Interfacing Biology and Systems Analysis in Pest Management

  • Wayne M. Getz
Conference paper
Part of the NATO ASI Series book series (volume 11)


Mathematical techniques for analysing population processes and designing pest management programs can be loosely divided into two broad categories: agroecosystem analysis, and statistical decision theory. Here the relative strengths and weaknesses of these two approaches are presented. Methodologies are discussed for using systems analysis to design pest management programs and for developing computer software to implement these programs. Although the material presented here deals with the general problem of interfacing biology and mathematics in the context of pest management, the discussions and examples focus to some extent on questions relating to fruit fly management.


Pest Management Gravid Female Pest Population Pest Management Program Phenology Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Auslander, D. M., G. F. Oster and C. B. Huffaker, 1974. Dynamics of interacting populations. J. Franklin Inst. 297: 345–376.CrossRefGoogle Scholar
  2. Berger, J. O., 1980. Statistical Decision Theory, Springer-Verlag, New York.Google Scholar
  3. Brillinger, D. R., J. Guckenheimer, P. Guttsop and G. Oster, 1980. Empirical modelling of population time series data: the case of age and density dependent vital rates. In: G. F. Oster (ed.), Some Mathematical Questions in the Life Sciences, Am. Math. Soc., Providence, Rhode Island.Google Scholar
  4. Debouzie, D. and J. Thioulouse, 1986. Statistics to find spatial and temporal structures in populations. This volume.Google Scholar
  5. Fletcher, B. S., 1986. Some issues in fruit fly ecology. This volume.Google Scholar
  6. Farnsworth, R. L., 1986. An economic model to measure costs and benefits of eradication. This volume.Google Scholar
  7. Gashnig, J., 1982. PROSPECTOR: an expert system for mineral exploration. In: D. Michie (ed.), Introductory Readings in Expert Systems, pp. 47–64, Garden Breach, New York.Google Scholar
  8. Getz, W. M. and A. P. Gutierrez, 1982. A perspective on systems analysis in crop production and insect pest management. Ann. Rev. Entomol. 27: 447-466.Google Scholar
  9. Getz, W. M., E. R. Lichtenberg and M. E. Hochberg, unpublished manuscript. Spray schedule decision models and the economics of ricefield mosquito control.Google Scholar
  10. Getz, W. M. and L. McGrail, 1981. Computer Aids for CP/IPM in Almonds. AG Comp Bulletin 2: 53–69.Google Scholar
  11. Gutierrez, A. P., J. E. DeVay, G. S. Pullman and G. E. Friebertshauser, 1983. A model of verticillium wilt in relation to cotton growth and development. Phytopathology 73: 89–95.CrossRefGoogle Scholar
  12. Hacker, C. S., D. W. Scott and J. R. Thompson, 1973. Time series analysis of mosquito population data. J. Med. Ent. 10: 533-543.Google Scholar
  13. Haniotakis, G. E., 1986. The role of sex pheromones in the contol of the olive fruit fly, Dacus oleae: present status—prospects. This volume.Google Scholar
  14. Hochberg, M. E., J. Pickering and W. M. Getz, in press. Evaluation of phenology models using field data: case study for the pea aphid Acyrthosiphon pisum Harris, and the blue alfalfa aphid Acyrthosiphon kondoi Shinji (Homoptera: Aphidoidea). J. Environ. Ent.Google Scholar
  15. Hooper, G.M.S., 1986. Some observations on the competitiveness of sterilized mediterranean fruit fly. This volume.Google Scholar
  16. Hooper, G.M.S., 1986. Some observations on the competitiveness of sterilized mediterranean fruit fly. This volume.Google Scholar
  17. Levins, R. and M. Wilson, 1980. Ecological theory and pest management. Ann. Rev. Entomol. 25: 287-308.Google Scholar
  18. Mangel, M., 1985. Decision and Control in Uncertain Resource Systems, Academic Press, Orlando, Florida.Google Scholar
  19. Mangel, M., R. E. Plant and J. R. Carey, 1984. Rapid delimiting of pest infestations: a casestudy of the Mediterranean fruit fly. J. Appl. Ecol. 21: 563-579.Google Scholar
  20. Mangel, M., S. E. Stefanou and J. E. Wilen, 1985. Modeling Lygus hesperus injury to cotton yields. J. Econ. Ent.Google Scholar
  21. Michalski, E. S., J. H. Davis, V. S. Bisht and J. B. Sinclair, 1983. A computer-based advisory system for diagnosing soybean diseases in Illinois. Plant Disease 67: 459–463.CrossRefGoogle Scholar
  22. Norton, G. A., 1986. Feasibility assessment of pest management. This volume.Google Scholar
  23. Plant, R. E., 1986. The sterile insect technique: a theoretical perspective. This volume.Google Scholar
  24. Plant, R. E. and M. Mangel, 1985. Modeling and simulation in agricultural pest management. Unpublished manuscript.Google Scholar
  25. Ruesink, W. G., 1976. Status of the systems approach to pest management. Ann. Rev. Entomol. 21: 27-44.Google Scholar
  26. Shortcliffe, E. H., 1976. Computer-based Medical Consultation: Mycin. American Elsevier, New York.Google Scholar
  27. Starfield, A. M. and A. L. Bleloch, 1983. Expert systems: an approach to problems in ecological management that are difficult to quantify. J. Environ. Mgmt. 16: 261-268.Google Scholar
  28. Starfield, A. M. and A. L. Bleloch, 1986. Building models for wildlife management. Macmillan, New York.Google Scholar
  29. Stefanou, S. E., M. Mangel and J. E. Wilen, in press. Information in agricultural pest control. J. Agrie. Econ.Google Scholar
  30. Stone, N., 1984. Analysis of the Pest Management of Pink Bollworm, PectinophoraGoogle Scholar
  31. gossypiella (Saunders) in Southwestern Desert Cotton, Gossypium hirsutum L. Ph.D. Thesis, Univ. of Calif., Berkeley.Google Scholar
  32. Taylor, L. R., 1971. Aggregation as a species characteristic. In: C. P. Patii, E. C. Pielou and W. E. Waters (eds.), Statistical Ecology, vol. 1, pp. 357–397, Pennsylvania State University Press.Google Scholar
  33. Van den Driessche, P., 1986. Some analytical models for biotechnical methods of pest control. This volume.Google Scholar
  34. Wagner, T. L., H. Wu, P. S. Sharpe, R. M. Schoolfield and R. W. Coulsen, 1984. Modeling insect development rates: a literature review and application of a biophysical model. Ann. Entomol. Soc. Am. 77: 208–225.Google Scholar
  35. Williams, D. W., H. L. Andris, R. H. Beede, D. A. Luvisi, M.V.K. Norton and L. E. Williams, 1985. Validation of a model for the growth and development of the Thompson Seedless grapevine. II. Phenology. Am. J. Enol. Vitic. 36: 283–289.Google Scholar
  36. Wilson, L. T., D. Gonzalez, T. F. Leigh, V. Maggi, C. Foristiere and P. Goodell, 1983. Within-plant distribution of spider mites (Acari: Tetranychidae) on cotton: developing an implementable monitoring program. Ens. Ent. 12: 128–134.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • Wayne M. Getz
    • 1
  1. 1.Division of Biological ControlUniversity of CaliforniaBerkeleyUSA

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