Incubation Temperature, But Not Pequi Oil Supplementation, Affects Methane Production, and the Ruminal Microbiota in a Rumen Simulation Technique (Rusitec) System
Bibliographic record
Abstract
Lipid supplementation is a promising strategy for methane mitigation in cattle and has been evaluated using several different lipid sources. However, limited studies have assessed the effect of temperature on methane emissions from cattle and changes in incubation temperature have also not been extensively evaluated. The aim of this study was to evaluate the combined effect of pequi oil (high in unsaturated fatty acids) and incubation temperature on fermentation characteristics and microbial communities using the rumen simulation technique. A completely randomized experiment was conducted over a 28-day period using a Rusitec system. The experiment was divided into four periods of 7 days each, the first of which was a 7-day adaptation period followed by three experimental periods. The two treatments consisted of a control diet (no pequi oil inclusion) and a diet supplemented with pequi oil (1.5 mL/day) which increased the dietary fat content to 6% (dry matter, DM-basis). Three fermenter vessels (i.e., replicates) were allocated to each treatment. In the first experimental period, the incubation temperature was maintained at 39°C, decreased to 35°C in the second experimental period and then increased again to 39°C in the third. Pequi oil was continuously supplemented during the experiment. Microbial communities were assessed using high-throughput sequencing of the archaeal and bacterial 16S rRNA gene. Methane production was reduced by 57% following a 4°C decrease in incubation temperature. Supplementation with pequi oil increased the dietary fat content to 6% (DM-basis) but did not affect methane production. Analysis of the microbiota revealed that decreasing incubation temperature to 35°C affected the archaeal and bacterial diversity and richness of liquid-associated microbes, but lipid supplementation did not change microbial diversity.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".