Sustained benefits of long-term biochar application for food security and climate change mitigation
Bibliographic record
Abstract
Biochar application offers significant potential to enhance food security and mitigate climate change. However, most evidence stems from short-term field experiments (≤3 y), leaving uncertainty about the long-term sustainability of these benefits, especially with annual biochar additions to soils. To address this knowledge gap, we analyzed a global dataset from 438 studies (3,229 observations) and found that long-term annual biochar application (≥4 y) not only sustains but often enhances its benefits. These include improved crop yields (+10.8%), reductions in CH 4 (–13.5%) and N 2 O (–21.4%) emissions, and increased soil organic carbon content (+52.5%). In contrast, these benefits tend to diminish over time with single biochar applications due to the aging effect of biochar. Results from 29 global long-term experiments (4 to 12 y) confirm these sustained benefits for crop yield and greenhouse gas mitigation, although the magnitude of effects varies with soil properties, climate, and management practices. To maximize biochar’s long-term benefits for global food security and climate change mitigation, it is essential to develop viable strategies, such as applying biochar at intervals of several years while tailoring practices to local soil, climate, and cropping conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".