Enteric methane emissions in response to ruminal inoculation of Propionibacterium strains in beef cattle fed a mixed diet
Bibliographic record
Abstract
The objective of this study was to test the efficacy of Propionibacterium strains to mitigate enteric methane (CH4) emissions in beef heifers fed a mixed diet. An experiment was conducted with 16 ruminally cannulated beef heifers fed a basal diet consisting of 60 : 40 barley silage : barley grain (DM basis). Treatments included: (1) Control, (2) Propionibacterium freudenreichii T114, (3) P. thoenii T159, and (4) P. freudenreichii T54. Strains (1 × 1011 colony forming units) were administered daily directly into the rumen before feeding. No treatment effects were observed for DM intake (P = 0.90), mean ruminal pH (P = 0.50) and total volatile fatty acids (P = 0.44). However, compared with the Control, proportions of individual volatile fatty acids changed with acetate being less with Propionibacterium T159 (P = 0.02), whereas ruminal isobutyrate (P < 0.01) and acetate : propionate ratio (P = 0.04) were greater with Propionibacterium T114. Total daily enteric CH4 production averaged 188 g/day and was not affected by Propionbacterium strains (P = 0.51). Methane yield averaged 22 g/kg of DMI intake and tended to be greater with Propionibacterium strains (P = 0.08). The relative abundance of total Propionibacteria was greater with the inoculation of Propionibacterium T159 relative to the Control heifers (P = 0.04). In conclusion, inoculation of Propionibacterium T159 decreased ruminal acetate proportion and Propionibacterium T114 increased acetate : propionate ratio. However, inoculated strains failed to lower total CH4 emissions possibly due to the inability of Propionibacterium strains to elevate ruminal propionate concentrations.
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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) | 0.001 | 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 | 0.001 |
| 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".