agronomy Article Disease Risk Forecasting with Bayesian Learning Networks: Application to Grape Powdery Mildew (Erysiphe necator) in Vineyards Weixun Lu 1 , Nathaniel K. Newlands 2,∗ , Odile Carisse 3, David E. Atkinson 1 and Alex J. Cannon 4 1 Department of Geography, University of Victoria, David Turpin Building, 3800 Finnerty Road, Victoria, BC V9P 5C2, Canada;
[email protected] (W.L.);
[email protected] (D.E.A.) 2 Science and Technology, Agriculture and Agri-Food Canada, Summerland Research and Development Centre, 4200 Highway 97 S, P.O. Box 5000, Summerland, BC V0H 1Z0, Canada 3 Science and Technology, Agriculture and Agri-Food Canada, Saint-Jean-sur-Richelieu Research and Development Centre, 430 Gouin Boulevard, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada;
[email protected] 4 Climate Research Division, Environment and Climate Change Canada, 3800 Finnerty Road, Victoria, BC V8P 5C2, Canada;
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[email protected] Received: 14 March 2020; Accepted: 17 April 2020; Published: 28 April 2020 Abstract: Powdery mildew (Erysiphe necator) is a fungal disease causing significant loss of grape yield in commercial vineyards. The rate of development of this disease varies annually and is driven by complex interactions between the pathogen, its host, and environmental conditions. The long term impacts of weather and climate variability on disease development is not well understood, making the development of efficient and durable strategies for disease management challenging, especially under northern conditions. We present a probabilistic, Bayesian learning network model to explore the complex causal interactions between environment, pathogen, and host for three different susceptible northern grape cultivars in Quebec, Canada.