Canola research meeting

On 16 February I attended the meeting of Australian canola pathology researchers at the Metro Inn in Perth, where current research projects in the canola industry were presented. The meeting gave me an insight into canola pathology; I learned how modelling of disease has been used to assist industry and it gave me an appreciation of my co-supervisor's role in this development. Presentations covered breeding, plant pathology and funding allocation.

I learned about canola diseases, such as blackleg, and the modelling developed for predicting this disease in canola. Of particular relevance to my PhD project was the Blackleg Sporacle model, presented by Dr Moin Salam.

Blackleg Sporacle model

The Blackleg Sporacle model was run using 10 years of weather data for each of the five canola growing areas of South Australia, Western Australia and Victoria and 10 areas of New South Wales. The Blackleg Sporacle model produces regional fortnightly forecasts of the timing of maturity of pseudothecia of Leptosphaeria maculans, the blackleg pathogen, which can assist in planting decisions.

Farmers in the northern agricultural region of WA sow canola early. This avoids ascospore showers in the early seedling growth stage when the crop is most vulnerable. In other regions, delayed sowing may incur yield penalties. The blackleg model can be used in the time leading up to seeding to identify seasons where early sowing may be effective.

Developing a model as a predictive tool for spore release of the chickpea ascochyta blight pathogen (Ascochyta rabiei) is an exciting tool that could be used to predict disease risk. The benefit of such a model is it can be used to predict the time of maturity of pseudothecia, containing ascospores of the pathogen, depending on weather, site and disease biology. This allows for effective and strategic plantings by farmers to reduce disease pressure. It can potentially predict the time when spore release would occur and when spore density would be the greatest. This would give the farmer the ability to decide the best spray application timing for the season or if a spray application is needed at all, saving on chemical use, time and expense. It also can allow for effective decision-making on crop selection each season. If disease pressure is high in one year it may be safer to plant an alternative crop.

Modelling development for chickpeas

During my visit, Dr Salam began to use Mathematica to develop a model for ascochyta blight of chickpeas based on the Anthracnose Tracer model (which he developed with Art Diggle in 2002). Field site weather data, latent period of spore maturity, disease incidence and other features, will be used to generate a model which can eventually be used as a tool to predict ascochyta blight of chickpea.

Dr Salam and I applied data of disease spread that I collected in my first field trial at Kingsford, South Australia (2006) combined with hourly weather data from the site. The adapted model created a prediction of disease spread which we could analyse in combination with my field data. There was little spread of disease in this trial due to a late start to the season and a dry year that were not conducive to disease spread.

The spread model generated by Mathematica allows simulation of a wet or average year using disease spread characteristics and previous historical weather data. This allows for disease forecasting in years with different rainfall intensity. The benefit of this is even if all the seasons I experience during this study are affected by drought we can potentially predict what would occur if rain had occurred during the season. During meetings, discussions were conducted on the most appropriate methods to analyse and collect data from my field trials in coming seasons to best fit the requirements of the model. Methods used to collect data in the first season could be refined to get the best out of the model.

In terms of future possibilities, such a model could be adapted for various diseases spread by rain-splashed spores and windborne spores, based on data such as on site weather, disease characteristics and host variability.

Other visits

During my visit to Perth I also had the pleasure of meeting and liaising with influential scientists such as Bill Mcleod, Art Diggle, Tamrika Hind and Darryl Hardie.

Location