New insights into the relationships between livestock grazing behaviors and soil organic carbon stock in an alpine grassland
Bibliographic record
Abstract
Grazing affects soil carbon (C) storage in grassland ecosystems through livestock trampling, defoliation , and excretion of urine and dung. However, independent effects of those grazing behaviors on soil organic C (SOC) remains unclear, particularly in alpine grassland ecosystems. To address this knowledge gap, a one-year field experiment was conducted on the eastern edge of the Qinghai-Tibetan Plateau to study the effect of grazing behaviors , including light vs. heavy trampling, light vs. heavy defoliation , and excretion of urine and dung, of yak ( Bos grunniens ) on SOC stock. Our results showed that trampling, particularly light trampling, significantly increased SOC stock in the 0–10 cm soil layer. The increase of soil bulk density and SOC concentration were the main reasons for the increase of surface SOC stock under trampling. Heavy defoliation significantly increased SOC stock in the 20–30 cm depth in comparison to light defoliation (4.4 vs. 2.9 kg C m -2 ), but neither affected SOC stock in the 0–30 cm soil profile. In contrast, excretion significantly lowered SOC stock by 9.3 kg C m -2 (0–30 cm), linked to the decreased soil bulk density and SOC concentration, and the decreased microbial biomass C, which might have lowered the microbial contribution to SOC storage . Our study of independent grazing effects showed that short-term livestock excretion caused the loss of SOC stock while intensive trampling or defoliation did not. Our findings have implications for managing livestock grazing behavior to maintain SOC stock in alpine grassland ecosystems.
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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 | 0.000 | 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 | 0.000 | 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 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".