Disease Risk Forecasting with Bayesian Learning Networks: Application to Grape Powdery Mildew (Erysiphe nector) in Vineyards
Date
2020
Authors
Lu, Weixun
Newlands, Nathaniel K.
Carisse, Odile
Atkinson, David E.
Cannon, Alex J.
Journal Title
Journal ISSN
Volume Title
Publisher
Agronomy
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. This approach combines environmental (weather,
climate), pathogen (development stages), and host (crop cultivar-specific susceptibility) factors.
The model is evaluated in an operational forecast mode with supervised and algorithm model learning
and integrating Global Forecast System (GFS) Ensemble Reforecasts (GEFSR). A model-guided
fungicide spray strategy is validated for guiding spray decisions up to 6 days with a 10-day forecast
of potential spray efficacy under rain washed off conditions. The model-guided strategy improves
fungicide spray decisions; decreasing the number of sprays, and identifying the optimal time to spray
to increase spray effectiveness.
Description
Keywords
Bayesian learning networks, forecasting, modeling, powdery mildew disease, risk, viticulture
Citation
Lu, W., Newlands, N. K., Carisse, O., Atkinson, D. E., & Cannon, A. J. (2020). Disease Risk Forecasting with Bayesian Learning Networks: Application to Grape Powdery Mildew (Erysiphe nector) in Vineyards. Agronomy, 10(5), 1-29. https://doi.org/10.3390/agronomy10050622.