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Temperature Probabilities and the Bayesian No Data' Problem

Published online by Cambridge University Press:  28 April 2015

Thomas L. Sporleder*
Affiliation:
Texas A&M University

Extract

Weather constitutes an exogenous factor in agriculture which may have considerable influence on production and marketing. For a particular commodity, weather may influence quantity produced, quality of the commodity marketed, and consequently influence prices received (or paid) by various firms associated with that commodity system. Although some has been written about the influence of weather on agriculture, little economic analysis is available which attempts to integrate estimated probabilities of some weather phenomenon (a notable exception is McQuigg and Doll). This latter situation may be attributed, at least partially, to the complexities of such an integrative analysis.

Type
Research Article
Copyright
Copyright © Southern Agricultural Economics Association 1972

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References

[1]Bullock, J. Bruce and Logan, S. H., “A Model for Decision Making Under Uncertainty,” Agricultural Economics Research 21: 109115, Oct. 1969.Google Scholar
[2]Connolly, C. C, “Marginal Returns of Alternative Freeze Control Systems,” proceedings of the annual conference of the Texas Agricultural Experiment Station, Jan. 7-9, 1970, Texas A&M University.Google Scholar
[3]Court, Arnold, ‘Temperature Extremes in the United States,” Geographical Review 43: 3949, 1953.CrossRefGoogle Scholar
[4]Gumbel, E. J., Statistics of Extremes, New York: Columbia University Press, 1958.CrossRefGoogle Scholar
[5]Hahn, Gerald J., and Shapiro, Samuel S., Statistical Models in Engineering, Englewood Cliffs, N.J.: Prentice Hall, Inc., 1958.Google Scholar
[6]Hildreth, R. J. and Thomas, G. W., “Farming and Ranching Risk as Influenced by Rainfall,” Texas Agr. Exp. Sta. Bull. MP-154, Jan. 1956.Google Scholar
[7]Knight, F.H., Risk, Uncertainty, and Profit, New York: Sentry, 1957.Google Scholar
[8]Lieblein, J., “A New Method of Analyzing Extreme-Value Data,” Technical Note 3053, National Advisory Committee for Aeronautics, Washington, D. C, 1954.Google Scholar
[9]McQuigg, James D. and Thompson, R. G., “Economic Value of Improved Methods of Translating Weather Information in Operational Terms,” Monthly Weather Review 94: 8387, Feb. 1966.2.3.CO;2>CrossRefGoogle Scholar
[10]McQuigg, James D.Foreseeing the Future,” Meteorological Monographs 6: 181188, July 1965.Google Scholar
[11]McQuigg, James D. and Doll, John P., “Weather Variability and Economic Analysis,” Missouri Agr. Exp. Sta. Bull. 777, June, 1961.Google Scholar
[12]Orton, Robertet al., “Climatic Guide: The Lower Rio Grande Valley of Texas,” Texas Agr. Exp. Sta. Bull. MP-841, Sept. 1967.Google Scholar
[13]Stalling, J. L., “Weather IndexesJournal of Farm Economics 42: 180186, Feb. 1960.CrossRefGoogle Scholar
[14]Sporleder, Thomas L., “TEMPROB: A FORTRAN IV PROGRAM For Calculating Temperature Probabilities From Extreme Minimum Temperature Data,” Market Research and Development Center Technical Report MRC 70-3, Texas A&M University, July 1970.Google Scholar
[15]Texas Agricultural Extension Service, “Rio Grande Valley Citrus Budget Bearing Grove, July 1971,” Unpublished data.Google Scholar
[16]Vestal, C. K.Fitting of Climatological Extreme Value Data,” Climatological Services Memorandum No. 89, United States Weather Bureau, Ft. Worth, Texas, August 1961.Google Scholar
[17]Zusman, Pinhas and Amiad, Amotz, “Simulation: A Tool for Farm Planning Under Conditions of Weather Uncertainty,” Journal of Farm Economics 47: 574594, August 1965.CrossRefGoogle Scholar