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Record W2767480006 · doi:10.1017/s1751731117002750

Associations of rumen parameters with feed efficiency and sampling routine in beef cattle

2017· article· en· W2767480006 on OpenAlexafffund
Stephanie Lam, Jasper Munro, Mi Zhou, Ling Guan, Flávio S. Schenkel, M.A. Steele, Stephen P. Miller, Y.R. Montanholi

Bibliographic record

Venueanimal · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsDalhousie UniversityUniversity of AlbertaBIO (Canada)University of Guelph
FundersNational Institutes of HealthAgricultural Adaptation CouncilOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsRumenAnimal scienceBiologyBeef cattleResidual feed intakeFeedlotPopulationDry matterAnimal feedFeed conversion ratioFood scienceBody weight

Abstract

fetched live from OpenAlex

Characterizing ruminal parameters in the context of sampling routine and feed efficiency is fundamental to understand the efficiency of feed utilization in the bovine. Therefore, we evaluated microbial and volatile fatty acid (VFA) profiles, rumen papillae epithelial and stratum corneum thickness and rumen pH (RpH) and temperature (RT) in feedlot cattle. In all, 48 cattle (32 steers plus 16 bulls), fed a high moisture corn and haylage-based ration, underwent a productive performance test to determine residual feed intake (RFI) using feed intake, growth, BW and composition traits. Rumen fluid was collected, then RpH and RT logger were inserted 5.5±1 days before slaughter. At slaughter, the logger was recovered and rumen fluid and rumen tissue were sampled. The relative daily time spent in specific RpH and RT ranges were determined. Polynomial regression analysis was used to characterize RpH and RT circadian patterns. Animals were divided into efficient and inefficient groups based on RFI to compare productive performance and ruminal parameters. Efficient animals consumed 1.8 kg/day less dry matter than inefficient cattle (P⩽0.05) while achieving the same productive performance (P⩾0.10). Ruminal bacteria population was higher (P⩽0.05) (7.6×1011 v. 4.3×1011 copy number of 16S rRNA gene/ml rumen fluid) and methanogen population was lower (P⩽0.05) (2.3×109 v. 4.9×109 copy number of 16S rRNA gene/ml rumen fluid) in efficient compared with inefficient cattle at slaughter with no differences (P⩾0.10) between samples collected on-farm. No differences (P⩾0.10) in rumen fluid VFA were also observed between feed efficiency groups either on-farm or at slaughter. However, increased (P⩽0.05) acetate, and decreased (P⩽0.05) propionate, butyrate, valerate and caproate concentrations were observed at slaughter compared with on-farm. Efficient had increased (P⩽0.05) rumen epithelium thickness (136 v. 126 µm) compared with inefficient cattle. Efficient animals also spent 318% and 93.2% more time (P⩽0.05) in acidotic (4.14% v. 1.30%) (pH⩽5.6) and optimal (5.6<pH<6.0) (8.53% v. 4.42%) RpH range compared with inefficient cattle. The circadian patterns revealed lower (P⩽0.05) RpH and no differences (P⩾0.10) in RT pre-, during, and post-prandial periods in efficient compared with inefficient cattle. In essence, superior feed efficiency in cattle seems linked to rumen features consistent with improved efficiency of feed utilization. Microbial abundance, rumen epithelial histomorphology, and RpH, may serve as indicators for feed efficiency in cattle. The divergences of assessments made on-farm and at slaughter should be considered in the development of proxies for feed efficiency.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.137

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.051
GPT teacher head0.276
Teacher spread0.225 · 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 teacher head, 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

Citations58
Published2017
Admission routes2
Has abstractyes

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