Biosecurity Research Articles

Pea seed-borne mosaic virus Pathosystem Drivers under Mediterranean-Type Climatic Conditions: Deductions from 23 Epidemic Scenarios

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Plant Disease


ISSN:0191-2917 e-ISSN:1943-7692


Agronomy and Crop Sciences | Biosecurity


Drivers of Pea seed-borne mosaic virus (PSbMV) epidemics in rainfed field pea crops were examined under autumn to spring growing conditions in a Mediterranean-type environment. To collect aphid occurrence and PSbMV epidemic data under a diverse range of conditions, 23 field pea data collection blocks were set up over a 6-year period (2010 to 2015) at five locations in the southwest Australian grain-growing region. PSbMV infection levels in seed sown (0.1 to 13%), time of sowing (22 May to 22 June), and cultivar (Kaspa or PBA Twilight) varied with location and year. Throughout each growing season, rainfall data were collected, leaf and seed samples were tested to monitor PSbMV incidence in the crop and transmission from harvested seed, and sticky traps were used to monitor flying aphid numbers. Winged migrant Acyrthosiphon kondoi, Lipaphis erysimi, Myzus persicae, and Rhopalosiphum padi were identified in green tile traps in 2014 and 2015. However, no aphid colonization of field pea plants ever occurred in the blocks. The deductions made from collection block data illustrated how the magnitude of PSbMV spread prior to flowering is determined by two primary epidemic drivers: (i) PSbMV infection incidence in the seed sown, which defines the magnitude of virus inoculum source for within-crop spread by aphids, and (ii) presowing rainfall that promotes background vegetation growth which, in turn, drives early-season aphid populations and the time of first arrival of their winged migrants to field pea crops. Likely secondary epidemic drivers included wind-mediated PSbMV plant-to-plant contact transmission and time of sowing. PSbMV incidence at flowering time strongly influenced transmission rate from harvested seed to seedlings. The data collected are well suited for development and validation of a forecasting model that informs a Decision Support System for PSbMV control in field pea crops.



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