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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".