Evaluation of binomial sequential classification sampling plan for leafmine of Liriomyza trifolii (Diptera : Agromyzidae) in greenhouse tomatoes

Abstract

Sequential binomial sampling plans for classifying the leafmine density of Liriomyza trifolii ( Burgess) were developed and evaluated in tomato greenhouses in southern Korea during 2003-2004. Two action thresholds (mAT), three and seven leafmines per tomato leaf, were set by examining the relationship between the leafmine density and the decrease in the tomato yield. An empirical P-T-m model, which is expressed as ln(-ln(1-P-T)) = gamma+delta ln(m), was used to examine the relationship between the proportion of the infested tomato leaves (P-T) with at least T (tally threshold) leafmines and the mean leafmine density (m). The empirical model showed an improvement in the fit up to T = 5, which then stabilised at T > 5. Wald's sequential probability ratio test was used to formulate the sequential sampling stop lines relative to m(AT) = 3 and 7 with T 1, 2, 3, 4, and 5. The sampling plans were evaluated using the operating characteristic (OC) and the average sample number (ASN) functions. The robustness of the sampling plans improved with increasing T values but the improvements were negligible at T >= 3. Simulation analysis with the OC and ASN functions indicated that a binomial model with T = 3 was optimal and less than 70 samples were required to classify the number of leafmines relative to either mAT values. Resampling simulation of eight independent data sets with T = 3 showed that the performance of the sampling plans was superior to that expected from the established OC and ASN functions. The correct classification rate was at least 99% and the matching sample size was < 57.