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Weed Thresholds: The Space Component and Considerations for Herbicide Resistance

Published online by Cambridge University Press:  12 June 2017

Bruce D. Maxwell
Bruce D. Maxwell*
Affiliation:
Dep. Agron. Plant Genet., 411 Borlaug Hall, Univ. Minn., St. Paul, MN 55108
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Abstract

As an extension of weed threshold models in which crop losses are based on weed density, an alternative model for grass weeds in cereal crops is proposed that incorporates the theoretical importance of selection for herbicide resistance, initial weed population frequency, and weed seed dispersal. Simulations suggest optimum weed population levels (thresholds) for maintaining genotypes that are susceptible to control practices and which minimize crop yield reductions. Weed population frequency, in combination with dispersal and competitive traits may determine optimum weed management strategies/Model simulations indicate that understanding how agricultural practices select for “weedy” traits (e.g. herbicide resistance, competitive ability, dispersal potential) may be important in determining weed density thresholds.

Keywords

green foxtail, Setaria viridis (L.) Beauv ♯ SETVIspring wheat, Triticum aestivum L.Population modelsseed dispersal
Type
Symposium
Information
Weed Technology , Volume 6 , Issue 1 , March 1992 , pp. 205 - 212
Copyright
Copyright © 1990 by the Weed Science Society of America 

