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Chapter 4 - Decision making and economic risk in IPM

Published online by Cambridge University Press:  01 September 2010

Edward B. Radcliffe
Affiliation:
University of Minnesota
William D. Hutchison
Affiliation:
University of Minnesota
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Summary

Understanding and communicating the value of an IPM program or specific IPM tactics in agriculture, forestry and other venues has historically been difficult for a number of reasons (Grieshop et al., 1988; Wearing, 1988; Kogan, 1998; Swinton & Day, 2003). Some of the more common barriers to adoption include a lack of practical sampling/monitoring tools (Wearing, 1988), challenges to fully integrating biologically based tactics (Kogan, 1998; Ehler & Bottrell, 2000), the need for multiple pest–damage relationships for multiple insect pests per crop (Pedigo et al., 1986), changing economic conditions (with or without government subsidy programs) and the fact that multiple human audiences with diverse backgrounds and motivations are on the receiving end of new IPM programs (e.g. Bechinski, 1994; Cuperus & Berberet, 1994; Bacic et al., 2006; Hammond et al., 2006), including known variability in the adoption of new technologies (e.g. Mumford & Norton, 1984; Grieshop et al., 1988; Rogers, 1995). Equally important barriers, however, could be the perceived complexity of IPM compared to current conventional pest approaches (e.g. Bechinski, 1994; Cuperus & Berberet, 1994; Grieshop et al., 1988), or a lack of up-front consultation with targeted audiences prior to the R&D investment for developing IPM programs (Norton et al., 2005; Bacic et al., 2006). Although many of the concepts discussed in this chapter are relevant to IPM audiences in forestry or residential-urban pest management, our focus will primarily be targeted to decision makers in agricultural systems, and primarily arthropod management in crops.

Type
Chapter
Information
Integrated Pest Management
Concepts, Tactics, Strategies and Case Studies
, pp. 33 - 50
Publisher: Cambridge University Press
Print publication year: 2008

