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A Dynamic Optimisation Model of Weed Control

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  • Cacho, Oscar J.
  • Jones, Randall E.

Abstract

It is argued in this paper that static approaches to weed management, where the benefits and costs are only considered within a single season, are inappropriate for assessing the economic benefits of weed control technologies. There are carryover effects from weed management as weeds that escape control in one season may reproduce and replenish weed populations in following seasons. Consequently, it is appropriate to view weed control in the context of a resource management problem where the goal is to determine the optimal inter-temporal level of weed control that maximises economic benefits over some pre-determined period of time. A dynamic optimisation model for weed control is presented. Using the tools of comparative static analysis and Pontryagin's maximum principle, the conditions for optimal input use (ie weed control) are compared for static and dynamic situations. It is shown that a higher level of input use for a given weed population is optimal using a dynamic framework than would be derived under a static framework. The analysis is further extended by the incorporation of uncertainty and shows that the optimal level of weed control is also affected by uncertainty in herbicide efficacy and the survival of weed seeds produced. A case study of the optimal long-term management under deterministic and stochastic conditions of an annual cropping weed, Avena fatua, is presented.

Suggested Citation

  • Cacho, Oscar J. & Jones, Randall E., 2000. "A Dynamic Optimisation Model of Weed Control," Working Papers 12902, University of New England, School of Economics.
    • Handle: RePEc:ags:uneewp:12902
    DOI: 10.22004/ag.econ.12902
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    References listed on IDEAS

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    1. David J. Pannell, 1995. "Optimal Herbicide Strategies for Weed Control under Risk Aversion," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 17(3), pages 337-350.
    2. Gershon Feder, 1979. "Pesticides, Information, and Pest Management under Uncertainty," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 61(1), pages 97-103.
    3. Kennedy, John O. S., 1988. "Principles of dynamic optimization in resource management," Agricultural Economics, Blackwell, vol. 2(1), pages 57-72, June.
    4. Pannell, David J., 1990. "An Economic Response Model Of Herbicide Application For Weed Control," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 34(3), pages 1-19, December.
    5. Kent D. Olson & Vernon R. Eidman, 1992. "A Farmer's Choice of Weed Control Method and the Impacts of Policy and Risk," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 14(1), pages 125-137.
    6. D. J. Pannell, 1990. "Responses To Risk In Weed Control Decisions Under Expected Profit Maximisation," Journal of Agricultural Economics, Wiley Blackwell, vol. 41(3), pages 391-401, September.
    7. Auld, Bruce A. & Tisdell, Clem A., 1987. "Economic thresholds and response to uncertainty in weed control," Agricultural Systems, Elsevier, vol. 25(3), pages 219-227.
    8. William Deen & Alfons Weersink & Calum G. Turvey & Susan Weaver, 1993. "Weed Control Decision Rules under Uncertainty," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 15(1), pages 39-50.
    9. Carlson, Gerald A., 1984. "Risk Reducing Inputs Related to Agricultural Pests," Regional Research Projects >1984: S-180 Annual Meeting, March 25-28, 1984, New Orleans, Louisiana 307236, Regional Research Projects > S-180: An Economic Analysis of Risk Management Strategies for Agricultural Production Firms.
    10. Richard Bellman, 1957. "On a Dynamic Programming Approach to the Caterer Problem--I," Management Science, INFORMS, vol. 3(3), pages 270-278, April.
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    Cited by:

    1. Nordblom, Thomas L. & Jones, Randall E. & Medd, Richard W., 2003. "Economics of factor adjusted herbicide doses: a simulation analysis of best efficacy targeting strategies (BETS)," Agricultural Systems, Elsevier, vol. 76(3), pages 863-882, June.
    2. Brennan, John P. & Lemerle, Deirdre & Martin, Peter J., 2001. "Economics of Increasing Wheat Competitiveness as a Weed Control Weapon," 2001 Conference (45th), January 23-25, 2001, Adelaide, Australia 125543, Australian Agricultural and Resource Economics Society.
    3. Nordblom, Thomas L. & Jones, Randall E. & Medd, Richard W., 2001. "Economics of factor-adjusted herbicide doses: Best-Efficacy Targeting or Best Fixed Doses vs 'blind' upper or lower label doses," 2001 Conference (45th), January 23-25, 2001, Adelaide, Australia 125800, Australian Agricultural and Resource Economics Society.
    4. Nordblom, Thomas L. & Jones, Randall E. & Medd, Richard W., 2002. "A herbicide dose strategy able to account for environmental variation allows lower overall herbicide use without compromising long run economic benefits," 2002 Conference (46th), February 13-15, 2002, Canberra, Australia 183421, Australian Agricultural and Resource Economics Society.
    5. Webster, Stewart & Mullen, John D., 2000. "Animal Disease Economics: The Case of Ovine Johne’s Disease in New South Wales," 2000 Conference (44th), January 23-25, 2000, Sydney, Australia 123737, Australian Agricultural and Resource Economics Society.

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