Binomial sampling plans for estimating and classifying population density of adult Bemisia tabaci in cotton

We used an empirical relationship to develop models for estimating and for classifying the population density of adult Bemisia tabaci (Gennadius) (Homoptera: Aleyrodidae) in cotton based on the proportion of infested leaves. We examined models based on tally thresholds (the minimum number of insects present before a leaf is considered infested) of 1, 2, 3, 4, 5, and 6 adults per fifth mainstem node leaf from the terminal. For the estimation of density, sampling precision (SE/mean) increased with higher tally thresholds (T); however, there was negligible improvement in precision with T>=3 adults per leaf. Using T=3 as few as 30 samples were necessary to achieve a precision of 0.25 over a wide range of population densities. To evaluate these binomial models for the classification of population density for pest management application, we used simulation analyses to determine operating characteristic curves (error probabilities), and to estimate average sample size and cost functions. Error probabilities and average sample sizes declined with higher values of T, but there was negligible decline in error probabilities using T>=3 adults per leaf, and the overall cost of sampling was lowest for T=3. Wald's sequential probability ratio test was used to formulate sequential sampling stop lines for classifying population density relative to two nominal action thresholds, 5 or 10 adults per leaf. Simulation analysis indicated that by using T=3, fewer than 30 samples, on average, were needed to classify populations relative to either action threshold. However, simulated error probabilities consistently exceeded the nominal error probabilities used to initially formulate sequential sampling stop lines regardless of the tally threshold. Comparing binomial models using T=1 or T=3 to independent data from four field sites, the model for T=1 was generally biased towards overprediction of mean density, but the T=3 model was a robust and relatively unbiased predictor of mean density. The bionomial sampling plans presented here should permit the rapid estimation of population density and enhance the efficiency of pest management programs based on the prescriptive suppression of B. tabaci in cotton.