Hostname: page-component-76fb5796d-zzh7m Total loading time: 0 Render date: 2024-04-26T19:00:39.276Z Has data issue: false hasContentIssue false
  • English
  • Français

An economic analysis of binomial sampling for weed scouting

Published online by Cambridge University Press:  20 January 2017

David W. Krueger ,
Gail G. Wilkerson ,
Harold D. Coble  and
Harvey J. Gold
Gail G. Wilkerson
Affiliation:
Department of Crop Science, North Carolina State University, Raleigh, NC 27695-7620
Harold D. Coble
Affiliation:
Department of Crop Science, North Carolina State University, Raleigh, NC 27695-7620
Harvey J. Gold
Affiliation:
Biomathematics Program, Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203
Get access Rights & Permissions [Opens in a new window]

Abstract

Full-count random sampling has been the traditional method of obtaining weed densities. Currently it is the recommended scouting procedure when using HERB, a herbicide selection decision aid. However, alternative methods of scouting that are quicker and more economical need to be investigated. One possibility that has been considered is binomial sampling. Binomial sampling is the procedure by which density is estimated from the number of random quadrats in which the count of individuals is equal to or less than a specified cutoff value. This sampling method has been widely used for insect scouting. There has also been interest in using binomial sampling for weed scouting. However, an economic analysis of this sampling method for weeds has not been performed. In this paper, the results of an economic analysis using simulations with binomial sampling and the HERB model are presented. Full-count sampling was included in the simulations to provide a benchmark for comparison. The comparison was made in terms of economic losses incurred when the estimated weed density obtained from sampling was inaccurate and a herbicide treatment was selected that did not maximize profits. These types of losses are referred to as opportunity losses. The opportunity losses obtained from the simulations indicate that in some situations binomial sampling may be a viable economic alternative to full-count sampling for fields with weed populations that follow a negative binomial distribution, assuming no prior knowledge of weed densities or negative binomial k values.

Keywords

Glycine max, soybeansNegative binomial distributionHERB
Type
Research Article
Information
Weed Science , Volume 48 , Issue 1 , February 2000 , pp. 53 - 60
Copyright
Copyright © 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

Baldwin, F. L. 1984. Arkansas pilot weed scouting program. Proc. Northeastern Weed Sci. Soc. 38 (Supplement): 13–16.Google Scholar
Berti, A., Zanin, G., Baldoni, G., Grignani, C., Mazzoncini, M., Montemurro, P., Tei, F., Vazzana, C., and Viggiani, P. 1992. Frequency distribution of weed counts and applicability of a sequential sampling method to integrated weed management. Weed Res. 32:39–44.Google Scholar
Binns, M. R. 1990. Robustness in binomial sampling for decision-making in pest incidence. Pages 63–80 In Bostanian, N. J., Wilson, L. T., and Dennehy, T. J., eds. Monitoring and Integrated Management of Arthropod Pests of Small Fruit Crops. Andover, Hampshire: Intercept Ltd.Google Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distributions on prediction of yield loss. J. Appl. Ecol. 27:735–742.Google Scholar
Cheshire, J. M., Funderburk, J. E., Zimet, D. J., Mack, T. P., and Gilreath, M. E. 1989. Economic injury levels and binomial sampling program for lesser cornstalk borer (Lepidoptera: Pyralidae) in seedling grain sorghum. J. Econ. Entomol. 82:270–274.CrossRefGoogle Scholar
Cho, K., Eckel, C. S., Walgenbach, J. F., and Kennedy, G. G. 1995. Spatial distribution and sampling procedures for Frankliniella spp. (Thysanoptera: Thripidae) in staked tomato. J. Econ. Entomol. 88:1658–1665.Google Scholar
Conn, J. S., Proctor, C. H., and Skroch, W. A. 1982. Selection of sampling methods to determine weed abundance in apple (Malus domestica) orchards. Weed Sci. 30:35–40.Google Scholar
Dieleman, A., Hamill, A. S., Fox, G. C., and Swanton, C. J. 1996. Decision rules for postemergence control of pigweed (Amaranthus spp.) in soybean (Glycine max) . Weed Sci. 44:126–132.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.Google Scholar
Johnson, G. A., Mortensen, D., Young, L. J., and Martin, A. R. 1995. The stability of weed seedling population models and parameters in eastern Nebraska corn (Zea mays) and soybean (Glycine max) fields. Weed Sci. 43:604–611.Google Scholar
Kwon, T., Young, D. L., Young, F. L., and Boerboom, C. M. 1995. PALWEED: WHEAT: A bioeconomic decision model for postemergence weed management in winter wheat (Triticum aestivum) . Weed Sci. 43:595–603.Google Scholar
Legg, D. E., Nowierski, R. M., Feng, M. G., Peairs, F. B., Hein, G. L., Elberson, L. R., and Johnson, J. B. 1994. Binomial sequential sampling plans and decision support algorithms for managing the Russian wheat aphid (Homoptera: Aphididae) in small grains. J. Econ. Entomol. 87:1513–1533.CrossRefGoogle Scholar
Lemieux, C., Cloutier, D. C., and Leroux, G. D. 1992. Sampling quackgrass (Elytrigia repens) population. Weed Sci. 40:534–541.CrossRefGoogle Scholar
Richter, O. and Söndgerath, D. 1990. Parameter estimation in ecology: The link between data and models. New York: VCH Publishers, New York. 218 p.Google Scholar
Sorenson, C. E., Van Duyn, J. W., Kennedy, G. G., Bradley, J. R. Jr., Eckel, C. S., and Fernandez, G.C.J. 1995. Evaluation of a sequential egg mass sampling system for predicting second-generation damage by European corn borer (Lepidoptera: Pyralidae) in field corn in North Carolina. J. Econ. Entomol. 88:1316–1323.CrossRefGoogle Scholar
Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44:313–335.Google Scholar
Thomas, A. G. 1985. Weed survey system used in Saskatchewan for cereal and oilseed crops. Weed Sci. 33:34–43.Google Scholar
Thompson, G. E. 1972. Statistics for decisions, an elementary introduction. Boston: Little, Brown and Company. 297 p.Google Scholar
Thompson, S. K. 1992. Sampling. New York: John Wiley and Sons. 343 p.Google Scholar
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. 9:337–342.Google Scholar
White, G. C. and Bennetts, R. E. 1996. Analysis of frequency count data using the negative binomial distribution. Ecol. 77:2549–2557.CrossRefGoogle Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992a. Modeling weed distribution for improved postemergence control decision. Weed Sci. 40:546–553.Google Scholar
Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G. 1992b. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Sci. 40:554–557.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.Google Scholar
Wilkerson, G. G., Coble, H. D., and Modena, S. A. 1995. HERB Version 5.0 User's Manual. Research Report 152. Raleigh, NC: North Carolina State University Crop Science Department.Google Scholar
Wilson, L. T. and Room, P. M. 1983. Clumping patterns of fruit and arthropods in cotton, with implications for binomial sampling. Environ. Entomol. 12:50–54.Google Scholar
York, A. C. 1994. Weed Management in Soybeans. Extension Publication AG-274. Raleigh, NC: North Carolina Cooperative Extension Service.Google Scholar