Early Crop Disease Alert System for Potato Growers
An innovative, easy-to-use alert system that identifies crop disease in the air, long before it can be seen on the potato field is now available.
Spornado Sampler is a highly sensitive airborne detection tool that identifies fungal diseases such as late blight, sclerotinia, fusarium head blight, powdery and downy mildews. It uses targeted DNA testing which detects very low levels of the pathogens.
“The rapid and precise data provides a valuable tool for pesticide management, delivering the information growers need to accurately apply fungicides when and where they’re needed,” explained the system developers.
Spornado Sampler doesn’t require training, teaching, or licensing. It is an out-of-the-box system that ships within a day or two after it is ordered and is as easy to learn as hammering a stake into the ground.
“Once in the field, the sampler relies on wind power to trap even microscopic-sized fungal-fragments on specialized filters that easily snap out of the sampler. The grower, their agronomist or crop consultant simply collects and ships the filter to an approved partner lab. It doesn’t rely on visible identification of spores; the highly sensitive DNA analysis can detect very low levels of fungal pathogens that cause crop disease,” the company’s experts added.
In as little as one day, growers receive an email or text message with clear, actionable information about their possible risks of fungal disease: “A new and valuable tool in a complicated, unpredictable line of work”.
For a crop disease to develop there must be a susceptible host crop, an airborne pathogen, and a favorable environment. Together these three elements are known as the Disease Triangle. When the three conditions of the triangle intersect, fungal crop disease may occur—and can have damaging, costly effects on crops and annual yield.
Predicting the onset of these conditions and resulting crop diseases can be inexact – as much an art as science. Environmental data such as weather conditions and predictive models help, but even for the most seasoned grower, it is difficult to accurately anticipate the onset of fungal diseases.