Effect of Phenotypic Residual Feed Intake and Dietary Forage Content on the Rumen Microbial Community of Beef Cattle
Bibliographic record
Abstract
Feed-efficient animals have lower production costs and reduced environmental impact. Given that rumen microbial fermentation plays a pivotal role in host nutrition, the premise that rumen microbiota may contribute to host feed efficiency is gaining momentum. Since diet is a major factor in determining rumen community structure and fermentation patterns, we investigated the effect of divergence in phenotypic residual feed intake (RFI) on ruminal community structure of beef cattle across two contrasting diets. PCR-denaturing gradient gel electrophoresis (DGGE) and quantitative PCR (qPCR) were performed to profile the rumen bacterial population and to quantify the ruminal populations of Entodinium spp., protozoa, Fibrobacter succinogenes, Ruminococcus flavefaciens, Ruminococcus albus, Prevotella brevis, the genus Prevotella, and fungi in 14 low (efficient)- and 14 high (inefficient)-RFI animals offered a low-energy, high-forage diet, followed by a high-energy, low-forage diet. Canonical correspondence and Spearman correlation analyses were used to investigate associations between physiological variables and rumen microbial structure and specific microbial populations, respectively. The effect of RFI on bacterial profiles was influenced by diet, with the association between RFI group and PCR-DGGE profiles stronger for the higher forage diet. qPCR showed that Prevotella abundance was higher (P < 0.0001) in inefficient animals. A higher (P < 0.0001) abundance of Entodinium and Prevotella spp. and a lower (P < 0.0001) abundance of Fibrobacter succinogenes were observed when animals were offered the low-forage diet. Thus, differences in the ruminal microflora may contribute to host feed efficiency, although this effect may also be modulated by the diet offered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".