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

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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Paper provided by Center for Agricultural and Rural Development (CARD) at Iowa State University in its series Center for Agricultural and Rural Development (CARD) Publications with number 10-wp511.

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Date of creation: Aug 2010
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Handle: RePEc:ias:cpaper:10-wp511
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  1. 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.
  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. Bruce A. Babcock & Joseph A. Herriges, 1994. "Input Demand Under Yield and Revenue Insurance," Center for Agricultural and Rural Development (CARD) Publications 94-wp127, Center for Agricultural and Rural Development (CARD) at Iowa State University.
  4. 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.
  5. 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.
  6. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  7. 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.
  8. 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.
  9. 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.
  10. David A. Hennessy, 2004. "On Monoculture and the Structure of Crop Rotations," Center for Agricultural and Rural Development (CARD) Publications 04-wp369, Center for Agricultural and Rural Development (CARD) at Iowa State University.
  11. 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.
  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 179-94, March-Apr.
  13. 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.
  14. 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.
  15. repec:cup:cbooks:9780521671736 is not listed on IDEAS
  16. Campbell R. Harvey & Akhtar Siddique, 2000. "Conditional Skewness in Asset Pricing Tests," Journal of Finance, American Finance Association, vol. 55(3), pages 1263-1295, 06.
  17. repec:cup:cbooks:9780521855716 is not listed on IDEAS
  18. Koop, Gary M & Poirier, Dale J & Tobias, Justin, 2007. "Bayesian Econometric Methods," Staff General Research Papers 12722, Iowa State University, Department of Economics.
  19. Mitchell, Paul D., 2004. "Nutrient Best Management Practice Insurance and Farmer Perceptions of Adoption Risk," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 36(03), December.
  20. 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.
  21. Phoebe Koundouri & Nikolaos Kourogenis, . "On the Distribution of Crop Yields: Does the Central Limit Theorem Apply?," DEOS Working Papers 1007, Athens University of Economics and Business.
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