Botcast: A forecasting system to time the initial fungicide spray for managing botrytis leaf blight of onions

Sutton, J.C.; James, T. D. W.; Rowell, P.M.

doi:10.1016/0167-8809(86)90136-2

Cited by 44 publications

(58 citation statements)

References 14 publications

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“…Relative humidity (RH) thresholds, especially number of hours of RH ≥ 90%, have been used as a full or partial proxy for PWD in disease management (Krause et al 1975;Madden et al 1978;Sutton et al 1986;Wilks and Shen 1991). Conversely, durations of dry periods, characterized by RH ≤ 75%, have been used to produce negative forecasts for disease development or spore production, thus identifying periods not conducive to the progress of the pathogen (WMO 1988).…”

Section: Methodsmentioning

confidence: 99%

Estimating wetness duration on maize ears from meteorological observations

Rao¹,

Gillespie²,

Schaafsma³

1998

Can. J. Soil. Sci.

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Estimating wetness duration on maize ears from meteorological observations. Can. J. Soil Sci. 78: 149-154. The onset and cessation of surface wetness on maize ears were simulated with six models, using hourly meteorological data, to examine the linkage between wetness duration and possible forecasting of fungal infections that produce mycotoxins. Two threshold models (using relative humidity and dew point temperature), one regression model (using humidity and wind speed), and three physical models based on the energy balance approach, were compared. Also, spatial and temporal variability in wetness duration was measured and simulated at three sites located at distances up to 29 km from a central weather station. The estimated wetness values were compared with observations from cylindrical wetness sensors placed near ear level in maize canopies. The results relate to potential mycotoxin warning systems and indicate that threshold models can provide reasonable estimates of ear wetness duration in this region, that a comprehensive physical model can give superior estimates, and that wetness estimates made from a central weather station data can be extended to nearby crop fields with a moderate degree of confidence.Key words: epidemiology, mycotoxins, surface wetness models Rao, P. S., Gillespie, T. J. et Schaafsma, A. W. 1998. Estimation de la durée d'humectation des épis de maïs à partir des observations météorologiques. Can. J. Soil Sci. 78: 149-154. Étant donné le lien observé entre la durée d'humectation et l'apparition éventuelle des infections fongiques mycotoxinogènes, nous avons simulé au moyen de 6 modèles, à partir des données météorologiques horaires, le début et la fin de l'humectation superficielle des épis de maïs. Deux modèles à seuils (utilisant l'humidité relative et la température au point de rosée), un modèle de régression utilisant l'humidité et la vitesse du vent et 3 modèles physiques basés sur le bilan énergétique étaient comparés. En outre, la variabilité dans le temps et dans l'espace de la durée d'humectation était mesurée et simulée à 3 emplacements situés à des distances allant jusqu'à 29 km d'une station météorologique centrale. Les valeurs d'humectation estimées étaient comparées à des observations enregistrées par des capteurs d'humectation cylindriques posés à la hauteur approximative des épis dans une culture de maïs. Les résultats sont intéressants pour les futurs systèmes d'avertissement de production de mycotoxines. Ils montrent aussi que les modèles à seuils peuvent fournir des valeurs raisonnables de la durée d'humectation des épis dans cette région. Ces valeurs peuvent encore être améliorées par l'utilisation d'un modèle physique intégré. On a enfin constaté que les valeurs de durée d'humectation provenant d'une station météorologique centrale peuvent être étendues aux cultures avoisinantes avec un degré de confiance modéré.

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Section: Methodsmentioning

confidence: 99%

Estimating wetness duration on maize ears from meteorological observations

Rao¹,

Gillespie²,

Schaafsma³

1998

Can. J. Soil. Sci.

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“…Several epidemiological models have been developed and integrated into functional forecast systems, usually coupled with weather forecast, for field crops including grapes and onions (Sutton et al, 1986;Vincelli and Lorbeer, 1989;Broome et al, 1995). Key elements in predicting disease are the occurrence of free water, as rain or dew, and high relative humidity.…”

Section: Development Of Epidemiological and Risk Prediction Modelsmentioning

confidence: 99%

“…Many such systems have been developed for a variety of host/disease combinations (Campbell and Madden, 1990). An example of a predictive system implicating Botrytis is "BOTCAST", used to time the initiation of fungicide programs for the protection of onions against leaf blight caused by B. squamosa (Sutton et al, 1986).…”

Section: Timing Of Applicationmentioning

confidence: 99%

Integrated Control of Botrytis Cinerea on Greenhouse Tomatoes

Nicot

Baille

Aerial Plant Surface Microbiology

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“…Primary inoculum from the sources described above and secondary inoculum produced as conidia on the leaves of infected crop plants, spore dispersal and survival, infection and colonization by and the sporulation capacity of B. squamosa are basic requirements for the development of leaf blight in onion crops (Sutton et al, 1986).…”

Section: Botrytis Squamosa (Leaf Rot Leaf Blight Blast) (A) Pathogementioning

confidence: 99%

“…From this, bioclimatological data thresholds are determined that indicate when fungicide sprays should be applied. This is the basis of the disease forecasting system known as BOTCAST (Sutton et al, 1986) which can be implemented at a farm level by personnel with disease management skills.…”

Section: Botrytis Squamosa (Leaf Rot Leaf Blight Blast) (A) Pathogementioning

confidence: 99%

Onion diseases

Maude¹

1998

The Epidemiology of Plant Diseases

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Botcast: A forecasting system to time the initial fungicide spray for managing botrytis leaf blight of onions

Cited by 44 publications

References 14 publications

Estimating wetness duration on maize ears from meteorological observations

Estimating wetness duration on maize ears from meteorological observations

Integrated Control of Botrytis Cinerea on Greenhouse Tomatoes

Onion diseases

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