Database Research Summaries
Increasing the rate of genetic gain for yield in soybean breeding programs

calendar_today Year of Research: 2019
update Posted On: 01/20/2020
group Leah McHale, principal investigator, The Ohio State University
bookmark North Central Soybean Research Program

Research Focus

The focus of this project is to increase the rate of genetic gain for yield in soybean breeding programs by increasing genetic variance, decreasing non-genetic sources of variability, and decreasing the length of the breeding cycle. This will be accomplished through coordinated activities to be carried out across twelve breeding programs in the north-central region.

Increases in soybean yield through breeding have been slower than growers expect, with the rate of yield increases for soybean substantially less than that for corn.  Reports of genetic gain in corn generally range from 1.0 to 1.2 bu/ac/yr, whereas a collaborative study led by Brian Diers of a historic set of MG II-IV soybean varieties released from 1923 to 2008 revealed a recent rate of genetic gain of 0.43 bu/ac/yr.

An introductory video about soybean breeding for yield, called Yield Potential, Yield Protection, and Genetic Gain has been developed and is posted on the North Central Soybean Research Program You Tube channel. Future short videos describing history and future developments of genetic gain are planned.

2019 NCSRP Annual Report summary


  1. Increase selection intensity and decrease non-genetic sources of variability through improved progeny row testing.
  2. Increase selection coefficient and decrease length of breeding cycle through genomic selection.
  3. Increase additive genetic variance. One of the key issues in the slow rate of soybean yield increase is the lack of genetic diversity in the commercial soybean gene pool.
  4. Develop a metric to estimate genetic gains on an annual basis.


Increasing the selection intensity and decreasing non-genetic sources of variability

Our 2019 yield tests based on selections from 2018 have been harvested. This will be the second preliminary yield test based on the progeny row selection models implemented in this study. All programs reporting have collected additional phenotypic data (observable characteristics) for each selection for two years.

Increasing selection coefficient and decreasing the length of the breeding cycle

A novel, rapid, and inexpensive genotyping method (MIP) was developed that can be used for genomic prediction and selection. From the different conditions tested we have been able to reduce the time it takes to prepare for sequencing by more than half. This will significantly increase the number of samples that can be processed.

We have continued to demonstrate that the lower reaction volumes and enzyme concentrations that we use produce good data and help get the cost of the protocol under $5.

Increasing the genetic variability in the commercial soybean gene pool

We have developed predictive model(s) that allow selection of superior high-yield genotypes from the USDA germplasm collection.

As this is likely the only time that such a collection of PIs will be evaluated in replicated trials across so many environments, we have been taking the opportunity to collect as much phenotypic data as possible. The phenotype data, plant developmental data, and environment data were all incorporated in the models.

High yielding lines derived from crosses of Glycine max with Glycine tomentella have been identified.

Multiple lines derived from wild soybean (Glycine soja) F2 crosses with Williams 82 or BC1 crosses with Williams 82 yield 80-97% and 89-95% of the checks over 3 years, respectively. A scientific manuscript that reports the contribution of wild type soybeans as a source of genomic diversity in soybean elite lines is in preparation.

A list of Alternative Elite Lines belonging to MG III and MG IV has been identified containing the combination of the most favorable haplotypes that could be used for increasing the performance in soybean breeding crosses

We are using publicly-available simulation software that is sufficiently flexible for a skilled graduate student to generate yields of potential varieties from multiple families grown in multiple environments in at least one stage of field trials.


  • Soybean breeders will be able to take advantage of the improved breeding methods that can be implemented in any soybean breeding program.
  • Diverse germplasm will be made available through selection methods. As a result, farmers will reap the benefits of higher yields from new cultivars.
  • Improved breeding methods as well as the development and release of community genotype and yield data and models data to soybean breeders will directly translate to greater yield gains in soybean breeding. Ultimately this will lead to higher yielding soybean varieties.
  • Extension materials aimed at explaining genetic gains to non-experts are a useful educational tool.

For more information about this research project, please visit the National Soybean Checkoff Research Database.

Funded in part by the soybean checkoff.