Estimating Farm-Level Yield Distributions For Corn And Soybeans In Illinois
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.
|Date of creation:||2000|
|Contact details of provider:|| Postal: 555 East Wells Street, Suite 1100, Milwaukee, Wisconsin 53202|
Phone: (414) 918-3190
Fax: (414) 276-3349
Web page: http://www.aaea.org
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- Weninger, Quinn & Just, Richard E., 1999. "Are Crop Yields Normally Distributed?," Staff General Research Papers Archive 5064, Iowa State University, Department of Economics.
- Deb, Partha & Sefton, Martin, 1996. "The distribution of a Lagrange multiplier test of normality," Economics Letters, Elsevier, vol. 51(2), pages 123-130, May.
- Urzua, Carlos M., 1996. "On the correct use of omnibus tests for normality," Economics Letters, Elsevier, vol. 53(3), pages 247-251, December.
- 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.
- 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.
- 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.
- 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.
- 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. Full references (including those not matched with items on IDEAS)
When requesting a correction, please mention this item's handle: RePEc:ags:aaea00:21720. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.