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Estimation of Crop Yield Loss Due to Interference by Multiple Weed Species

Published online by Cambridge University Press:  12 June 2017

Scott M. Swinton ,
Douglas D. Buhler ,
Frank Forcella ,
Jeffrey L. Gunsolus  and
Robert P. King
Scott M. Swinton
Affiliation:
Dep. Agric. Econ., Michigan State Univ., E. Lansing, MI 48824–1039
Douglas D. Buhler
Affiliation:
Plant Sci. Res. Unit, U.S. Dep. Agric, Agric. Res. Serv. and Assoc. Prof., Dep. Agron. and Plant Genet., Univ. Minnesota, St. Paul, MN 55108
Frank Forcella
Affiliation:
U.S. Dep. Agric, Agric. Res. Serv., North Cent. Soil Conserv. Res. Lab., Morris, MN 56267
Jeffrey L. Gunsolus
Affiliation:
Dep. Agron. and Plant Genet., Univ. Minnesota, St. Paul, MN 55108
Robert P. King
Affiliation:
Dep. Agric. and Appl. Econ., Univ. Minnesota, St. Paul, MN 55108
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Abstract

Previous efforts to model crop yield loss from multiple weed species constructed competitive indices based on yield loss from individual weed species. Our model uses a multispecies modification of Cousens’ rectangular hyperbolic yield function to estimate a nonlinear competitive index for weed-crop interference. Results from 13 Minnesota and Wisconsin data sets provide measures of the relative competitiveness of mixed green and yellow foxtails, common lambsquarters, redroot pigweed, velvetleaf, and several other weed species. Competition coefficient estimates are stable over years, but not locations.

Keywords

Corn, Zea mays L.soybean, Glycine max Merr.green foxtail, Setaria viridis (L.) Beauv. # SETVIyellow foxtail S. glauca (L.) Beauv. # SETLUcommon lambsquarters, Chenopodium album L. # CHEALredroot pigweed, Amaranthus retroflexus L. # AMAREvelvetleaf Abutilon theophrasti Medik. # ABUTHbarnyardgrass, Echinochloa crus-galli L. Beauv. # ECHCGcommon ragweed, Ambrosia artemisiifolia L. # AMBELEastern black nightshade, Solanum ptycanthum Dun. # SOLPTBioeconomic modelcompetitioncompetitive index
Type
Special Topics
Information
Weed Science , Volume 42 , Issue 1 , March 1994 , pp. 103 - 109
Copyright
Copyright © 1994 by the Weed Science Society of America 

