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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
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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 

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