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Economics and Effectiveness of Alternative Weed Scouting Methods in Peanut

Published online by Cambridge University Press:  20 January 2017

Bridget L. Robinson ,
Jodie M. Moffitt ,
Gail G. Wilkerson  and
David L. Jordan
Bridget L. Robinson
Affiliation:
Department of Crop Science, North Carolina State University, Campus Box 7620, Raleigh, NC 27695
Jodie M. Moffitt
Affiliation:
Department of Crop Science, North Carolina State University, Campus Box 7620, Raleigh, NC 27695
Gail G. Wilkerson*
Affiliation:
Department of Crop Science, North Carolina State University, Campus Box 7620, Raleigh, NC 27695
David L. Jordan
Affiliation:
Department of Crop Science, North Carolina State University, Campus Box 7620, Raleigh, NC 27695
*
Corresponding author's E-mail: gail_wilkerson@ncsu.edu
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Abstract

On-farm trials were conducted in 16 North Carolina peanut fields to obtain estimates of scouting times and quality of herbicide recommendations for different weed scouting methods. The fields were monitored for weed species and population density using four scouting methods: windshield (estimate made from the edge of the field), whole-field (estimate based on walk through the field), range (weed densities rated on 1–5 scale at six locations in the field), and counts (weeds estimated by counting at six locations in the field). The herbicide application decision support system (HADSS) was used to determine theoretical net return over herbicide investment and yield loss ($ and %) for each treatment in each field. Three scouts estimated average weed population densities using each scouting method. These values were entered into HADSS to obtain treatment recommendations. Independently collected count data from all three scouts were combined to determine the optimal treatment in each field and the relative ranking of each available treatment. When using the whole-field method, scouts observed a greater number of weed species than when using the other methods. The windshield, whole-field, and range scouting methods tended to overestimate density slightly at low densities and underestimate density substantially at high densities, compared to the count method. The windshield method required the least amount of time to complete (6 min per field), but also resulted in the greatest average loss. Even for this method, recommendations had theoretical net returns within 10% of the return for the optimal treatment 80% of the time. The count method appears to have less economic risk than the windshield, whole-field, and range scouting methods.

