POMICS: A simulation disease model for timing fungicide applications in management of Powdery Mildew of Cucurbits

A weather-based simulation model, called Powdery Mildew of Cucurbits Simulation (POMICS), was constructed to predict fungicide application scheduling to manage powdery mildew of cucurbits. The model was developed on the principle that conditions favorable for Podosphaera xanthii, a causal pathogen o...

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Bibliographic Details
Published in:Phytopathology
Main Author: Sapak Z.; Salam M.U.; Minchinton E.J.; MacManus G.P.V.; Joyce D.C.; Galea V.J.
Format: Article
Language:English
Published: American Phytopathological Society 2017
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027576222&doi=10.1094%2fPHYTO-11-16-0413-R&partnerID=40&md5=f9b84c84f83542811dd4614ca6f9051d
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Summary:A weather-based simulation model, called Powdery Mildew of Cucurbits Simulation (POMICS), was constructed to predict fungicide application scheduling to manage powdery mildew of cucurbits. The model was developed on the principle that conditions favorable for Podosphaera xanthii, a causal pathogen of this crop disease, generate a number of infection cycles in a single growing season. The model consists of two components that (i) simulate the disease progression of P. xanthii in secondary infection cycles under natural conditions and (ii) predict the disease severity with application of fungicides at any recurrent disease cycles. The underlying environmental factors associated with P. xanthii infection were quantified from laboratory and field studies, and also gathered from literature. The performance of the POMICS model when validated with two datasets of uncontrolled natural infection was good (the mean difference between simulated and observed disease severity on a scale of 0 to 5 was 0.02 and 0.05). In simulations, POMICS was able to predict high- and low-risk disease alerts. Furthermore, the predicted disease severity was responsive to the number of fungicide applications. Such responsiveness indicates that the model has the potential to be used as a tool to guide the scheduling of judicious fungicide applications. © 2017 The American Phytopathological Society.
ISSN:0031949X
DOI:10.1094/PHYTO-11-16-0413-R