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

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  • Du, Xiaodong
  • Hennessy, David A.
  • Yu, Cindy

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 nitrogen use at low levels but not at higher levels. Employing four corn yield 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. 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 confirmed by the quantile-based measure. We also find evidence that skewness becomes more negative upon moving from corn-after-corn to corn-after-soybean.

Suggested Citation

  • Du, Xiaodong & Hennessy, David A. & Yu, Cindy, 2012. "Testing Day's Conjecture That More Nitrogen Decreases Crop Yield Skewness," Staff General Research Papers Archive 35022, Iowa State University, Department of Economics.
  • Handle: RePEc:isu:genres:35022
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    4. 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.
    5. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
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    7. 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.
    8. 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.
    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. 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.
    11. 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.
    12. 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.
    13. 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.

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