Effects of grain supplementation on methane production of grazing steers using the sulphur (SF<sub>6</sub>) tracer gas technique
Bibliographic record
Abstract
The objective of the study was to examine the effect of supplemental grain on methane (CH 4 ) production of grazing steers. Eight beef steers (344.6 ± 6.4 kg) were assigned to legume-grass pasture (C; n = 4) or legume-grass pasture plusa rolled barley supplement (S; n = 4). In a completely randomized design with repeated measures, CH 4 output was measured for two 24-h periods, using the SF 6 tracer gas technique as steers entered (IN) and exited (OUT) paddocks. Two, 4 and 4 kg of rolled-barley grain was fed daily to S steersduring the EARLY, MID and LATE periods of the grazing season, respectively. Supplementation reduced forage dry matter intake (DMI) by 11% (P = 0.03) and increased total organic matter intake (TOMI) by 14% (P = 0.001). Daily CH 4 production was similar for C and S steers (P > 0.05). Methane production, increased (P < 0.05) from 256 L d -1 in the EARLY period to 364 L d -1 at the MID and 342 L d -1 at the LATE period. Energy lost as CH 4 , % total gross energy intake (TGEI) ranged from 4.7 to 8.4% (mean 6.5 ± 0.3%) during the grazing season, and there was no difference between S (6.4 ± 0.6%) and C (6.7 ± 0.6%) steers (P = 0.71). Methane production declined with grazing on high-quality forages; steers on EARLY pastures had 44% and 29% lower (P < 0.05) energy loss as CH 4 than animals on MID and LATE pastures, respectively. There was also a 54% lower CH 4 loss when animals entered new paddocks relative to those exiting the paddocks (P < 0.05). It can be concluded that the effects of supplementation on CH 4 production were marginal in grazing steers. The study suggests that pasture quality plays a major role in the extent to which CH 4 production can be reduced with grain supplementation in grazing animals. Key words: Methane, grazing steers, grain supplementation, pasture quality
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".