Hostname: page-component-7bb8b95d7b-w7rtg Total loading time: 0 Render date: 2024-10-05T13:59:03.176Z Has data issue: false hasContentIssue false
  • English
  • Français

A Bioeconomic Simulation Approach to Multi-Species Insect Management

Published online by Cambridge University Press:  28 April 2015

William G. Boggess ,
Dino J. Cardelli  and
C. S. Barfield
William G. Boggess
Affiliation:
University of Florida
Dino J. Cardelli
Affiliation:
Tropicana Inc., Bradenton, Florida
C. S. Barfield
Affiliation:
University of Florida
Get access Rights & Permissions [Opens in a new window]

Abstract

Classical approaches to the economics of pest management have focused almost exclusively on single-species models. This study develops and implements a methodology with which to evaluate multi-species, non-stochastic, managerial decisions subject to stochastic elements of the plant-insect system. Multi-species insect management strategies (combinations of scouting interval, threshold value, and choice of pesticide) are analyzed using a physiological mechanistic soybean plant growth model coupled to three insect population dynamics models. Preliminary results indicate that net returns are maximized and variance is reduced with lower thresholds and more frequent scouting than current recommendations.

Keywords

pest managementmulti-speciessimulationstochastic dominance
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 17 , Issue 2 , December 1985 , pp. 43 - 56
Copyright
Copyright © Southern Agricultural Economics Association 1985

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

Anderson, Jock R., Dillion, John L., and Hardaker, Brian. Agricultural Decision Analysis, Iowa State University Press, Ames, Iowa, 1977.Google Scholar
Boote, K. J.Unpublished data, Agronomy Department, University of Florida.Google Scholar
Brown, L. G., McClendon, R. W., and Jones, J. W.. “Cotton and Insect Management Simulation Model.” Chapter 17 in Cotton Insect Management with Special References to the Boll Weevil, Edited by Ridgway, R. L., Lloyd, R. P. and Corss, W. H., USDA, ARS, Agricultural Handbook 589, pp. 437–480, 1983Google Scholar
Carlson, G. A. “A Decision Theoretic Approach to Crop Disease Prediction and Control.” Amer. J. Agr. Econ., 52(1970):216–223.CrossRefGoogle Scholar
Gutierrez, A. P., Leigh, T. F., Wang, Y., and Cave, R. D.. “An Analysis of Cotton Production in California: Lygus Hesperus Injury-An Evaluation.” Can. Entomol., 109(1977):1,375–1,386.CrossRefGoogle Scholar
Hall, D. C. and Norgaard, R. B.. “On the Timing and Application of Pesticides.” Amer. J. Agr. Econ., 55(1973):198–201.CrossRefGoogle Scholar
Herzog, D. C, Newsom, L. D., and McPherson, R. M.. Unpublished data, 1975.Google Scholar
Hewitt, Tim. “Budgets Generated for the Farm System Lab.” Dept. of Food and Resource Econ., University of Florida, 1983.Google Scholar
Hueth, D. and Regev, U.. “Optimal Agricultural Pest Management with Increasing Pest Resistance.” Amer. J. Agr. Econ., 56(1974):543–550.CrossRefGoogle Scholar
Johnson, F. A., Herzog, D. C., and Sprenkel, R. K.. “Soybean Insect Control.” Extension Entomology Report *58, University of Florida, Gainesville, May, 1984.Google Scholar
Kiritani, K. N., Hokyo, N., and Kiroma, K.. “Survival Rate and Reproductivity of Adult Southern Green Stinkbug, Nezara viridula, in the Cage.” Jap. J. Appl. Entomol., 7(1963):113–118.CrossRefGoogle Scholar
Marsolan, N. F. and Rudd, W. G.. “Modeling and Optimal Control of Insect Pest Populations.” Math Biosciences, 30(1976):231–244.CrossRefGoogle Scholar
Moscarodi, F.Effect of Soybean Crop Phenology on Development, Leaf Consumption, and Oviposition of Anticarsia gemmatalis Hubhner, Ph.D. dissertation, University of Florida, Gainesville, 1979.Google Scholar
Reichelderfer, K. H. and Bender, F. E.. “Application of a Simulation Approach to Evaluating Methods for the Control of Agricultural Pests.” Amer. J. Agr. Econ., 61(1979):258–267.CrossRefGoogle Scholar
Rudd, W. G. “Status of Systems Approach to Pest Management.” Ann. Rev. Entomol., 21(1976):27–44.Google Scholar
Shoemaker, C. A. “Optimization Analysis of the Integration of Biological, Cultural, and Chemical Control of Alfalfa Weevil ( Coleoptera curculionidae).” Env. Entomol., 21(1983):286–295.CrossRefGoogle Scholar
Stinner, R. E., Rabb, R. L., and Bradley, J. R.. “Population Dynamics of Heliothis zea (Boddie) and H. virescens (F.) in North Carolina: A Simulation Model.” Env. Entomol. 3, 1(1974):163–168.CrossRefGoogle Scholar
Talpaz, H., Curry, G. L., Sharpe, P. J., DeMichele, D. W., and Frisbie, R. E..”Optimal Pesticide Application for Controlling the Boll Weevil on Cotton.” Amer. J. Agr. Econ., 60(1978):469–475.CrossRefGoogle Scholar
Watt, K. E. F. “The Use of Mathematics and Computers to Determine Optimal Strategy and Tactics for a Given Insect Pest Control Problem.” Can. Entomol., 96(1964):202–220.CrossRefGoogle Scholar
Wilkerson, G. G., Mishoe, J. W., Jones, J. W., Boggess, W. G., and Swaney, D. P.. “Within-Season Decision making for Pest Control in Soybeans.”Agr. Eng. Dep. Paper No. 834044, University of Florida, 1983a.Google Scholar
Wilkerson, G. G., Mishoe, J. W., Jones, J. W., Stimac, J. L., Swaney, D. P., and Boggess, W. G.. “SICM Florida Soybean Integrated Crop Management Model: Model Description User's Guide.” Agr. Eng. Dep. Rpt. AGE 83-1, University of Florida, November, 1983b.Google Scholar
Wilkerson, G. G., Jones, J. W., Boote, K.J.Ingram, K. T., and Mishoe, J. W.. “Modeling Soybean Growth for Crop Management.” Transac. ASAE; 26, 2(1983c):562–568.CrossRefGoogle Scholar
Wilkerson, G. G., Mishoe, J. W., and Stimac, J. L.. “Modeling Velvetbean Caterpillar Populations for Crop Management.” 1985 (In Review).Google Scholar
Zavaleta, Luis R. and Ruesink, William G.. “Expected Benefits from Non-chemical Methods of Alfalfa Weevil Control.” Amer. J. Agr. Econ., 62(1980):801–805.CrossRefGoogle Scholar