Rust never sleeps: New approach can predict outbreaks of corn disease
With data collected using aerial drones, Texas A&M AgriLife researchers have developed a method to predict outbreaks of southern rust in corn crops before the disease causes significant economic damage.
Scientific Reports has selected a paper outlining the work for publication.
The lead author is Aaron DeSalvio, a Department of Soil and Crop Sciences graduate student in the Genetics and Genomics program at Texas A&M University.
Leadership for the project was provided by Seth Murray, Texas A&M AgriLife Research corn breeder and Eugene Butler Endowed Chair in the Department of Soil and Crop Sciences, and Tom Isakeit, Texas A&M AgriLife Extension Service plant pathologist in the Department of Plant Pathology and Microbiology. Other contributors included post doctorate researcher Alper Adak, who helped analyze data, and Scott Wilde, who helped with drone flights.
Southern rust is the most important foliar disease of corn in the Upper Coast region of Texas, Isakeit said.
“Severe epidemics of this disease do not occur annually,” Isakeit said. “The sporadic occurrence of the disease makes it difficult to get good data on hybrid susceptibility from variety trials.”
Isakeit said early identification allows for informed decision making and fungicide application to prevent damages. The fungicide should be applied when there is a low severity of disease on the mid-to-upper canopy.
Through data analysis, DeSalvio said, researchers revealed a positive correlation between the presence of southern rust at grain-filling time and yield, which will have practical implications for precision agricultural practices.
“As a breeder, I can never seem to find time to take notes on rust,” Murray said. “Now, with UAS/drone tools, I don’t have to, and these tools are more accurate. But most exciting to me, we can approximate the time of grain fill, which is highly correlated with grain-yield prediction. This could not practically be done before.”