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Ontario Grain Farmer Magazine

Soybean cultivar & germplasm development: Focusing on seed yield & quality and disease resistance

Soybean cultivar & germplasm development: Focusing on seed yield & quality and disease resistance

Principal Investigator: Milad Eskandari

Research Institution: University of Guelph (U of G)

Timeline: April 2018 – March 2023   

Objectives:

  • Developing new high-yielding full-season soybeans with ≥2% per year yield increase until 2023 for food-grade and oil crush markets using classical, genetic, and genomic approaches. To achieve these goals, modern soybean cultivars from Canada, Japan, China, as well as the US high-yielding modern and exotic-derived experimental lines were used for incorporating new yield-related favorable genes/alleles into new Canadian cultivars.
  • Developing food-grade and specialty soybeans with improved value-added characteristics such as, protein content, seed size, isoflavones, sucrose content, and altered fatty acids and amino acids for niche end use markets. The genetic control of the above traits are being studied to discover molecular markers and develop genomic tools that will facilitate improvement of the target traits.           
  • Enhancing Canadian soybean germplasm for seed yield and composition traits as well as resistance to biotic stresses by incorporating durable disease resistance and new favourable alleles for the target traits from US, Chinese, and Japanese germplasm.
  • Developing new soybean cultivars with resistance to soybean cyst nematode (SCN) and expanding the genetic diversity of SCN resistance. In addition to using the most common source of SCN (i.e., PI 88788) for the continuation of developing SCN-resistant soybeans, one of the best sources of SCN resistance, PI 437654 (a.k.a. Hartwig), was used to expand the genetic diversity and improve the resistance in Canadian soybeans. 

Impacts:

  • Canadian soybean growers and industries benefit from the development of new high-yielding full-season soybeans with value-added traits (e.g., high protein, sucrose and oil content, modified fatty and amino acids, and seed size) that are resistant to the most important biotic stress in Canada (i.e., SCN). These new superior soybeans with value-added traits help Canadian soybean growers remain globally competitive and allow the food-grade soybean industry to maintain its global leadership.
  • The new germplasm and high-throughput genetic and genomic tools that have been developed through this project will be beneficial to this breeding program, as well as other Canadian soybean breeders, for pyramiding new favourable genes into the available or new cultivars that are adapted for Ontario.

Scientific Summary:

Demand for both GMO and non-GMO food-grade soybeans continues to increase worldwide. Canada accounts for only 2% of the world’s soybean production; however, it is a global leader in producing specialty, high-seed-quality soybeans. The value of soybeans to the Canadian economy is continuing to increase as production trends higher. Over the last decade, Canadian production of soybeans has increased (in tonnes) by over 37%.

The University of Guelph soybean breeding and genetics program at Ridgetown Campus is a public breeding program focusing on developing new full-season soybean cultivars and germplasm adapted to southwestern Ontario (maturity groups (MG) ranging from MG1 to early MG3). This breeding program is committed to developing new high-yielding high-quality cultivars that maximize farm gate value to the Canadian soybean growers and also meet the demands of domestic and international end users.

Through this proposed project, we aimed to develop new high-yielding food-grade soybean elite cultivars with improved value-added traits by identification and introgression of favourable genes / alleles from exotic germplasm, including North American, Chinese and Japanese modern cultivars and PI-derived experimental lines, using classical and modern genetic and genomic tools. In addition to providing the knowledge and technology that is important for the continued development of superior food-grade soybeans adapted to Canada, the outcome is also expected to have significant impact on the Canadian soybean growers’ productivity and the soybean export industry through the development and commercial release of new value-added high-yielding soybeans.

Results:

Every year (2018-2023) F1 seedlings of 110 crosses that have been made in winter using 29-35 different soybean elite and exotic germplasm were planted in the field at Ridgetown during the summer and the F2 seeds harvested. After threshing, 500 F2 seeds for each cross were packed and sent to Costa Rica (winter nursery) to be advanced to F4 generation using Single Seed Descent (SSD) breeding procedure during winters. Then, over 50,000 single F4 plants were annually evaluated in our F4 nursery at Ridgetown, and about 15-20% of the F4 plants with promising visual agronomic performance and suitable maturity were selected to form F4-5 generation materials for further evaluation. On an annual basis we have also evaluated up to 12,000 F4-5 soybean lines in head-rows at Ridgetown and about 15-20% have been selected for further evaluation through our preliminary yield trials (at two locations using 2-rep randomized complete block designs).

