After working in the field with New York state growers, Heck, a postdoctoral associate from Brazil working in the lab of Sarah Pethybridge, associate professor of plant pathology and plant-microbe biology, said one challenge was that too many fields needed to be assessed in a short time, and there was a lack of decision-making tools. Using his own research and resources available at Cornell AgriTech, Heck developed the app, called Sampling, as a solution for growers.
We recently asked Heck to share information about the app’s development and benefits.
What problems can this app help growers potentially solve? We want to assist growers in making decisions about when a management practice, such as applying fungicides and pesticides, is needed. Sequential sampling plans for agriculture pests were developed to save time in scouting and assessing for diseases and insects. However, merging these plans with the action thresholds for integrated pest management practices has created a more powerful tool for decision-making. For example, using the new app, the grower will apply a fungicide only if the disease has crossed the action threshold; we define the action threshold as the limit when the cost of control is equal to the damages caused by the disease. The additional benefits include reducing pesticide applications, reducing the cost of crop production, reducing the load of pesticides in the food and environment, and reducing the selection pressure of plant pathogens and insect pests resistant to chemicals.
What research at Cornell AgriTech inspired you most to develop this app?
Most of the algorithms that we developed were based on scientific literature, and from these, Jan Nyrop, director at Cornell AgriTech, is one of the main researchers in the field of sequential sampling. I used many of his published manuscripts to learn and understand this technique and ultimately develop the idea for the Sampling app.
How many diseases can this app be used for sampling?
This is the first version of the app, so for now, we have implemented the sequential sampling plans for the Cercospora leaf spot disease. We developed the app to serve as a repository for sampling plans for multiple diseases and insect pests. We plan to add sampling plans for Stemphylium leaf blight of onions and a few more diseases and insect pests this year. We also have some requests from researchers abroad to incorporate Fusarium wilt of bananas, white mold of soybeans and coffee rust into the app.
How do you use the app?
The Sampling app allows users to select a disease or pest from a prepopulated list and specify the objective of sampling: estimation or classification. Later, the precision of sampling or the action threshold can be selected.
Users can navigate on the map to select a field to be inspected. When sampling begins, users can choose a random sampling unit to start scouting for diseases and enter the number of diseased individuals at each sampling unit assessed. The app will then inform the user when to stop sampling for the goals selected and return the final incidence and threshold achieved. With this information, stakeholders can make a decision if treatment is needed (or not) for disease management. For example, if the disease incidence in the field is above the action threshold, a fungicide spray is required to keep the disease under control.
What is one thing that you think would surprise people about New York state crop diseases?
One thing that surprised me most about crop diseases in New York state is how dynamic the crops and the plant diseases are. I was not expecting to see this, mainly because of my research experiences in tropical and subtropical regions. Like human diseases epidemics, plant disease epidemics work very similarly in terms of population variants. A pathogen population threatens a host population, and a variant can quickly overcome a well-established management practice and become predominant. We are seeing this for the Stemphylium leaf blight disease of onion. It is well known that plant pathogens can adapt and evolve in the environment according to the different stimuli being applied. Still, in some crops in New York, these changes in the pathogen population are incredibly dynamic and fast. The whole SLB population can become resistant to a chemical in four to five crop seasons. This would typically take at least double this time in most pathosystems.
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