Global evaluation of inhibitor impacts on ammonia and nitrous oxide emissions from agricultural soils: A meta‐analysis
Bibliographic record
Abstract
Abstract Inhibitors are widely considered an efficient tool for reducing nitrogen (N) loss and improving N use efficiency, but their effectiveness is highly variable across agroecosystems. In this study, we synthesized 182 studies (222 sites) worldwide to evaluate the impacts of inhibitors (urease inhibitors [UI], nitrification inhibitors [NI] and combined inhibitors) on crop yields and gaseous N loss (ammonia [NH3] and nitrous oxide [N2O] emissions) and explored their responses to different management and environmental factors including inhibitor application timing, fertilization regime, cropping system, water management, soil properties and climatic conditions using subgroup meta‐analysis, meta‐regression and multivariate analyses. The UI were most effective in enhancing crop yields (by 5%) and reducing NH3 volatilization (by 51%), whereas NI were most effective at reducing N2O emissions (by 49%). The application of UI mitigates NH3 loss and increases crop yields especially in high NH3‐N loss scenarios, whereas NI application would minimize the net N2O emissions and the resultant environmental impacts especially in low NH3‐N loss scenarios. Alternatively, the combined application of UI and NI enables producers to balance crop production and environmental conservation goals without pollution tradeoffs. The inhibitor efficacy for decreasing gaseous N loss was dependent upon soil and climatic conditions and management practices. Notably, both meta‐regression and multivariate analyses suggest that inhibitors provide a greater opportunity for reducing fertilizer N inputs in high‐N‐surplus systems and presumably favor crop yield enhancement under soil N deficiency situations. The pursuit of an improved understanding of the interactions between plant‐soil‐climate‐management systems and different types of inhibitors should continue to optimize the effectiveness of inhibitors for reducing environmental losses while increasing productivity.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".