Hostname: page-component-848d4c4894-sjtt6 Total loading time: 0 Render date: 2024-06-24T14:27:03.232Z Has data issue: false hasContentIssue false
  • English
  • Français

Probability Distributions of Crop Prices, Yields, and Gross Revenue

Published online by Cambridge University Press:  10 May 2017

Bernard V. Tew  and
Donald W. Reid
Bernard V. Tew
Affiliation:
University of Kentucky
Donald W. Reid
Affiliation:
University of Georgia
Get access

Abstract

This study shows that the price-yield correlation is a major influence in determining the skewness of revenue. Therefore, normality for revenue may not be rejected even if the price and/or yield distributions are significantly skewed. Analysis of cotton revenue for Mississippi shows that this can be the case empirically when the correlation between price and yield is moderately negative and the relative variability of yield and price is not too high. Hence, for crops produced in their major production regions where negative correlations between prices and yields are the greatest, revenue distributions may have a greater tendency toward normal.

Type
Articles
Information
Northeastern Journal of Agricultural and Resource Economics , Volume 17 , Issue 2 , October 1988 , pp. 118 - 124
Copyright
Copyright © 1988 Northeastern Agricultural and Resource Economics Association 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

Senior authorship is equally shared.

References

Alexander, Vickie J., Musser, Wesley N., and Mason, George. “Futures Markets and Firm Decisions Under Price, Production, and Financial Uncertainty.” Southern Journal of Agricultural Economics 18 (1986):39–50.Google Scholar
Boggess, W. G., Lynne, G. D., Jones, J. W., and Swaney, D. P. “Risk-Return Assessment of Irrigation Decisions in Humid Regions.” Southern Journal of Agricultural Economics 15 (1983):135–144.Google Scholar
Bohrnstedt, George W. and Goldberger, Arthur S. “On the Exact Covariance of Products of Random Variables.” Journal of the American Statistics Association 64 (1969):1439–1442.Google Scholar
Buccola, Steven T. “Testing for Nonnormality in Farm Net Returns.” American Journal of Agricultural Economics 68 (1986):334–343.Google Scholar
Burt, O. R., and Finley, R. M. “Statistical Analysis of Identities in Random Variables.” American Journal of Agricultural Economics 50 (1968):734–744.Google Scholar
Collender, Robert N. and Zilberman, David. “Land Allocation Under Uncertainty for Alternative Specifications of Return Distributions.” American Journal of Agricultural Economics 67 (1985):779–786.Google Scholar
Day, R. H. “Distributions of Field Crop Yields.” American Journal of Agricultural Economics 47 (1965):713–741.Google Scholar
Gallagher, Paul. “U.S. Corn Yield Capacity and Probability: Estimation and Forecasting With Nonsymmetric Disturbances.” North Central Journal of Agricultural Economics 8 (1986):109–122.Google Scholar
Goodman, Leo A. “On Exact Variance of Products.” Journal of the American Statistics Association 55 (1960):708–713.Google Scholar
Grant, Dwight. “Theory of the Firm with Joint Price and Output Risk and a Forward Market.” American Journal of Agricultural Economics 67 (1985):630–635.Google Scholar
Grissom, Perrin H. “Sources and Rates of Nitrogen.” Mississippi State College Agricultural Experiment Station Information Sheet #575. February 1958.Google Scholar
Grissom, Perrin H. and Spurgeon, W I. “Fertility Practices for Cotton and Corn in the Yazoo-Mississippi Delta.” Mississippi State College Agricultural Experiment Station Bulletin 614, April 1961.Google Scholar
Haldane, J. B. S. “Moments of the Distributions of Powers and Products of Normal Variates.” Biometrika 32 (1942):226–242.Google Scholar
Harris, Thomas R. and Mapp, Harry P. “A Stochastic Dominance Comparison of Water-Conserving Irrigation Strategies.” American Journal of Agricultural Economics 68 (1986):298–305.Google Scholar
Klemme, Richard M. “A Stochastic Dominance Comparison of Reduced Tillage Systems in Corn and Soybean Production Under Risk.” American Journal of Agricultural Economics 6 (1985):550–557.Google Scholar
Pearson, E. S. “The Distribution of Frequency Constants in Small Sample From Nonnormal Symmetrical and Skew Populations.” Biometrika 20A (1928):356.Google Scholar
Pearson, E. S. “A Further Development of Tests for Normality.” Biometrika 22 (1930-31):237–249.Google Scholar
Shapiro, S. S. and Wilk, M. B. “A Comparative Study of Various Tests for Normality.” Invited paper, American Statistics Association Annual Meeting, Chicago, Ill., December 1964.Google Scholar
Shapiro, S. S. and Wilk, M. B. “An Analysis of Variance Test for Normality (Complete Samples).” Biometrika 52 (1965):591–611.Google Scholar
Tew, Bernard V. and Boggess, William G. “Risk-Return Assessment of Irrigation Decisions in Humid Regions: An Extension.” Southern Journal of Agricultural Economics 16(2) (1984):159–160.Google Scholar
U.S. Department of Agriculture. Agricultural Statistics, various issues. United States Gov. Printing Office, Washington, D.C.Google Scholar
Yassour, Joseph, Zilberman, David, and Rausser, Gordon C. “Optimum Choices Among Alternative Technologies With Stochastic Yield.” American Journal of Agricultural Economics 63 (1981):718–723.Google Scholar