Keywords

Peanut, Arachis hypogaea L.Decision support systemeconomic thresholdintegrated pest managementweed managementweed interference
Type
Research
Information
Weed Technology , Volume 21 , Issue 1 , March 2007 , pp. 88 - 96
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bange, M. P., Deutscher, S. A., Larsen, D., Linsley, D., and Whiteside, S. 2004. A handheld decision support system to facilitate insect pest management in Australian cotton systems. Comp. Elec. Ag. 43:131–147.Google Scholar
Bennett, A. C., Price, A. J., Sturgill, M. C., Buol, G. S., and Wilkerson, G. G. 2003. HADSS™, Pocket HERB™, and WebHADSS™: decision aids for field crops. Weed Technol. 17:412–420.Google Scholar
Berti, A., Bravin, F., and Zanin, G. 2003. Application of decision-support software for postemergence weed control. Weed Sci. 51:618–627.CrossRefGoogle Scholar
Buhler, D. D., Liebman, M., and Obrycki, J. J. 2000. Theoretical and practical challenges to an IPM approach to weed management. Weed Sci. 48:274–280.CrossRefGoogle Scholar
Bullen, S. G., Smith, N., and Pease, J. 2002. in Sholar, R.S., ed. Regional and Farm Level Economic Impacts of Peanut Quota Changes. Stillwater, OK Proceedings of the American Peanut Research and Education Society. 42.Google Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Sci. 45:364–373.CrossRefGoogle Scholar
Chvosta, J., Thurman, W. N., and Brown, B. 2002. The Economic Effects of Considered Change in Federal Peanut Policy. in Sholar, R.S., ed Stillwater, OK Proceeding of the American Peanut Research and Education Society. 42–43.Google Scholar
Clay, , and Johnson, . 2002. Scouting for Weeds. Crop Manage Online Resource: DOI:10.1094/cm_2002_1206_01. Published Dec 2002.Google Scholar
Clay, S. A., Lems, G. J., Clay, D. E., Forcella, F., Ellsbury, M. M., and Carlson, C. G. 1999. Sampling weed spatial variability on a fieldwide scale. Weed Sci. 47:674–681.CrossRefGoogle Scholar
Colbach, N., Dessaint, F., and Forcella, F. 1999. Evaluating field-scale sampling methods for the estimation of mean plant densities of weeds. Weed Res. 40:411–430.CrossRefGoogle Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.CrossRefGoogle Scholar
Cousens, R., Peters, N. C. B., and Marshal, C. J. 1984. Models of Yield Loss—Weed Density Relationships. Proceedings of the 7th International Symposium On Weed Biology, Ecology and Systematics 367–374.Google Scholar
Gerhards, R. and Christensen, S. 2003. Real-time weed detection, decision making and patch spraying in maize, sugarbeet, winter wheat and winter barley. Weed Res. 43:385–392.Google Scholar
Gold, H. J., Bay, J., and Wilkerson, G. G. 1996. Scouting for weeds, based on the negative binomial distribution. Weed Sci. 44:504–510.CrossRefGoogle Scholar
Johnson, G. A., Mortensen, D. A., Young, L. J., and Martin, A. R. 1996. Parametric sequential sampling based on multistage estimation of the negative binomial parameter k . Weed Sci. 44:555–559.CrossRefGoogle Scholar
Jordan, D. L. 2005. Weed Management in Peanut. North Carolina Cooperative Extension Service AG-331 2005 Peanut Information. 35–60.Google Scholar
Jordan, D. L., Brandenburg, R. L., Bailey, J. E., Johnson, P. D., Royals, B. M., and Curtis, V. L. 1999. Cost effectiveness of pest management systems in peanut (Arachis hypogaea L.) grown in North Carolina. Peanut Sci. 26:85–94.CrossRefGoogle Scholar
Jordan, D. L., Wilkerson, G. G., and Krueger, D. W. 2003. Evaluation of scouting methods in peanut (Arachis hypogaea) using theoretical net returns from HADSS™. Weed Technol. 17:358–365.Google Scholar
Krueger, D. W., Wilkerson, G. G., Coble, H. D., and Gold, H. J. 2000. An economic analysis of binomial sampling for weed scouting. Weed Sci. 48:53–60.Google Scholar
MacDonald, G. E., Bridges, D. C., and Brecke, B. J. 1998. Validation of HERB computer decision aid model for peanuts. Proc. S. Weed Sci. Soc. 51:216.Google Scholar
Munier-Jolain, N. M., Chauvel, B., and Gasquez, J. 2002. Long-term modeling of weed control strategies: analysis of threshold-based options for weed species with contrasted competitive abilities. Weed Res. 42:107–122.Google Scholar
Murdock, S. W. and Murray, D. S. 2002. Obtaining weed populations for computerized Decision Support System (DSS) inputs: counts versus estimations. Proc. S. Weed Sci. Soc. 55:130.Google Scholar
Mutch, D. R. and Michalak, P. S. 1985. A comparative analysis of three weed sampling methods in corn. Weeds Today. 16/4:10–11.Google Scholar
Neeser, C., Dille, J. A., Krishman, G., Mortensen, D. A., Rawlinson, J. T., Martin, A. R., and Bills, L. B. 2004. WeedSoft: a weed management decision support system. Weed Sci. 52:115–122.CrossRefGoogle Scholar
Pasqual, G. M. 1994. Development of an expert system for the identification and control of weeds in wheat, triticale, barley and oat crops. Comp. Elec. Ag. 10:117–134.Google Scholar
Rew, L. J. and Cousens, R. D. 2000. Spatial distribution of weeds in arable crops; are current sampling and analytical methods appropriate? Weed Res. 41:1–18.CrossRefGoogle Scholar
Scott, G. H., Askew, S. D., Wilcut, J. W., and Bennett, A. C. 2002. Economic evaluation of HADSS™ computer program in North Carolina peanut. Weed Sci. 50:91–100.CrossRefGoogle Scholar
Sturgill, M. C., Wilkerson, G. G., Wilcut, J. W., Bennett, A. C., and Buol, G. S. 2003. HADSS 2003 User's Manual. Res. Bull. 192, Raleigh, NC Crop Science Department, NCSU.Google Scholar
White, A. D. and Coble, H. D. 1997. Validation of HERB for use in peanut. Weed Technol. 11:573–579.CrossRefGoogle Scholar
Wilcut, J. W., York, A. C., Grichar, W. J., and Wehtje, G. R. 1995. The biology and management of weeds in peanut (Arachis hypogaea). in Pattee, H.E., Stalker, H.T., eds. Advances in Peanut Science. Stillwater, OK American Peanut Research and Education Society. 207–244.Google Scholar
Wiles, L. J., Gold, H. J., and Wilkerson, G. G. 1993. Modeling the uncertainty of weed density estimates to improve post-emergence herbicide control decisions. Weed Res. 33:241–252.CrossRefGoogle Scholar
Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G. 1992c. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Sci. 40:554–557.Google Scholar
Wiles, L. J., Wilkerson, G. G., and Gold, H. J. 1992a. Value of information about weed distribution for improving postemergence control decisions. Crop Prot. 11:547–554.CrossRefGoogle Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992b. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40:546–553.CrossRefGoogle Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J. 83:413–417.CrossRefGoogle Scholar
Wilkerson, G. G., Wiles, L. J., and Bennett, A. C. 2002. Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach. Weed Sci. 50:411–424.CrossRefGoogle Scholar