Review: Strategies to increase nitrogen use efficiency of spring barley
Bibliographic record
Abstract
Anbessa, Y. and Juskiw, P. 2012. Review: Strategies to increase nitrogen use efficiency of spring barley. Can. J. Plant Sci. 92: 617-625. Improvement in nitrogen use efficiency (NUE) is important to reduce input costs and the negative impact of excessive N on the environment. This review found that barley growers in western Canada have over the years adopted a number of improved N management strategies including soil testing and adjusting rate of N fertilization accordingly, switching from fall application to spring application of N fertilizers, and side-dressing placement of N that gives plant roots easier access to N nutrition. However, it is our opinion that use of variable N rates, choice of N fertilizer type that is less susceptible to losses, and improved manure management are some of the areas where further increase in NUE should be sought. As well, barley germplasms show substantial differences in NUE and genetic selection could increase NUE. Genetic improvement of NUE in barley should be possible both by the traditional breeding approach of crossing and pyramiding NUE genes from across different sources as well as through the development of transgenic barley. The integration of improved N management practices and more efficient cultivars may bring about a significant increase in NUE and ultimately grain yield of barley under the target moderate rate of N application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".