Hostname: page-component-5c6d5d7d68-thh2z Total loading time: 0 Render date: 2024-08-21T23:51:52.020Z Has data issue: false hasContentIssue false
  • English
  • Français

Acreage Planting Decision Analysis of South Carolina Tomatoes: Nerlovian Versus Just Risk Model

Published online by Cambridge University Press:  05 September 2016

T. T. Fu ,
S. M. Fletcher  and
J. E. Epperson
T. T. Fu
Affiliation:
Division of Agricultural Economics, University of Georgia
S. M. Fletcher
Affiliation:
Division of Agricultural Economics, University of Georgia
J. E. Epperson
Affiliation:
Division of Agricultural Economics, University of Georgia
Get access Rights & Permissions [Opens in a new window]

Abstract

Factors which explain supply response behavior of South Carolina tomato growers were determined. Two well known supply response models were used for comparison: the Nerlovian structural model and the Just risk model. The Just risk model reflected the significance of the risk effect in both stable and unstable periods. An evaluation of forecasting power between the two models was indeterminate. Growers are apparently willing to invest in more information with increased market instability because growers were influenced by the Florida winter price of tomatoes in planting decisions during the period of instability.

Keywords

supply responseuncertaintyinformationtomatoes
Type
Submitted Articles
Information
Journal of Agricultural and Applied Economics , Volume 18 , Issue 2 , December 1986 , pp. 65 - 72
Copyright
Copyright © Southern Agricultural Economics Association 1986

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.)

References

Behrman, J. Supply Response in Underdeveloped Agriculture, Amsterdam: North-Holland Publishing Company, 1968.Google Scholar
Dhrymes, P. J.. Distributed Lags: Problems of Estimation and Formulation, San Francisco: Holden-Day, 1971.Google Scholar
Eckstein, Z. “A Rational Expectations Model of Agricultural Supply.” J. Polit. Econ., 92(1984):1–19.Google Scholar
Florida Crop and Livestock Reporting Service. Florida Agricultural Statistics: Vegetable Summary, Orlando, 1971–1983.Google Scholar
Just, R. E..“An Investigation of the Importance of Risk in Farmers' Decisions.” Amer. J. Agr. Econ., 56,1(1974):14–25.Google Scholar
Klein, L. R..“The Estimation of Distributed Lags.” Econometrica, 26(1958):553–565.Google Scholar
Lin, W. “Measuring Aggregate Supply Response Under Instability.” Amer. J. Agr. Econ., 59,5(1977):903–907.CrossRefGoogle Scholar
Pope, R. D..“Supply Response and the Dispersion of Price Expectations.” Amer. J. Agr. Econ., 63,2(1981):161–163.Google Scholar
Rose, G. N.. Tomato Production in Florida: A Historic Series. Econ. Report 48, Food and Res. Econ. Dept., University of Florida, June, 1973.Google Scholar
Schmitz, A., Shalit, H., and Turnovsky, S. J.. “Producer Welfare and the Preference for Price Stability.” Amer. J. Agr. Econ., 63,2(1981):157–160.Google Scholar
Tomek, W. and Robinson, K.. Agricultural Product Prices. Ithaca, New York: Cornell University Press, 1981.Google Scholar
Traill, W. B..“Risk Variables in Econometric Supply Response Models.” J. Agr. Econ., 29,1(1978):53–61.Google Scholar
U.S. Department of Agriculture, Statistical Reporting Service. Fresh Market Vegetable Prices, Washington D.C.: Stat. Bull. No. 318, June, 1962.Google Scholar
U.S. Department of Agriculture, Statistical Reporting Service. Vegetables: Annual Summary, Washington, D.C., 1952–1983.Google Scholar
U.S. Department of Agriculture, Agricultural Marketing Service. Fresh Fruit and Vegetable Shipments by Commodities, States, and Months, Washington D.C.: FVUS-7, 1953–1983.Google Scholar
Wells, G. “Forecasting South Carolina Tomato Prices Prior to Planting.” So. J. Agr. Econ., 12,1(1980):109–118.Google Scholar
Young, D. L..“Risk Preferences of Agricultural Producers: Their Use in Extension and Research.” Amer. J. Agr. Econ., 61,5(1979):1,063–1,070.Google Scholar