CFCRA Oat Project: Activity 1 – Breeding, genomics and agronomy research to improve oat yield and quality

Objectives:

• Develop new oat cultivars with improved grain yield and quality.
• Identify optimal agronomic practices to achieve high and stable grain yield and quality.
• Enhance current oat breeding procedures in both the Ottawa and Brandon breeding programs with genomic selection.
• Screen advanced oat breeding lines for crown rust resistance.
• Enhance information and germplasm exchange among North American oat breeding programs through a joint testing and genotyping network referred as ENCORE.
• Develop a multi-faceted approach to data and knowledge management that enhances all objectives of this project and benefits world-wide pre-competitive oat research.

Impacts:

• The development of new oat cultivars with improved resistance to crown rust will provide oat growers in eastern Canada with high yielding and reliable oat cultivars that can be used as food, feed, or cover crop.
• The development of new oat cultivars that meet the desired levels of beta-glucan content will provide consumers with healthier and more nutritious oat products from the oat milling industry.
• The development of agronomic recommendations (i.e., nitrogen management and seeding rate) for oat production areas across Canada will improve oat production systems on farm.

Scientific Summary:

This research project had three distinct components: breeding, genomic selection, and agronomy, with the following objectives:

1. To develop new oat cultivars for eastern Canada with improved grain yield and milling quality for end users.
2. To improve the selection accuracy in the oat breeding programs at Agriculture & Agri-Food Canada in Ottawa, ON and Brandon, MB using modern genomic tools and approaches.
3. To identify optimum nitrogen amounts and application methods, as well as optimum planting density for oat production in different oat growing regions of Canada.

Results:

Breeding efforts resulted in 16 new oat cultivars developed and released between 2018 and 2023. Among these were five for mega-environment (ME) 1 (southern and eastern Ontario), eight for ME2 (northern Ontario, Quebec, and Maritimes), and three for ME3 (Canadian Prairies).

The four cultivars released in 2019 have already shown significant impact on oat production in Ontario and Quebec. Specifically, AAC Excellence has shown superior yield, grain quality, and b-glucan levels and was listed as the top variety on the 2023 Quebec oat recommendation list. AAC Reid showed 25% higher yield than the Ontario Cereal Crop Committee (OCCC) trial mean in ME1, superior grain and compositional quality, lodging resistance, and resistance to multiple diseases (crown rust, barley yellow dwarf virus, and powdery mildew). AAC Chandler was one of the top yielders in northern Ontario (ME2). The newer cultivars, which are expected to be better still, are yet to show their impact, but are expected to perform very well in the coming years.

Genomic selection (GS) was introduced into the breeding program at AAFC Ottawa and AAFC Brandon to complement traditional breeding by visual selection. GS was shown to be effective in selecting yield and β-glucan. Two of the 16 cultivars developed under this activity were among the 60 lines selected through genomic prediction in 2017 in the observation nursery. While OA1655-1 was also selected visually, OA1675-1GS was discarded by visual selection for its tall plant height.

The GS prediction was effective for yield at Normandin QC and Harrington PE, but it was negative for yield at Ottawa in 2022. This strongly suggests that mega-environment specific GS models, particularly for yield in ME1, must be developed and utilized in the future. Multiple cohorts of genomic selection (GS) up to the present indicate that GS was not more effective than visual selection (VS) in developing high-yielding cultivars. This is because cultivars are defined by many traits other than yield and these traits are often unfavorably associated. Therefore, GS models for all key traits must be developed and utilized in the prediction if GS is to be used as an alternative to VS. A more rational approach would be to combine GS with VS prior to yield trials. We propose to use VS for observable traits, which are literally countless, and to use GS for traits that are important but cannot be easily observed or measured (e.g., yield and compositional quality). Cost efficiency has to be investigated for adding GS to the current breeding system; hopefully the additional cost will be justified by improved selection accuracy.

Agronomy studies on optimum nitrogen fertilization rates revealed that the maximum economic rate of nitrogen (MERN) in Canada averaged 127 kg/ha at market prices* and varied by location and years. Estimated yields from MERN fertilization were 5,560 kg/ha in western Canada and 6,240 kg/ha in eastern Canada. MERN is lowered as the cost of nitrogen fertilizer increases. Split use nitrogen was also found to improve nitrogen use efficiency. Plant density studies revealed that the optimum seeding rate for oats was 420 seeds/m² for eastern Canada, which is considerably higher than the OMAFRA-recommended seeding rate of 200-300 in seeds/m² in Ontario.

*Cost of grain and N used in economic calculations for the agronomy studies varied by year. 2018-2019: grain @ $246/tonne & N @ $750/tonne; 2020-2023: grain @ $400/tonne & N @ $2,200/tonne.

External Funding Partners:

This activity was funded in part by the Government of Canada under the Canadian Agricultural Partnership’s AgriScience Program, with industry support from the Canadian Field Crop Research Alliance (CFCRA) whose members include: Atlantic Grains Council; Producteurs de grains du Quebec; Grain Farmers of Ontario; Manitoba Corn Growers Association; Manitoba Pulse & Soybean Growers; Saskatchewan Pulse Growers; Prairie Oat Growers Association; SeCan; and FP Genetics.  Additional industry funding beyond the core CFCRA members was provided by oat millers across Canada.

Project Related Publications:

Blake, V.C., et al. 2022. GrainGenes: Tools and Content to Assist Breeders Improving Oat Quality. Foods. 11(7): 914.

Ma, B.L., De Haan, B., Zheng, Z., Xue, A.G., Chen, Y., de Silva, N.D.G., Byker, H., 2022. Exploring the relationships between biomass production, nutrient acquisition, and phenotypic traits: testing oat genotypes as a cover crop. Journal of Plant Nutrition. 45 (19): 2931-2944.

Tinker, N.A., Wight, C.P., Bekele, W.A., Yan, W., Jellen, E.N., Renhuldt, N.T. 2022. Genome analysis in Avena sativa reveals hidden breeding barriers and opportunities for oat improvement. Communications Biology. 5 (1): 474.

Yao, E., et al. 2022. GrainGenes: a data-rich repository for small grains genetics and genomics. Database. Volume 2022.

Yan, W., Fregeau-Reid, J., DeHaan, B., Thomas, S., Hayes, M., Martin, R. 2022. AAC Excellence oat. Canadian Journal of Plant Science. 102 (3): 776-780.

Yan, W., Fregeau-Reid, J., DeHaan, B., Thomas, S., Hayes, M., Martin, R. 2022. AAC Reid oat. Canadian Journal of Plant Science. 102 (3): 781-784.

Yan, W., Nilsen, K.T. 2022. Mega‐environment analysis and breeding for specific adaptation. Crop Science. 63 (2): 480-494.