Genotype × environment interaction patterns of dry matter yield in meadow brome, orchardgrass, tall fescue, and timothy evaluated at harsh winter sites
Bibliographic record
Abstract
Abstract Background Genotype × environment interaction (GEI) slows genetic gains and complicates selection decisions in plant breeding programs. Forage breeding program seed sales often encompass large geographic regions to which the cultivars may not be adapted. An understanding of the extent of GEI in perennial, cool‐season forage grasses will facilitate improved selection decisions and end‐use in areas with harsh winters. Methods We evaluated the dry matter yield of nine meadow brome ( Bromus biebersteinii Roemer & J. A. Schultes), nine orchardgrass ( Dactylis glomerata L.), seven tall fescue ( Lolium arundinaceum (Schreb.) Darbysh.), and 10 timothy ( Phleum pratense L.) cultivars or breeding populations at seven high latitude and/or elevation locations in Canada and the United States from 2019 to 2021. Results For each of the species, we found significant differences among the genotypes for dry matter yield across environments and found significant levels of GEI. Using site regression analysis and GGE biplot visualizations, we then characterized the extent of the interactions in each species. Except for tall fescue, there was little evidence for the broad adaptation of genotypes across locations. Conclusions This research adds further evidence to the limitations of perennial, forage breeding programs to develop widely adapted cultivars and the need to maintain regional breeding efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".