Nitrous oxide emissions and nitrogen use efficiency in wheat: Nitrogen fertilization timing and formulation, soil nitrogen, and weather effects
Bibliographic record
Abstract
Abstract Improving N fertilization in croplands could minimize soil emissions of nitrous oxide (N 2 O) and mitigate climate change. This study investigated the effects of spring vs. fall N applications of conventional vs. enhanced‐efficiency N fertilizers (EENFs) on N 2 O emissions and N use efficiency in spring wheat ( Triticum aestivum L.) over 2.5 yr in Alberta, Canada. Fertilizers were anhydrous ammonia and urea and the EENF formulations included urease and nitrification inhibitors and a polymer coating. We measured a fertilizer N 2 O emission factor of 0.31 ± 0.04%. Irrespective of N fertilizer and timing options peak N 2 O emissions were evident following soil thawing and major rainfalls. Because most of the annual N 2 O emissions were associated with soil thawing, spring‐applied N emitted half the N 2 O of the fall‐applied N during the second study year ( P < .001). Conversely, the opposite was observed for the first study year when overall N 2 O emissions were 36% larger for spring‐ than fall‐applied N ( P = .031) as major rainfalls occurred shortly after the spring N fertilization. Nevertheless, within this first study year, EENFs significantly reduced N 2 O emissions (by 26% on average; P = .019), with a tendency for 11% higher grain yield across springtime EENFs than for conventional fertilizers. Concomitantly, spring‐applied N doubled the fertilizer N recovery efficiency in the same year ( P = .023). The soil at the study site inherently had high N availability (NH 4 and NO 3 ) and this probably moderated the beneficial effects of EENFs on N 2 O emissions and grain yields. Results suggest that spring EENFs can mitigate the risk for N 2 O emissions while sustaining high yields even under scenarios with high availability of native soil N.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".