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Carisse O.,Agriculture and Agri Food Canada | Levasseur A.,Agriculture and Agri Food Canada | Van der Heyden H.,Compagnie de recherche Phytodata Inc.
Plant Pathology

Botrytis leaf blight (BLB) caused by Botrytis squamosa is a major leaf disease of onion. Various forecasting systems have been developed to help growers manage the disease. To improve forecasting reliability, the influence of temperature and wetness duration on B. squamosa infection was quantified by inoculating onion leaves with a conidial suspension and incubating them under various combinations of temperature (10-30°C) and leaf wetness duration (0-84h). Infection was measured as the number of lesions per cm 2 of leaf and converted to the proportion of maximum infection (PMI). Regardless of leaf wetness duration, only a few lesions developed at 30°C and the number of lesions increased as the temperature rose from 10 to 20°C but decreased at 25°C. Between 10 and 25°C the number of lesions per cm 2 of leaf area increased gradually with increasing leaf wetness duration from 12 to 72h. Relative infection was modelled as a function of both temperature and wetness duration using a modified version of the Weibull equation, which provided a precise description of the response of B. squamosa (R 2=0·88). To facilitate field validation, receiving operating characteristic curve analysis was performed to determine the accuracy of various sets of criteria for establishing the length of an infection event based on field weather data. The total number of leaf wetness and RH >90% hours over a 72h period was the best criterion, regardless of the wetness interruption pattern (sensitivity=90·91, specificity=84·62, area under the receiving operating curve=0·878). The model describing the relationship of PMI to temperature and leaf wetness duration, and field observations on airborne conidium concentration (ACC) were used to calculate the risk of infection (RI BLB) as RI BLB=PMI×ACC. In 2009 and 2010, this risk index was compared to the observed rate of BLB progress (Rate BLB+5days) during the following 5days. There was a linear relationship between RI BLB and Rate BLB+5days indicating that this new risk indicator was reliable for predicting the risk of BLB development. These findings will help to improve the timing of fungicide applications for BLB management. © 2012 The Authors. Plant Pathology © 2012 BSPP. Source

Carisse O.,Agriculture and Agri Food Canada | Van Der Heyden H.,Compagnie de recherche Phytodata Inc.
Plant Disease

Gray mold, caused by Botrytis cinerea, is an important threat for tomato greenhouse producers. The influence of airborne conidia concentration (ACC) on both flower and stem-wound infections was studied in a greenhouse maintained at a temperature of 15, 20, or 25°C using diseased tomato leaves as the unique source of dry inoculum. Spore samplers were used to monitor ACC, and a previously developed real-time qPCR assay was used to quantify airborne B. cinerea conidia. The proportion of infected flowers remained low at ACC < 10 conidia/m3; above this concentration, flower infection increased with increasing ACC. The influence of ACC on proportion of infected flowers was well described by a sigmoid model (R2 = 0.90 to 0.92). The mean proportion of infected stem wounds over the three trials was 0.021; no infected wounds were observed at ACC < 100 conidia/m3. Based on logistic regression, the probability that a stem becomes infected increased rapidly with mean probabilities of 0.24 and 0.87 at ACCs of 315 and 3,161 conidia/m3, respectively. The results suggest that the amount of airborne B. cinerea inoculum in the greenhouse is often above the action threshold for flower infection and that monitoring airborne B. cinerea inoculum could help in timing de-leafing operations. © 2015 The American Phytopathological Society. Source

Carisse O.,Agriculture and Agri Food Canada | Morissette-Thomas V.,Universite de Sherbrooke | Van Der Heyden H.,Compagnie de recherche Phytodata Inc.

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, log 10-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. © 2013 The American Phytopathological Society. Source

Van der Heyden H.,Compagnie de recherche Phytodata Inc. | Lefebvre M.,Compagnie de recherche Phytodata Inc. | Roberge L.,Compagnie de recherche Phytodata Inc. | Brodeur L.,Compagnie de recherche Phytodata Inc. | Carisse O.,Agriculture and Agri Food Canada
Plant Disease

The relationship between strawberry powdery mildew and airborne conidium 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 × ananassa) cultivar Jewel. Disease incidence, expressed as the proportion of diseased leaflets, and ACC were monitored in fields divided into 3 × 8 grids containing 24 100 m2 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 (ACC0.35m) and for ACC sampled at 1.0 m from the ground (ACC1.0m), 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 ACC1.0m and ACC0.35m. For disease incidence data, the beta-binomial distribution provided a good fit of 75% of the data sets. Taylor's power law indicated that, for ACC at both sampling heights, heterogeneity increased with increasing mean ACC, whereas the binary form of the power law suggested that heterogeneity was not dependent on the mean for disease incidence. When the spatial location of each sampling location was taken into account, Spatial Analysis by Distance Indices showed low aggregation indices for both ACCs and disease incidence, and weak association between ACC and disease incidence. Based on these analyses, it was found that the distribution of strawberry powdery mildew was weakly aggregated. Although a higher level of heterogeneity was observed for airborne inoculum, the heterogeneity was low with no distinct foci, suggesting that epidemics are induced by well-distributed inoculum. This low level of heterogeneity allows mean airborne inoculum concentration to be estimated using only one sampler per field with an overall accuracy of at least 0.841. The results obtained in this study could be used to develop a sampling scheme that will improve strawberry powdery mildew risk estimation. © 2014 Department of Agriculture and Agri-Food, Government of Canada. Source

Fall M.L.,Universite de Sherbrooke | Fall M.L.,Compagnie de recherche Phytodata Inc. | Van Der Heyden H.,Compagnie de recherche Phytodata Inc. | Carisse O.,Agriculture and Agri Food Canada

Lettuce downy mildew, caused by the oomycete Bremia lactucae Regel, is a major threat to lettuce production worldwide. Lettuce downy mildew is a polycyclic disease driven by airborne spores. A weather-based dynamic simulation model for B. lactucae airborne spores was developed to simulate the aerobiological characteristics of the pathogen. The model was built using the STELLA platform by following the system dynamics methodology. The model was developed using published equations describing disease subprocesses (e.g., sporulation) and assembled knowledge of the interactions among pathogen, host, and weather. The model was evaluated with four years of independent data by comparing model simulations with observations of hourly and daily airborne spore concentrations. The results show an accurate simulation of the trend and shape of B. lactucae temporal dynamics of airborne spore concentration. The model simulated hourly and daily peaks in airborne spore concentrations. More than 95% of the simulation runs, the daily-simulated airborne conidia concentration was 0 when airborne conidia were not observed. Also, the relationship between the simulated and the observed airborne spores was linear. In more than 94% of the simulation runs, the proportion of the linear variation in the hourly-observed values explained by the variation in the hourly-simulated values was greater than 0.7 in all years except one. Most of the errors came from the deviation from the 1:1 line, and the proportion of errors due to the model bias was low. This model is the only dynamic model developed to mimic the dynamics of airborne inoculum and represents an initial step towards improved lettuce downy mildew understanding, forecasting and management. ©2016 Fall et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Source

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