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Validation of HERB for Use in Peanut (Arachis hypogaea)

Published online by Cambridge University Press:  12 June 2017

Anthony D. White and
Harold D. Coble
Anthony D. White
Affiliation:
Department of Crop Science, North Carolina State University, Raleigh, NC 27695
Harold D. Coble
Affiliation:
Department of Crop Science, North Carolina State University, Raleigh, NC 27695
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Abstract

Researchers are currently developing predictive weed management models to aid producers in maintaining or improving economic profitability of peanut production while minimizing herbicide inputs and reducing environmental impact. HERB (Version 2.1.P), a computer decision model, has recently been developed for peanut and is now awaiting validation of weed control decisions before being released to the public. Field validation trials in 1994 and 1995 indicate that the current competitive index parameters in the HERB model are invalid, and statistically estimated competitive indices were generated. Estimating new parameters improved R 2 values from 0.37 to 0.61. New competitive index parameters allow the HERB model to more accurately predict the level of yield loss at a given weed density.

Keywords

Peanut, Arachis hypogaea L. ‘NC-10C’Computer decision modelcrop competitionCI, competitive indexCL, competitive loadDL, given weed densityHERB, computerized economic threshold decision modelNi, number of weed species per unit areaPOST, postemergenceTCL, total competitive load

Information

Type
Research
Information
Weed Technology , Volume 11 , Issue 3 , September 1997, pp. 573 - 579
Copyright
Copyright © 1997 by the Weed Science Society of America 

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References

Literature Cited

Auld, B. A. and Tisdell, C. A. 1987. Economic thresholds and response to uncertainty in weed control. Agric. Syst. 25:219–227.CrossRefGoogle Scholar
Barbour, J. C. and Bridges, D. C. 1995. A model of competition for light between peanut and broadleaf weeds. Weed Sci. 43:247–257.CrossRefGoogle Scholar
Coble, H. D. 1986. Development and implementation of economic thresholds for soybeans. In Frisbie, R. E. and Adkisson, P. L., eds. EIPM: Integrated Pest Management on Major Agricultural Systems. College Station, TX: Texas A & M University. pp. 295–307.Google Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6:191–195.CrossRefGoogle Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.CrossRefGoogle Scholar
Cousens, R., Doyle, C. J., Wilson, B. J., and Cussans, G. W. 1986. Modeling economics of controlling Arena fatua in winter wheat. Pestic. Sci. 17:1–12.CrossRefGoogle Scholar
Cousens, R., Peters, N.C.B., and Marshall, C. J. 1984. Models of yield loss weed density relationships. In Proceedings of the 7th International Colloquium on Weed Ecology, Biology and Systematics. Paris: Brighton Metropole. pp. 367–374.Google Scholar
Cousens, R., Wilson, B. J., and Cussans, G. W. 1985. To spray or not to spray: the theory behind the practice. In Proceedings of the 1985 British Crop Protection Conference on Weeds. Croyden, England: BCPC Publications. pp. 671–678.Google Scholar
Harris, T. C. and Ritter, R. L. 1987. Giant green foxtail (Setaria viridis var. major) and fall panicum (Panicum dichotomiflorum) competition in soybeans (Glycine max). Weed Sci. 35:663–668.CrossRefGoogle Scholar
Harvey, R. G. and Wagner, C. R. 1994. Using estimates of weed pressure to establish crop yield loss equations. Weed Technol. 8:114–118.CrossRefGoogle Scholar
Mortensen, D. A. and Coble, H. C. 1989. The influence of soil water content on common cocklebur (Xanthium strumarium) interference in soybeans (Glycine max). Weed Sci. 37:76–83.CrossRefGoogle Scholar
Pantone, D. J. and Baker, J. B. 1991. Weed–crop competition models and response–surface analysis of red rice competition in cultivated rice: a review. Crop Sci. 31:1105–1110.CrossRefGoogle Scholar
Radosevich, S. R. 1988. Methods to study crop and weed interactions. In Altieri, M. A. and Liebman, M., eds. Weed Management in Agroecosystems: Ecological Approaches. Boca Raton, FL: CRC Press. pp. 122–143.Google Scholar
[SAS] Statistic Analysis Systems. 1990. The NLIN procedure. In SAS User's Guide. Version 6, 4th ed., Volume 2. Cary, NC: Statistical Analysis Systems Institute. pp. 1135–1194.Google Scholar
Schreiber, M. M. 1985. Methods for crop–weed competition research. In World Soybean Research Conf. III: Proceedings. Boulder, CO: West View Press. pp. 1112–1115.Google Scholar
Wilcut, J. W., York, A. C., and Wehtje, G. R. 1994. The control and interaction of weeds in peanut (Arachis hypogaea). Rev. Weed Sci. 6:177–205.Google Scholar
Wiles, L. J., Wilkerson, G. G., and Coble, H. D. 1991. Weeding: A Weed Ecology and Economic Decision making INstructional Game. Weed Technol. 5:887–893.CrossRefGoogle Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybeans. Agron. J. 83:413–417.CrossRefGoogle Scholar