Enhanced nitrogen management strategies for winter wheat production in the Canadian prairies
Bibliographic record
Abstract
To address knowledge gaps around enhanced efficiency urea fertilizer efficacy for nitrogen (N) management, a study was designed to improve integrated nutrient management systems for western Canadian winter wheat producers. Three factors were included in Experiment 1: (i) urea type [urea, urea + urease inhibitor—Agrotain ® ; urea + urease and nitrification inhibitor—SuperU ® , polymer-coated urea—Environmentally Smart Nitrogen ® (ESN ® ), and urea ammonium nitrate (UAN)], (ii) application method (side-band vs. spring-broadcast vs. 50% side-band: 50% spring-broadcast), and (iii) cultivar (AC Radiant hard red winter wheat vs. CDC Ptarmigan soft white winter wheat). The Agrotain ® and CDC Ptarmigan treatments were removed in Experiment 2 to allow for additional application methods: (i) fall side-band, (ii) 50% side-band — 50% late fall broadcast, (iii) 50% side-band — 50% early spring broadcast, (iv) 50% side-band — 50% mid-spring broadcast, and (v) 50% side-band — 50% late spring broadcast. CDC Ptarmigan produced superior grain yield and N utilization over AC Radiant. Grain yield and protein content were influenced by N form and application method. Split applications of N usually provided the maximum yield and protein, particularly with Agrotain ® or SuperU ® . An exception to the poor fall-application results was the SuperU ® treatments, which produced similar yield to the highest-yielding treatments. The results suggest that split applications of N might be most efficient for yield and protein optimization when combined with an enhanced efficiency urea product, particularly with urease or urease + nitrification inhibitors, and if the majority of N is applied in spring.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".