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Validation of Four Bioeconomic Weed Management Models for Sugarbeet (Beta vulgaris) Production

Published online by Cambridge University Press:  12 June 2017

John M. Shribbs ,
Edward E. Schweizer ,
Lauren Hergert  and
Donald W. Lybecker
John M. Shribbs
Affiliation:
ICI Americas Inc., P.O. Box 760, Mt. View, CA 94042
Edward E. Schweizer
Affiliation:
Agric. Res. Serv., U.S. Dep. Agric., Crops Res. Lab., 1701 Center Ave., Fort Collins, CO 80526
Lauren Hergert
Affiliation:
Western Sugar Co., P.O. Box 643, Alliance, NB 69301
Donald W. Lybecker
Affiliation:
Dep. Agric. and Res. Econ., Colorado State Univ., Fort Collins, CO 80523
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Abstract

The performance of four sequential weed management models that assumed either low or high risk was compared to the performance of two sugarbeet consultants, one who assumed low risk and the other high risk. Weed management recommendations were performed over one growing season at two locations, each with several levels of weed populations. Recommendations for preplant, postemergence, and layby herbicide treatments or late-season handweeding differed among the four weed management levels. The high-risk management level was labor intensive and the low-risk management level was herbicide intensive. Weed populations at harvest, recoverable sucrose, and net return above weed control costs were not different among the four weed management levels. Weeds can be controlled in sugarbeets by employing weed management practices based on bioeconomic modeling.

Keywords

SugarbeetBeta vulgaris L. ‘Monohikari’Economicsherbicidesweed controlweed thresholds
Type
Special Topics
Information
Weed Science , Volume 38 , Issue 4-5 , September 1990 , pp. 445 - 451
Copyright
Copyright © 1990 by the Weed Science Society of America 

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References

Literature Cited

1. Anonymous. 1984. Crop losses due to weeds in Canada and the United States. Weed Sci. Soc. Am., Champaign, IL. 22 pp.Google Scholar
2. Association of Official Agriculture Chemists. 1955. Official Methods of Analysis. 8th ed. Washington, DC. Pages 564–578.Google Scholar
3. Carruthers, A. and Oldfield, J.F.T. 1961. Methods for the assessment of beet quality. Int. Sugar J. 63:72–74, 103-105, 137-139.Google Scholar
4. 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
5. 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
6. 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
7. McWhorter, C. G. and Shaw, W. C. 1982. Research needs for integrated weed management systems. Weed Sci. 30:40–45 (Suppl. 1).CrossRefGoogle Scholar
8. Schweizer, E. E. and Dexter, A. G. 1987. Weed control in sugarbeets (Beta vulgaris) in North America. Rev. Weed Sci. 3:113–133.Google Scholar
9. Shribbs, J. M., Schweizer, E. E., and Lybecker, D. W. 1990. Bioeconomic weed management models for sugarbeet (Beta vulgaris) production. Weed Sci. 38:436–444.CrossRefGoogle Scholar
10. Walker, P. T. 1987. Quantifying the relationship between insect populations, damage, yield, and economic thresholds. Pages 114–125 in Teng, P. S., ed. Crop Loss Assessment and Pest Management. APS Press, St Paul. Google Scholar
11. Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1988. HERB V2.0: Herbicide decision model for postemergence weed control in soybeans–User's Manual. Bulletin No. 113. Crop Sci. Dep., North Carolina State Univ. 98 p.Google Scholar
12. Zadoks, J. C. 1985. On the conceptual basis of crop loss assessment: threshold theory. Annu. Rev. Phytopathol. 23:455–473.CrossRefGoogle Scholar