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Testing Day's Conjecture that More Nitrogen Decreases Crop Yield Skewness

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Abstract

While controversy surrounds skewness attributes of typical yield distributions, a better understanding is important for agricultural policy assessment and for crop insurance rate setting. Day (1965) conjectured that crop yield skewness declines with an increase in low levels of nitrogen use, but higher levels have no effect. In a theoretical model based on the law of the minimum (von Liebig) technology, we find conditions under which Day's conjecture applies. Employing four experimental plot datasets, we investigate the conjecture by introducing (a) a flexible Bayesian extension of the Just-Pope technology to incorporate skewness, and (b) a quantile-based measure of skewness shift. For corn yields, the Bayesian estimation provides strong evidence in favor of negative skewness at commercial nitrogen rates and for Day's conjecture. There was weaker evidence in favor of positively skewed cotton yield and little evidence in favor of the conjecture. The results are also confirmed by the quantile-based measure.

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  • Xiaodong Du & David A. Hennessy & Cindy L. Yu, 2010. "Testing Day's Conjecture that More Nitrogen Decreases Crop Yield Skewness," Center for Agricultural and Rural Development (CARD) Publications 10-wp511, Center for Agricultural and Rural Development (CARD) at Iowa State University.
  • Handle: RePEc:ias:cpaper:10-wp511
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    Cited by:

    1. 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.
    2. Yu, Tian, 2011. "Three essays on weather and crop yield," ISU General Staff Papers 201101010800002976, Iowa State University, Department of Economics.
    3. 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.
    4. Tor N. Tolhurst & Alan P. Ker, 2015. "On Technological Change in Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 137-158.
    5. Jesse Tack & Andrew Barkley & Lawton Nalley, 2014. "Heterogeneous effects of warming and drought on selected wheat variety yields," Climatic Change, Springer, vol. 125(3), pages 489-500, August.
    6. Du, Xiaodong & Hennessy, David & Feng, Hongli, 2014. "Tail Dependence is to be Expected Among Crop Yields," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 174315, Agricultural and Applied Economics Association.
    7. 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.
    8. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    9. Du, Xiaodong & Dong, Fengxia, 2024. "Climate Change and Dynamics of Crop Yield Distribution," 2024 Annual Meeting, July 28-30, New Orleans, LA 343786, Agricultural and Applied Economics Association.
    10. Menale Kassie & Hailemariam Teklewold & Paswel Marenya & Moti Jaleta & Olaf Erenstein, 2015. "Production Risks and Food Security under Alternative Technology Choices in Malawi: Application of a Multinomial Endogenous Switching Regression," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(3), pages 640-659, September.
    11. Agarwal, Sandip & Jacobs, Keri L. & Weninger, Quinn, 2016. "Unfolding the Bias in Farm Nitrogen Management," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 237380, Agricultural and Applied Economics Association.
    12. Brorsen, B. Wade, 2013. "Using Bayesian Estimation and Decision Theory to Determine the Optimal Level of Nitrogen in Cotton," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 142951, Southern Agricultural Economics Association.

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    Keywords

    crop insurance; Gibbs sampler; Just and Pope technology; negative skewness; quantile regression.;
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