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 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) | 0.001 | 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 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".