Response of Soil Carbon Mineralization to Grassland Management Practices on the Qinghai‐Tibetan Plateau
Bibliographic record
Abstract
Abstract Grassland management practices strongly influence soil carbon dynamics, yet their effects on carbon mineralization processes in high‐altitude regions remain poorly understood. We examined soil carbon mineralization patterns under four common grassland management practices implemented on the Qinghai‐Tibetan Plateau (i.e., seasonal grazing, continuous grazing, perennial artificial grasslands, and annual artificial grasslands) using a 147‐day incubation experiment. We also analyzed soil properties, microbial communities, and carbon degradation genes to understand the mechanisms driving carbon mineralization. We observed distinct depth‐dependent responses to management practices. In surface soils (0–0.15 m), seasonal grazing exhibited the highest cumulative carbon mineralization (2993.32 mg CO 2 ‐C kg −1 ), 1.5‐fold higher than annual artificial grasslands. However, in subsurface soils (0.15–0.30 m), continuous grazing showed the greatest cumulative carbon mineralization (2355.18 mg CO 2 ‐C kg −1 ), 1.5‐fold higher than perennial artificial grasslands. Collectively, soil properties, carbon degradation genes, and fungal diversity explained 74% of the variation in cumulative carbon mineralization, with soil properties showing the strongest direct effect (path coefficient = 0.62). Interestingly, bacterial diversity exhibited a negative relationship with cumulative carbon mineralization, suggesting previously underappreciated mechanisms of carbon preservation involving microbial‐derived compounds and their interaction with soil minerals. The variability in the abundance of specific carbon degradation genes across grassland management practices revealed that peroxidase and limonene 1,2‐epoxide hydrolase genes showed positive correlations with cumulative carbon mineralization. Our results suggest that optimal soil carbon management in high‐altitude grasslands is challenging and requires careful consideration of both grassland management practices and soil depth, especially spatial and temporal patterns of grazing pressure.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".