Spatial Distribution Pattern and Sampling Plans for Thrips palmi (Thysanoptera: Thripidae) in Eggplant Fields

Abstract

Understanding the spatial distribution and population dynamics of insect pests is essential for developing reliable monitoring and integrated pest management strategies in vegetable cropping systems. The present study investigated the spatial distribution pattern and developed sampling plans for Thrips palmi (Thysanoptera: Thripidae) in eggplant fields under field conditions. Thrips populations were sampled periodically from different sections of eggplant fields during the cropping season, and population density data were analyzed using dispersion indices and aggregation models. Statistical approaches including variance-to-mean ratio, Taylor’s power law, and Iwao’s patchiness regression were employed to determine the distribution behavior of T. palmi populations. The results revealed that T. palmi exhibited an aggregated distribution pattern within eggplant fields, with clustering intensity varying according to crop growth stage and environmental conditions. Higher population densities were observed during warm and dry periods favorable for thrips multiplication. Aggregated distribution was particularly pronounced on young leaves and flowering parts of the plants, indicating preferred feeding and reproductive sites. Based on the distribution parameters, sequential and fixed-precision sampling plans were developed to improve monitoring efficiency and reduce sampling effort in pest surveillance programs. The proposed sampling strategies provided reliable estimation of thrips population density with acceptable precision levels for practical field applications. Understanding the spatial ecology of T. palmi can support timely decision-making and optimize pest control interventions in eggplant production systems. The findings contribute valuable information for integrated pest management programs aimed at minimizing crop losses and reducing unnecessary insecticide applications through efficient pest monitoring techniques.