Genetic Variability in Nitrogen Use Efficiency of Spring Barley
Bibliographic record
Abstract
Increasing costs of N fertilizers and the negative impact of excessive N on the environment have made improvement in nitrogen use efficiency (NUE) a desirable goal in barley ( Hordeum vulgare L.) breeding. Seventeen replicated trials, each consisting of 15 to 20 genotypes, were performed across different environments in Alberta, Canada from 1998 to 2007 to determine genetic variability in NUE. Further, 25 genotypes were grown at six environments in 2007 for analysis of the pattern of genotypic variation for NUE. Analysis of variance revealed significant effects of genotype and environment on NUE. The majority of the phenotypic variation in NUE was accounted for by genotypic variance and heritability estimates for this trait ranged from 0.5 to 0.86. Genotypes H97097001001, H96014002, ‘Vivar’, and ‘Xena’, were superior in NUE, yielding 47 to 48 kg kg −1 N as compared to about 35 kg kg −1 N yield for the relatively inefficient genotypes. There was no clear distinction between two‐rowed and six‐rowed types in NUE, but rather significant differences were observed among genotypes within each spike‐type group. Reduction in N fertilizer requirements in barley while maintaining yield may be achieved through breeding by targeting increased yield potential in association with higher NUE.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".