2020 journal article
Cotton thrips infestation predictor: a practical tool for predicting tobacco thrips (Frankliniella fusca) infestation of cotton seedlings in the south-easternUnited States
PEST MANAGEMENT SCIENCE, 76(12), 4018–4028.
author keywords: cotton; thrips; management; forecasting; decision support; Gossypium hirsutum; Frankliniella fusca
MeSH headings : Animals; Gossypium; Seedlings; Thysanoptera; Tobacco; United States
TL;DR:
A composite model of thrips phenology and cotton seedling susceptibility is empirically developed to predict site-specific infestation risk so that monitoring and other resources can be allocated efficiently, and to inform stakeholders about the dynamics of thriPS infestation andotton seedling injury at a time when thrips are evolving resistance to commonly-used pesticides.
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UN Sustainable Development Goal Categories
2. Zero Hunger
(Web of Science)
13. Climate Action
(Web of Science)
15. Life on Land
(Web of Science)
Source: Web Of Science
Added: August 10, 2020
AbstractBackgroundThrips (order Thysanoptera) infestations of cotton seedlings result in plant injury, increasing the detrimental consequences of other challenges to production agriculture, such as abiotic stress or infestation by other pests. Using Frankliniella fusca as a thrips species of focus, we empirically developed a composite model of thrips phenology and cotton seedling susceptibility to predict site‐specific infestation risk so that monitoring and other resources can be allocated efficiently, to optimize the timing of thrips control measures to maximize effectiveness, and to inform stakeholders about the dynamics of thrips infestation and cotton seedling injury at a time when thrips are evolving resistance to commonly‐used pesticides.ResultsA mixture distribution model of thrips infestation potential, fit to data describing F. fusca adult dispersal in time, proved best for predicting infestations of F. fusca on cotton seedlings. Thrips generations occurring each year as a function of weather are represented as a probability distribution. A model of cotton seedling growth was also developed to predict susceptibility as a function of weather. Combining these two models resulted in a model of seedling injury, which was validated and developed for implementation as a software tool.ConclusionsExperimental validation of the implemented model demonstrated the utility of its output in predicting infestation risk. Successful implementation and use of the software tool derived from this model was enabled by close cooperation with university extension personnel, agricultural consultants, and growers, underscoring the importance of stakeholder and expert input to the success of applied analytical research. © 2020 Society of Chemical Industry