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 (m AT), 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 )) = γ + δ 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 m AT 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.