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Economic Thresholds and the Case for Longer Term Approaches to Population Management of Weeds

Published online by Cambridge University Press:  20 January 2017

Randall E. Jones  and
Richard W. Medd
Randall E. Jones*
Affiliation:
Cooperative Research Centre for Weed Management Systems, New South Wales Agriculture, Orange Agricultural Institute, Forest Road, Orange, New South Wales 2800, Australia
Richard W. Medd
Affiliation:
Cooperative Research Centre for Weed Management Systems, New South Wales Agriculture, Orange Agricultural Institute, Forest Road, Orange, New South Wales 2800, Australia
*
Corresponding author's E-mail: randall.jones@agric.nsw.gov.au.
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Abstract

The economic threshold is a concept strongly embedded within the weed management literature. There are some theoretical concerns with applying a static approach such as the economic threshold to weed management decision making. An improvement is to adopt a population management approach where the intertemporal effects of decisions are taken into account. The focus should be on managing weed populations through time rather than minimizing the yield effect of weeds in a single season or year. Rather than viewing weeds as an annual production problem, the weed seed bank can be considered a renewable resource stock, and the management goal is to deplete this resource stock through time. The principles of natural resource economics illustrate that including the intertemporal effects of weed control will, for a given size of a seed bank, result in a greater level of weed control and a higher economic benefit than if control decisions were based solely on the current period effects. A dynamic economic model was developed of an extensive Australian spring wheat (Triticum aestivum) cropping system to test these principles using wild oat (Avena fatua and A. ludoviciana) as an example. The model was solved for a 20-yr time horizon for a population management approach and the traditional static economic threshold. The economic benefits from a population management approach were significantly greater than those generated by the economic threshold, and the final seed bank was considerably lower. This result suggests that a paradigm shift from thresholds to longer term population management is warranted.

Keywords

Diclofop-methyl, (±)-2-[4-(2,4-dichlorophenoxy)phenoxy]propanoic acidflamprop-methyl, N-benzoyl-N-(3-chloro-4-fluorophenyl)-dl-alanineglyphosate, N-(phosphonomethyl)glycinewild oat, Avena fatua L. AVEFA, winter wild oat Avena ludoviciana Dur. AVELUwheat Triticum aestivum L.Dynamic programmingeconomic optimum thresholdsoptimum dose rateeconomicsnet present valuemodelingintegrated weed managementrigid ryegrassannual ryegrassLolium rigidum.DP, dynamic programmingEOT, economic optimum thresholdET, economic thresholdIWM, integrated weed managementNPV, net present valueODR, optimum dose rate

