Genotype × Region Interaction for Two‐Row Barley Yield in Canada
Bibliographic record
Abstract
Barley (Hordeum vulgare L.) breeding programs recognize eastern and western Canada as separate target regions, but the extent of local adaptation to regions and subregions within them has not been studied. Genotype × region and subregion interactions were estimated in 145 lines from the two‐row barley cross Harrington/TR306 in 22 trials in 1992‐1993. The trials were grouped into five subregions (Maritimes–Quebec, Ontario, Manitoba–North Dakota, Saskatchewan, and Alberta) and two regions (eastern Canada and western Canada plus North Dakota). Variance components were estimated by a model in which the genotype × location (σ2GL) variance was subdivided into a genotype × region (or subregion) variance (σ2GS), and a within‐region or ‐subregion σ2GL No σ2GS was observed within the eastern or western regions, and genotypic correlations across subregions within regions approached 1.0. Significant σ2GS was observed for eastern versus western Canada, but the correlation between genotypic effects across these regions was 0.83. In a selection experiment, subdivision of the eastern or western regions did not increase response. Selection in the east produced greater yields in both the east and west. The same genotype ranked first for yield in both regions. There was little specific adaptation to subregions, and two‐row barley genotypes were broadly adapted across northern North America. Further subdivision of the regions is unwarranted, and selection in either region is likely to result in response in the other. The lack of local adaptation indicates that breeding programs that test broadly are likely to outperform ones that are narrowly targeted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".