Selection Efficiency across Environments in Improvement of Barley Yield for Moderately Low Nitrogen Environments
Bibliographic record
Abstract
Developing barley (Hordeum vulgare L) cultivars suitable for low‐N conditions is important for sustainable production. In breeding for low‐N environments, it must be decided whether a separate breeding program is necessary for this environment or if it can be performed as part of a multienvironmental testing and selection strategy. The objective of this study was to determine the efficiency of indirect selection based on performance under the traditional multiple high N environments versus direct selection under the low‐N conditions. Twelve experiments, each consisting of 18 to 25 barley genotypes, were conducted in five to eight environments including a low‐N environment in Alberta, Canada, from 2001 to 2006. The low‐N conditions used in this study simulated reduced N application as would be used to produce malting barley in western Canada, so the level of N‐stress imposed would be considered moderate. Genetic correlations between mean grain yield across multiple high N environments and the yield in the low‐N trial was positive and high, ranging from 0.83 to 1.00. The predicted correlated response in grain yield under low N to selection based on mean yields across multiple high‐N environments relative to the predicted response to direct selection in the low‐N environment ranged from 0.81 to 1.18. This implies that breeding for low‐N conditions relevant to malting barley cultivation in western Canada and similar circumstances can be performed as part of the selection strategy for broad adaptation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".