WA RESEARCH has made another breakthrough in understanding the progress of blackleg in canola, a disease that can lead to substantial yield losses.
Computer modellers Moin Salam and Art Diggle of the WA Department of Agriculture have developed Australia's first model of blackleg epidemiology, enabling simulation of the discharge of blackleg spores . Canol a pathologists in the eastern states are keen to use the model and, in collaboration with Dr Salam, will test it in their environments. The model was developed together with the Department's canola pathologists Ravjit Khangura and Martin Barbetti and with support from growers and the Federal Government through the GRDC.
Avoiding spore showers
Blackleg inoculum largely comes from infected canola stubble from the previous year's crop. Depending on rainfall and temperature, the fruiting bodies or pseudothecia (the fungal organs where blackleg spores develop) mature on the stubble in summer, autumn or winter. Rainfall then triggers fun gal spore release.
While canola cultivars have been developed with adult plant resistance to blackleg, most are still vulnerable as seedlings.
Using temperature and rainfall inputs, the model can predict when major spore showers wi ll occur, allowing growers to determine the best sowing time to avoid them. In high-rainfall areas, major showers can release 1,000-2,000 spores per cubic metre per hour.
Depending on rainfall events, the rate of release gives major showers between March and June and even beyond, with smaller spore showers continuing until the end of October. In low-rainfall areas this process is usually delayed 6- 12 weeks, depending on seasonal and regional conditions.
The model can calculate the stages of pseudothecia maturity in pre-season, giving a good indication of the poss ibility of early spore showers. Research will continue to further refine the model so that it can be used to predict yield lo sees from such spore showers.
Program 3 Contact: Dr Moin Salam 0893683669 email email@example.com Dr Ravjit Khangura 08 9368 3374 email firstname.lastname@example.org