Affiliations 

  • 1 First author: Faculty of Plantation and Agrotechnology, MARA University of Technology, Shah Alam, Selangor, 40450, Malaysia; first, fifth, and sixth authors: School of Agriculture and Food Sciences, The University of Queensland, Gatton 4343, QLD, Australia; second author: Food and Agriculture Organization of the United Nations (FAO), FAO Representation in Bangladesh, House 37; Road 08; Dhanmondi R/A, Dhaka-1205, Bangladesh; third author: Department of Economic Development, Jobs, Transport and Resources, 475 Mickleham Road, Attwood, VIC 3047, Australia; fourth author: Horticulture and Forestry Science, Agri-Science Queensland, Department of Employment, Economic Development and Innovation, PO Box 15, Ayr, QLD 4807, Australia; and fifth author: Department of Agriculture and Fisheries, Level 2C West, Ecosciences Precinct, Box 267, Brisbane, QLD 4001, Australia
Phytopathology, 2017 09;107(9):1022-1031.
PMID: 28517959 DOI: 10.1094/PHYTO-11-16-0413-R

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.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.