"Towards climate-smart agriculture: Monitoring and predicting crop suitability, plant diseases and pests"

Abstract :

Agroecosystems are increasingly challenged by invasive pests, extreme weather events, and rapidly changing environmental conditions. Yet, our ability to predict their impacts on agricultural systems remains limited. Current approaches are largely retrospective, statistically linking crop response to global change dynamics, without explicitly exploring the complex interactions between abiotic drivers and crop, pathogen and pest dynamics. Therefore, improving our ability to robustly predict future risks to food security and to assess the effectiveness of different management and control strategies is essential. In this talk, I explore how climate-driven mathematical modeling tools can be exploited to understand the environmental response of agroecosystems and predict their dynamics under scenarios of climate change. I use brown rot disease in peach cultivars as main study system to assess the climatic dependence of disease spread and plant phenology and predict how climate change is expected to alter fruit production areas and disease patterns. I also discuss a biogeographical modeling approach that integrates ecological theory with deep learning techniques to assess crop and pest suitability using species presence records derived from citizen science observations. Strengthening the predictive power of computational tools, while providing a deeper understanding of climate-driven processes, is critical for supporting decision-making and improving the resilience of agroecosystems to climate change.