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References

Literature Cited

1. Auld, B. A., and Tisdell, C. A. 1986. Economic threshold/critical density models in weed control. Proc. Eur. Weed Res. Soc. Symp. 1986, Economic Weed Control. p. 261–268.Google Scholar
2. Auld, B. A., Menz, K. M., and Tisdell, C. A. 1987. Weed control economics. Applied Botany and Crop Science Series. Academic Press, London. p. 55–56.Google Scholar
3. Auld, B. A., and Tisdell, C. A. 1987. Economic thresholds and response to uncertainty in weed control. Agric. Syst. 25:219–227.CrossRefGoogle Scholar
4. Ballare, C. L., Scopel, A. L., Ghersa, C. M., and Sanchez, R. A. 1987. The population ecology of Datura ferox in soybean crops. A simulation approach incorporating seed dispersal. Agric. Ecosystems. Environ. 19:177–188.CrossRefGoogle Scholar
5. Coble, H. D., and Ritter, R. L. 1978. Pennsylvania smartweed (Polygonum pensylvanicum) interference in soybeans (Glycine max). Weed Sci. 26:556–9.CrossRefGoogle Scholar
6. Coble, H. D., Williams, F. M., and Ritter, R. L. 1981. Common ragweed (Ambrosia artemisiifolia) interference in soybeans (Glycine max). Weed Sci. 29:339–342.CrossRefGoogle Scholar
7. Cousens, R., Wilson, B. J., and Cussans, G. W. 1985. To spray or not to spray: the theory behind the practice. Proc. 1985. Br. Crop Prot. Conf.–Weeds, p. 671–678.Google Scholar
8. Cousens, R., Doyle, C. J., Wilson, B. J., and Cussans, G. W. 1986. Modeling the economics of controlling Avena fatua in winter wheat. Pestic. Sci. 17:1–12.CrossRefGoogle Scholar
9. Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q. 2:13–20.Google Scholar
10. Cousens, R., Moss, S. R., Cussans, G. W., and Wilson, B. J. 1987. Modeling weed populations in cereals. Rev. Weed Sci. 3:93–112.Google Scholar
11. Doyle, C. J., Cousens, R., and Moss, S. R. 1986. A model of the economics of controlling Alopecurus myosuroides in winter wheat. Crop Prot. 5:143–150.CrossRefGoogle Scholar
12. Fuller, E., Lazarus, B., and Nordquist, D. 1990. What to grow in 1990. Crop Budgets for Soil Area 1–12. Minn. Ext. Serv. Bull. AG-FS-0934-0945. University of Minnesota, St. Paul. Google Scholar
13. Gould, F. 1990. Ecological Genetics and Integrated Pest Management. p. 441–458 in Carroll, C. R., Vandermeer, J. H., and Rossett, P. M., eds. Agroecology. McGraw-Hill Publishing, New York.Google Scholar
14. Klaseus, T. G., Buzicky, G. C., and Schneider, E. G. 1988. Pesticides and Groundwater: Surveys of selected Minnesota wells. Prepared for the Legislative Commission on Minnesota Resources by the Minnesota Department of Agriculture and Health, St. Paul, Minn. Google Scholar
15. Mathews, G. A. 1984. Pest Management. Longman Inc., New York. 231 p.Google Scholar
16. Maxwell, B. D., Wilson, M. V., and Radosevich, S. R. 1988. Population modeling approach for evaluating leafy spurge (Euphorbia esula) development and control. Weed Technol. 2:132–138.CrossRefGoogle Scholar
17. Maxwell, B. D., Rousch, M. L., and Radosevich, S. R. 1990. Predicting the evolution and dynamics of herbicide resistance in weed populations. Weed Technol. 4:2–13.CrossRefGoogle Scholar
18. Maxwell, B. D., Roush, M. L., and Radosevich, S. R. 1990. Prevention and management of herbicide resistant weeds. Proc. 9th Aust. Weed Control Conf., August 6, 1990, Adelaide, Australia. p. 260–267.Google Scholar
19. Maxwell, B. D., and Ghersa, C. 1991. The influence of weed seed dispersion versus the effect of competition on crop yield reduction. Weed Technol. 6:196–204.CrossRefGoogle Scholar
20. Moody, K. 1983. Weeds: definitions, costs, characteristics, classification and effects. PLITS 1:11–32.Google Scholar
21. Mortensen, D. A., Von Bargen, K., Meyer, G. E., and Shropshire, G. J. 1991. Plant imaging sensors for intermittent sprayer control. Weed Sci. Soc. Am. 31:80.Google Scholar
22. Norgaard, R. B. 1976. Integrating economics and pest management. p. 17–27. in Apple, J. L., and Smith, R. F. eds. Integrated Pest Management. Plenum Press, New York.CrossRefGoogle Scholar
23. Roush, M. L., Radosevich, S. R., and Maxwell, B. D. 1990. Future outlook for herbicide-resistance research. Weed Technol. 4:208–214.CrossRefGoogle Scholar
24. Schweizer, E. E., and Lauridson, T. C. 1985. Powell amaranth (Amaranthus powellii) interference in sugarbeet (Beta vulgaris). Weed Sci. 33:518–220.CrossRefGoogle Scholar
25. Stern, V. M., Smith, R. E., van den Bosch, R., and Hagen, K. S. 1959. The integrated control concept. Hilgardia 29:81–101.CrossRefGoogle Scholar
26. Streibig, J. C., Combellack, J. H., Pritchard, G. H., and Richardson, R. G. 1989. Estimation of thresholds for weed control in Australian cereals. Weed Res. 29:117–126.CrossRefGoogle Scholar
27. Thomas, G. D., Smith, D. B., and Ignoffo, C. M. 1979. Economic thresholds for insect control. p. 419–429. in Ennis, W. B. Jr. ed. Introduction to Crop Protection. The American Society of Agronomy, Inc. and The Crop Science Society of America, Madison, Wisconsin.Google Scholar
28. Wagner, R. G., Petersen, T. D., Ross, D. W., and Radosevich, S. R. 1989. Competition thresholds for the survival and growth of ponderosa pine seedlings associated with woody and herbaceous vegetation. New Forests. 3:151–170.CrossRefGoogle Scholar
29. Weston, J. F., and Copeland, T. E. 1986. Managerial Finance. 8th edition. Dryden Press, Chicago, Ill. Google Scholar
30. Wetala, M.P.E. 1976. The relationship between weeds and soyabeans yields. Proc. 6th East Afr. Weed Sci. Conf. p. 156–68.Google Scholar