Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-05T01:45:49.441Z Has data issue: false hasContentIssue false

Expert System Evaluation and Implementation for Soybean (Glycine max) Weed Management

Published online by Cambridge University Press:  12 June 2017

C. Dale Monks
Affiliation:
Agron., Soils Dep., Auburn Univ., Auburn, AL 36849
David C. Bridges
Affiliation:
Dep. Agron., Univ. Georgia, Griffin, GA 30223
John W. Woodruff
Affiliation:
Coop. Ext. Serv., Tifton, GA 31993
Tim R. Murphy
Affiliation:
Coop. Ext. Serv., Tifton, GA 31993
Daniel J. Berry
Affiliation:
Dep. Agron., Griffin, GA 30223

Abstract

HERB, a computer-based expert system for soybean weed management developed at North Carolina State University, was evaluated for managing weeds under Georgia conditions. The project was initiated in two phases: a) training Cooperative Extension county agents followed by evaluation in six Georgia counties and b) revision, licensing, and distribution across the state. Field evaluations indicated that HERB was not highly accurate for predicting final yield loss because of weed species senescence and environmental extremes later in the growing season. HERB generally provided a reasonable prediction for a positive economic return due to treatment approximately 60% of the time. Accuracy was directly dependent upon the accuracy of weed-free yield estimates and extremes in growing conditions. HERB should not be the sole source of weed management information but may be useful to producers and county agents where mixed or low populations of weeds exist. The program was distributed statewide in 1993 after revision, duplication, and training was completed.

Type
Research
Copyright
Copyright © 1995 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.)

References

Literature Cited

1. Anonymous. 1992. A new technological era for American agriculture. Off. Technol. Assess. Congress. Board of the 102nd Congress. p. 5.Google Scholar
2. Anonymous. 1990. Integration of food safety and water quality concepts. Oklahoma State University, Stillwater. Circular E-903.Google Scholar
3. Castner, E. P. and Banks, P. A. 1989. Soybean herbicide decision model verification in Georgia. Proc. South. Weed Sci. Soc. 42:324.Google Scholar
4. Green, J. M. 1991. Maximizing herbicide efficiency with mixtures and expert systems. Weed Technol. 5:894897.Google Scholar
5. Jensen, P. K. and Kudsk, P. 1988. Prediction of herbicide activity. Weed Res. 28:473478.Google Scholar
6. Jones, R. E. and Banks, P. A. 1988. Verification of a herbicide decision model in Georgia. Proc. South. Weed Sci. Soc. 41:314.Google Scholar
7. Keisling, T. C., Oliver, L. R., Crowley, R. H., and Baldwin, F. L. 1984. Potential use of response surface analyses for weed management in soybeans (Glycine max). Weed Sci. 32:552557.Google Scholar
8. Kropff, M. J. and Lotz, L.A.P. 1992. Optimization of weed management systems: the role of ecological models of interplant competition. Weed Technol. 6:462470.Google Scholar
9. Kropff, M. J., Weaver, S. E., and Smits, M. A. 1992. Use of ecophysiological models for crop-weed interference: relations among weed density, relative time of weed emergence, relative leaf area and yield loss. Weed Sci. 40:296301.CrossRefGoogle Scholar
10. 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
11. Marra, M. C. and Carlson, G. A. 1983. An economic threshold model for weeds in soybeans (Glycine max). Weed Sci. 31:604609.Google Scholar
12. Murphy, T. R. and Bridges, D. C. 1993. HERB user's guide for Georgia soybeans. The University of Georgia Coop. Ext. Ser. No. CSS 94-02.Google Scholar
13. Osteen, C. D. and Smezdra, P. I. 1989. Agricultural pesticide use trends and policy issues. U.S. Dep. of Ag. Econ. Res. Ser. Rep. No. 622.Google Scholar
14. Russell, M. H. and Layton, R. J. 1992. Models and modeling in a regulatory setting: considerations, applications and problems. Weed Technol. 6:673676.Google Scholar
15. Weaver, S. E., Kropff, M. J., and Growneveld, R.M.W. 1992. Use of ecophysiological models for crop-weed interference: the critical period of weed interference. Weed Sci. 40:302307.Google Scholar
16. Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G. 1992. Spatial distribution of broadleaf weed in North Carolina soybean (Glycine max) fields. Weed Sci. 40:554557.Google Scholar
17. Wiles, L. J., Wilkerson, G. G., and Coble, H. D. 1991. WEEDING: A weed ecology and economic decision making instructional game. Weed Technol. 5:887893.Google Scholar
18. Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: Decision model for postemergence weed control in soybean. Agron. J. 83:413417.Google Scholar