Nitrous oxide emissions with enhanced efficiency and conventional urea fertilizers in winter wheat
Bibliographic record
Abstract
Abstract Optimizing nitrogen fertilizer management can reduce nitrous oxide (N 2 O) emissions. This study tested if split applying enhanced efficiency fertilizers (EEFs) resulted in lower N 2 O emissions than applying equivalent rates of urea at planting. In semiarid southern Alberta, field trials were conducted during three years (planting to harvest) in rainfed winter wheat crops. Annual fertilizer rates ranged from 146 to 176 kg N ha −1 . Fertilizer types were urea, and three EEFs (polymer-coated urea, urea with urease and nitrification inhibitors, and urea with a nitrification inhibitor). Each fertilizer type was applied three ways: 100% banded at planting, split applied 30% banded at planting and 70% broadcast in late fall, and split applied 30% banded at planting and 70% broadcast at Feekes growth stage 4 (GS4, post-tiller formation, wheat entering the greening up phase in the early spring). Nitrous oxide was measured using static chambers between sub-weekly and monthly from planting to harvest. Over three years, cumulative N 2 O emissions ranged from 0.16 to 1.32 kg N ha −1 . This was equivalent to emissions factors between 0.009 and 0.688%. Cumulative N 2 O emissions and emissions factors did not differ between fertilizer types, but they were lower when fertilizer was split applied at GS4 compared to in late fall ( P ≤ 0.10). Our study suggests that EEFs do not reduce N 2 O emissions from rainfed winter wheat crops, but a well-timed split application with a majority of fertilizer applied after winter can minimize N 2 O emissions.
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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 | 0.000 | 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 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".