Impact of rangeland enclosure and seasonal grazing on protected and unprotected rangelands in Chakwal region, Pakistan
Bibliographic record
Abstract
Abstract Scrub rangelands support livestock grazing and provide ecosystem services to their inhabitants. The present study was conducted in Chakwal, an important tract of the Pothwar Plateau, which sustains herds of small domestic and nomadic ruminants. Urbanization and uncontrolled grazing practices have reduced rangeland productivity and increased soil erosion and resulted in poor land conditions. This study assessed the influence of two years of rangeland protection on aboveground vegetation biomass and the chemical composition of plants and aimed to determine the influence of seasonal grazing on the live-weight gain of small ewes. Using the line intercept method, vegetation data from protected and unprotected plots in 2015 and 2016 were collected at two sites, Dhulli and Begal. Vegetation cover was assessed from images using VegMeasure. Results showed that protected study sites displayed higher vegetation biomass (834 and 690 kg ha -1 ) compared to the unprotected study site (477 and 326 kg ha -1 ) during April and August of both years. In the seasonal grazing trial, the experimental ewes that grazed on protected rangelands showed higher live-weight gain (33–63 g day -1 ) compared to live weight of ewes (17–21 g day -1 ) that grazed on unprotected rangelands during the experimental period of 127 days at both sites. The results suggest that the Chakwal rangeland has tremendous potential to improve vegetation productivity but modern livestock management and seasonal grazing practices are needed to improve carrying capacity and livestock productivity.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".