Researchers Develop Faster, More Efficient Test for Soybean Cyst Nematode

The soybean cyst nematode (SCN) is the most pervasive plant pest in agriculture. University of Illinois researchers have developed a system to test for the nematode that is approximately 20 times faster than current technology and provides more precise data than existing solutions. Kaustubh Bhalerao is the principal investigator for the project.

“Producers typically lose 5 to 10 percent of their yield to the SCN,” said Bhalerao. “To combat the problem, they need to evaluate their crops each year. Did the resistant variety they used last year work? Should they use a different variety this year, or should they rotate to corn?”

To make those management decisions, Bhalerao said, most producers pull 10 soil samples from as many acres and pool the samples to get one reading on the SCN population. “The test costs about $50, so they spend $5 per acre to get one result, or point, of very poor reproducibility, quality, and spatial resolution.”

Bhalerao said the new system can bring that cost down markedly; producers can get 20 points per 10 acres, or about two points per acre, which increases the spatial resolution by 20 times. Experts recommend 10 points per acre for the most accurate results, and Bhalerao said with properly streamlined operations, the results can be provided for about $20, or $2 per sample.

The system includes an “extractor” that processes the soil samples to separate the eggs. One extractor unit has four channels for processing, so multiple units can be used as needed. An automated image acquisition and analysis system counts the eggs.

“The extractor basically makes a milkshake out of a clod of dirt and water,” Bhalerao said. “The operator inserts a plug of soil, and water is added so that the cysts float and the dirt sinks. Once the cysts float, we raise the water level so the cysts flow out onto a set of sieves, where they’re crushed by an automated roller. The eggs from the cysts pass through the sieves and are washed into a sample container. The eggs are stained and counted using the image analysis system.”

A website will be developed so producers can monitor and organize the data they receive from the SCN testing and possibly integrate it with other information they collect, such as data from yield monitors on tractors. Bhalerao said the research team hopes to provide this group of technologies primarily through soil testing companies.

“With the current technology, an average company is testing about 3,000 samples a year,” he said. “With our technology, they should be able to do 30,000 or more.”

Bhalerao is the head of a multidisciplinary research team that includes Farhan Syed Abbas, a graduate student in technical systems management in Agricultural and Biological Engineering (ABE); Allante Whitmore, a graduate student in ABE; Sadia Bekal, a research associate in Bhalerao’s lab; Chinmay Soman, a former postdoctoral researcher in the lab; and Kris Lambert, an associate professor in crop sciences.

“My contribution includes the image analysis program,” said Bhalerao. “Farhan did the grant submission and presentation, Allante has done all the engineering drawing for the extractor, and Kris is an advisor on the grant we received for the research. The project is a direct result of experts from multiple backgrounds working together to solve an important problem.

“The agricultural industry is showing a lot of interest in this area,” Bhalerao concluded. “It’s kind of the ‘wild west’ right now, but we’re positioned very well, and we have a good prototype in the market.”

Funding for this research was provided in part by a University of Illinois Proof-of-Concept (iPOC) award.

Source: Kaustubh Bhalerao and Leanne Lucas, University of Illinois

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