Straw mulching increased soil organic carbon content and stability by stimulating mineral protection in a Moso bamboo plantation
Bibliographic record
Abstract
Straw mulching significantly affects the soil carbon cycle. However, the impact of straw mulching on soil organic carbon (SOC) fractions in Moso bamboo plantations remains unclear. To address this gap in research, a 3-year field trial was established in a cultivated Moso bamboo ( Phyllostachys edulis ) plantation stand in a subtropical bamboo habitat. The experiment employed a space-for-time substitution design to compare three straw mulching strategies: Control (0-year mulching), SM1 (1-year application), and SM3 (3-year application). We specifically examined straw mulching-induced variation in SOC fractions and their underlying mechanisms. The application of straw mulch enhanced SOC accumulation by 27.2–30.9%, while elevating particulate (POC) and mineral-associated organic carbon (MAOC) pools by 15.0–37.5% and 28.6–33.9%, respectively. MAOC was dominant in SOC and was more sensitive to straw mulching than POC. Additionally, straw mulching significantly increased fungal residue carbon and iron-aluminum oxide content. POC and MAOC contents exhibited significant positive correlations with iron-aluminum oxide. These results indicate that straw mulching can significantly increase SOC content and stability in Moso bamboo plantations and thus is a potential management measure to increase soil carbon sequestration in Moso bamboo plantations. • Straw mulching significantly increased SOC in subtropical bamboo plantations. • Both POC and MAOC pools increased significantly under straw mulching treatments. • MAOC was more responsive to straw mulching than POC. • Increase in POC and MAOC was attributed to enhanced protection from Fe/Al oxides.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".