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Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems

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Using phenotype data of three spring wheat populations evaluated at 6–15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa.

Abstract

There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3–9 conventionally and 3–6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0M) or environments (CV0), a subset of lines that have been evaluated in either management (CV2M) or some environments (CV2), and the performance of newly developed lines in either management (CV1M) or environments (CV1). The average prediction accuracies of the model that incorporated genotype × environment interactions with CV0 and CV2 schemes varied from 0.69 to 0.97. In the CV1 and CV1M schemes, prediction accuracies ranged from − 0.12 to 0.77 depending on the reaction norm models, the traits, and populations. In most cases, grain protein showed the highest prediction accuracies. The phenotype data collected under the organic management effectively predicted the performance of lines under conventional management and vice versa. This is the first comprehensive GS study that investigated the effect of the organic management system in wheat.

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Acknowledgements

The authors would also like to express appreciation to Klaus Strenzke, Joseph Moss, Izabela Ciechnowska, Fabiana Dias, Katherine Chabot, Tom Keady, and Russel Puk for their technical support with phenotypic data collection in both the conventional and organic managements.

Funding

This study was supported by grants to the University of Alberta wheat breeding program from the Alberta Crop Industry Development Fund (ACIDF), Alberta Wheat Commission (AWC), Saskatchewan Wheat Development Commission (Sask Wheat), Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery and Collaborative Grant, Agriculture and Agri-Food Canada (AAFC), Western Grains Research Foundation Endowment Fund (WGRF), and Core Program Check-off funds to DS.

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Authors and Affiliations

  1. Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada

    Kassa Semagn, Muhammad Iqbal, Hua Chen, Darcy H. Bemister & Dean Spaner

  2. International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico

    José Crossa

  3. University of Nebraska – Lincoln, Lincoln, NE, 68583, USA

    Diego Jarquin & Reka Howard

  4. Department of Agronomy, School of Life Science and Engineering, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang, 621010, Sichuan, China

    Hua Chen

  5. Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada

    Brian L. Beres & Harpinder Randhawa

  6. Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada

    Amidou N’Diaye & Curtis Pozniak

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  1. Kassa Semagn
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Contributions

KS conceptualized the work, curated and analyzed the data, and wrote the paper. MI supervised and managed the field data collection, contributed to data curation and analyses, and edited the paper. JC, DR, and RH were involved in data analyses and edited the paper. HC, DHB, HR, BLB, AN, and CP were involved in data generation and edited the paper. DS conceptualized the project, acquired funding, supervised the project, and edited the paper.

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Correspondence to Dean Spaner.

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Communicated by Xianchun Xia.

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Semagn, K., Iqbal, M., Crossa, J. et al. Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems. Theor Appl Genet 135, 537–552 (2022). https://doi.org/10.1007/s00122-021-03982-0

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