The effects of split application of enhanced efficiency fertilizers on non-winter nitrous oxide emissions from winter wheat
Bibliographic record
Abstract
This study tested if non-winter cumulative nitrous oxide (N2O) emissions, emission factors, and yield-scaled N2O emissions were affected by split application of enhanced efficiency nitrogen fertilizers in a rain-fed winter wheat crop. Based on initial soil tests, fertilizers were applied at 84 kg N ha−1 in year 1 and 72 kg N ha−1 in year 2. Two trials were completed each year. Trial 1 applied (1) urea, (2) urea with nitrification inhibitor, (3) nitrification and urease inhibitors, and (4) polymer-coated urea as (1) 100% side-banded at planting, 30% side-banded at planting and (2) 70% surface-applied in late fall, or (3) 70% surface-applied in spring at Feekes growth stage 4 (GS4). Trial 2 applied (1) urea–ammonium nitrate (UAN), (2) UAN treated with nitrification inhibitor, (3) urease inhibitor, (4) a combination of both, (5) granular urea, and (6) polymer-coated urea, all applied 50% side-banded at planting and 50% surface-applied at GS4. Cumulative N2O emissions from fertilized soils ranged from 0.101 to 0.433 kg N ha−1. The emission factors for trial 1 were greater in year 1 than year 2 (P ≤ 0.05). There were no treatment differences in cumulative N2O emissions in trial 2. However, cumulative N2O emissions, emission factors, and yield-scaled N2O emissions from trial 1 were higher when fertilizer was split-applied in late fall compared with at GS4 (all P ≤ 0.05). This study demonstrates that under best management practices, it is better to apply the required rate in the form of conventional fertilizer at planting rather than split application.
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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) | 0.001 | 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) | 0.001 | 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".