The Effects of Supplemental Feeding on Methane Emissions from Yak Grazing in the Warm Season
Bibliographic record
Abstract
The yak industry constitutes a pivotal segment of livestock development across the Qinghai–Tibetan Plateau and adjacent pastoral regions. Existing studies have shown that supplemental feeding for grazing yaks significantly improves meat quality and growth performance, but its effect on enteric methane (CH4) emission from yaks has never been reported, so the present experiment aimed to investigate the effect of supplemental feeding of grazing yaks on CH4 emissions in three different periods of the warm season. Thirty male yaks were randomly assigned to two groups, with 15 yaks per group, The groups were designated as the grazing group (GR), with traditional grazing methods and with an initial body weight of 94.56 ± 3.9 kg, and the supplemental feeding group (GRS) with an initial body weight of 95.01 ± 4.1 kg, which received 1500 g/d of supplemental feed for 120 days. The methane emissions of the two groups were measured at the late regreening period (LRP), the greening-grass period (GGP), and the browning period (BRP) using the SF6 tracer method. The results showed that GRS increased (p < 0.01) the total DMI during warm season but decreased (p < 0.01) the forage intake at the LRP and GGP, resulting in a significantly reduced methane yield per kg of BW gain and per kg of DMD in warm-season grazing yaks (p < 0.01), a significantly reduced methane production per kg of DMI and methane energy to gross energy ratio in grazing yaks during the GGP (p < 0.05), and a similarly significantly decrease in methane production per kg of DMI and methane energy to gross energy ratio in grazing yaks during the BRP (p < 0.01). In conclusion, supplemental nutrition for grazing yaks during the warm season in alpine grasslands significantly enhances growth performance, reduces methane emissions, and improves dietary energy utilization efficiency.
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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 | 0.000 |
| 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".