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

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

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. Copyright 2012, Oxford University Press.

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  • 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.
  • Handle: RePEc:oup:ajagec:v:94:y:2012:i:1:p:225-237
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    File URL: http://hdl.handle.net/10.1093/ajae/aar091
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    1. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
    2. Richard E. Just & Quinn Weninger, 1999. "Are Crop Yields Normally Distributed?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 287-304.
    3. Tian Yu & Bruce A. Babcock, 2010. "Are U.S. Corn and Soybeans Becoming More Drought Tolerant?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(5), pages 1310-1323.
    4. john M. Antle, 2010. "Asymmetry, Partial Moments, and Production Risk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(5), pages 1294-1309.
    5. 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.
    6. Bruce A. Babcock & David A. Hennessy, 1996. "Input Demand under Yield and Revenue Insurance," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 416-427.
    7. 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.
    8. Mitchell, Paul D., 2004. "Nutrient Best Management Practice Insurance and Farmer Perceptions of Adoption Risk," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 36(03), pages 657-673, December.
    9. 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.
    10. Just, Richard E. & Pope, Rulon D., 1978. "Stochastic specification of production functions and economic implications," Journal of Econometrics, Elsevier, vol. 7(1), pages 67-86, February.
    11. Seo, Sangtaek & Mitchell, Paul D. & Leatham, David J., 2005. "Effects of Federal Risk Management Programs on Optimal Acreage Allocation and Nitrogen Use in a Texas Cotton-Sorghum System," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 37(03), December.
    12. Terrell, Dek, 1996. "Incorporating Monotonicity and Concavity Conditions in Flexible Functional Forms," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(2), pages 179-194, March-Apr.
    13. David A. Hennessy, 2006. "On Monoculture and the Structure of Crop Rotations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(4), pages 900-914.
    14. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, April.
    15. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    16. Salvatore Di Falco & Jean-Paul Chavas, 2007. "On Crop Biodiversity, Risk Exposure, and Food Security in the Highlands of Ethiopia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(3), pages 599-611.
    17. Phoebe Koundouri & Céline Nauges & Vangelis Tzouvelekas, 2006. "Technology Adoption under Production Uncertainty: Theory and Application to Irrigation Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(3), pages 657-670.
    18. Antle, John M, 1983. "Testing the Stochastic Structure of Production: A Flexible Moment-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 192-201, July.
    19. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, June.
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    Cited by:

    1. 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.
    2. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    3. 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.
    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. Yu, Tian, 2011. "Three essays on weather and crop yield," ISU General Staff Papers 201101010800002976, Iowa State University, Department of Economics.
    6. 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.
    7. 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.
    8. 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.
    9. 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.

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