Degradable film mulching increases soil carbon sequestration in major Chinese dryland agroecosystems
Bibliographic record
Abstract
Plastic film mulching (PM), which contributes to nearly half of the increased crop yields in dryland agroecosystems, exacerbates environmental burdens due to its non-degradable nature. Globally, there is a growing demand to replace non-degradable PM with degradable film mulching (DM), yet its impacts on soil organic carbon (SOC) in dryland agroecosystems remains unknown. Here, using multi-field studies and mesocosm experiments, we found that DM strongly increased but PM reduced SOC storage (0–1 m). This difference is likely attributable to the higher microbial C use efficiency in soil under DM, leading to increased microbial-derived C compared to PM. Under the high roading scenario for 2100, DM could reduce the decomposition of SOC (0–1 m) in China’s drylands by 9.0 ± 1.0 Mg ha–1 year–1 (one standard error) compared with PM. Our findings highlight that DM is a promising alternative to PM for sequestrating SOC and alleviating C loss under climate change in dryland agroecosystems. Sustainable management of soil organic carbon (SOC) in farmland is critical for mitigating climate change and improving soil health. Degradable film mulching is a promising alternative to plastic film mulching, sequestering SOC and reducing C loss in dryland agroecosystems under climate change.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".