Effects of the inclusion of Moringa oleifera seed on rumen fermentation and methane production in a beef cattle diet using the rumen simulation technique (Rusitec)
Bibliographic record
Abstract
Moringa oleifera seeds are currently being used as a livestock feed across tropical regions of the world due to its availability and palatability. However, limited knowledge exists on the effects of the raw seeds on ruminant metabolism. As such, the rumen stimulation technique was used to evaluate the effects of substituting increasing concentrations of ground Moringa seeds (0, 100, 200 and 400 g/kg concentrate dry matter (DM)) in the diet on rumen fermentation and methane production. Two identical, Rusitec apparatuses, each with eight fermenters were used with the first 8 days used for adaptation and days 9 to 16 used for measurements. Fermenters were fed a total mixed ration with Urochloa brizantha as the forage. Disappearance of DM, CP, NDF and ADF linearly decreased (P<0.01) with increasing concentrations of Moringa seeds in the diet. Total volatile fatty acid production and the acetate to propionate ratio were also linearly decreased (P<0.01). However, only the 400 g/kg (concentrate DM basis) treatment differed (P<0.01) from the control. Methane production (%), total microbial incorporation of 15N and total production of microbial N linearly decreased (P<0.01) as the inclusion of Moringa seeds increased. Though the inclusion of Moringa seeds in the diet decreased CH4 production, this arose from an unfavourable decrease in diet digestibility and rumen fermentation parameters.
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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) | 0.000 | 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 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".