Table of Contents
Agronomy, Volume 10, Issue 5 (May 2020) – 144 articles
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Cover Story (view full-size image) Disease causes significant loss of grape yield in commercial vineyards and is driven by complex [...] Read more. Disease causes significant loss of grape yield in commercial vineyards and is driven by complex interactions between the pathogen, its host, and environmental conditions. We showcase a novel hybrid, machine-learning modeling approach to forecast grapevine disease risk that combines Bayesian network learning with site-specific calibration equations. We validate this approach using 13 years of data for three different susceptible northern grape cultivars in Quebec, Canada. This model-based approach can accurately forecast 16 days ahead using Global Forecast System (GFS) Ensemble Reforecasts (GEFSR), guiding spray decisions within a 6-day moving window through the growing season, and optimizing spray timing, efficiency, and costs.View this paper.