Effect of Land Use on Soil Degradation in Alpine Grassland Soil, China
Bibliographic record
Abstract
Grassland soils in Northern China are being seriously degraded under cultivation and grazing. This study investigated the impacts of land use on soil erosion and soil fertility in alpine grassland of China. Land uses included three levels of pasture degradation, lightly (LDP), moderately (MDP), and heavily degraded pasture (HDP) classified based on vegetation cover, and cultivated fields ranging from 1 to 50 yr of cultivation. Soil samples were collected from 18 sites at seven locations in Chernozemic soils between elevations of 2600 to 3000 m. Soil erosion was estimated by 137 Cs radioactivity. When pasture was heavily degraded, 137 Cs activity was significantly reduced, and organic C (OC), total N, and cation‐exchange capacity declined by 33, 28, and 18% respectively. Cultivation of grassland worsened soil erosion, and after 8‐, 16‐, and 41‐yr cultivation soil OC decreased by 25, 39, and 55%, respectively. Regionally, 59% of OC was lost within 30 to 50 yr of cultivation. There were concomitant losses of total N and exchange capacity. On cultivated soils, soil erosion and mineralization were equally responsible for organic C losses. Pasture degradation and cultivation also caused changes in soil P. Mineralization of organic P, incorporation of subsoil by tillage following erosion, and fertilization increased levels of Ca‐P in cultivated fields. This study indicated that grassland degradation and cultivation caused not only severe soil erosion, but also fertility decline and chemical changes of P dynamics.
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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 | 0.001 |
| 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 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".