Binomial Sampling of Western Flower Thrips Infesting Flowering Greenhouse Crops Using Incidence-Mean Models
Ugine, Todd A. ; Sanderson, John P. ; Wraight, Stephen P. ; Shipp, Les ; Wang, K. ; Nyrop, Jan P.
In: Environmental Entomology, 2011, vol. 40, no. 2, p. 381-390
Ajouter à la liste personnelle- Summary
- Accurate assessments of thrips density are important for effective thrips management programs. Complicating the development of sampling plans for western flower thrips (WFT) Frankliniella occidentalis (Pergande) in greenhouse crops are the facts that they are small, difficult to detect, and attack a variety of crops, which may be grown concurrently within the same greenhouse. Binomial sampling was evaluated as an alternative to sampling plans based on complete enumeration. This work included comparison of incidence-mean models across diverse plant species (impatiens, cucumber, and marigold) to determine the possibility of using a generic model for sampling WFT in mixed crops. Data from laboratory-processed flower samples revealed that infestation rates calculated using a tally threshold of three thrips per flower provided the best estimates of thrips population densities in each tested crop and in the combined crops (composite data set). Distributions of thrips populations were similar across the three plant species, indicating potential for development of a generic sampling plan for mixed floral crops. Practical sampling methods for simple and complex flowers tested in the greenhouse (in situ) were evaluated via construction of binomial count operating characteristic functions. In the case of simple flowers (impatiens), visual inspections provided adequate estimates of thrips infestation rates at a low tally threshold, which ultimately enabled accurate estimation of thrips densities. However, visual inspection and tap-sampling of complex flowers (marigold) provided unreliable results. These findings indicate that use of binomial sampling methods in mixed floral crops will require development of more accurate sampling techniques