Influence of N fertilization method on weed growth, grain yield and grain protein concentration in no-till winter wheat
Bibliographic record
Abstract
Applying polymer-coated urea (PCU) instead of uncoated urea may benefit winter wheat (Triticum aestivum L.) production by reducing weed growth or increasing grain yield and protein concentration. Field trials were conducted for 3 yr under rainfed and irrigated conditions in Lethbridge and 2 yr under rainfed conditions in Lacombe to determine potential benefits of substituting urea with PCU for typical winter wheat production practices in Alberta. Four factors were included in each experiment: (1) urea type (urea, PCU and a 50:50 blend of urea and PCU), (2) application method (fall side-band vs. spring broadcast), (3) N rate (1× and 1.5× recommended N rate), and (4) herbicide application (none vs. full). Herbicide application substantially reduced weed biomass at all site-years, but only increased average grain yield by 9%. Dicot weed biomass was not affected by fertilizer treatment, but monocot weed biomass was less for fall banded than spring broadcast application and less for urea than PCU or blend at the 1× N rate. Over all site-years, substitution of 50 or 100% of urea with PCU increased grain yield by an average of 4.3% and reduced protein concentration by 1.3%. Substitution of urea with PCU had the largest impact at the site-year with most severe drought stress, indicating that PCU benefits were probably due to factors other than reduced N loss. Further study is required to evaluate potential benefits from substitution of urea with PCU over a wide range of environmental conditions.Key words: Triticum aestivum, nitrogen fertilizer, placement, timing, split N, grain protein concentration, polymer-coated urea
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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 | 0.000 | 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 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".