A NEW management app is set to enhance decision making by Australian canola growers in their efforts to reduce yield and profit losses caused by the potentially devastating disease, blackleg.

Oilseeds disease expert Steve Marcroft demonstrates the new BlacklegCM app to Dagro agronomist Craig Drum, Tatyoon, Victoria.

BlacklegCM, a Grains Research and Development Corporation (GRDC) investment, will provide growers with a tool that can forecast the likelihood and severity of the disease, associated yield loss and economic returns on a paddock-by-paddock basis ahead of sowing.

It has been built by the Department of Primary Industries and Regional Development (DPIRD) in Western Australia as part of the national pathogen management modelling and delivery of decision support project and has had input from all members of the GRDC’s National Canola Pathology Program.

Oilseeds disease expert Dr Steve Marcroft said the tool would provide growers and advisers with “an interactive interface” to explore and compare the economic outcomes of different management strategies for blackleg, which can cause up to 90 percent yield loss where a cultivar’s blackleg resistance has been overcome.

Dr Marcroft, of Marcroft Grains Pathology, said the app was an extension of the GRDC’s Blackleg Management Guide which was updated twice a year to reflect any changes in the resistance status of individual cultivars.

“Although the Management Guide provides useful information, it has some limitations in its current form. It is difficult to consider complex interactions such as the use of cultivars with different blackleg ratings in high or low rainfall environments and the effect of fungicide use,” he said.

“There has been a need to develop a management tool that can provide disease forecasting based on the management principles proposed by the manager of an individual paddock, and this has led to the development of BlacklegCM.

“BlacklegCM assists growers to manage blackleg by integrating the information provided in the Blackleg Management Guide and producing a predicted economic outcome.

“It can be modified to account for some of the major factors that relate to risk of yield loss due to blackleg in a particular paddock. It allows the user to compare the likely relative profitability of different disease management strategies, including paddock selection, cultivar choice, seed dressing, banded fungicide and sprayed fungicide.”

BlacklegCM takes account of costs, yield benefits and grain prices to give the best case, worst case and most likely estimates of economic return. It also accounts for the major factors that influence blackleg severity.

“The user has the option to change each parameter to tailor the output to their cropping circumstance,” Dr Marcroft said.

“Consequently, the user can explore their options for disease control and understand the relative importance of each factor.

“For example, distance to one-year-old stubble has a large influence on disease severity, while two-year-old stubble has less of an influence. Foliar fungicide has a small influence if used in isolation but is very effective if used in combination with a seed dressing fungicide. Foliar fungicide on a one-tonne crop is likely to cause an economic loss while fungicide on a three-tonne crop is more likely to result in a large profit.

“Once all the parameters have been entered by the user, the real power of the app becomes apparent as it determines blackleg severity, yield loss and economic return from the parameters entered.”

Dr Marcroft said the app could also be used during the growing season.

“For instance, in 2016 many growers planned for a two tonne/hectare crop but soon realised that yield potentials were much higher. Members of the National Canola Pathology team then warned of a very high blackleg lesion severity,” he said.

“In this scenario in July, growers could have re-run the app with a three t/ha yield target, rather than a 2 t/ha yield target, and compared plus or minus foliar fungicide.”

The app is now available via the App Store and Play Store.

Source: GRDC