Used in everything from baked goods to trendy edamame and livestock feed to cooking oil, the huge array of uses for soybeans has scientists looking for the most efficient ways to grow them.
That interest inspired the Kansas State University soybean breeding program to team up with the spectral analysis lab of Kevin Price, K-State professor of agronomy, to explore ways to increase the efficiency of the soybean breeding line selection process.
“The most time-consuming, land-intensive and expensive aspect of our breeding program at K-State is in harvesting the many thousands of early generation lines, weighing the seed and determining yield,” said Bill Schapaugh, K-State soybean breeder. “If we can find a way to separate out 50 percent or more of the very low-yielding lines without the need to combine harvest and weigh the seed, that would reduce the time and cost of our breeding program considerably,” Schapaugh said.
Spectral analysis, a method of analyzing the electromagnetic radiation coming from plants and other objects, is being used in the K-State Agronomy Department to determine the level of photosynthetic activity of vegetation in many different situations. The work is conducted with financial support from the Kansas Soybean Commission.
“We decided to work with Dr. Price’s spectral analysis team to try using this new technology in our soybean breeding nursery,” Schapaugh said. “The goal was to find out how effective this technology might be in predicting yields, stress tolerance and disease resistance as a way to eliminate unpromising lines early in the process.”
To do this, the K-State team, including graduate students Nan An, Brent Christenson, and Nathan Keep, used a ground-based spectroradiometer to gather spectral data in the visible and infrared spectra at various stages of growth, and correlated the results with actual yield data. They have spent the last two years trying to determine exactly what data to collect and how often, and whether any of the spectral regions being measured would have a good correlation to yield.
“Spectral analysis doesn’t have to be accurate enough to separate lines with a yield difference of just one or two bushels per acre. If it can separate lines with a yield difference of five to 10 bushels, that would be a great help in the preliminary stages of line evaluation,” Schapaugh said.
The initial model, developed by Christenson, correlated various spectral data at different growth stages with actual yields. The correlation using that model was not perfect, but was close enough to encourage further work.
“With this model, and using only the spectral data taken at the seed fill stage to make selections, we would have retained all of the highest yielding varieties by selecting the best half,” Schapaugh said.
“If we can repeat the kind of results we have achieved in the training population with experimental varieties from other populations, the precision should be accurate enough to cull out lines having a low yield potential at the earliest stage of evaluation. If we can discard low-yielding lines without having to harvest them and weigh the seed for yields, this will have tremendous value to the breeding program in terms of saving time, space and money,” he said.
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