ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Plant Breeding
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1579376
Estimation of Ryegrass (Lolium) Dry Matter Yield Using Genomic Prediction Considering Genotype by Environment Interaction Across South-eastern Australia
Provisionally accepted
- 1The University of Melbourne, Parkville, Australia
- 2AgriBio, La Trobe University, Melbourne, Victoria, Australia
- 3Agriculture Victoria Research, Melbourne, Australia
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Genomic Prediction (GP) considering Genotype by Environment (G×E) interactions was, for the first time, used to assess the environment-specific seasonal performance and genetic potential of perennial ryegrass (Lolium perenne L.) in a regional evaluation system across southeastern Australia. The study analysed the Dry Matter Yield (DMY) of 72 base cultivars and endophyte symbiotic effects using multi-harvest, multi-site trial data, and genomic data in a best linear unbiased prediction framework. Spatial analysis corrected for field heterogeneities, while Leave-One-Out Cross Validation assessed predictive ability. Results identified two distinct mega-environments: mainland Australia (AUM) and Tasmania (TAS), with cultivars showing environment-specific adaptation (Base and Bealey in AUM; Platinum and Avalon in TAS) or broad adaptability (Shogun). The G×E-enhanced GP model demonstrated an overall 24.9% improved predictive accuracy (Lin's Concordance Correlation Coefficient, CCC: 0.542) over the Australian industry-standard best linear unbiased estimation model (CCC: 0.434), with genomic information contributing a 12.7% improvement (CCC: from 0.434 to 0.489) and G×E modelling providing an additional 10.8% increase (CCC: from 0.489 to 0.542). Narrow-sense heritability increased from 0.31 to 0.39 with G×E inclusion, while broad-sense heritability remained high in both mega-environments (AUM: 0.73, TAS: 0.74). These findings support informed cultivar selection for the Australian dairy industry and enable genomics-based parental selection in future breeding programs.
Keywords: Reginal evaluation system, Environmental adaptability, Sustainable forage production, Multi-harvest multi-site trials, genomic selection
Received: 19 Feb 2025; Accepted: 19 May 2025.
Copyright: © 2025 Zhu, Giri, Lin, Cogan, Jacobs and Smith. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jiashuai Zhu, The University of Melbourne, Parkville, Australia
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