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References

Babcock, B. A., Choi, E. K. & Feinerman, E. (1993). Risk and probability premiums for CARA utility functions. Journal of Agricultural and Resource Economics, 18, 17–24.Google Scholar
Bacic, I. L. Z., Bregt, A. K. & Rositer, D. G. (2006). A participatory approach for integrating risk assessment into rural decision-making: a case study in Santa Catarina, Brazil. Agricultural Systems, 87, 229–244.CrossRefGoogle Scholar
Barry, P. (ed.) (1984). Risk Management in Agriculture. Ames, IA: Iowa State University Press.Google Scholar
Bechinski, E. J. (1994). Designing and delivering in-field scouting programs. In Handbook of Sampling Methods for Arthropods in Agriculture, eds. Pedigo, L. P. & Buntin, B. D., pp. 683–706. Boca Raton, FL: CRC Press.Google Scholar
Benbrook, C. M., Sexson, , Wyman, J. A, D. L. et al. (2002). Developing a pesticide risk assessment tool to monitor progress in reducing reliance on high-risk pesticides. American Journal of Potato Research, 79, 183– 200.CrossRefGoogle Scholar
Boehlje, M. D. & Eidman, V. R. (1984). Farm Management. New York: John Wiley.Google Scholar
Burkness, E. C. & Hutchison, W. D. (2008). Implementing reduced-risk IPM in fresh-market cabbage: Improved net returns via scouting and timing of effective management. Journal of Economic Entomology, 101, 461–471.CrossRefGoogle Scholar
Burkness, E. C., Hutchison, W. D., Weinzierl, R. A.et al. (2002). Efficacy and risk efficiency of sweet corn hybrids expressing a Bacillus thuringiensis toxin for lepidopteran pest management in the Midwestern U.S. Crop Protection, 21, 157–169.CrossRefGoogle Scholar
Camerer, C. (2003). Behavioral Game Theory: Experiments in Strategic Interaction. Princeton, NJ: Princeton University Press.Google Scholar
Carlson, G. A., (1970). The decision theoretic approach to crop disease prediction and control. American Journal of Agricultural Economics, 52, 216–223.CrossRefGoogle Scholar
Chavas, J. P. (2004). Risk Analysis in Theory and Practice. New York: Elsevier.Google Scholar
Crowder, D. W., Onstad, D. W., Gray, M. E.et al. (2005). Economic analysis of dynamic management strategies utilizing transgenic corn for control of western corn rootworm (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 98, 961–975.CrossRefGoogle Scholar
Cuperus, G. W. & Berberet, R. C. (1994). Training specialists in sampling procedures. In Handbook of Sampling Methods for Arthropods in Agriculture, eds. Pedigo, L. P. & Buntin, B. D., pp. 669–681. Boca Raton, FL: CRC Press.Google Scholar
Edson, C., Swinton, S., Nugent, J.et al. (2003). Cherry Orchard Floor Management: Opportunities to Improve Profit and Stewardship, MSU Ext. Bull. E-2890. East Lansing, MI: Michigan State University.Google Scholar
Eeckhoudt, L., Gollier, C. & Schlesinger, H. (2005). Economic and Financial Decisions under Risk. Princeton, NJ: Princeton University Press.Google Scholar
Ehler, L. E. & Bottrell, D. G., (2000). The illusion of integrated pest management. Issues in Science and Technology, 16, 61–64.Google Scholar
Ernst, M. & Paulus, M. P. (2005). Neurobiology of decision making: a selective review from neurocognitive and clinical perspective. Biological Psychiatry, 58, 597–604.CrossRefGoogle ScholarPubMed
Evans, M., Hastings, N. & Peacock, B. (2000). Statistical Distributions, 3rd edn. New York: John Wiley.Google Scholar
Feather, P. & Cooper, J. (1995). Voluntary Incentives for Reducing Agricultural Nonpoint Source Water Pollution, Agricultural Information Bulletin No. 716. Washington, DC: US Department of Agriculture, Economic Research Service.Google Scholar
Fecteau, S., Pascual-Leone, , Zald, D. H, A.et al. (2007). Activation of prefrontal cortex by transcranial direct current stimulation reduces appetite for risk during ambiguous decision making. Journal of Neuroscience, 27, 6212–6218.CrossRefGoogle ScholarPubMed
Feder, G. (1979). Pesticides, information, and pest management under uncertainty. American Journal of Agricultural Economics, 62, 97–103.CrossRefGoogle Scholar
Foster, R. E., Tollefson, J. J., Nyrop, J. P. & Hein, G. L. (1986). Value of adult corn rootworm (Coleoptera: Chrysomelidae) population estimates in pest management decision making. Journal of Economic Entomology, 79, 303–310.CrossRefGoogle Scholar
Fox, G., Weersink, A., Sarwar, G., Duff, S. & Deen, B. (1991). Comparative economics of alternative agricultural production systems: a review. Northeastern Journal of Agricultural and Resource Economics, 20, 124–142.Google Scholar
Freund, J. E. (1992). Mathematical Statistics, 5th edn. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Gollier, C. (2001). The Economics of Risk and Time. Cambridge, MA: MIT Press.Google Scholar
