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Record W2034155995 · doi:10.3168/jds.2011-4492

Changes in ruminal bacterial community composition following feeding of alfalfa ensiled with a lactic acid bacterial inoculant

2011· article· en· W2034155995 on OpenAlexaff
Riazuddin Mohammed, David M. Stevenson, K. A. Beauchemin, R. E. Muck, Paul J. Weimer

Bibliographic record

VenueJournal of Dairy Science · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsnot available
FundersAgricultural Research ServiceU.S. Department of Agriculture
KeywordsMicrobial inoculantLactic acidSilageComposition (language)Food scienceChemistryAnimal scienceBiologyBacteria

Abstract

fetched live from OpenAlex

Some silage inoculants help to improve silage quality and promote an increase in milk production, possibly through altering the rumen microflora. We hypothesized that rumen bacterial community composition (BCC) would be different in cows fed alfalfa ensiled with the inoculant Lactobacillus plantarum MTD/1 (LP) compared with those fed alfalfa ensiled without the inoculant (Ctrl). Eight ruminally cannulated Holstein cows were allotted to 2 diets (Ctrl or LP) in a double crossover design with four 28-d periods. Diets were formulated to contain (% dry matter basis) 28.0% neutral detergent fiber and 16.2% crude protein, and contained alfalfa silage, 50.9; corn silage, 20.6; high-moisture shelled corn, 21.4; soy hulls, 4.7; plus minerals and vitamins, 2.4. Ruminal digesta were collected just before feeding on 3 consecutive days near the end of each period, and were separated into solid and liquid phases. Microbial DNA was extracted from each phase, amplified by PCR using domain-level bacterial primers, and subjected to automated ribosomal intergenic spacer analysis. The pH was 4.56 and 4.86 and the lactate-to-acetate ratio 9.8 and 4.4, respectively, for the treated and untreated alfalfa silages. Dry matter intakes and milk production data were not influenced by diets but showed a cow effect. Total volatile fatty acids (mM) tended to be greater for LP compared with Ctrl. Individual volatile fatty acids were not influenced by diets but showed a significant cow effect. Ruminal acetate (mol/100 mol) and acetate-to-propionate ratio were lower and propionate (mol/100 mol) greater for the 2 milk fat-depressed (MFD; <3.2% fat content) cows compared with the other 6 cows. Correspondence analysis of the 265 peaks in the automated ribosomal intergenic spacer analysis profile across the 188 samples revealed that the first 2 components contributed 7.1 and 3.8% to the total variation in the profile. The ordination points representing the liquid and solid phases clustered separately, indicating that these phases differed in BCC. The analysis of similarity data showed differences between Ctrl and LP. The lactic acid bacterial counts (log(10) cfu/g of wet silage) were 3.94 and 4.53 for the untreated and treated silage, respectively, at ensiling. The relative population size (RPS) of L. plantarum, determined by real-time PCR of 16S rRNA gene copies, was greater in LP compared with Ctrl. The ordination points corresponding to certain individual cows clustered separately, and the most distinctive bacterial communities were those associated with MFD cows. The RPS of Megasphaera elsdenii was greater in 1 of the 2 MFD cows, although mean RPS of M. elsdenii did not differ between the treatments. In addition to the differences in rumen BCC between LP and Ctrl, MFD cows also displayed differences in BCC compared with cows with normal milk fat yield.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.046
GPT teacher head0.247
Teacher spread0.200 · 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 designObservational
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

Citations43
Published2011
Admission routes1
Has abstractyes

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