Quickly predicting sclerotinia is closer to reality
By Madeleine Baerg
University of Alberta PhD candidate, Barbara Ziesman, is currently developing a DNA-based canola petal test that will reliably identify sclerotinia inoculum on canola flower petals in as little as six hours. Photo by Janet Kanters.
As the crop’s first flowers start to bloom in as many as 20 million acres of canola next summer, Canadian farmers will yet again face an impossible decision. Some farmers will opt to fight sclerotinia stem rot, canola’s most common and costly disease, with an expensive, time consuming, and quite possibly unnecessary fungicide application. Others will choose to save input dollars by skipping a preventative fungicide application, thereby risking as much as 50 per cent of their canola crop’s ultimate yield if the crop becomes infected with sclerotinia.
Either way, the farmers’ decisions will be made mostly blindly: calculations based on experience, gut feel and prediction. This is because, unlike almost every other disease, sclerotinia needs to be controlled before it is visible, before science can reliably detect its presence or forecast its damage in a crop. But that frustrating reality may change in the near future.
University of Alberta PhD candidate, Barbara Ziesman, is currently developing a DNA-based canola petal test that will reliably identify sclerotinia inoculum on canola flower petals in as little as six hours. Unlike the current sclerotinia culture test, which takes four to five days to identify sclerotinia’s presence in a crop, the DNA-based test will provide results quickly enough that a producer has time to control an infected crop with fungicide.
“The DNA-based test allows us to see a spike in inoculum much faster than a traditional plate test. By giving us these quick results, a farmer will be able to make better control decisions,” explains Ziesman. “The information provided is more than the per cent petal infestation estimates that the traditional plate test provides. Because the DNA test identifies how much inoculum is present on each petal, if a farmer were to test several times in a growing season, they’d be able to apply fungicide when the inoculum is at its highest.”
In addition to faster results, the test also offers the added benefit of greater accuracy. Unlike a culture test in which a technician identifies sclerotinia by its physical characteristics, the DNA test is entirely machine controlled, reducing the possibility of human error.
Already, Ziesman has achieved the first major hurdle: developing the process to isolate sclerotinia DNA and amplify these genes to calculable levels in petal samples.
“The test itself is validated: it is sensitive, specific and reliable,” she says.
That said, a quick and effective petal test is only the first component of a reliable forecasting method. Currently, Ziesman is working on developing an outline for the second, less easily defined and far more challenging component of forecasting: combining petal test results with the many external factors that influence disease development.
“What we’re seeing right now is that there is a relationship between how much sclerotinia DNA is identified and the risk of developing a specific level of disease. The challenge is that so many conditions and factors influence the actual development of the disease,” she says. “Though we’ll never be able to predict with certainty exactly how much disease will develop in any given field, what we are working to develop now are risk level thresholds. To be useful as a risk assessment tool for growers, sclerotinia risk thresholds have to take multiple factors into consideration, because multiple factors will affect the actual disease development.”
For example, the thresholds may need to quantitatively or qualitatively consider everything from crop characteristics like density, seeding rate, crop height and yield potential, to environmental conditions including precipitation, humidity and temperature.
Ziesman and a team of collaborators have collected petal samples from the Edmonton region and analyzed them in relation to growing conditions in the same area for the past four years.
In 2011, Ziesman noted a discernable trend in the percentage of petals that tested positive for disease in the test fields versus disease development. However, because the year’s growing conditions were not ideal for disease development, the results were not substantial enough to be considered statistically significant.
In 2012, environmental conditions were very conducive to disease: between 22 and 90 per cent of plants not treated with fungicide became infected with sclerotinia in all 10 of the commercial fields they sampled. Considering only petal infestation at the 30 to 40 per cent bloom stage, Ziesman and her team were able to account for a full 70 per cent of the variation in actual stem rot development.
The results from 2013 in the Edmonton region were the most noteworthy or, as Ziesman says, “the most fun” from a research perspective. Initial analysis showed a disappointing, non-significant relationship between sclerotinia DNA at the petal test and eventual disease development. However, farmers will remember 2013 as a very wet spring, which resulted in much later than normal seeding in many acres. When Ziesman factored in this detail, the results proved immediately different.
“We looked at the fact that maybe the crop wasn’t at a susceptible stage when the inoculum was present in the fields. When we took away the late seeded fields from the analysis, results from the DNA test again accounted for a majority of the observed variation in disease development.”
While this research remains in its early stages, its potential for applicability in farm fields is very high. Ziesman’s hope is to develop a risk assessment tool that indicates risk levels based on the DNA-based petal infestation levels while taking into account other important factors for disease development.
“I do feel that it’s entirely possible that this could be the new way producers test for sclerotinia stem rot, maybe even in the near future,” says Ziesman. “The science side is important but our goal is to develop something that can be of benefit to producers.”
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