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

Sapak, Z., Salam, M. U., Minchinton, E. J., MacManus, G. P. V., Joyce, D. C. and Galea, V. J. (2017) POMICS: a simulation disease model for timing fungicide applications in management of Powdery Mildew of Cucurbits. Phytopathology, 107 9: 1022-1031. doi:10.1094/PHYTO-11-16-0413-R


Author Sapak, Z.
Salam, M. U.
Minchinton, E. J.
MacManus, G. P. V.
Joyce, D. C.
Galea, V. J.
Title POMICS: a simulation disease model for timing fungicide applications in management of Powdery Mildew of Cucurbits
Journal name Phytopathology   Check publisher's open access policy
ISSN 0031-949X
1943-7684
Publication date 2017-09-01
Sub-type Article (original research)
DOI 10.1094/PHYTO-11-16-0413-R
Open Access Status Not yet assessed
Volume 107
Issue 9
Start page 1022
End page 1031
Total pages 10
Place of publication St. Paul, MN, United States
Publisher American Phytopathological Society
Language eng
Subject 1102 Agronomy and Crop Science
1110 Plant Science
Abstract 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.
Keyword Temperature
Vapor pressure deficit
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Agriculture and Food Sciences
 
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