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Estimating Insecticide Application Frequencies: A Comparison of Geometric and Other Count Data Models

Published online by Cambridge University Press:  28 April 2015

Bryan J. Hubbell
Bryan J. Hubbell*
Affiliation:
Department of Agricultural and Applied Economics, University of Georgia
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Abstract

The number of insecticide applications made by an apple grower to control an insect infestation is modeled as a geometric random variable. Insecticide efficacy, rate per application, month of treatment, and method of application all have significant impacts on the expected number of applications. The number of applications to control a given insect population is dependent on the probability of achieving successful control with a given application. Results suggest that northeastern growers have the highest and mid-Atlantic growers the lowest probability of controlling an infestation with a given application. Results also indicate that scales require the least and moths the most number of applications. Growers are not responsive to per unit insecticide prices, but respond negatively to insecticide toxicity, supporting findings from previous pesticide demand analyses.

Keywords

applescount datageometricinsect controlpesticides
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 29 , Issue 2 , December 1997 , pp. 225 - 242
Copyright
Copyright © Southern Agricultural Economics Association 1998

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References

Barmby, T., and Doornik, J.. “Modeling Trip Frequency as a Poisson Variable.” J. Transport Econ. and Policy 23(1989):309-15.Google Scholar
Beach, E.D., and Carlson, G.A.. “A Hedonic Analysis of Herbicides: Do User Safety and Water Quality Matter?” Amer. J. Agr. Econ. 75(1993):612-23.CrossRefGoogle Scholar
Carlson, G.A. “Long-Run Productivity of Insecticides.” Amer. J. Agr. Econ. 59(1977):543-48.CrossRefGoogle Scholar
Creel, M.D., and Loomis, J.B.. “Theoretical and Empirical Advantages of Truncated Count Data Estimators for Analysis of Deer Hunting in California.” Amer. J. Agr. Econ. 72(1990):434-41.CrossRefGoogle Scholar
Crop Protection Chemicals Reference. New York: Chemical and Pharmaceutical Press, 1991.Google Scholar
DPRA, Inc. AGCHEMPRICE 1991. Manhattan KS: DPRA, Inc., 1991.Google Scholar
Fernandez-Cornejo, J. “Short- and Long-Run Demand and Substitution of Agricultural Inputs.” Northeast. J. Agr. and Re sour. Econ. 21(1992):36–49.CrossRefGoogle Scholar
Gomez, I.A., and Ozuna, T. Jr. “Testing for Over-dispersion in Truncated Count Data Recreation Demand Functions.” J. Environ. Manage. 37(1993):117-25.CrossRefGoogle Scholar
Grogger, J.T., and Carson, R.T.. “Models for Truncated Counts.” J. Appl. Econometrics 6(1991):225-38.CrossRefGoogle Scholar
Hausman, J., Hall, B.H., and Griliches, Z.. “Econometric Models for Count Data with an Application to the Patents-R&D Relationship.” Econometrica 52(1984):909-37.CrossRefGoogle Scholar
Headley, J.C. “Defining the Economic Threshold.” In Pest Control Strategies for the Future, Agricultural Board, pp. 100-08. Washington DC: National Academy Press, 1972.Google Scholar
Higley, L.G., and Pedigo, L.P.. “Economic Injury Level Concepts and Their Use in Sustaining Environmental Quality.” Agr., Ecosystems, and Environ. 46(1993):233-43.CrossRefGoogle Scholar
Hubbell, B.J. “A Discrete/Continuous Model of Insecticide Active Ingredient Selection and Use: Decisions by U.S. Apple Growers.” Unpublished Ph.D. dissertation, North Carolina State University, 1995.Google Scholar
Lee, J. “The Demand for Varied Diet with Econometric Models for Count Data.” Amer. J. Agr. Econ. 69(1987):687-92.CrossRefGoogle Scholar
Marra, M.C., Gould, T.D., and Porter, G.A.. “A Computable Economic Threshold Model for Weeds in Field Crops with Multiple Pests, Quality Effects, and an Uncertain Spraying Period Length.” Northeast. J. Agr. and Resour. Econ. 18(1989):12–17.CrossRefGoogle Scholar
Mendenhall, W., Wackerly, D.D., and Scheaffer, R.L.. Mathematical Statistics with Applications. Boston: PWS-Kent Publishing Co., 1990.Google Scholar
Miranowski, J.A. “The Demand for Agricultural Crop Chemicals Under Alternative Farm Program and Pollution Control Solutions.” Unpublished Ph.D. dissertation, Harvard University, 1975.Google Scholar
Moffitt, L.J. “Incorporating Environmental Considerations in Pest Control Advice for Farmers.” Amer. J. Agr. Econ. 70(1988):628-34.CrossRefGoogle Scholar
Royal Society of Chemistry. The Agrochemicals Handbook. Surrey UK: Unwin Brothers, Ltd., 1983.Google Scholar
Smith, V.K., Liu, J.L., and Palmquist, R.B.. “Marine Pollution and Sport Fishing Quality.” Econ. Letters 42(1993):11–16.CrossRefGoogle Scholar
U.S. Department of Agriculture, National Agricultural Statistics Service. “1991 Fruit and Nut Chemical Use Survey.” USDA/NASS, Washington DC, 1992.Google Scholar
Virginia and West Virginia Cooperative Extension. 1994 Spray Bulletin for Commercial Tree Fruit Growers. Blacksburg VA: Virginia Polytechnic Institute and State University Press, 1993.Google Scholar
Washington State University Cooperative Extension. 1994 Crop Protection Guide for Tree Fruits in Washington. Pullman WA: Washington State University Bulletin Office, 1994.Google Scholar
Wauchope, R.D., Buttler, T.M., Hornsby, A.G., Augustijn-Beckers, P.W.M., and Burt, J.P.. “The SCS/ARS/CES Pesticide Properties Database for Environmental Decision Making.” Rev. Environ. Contamination and Toxicology 123(1992):1–156.Google ScholarPubMed
Wiswesser, W.J., ed. The Pesticide Index. Entomology Society of America, 1976.CrossRefGoogle Scholar
Worthing, C.R., ed. The Pesticide Manual. London: The British Crop Protection Council, 1983.Google Scholar
Yen, S.T., and Adamowicz, W.L.. “Statistical Properties of Welfare Measures from Count-Data Models of Recreation Demand.” Rev. Agr. Econ. 15(1993):205-15.CrossRefGoogle Scholar