Stacking nitrogen management practices: Combining double‐slot fertilizer injection with urease and nitrification inhibitors improves yields and reduces ammonia and nitrous oxide emissions
Bibliographic record
Abstract
Abstract Ammonia loss following nitrogen fertilization can degrade air quality and impact human health, whereas nitrous oxide (N 2 O) can contribute to global warming and climate change. Mitigation practices that target only one N‐loss pathway can lead to pollution swamping; hence, practices targeting both N‐losses are required. A 3‐year field study examined fertilizer N‐placement (broadcast urea, single‐slot injection of urea ammonium nitrate [UAN], double‐slot UAN injection) and N‐metabolization inhibitors (with/without urease and nitrification inhibitors) impacts on NH 3 and N 2 O losses and corn yields. Ammonia volatilization was reduced ( p < 0.05) by 26% with single‐slot UAN injection (10.6 kg N ha −1 ) and by 63% with double‐slot UAN injection (5.32 kg N ha −1 ) compared to broadcast urea (14.3 kg N ha −1 ). Dual urease and nitrification inhibitors reduced NH 3 volatilization (0.84–3.86 kg N ha −1 ) by 57%–92% compared to no inhibitors (5.32–14.3 kg N ha −1 ). When no inhibitors were applied, N 2 O emissions from slot injection (6.43–7.62 kg N ha −1 ) were 2.6–3.1 times greater than from broadcast urea (2.43 kg N ha −1 ). Dual inhibitors reduced N 2 O emissions by 43% from 6.43 to 3.66 kg N ha −1 with double‐slot injection. Double‐slot UAN injection increased corn grain yields (9.73 t ha −1 ) by 12%–13% compared to single‐slot UAN injection (8.71 t ha −1 ) and broadcast urea (8.6 t ha −1 ). Double‐slot UAN injection effectively decreased NH 3 losses and increased corn grain yields, but dual N inhibitors were required to also reduce N 2 O. Hence, combined productivity and environmental benefits were accrued only when fertilizer containing urease and nitrification inhibitors was combined with double‐slot injection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".