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Lagged Association Between Powdery Mildew Leaf Severity, Airborne Inoculum, Weather, and Crop Losses in Strawberry

First author: Agriculture and Agri-Food Canada, Horticulture Research and Development Centre, 430 Gouin Blvd., St. Jean-sur-Richelieu, QC, J3B 3E6, Canada; second author: Department of Mathematics, Sherbrooke University, Sherbrooke, QC, J1K 2R1, Canada; third author: Compagnie de recherche Phytodata Inc., 111 Rang St. Patrice, Sherrington, QC, J0L 2N0, Canada.

Knowledge about epidemiology and the impact of disease on yield is fundamental for establishing effective management strategies. The purpose of this study was to investigate the relationship between foliar strawberry mildew severity, Podosphaera aphanis airborne inoculum concentration, weather, and subsequent crop losses for day-neutral strawberry. The experiment was conducted at three, five, and four sites in 2006, 2007, and 2008, respectively, for a total of 12 epidemics. At each site, data were collected on 25 plants at 2-day intervals from the end of May to early October for a total of 60 to 62 samplings annually. First, seasonal crop losses were statistically described; then, a lagged regression model was developed to describe crop losses from the parameters that were significantly associated with losses. There was a strong positive linear relationship between seasonal crop losses and the area under the leaf disease progress curve (R² = 0.90) and daily mean airborne conidia concentration (R² = 0.86), and a negative linear relationship between crop losses and time to 5% loss (R² = 0.76) and time to 5% leaf area diseased (R² = 0.61). Among the 53 monitoring- and weather-based variables analyzed, percent leaf area diseased, log₁₀-transformed airborne inoculum concentration, and weather variables related to temperature were significantly associated with crop losses. However, polynomial distributed lag regression models built with weather variables were not accurate in predicting losses, with the exception of a model based on a combined temperature and humidity variable, which provided accurate prediction of the data used to construct the model but not of independent data. Overall, the model based on log₁₀-transformed airborne inoculum concentration did not provide accurate crop loss predictions. The model built using percent leaf area diseased with a time lag of 8 days (n = 4) and a polynomial degree of 2 provided a good description of the crop-loss data used to construct the model (r = 0.99 and 0.90) and of independent data (r = 0.92). For the 12 epidemics studied, 5% crop loss was reached when an average of 17% leaf area diseased was observed since the beginning of symptom development. These results indicate that information on foliar powdery mildew must be considered when making strawberry powdery mildew management decisions.