Enteric methane emissions from backgrounded cattle consuming all-forage diets
Bibliographic record
Abstract
To quantify enteric methane (CH 4 ) emissions of growing cattle consuming allforage diets, a field study utilizing 144 British × Continental crossbred steers (262 ± 4 kg) was conducted during an 84-d overwintering period followed by a 56-d grazing period in one of two, grass-based pastures. Enteric CH 4 emissions were quantified using the sulphur hexaflouride (SF 6 ) tracer gas technique. During the overwintering period, four qualities of chopped alfalfa-grass silage, ranging in NDF content from 46.4 to 60.8%, DM basis, were utilized. Steers fed the lowest quality forage (60.8% NDF) had lower DMI, (6.8 ± 0.4 kg head -1 , P = 0.0075) and lower ADG (0.83 ± 0.03 kg d -1 , P = 0.0028) compared with those fed higher quality forage whose intake ranged from 8.2 to 9.1 ± 0.4 kg d -1 , with gains ranging from 1.00 to 1.06 ± 0.03 kg d -1 . Enteric emissions (% GE intake) were not influenced by forage quality across this range of NDF values; however, CH 4 losses did decrease from 6.8 to 4.7 ± 0.3% GE intake as the winter period progressed. Increased DMI, accompanied by a decrease in the proportion of feed energy lost as enteric CH 4 emissions, suggests that utilization of the lower-quality forage improved as steers reached higher body weights. Emissions were influenced by pasture quality and availability, as highest CH 4 emissions (11.3% GE intake, P = 0.0005) were observed when quality was low and DM availability was limited (738 kg ha -1 ). This study demonstrates that growing cattle consuming all-forage diets typical of those utilized in Western Canada during the winter feeding period will lose 5.1 to 5.9% of feed energy as CH 4 . Further, it has shown that emissions from growing cattle on grass-based pastures may exceed 10% GE intake – a value that is greater than those previously reported for growing cattle grazing legume-based pastures. Key words: Enteric methane emissions, cattle, forage, backgrounding, pasture
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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 | 0.000 | 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".