Analysis of Labor Productivity in Single and Multi-household Grassland Management Patterns: A Case Study in Maqu County, Qinghai-Tibetan Plateau
Bibliographic record
Abstract
This study investigated labor productivity in meat and milk/dairy production within single and multi-household management patterns, based on primary data collected from 156 randomly selected herder households in Maqu County, Tibetan Plateau. The results showed that in the rotational grazing system, herder households in both single and multi-household management patterns achieved higher labor productivity for meat production (70.36 Kg/man-day and 51.21 Kg/man-day, respectively) compared to the overall study households (40.89 Kg/man-day). In contrast, within the continuous grazing system, the single-household management pattern recorded lower labor productivity for meat production (23.04 Kg/man-day). Significantly, regional variations in the distance between pastures and market centers led herder households in the single-household management pattern within the continuous grazing system to achieve superior labor productivity for milk and dairy production (19.74 $/man-day) compared to the overall study households (15.44 $/man-day). In the rotational grazing system, labor productivity for milk and dairy production stood at 12.63 $/man-day for the single-household management pattern and 8.30 $/man-day for the multi-household management pattern. These findings underscore the complexities associated with achieving high labor productivity simultaneously in both meat and milk/dairy production within the same grassland management pattern. While the multi-household management pattern shows promise in reducing labor inputs, it also grapples with challenges in achieving substantial production levels for meat and milk/dairy products. To address these challenges, policymakers should consider follow-up measures that prioritize the simultaneous enhancement of meat and milk/dairy production within the multi-household management pattern. Special attention should be given to reducing the distance between herder households and market centers to facilitate the sale of milk/dairy products. Simply advocating for the broader adoption of the multi-household management pattern may fall short without addressing these production-related hurdles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".