Biochar affects greenhouse gas emissions in various environments: A critical review
Bibliographic record
Abstract
Abstract Biochar application to the soil is a novel approach to carbon sequestration. Biochar application affects the emission of greenhouse gases (GHGs), such as CO 2 , CH 4 , and N 2 O, from different environments (e.g., upland soils, rice paddies and wetlands, and composting environments). In this review, the effect of biochar on GHGs emissions from the above three typical environments are critically evaluated based on a literature analysis. First, the properties of biochar and engineered biochar related to GHGs emissions was reviewed, targeting its relationship with climate change mitigation. Then, a meta‐analysis was conducted to assess the effect of biochar on the emissions of CO 2 , CH 4 , and N 2 O in different environments, and the relevant mechanisms. Several parameters were identified as the main influencing factors in the meta‐analysis, including the pH of the biochar, feedstock type, pyrolysis temperature, biochar application rate, C/N ratio of the biochar, and experimental scale. An overall suppression effect among different environments was found, in the following order for different greenhouse gases: N 2 O > CH 4 > CO 2 . We conclude that biochar can change the physicochemical properties of soil and compost in different environments, which further shapes the microbial community in a specific environment. Biochar addition affects CO 2 emissions by influencing oligotrophic and copiotrophic bacteria; CH 4 emissions by regulating the abundance of functional genes, such as mcrA (a methanogen) and pmoA (a methanotroph); and N 2 O emissions by controlling N‐cycling functional genes, including amoA , nirS , nirK , nosZ . Finally, future research directions for mitigating greenhouse gas emissions through biochar application are suggested.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".