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Economic decision rules for postemergence herbicide control of barnyardgrass (Echinochloa crus-galli) in corn (Zea mays)

Published online by Cambridge University Press:  12 June 2017

Aca Č. Bosnić  and
Clarence J. Swanton
Aca Č. Bosnić
Affiliation:
Department of Crop Science, University of Guelph, Guelph, ON, Canada N1G 2W1
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Abstract

Economic decision rules for postemergence herbicide control of barnyardgrass in corn were developed. Damage and control functions that formed the basis of an economic model were estimated. Barnyardgrass density and time of emergence relative to the crop were fundamental to calculate the damage function. The control function described barnyardgrass dry weight response to variable doses of two herbicides. Both the biologist's and economist's weed control decision rules, derived from the economic model, were influenced by time of weed emergence relative to the crop, corn yield, and price. Inclusion of time of weed emergence relative to the crop improved our interpretative ability of derived decision rules. The biologist's threshold weed density was more sensitive to changes in parameter values than the economist's optimal herbicide dose strategy. Herbicide use with recommended label dose was greater than either the economically optimal or the biologically effective doses. Use of the biologically effective dose for postemergence weed control decisions was cost efficient and could be of practical significance to corn growers.

Keywords

Barnyardgrass, Echinochloa crusgalli (L.) Beauv. ECHCGcorn, Zea mays L. ‘Pioneer 3902.’DPX-79406economicsintegrated weed managementrimsulfurontime of emergenceweed densityyieldECHCG
Type
Weed Management
Information
Weed Science , Volume 45 , Issue 4 , August 1997 , pp. 557 - 563
Copyright
Copyright © 1997 by the Weed Science Society of America 

