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Developing Flexible Economic Thresholds for Pest Management Using Dynamic Programming

Published online by Cambridge University Press:  28 April 2015

Jayson K. Harper ,
James W. Mjelde ,
M. Edward Rister ,
Michael O. Way  and
Bastiaan M. Drees
Jayson K. Harper
Affiliation:
Department of Agricultural Economics and Rural Sociology, The Pennsylvania State University
James W. Mjelde
Affiliation:
Department of Agricultural Economics, Texas A&M University
M. Edward Rister
Affiliation:
Department of Agricultural Economics, Texas A&M University
Michael O. Way
Affiliation:
Texas A&M UniversityAgricultural Research and Extension Center, Beaumont, TX
Bastiaan M. Drees
Affiliation:
Texas Agricultural Extension Service
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Abstract

The rice stink bug is a major pest of rice in Texas, causing quality related damage. The previous thresholds used for assisting in rice stink bug spray decisions lacked flexibility in economic and production decision variables and neglected the dynamics of the pest population. Using stochastic dynamic programming, flexible economic thresholds for the rice stink bug were generated. The new thresholds offer several advantages over the old, static thresholds, including increased net returns, incorporation of pest dynamics, user flexibility, ease of implementation, and a systematic process for updating.

Keywords

economic thresholdsdynamic programmingpest managementrice

Information

Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 26 , Issue 1 , July 1994 , pp. 134 - 147
Copyright
Copyright © Southern Agricultural Economics Association 1994

