Winter wheat responses to enhanced efficiency granular nitrogen fertilizer in the Canadian Prairies
Bibliographic record
Abstract
Optimizing the timing of nitrogen (N) enhanced efficiency fertilizers (EEFs) may maximize winter wheat ( Triticum aestivum L.) grain yield, protein content, and N-use efficiency (NUE). From 2013 to 2018, experiments were conducted at two irrigated and six rain-fed sites across the Canadian Prairies (24 site-years) to evaluate winter wheat responses to N source and timing/placement effects of EEFs. Nitrogen sources included untreated urea, nitrification inhibitor nitrapyrin treated urea, urease inhibitor N-( n-butyl) thiophosphoric triamide (NBPT) plus nitrification inhibitor dicyandiamide (DCD)-treated urea (NBPT + DCD), and polymer-coated urea (PCU). The N sources were all side-banded at planting, 30% side-banded at planting plus 70% broadcast in-crop late-fall (averaged 38 days after planting; split-applied late-fall), or 30% side-banded at planting plus 70% broadcast in-crop early-spring (averaged 224 days after planting; split-applied early-spring). Nitrous oxide and methane emissions were measured at one rain-fed site to test whether N source and timing/placement influenced CO 2 -equivalents (CO 2 -eq; nitrous oxide + methane). Under irrigation, NBPT + DCD consistently produced the highest yields regardless of timing/placement; however, the 80% of the recommended rate caused suboptimal protein responses (≤11%) unless split-application of N was adopted. Untreated urea produced the highest net CO 2 -eq and yield-scaled CO 2 -eq emissions, with the highest emissions when urea was split-applied early-spring. To optimize winter wheat production and NUE, we conclude that NBPT + DCD all-banded during seeding operations or split-applied early-spring provided similar and often superior results to other sources, including a more typical system of urea side-banded at the time of seeding.
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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 | 0.001 |
| 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 | 0.000 |
| 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".