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References

Literature Cited

Adcock, T. E. and Banks, P. A. 1991. Effects of preemergence herbicides on the competitiveness of selected weeds. Weed Sci. 39: 54–56.CrossRefGoogle Scholar
Anonymous. 1989. Grain and Forage Crops: Estimated Production Costs Ontario, 1989. Toronto, Ontario, Canada: Economics Information Report No. 89-05. Ontario Ministry of Agriculture and Food. 26 p.Google Scholar
Anonymous. 1995. Agriculture Statistics for Ontario. Toronto, Ontario, Canada: Ontario Ministry of Agriculture and Food. Publication 20. 100 p.Google Scholar
Auld, B. A., Menz, K. M., and Tisdell, C. A. 1987. Weed Control Economics. London: Academic Press, pp. 47–62.Google Scholar
Bartley, M. R. 1993. Assessment of herbicide selectivity. in Streibig, J. C. and Kudsk, P., eds. Herbicide Bioassays. Boca Raton, FL: CRC Press, pp. 57–73.Google Scholar
Bosnić, A.Č. and Swanton, C. J. 1997. Influence of barnyardgrass (Echinochloa crusgalli) time of emergence and density on corn (Zea mays). Weed Sci. 45: 276–282.CrossRefGoogle Scholar
Chikoye, D. and Swanton, C. J. 1995. Evaluation of three empirical models depicting Ambrosia artemisiifolia competition in white bean. Weed Res. 35: 421–428.CrossRefGoogle Scholar
Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci. 43: 375–380.CrossRefGoogle Scholar
Chism, W. J., Birch, J. B., and Bingham, S. W. 1992. Nonlinear regressions for analysing growth stage and quinclorac interactions. Weed Technol. 6: 898–903.CrossRefGoogle Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. App. Biol. 107: 239–252.CrossRefGoogle Scholar
Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q. 2: 13–20.Google Scholar
Cousens, R., Brain, P., O'Donovan, J. T., and O'Sullivan, P. A. 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Sci. 35: 720–725.CrossRefGoogle Scholar
Cousens, R., Doyle, C. J., Wilson, B. J., and Cussans, G. W. 1986. Modelling the economics of controlling Avena fatua in winter wheat. Pesticide Sci. 17: 1–12.CrossRefGoogle Scholar
Deen, W. 1991. Determination of thresholds of weed control in Ontario corn and soybeans. . University of Guelph, Guelph, Ontario, Canada, pp. 29–33.Google Scholar
Deen, W., Weersink, A., Turvey, C. G., and Weaver, S. E. 1993. Weed control decision rules under uncertainty. Rev. Agric. Econ. 15: 39–50.CrossRefGoogle Scholar
Dieleman, A., Hamill, A. S., Fox, G. C., and Swanton, C. J. 1996. Decision rules for postemergence control of pigweed (Amaranthus spp.) in soybean (Glycine max). Weed Res. 44: 126–132.CrossRefGoogle Scholar
Dieleman, A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (Amaranthus spp.) interference in soybean (Glycine max). Weed Res. 43: 612–618.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
Draper, N. R. and Smith, H. 1981. Applied Regression Analysis. New York: J. Wiley, pp. 458–517.Google Scholar
FIG-P. 1992. The Scientific Figure Processor. Durham, NC: FIG-P Software Corporation.Google Scholar
Gillmeister, W. J., Moffit, L. J., Bhowmik, P. C., and Allen, P. G. 1990. Informational value in weed management. Northeast. J. Agric. Res. Econ. 19: 24–27.CrossRefGoogle Scholar
Green, J. M. 1991. Maximizing herbicide efficiency with mixtures and expert systems. Weed Technol. 5: 894–897.CrossRefGoogle Scholar
Hamill, A. S. and Thomas, A. G. 1985. Survey for Weeds and Their Competitive Effect in Corn and Soybean Fields of Essex and Kent counties in Ontario. Agriculture Canada, Ottawa, ON, Canada: Weed Survey Series Publication 85-2. 54 p.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42: 568–573.CrossRefGoogle Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1995. Comparison of empirical models depicting density of Amaranthus retroflexus L., and relative leaf area as predictors of yield loss in maize (Zea mays L.). Weed Res. 35: 207–215.CrossRefGoogle Scholar
Kropff, M. J. and Lotz, L.A.P. 1992. Systems approaches to quantify crop-weed interactions and their application in weed management. Agric. Syst. 40: 265–282.CrossRefGoogle 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
Moffitt, L. J. 1988. Incorporating environmental considerations in pest control advice for farmers. Am. J. Agric. Econ. 70: 628–634.CrossRefGoogle Scholar
Moffitt, L. J., Hall, D. C., and Osteen, C. D. 1984. Economic thresholds under uncertainty with application to corn nematode management. South. J. Agric. Econ. 16: 151–157.Google 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 maximisation. J. Agric. Econ. 41: 391–404.CrossRefGoogle Scholar
[SAS] Statistical Analysis Systems. 1990. PROC NLIN Method DUD. Version 6.06. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Swanton, C. J., Chandler, K., Elmes, M. J., Murphy, S. D., and Anderson, G. W. 1996. Postemergence control of annual grasses and corn (Zea mays) tolerance using DPX-79406. Weed Technol. 10: 288–294.CrossRefGoogle Scholar
Weaver, S. E. 1991. Size dependent economic thresholds for three broadleaf weed species in soybeans. Weed Technol. 5: 674–679.CrossRefGoogle Scholar
Weersink, A., Deen, W., and Weaver, S. 1991. Defining and measuring economic threshold levels. Can. J. Agric. Econ. 39: 619–625.CrossRefGoogle Scholar
Weersink, A., Deen, W., and Weaver, S. 1992. Evaluation of alternative decision rules for postemergence herbicide treatments in soybeans. J. Prod. Agric. 5: 298–303.CrossRefGoogle Scholar
Wiese, A. M. and Binning, L. K. 1987. Calculating the threshold temperature of development for weeds. Weed Sci. 35: 177–179.CrossRefGoogle Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992. Modelling weed distribution for improved postemergence control decisions. Weed Sci. 40: 546–553.CrossRefGoogle Scholar
Zimdahl, R. L. 1980. Weed-crop Competition: A Review. Corvallis, OR: International Plant Protection Center. 196 p.Google Scholar