Research HighlightsBreeders Continue to Improve Tools for Soybean Genetic Gain
By Carol Brown
Through their breeding programs, soybean geneticists at public universities in the North Central region have developed improved germplasm, cultivars, and soybean lines. Because they are at public institutions, they can share germplasm to continue to improve soybean genetics.
These scientists also contribute their findings to the Northern Uniform Soybean Tests (NUST), overseen by the USDA since 1941. The first USDA report states the tests were created to “develop improved varieties and strains of soybeans for industrial utilization.” Today, the purpose reflects the advancements made over the last 80 years: “to critically evaluate the best of the experimental soybean lines developed by federal and state research personnel in the U.S. and Canada for their potential release as new varieties.” Similar reports for the Southern Uniform Soybean Tests began in 1943.
Through several projects funded over the last decade by the North Central Soybean Research Program, breeders in the region have been refining their research and the data they collect for improved soybean genetics as well as for better communication between breeders — and to add key information to the NUST.
“This project has facilitated communication between breeders on updates, methods and data acquisition,” says Leah McHale, a soybean breeder and geneticist at Ohio State University who led this NCSRP-funded project. “Unlike private industry, breeders at academic institutions generally have a limited capacity to independently carry out multi-state yield trials. Instead, through the NUST, we work with each other to accomplish this. The NUST provides breeders with yield data across more environments as well as information on what germplasm might be available to incorporate in our specific breeding programs.”
The NCSRP project started in 2017, and leaders named it the SOYGEN project (Science-Optimized Yield Gains across ENvironments) in 2018. McHale led the SOYGEN and SOYGEN2 projects. Aaron Lorenz at the University of Minnesota leads the SOYGEN3 project, which began in October 2022.
“There is a wealth of data available that we can use to make our selections, and to identify germplasm from other breeders that would be worthwhile to integrate into our breeding programs,” says McHale. “But previously, that had been the limit of what we’ve used the NUST data for. This project is helping us to make the data even better.”
Among the improvements, the team asked participating breeders to allow their germplasm entries to be genotyped and to add GPS coordinates to their trial locations, both current and historical. Detailed location information helps researchers select appropriate germplasm for their studies. People can then do more complex work, McHale says, such as genotype-by-environment (GxE) analyses. These analyses allow breeders to tease out which characteristics are truly caused by genetic differences, providing valuable information for soybean geneticists.
The NUST trials are conducted by Maturity Group, and the recorded data includes information on phenotype, or agronomic traits, and now genotype as well. They also record specific test locations and their rainfall data.
“This additional data will help us identify genetics that perform well in certain environments or parameters,” explains McHale. “It will allow us to develop genomic selection models from a wide set of germplasm and environments. And the data is in a uniform format, so it should create some synergy across the group.”
Additional Studies for Better Research
Another objective within the project looks at different ways to improve progeny selection, which is determining which plants to breed together based on the traits of their offspring. This objective, McHale says, has a lot of specific projects and experiments within it. One component explores whether breeders could use soybean canopy UAV imagery to be more efficient in their plant selection process. The team is in the process of analyzing this data for its feasibility.
Another method the team is testing is rapid cycling genomic selection. William Schapaugh at Kansas State University is leading this component of the project. As the topic suggests, Schapaugh is trying to reduce the time to complete an individual cycle of selection.
“A cycle of selection is the time it takes from the beginning of the breeding process until superior progeny are identified that can be used as parents to develop new varieties,” Schapaugh says. “Using traditional breeding methods, the cycle time is several years, and may be as long as 7 to 10 years. Genomic selection tools help reduce this cycle time by identifying superior progeny sooner in the breeding process.”
In traditional breeding methods, plant parents are crossed to form an F1 generation. The F1 and following generations are self-pollinated to produce inbred lines that are evaluated over several growing seasons to identify superior varieties. However, Schaupaugh’s rapid cycling experiment is testing genomic selection on the F1 generation.
“Our F1 generations are produced from a random mating population where every F1 plant represents a different genotype. Using genomic selection, we are predicting the yield potential, seed protein and oil composition, plant maturity and genetic diversity of each plant,” he explains. “Based only on genomic predictions, we select superior plants and intermate them to form a new F1 generation. The process is repeated to form a base population (C0), and three cycles of selection: C1, C2 and C3. Inbred lines from each of these cycles have been developed, all the lines have been genotyped and are now being evaluated in field trials.”
Once the field trials are complete, the collected data will help the researchers understand the effectiveness of this approach to reduce cycle time, increase genetic gain, and maintain genetic diversity in the breeding material.
“Theoretically, if genetic diversity isn’t added in, we’ll reach a plateau of genetic improvements after a certain number of cycles,” McHale comments. “We want to know when that plateau will be reached and where will we see the biggest gains for the research dollar investment. This portion of the project required a lot of coordination and effort.”
The NCSRP funding enables soybean breeders across the region to improve soybean lines and how they exchange information about the work they are doing. This translates to improved soybean quality including better yielding beans for farmers.
The scientists involved with the SOYGEN project have made positive strides in the soybean breeding arena, as reported in previous articles. Read about the team’s progress over the last few years:
Purdue University article on SOYGEN3: Drone Imagery Analysis to Help Increase Soybean Yield in Wake of Climate Change
Published: Jun 5, 2023
The materials on SRIN were funded with checkoff dollars from United Soybean Board and the North Central Soybean Research Program. To find checkoff funded research related to this research highlight or to see other checkoff research projects, please visit the National Soybean Checkoff Research Database.