Impact of grassland contract policy on soil organic carbon losses from alpine grassland on the Qinghai–Tibetan Plateau
Bibliographic record
Abstract
Abstract Carbon storage in the soils on the Qinghai–Tibetan Plateau plays a very important role in the global carbon budget. In the 1990s, a policy of contracting collective grasslands to smaller units was implemented, resulting in a change from the traditional collective grassland management to two new management patterns: a multi‐household management pattern ( MMP : grassland shared by several households without enclosures) and a single‐household management pattern ( SMP : grassland enclosed and used by only one household). In 2016, 50 MMP and 54 SMP winter pastures on the Qinghai–Tibetan Plateau were sampled to assess the differences in soil organic carbon ( SOC ) between the two management patterns. Results showed that average SOC was significantly greater under MMP than under SMP , with an estimated 0.41 Mg C/ha/yr lost due to SMP following the new grassland contract. Based on the government's grassland policy, four grassland utilization scenarios were developed for both summer and winter pastures. We found that if the grassland were managed under SMP , likely C losses ranged between 0.31 × 10 7 and 6.15 × 10 7 Mg C/yr across the Qinghai–Tibetan Plateau relative to MMP , which more closely resembles pre‐1990s grassland management. Previous estimates of C losses have only considered land use change (with cover change) and ignored the impacts driven by land management pattern changes (without cover change). The new data suggest that C losses from the Qinghai–Tibetan Plateau are greater than previously estimated, and therefore that the grassland contract policy should be reviewed and SMP households should be encouraged to reunite into the MMP . These findings have potential implications for land management strategies not only on the Qinghai–Tibetan Plateau but also other grazing regions globally where such practices may exist.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".