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The Value of Pest Information in a Dynamic Setting: The Case of Weed Control

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  • Swinton, Scott M.
  • King, Robert P.

Abstract

The value of weed scouting information for soil-applied and post-emergence weed management is estimated using a dynamic, whole-farm, stochastic simulation model. The model simulates outcomes of four expected utility functions from management strategies using three levels of weed information. Results from a representative Minnesota corn and soybean farm indicate high value of weed seedling counts (for post-emergence control) but relatively low value of weed seed counts (for soil-applied control). While herbicide use is often reduced under information based management, this is not always the case.
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  • Swinton, Scott M. & King, Robert P., 1992. "The Value of Pest Information in a Dynamic Setting: The Case of Weed Control," Staff Paper Series 201163, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    • Handle: RePEc:ags:midasp:201163
    DOI: 10.22004/ag.econ.201163
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    Cited by:

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    2. Dirksmeyer, Walter, 2007. "Ist Eine Reduzierung Des Pflanzenschutzmitteleinsatzes Im Freilandgemüsebau Möglich? Ergebnisse Eines Bioökonomischen Simulationsmodells," 47th Annual Conference, Weihenstephan, Germany, September 26-28, 2007 7592, German Association of Agricultural Economists (GEWISOLA).
    3. Poon, Kenneth & Weersink, Alfons & Deaton, Brady J., Jr., 2011. "Demand and Supply Analysis of Farm, Farmer and Farm Family Data," Working Papers 114094, Structure and Performance of Agriculture and Agri-products Industry (SPAA).
    4. Bennett, Anne L. & Pannell, David J., 1998. "Economic evaluation of a weed-activated sprayer for herbicide application to patchy weed populations," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 42(4), pages 1-20.
    5. Woongchan Jeon & Kwansoo Kim, 2017. "Optimal Weed Control Strategies in Rice Production under Dynamic and Static Decision Rules in South Korea," Sustainability, MDPI, vol. 9(6), pages 1-11, June.
    6. Haight, Robert G. & Polasky, Stephen, 2010. "Optimal control of an invasive species with imperfect information about the level of infestation," Resource and Energy Economics, Elsevier, vol. 32(4), pages 519-533, November.
    7. Dirksmeyer, W., 2008. "Ist eine Reduzierung des Pflanzenschutzmitteleinsatzes im Freilandgemüsebau möglich? Ergebnisse eines bioökonomischen Simulationsmodells," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 43, March.
    8. Wu, JunJie, 2001. "Optimal weed control under static and dynamic decision rules," Agricultural Economics, Blackwell, vol. 25(1), pages 119-130, June.
    9. Vaughn, Gerald F. & Breimyer, Harold F. & Paarlberg, Don & Lovell, Sabrina J. & Kuch, Peter J. & Otte, John & Gardner, Bruce L. & Randall, Alan & Cunfer, Barry M., 1999. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 14(3), pages 1-3.
    10. David J. Pannell, 2002. "Prose, Psychopaths and Persistence: Personal Perspectives on Publishing," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 50(2), pages 101-115, July.
    11. Cobourn, Kelly M. & Goodhue, Rachael E. & Williams, Jeffrey C., 2009. "The Role of Harvest Timing in Pest Management: Grower Response to Infestation by the California Olive Fruit Fly," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49475, Agricultural and Applied Economics Association.
    12. Mitchell, Paul D., 2001. "Additive Versus Proportional Pest Damage Functions: Why Ecology Matters," 2001 Annual meeting, August 5-8, Chicago, IL 20775, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    13. Jonathan R. McFadden & Alicia Rosburg & Eric Njuki, 2022. "Information inputs and technical efficiency in midwest corn production: evidence from farmers' use of yield and soil maps," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(2), pages 589-612, March.
    14. Wiles, L. J. & King, R. P. & Schweizer, E. E. & Lybecker, D. W. & Swinton, S. M., 1996. "GWM: General weed management model," Agricultural Systems, Elsevier, vol. 50(4), pages 355-376.

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