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Estimating Farm-Level Yield Distributions For Corn And Soybeans In Illinois

Listed author(s):
  • Zanini, Fabio C.
  • Irwin, Scott H.
  • Schnitkey, Gary D.
  • Sherrick, Bruce J.

Many yield modeling approaches have been developed in attempts to provide accurate characterizations of farm-level yield distributions due to the importance of yield uncertainty in crop insurance design and rating, and for managing farm-level risk. Competing existing models of crop yields accommodate varying skewness, kurtosis, and other departures from normality including features such as multiple modes. Recently, the received view of crop yield modeling has been challenged by Just and Weninger who indicate that there is insufficient evidence to reject normality given data limitations and potential methodological shortcomings in controlling for deterministic components (trend) in yields. They point out that past empirical efforts to estimate and validate specific-farm distributional characterizations have been severely hampered by data limitations. As a result, they argue in favor of normality as an appropriate parameterization of crop yields. This paper investigates alternate representations of farm-level crop yield distributions using a unique data set from the University of Illinois Endowment farms, containing same-site yield observations for a relatively long period of time, and under conditions that closely mirror actual farm conditions in Illinois. Results from alternate econometric model specifications controlling for trend effects suggest that a linear trend provides an adequate representation of crop yields at the farm level during the period covered by the estimations. Specification tests based on a linear-trend model suggest significant heteroskedasticity is present in only a few farms, and that the residuals are white noise. With these data, Jarque-Bera normality test results indicate that normality of detrended yield residuals is rejected by a far greater number of fields than would be explained due to randomness. Thus, to further clarify the issue of yield distribution characterizations, more complete goodness-of-fit measures are compared across a larger set of candidate distributions. The results indicate that the Weibull distribution consistently ranks better than the normal distribution both in fields where normality is rejected and in fields where normality is not rejected. The results highlight the fact that failing to reject normality is not the same as identifying normality as a "best" parameterization, and provide guidance for progressing toward better representations of farm-level crop yields.

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Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2000 Annual meeting, July 30-August 2, Tampa, FL with number 21720.

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Date of creation: 2000
Handle: RePEc:ags:aaea00:21720
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  1. Goodwin, Barry K., 1994. "Premium Rate Determination In The Federal Crop Insurance Program: What Do Averages Have To Say About Risk?," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(02), 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. Deb, Partha & Sefton, Martin, 1996. "The distribution of a Lagrange multiplier test of normality," Economics Letters, Elsevier, vol. 51(2), pages 123-130, May.
  4. Urzua, Carlos M., 1996. "On the correct use of omnibus tests for normality," Economics Letters, Elsevier, vol. 53(3), pages 247-251, December.
  5. 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.
  6. Marra, Michele C. & Schurle, Bryan W., 1994. "Kansas Wheat Yield Risk Measures And Aggregation: A Meta- Analysis Approach," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 19(01), July.
  7. Steven D. Hanson & Robert J. Myers & J. Roy Black, 1998. "The Effects of Crop Yield Insurance Designs on Farmer Participation and Welfare," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(4), pages 806-820.
  8. 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.
  9. Pease, James W., 1992. "A Comparison of Subjective and Historical Crop Yield Probability Distributions," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 24(02), pages 23-32, December.
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