Grieshop, J. I., Zalom, F. G. & Miyao, G. (1988). Adoption and diffusion of Integrated Pest Management Innovations in Agriculture. Bulletin of the Entomological Society of America, 34, 77–78.CrossRefGoogle Scholar
Gutierrez, A. P. & Baumgärtner, J. (2007). Modeling the dynamics of tritrophic population interactions, In Perspectives in Ecological Theory and Integrated Pest Management, eds. Kogan, M. & Jepson, P., pp. 301–360. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Hammond, C. M., Luschel, E. C., Boerboom, C. M. & Nowak, P. J. (2006). Adoption of integrated pest management tactics by Wisconsin farmers. Weed Technology, 20, 756–767.CrossRefGoogle Scholar
Hammond, R. B. (1996). Limitations to EILs and thresholds, In Economic Thresholds for Integrated Pest Management, eds. Higley, L. G. & Pedigo, L. P., pp. 58–73. Lincoln, NE: University of Nebraska Press.Google Scholar
Hardaker, J. B., Huirne, B. R. M., Anderson, J. R. & Lien, G. (2004). Coping with Risk in Agriculture, 2nd edn. Wallingford, UK: CABI Publishing.CrossRefGoogle Scholar
Hoverstad, T. R., Gunsolus, J. L., Johnson, G. A. & King, R. P. (2004). Risk-efficiency criteria for evaluating economics of herbicide-based weed management systems in corn. Weed Technology, 18, 687–697.CrossRefGoogle Scholar
Hrubovcak, J., Vasavada, U. & Aldy, J. E. (1999). Green Technologies for a More Sustainable Agriculture, Agricultural Information Bulletin No. 752. Washington, DC: US Department of Agriculture, Economic Research Service.Google Scholar
Hurley, T. M., Mitchell, P. D. & Rice, M. E. (2004). Risk and the value of Bt corn. American Journal of Agricultural Economics, 86, 345–358.CrossRefGoogle Scholar
Hutchins, S. H. (1997). IPM: opportunities and challenges for the private sector. In Radcliffe's IPM World Textbook, eds. Radcliffe, E. B. & Hutchison, W. D.. St. Paul, MN: University of Minnesota. Available at http://ipmworld.umn.edu.Google Scholar
Hutchison, W. D., Burkness, E. C., Carrillo, M. A., Galvan, T. L., Mitchell, P. D. & Hurley, T. M. (2006a). IPM: A Risk Management Framework to Improve Decision-making, Public. No. 08229. St. Paul, MN: University of Minnesota Extension Service.Google Scholar
Hutchison, W. D., Burkness, E. C., Carrillo, M. A., Hurley, T. M. & Pahl, G. (2006b). Fresh-Market Cabbage: Increasing Economic Returns while Reducing Risk, Public. No. 08230. St. Paul, MN: University of Minnesota Extension Service.Google Scholar
Isard, S. A., Gage, S. H., Comtois, P. & Russo, J. M. (2005). Principles of the atmospheric pathway for invasive species applied to soybean rust. BioScience, 55, 851–861.CrossRefGoogle Scholar
Jepson, P. C. (2007). Ecotoxicology: the ecology of interactions between pesticides and non-target organisms. In Perspectives in Ecological Theory and Integrated Pest Management, eds. Kogan, M. & Jepson, P., pp. 522–551. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Kogan, M. (1998). Integrated pest management: historical perspectives and contemporary developments. Annual Review of Entomology, 43, 243–270.CrossRefGoogle ScholarPubMed
Kovach, J., Petzoldt, C., Degni, J. & Tette, J. (1992). A Method to Measure the Environmental Impact of Pesticides, New York Food and Life Science Bulletin No. 139. Geneva, NY: Cornell University, New York Agricultural Experiment Station. Available at http://ecommons.library.cornell.edu/handle/1813/ 5203.Google Scholar
Manfredo, M. R. & Leuthold, R. M. (1999). Value-at-risk analysis: a review and the potential for agricultural applications. Review of Agricultural Economics, 21, 99–111.Google Scholar
McCarl, B. & Bessler, D. (1989). Estimating an upper bound on the Pratt risk aversion coefficient when the utility function is unknown. Australian Journal of Agricultural Economics, 33, 56–63.CrossRefGoogle Scholar
Mitchell, P. D. (2005). The Expected Net Benefit and Break-Even Probability for Bt Corn in Wisconsin, UWEX Information Bulletin with companion spreadsheet, October. Madison, WI: University of Wisconsin Extension. Available at www.aae.wisc.edu/mitchell/extension.htm.Google Scholar
Mitchell, P. D. (2008). Risk, Farmer Returns and Integrated Pest Management (IPM), Agricultural and Applied Economics Staff Paper No. 526. Madison, WI: University of Wisconsin–Madison.Google Scholar
Mitchell, P. D. & Onstad, D. W. (2005). Effect of extended diapause on the evolution of resistance to transgenic Bacillus thuringiensis corn by northern corn rootworm (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 98, 2220–2234.CrossRefGoogle ScholarPubMed
Mitchell, P. D. & Onstad, D. W. (2008). Valuing insect resistance in an uncertain future. In Insect Resistance Management: Biology, Economics, and Prediction, ed. Onstad, D. W., pp. 17–38. San Diego, CA: Academic Press.Google Scholar
