MétaCan
Menu
Back to cohort
Record W2792886149 · doi:10.1071/an17365

Effects of gallic acid on in vitro rumen fermentation and methane production using rumen simulation (Rusitec) and batch-culture techniques

2018· article· en· W2792886149 on OpenAlexafffund
Chen Wei, Jessie Guyader, K. A. Beauchemin, Guanglei Zhao

Bibliographic record

VenueAnimal Production Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of ChinaEmissions Reduction Alberta
KeywordsRumenDry matterFermentationNeutral Detergent FiberAnimal scienceIsovalerateChemistryRandomized block designGallic acidFood scienceOrganic matterTotal mixed rationStarchBiochemistryButyrateBiologyAgronomyOrganic chemistry

Abstract

fetched live from OpenAlex

Two experiments were conducted to investigate the effects of adding gallic acid (GA) to ruminant diets on long- and short-term in vitro rumen fermentation and methane (CH4) production, and to test possible interactions between GA and ethanol on fermentation. The first experiment was conducted using the rumen simulation technique (Rusitec), as a completely randomised block design with four replications and the following four doses of GA: 0, 5, 10 and 20 mg GA/g dry matter (DM). Ethanol was used in all treatments to increase the solubilisation of GA in rumen fluid. The experimental period lasted 16 days, of which the first 7 days were for adaptation and the subsequent 9 days were for sampling. The second experiment was a 48-h batch-culture incubation conducted as a completely randomised design with a 4 (GA dose; 0, 10, 20, and 40 mg GA/g DM) × 2 (with or without ethanol) arrangement of treatments. In the Rusitec experiment, addition of GA up to 20 mg/g DM did not affect DM disappearance (DMD), organic matter (OM) disappearance, neutral detergent-fibre disappearance (NDFD), acid detergent-fibre disappearance (ADFD) or starch disappearance (P > 0.05), but crude protein disappearance was linearly decreased (P = 0.04) from 78.3% to 72.0%. Daily gas production and CH4 production expressed as mL/g DM and mL/g DMD were not affected by addition of GA (P > 0.05). Addition of GA up to 20 mg/g DM increased butyrate and isovalerate production (P < 0.05) and tended to increase isobutyrate (P = 0.09) and decrease heptanoate production (P = 0.07). In the batch-culture experiment, adding GA up to 40 mg/g DM linearly increased 48-h DMD, NDFD and ADFD (P < 0.05) and decreased (P < 0.05) CH4 expressed as mL/g DMD, mL/g NDFD and mL/g ADFD. Methane production was decreased after 24 h and 48 h only when GA was added at 10 mg/g DM without ethanol. Fermentation liquid pH and concentration of ammonia-nitrogen (ammonia-N) were also reduced (P < 0.05) with an increasing concentration of GA. Treatments with ethanol notably enhanced 48-h DMD, NDFD, ADFD, gas production (mL/g DM, mL/g OM or mL/g DMD), CH4 production (mL/g DM, mL/g DMD or mL/g NDFD), total volatile fatty acid concentration, the acetate : propionate ratio, acetate, valerate, isovalerate and caproate molar proportions (P < 0.01) and decreased propionate, butyrate and isobutyrate molar proportions (P < 0.01). Significant dose of GA × ethanol interaction was observed only for acetate molar proportion (P = 0.03). In conclusion, our study suggests that the beneficial effects of GA on feed digestion and CH4 production may be short term, while improvements in N metabolism may be sustained over the long term. It may be useful to conduct long-term in vivo studies using a range of diets and doses to verify whether GA can be used as a feed additive to mitigate enteric CH4 production and improve N metabolism of ruminants.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.297
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations25
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueAnimal Production ScienceSame topicRuminant Nutrition and Digestive PhysiologyFrench-language works237,207