Leafhopper population dynamics in Canadian prairies: Towards AI-based forecasting tools

Canola is one of the most economically important crops for Canadian agriculture, notably in the Prairies. Aster Yellows disease (AY) outbreaks in canola have become more frequent and devastating since the first symptomatic rapeseed plant was recorded in Saskatchewan in 1953, yet it is still largely unpredictable. Over 20 species of the insect family Cicadellidae (Hemiptera), mainly Macrosteles quadrilineatus (Forbes), can transmit Aster Yellows phytoplasma, the pathogen causing AY. However, canola growers have limited tools to monitor and manage this pest, as its population dynamics remain poorly understood. To bridge this knowledge gap and advance Canadian prairie agriculture, this study will provide the ecological foundation for developing a leafhopper population prediction model using artificial intelligence, as part of a Canada-wide initiative called the “LeafHope” project. This first sampling year uses sticky cards along the edge of canola fields throughout Alberta, Saskatchewan, and Manitoba to monitor leafhoppers, which provides data on the emergence and movements of leafhopper species throughout the Prairie provinces. Abundance, diversity, and population dynamics are analyzed according to time (seeding and sampling date), place, surrounding environment, agricultural practices (e.g. seed coat treatments, pesticide and fertilizer applications), and climatic factors (migratory winds, temperature, relative humidity, and precipitation). By understanding leafhopper population dynamics, canola growers can more accurately prevent and prepare for AY outbreaks in their crop fields. Furthermore, this same study system can be expanded to cover other affected crops, like cereals, in the future.