Contrasting effects of different pH ‐raising materials on N 2 O emissions in acidic upland soils
Bibliographic record
Abstract
Abstract Acidic soils, occupying ca. 40% of the world's arable soils, often need to be managed (e.g., to raise their pH and to improve crop productivity); however, the environmental impact of raising soil pH is often difficult to assess. Increasing soil pH stimulates the reduction of N 2 O to N 2 , thus lowering N 2 O emissions associated with denitrification, but can also increase autotrophic nitrification rates and related N 2 O emission. Using a 15 N tracing technique, we provide process‐based insights into the effects of two acid‐neutralizing materials (quicklime [CaO] vs. pig manure) on N 2 O emissions in an acidified upland soil that had experienced excessive N application. Without pH adjustments we found that N 2 O emissions, stimulated by supply of reactive N, were related to denitrification‐ and heterotrophic nitrification‐derived N 2 O emissions, whereas autotrophic nitrification‐derived N 2 O emissions declined with decreasing soil pH. These effects were reversed by increasing soil pH via liming. However, increasing the soil pH via application of pig manure significantly increased soil N 2 O emissions from both nitrification and denitrification. Our study highlights that pH‐amelioration practices may enhance N 2 O emissions depending on the type of material applied to the soil. Therefore, both pH remediation and greenhouse gas mitigation options need to be considered together to avoid adverse environmental effects. The effect of different acid‐neutralizing materials on soil N 2 O emissions should be incorporated into ecosystem models to better estimate global N 2 O emissions when pH amelioration is practised. Highlights Enhanced N 2 O emission by N input was from denitrification and heterotrophic nitrification. Chemical N input and liming have reversible effects on N 2 O emission. Soil N 2 O emission was decreased by liming but increased by animal manure input. Careful consideration of pH raising substrates is needed to avoid adverse effects.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".