172 Enteric methane emissions from pregnant beef heifers as affected by multiple mitigation strategies
Bibliographic record
Abstract
Abstract This study was conducted to determine if enteric methane (CH4) emissions from pregnant beef heifers could be reduced by using multiple dietary mitigation strategies. The trial was designed as a 4 x 4 Latin square consisting of a 21-d adaptation phase followed by a 21-d data collection phase. Forty Aberdeen Angus cross pregnant beef heifers were randomly assigned to one of the four dietary treatments (n = 10): i) low protein grass hay (L; 6.1% CP; 1.9% fat); ii) adequate protein grass hay (AD; 10.8% CP; 1.9% fat); iii) AD supplemented with sunflower screenings (ADSS; 9.8% CP; 5.6% fat); and iv) above adequate protein legume grass mix hay (AAD; 12.8% CP; 1.7% fat). Total dry matter intake (DMI) was 32%, 27% and 39% greater (P < 0.0001) for AD, ADSS and AAD respectively, relative to the L diet (6.5 kg d-1). Average daily gain (ADG; kg d-1) was influenced by dietary treatment (P < 0.0001), as heifers offered L, AD, ADSS and AAD diets gained 0 ± 0.2, 0.6 ± 0.2, 0.5 ± 0.2 and 0.7 ± 0.2 kg d-1, respectively. Enteric CH4 emissions (L d-1), were influenced by dietary treatment (P < 0.0001) with 184 ± 18.9, 214 ± 19.0, 204 ± 19.1 and 232 ± 19.1 L d-1 for heifers offered L, AD, ADSS and AAD diets, respectively. Further, heifers offered AD, ADSS and AAD diets emitted 19%, 22% and 14% less (P=0.03) enteric CH4 (% GEI) relative to diet L, respectively. This study demonstrates that supplementation to meet nutrient requirements for protein or increasing the fat content of forage-based diets using low-cost by-products such as sunflower screenings can decrease enteric CH4 emissions without adversely impacting total DMI and ADG.
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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".