We also annually evaluated over 300 F4-6 lines for yield and other important traits across six locations in southwestern Ontario. Seed composition traits (e.g., protein, oil, sucrose, different fatty acids) of all harvested seeds were measured using NIR during the fall. In addition to evaluating for yield in three locations, SCN resistant tests were implemented on F4-7 advanced lines using KASP markers for both rhg1 and Rhg4 genes. Lines that carry rhg1 or P188788 based-SCN resistant gene were identified using rhg1 KASP marker. On average, each year around 50-60 lines out of 100-120 advanced lines have P188788-type SCN resistant gene. These identified lines tested for rhg1 copy number (CN) using TaqMan assay. Genotypes with three or more rhg1 copy number show resistance to SCN, and those with one or two CN are susceptible. On the other hand, all advanced lines were tested for Rhg4 KASP marker and Peking-type SCN resistant gene were identified. Then Rhg4 CN were evaluated in these SCN resistant lines. In general, it is hypothesized that a high CN of Rhg4 can make soybeans resistant to the broad spectrum of SCN HG types. In our program most of the resistant lines had around 8 CN of rhg1 gene but none of them had more than one copy number Rhg4. The molecular marker analyses is fast, cost-efficient, and a reliable tool to select SCN resistant lines and introduce them to Ontario-based seed industries for licensing and commercial release.

Cultivars Developed at the U of G Ridgetown Soybean Breeding Program from 2018 – 2023
Variety NameLine NumberYear of Release to SeCan Members
NA 2300SC 6014N2019
OAC ParisSC 5714N2019
SC 3116NSC 3116N2019
SC 4317NSC 4317N2020
SC 8515NSC 8515N2020
OAC UnionSC 6218N2021
OAC StirlingSC 8518N2021
SC 5420NSC 5420N2023
SC 0621NSC 0621NAt Breeder Seed production stage in 2023 – will be released to farmers in 2024
SC 0821NSC 0821N
SC 2521NSC 2521N

External Funding Partners:

SeCan  

Project Related Publications:

Yoosefzadeh-Najafabadi, M., Earl, H., Tulpan, D., Sulik, J. and Eskandari, M. 2021. Application of machine learning algorithms in plant breeding: Predicting yield from hyperspectral reflectance in soyabean. Frontiers of Plant Science. 11.

Yoosefzadeh-Najafabadi, M., Tulpan, D., Eskandari, M. 2021. Application of machine learning and genetic optimization algorithms for modelling and optimizing soyabean yield using its component traits. PLoS ONE. 16(4).

Yoosefzadeh-Najafabadi, M., Rajcan, I., and Eskandari, M. 2022. Optimizing genomic selection in soyabean: An important improvement in agricultural genomics. Heliyon. 8(11).

Yoosefzadeh-Najafabadi, M., Eskandari, M., Torabi, S., Torkamaneh, D., Tulpan, D., and Rajcan, I. 2022. Machine learning based genome wide association studies for uncovering QTL underlying soybean yield and its components. International Journal of Molecular Science. 23(5538).

Yoosefzadeh-Najafabadi, M., Hesami, M., and Eskandari, M. Machine learning-assisted approaches in modernized plant breeding programs. 2023. Genes 14 (4), 777.

Yoosefzadeh Najafabadi, M., Stirling, B., Brandt, R., Torabi, S., and Eskandari, M. 2023. OAC Union Soybean. Canadian Journal of Plant Science.

Yoosefzadeh Najafabadi, M., Stirling, B., Brandt, R., Torabi, S., and Eskandari, M. 2023. OAC Stirling Soybean. Canadian Journal of Plant Science.