Hostname: page-component-5b777bbd6c-6lqsf Total loading time: 0 Render date: 2025-06-20T15:53:16.905Z Has data issue: false hasContentIssue false
  • English
  • Français

Bioeconomic Modeling to Simulate Weed Control Strategies for Continuous Corn (Zea mays)

Published online by Cambridge University Press:  12 June 2017

Robert P. King ,
Donald W. Lybecker ,
Edward E. Schweizer  and
Robert L. Zimdahl
Robert P. King
Affiliation:
Dep. Agric. and Appl. Econ., Univ. Minnesota, St. Paul, MN 55108
Donald W. Lybecker
Affiliation:
Dep. Agric. and Nat., Res. Econ.
Edward E. Schweizer
Affiliation:
Agric. Res. Serv., U.S. Dep. Agric., Crops Res. Lab.
Robert L. Zimdahl
Affiliation:
Dep. Plant Path. and Weed Sci., Colorado State Univ., Fort Collins, CO 80523
Get access Rights & Permissions [Opens in a new window]

Abstract

Grass and broadleaf weed densities and seed numbers, weed control practices, and grain yields were included in a bioeconomic model that evaluates alternative weed management strategies for continuous corn (Zea mays L.). Weed seed numbers in soil and herbicide carry-over provided intertemporal links. Four weed management strategies – two fixed, one mixed, and one flexible – were evaluated with annualized net returns as the performance indicator. The flexible strategy (weed control based on observed conditions) had the largest annualized net return for high and low initial weed seed numbers. The fixed weed management strategy (weed control predetermined) of an annual application of only a preemergence herbicide ranked second in terms of annualized net returns for high weed seed numbers. The mixed weed management strategy of alternative year applications of preemergence herbicide and “as needed” applications of postemergence herbicide ranked second for low initial weed seed numbers. The fixed weed management strategy of alternate year application of preemergence herbicide only generated the lowest annualized net return, regardless of initial weed seed numbers.

Keywords

Interdisciplinary weed researchweed management systemsannualized net returnsweed seed numbers
Type
Special Topics
Information
Weed Science , Volume 34 , Issue 6 , November 1986 , pp. 972 - 979
Copyright
Copyright © 1986 by the Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Literature Cited

1. Chisaka, H. 1977. Weed damage to crops: Yield loss due to weed competition. Pages 1–16 in Fryer, J. D. and Matzunaka, S., eds. Integrated Control of Weeds. Univ. of Tokyo Press, Tokyo, Japan.Google Scholar
2. Cussans, G. W. 1981. Strategic planning for weed control – A researcher's view. Pages 823–832 in Proc. 1980 Br. Crop Protection Conf. – Weeds. ARC Weed Res. Organization, Yarnton, Oxford, U.K. Google Scholar
3. Headley, J. C. 1972. Defining the economic threshold. Pages 100–108 in Pest Control Strategies for the Future. Nat. Acad. Sci., Washington, DC.Google Scholar
4. Headley, J. C. 1975. The economics of pest management decisions by individual growers. Iowa State J. Res. 49:623–628.Google Scholar
5. Johnston, J. 1972. Econometric Methods. 2d ed. McGraw-Hill Book Co., New York, NY. 437 pp.Google Scholar
6. Kmenta, J. 1971. Elements of Econometrics. The MacMillan Co., New York. 655 pp.Google Scholar
7. Lybecker, D. W., King, R. P., Schweizer, E. E., and Zimdahl, R. L. 1984. Economic analysis of two weed management systems for two cropping rotations. Weed Sci. 32:90–95.Google Scholar
8. Marra, M. C. and Carlson, G. A. 1983. An economic threshold model for weeds in soybeans (Glycine max). Weed Sci. 31:604–609.Google Scholar
9. McWhorter, C. G. and Shaw, W. C. 1982. Research needs for integrated weed management systems. Weed Sci. Suppl. 30: 40–45.Google Scholar
10. Mortimer, A. M. 1977. Aspects of the seed population dynamics of Dactylis glomerata L., Holcus lanatus L., Plantago lanceolata L., and Poa annua L. Pages 687–694 in Proc. 1976 Br. Crop Protection Conf. – Weeds. ARC Weed Res. Organization, Yarnton, Oxford, U.K. Google Scholar
11. Nie, N. H., Hull, C. H., Jenkins, J. G., Steinbrenner, K., and Bent, D. H. 1975. Statistical package for the social sciences. 2d ed. McGraw-Hill Book Co., New York. 675 pp.Google Scholar
12. Norton, G. A. 1976. Analysis of decision making in crop protection, Agro-Ecosystems 3:27–44.Google Scholar
13. Schweizer, E. E. and Zimdahl, R. L. 1984. Weed seed decline in irrigated soil after six years of continuous corn (Zea mays) and herbicides. Weed Sci. 32:76–83.Google Scholar
14. Taylor, R. C. and Burt, O. R. 1984. Near-optimal management strategies for controlling wild oats in spring wheat. Am. J. Agric. Econ. 66:50–60.Google Scholar
15. Wilson, B. J. 1979. The cost of wild oats (Avena spp.) in Australian wheat production, Proc. 7th Asian-Pacific Weed Sci. Soc. Conf. 441–444.Google Scholar
16. Zimdahl, R. L. 1979. Weed competition. Do we know enough? Proc. West. Soc. Weed Sci. 32:13–29.Google Scholar
17. Zimdahl, R. L. 1980. Weed-crop competition, a review. Int. Plant Prot. Ctr., Oregon State Univ., Corvallis. 195 pp.Google Scholar