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 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 | 0.000 |
| 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 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".