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Record W2981381899 · doi:10.1093/tas/txz168

Use of barley or corn silage when fed with barley, corn, or a blend of barley and corn on growth performance, nutrient utilization, and carcass characteristics of finishing beef cattle

2019· review· en· W2981381899 on OpenAlexafffund
Jordan Johnson, B.D. Sutherland, J. J. McKinnon, Tim A. McAllister, G.B. Penner

Bibliographic record

VenueTranslational Animal Science · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
FundersDuPont PioneerMinistry of Agriculture - Saskatchewan
KeywordsSilageDry matterAnimal scienceNutrientAgronomyFactorial experimentCompletely randomized designMathematicsFecesBeef cattleBiologyChemistry

Abstract

fetched live from OpenAlex

Abstract The objective of this study was to evaluate the effects of the source of silage, cereal grain, and their interaction on growth performance, digestibility, and carcass characteristics of finishing beef cattle. Using a completely randomized design within an 89-d finishing study, 288 steers were randomly assigned to 1 of 24 pens (12 steers/pen) with average steer body weight (BW) within a pen of 464 kg ± 1.7 kg (mean ± SD). Diets were arranged in a 2 × 3 factorial with corn silage (CS) or barley silage (BS) included at 8% (dry matter [DM] basis). Within each silage source, diets contained dry-rolled barley grain (BG; 86% of DM), dry-rolled corn grain (CG; 85% of DM), or an equal blend of BG and CG (BCG; 85% of DM). Total tract digestibility of nutrients was estimated from fecal samples using near-infrared spectroscopy. Data were analyzed with pen as the experimental unit using the Mixed Model of SAS with the fixed effects of silage, grain, and the two-way interaction. Carcass and fecal kernel data were analyzed using GLIMMIX utilizing the same model. There were no interactions detected between silage and grain source. Feeding CG increased (P < 0.01) DM intake by 0.8 and 0.6 kg/d relative to BG and BCG, respectively. Gain-to-feed ratio was greater (P = 0.04) for BG (0.172 kg/kg) than CG (0.162 kg/kg) but did not differ from BCG (0.165 kg/kg). Furthermore, average daily gain (2.07 kg/d) and final body weight did not differ among treatments (P ≥ 0.25). Hot carcass weight (HCW) was 6.2 kg greater (372.2 vs. 366.0 kg; P < 0.01) and dressing percentage was 0.57 percentage units greater (59.53 vs. 58.96 %; P = 0.04) for steers fed CS than BS, respectively. There was no effect of dietary treatment on the severity of liver abscesses (P ≥ 0.20) with 72.0% of carcasses having clear livers, 24.4% with minor liver abscesses, and 3.6% with severe liver abscesses. Digestibility of DM, organic matter, crude protein, neutral detergent fiber, and starch were greater for BG (P < 0.01) than CG or BCG. As expected, grain source affected the appearance of grain kernels in the feces (P ≤ 0.04). Feeding CS silage increased the appearance of fractured corn kernels (P = 0.04), while feeding BS increased fiber appearance in the feces (P = 0.02). Current results indicate that when dry rolled, feeding BG resulted in improved performance and digestibility compared with CG and BCG. Even at low inclusion levels (8% of DM), CS resulted in improved carcass characteristics relative to BS.

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)0.001
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: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.130
GPT teacher head0.291
Teacher spread0.161 · 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
GenreReview

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

Citations24
Published2019
Admission routes2
Has abstractyes

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