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Record W4388540020 · doi:10.1093/jas/skad281.651

PSXIV-2 Inclusion of a Slow-Release Nitrogen Ingredient Associated Or Not with Monensin on Performance, Metabolism, and Carcass Parameters of Finishing Beef Cattle Fed High Starch Diets

2023· article· en· W4388540020 on OpenAlexaff
Gabriel Damasio, Karolina Batista Nascimento, Thaís Correia Costa, Luana Santos, Lusiane Pinto, Andrey Miranda, Germán Darío Ramírez-Zamudio, Márcio de Souza Duarte, Mateus Pies Gionbelli

Bibliographic record

VenueJournal of Animal Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsMonensinFeedlotDry matterAnimal scienceBeef cattleStarchIngredientSilageBiologyChemistryFood science

Abstract

fetched live from OpenAlex

Abstract This study aimed to assess the effects of a slow-released nitrogen (N) ingredient in association or not with monensin on the performance, metabolism, and carcass parameters of finishing young bulls. To investigate these outcomes, 112 Nellore young bulls (380 kg ± 16.2) were used in a 2 × 2 factorial arrangement. The young bulls were allocated to 28 feedlot pens (four animals per pen) and fed a finishing high starch (51% starch) corn grain and corn silage-based diet. The following treatments were randomly assigned to the experimental units: 1) Control (CON, n = 7), finishing diet without additives; 2) Monensin-enriched diet (MON, n = 7), monensin (Rumensin, Elanco Animal Health, Greenfield, IN) provided at a level of 30 mg per kg of dry matter (DM); 3) Gradual-N-release enriched diet (SRN, n = 7), slow-N-release supplement (Timafeed Boost, Roullier Group, Saint-Malo, France) provided at a dose of 250 g per animal per day; or 4) Monensin + SRN diet (MON + SRN, n = 7), monensin (30 mg per kg of DM) associated with the SRN (250 g per animal per day). The experimental period comprised 102 days, with the first 15 days designated for diet adaptation. The average daily gain (ADG) over the experimental period was reduced for young bulls fed SRN + MON (P = 0.02) compared with those fed only SRN. Young bulls fed MON had greater DMI during the finishing phase than MON × SRN (P = 0.03). Overall, young bulls fed CON diet had greater day-by-day dry matter intake (DMI) variation than other treatments (P ≤ 0.05). The SRN inclusion in the diet improved feed efficiency by 5.7% (P = 0.04). Young bulls fed diets without SRN inclusion tended to have higher blood urea concentration (P = 0.06). Blood D-lactate and glucose levels were similar between treatments (P ≥ 0.21). The SRN use tended to increase (P = 0.09) DM digestibility (5.6% increase). The microbial crude protein was reduced by MON + SRN association (P = 0.02) compared with other treatments. The hot carcass weight was greater for the SRN group compared with MON × SRN (P = 0.02). The use of SRN in the diet increased (P ≤ 0.04) total and daily carcass gain, carcass yield, and biological efficiency. In summary, although additional studies are suggested, these data indicate that the association between SRN and monensin might require careful consideration when used in finishing diets. The SRN inclusion in finishing diets without monensin shows important potential for increasing animal performance and 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 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 score0.003
Threshold uncertainty score0.006

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 head0.034
GPT teacher head0.253
Teacher spread0.219 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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