Poster Presentation Science Protecting Plant Health 2017

An area wide surveillance network to monitor airborne inoculum of plant pathogens affecting the grains industry (#255)

Rohan Kimber 1 , Marg Evans 1 , Alan McKay 1 , Daniele Giblot-Ducray 1 , Herdina . 1 , Jenny Davidson 1
  1. South Australian Research and Development Institute (SARDI), GPO Box 397, Adelaide, South Australia, 5001

Burkard volumetric spore traps were deployed in a network to monitor airborne spore dispersal patterns within major South Australian grain growing regions. Stubbles infested with either yellow leaf spot (synonym: tan spot) of wheat (Pyrenophora tritici-repentis), net form net blotch (P. teres f. teres) of barley or blackspot (complex including Didymella pinodes) of field pea, were placed around the traps. Spore tapes retrieved every 36 days over the growing season and particles deposited on the adhesive tapes were analysed for fungal identification. Molecular assays specific to P. tritici-repentis and P. teres f. teres were developed to quantity fungal DNA and an assay for blackspot was also included. Trap samples were processed as 2-day tape segments separated by 3 trapping days to allow analysis of spore release over time. In the first two years, the trapping and diagnostic protocols were optimised and evaluated at five locations with various climates using data from the diseased stubble. Additional molecular assays were then incorporated to detect and quantify spores of white grain disorder (Eutiarosporella spp.), eyespot (Oculimacula yallundae), spot form net blotch (P. teres f. maculata), septoria triticii blotch of wheat (Septoria triticii), blackleg of canola (Leptosphaeria maculans) and ascochyta blight of chickpea (Ascochyta rabiei). Traps now operate at eight strategic locations to analyse endemic spore dispersal patterns of this suite of fungal diseases over the length of the growing season. Our aim is to establish relationships between spore release patterns and climate triggers to provide valuable information on disease dynamics in a changing farming environment, as well as generate data to improve strategies in disease management and forecasting. New innovations are now being combined with this surveillance network to incorporate rapid detection and data interpretation for more accurate and timely interrogation of spatial data generated from a network of samplers.