Cotton thrips infestation predictor: a practical tool for predicting tobacco thrips (Frankliniella fusca) infestation of cotton seedlings in the south‐eastern United States

Abstract

Background: Thrips (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. Results: A 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. Conclusions: Experimental 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.

Learnings from over a decade of increasing pesticide resistance in the redlegged earth mite, Halotydeus destructor (Tucker)

“…This included the southwest region in WA, and both Kangaroo Island and the Fleurieu Peninsula in SA. This spatial prediction model is novel in resistance management, although predictive models have more generally been used to direct monitoring efforts in other aspects of pest management, 34,35 and in surveillance programmes for invasive pests 36–38 …”

“…This included the southwest region in WA, and both Kangaroo Island and the Fleurieu Peninsula in SA. This spatial prediction model is novel in resistance management, although predictive models have more generally been used to direct monitoring efforts in other aspects of pest management, 34,35 and in surveillance programmes for invasive pests. [36][37][38] In Victoria in 2018, three adjacent fields were found with low resistance to organophosphates, with resistance ratios between fiveand seven-fold for omethoate and between seven-and 70-fold for malathion.…”

Spatial and temporal patterns of Frankliniella fusca (Thysanoptera: Thripidae) in wheat agroecosystems

“…Importantly, our two-year study may not have a sufficient temporal span to detect consistent trends in abiotic weather factors reported in previous studies (Chappell, Ward, et al, 2020;Morsello et al, 2008). We do know that F. fusca biology is greatly influenced by temperature and precipitation both directly and indirectly.…”

The Epidemiology of Plant Virus Disease: Towards a New Synthesis

“…Although the false-positive rates were high in both cases, this was considered reasonable given the inbuilt bias of the system in relation to uncertainty in the risk assessment. Weather variables were also used as predictors for both cotton seedling susceptibility to thrips infestation and thrips generation times [83]. Combining these two aspects gave a model for seedling damage that was further developed as software for a prediction tool.…”

“…Compared with fungal plant pathogens, there is a need for further evidence on climate change effects on plant viruses in crops and wild populations [75] especially where there are different effects on host growth and aphid performance depending on virus infection [263]. In terms of linking epidemiological research and analysis to disease management, there is now a greater recognition that appropriate spatial and temporal scales are required for forecasting [82,83]. Crop heterogeneity is known to mitigate the population build-up of arthropod pests and the damage they cause, and these effects should be extended to consequent effects on vectored plant virus epidemics [103,105,107].…”