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RESEARCH ARTICLE
Contents Vol 61(2)

An epidemiological model for externally acquired vector-borne viruses applied to Beet western yellows virus in Brassica napus crops in a Mediterranean-type environment

T. Maling A B , A. J. Diggle A B , D. J. Thackray B , K. H. M. Siddique B C and R. A. C. Jones A C D
+ Author Affiliations
- Author Affiliations

A Agricultural Research Western Australia, Department of Agriculture and Food, Locked Bag No. 4, Bentley Delivery Centre, Perth, WA 6983, Australia.

B Centre for Legumes in Mediterranean Agriculture, Faculty of Natural and Agricultural Sciences, The University of Western Australia, Crawley, Perth, WA 6009, Australia.

C School of Plant Biology and Institute of Agriculture, The University of Western Australia, Faculty of Natural and Agricultural Sciences, Crawley, Perth, WA 6009, Australia.

D Corresponding author. Email: rjones@agric.wa.gov.au

Crop and Pasture Science 61(2) 132-144 https://doi.org/10.1071/CP09180
Submitted: 25 June 2009  Accepted: 13 November 2009   Published: 8 February 2010

Abstract

A hybrid mechanistic/statistical model developed previously to predict vector activity and epidemics of vector-borne viruses was modified to simulate virus epidemics in the Beet western yellows virus (BWYV) – Brassica napus pathosystem. BWYV, which is persistently aphid-borne, spreads to B. napus crops from external sources and causes substantial yield losses when there is widespread infection of young plants. Risk that such losses may occur depends on the magnitude and availability of viral inoculum in the external source, the amount of biomass available to support aphid vectors, its duration before crop emergence, and the time of arrival of vector aphids in the crop. The model uses daily rainfall, temperature, and evaporation data from over 450 sites in the grainbelt of south-western Australia to track biomass levels throughout the growing season. This information is then used to simulate aphid vector populations and virus incidence, initially in the external source environment, then in the crop, and ultimately to provide risk forecasts. The model predicted BWYV spread successfully for 10 of 12 different datasets from 3 years of field observations on B. napus blocks at 4 sites representing different rainfall and geographic zones of the grainbelt. Sensitivity analysis was used to determine the relative importance of the main parameters that describe the pathosystem and to predict which control measures are likely to be useful. An analysis of timing of predictions v. their accuracy was also done to establish optimum timing of forecasts for BWYV epidemics in B. napus crops.

Additional keywords: risk, simulation, prediction, BWYV spread, canola, oil-seed rape, quantitative epidemiology, integrated management, decision support.


Acknowledgments

We thank Jenny Hawkes and other present and past members of the DAFWA plant virology group, and Research Station staff, for their contribution to the data used in development of this model. Financial support was provided by the Australian Research Council (ARC) and the Department of Agriculture and Food for Western Australia (DAFWA) through an ARC Linkage Project. The Grains Research and Development Corporation funded previous data collection at the validation sites. We acknowledge use of SILO climate data from the Queensland Department of Natural Resources.


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