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 (CH4) 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, CH4 output was measured for two 24-h periods, using the SF6 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 CH4 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 CH4, % 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 CH4 than animals on MID and LATE pastures, respectively. There was also a 54% lower CH4 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 CH4 production were marginal in grazing steers. The study suggests that pasture quality plays a major role in the extent to which CH4 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".