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References

Antle, J.M. “Sequential Decision Making in Production Models.” Amer. J. Agr. Econ. 65(1983a): 282–290.CrossRefGoogle Scholar
Antle, J.M. “Incorporating Risk in Production Analysis.” Amer. J. Agr. Econ. 65(1983b): 1099–1106.CrossRefGoogle Scholar
Bradshaw, D. Crop and Agricultural Consultants, El Campo, TX. Personal Communication. 1991.Google Scholar
Brorsen, B.W., Grant, W.R., and Rister, M.E.. “Some Effects of Rice Quality on Rough Rice Prices.” So. J. Agr. Econ. 20(1988): 131–140.Google Scholar
Burt, O.R. “Dynamic Programming: Has its Day Arrived?” West. J. Agr. Econ. 7(1982): 381–394.Google Scholar
Burt, O.R. and Stauber, M.S.. “Economic Analysis of Irrigation in a Subhumid Climate.” Amer. J. Agr. Econ. 53(1971): 33–46.CrossRefGoogle Scholar
Chatterjee, S. “A Mathematical Model for Pest Control.” Biometrics. 29(1973): 727–734.CrossRefGoogle Scholar
Crane, G. Coastal Agricultural Consulting, Inc., East Bernard, TX. Personal Communication. 1991.Google Scholar
Dismukes, R.U.S. Rice Farms: A Regional Comparison. ERS Staff Report No. AGES880119. Washington, DC: USDA, ERS, ARED, February 1988.Google Scholar
Domangue, M. “Land Management and Objective Decisions Keep Texan On Top.” Rice Farmer. May 1990. pp. 10–11.Google Scholar
Domangue, M. “Pre-Plant Liquid Fertilizer - Easy on Costs & Environment.” Rice Farmer. January 1991. pp. 14–16.Google Scholar
Frisbie, R.E., and Adkisson, P.L.. eds. Integrated Pest Management on Major Agricultural Systems. Symposium of the Consortium for Integrated Pest Management and USDA/CSRS. Texas Agricultural Experiment Station, MP-1616. October, 1985.Google Scholar
Fryar, E.O., Parsch, L.D., Holder, S.H., and Tugwell, N.P.. “The Economics of Controlling Peck in Arkansas Rice.” Ark. Farm Res. 35-3(1986): 7.Google Scholar
Grant, W.R., Rister, M.E., and Brorsen, B.W.. Rice Quality Factors: Implications for Management Decisions. B-1541. College Station, TX: Texas Agricultural Experiment Station, July 1986.Google Scholar
Hall, D.C. and Moffitt, L.J.. “Application of the Economic Threshold for Interseasonal Pest Control.” West. J. Agr. Econ. 10(1985): 223–229.Google 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
Harper, J.K. “Developing Economics Thresholds for Rice Stink Bug Management in Texas Using Dynamic Programming.” Unpublished Ph.D. Dissertation. Texas A&M University, August 1988.Google Scholar
Harper, J.K., Way, M.O., Drees, B.M., Rister, M.E., and Mjelde, J.W.. “Damage Function Analysis for the Rice Stink Bug (Hemiptera: Pentatomidae).” J. Econ. Entom. 86(1993): 1250–1258.CrossRefGoogle Scholar
Headley, J.C. “Defining the Economic Threshold.” in Pest Control Strategies for the Future. Washington, DC: National Academy of Sciences, 1972.Google Scholar
Hueth, D. and Regev, U.. “Optimal Agricultural Pest Management with Increasing Pest Resistance.” Amer. J. Agr. Econ. 57(1974): 543–551.CrossRefGoogle Scholar
Kennedy, J.O.S. “Application of Dynamic Programming to Agriculture, Forestry, and Fisheries: Review and Prognosis.” Rev. Market. Agr. Econ. 49(1981): 141–173.Google Scholar
Marra, M.C. and Carlson, G.A.. “An Economic Threshold Model for Weeds in Soybeans (Glycine max.).” Weed Sci. 32(1983): 604–609.CrossRefGoogle Scholar
Marra, M.C., Gould, T.D., and Porter, G.A.. “A Computable Economie Threshold Model for Weeds in Field Crops with Multiple Pests, Quality Effects, and an Uncertain Spraying Period Length.” N.E. J. Agr. Res. Econ. 18(1989): 12–17.Google Scholar
McIlveen, G., Bowling, C.C., and Drees, B.M.. “1981 Rice Insect Control.” Entomology Notes. Texas Agricultural Extension Service, Vol. XXII, Special Issue No. 1, February 17, 1981.Google Scholar
Mjelde, J.W., Dixon, B.L., and Sonka, S.T.. “Estimating the Value of Sequential Updating Solutions for Intra-Year Crop Management.” West. J. Agr. Econ. 13(1988): 184–192.Google Scholar
Moffitt, L.J., Hall, D.C., and Osteen, C.D.. “Economic Thresholds Under Uncertainty with Application to Corn Nematode Management.” So. J. Agr. Econ. 16(1984): 151–157.Google Scholar
Stem, V.M., Smith, R.F., van den Bosch, R., and Hagen, K.S.. “The Integrated Control Concept.” Hilgardia 29(1959): 81–101.Google Scholar
Stone, J.D. and Pedigo, L.D.. “Development and Economic Injury Level of the Green Clover Worm in Soybeans in Iowa.” J. Econ. Entom. 65(1972): 197–201.CrossRefGoogle Scholar
Swanson, M.C. and Newsom, L.D.. “Effect of Infestation by the Rice Stink Bug, Oebulus Pugnax, on Yield and Quality of Rice.” J. Econ. Entom. 55(1962): 877–879.CrossRefGoogle Scholar
Talpaz, H. and Frisbee, R.. “An Advanced Method for Economic Threshold Determination: A Positive Approach.” So. J. Agr. Econ. 7(1975): 19–25.Google Scholar
Taylor, C.R. “A Flexible Method for Empirically Estimating Probability Functions.” West. J. Agr. Econ. 9(1984): 66–76.Google Scholar
Taylor, C.R. “Risk Aversion Versus Expected Profit Maximization with a Progressive Income Tax.” Amer. J. Agr. Econ. 68(1986): 137–143.CrossRefGoogle Scholar
Texas Agricultural Extension Service. “1988 Rice Production Guidelines.” D-1253. College Station, TX: February 1988.Google Scholar
Traylor, H.D., Denison, J.F., and Conger, S.L. Jr. “Quality Factors on Prices Paid Producers for Rough Rice in Louisiana.” La. Rural Econ. 49-4(1987): 5–7.Google Scholar
U.S. Environmental Protection Agency. The Federal Insecticide, Fungicide, and Rodenticide Act as Amended (Section 3), 540/09-89-012. Office of Pesticide Programs. Washington D.C., 1988.Google Scholar
United States Department of Agriculture, Federal Grain Inspecting Service. United States Standards for Rough Rice, Brown Rice for Processing and Milled Rice. Washington, DC: GPO, August 1977.Google Scholar
Way, M.O., Bowling, C.C., and Wallace, R.G.. “Initial and Residual Activity of Insecticides for Control of Rice Stink Bug, Oebalus pugnax (Fabricius).” Texas Agricultural Experiment Station. PR-4504, College Station, TX. March 1987.Google Scholar
Zacharias, T.P., Liebman, J.S., and Noel, G.R.. “Management Strategies for Controlling Soybean Cyst Nematode: An Application of Stochastic Dynamic Programming.” N. Cent. J. Agr. Econ. 8(1986): 175–188.Google Scholar