The role of conservation agriculture practices in mitigating N2O emissions: A meta-analysis
Bibliographic record
Abstract
Abstract Conservation agriculture is often assumed to reduce soil N 2 O emissions. Yet, studies analyzing the specific effect of conservation agriculture practices on N 2 O emissions give contradictory results. Herein, we synthesized a comprehensive database on the three main conservation agriculture practices (cover crops, diversified crop rotations, and no-till and/or reduced tillage (NT/RT)) to elucidate the role of conservation practices on N 2 O emissions. Further, we used a random meta-forest approach to identify the most important predictors of the effects of these practices on soil N 2 O emissions. Averaged across all comparisons, NT/RT significantly decreased soil N 2 O emissions by 11% (95% CI: –19 to –1%) compared to conventional tillage. The reductions due to NT/RT were more commonly observed in humid climates and in soils with an initial carbon content < 20 g kg –1 . The implementation of cover crops and diversified crop rotations led to variable effects on soil N 2 O emissions. Cover crops were more likely to reduce soil N 2 O emissions at neutral soil pH, and in soils with intermediate carbon (~20 g kg –1 ) and nitrogen (~3 g kg –1 ) contents. Diversified crop rotations tended to increase soil N 2 O emissions in temperate regions and neutral to alkaline soils. Our results provide a comprehensive predictive framework to understand the conditions in which the adoption of various conservation agriculture practices can contribute to climate change mitigation. Combining these results with a similar mechanistic understanding of conservation agriculture impacts on ecosystem services and crop production will pave the way for a wider adoption globally of these management practices.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.047 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".