Investigating Genetic Progress and Variation for Nitrogen Use Efficiency in Spring Wheat
Bibliographic record
Abstract
Improved N use efficiency (NUE) increases wheat ( Triticum aestivum L.) yields and reduces N losses in the environment. We investigated genetic variation and correlations among agronomic and NUE traits in Canada Western Red Spring wheat cultivars to further improve NUE. Trials were conducted for 3 yr at two locations in Alberta, Canada, under two levels of N (200 and ~50 kg ha −1 ). Genotype × environment interaction was significant for traits associated with vegetative growth, and genotype × N fertilizer treatment interaction was significant for important yield and NUE traits. There were significant positive correlations between total dry matter and N uptake efficiency (NUpE) in the high ( r = 0.74, P < 0.05) and low ( r = 0.83, P < 0.05) N treatments. The effect of dwarfing Rht‐1b allele was more prominent under high N treatment in increasing NUE. However, cultivars with Rht‐1b allele showed inconsistent results for NUpE, indicating that Rht alleles might have pleiotropic effects on N uptake. Grain yield, NUE, and N utilization efficiency (NUtE) exhibited genetic improvement over time only under high N treatment. Our results indicated that grain yield increased mainly due to improved harvest index (HI), suggesting improvement in C assimilation rather than N partitioning efficiency. Nitrogen use efficiency may further be improved by intercrossing cultivars with high HI, N harvest index, and NUtE and those with good NUpE, while using total dry matter production as a selection criterion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".