Enteric methane emissions from pregnant beef heifers as affected by multiple dietary mitigation strategies
Bibliographic record
Abstract
This project was conducted to determine the effect of increased dietary crude protein (CP) alone or in combination with a lipid supplement or inclusion of a legume on animal performance, exploring the efficacy multiple dietary mitigation strategies to reduce enteric CH4 emissions in pregnant beef heifers. Forty Aberdeen Angus cross heifers (361.5 ± 29.18 kg) in the second month of pregnancy were blocked according to body weight and randomly assigned to one of four feedlot pens (n = 10 animals pen-1) in a 4 x 4 Latin Square design consisting of a 21-d adaptation and 21-d data collection phase. Diets were: 1) 6.1% CP grass hay supplemented with canola meal (CP6.1); 2) 10.8% CP grass hay (CP10.8); 3) 9.8% CP grass hay supplemented with sunflower screenings (CP9.8 + SS); or 4) 12.8% CP legume grass hay (CP12.8 + L; approximately 50% sainfoin and 50% grass). Pregnant heifers offered the CP6.1 diet had significantly lower total dry matter intake (DMI), average daily gain (ADG) and blood urea nitrogen compared to the CP10.8, CP9.8 + SS and CP12.8 + L diets. Total DMI and ADG was not significantly different between the CP10.8 and CP9.8 + SS diets indicating that lipid supplementation did not negatively impact total DMI nor ADG. There was no significant difference between the CP10.8, CP9.8 + SS or CP12.8 + L diets for enteric CH4 emissions (% GEI) compared to the CP10.8, suggesting that supplemental lipid or addition of a legume did not further reduce enteric CH4 emissions. In conclusion, it was found that the additional dietary mitigation strategies of lipid (CP9.8 + SS) or legume (CP12.8 + L) inclusion in the diet did not further reduce enteric CH4 emissions compared to the hay only diet (CP10.8). The combination of multiple or layered mitigation strategies is still a relatively new concept for reducing enteric CH4 emissions. Therefore, further research is required in this area to understand how multiple mitigation strategies interact when used in combination, if larger reductions of enteric CH4 emissions are to be realized.
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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) | 0.000 | 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".