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An Epidemiological Model for Externally Sourced Vector-Borne Viruses Applied to Bean yellow mosaic virus in Lupin Crops in a Mediterranean-Type Environment

First, second, third, fourth, and fifth authors: Centre for Legumes in Mediterranean Agriculture, M080, The University of Western Australia, Crawley, WA 6009, Australia; second and fifth authors: Agricultural Research Western Australia, Locked Bag No. 4, Bentley Delivery Centre, Perth, WA 6983, Australia; fourth author: Institute of Agriculture, The University of Western Australia M082, Crawley, WA 6009, Australia; and fifth author: School of Plant Biology, The University of Western Australia, Crawley, WA 6009, Australia.

A hybrid mechanistic/statistical model was developed to predict vector activity and epidemics of vector-borne viruses spreading from external virus sources to an adjacent crop. The pathosystem tested was Bean yellow mosaic virus (BYMV) spreading from annually self-regenerating, legume-based pastures to adjacent crops of narrow-leafed lupin (Lupinus angustifolius) in the winter--spring growing season in a region with a Mediterranean-type environment where the virus persists over summer within dormant seed of annual clovers. The model uses a combination of daily rainfall and mean temperature during late summer and early fall to drive aphid population increase, migration of aphids from pasture to lupin crops, and the spread of BYMV. The model predicted time of arrival of aphid vectors and resulting BYMV spread successfully for seven of eight datasets from 2 years of field observations at four sites representing different rainfall and geographic zones of the southwestern Australian grainbelt. Sensitivity analysis was performed to determine the relative importance of the main parameters that describe the pathosystem. The hybrid mechanistic/statistical approach used created a flexible analytical tool for vector-mediated plant pathosystems that made useful predictions even when field data were not available for some components of the system.

Additional keywords:forecasting, integrated disease management, risk, simulation.