Grazing exclusion—An effective approach for naturally restoring degraded grasslands in Northern China
Bibliographic record
Abstract
Abstract Nearly 90% of the 390 million ha of grasslands in northern China are degraded. ‘Grazing exclusion’ has been implemented as a nature‐based solution to rejuvenate degraded grasslands, but the effectiveness of the rejuvenation processes is uncertain. Here, we investigated the effects of grazing exclusion on aboveground plant community traits, soil physiochemical and biological properties, and the mechanisms responsible for enhanced grassland rejuvenation. A meta‐analysis across various studies was used to assess the effectiveness. On average, grazing exclusion improved vegetation coverage by 18.5 percentage points and increased aboveground biomass by 1.13 t ha−1 and root biomass by 1.27 t ha−1, which represent an increase of 84%, 246%, and 31%, respectively, compared with continuous grazing practices. Grazing exclusion reduced soil bulk density by 13.7% and increased soil water content by 68.9%. Grasslands under grazing exclusion increased soil organic carbon (SOC) in the 0‐ to 15‐cm depth by 3.95 (±0.35 Std err) t ha−1 and total soil N, available N, and total soil P in the 0‐ to 40‐cm depth by 2.39 (±0.14), 0.83 (±0.37), and 1.96 (±0.44) t ha−1, respectively, compared with continuous grazing; these values represent an increase of 31%, 25%, 23%, and 14%, respectively. Prolonging the duration (years) of grazing practices enlarged the differences in SOC and soil N content between grazing exclusion and continuous grazing. Grazing exclusion has improved plant community traits and enhanced soil physiochemical and biological properties of degraded grasslands, and thus, this ‘nature‐based’ approach can serve as an effective means to rejuvenate degraded grasslands.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".