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 (Genna dius) (Homoptera: Aleyrodidae) in cotton based on the proportion of in fested leaves. We examined models based on tally thresholds (the minim um 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 term inal. For the estimation of density, sampling precision (SE/mean) incr eased with higher tally thresholds (T); however, there was negligible improvement in precision with T greater than or equal to 3 adults per leaf. Using T = 3 as few as 30 samples were necessary to achieve a pre cision 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 Co determine operating characteristic curves (error probabilities), and to estimat e average sample size and cost functions. Error probabilities and aver age sample sizes declined with higher values of T, but there was negli gible decline in error probabilities using T greater than or equal to 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 seq uential sampling stop lines for classifying population density relativ e to two nominal action thresholds, 5 or 10 adults per leaf. Simulatio n analysis indicated that by using T = 3, fewer than 30 samples, on av erage, were needed to classify populations relative to either action t hreshold. However, simulated error probabilities consistently exceeded the nominal error probabilities used to initially formulate sequentia l sampling stop lines regardless of the tally threshold. Comparing bin omial 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 unbi ased predictor of mean density. The binomial sampling plans presented here should permit the rapid estimation of population density and enha nce the efficiency of pest management programs based on the prescripti ve suppression of B. tabaci in cotton.