Disease Risk Forecasting with Bayesian Learning Networks: Application to Grape Powdery Mildew (Erysiphe nector) in Vineyards

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dc.contributor.authorLu, Weixun
dc.contributor.authorNewlands, Nathaniel K.
dc.contributor.authorCarisse, Odile
dc.contributor.authorAtkinson, David E.
dc.contributor.authorCannon, Alex J.
dc.date.accessioned2020-10-09T18:01:32Z
dc.date.available2020-10-09T18:01:32Z
dc.date.copyright2020en_US
dc.date.issued2020
dc.description.abstractPowdery 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.en_US
dc.description.reviewstatusRevieweden_US
dc.description.scholarlevelFacultyen_US
dc.description.sponsorshipThis research was supported by funding awarded to OC and NKN under the Canadian Agricultural Partnerships (CAP) program (AAFC), Project #2336 (J0001792.001.08); Influence of cultural practices and climate change on sustainability of grape production under northern conditions. W.L. was an AAFC Research Affiliate (RAP) funded through this research grant. DEA was supported by NSERC discovery grant funding and AC by Environment and Climate Change Canada (ECCC) research funding.en_US
dc.identifier.citationLu, 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.en_US
dc.identifier.urihttps://doi.org/10.3390/agronomy10050622
dc.identifier.urihttp://hdl.handle.net/1828/12187
dc.language.isoenen_US
dc.publisherAgronomyen_US
dc.subjectBayesian learning networksen_US
dc.subjectforecastingen_US
dc.subjectmodelingen_US
dc.subjectpowdery mildew diseaseen_US
dc.subjectrisken_US
dc.subjectviticultureen_US
dc.titleDisease Risk Forecasting with Bayesian Learning Networks: Application to Grape Powdery Mildew (Erysiphe nector) in Vineyardsen_US
dc.typeArticleen_US

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