Disease risk forecasting with Bayesian learning networks: Application to grape powdery mildew (Erysiphe necator) in vineyards
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 necator) in vineyards. Agronomy, [online] 10(5), http://dx.doi.org/10.3390/agronomy10050622
Plain language summary
Powdery mildew (Erysiphe necator) is a fungal disease causing significant loss in 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. We present a
Bayesian learning network approach to explore the complex causal interactions between environment,
pathogen, and host for three different susceptible grape cultivars (Chancellor, Geisenheim, and Frontenac)
in Quebec, Canada. This approach combines environmental (weather, climate), pathogen
(development stages), and host (crop cultivar-specific susceptibility) conditions. The efficacy of this
approach was compared using both supervised and algorithm model learning modes, and assessed
in an operational-context using Global Forecast System (GFS) Ensemble Reforecasts (GEFSR). we
present a model-based fungicide spray strategy for guiding spray decisions up to 6 days with a
10-day forecast of potential spray efficacy under rain washed off conditions. Our model improves
fungicide spray decisions; decreasing the number of sprays, while increasing spray effectiveness by
identifying the best time to spray.
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.