Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-05-08T12:10:49.011Z Has data issue: false hasContentIssue false

Two Approaches to Weed Control Decision-Aid Software

Published online by Cambridge University Press:  12 June 2017

David A. Mortensen
Affiliation:
Dep. Agron., Univ. Nebraska, Lincoln, NE 68583-0915
Harold D. Coble
Affiliation:
Dep. Crop Sci., North Carolina State Univ. Raleigh, NC 27695-7620

Abstract

The number of computer applications for purposes of weed control decisions has increased dramatically in the past eight yr. During this time, many efficacy-, and population-based weed control decision aids have been developed. The complexity of decisions range from those based on optimal weed control (independent of net profitability) to those predicting weed population effects before most profitable treatments are selected. While the number of software applications has increased sharply, national software databases have not been funded and tracking has been made difficult. The purpose of this paper is to review the criteria upon which efficacy- and population-based weed control decision aids are founded, and to identify software currently available in each of the two categories.

Type
Education
Copyright
Copyright © 1991 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.)

References

Literature Cited

1. Bauer, T. A., Mortensen, D. A., and Wicks, G. A. 1990. Environmental variability and economic thresholds for soybeans. Weed Sci. Soc. Am. Abstr. 30:53.Google Scholar
2. Bolte, J. P., Moss, D. N., Burrill, L., and Appleby, A., 1988. A herbicide recommendation expert system. Abstr. Am. Soc. Agron., p. 66.Google Scholar
3. Brannstrom, A. 1988. AgCites: The Agricultural Software Citations Database. North Cent. Comp. Inst., Univ. Wisconsin. Google Scholar
4. Coble, H. D. 1987. Using economic thresholds for weeds in soybeans. Abstr. Weed Sci. Soc. Am. 27:94.Google Scholar
5. Coble, H. D. 1985. The development and implementation of economic thresholds for soybeans. p. 295307 in Frisbie, R. E. and Adkisson, P. L., eds. Integrated Pest Management on Major Agricultural Systems. Texas A&M Univ. Google Scholar
6. 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. 671678.Google 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. 17:112.CrossRefGoogle Scholar
8. Davis, J. R., and Clark, J. L. 1989. A selective bibliography of expert systems in natural resource management. AI Appl. 3:118.Google Scholar
9. 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. 34:972979.CrossRefGoogle Scholar
10. Lambert, D. K., and Wood, T. K. 1989. Partial survey of expert systems for agriculture and natural resources management. AI Appl. 3:4152.Google Scholar
11. Linker, H. M., York, A. C., and Wilhite, D. R. Jr. 1990. WEEDS–a system for developing a computer-based herbicide recommendation program. Weed Technol. 4:380385.Google Scholar
12. Matthews, G. A. 1984. Pest Management. Longman, London, p. 22.Google Scholar
13. Mortensen, D. A., and Coble, H. D. 1989. The influence of soil water content on common cocklebur (Xanthium strumarium) interference in soybeans. Weed Sci. 37:7683.Google Scholar
14. Niemann, P. 1986. Mehrjahrige anwedung des schadensschellen prinzeps bei der unkrautbekampfung auf einem landwirtschaftlichen betrieb. Proc. Eur. Weed Res. Soc. Symp. 1986, Economic Weed Control. p. 385392.Google Scholar
15. Reichelderfer, K. H. 1980. Economics of integrated pest management: discussion. Am. J. Agric. Econ. 62:10121013.Google Scholar
16. Schmidt, J. R. 1984. Computer utilization in weed science. N. Cent. Comp. Inst. p. 200.Google Scholar
17. 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. 32:7683.Google Scholar
18. Stern, V. M. 1973. Economic Thresholds. Annu. Rev. Entomol. 18:259280.Google Scholar
19. Strain, J. R., and Simmons, S. 1984. Updated inventory of computer programs. Coop. Ext. Serv. Univ. Fla. Google Scholar
20. Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9:337342.Google Scholar
21. Wagner, R. G., Opalach, D., Harrington, T. B., and Radosevich, S. R. 1989. A computerized system fox optimizing forest vegetation management treatment prescriptions. Proc. Weed Sci. Soc. Am. 29:46.Google Scholar
22. Weaver, S. E. 1990. Size-dependent economic thresholds for velvetleaf (Abutilon theophrasti Medic), cocklebur (Xanthium strumarium L.), and jimsonweed (Datura stramonium L.) in soybeans [Glycine max (L.) Merr.]. Weed Sci. Soc. Am. Abstr. 30:298.Google Scholar
23. Wiles, L. J., Gold, H. J., and Wilkerson, G. G. 1991. Weed spatial arrangement and improved scouting protocols for HERB users. Proc. Weed Sci. Soc. Am. 31:85.Google Scholar
24. Wilkerson, G. G., Coble, H. D., and Modena, S. A. 1987, A postemergence herbicide decision model for soybeans. Weed Sci. Soc. Am. 27: 95.Google Scholar
25. Wilson, R. G. 1988. Biology of weed seeds in the soil. p. 2539 in Altieri, M. A. and Liebman, M., eds. Weed Management in Agroecosystems: Ecological Approaches. CRC Press, Boca Raton, FL.Google Scholar
26. Agricultural Computing: 1990 Ag Software Directory. Doane Information Services. St. Louis, MO.Google Scholar