Carisse, O., Lefebvre, A., Van der Heyden, H., Roberge, L., and Brodeur, L. 2013. Analysis of incidence-severity relationships for strawberry powdery mildew as influenced by cultivar, cultivar type, and production systems. Plant Dis. 97:354-362.The relationships between strawberry powdery mildew incidence (I) and severity (S) were investigated for various cultivars, for Junebearing and day-neutral cultivars, and for production systems (openfield and plastic-tunnel) with the objective of deriving a simple relationship for predicting severity (proportion of leaf area diseased [PLAD]) from incidence (proportion of diseased leaves). Data were collected from 2006 to 2011 at II commercial and experimental sites, for a total of 2,326 observations {n). For the cultivars grown in open fields, higher severity was observed on 'Seascape', with mean PLAD of 0.299 {n = 427); followed by 'Chambly', with 0.133 {n = 334); 'Cavendish', with 0.115 {n = 250); 'Darselect', with 0.111 (n = 321); and 'Jewel', with 0.105 {n = 276). In general, mean severity was higher when the strawberry plants were grown in plastic tunnels, with PLAD of 0.204, 0.199, and 0.181 for Chambly (n = 204), Darselect (n = 261), and Jewel {n = 253), respectively. A linear model based on complementary log-log transformation of I and S provided a good fit of the data (coefficient of determination [R~] adjusted for degrees of freedom from 0.82 to 0.96).A covariance analysis indicated that the sampling year and site of sampling did not significantly influence the estimated slope of the I-S relationship, nor did the specific cultivar among the June-bearing ones, whereas the production system (open-field versus plastic-tunnel) and the cultivar type (June-bearing versus day-neutral) significantly influenced the estimated slope. From this analysis, we were able to develop three specific models for open-ficld-grown Junehearing cultivars {R-= 0.90), for the open-field-grown day-neutral cultivar (Seascape, R^ = 0.91), and for June-hearing cultivars grown in plastic tunnels (R-= 0.92). From these results, it was concluded that strawherry powdery mildew leaf severity can be accurately estimated from incidence of diseased leaves. The I-S relationships developed in the present study may be used in making practical disease management decisions, especially for management programs that use information on disease level in the field to initiate fungicide spraying programs or to time the interval between sprays.

Van der Heyden, H., Lefebvre, M., Roberge, L., Brodeur, L., and Carisse, O. 2014. Spatial pattern of strawberry powdery mildew (Podosphaera aphanis) and airborne inoculum. Plant Dis. 98:43-54.The relationship between strawberry powdery mildew and airborne eonidium concentration (ACC) of Podosphaera aphanis was studied using data collected from 2006 to 2009 in 15 fields, and spatial pattern was described using 2 years of airborne inoculum and disease incidence data collected in fields planted with the June-bearing strawberry (Fragaria x ananassa) cultivar Jewel. Disease incidence, expressed as the proportion of diseased leaflets, and ACC were monitored in fields divided into 3x8 grids containing 24 100 m-quadrats. Variance-tomean ratio, index of dispersion, negative binomial distribution. Poisson distribution, and binomial and beta-binomial distributions were used to characterize the level of spatial heterogeneity. The relationship between percent leaf area diseased and daily ACC was linear, while the relationship between ACC and disease incidence followed an exponential growth curve. The V/M ratios were significantly greater than 1 for 100 and 96% of the sampling dates for ACC sampled at 0.35 m from the ground (ACCo 35^) and for ACC sampled at 1.0 m from the ground (ACCiom). respectively. For disease incidence, the index of dispersion D was significantly greater than 1 for 79% of the sampling dates. The negative binomial distribution fitted 86% of the data sets for both ACC| om and ACCoijn,. For disease incidence data, the beta-binomial distribution provided a good fit of 75% of the data sets.