LB 35-294 - Climate smart pest management: Using pest seasonal forecasts to build sustainable agricultural systems

My research aims to explore the potential of population dynamics modelling and seasonal forecasting for sustainable and environmentally-friendly agricultural systems. Specifically, I focus on using these tools to assist farmers in making informed decisions about pest population management programs, with a particular focus on the Mediterranean fruit fly, Ceratitis capitata. This pest is considered one of the most damaging fruit fly pests worldwide, infesting more than 250 species of fruits, including citrus and deciduous fruits.The current pest control policy for C. capitata is largely based on manual trap monitoring, population suppression by mass trapping, and complementary ground and aerial insecticide spraying. However, decision support tools that allow for the minimization of spraying are scarce due to the lack of a deep understanding of pest presence and infestation risks at a fine spatiotemporal scale, as well as the difficulty in identifying the optimal combination of control tactics that maximize the environmental and economic benefits.The objectives of this study are to develop a spatiotemporal modelling framework that accurately predicts the population dynamics of C. capitata and includes an optimization module to find optimal treatment strategies that minimize pesticide use while maximizing productivity and environmental sustainability.To achieve these objectives, we are developing an integrated machine-learning tool that uses weather, environmental, and pest monitoring datasets to provide sub-seasonal projections of pest presence and infestation levels. Our data archives include meteorological observations and forecasts, environmental factors such as NDVI, and a long record (2012 onwards) of pest monitoring observations in orchards located in Northern Israel. The framework includes spatial analyses of results to search for pest population hotspots, hypothesized areas where early population build-up is repetitively early, year after year. We also incorporate bioeconomic models to determine the optimal combination of control tactics that maximize environmental and economic benefits.By improving our understanding of C. capitata presence and infestation risks at a fine spatiotemporal scale, this study will contribute to the development of decision support tools that allow farmers to minimize spraying and establish stable, environmentally friendly agricultural systems. While predicting pest population dynamics is challenging, we believe that this study provides valuable insights and potential solutions for managing C. capitata and other insect pests in the face of global warming and changing agricultural conditions.