Information

Type
Research Article
Information
Weed Technology , Volume 14 , Issue 2 , June 2000 , pp. 337 - 350
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Auld, B. A., Menz, K. M., and Tisdell, C. A. 1987. Weed Control Economics. London: Academic Press. 177 p.Google Scholar
Bauer, T. A. and Mortensen, D. A. 1992. A comparison of economic and economic optimum thresholds for two annual weeds in soybeans. Weed Technol. 6: 228–235.CrossRefGoogle Scholar
Coble, H. D. 1998. My view. Weed Sci. 46:509.CrossRefGoogle Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6: 191–195.CrossRefGoogle Scholar
Conrad, J. M. and Clark, C. W. 1987. Natural Resource Economics: Notes and Problems, Cambridge, MA: Cambridge University Press. 231 p.CrossRefGoogle Scholar
Cousens, R. 1985. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105: 513–521.CrossRefGoogle Scholar
Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q. 2: 13–20.Google Scholar
Cousens, R., Peters, N.C.B., and Marshall, C. B. 1984. Models of yield loss-weed density relationships. Proceedings of the Seventh International Colloquium on Weed Biology, Ecology and Systematics. Columa—European Weed Research Society, Paris, pp. 367–374.Google Scholar
Cousens, R., Doyle, C. J., Wilson, B. J., and Cussans, G. W. 1986. Modelling the economics of controlling Avena fatua in winter wheat. Pestic. Sci. 17: 1–12.CrossRefGoogle Scholar
Czapar, G., Curry, M. P., and Wax, L. M. 1997. Grower acceptance of economic thresholds for weed management in Illinois. Weed Technol. 11: 828–831.CrossRefGoogle Scholar
Deen, W., Weersink, A., Turvey, C. G., and Weaver, S. 1993. Weed control decision rules under uncertainty. Rev. Agric. Econ. 15: 39–50.CrossRefGoogle Scholar
Doyle, C. J., Cousens, R., and Moss, S. R. 1986. A model of the economics of controlling Alopecurus myosuroides Huds in winter wheat. Crop Prot. 5: 143–150.CrossRefGoogle Scholar
Fisher, B. S. and Lee, R. R. 1981. A dynamic programming approach to the economic control of weed and disease infestations. Rev. Mark. Agric. Econ. 49: 175–87.Google Scholar
Genstat 5 Committee. 1996. Genstat™ 5 Release 3 Reference Manual. Oxford, UK: Clarendon Press. 749 p.Google Scholar
Higley, L. G. and Pedigo, L. P. 1993. Economic injury concepts and their use in sustaining environmental quality. Agric. Ecosyst. Environ. 46: 233–243.CrossRefGoogle Scholar
Jones, R. E. and Medd, R. W. 1997. Economic analysis of integrated management of wild oats involving fallow, herbicide and crop rotational options. Aust. J. Exp. Agric. 37: 683–691.CrossRefGoogle Scholar
Jordan, N. 1992. Weed demography and population dynamics: implications for threshold management. Weed Technol. 6: 184–190.CrossRefGoogle Scholar
Kennedy, J.O.S. 1988. Principles of dynamic optimization in resource management. Agric. Econ. 2: 57–72.CrossRefGoogle Scholar
Lapham, J. 1987. Population dynamics and competitive effects of Cyperus esculentus (yellow nutsedge)—prediction of cost-effective control strategies. Proceedings of the British Crop Protection Conference—Weeds, Brighton, UK. pp. 1043–1050.Google Scholar
Marra, M. C. and Carlson, G. A. 1983. An economic threshold model for weeds in soybeans (Glycine max). Weed Sci. 31: 604–609.CrossRefGoogle Scholar
Marra, M. C., Gould, T. D., and Porter, G. A. 1989. A computable economic threshold model for weeds in field crops with multiple pests, quality effects and an uncertain spraying period length. Northeast J. Agric. Econ. 18: 12–17.CrossRefGoogle Scholar
Martin, R. J. and Felton, W. L. 1993. Effect of crop rotation, tillage practice, and herbicides on population dynamics of wild oats in wheat. Aust. J. Exp. Agric. 33: 159–165.CrossRefGoogle Scholar
Martin, R. J., Cullis, B. R., and McNamara, D. W. 1987. Prediction of wheat yield loss due to competition by wild oats (Avena spp.). Aust. J. Agric. Res. 38: 487–499.CrossRefGoogle Scholar
Maxwell, B. D. 1992. Weed thresholds: the space component and considerations for herbicide resistance. Weed Technol. 6: 205–212.CrossRefGoogle Scholar
McInerney, J. 1976. The simple analytics of natural resource economics. J. Agric. Econ. 27: 31–52.CrossRefGoogle Scholar
Medd, R. W., McMillan, M. G., and Cook, A. S. 1992. Spray-topping of wild oats (Avena spp.) in wheat with selective herbicides. Plant Prot. Q. 7: 62–65.Google Scholar
Medd, R. W., Nicol, H. I., and Cook, A. S. 1995. Seed kill and its role in weed management systems: a case study of seed production, seed banks and population growth of Avena species (wild oats). Proceedings of the Ninth European Weed Research Society Symposium, Budapest, Hungary. Volume 2. pp. 627–632.Google Scholar
Mortensen, D. A., Martin, A. R., Harvill, T. E., and Bauer, T. A. 1993. The influence of rotational diversity on economic optimum thresholds in soybean. Proceedings of the Eighth European Weed Research Society Symposium, Braunschweig, Germany. pp. 815–823.Google Scholar
Murdoch, A. J. 1988. Long-term profit from weed control. Asp. Appl. Biol. 18: 91–98.Google Scholar
Norris, R. F. 1999. Ecological implications of using thresholds for weed management. J. Crop Prot. 2: 31–58.CrossRefGoogle Scholar
O'Donovan, J. T. 1996. Weed economic thresholds: useful agronomic tool or pipe dream? Phytoprotection. 77: 13–28.CrossRefGoogle Scholar
Pandey, S. and Medd, R. W. 1990. Integration of seed and plant kill tactics for control of wild oats: an economic evaluation. Agric. Systems. 34: 65–76.CrossRefGoogle Scholar
Pandey, S. and Medd, R. W. 1991. A stochastic dynamic programming framework for weed control decision making: an application to Avena fatua L. Agric. Econ. 6: 115–128.CrossRefGoogle Scholar
Pannell, D. J. 1990a. An economic response model of herbicide application for weed control. Aust. J. Agric. Econ. 34: 223–241.Google Scholar
Pannell, D. J. 1990b. Responses to risk in weed control decisions under expected profit maximization. J. Agric. Econ. 41: 391–403.CrossRefGoogle Scholar
Pannell, D. J. 1995. Optimal herbicide strategies for weed control under risk aversion. Rev. Agric. Econ. 17: 337–350.CrossRefGoogle Scholar
Philpotts, H. 1975. The control of wild oats in wheat by winter fallowing and summer cropping. Weed Res. 15: 221–225.CrossRefGoogle Scholar
Sattin, M., Zanin, G., and Berti, A. 1992. Case study for weed competition/population ecology: velvetleaf (Abutilon theophrasti) in corn (Zea mays). Weed Technol. 6: 213–219.CrossRefGoogle Scholar
Streibig, J. C. 1988. Herbicide bioassays. Weed Res. 28: 479–484.CrossRefGoogle Scholar
Swanton, C.J., Weaver, S., Cowan, P., Van Acker, R., Deen, W., and Shrehta, A. 1999. Weed thresholds: theory and application. J. Crop Prot. 2: 9–29.CrossRefGoogle Scholar
Swinton, S. M. and King, R. P. 1994a. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44: 313–335.CrossRefGoogle Scholar
Swinton, S. M. and King, R. P. 1994b. The value of pest information in a dynamic setting: the case of weed control. Am. J. Agric. Econ. 76: 36–46.CrossRefGoogle Scholar
Taylor, C. R. and Burt, O. R. 1984. Near-optimal management strategies for controlling wild oats in spring wheat. Am. J. Agric. Econ. 66: 50–60.CrossRefGoogle Scholar
Vangessel, M. J., Schweizer, E. D., Lybecker, D. W., and Westra, P. 1996. Integrated weed management systems for irrigated corn (Zea mays) production in Colorado—a case study. Weed Sci. 44: 423–428.CrossRefGoogle Scholar
Wallinga, J. 1998. Analysis of the rational long-term herbicide use: evidence for herbicide efficacy and critical weed kill rate as key factors. Agric. Syst. 56: 323–340.CrossRefGoogle Scholar
Wallinga, J. and Van Oijen, M. 1997. Level of threshold weed density does not affect the long-term frequency of weed control. Crop Prot. 16: 273–278.CrossRefGoogle Scholar