Demand for soybeans is extraordinarily high and is expected to remain high, being driven by demand for soybean oil as a renewable fuel feedstock, demand for protein, and production disruptions across the world. To meet the global demand for food and fuel without cultivating marginal and sensitive land, U.S. soybean farmers need to maximize yield per acre in the face of rapidly changing environments. A key driver of U.S. soybean yield since the 1940s has been the development of new varieties through plant breeding. After the implementation of scientific soybean breeding in the 1930s and 1940s in the U.S., soybean yields have dramatically increased, regions of production have expanded, varieties with defensive traits have been developed, and seed composition has been altered to meet various premium-based specialty markets. These foregoing facts show that soybean breeding is a powerful activity capable of transforming the agricultural landscape and making U.S. farms more competitive and profitable.
Despite these successes of soybean breeding, formidable challenges remain. One major challenge is the ubiquity of genotype-by-environment interactions. Genotype-by-environment interactions occur when varieties that do relatively well in some environments perform relatively poorly in other environments. This phenomenon slows the progress in developing broadly adapted varieties and necessitates more field testing across years and locations. It commonly occurs across years, which is particularly frustrating to the breeder. The timespan of a variety development program (7-10 years from cross to variety release) combined with genotype-by-environment interaction and climate change effectively makes it necessary for breeders to somehow breed for future environments, not necessarily the ones they are testing in now. On the other hand, genotype-by-environment interactions can be viewed as an opportunity to develop locally adapted varieties if it can be sufficiently exploited through well-defined target environments and their characterization for purposes of prediction.
Advances in DNA sequencing and the science of genomics has been revolutionizing crop breeding for more than a decade now, making it easier to identify genes underlying economically important traits, search for useful genetic diversity, and make faster and more effective selections through “genomic selection”. Genomic prediction and selection is a breeding method in which line selection and advancement decisions are made on the basis of genomic data only, allowing breeders to save time and resources. Numerous scientific articles have been published on the development and optimization of genomics-assisted breeding techniques. However, implementation in actual breeding programs still lags, especially in public-sector programs and small- to mid-size industry programs. Since the inception of the SOYGEN (Science Optimized Yield Gains across ENvironments) initiative funded by NCSRP, we have made a concerted effort to develop the resources and tools needed implement genomics-assisted breeding techniques. The SOYGEN network consists of all public soybean breeding programs located in the North Central region along with key collaborators in the areas of genomics, genotyping technology, and precision agriculture (Figure 1). We have compiled and curated existing variety performance data from our regional trial network and deposited them in a relational and searchable database from which data can be easily retrieved for analysis (https://soybase.org/ncsrp/queryportal/). We collectively genotyped nearly 3,300 advanced elite breeding lines entered in our regional trial network with genome-wide markers and developed low-cost low-density DNA marker technology necessary for conducting cost-effective genomic selection. To help use this genotypic data in making breeding decisions, we developed workflows and analysis tools (Figure 2). During the course of this initiative we have made over 10,000 genomic predictions, predicted cross value of over 1.2 million potential cross combinations, and dramatically increased our genotyping capacity. These advancements have been used to facilitate rapid-cycling genomic selection to increase genetic per year, select upon early-generation progenies at the plant row stage increase program efficiency, and identify parental combinations expected to create promising breeding populations in terms of average performance and variation.
Despite this progress, there is still work to do to continue to completely infuse genomics-assisted breeding into public soybean breeding programs. There are three new challenges we would like to tackle to advance genomics-assisted breeding in soybean: 1) Collect and model extremely dense “low pass sequencing” data, project sequence data onto breeding populations, and use it routinely in breeding programs; 2) Predict performance of varieties in future environments through modelling genotype-by-environment interaction effects and environmental parameters to improve varietal stability, increase efficiency, and more effectively develop varieties for future environments and local adaptation; 3) Use of structural variant data for enhancing genomic predictions and connecting yield stability to underlying genetics. We have deliberately chosen these objectives because they are not only major questions facing public programs but are also major questions facing large multi-national companies striving to leverage genomics to deliver new higher yielding products more rapidly and effectively to farmers. Such companies – large, mid-sized, and small – look to public programs to investigate such questions of general interest that sometimes involve high-risk, high-reward experimentation (see letters of support).
Accomplishing the foregoing objectives will advance soybean breeding methodology to help ensure continued genetic gain is made for yield, defensive traits, and seed composition well into future. Findings from our studies will be published in peer-reviewed open-access journals so that the knowledge we generate is available to everyone in the soybean seed industry. Findings will also be integrated into our current public programs to enhance their effectiveness and efficiency. Finally, keeping our public programs on the leading edge of breeding technology contributes to graduate and undergraduate education and thus produces future plant breeders, geneticists and other agricultural scientists well equipped to join the seed industry and create ever higher yielding soybean varieties.