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On Technological Change in Crop Yields

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  • Tolhurst, Tor
  • Ker, Alan P.

Abstract

Technological change in plant research rarely shifts the entire yield distribution upwards as assumed in the agricultural economics literature. Rather, technologies have been targeted at a speci c subpopulation of the yield distribution|for example, drought resistant seeds or so-called racehorse seeds|therefore, it is unlikely technological advancements are equal across subpopulations. In this manuscript we introduce a mixture model of crop yields with an embedded trend function in the component means, which allows di erent rates of technological change in each mixture or subpopulation. By doing so, we can test some inter- esting hypotheses that have been previously untestable. While previous literature assumes an equivalent rate of technological change across subpopulations we reject the null in 84.0%, 82.3%, and 64.0% of the counties for corn, soybean, and wheat respectively. Conversely, with respect to stable subpopulations through time (i.e. climate change) we reject in only 12.0%, 5.4%, and 4.6% of the counties for corn, soybean, and wheat respectively. These results have implications for modelling yields, directing funds regarding plant science research, and explaining the prevalence of heteroscedasticity in yield data.

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Bibliographic Info

Paper provided by Agricultural and Applied Economics Association in its series 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. with number 151269.

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Date of creation: 2013
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Handle: RePEc:ags:aaea13:151269

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Keywords: Crop Production/Industries;

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  1. Ardian Harri & Keith H. Coble & Alan P. Ker & Barry J. Goodwin, 2011. "Relaxing Heteroscedasticity Assumptions in Area-Yield Crop Insurance Rating," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(3), pages 703-713.
  2. Weninger, Quinn & Just, Richard E., 1999. "Are Crop Yields Normally Distributed?," Staff General Research Papers 5064, Iowa State University, Department of Economics.
  3. Featherstone, Allen M. & Kastens, Terry L., 2000. "Non-Parametric And Semi-Parametric Techniques For Modeling And Simulating Correlated, Non-Normal Price And Yield Distributions: Applications To Risk Analysis In Kansas Agriculture," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 32(02), August.
  4. Alan P. Ker & Keith Coble, 2003. "Modeling Conditional Yield Densities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 291-304.
  5. 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.
  6. Joseph Atwood & Saleem Shaik & Myles Watts, 2003. "Are Crop Yields Normally Distributed? A Reexamination," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(4), pages 888-901.
  7. Miller, Edward M, 1977. "Risk, Uncertainty, and Divergence of Opinion," Journal of Finance, American Finance Association, vol. 32(4), pages 1151-68, September.
  8. Robert Finger, 2010. "Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 205-211.
  9. Roger Claassen & Richard E. Just, 2010. "Heterogeneity and Distributional Form of Farm-Level Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(1), pages 144-160.
  10. Zanini, Fabio C. & Sherrick, Bruce J. & Schnitkey, Gary D. & Irwin, Scott H., 2001. "Crop Insurance Valuation Under Alternative Yield Distributions," 2001 Conference, April 23-24, 2001, St. Louis, Missouri 18953, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
  11. Gallagher, Paul W., 1987. "U.S. Soybean Yields: Estimation and Forecasting with Non-Symmetric Disturbances," Staff General Research Papers 10779, Iowa State University, Department of Economics.
  12. Barry K. Goodwin & Alan P. Ker, 1998. "Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 139-153.
  13. Octavio A. Ram�rez & Tanya McDonald, 2006. "Ranking Crop Yield Models: A Comment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(4), pages 1105-1110.
  14. Pease, James W., 1992. "A Comparison Of Subjective And Historical Crop Yield Probability Distributions," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 24(02), December.
  15. Ker, Alan P. & McGowan, Pat, 2000. "Weather-Based Adverse Selection And The U.S. Crop Insurance Program: The Private Insurance Company Perspective," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(02), December.
  16. 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.
  17. 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.
  18. Jerry R. Skees & J. Roy Black & Barry J. Barnett, 1997. "Designing and Rating an Area Yield Crop Insurance Contract," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 430-438.
  19. Bailey Norwood & Matthew C. Roberts & Jayson L. Lusk, 2004. "Ranking Crop Yield Models Using Out-of-Sample Likelihood Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1032-1043.
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