Timing and Method of<sup>15</sup>Nitrogen-Labeled Fertilizer Application on Grain Protein and Nitrogen Use Efficiency of Spring Wheat
Bibliographic record
Abstract
Grain protein content is one of the most important quality constraints for bread wheat (Triticum aestivum L.) production in eastern Canada. A field experiment was conducted for two years (1999 and 2000) on the Central Experimental Farm, Ottawa, Canada, to study whether split application of nitrogen (N) fertilizer improved grain protein content and nitrogen-use efficiency (NUE). Two cultivars (‘Celtic,’ as N-responsive and ‘Grandin’, as N-non-responsive) were grown using three different N doses and application methods: (1) 100 kg N ha−1 as NH4NO3, soil-applied at seeding with 15N2-labeled NH4NO3 to microplots, (2) 60 kg N ha−1 soil-applied at seeding plus 40 kg N ha−1 foliar-applied at the boot stage with 15N2-labeled urea to microplots, and (3) 90 kg N ha−1 as soil-applied at seeding plus 10 kg N ha−1 foliar-applied at the boot stage with 15N2-labeled urea to microplots. Plants were sampled at heading and maturity. While dry-matter production and grain yields were not affected by the treatments in either year, N application methods influenced tissue N concentration and NUE. In 1999, extended drought stress led to significant yield reduction; in 2000, foliar application of 10 kg N ha−1 at the boot stage significantly increased grain N concentration when grain protein was under the limit for bread quality, suggesting that later-applied N can contribute to grain protein content. At maturity, the average NUE was 22.3% in 1999 and 34.5% in 2000, but was always greater when all N was applied at seeding (42.5%) than when N was foliar-applied at the boot stage (18.5% to 24.5%). We conclude that application of a small amount of fertilizer N at the boot stage can improve the bread-making quality of spring wheat by increasing grain protein concentration.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".