Mitchell, P. D., Gray, M. E. & Steffey, K. L. (2004). A composed error model for estimating pest-damage functions and the impact of the western corn rootworm soybean variant in Illinois. American Journal of Agricultural Economics, 86, 332–344.CrossRefGoogle Scholar
Moffitt, L. J., Tanagosh, L. K. & Baritelle, J. L. (1983). Incorporating risk in comparisons of alternative pest management methods. Environmental Entomology, 12, 1003–1111.CrossRefGoogle Scholar
Mullen, J. D., Norton, G. W. & Reaves, D. W. (1997). Economic analysis of environmental benefits of integrated pest management. Journal of Agriculture and Applied Economics, 29, 243–253.CrossRefGoogle Scholar
Mumford, J. D. & Norton, G. A. (1984). Economics of decision making in pest management. Annual Review of Entomology, 29, 157–174.CrossRefGoogle Scholar
Musser, W. N., Tew, B. V. & Epperson, J. E. (1981). An economic examination of an integrated pest management production system with a contrast between E-V and stochastic dominance analysis. Southern Journal of Agricultural Economics, 13, 119–124.CrossRefGoogle Scholar
Norton, G. A. (1976). Analysis of decision making in crop protection. Agroecosystems, 3, 27–44.Google Scholar
Norton, G. A. (1982). A decision analysis approach to integrated pest control. Crop Protection, 1, 147– 164.CrossRefGoogle Scholar
Norton, G. A., Heinrichs, E. A., Luther, G. C. & Irwin, M. E. (eds.) (2005). Globalizing Integrated Pest Management: A Participatory Research Approach, Ames, IA: Blackwell.CrossRefGoogle Scholar
Nowak, P. (1992). Why farmers adopt production technology. Journal of Soil and Water Conservation, 47, 14–16.Google Scholar
Olson, K. D., (2004). Farm Management: Principles and Strategies. Ames, IA: Iowa State University Press.Google Scholar
Onstad, D. W. (1987). Calculation of economic injury levels and economic thresholds for pest management. Journal of Economic Entomology, 80, 297–303.CrossRefGoogle Scholar
Onstad, D. W. & Rabbinge, R. (1985). Dynamic programming and the computation of economic injury levels for crop disease control. Agricultural Systems, 18, 207–226.CrossRefGoogle Scholar
Onstad, D. W., Crowder, D. W., Mitchell, P. D. et al. (2003). Economics versus alleles: balancing IPM and IRM for rotation-resistant western corn rootworm (Coleoptera: Chrysomelidae). Journal of Economic Entomology, 96, 1872–1885.CrossRefGoogle Scholar
Pedigo, L. P. & Rice, M. E. (2006). Entomology and Pest Management, 5th edn. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Pedigo, L. P., Hutchins, S. H. & Higley, L. G. (1986). Economic injury levels in theory and practice. Annual Review of Entomology, 31, 341–368.CrossRefGoogle Scholar
Peterson, R. K. D. & Hunt, T. E. (2003). The probabilistic economic injury level: incorporating uncertainty into pest management decision-making. Journal of Economic Entomology, 96, 536–542.CrossRefGoogle ScholarPubMed
Plant, R. E. (1986). Uncertainty and the economic threshold. Journal of Economic Entomology, 79, 1–6.CrossRefGoogle Scholar
Plant, R. E. & Stone, N. D. (1991). Knowledge-Based Systems in Agriculture. New York: McGraw-Hill.Google Scholar
Ragsdale, D. W., McCornack, , Venette, R. C, B. P. et al. (2007). Economic threshold for soybean aphid. Journal of Economic Entomology, 100, 1258–1267.CrossRefGoogle ScholarPubMed
Rogers, E. M. (1995). Diffusion of Innovations, 4th edition. New York: The Free Press.Google ScholarPubMed
Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21, 49–58.CrossRefGoogle Scholar
Shiferaw, B., Freeman, H. A. & Swinton, S. M. (eds.) (2004). Natural Resource Management in Agriculture: Methods for Assessing Economic and Environmental Impacts. Wallingford: UK: CABI Publishing.Google Scholar
Shoemaker, C. (1982). Optimal integrated control of univoltine pest populations with age structure. Operations Research, 30, 40–61.CrossRefGoogle Scholar
Stone, N. D., Gutierrez, A. P., Getz, W. M. & Norgaard, R. (1986). III. Strategies for pink bollworm control in southwestern desert cotton: an economic simulation study. Hilgardia, 54, 42–56.CrossRefGoogle Scholar
Swinton, S. M. & Day, E. (2003). Economics in the design, assessment, adoption, and policy analysis of IPM. In Integrated Pest Management: Current and Future Strategies, R-140, ed. Barker, K. R.. Ames, IA: Council for Agricultural Science and Technology.Google Scholar
Venette, R. C. & Ragsdale, D. W. (2004). Assessing the invasion by soybean aphid (Homoptera: Aphididae): where will it end? Annals of the Entomological Society of America, 97, 219–226.CrossRefGoogle Scholar
Wearing, C. H. (1988). Evaluating the IPM implementation process. Annual Review of Entomology, 33, 17–38.CrossRefGoogle Scholar

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