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References

Literature Cited

1. Auld, B. A., Menz, K. M., and Tisdell, C. A. 1987. Weed Control Economics. Academic Press, London.Google Scholar
2. Beckett, T. H., Stoller, E. W., and Wax, L. M. 1988. Interference of four annual weeds in corn (Zea mays). Weed Sci. 36:764–769.CrossRefGoogle Scholar
3. Buhler, D. D. Unpublished data. 1989–1990. Rosemount, MN.Google Scholar
4. Chandler, J. M., Hamill, A. S., and Thomas, A. G. 1984. Crop losses due to weeds in Canada and the United States. Weed Sci. Soc. Am., Champaign, IL.Google Scholar
5. Coble, H. D. 1986. Development and implementation of economic thresholds for soybean. Pages 295–307 in Frisbie, R. E. and Adkisson, P. L., eds. CIPM: Integrated Pest Management on Major Agricultural Systems. Texas A & M Univ., College Station, TX.Google Scholar
6. Cousens, R. 1986. A simple model related yield loss to weed density. Ann. Appl. Biol. 107:239–252.CrossRefGoogle Scholar
7. 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
8. Dew, D. A. An index of competition for estimating crop loss due to weeds. 1972. Can. J. Plant Sci. 52:921–927.CrossRefGoogle Scholar
9. Doyle, C. J., Cousens, R., and Moss, S. R. 1986. A mode) of the economics of controlling Alopecurus myosuroides Huds. in winter wheat. Crop. Prot. 5:143–150.CrossRefGoogle Scholar
10. Forcella, F. and Lindstrom, M. J. 1988. Movement and germination of weed seeds in ridge-till crop production systems. Weed Sci. 36:56–59.CrossRefGoogle Scholar
11. Gunsolus, J. L. Unpublished data. 1989–1991. Lamberton, Morris, and Waseca, MN.Google Scholar
12. Kells, J. J. and Black, J. R. 1990. CORNHERB: Herbicide options program for weed control in corn. Software Version 2.0. Agric. Exp. Stn., Michigan State Univ., E. Lansing.Google Scholar
13. Kennedy, P. 1992. A Guide to Econometrics. 3rd ed. MIT Press, Cambridge, MA. Page 71.Google Scholar
14. Kidder, D., Posner, B., and Miller, D. 1989. WEEDIR: Weed control directory. Software Version 3.0. Minnesota Ext. Serv. AG-CS-2163. Univ. Minnesota, St. Paul. Google Scholar
15. King, R. P., Lybecker, D. W., Schweizer, E. E., and Zimdahl, R. L. 1986. Bioeconomic modeling to simulate weed control strategies for continuous corn (Zea mays). Weed Sci. 34:972–979.CrossRefGoogle Scholar
16. Knake, E. L. and Slife, F. W. 1962. Competition of Setaria faberii with corn and soybeans. Weeds 10:26–29.CrossRefGoogle Scholar
17. Kroppf, M. J. and Spitters, C. J. T. 1991. A simple model of crop loss by weed competition from early observations on relative area of the weeds. Weed Res. 31:97–105.CrossRefGoogle Scholar
18. Lybecker, D. W., Schweizer, E. E., and Westra, P. 1991. Computer aided decisions for weed management in corn. Abstracted in W. J. Agric. Econ. 16:456.Google Scholar
19. Murdoch, A. J. 1988. Long-term profit from weed control. Aspects Appl. Biol. 18:91–98.Google Scholar
20. Pandey, S. 1989. Economics of wild oats control: An application of a stochastic dynamic programming model. Agricultural Economics Discussion Paper 2/89. Univ. Western Australia. Nedlands.Google Scholar
21. Pannell, D. J. 1990. An economic response model of herbicide application for weed control. Aust. J. Agric. Econ. 34:223–241.Google Scholar
22. Pavlychenko, T. K. and Harrington, J. B. 1934. Competitive efficiency of weeds and cereal crops. Can. J. Res. 10:77–94.CrossRefGoogle Scholar
23. Pimentel, D., McLaughlin, L., Zepp, A., Lakitan, B., Kraus, T., Kleinman, P., Vancini, F., Roach, W. J., Graap, E., Keeton, W. S., and Selig, G. 1991. Environmental and economic effects of reducing pesticide use. Bioscience 41:402–409.CrossRefGoogle Scholar
24. Renner, K. A. and Black, J. R. 1991. SOYHERB: A computer program for soybean herbicide decision making. Agron. J. 83:921–925.CrossRefGoogle Scholar
25. Roush, M. L. and Radosevich, S. R. 1985. Relationships between growth and competitiveness of four annual weeds. J. App. Ecol. 22:895–905.CrossRefGoogle Scholar
26. Staniforth, D. W. 1957. Effects of annual grass weeds on the yield of corn. Agron. J. 49:551–555.CrossRefGoogle Scholar
27. Stoller, E. W., Harrison, S. K., Wax, L. M., Regnier, E. E., and Nafziger, E. D. 1987. Weed interference in soybeans (Glycine max). Rev. Weed Sci. 3:155–181.Google Scholar
28. Swinton, S. M. and King, R. P. (1994). A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44 (in press).Google Scholar
29. Swinton, S. M. 1991. A bioeconomic model for weed management in corn and soybean. Unpublished Ph.D. Dissertation. Dep. Agric. and Appl. Econ., Univ. Minnesota, St. Paul, MN. Pages 94–95.Google Scholar
30. 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
31. Welbank, P. J. 1963. A comparison of competitive effects of some common weed species. Ann. App. Biol. 51:107–125.CrossRefGoogle Scholar
32. White, K. J., Haun, S. A., Horsman, N. G., and Wong, S. D. 1988. SHAZAM Econometrics Computer Program: User's Reference Manual. McGraw-Hill, Montreal.Google Scholar
33. Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: Decision model for postemergence weed control in soybean. Agron. J. 83:413–417.CrossRefGoogle Scholar
34. Wilson, B. J. Yield responses of winter cereals to the control of broad-leaved weeds. 1986. Pages 75–82 in Proc. E.W.R.S. Symp., 1986: Economic Weed Control. European Weed Res. Soc. Stuttgart-Hohenheim.Google Scholar