Evaluation of presence-absence sampling plans for the diamondback moth (Plutella xylostella) (Lepidoptera: Plutellidae)

Two sets of sequential presence-absence sampling plans for the management of diamondback moth (DBM) were developed and evaluated, one for the classification of levels of the proportions of plants infested with larvae and the other for the classification of levels of larval density. The action thresholds investigated were 0.15, 0.25, 0.35 and 0.45 for proportion-based sampling plans and 0.2, 0.4, 0.6 and 0.8 larvae/plant for density-based sampling plans. Under the proportion-based sampling plans, the expected correct decision rates were 95% for 86-87% of all possible population levels and the expected average sample size was 50 plants for 73-87% of all possible population levels. Re-sampling analyses showed average sample sizes of <40 plants in reaching the 95% accuracy. For density-based sampling plans, an empirical proportiondensity model was first established. The resulting model was highly significant (P<0.001) and explained 97% of the total variation in the independent variable. Satisfactory performance ( 95% accuracy at 50 plants sample size) of the density-based sampling plans can be expected when the true population density does not lie in the vicinity of the action threshold. In conclusion, the sequential binomial sampling plans presented here can be used effectively in the monitoring of DBM populations for decision making. Introduction Diamondback moth (DBM), Plutella xylostella (L.) (Lepidoptera: Plutellidae), is a key pest of the important vegetable group that includes cabbage, broccoli, cauliflower, collards, rapeseed, mustard and Chinese cabbage. DBM is notorious for its rapid development of resistance to insecticides. In recent years, concerns over the resistance problem, human health and environment have forced the Brassica vegetable industry in many countries to implement some form of integrated pest management (IPM) and insecticide resistance management (IRM) (Talekar & Shelton 1993). However, grower adoption of IPM/IRM strategies has been slow. In Australia, most growers still spray their crops largely on a calendar basis. To encourage more growers to adopt threshold-based spray programs, which is the cornerstone of IPM and IRM, easy-to-use and time-efficient sampling plans are needed, as well as extension effort to convey the benefits of IPM and IRM. One easy-to-use sampling method is presence-absence sampling. Recording for each sampling unit only the presence/absence of the target pest, presence-absence sampling provides an attractive alternative to enumerative sampling, in which the number of pests in each sampling unit has to be recorded. The advantage is obvious for pest species forming aggregation clusters or those that are easily overlooked because of small size or cryptic behaviour of the pests and when the density of the pest population is high (Jones 1994). Presence-absence sampling is also the logical choice for IPM programs using proportionbased action thresholds, which are commonly used in horticulture crops. Sequential presence-absence sampling, or presence-absence sampling implemented under a sequential rule, provides the additional attractiveness of being potentially time-efficient as it enables decisions regarding pest population levels being made at minimal sample sizes. Although DBM does not form clusters, its earlier instars are quite small (<5mm) and easily overlooked on their leafy host plants. The larvae also tend to feed in hidden locations and wriggle away or drop down when disturbed. As a result, accurate recording of the number of larvae on a plant can be very difficult under field conditions. This paper investigates the efficiency and reliability of presence-absence sampling in the sequential classifications of DBM population levels under Wald’s (1947) sequential probability ratio test (SPRT). Sequential sampling plans for both proportion-based and density-based action thresholds were developed and evaluated. The action thresholds used were based on those practised by Brassica vegetable growers in Australia. Practical applications of the sampling plans are discussed. The management of diamondback moth and other crucifer pests Proceedings of the 4th International Workshop, Nov. 2001, Melbourne, Australia 170 Methods Data description Sampling data from four host crops, Brussels sprouts, cabbage, cauliflower and broccoli, collected in three states, South Australia (SA), Victoria (Vic) and Queensland (Qld), were used in this study. Sample sizes ranged from 35 to 300 plants. Data sets with a sample size of less than 100 plants were used for setting up the sampling plans and the rest for validating the sampling plans. Sequential sampling plans Under SPRT, a population is classified as below or above a prescribed action threshold (the AT) according to the positions of sample points relative to two parallel stop lines, the lower stop line and the upper stop line. The position of a sample point (n, Tn) is determined by the total number of plants sampled (n) and the number of infested plants found (Tn). The two stop lines are determined by the value of the AT and the distribution of the number of infested plants in the target population. In presence-absence sampling, the subject of interest is the proportion of infested plants in the target population. Since the real proportion of infested plants in a population at any given time is a fixed value, the number of infested plants found during a random sampling process observes binomial distribution, regardless of the patchiness of the distribution of the target organism causing the infestation. Stop lines under binomial distribution are calculated according to Fowler and Lynch (1987):