Soil GHG fluxes are altered by N deposition: New data indicate lower N stimulation of the N2O flux and greater stimulation of the calculated C pools
Bibliographic record
Abstract
The effects of nitrogen (N) deposition on soil organic carbon (C) and greenhouse gas (GHG) emissions in terrestrial ecosystems are the main drivers affecting GHG budgets under global climate change. Although many studies have been conducted on this topic, we still have little understanding of how N deposition affects soil C pools and GHG budgets at the global scale. We synthesized a comprehensive dataset of 275 sites from multiple terrestrial ecosystems around the world and quantified the responses of the global soil C pool and GHG fluxes induced by N enrichment. The results showed that the soil organic C concentration and the soil CO2 , CH4 and N2 O emissions increased by an average of 3.7%, 0.3%, 24.3% and 91.3% under N enrichment, respectively, and that the soil CH4 uptake decreased by 6.0%. Furthermore, the percentage increase in N2 O emissions (91.3%) was two times lower than that (215%) reported by Liu and Greaver (Ecology Letters, 2009, 12:1103-1117). There was also greater stimulation of soil C pools (15.70 kg C ha-1 year-1 per kg N ha-1 year-1 ) than previously reported under N deposition globally. The global N deposition results showed that croplands were the largest GHG sources (calculated as CO2 equivalents), followed by wetlands. However, forests and grasslands were two important GHG sinks. Globally, N deposition increased the terrestrial soil C sink by 6.34 Pg CO2 /year. It also increased net soil GHG emissions by 10.20 Pg CO2 -Geq (CO2 equivalents)/year. Therefore, N deposition not only increased the size of the soil C pool but also increased global GHG emissions, as calculated by the global warming potential approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".