limiting Beta Distribution," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 36(1), pages 1-15, April.
    14. 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.
    15. Ouedraogo, Frederic B. & Brorsen, B. Wade, 2014. "Bayesian Estimation of Optimal Nitrogen Rates with a Non-Normally Distributed Stochastic Plateau Function," 2014 Annual Meeting, February 1-4, 2014, Dallas, Texas 162447, Southern Agricultural Economics Association.
    16. Shukla, Sumedha & Arora, Gaurav & Agarwal, Sandip K., 2022. "Estimation of crop yield density conditional on input choices for smallholder farms using a BetaIV framework," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322580, Agricultural and Applied Economics Association.
    17. Zaura Fadhliani & Jeff Luckstead & Eric J. Wailes, 2019. "The impacts of multiperil crop insurance on Indonesian rice farmers and production," Agricultural Economics, International Association of Agricultural Economists, vol. 50(1), pages 15-26, January.
    18. 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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    Keywords

    conditional distribution; crop insurance; negative skewness; normal distribution; statistical laws;
    All these keywords.

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