The development of a dynamic diseaseforecasting model to controlEntomosporium mespilionAmelanchier alnifolia

Abstract

Entomosporium leaf and berry spot, which is caused by the fungal pathogen Entomosporium mespili, can cause up to 100% yield loss in Amelanchier alnifolia (saskatoon) in years when weather conditions are conducive to disease development. In an effort to optimize the effectiveness and minimize the use of fungicides, a dynamic diseaseforecasting model was developed. The model uses a disease pressure index equation, which integrates information regarding the phenological development of saskatoon, the relationship of disease to inoculum potential and production, leaf-wetness duration and temperature, inoculum release, and host susceptibility, to provide an estimate of disease pressure. When the model was evaluated, a strong correlation was found between predicted disease pressure and observed disease symptom development. After the field data were combined, the model could account for 82% of the variation in the increase of mean lesion number per leaf observed during the preharvest period. Control thresholds, together with application and control guidelines for use of the fungicide propiconazole, were added to the model, which was then evaluated in a field trial. A preliminary field test of the model showed that its use resulted in reduced disease development prior to the completion of fruit harvest.Key words: dynamic disease-forecasting model, disease pressure index, entomosporium leaf and berry spot, saskatoon.Résumé : L'entomosporiose, causée par le champignon pathogène Entomosporium mespili, peut engendrer jusqu'à 100 % de perte de rendement chez l'Amelanchier alnifolia (amélanchier à feuilles d'aulne) lors des années où les conditions climatiques sont favorables à la maladie. Dans le but d'optimiser l'efficacité et de diminuer l'usage de fongicides, un modèle dynamique de prévision de la maladie fut développé. Afin de fournir un estimé de la pression de maladie, le modèle utilise une équation de l'indice de pression de maladie qui intègre de l'information sur le développement phénologique de l'amélanchier, la relation entre la maladie et le potentiel et la production d'inoculum, la durée et la température d'humectation des feuilles, la libération de l'inoculum et la sensibilité de l'hôte. Lors de l'évaluation du modèle, une forte corrélation entre la pression de maladie prédite et les symptômes de maladie observés fut remarquée. Après combinaison des données de terrain, le modèle pouvait rendre compte de 82 % de la variation dans l'augmentation du nombre moyen de lésions par feuille observées pendant le délai avant récolte. Les seuils d'intervention, ainsi que les directives d'application et de vérification pour l'usage du fongicide propiconazole, furent ajoutés au modèle qui fut par la suite évalué lors d'essais en champ. Un test préliminaire du modèle sur le terrain a montré que son utilisation entraînait une diminution du développement de la maladie avant la fin de la récolte des fruits.

Statistical modelling of conidial discharge of entomophthoralean fungi using a newly discovered Pandora species

“…Our work can also be seen in the perspective of disease forecasting and fungal pathogen modelling [29, 30], where good models for spore discharge are an important ingredient, and we believe there is an important potential (in this direction) for future research.…”

Development and Validation of a Weather-Based Model for Predicting Infection of Loquat Fruit by Fusicladium eriobotryae

“…With respect to oversummering, modeling the dormant stage of fungal pathogens is challenging [66] , and the dormant stage has therefore been included in only a few models [70] – [74] . For this purpose, two key aspects must be addressed: (i) the inoculum dose (i.e., the quantity of inoculum that oversummers), which depends on the severity of the disease in each orchard at the end of the previous season; and (ii) the time when the primary inoculum begins to be available for infection.…”

“…For this purpose, two key aspects must be addressed: (i) the inoculum dose (i.e., the quantity of inoculum that oversummers), which depends on the severity of the disease in each orchard at the end of the previous season; and (ii) the time when the primary inoculum begins to be available for infection. In other models, the inoculum dose was directly measured in the field [70] , [74] or broadly estimated as low/high disease pressure [72] . In our case, incorporation into the model of the specific farmer’s assessment of the disease severity in the previous season may represent useful information regarding the potential primary inoculum dose.…”

Biology, epidemiology and control of Fusicladium eriobotryae, causal agent of loquat scab

“…With respect to oversummering, modeling the dormant stage of fungal pathogens is challenging (De Woolf and Isard, 2007), and the dormant stage has therefore been included in only a few models (Holtslag et al, 2004;Legler et al, 2013;Luo and Michailides, 2001;Rossi et al, 2005). For this purpose, two key aspects must be addressed: (i) the inoculum dose (i.e., the quantity of inoculum that oversummers), which depends on the severity of the disease in each orchard at the end of the previous season; and (ii) the time when the primary inoculum begins to be available for infection.…”

“…For this purpose, two key aspects must be addressed: (i) the inoculum dose (i.e., the quantity of inoculum that oversummers), which depends on the severity of the disease in each orchard at the end of the previous season; and (ii) the time when the primary inoculum begins to be available for infection. In other models, the inoculum dose was directly measured in the field (Holtslag et al, 2004;Gadoury and MacHardy, 1986) or broadly estimated as low/high disease pressure (Luo and Michailides, 2001). In our case, incorporation into the model of the specific farmer's assessment of the disease severity in the previous season may represent useful information regarding the potential primary inoculum dose.…”