Sampling Whiteflies in Cotton: Validation and Analysis of Enumerative and Binomial Plans

We tested enumerative and binomial sampling plans developed for Bemisia tabaci (Gennadius) in 3,240 ha of commercial cotton as part of the implementation of a community-wide integrated pest management (IPM) program in Laveen and Tolleson, AZ, in 1994. We compared new field observations to sampling distribution models developed previously for all lifestages, and validated and analyzed the performance of 5 sampling plans based on these models by resampling field data from 129 to 284 sites. Mean-variance relationships for the new data differed statistically from mean-variance models previously developed for adults, but not for eggs or nymphs. Resampling analyses indicated that desired precision (SE to mean ratio) was rarely achieved, on average, by fixed-precision sequential sampling plans. These enumerative sampling plans provided better precision than desired at moderate to high densities of eggs and adults and worse precision than desired at most densities of nymphs. An empirical model relating mean density to the proportion of leaves infested with 3 or more adult B. tabaci was accurate at mean densities < 2 adults per leaf but over-predicted mean density at higher densities. Resampling analysis revealed that a sequential sampling plan based on this empirical model was accurate at classifying population density relative to an action threshold of 5 adults per leaf. At nominal α and β error rates of 0.10, population density was correctly classified ≈87% of the time. Accuracy was not improved by reducing nominal error rates to 0.05. Resampling analysis of a fixed-sample size plan based on n = 30 gave similar results and increasing sample size to 50 increased accuracy only 3%. Further resampling analyses that more closely approximated scouting protocols (15 sample units drawn from each of 2 quadrants in the field) resulted in an average accuracy of ≈70%. Accuracy declined when populations densities differed greatly among quadrants in a field. Most of this error was associated with making a decision to control when pest density was below the action threshold. Based on a robust validation technique using field observations representing a wide range of environmental and agronomic conditions, our sampling plans performed well and should be useful for estimating and classifying population densities of B. tabaci in cotton over a wide area.