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Activity Number: 172 - Risk Prediction and Analysis
Type: Contributed
Date/Time: Monday, July 30, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Risk Analysis
Abstract #327240 Presentation 1 Presentation 2
Title: Forecasting of Grape Powdery Mildew Disease Risk in Vineyards Using a Bayesian Learning Network Model
Author(s): Nathaniel Newlands* and Weixun Lu and Odile Carisse and David E. Atkinson
Companies: Agriculture and Agri-Food Canada (Science and Technology Branch) and Agriculture and Agri-Food Canada (Science and Technology Branch) and Agriculture and Agri-Food Canada (Science and Technology Branch) and University of Victoria
Keywords: Agriculture; Bayesian; Disease Risk; Forecasting; Machine-Learning; Viticulture
Abstract:

Vineyards suffer substantial grape yield losses from damaging diseases, such as powdery mildew (Erysiphe necator) - a polycyclic, airborne disease. The severity and transmission of this grapevine disease depends on a complex interaction of genetic (pathogen and host) and environmental factors, including crop management practices. Climate change and variability is also contributing uncertainty in managing this disease by raising temperatures affecting winter chill requirements, lengthening growing seasons in northern climates, producing more days without frost, and more intense heatwave and rainfall events. It remains unclear how to best protect and minimize the impact of this disease in commercial vineyards. We present a Bayesian learning network model for forecasting disease risk in time and space. This approach combines diverse types of data, and complex causal relationship between variables. We present findings from validating this model against for grapevine disease data collected within Quebec vineyards. Key scientific recommendations and challenges associated with its use as a precision viticulture tool are discussed.


Authors who are presenting talks have a * after their name.

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