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Record W4402541707 · doi:10.1093/jas/skae234.347

63 Impacts of protein supplementation frequency on animal performance, rumen fermentation and methane emission from growing beef cattle

2024· article· en· W4402541707 on OpenAlexaff
Aklilu W. Alemu, A. D. Iwaasa

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsRumenBeef cattleAnimal scienceMethane emissionsFermentationMethaneBiologyFood scienceChemistryEcology

Abstract

fetched live from OpenAlex

Abstract Nutrient supplementation is one of largest expenditure in forage-based beef production systems, especially when forage quality is poor or limited. Decreasing supplementation frequency may have a role in improving sustainability by increasing animal performance and minimizing cost of labor and fuel. This study evaluated the impacts of protein supplementation frequency (canola meal pellet; CMP) on animal performance, rumen fermentation, and enteric methane production from beef cattle fed forage-based diet. A total of 60 crossbred backgrounding steers [body weight (BW) = 278 ± 10 kg) was randomly assigned to one of the three treatments (n = 20/treatment): i) control (no CMP supplementation), ii) control + everyday CMP supplementation (0.83 kg/d per steer), and iii) control + every other day supplementation of CMP. The study was comprised of 21 d of adaptation followed by three 21-d periods, with enteric methane measured for 7 d per period. Animals were fed grass hay-based diets once a day at 0800 h and remained on their respective diets throughout the 63-d study. Diets were formulated considering animal BW, feed nutrient density and environmental condition in accordance with the National Academies of Sciences, Engineering and Medicine. Each treatment group was assigned to a pen, with the cattle and diets rotated among pens weekly to allow the animals to access the GreenFeed system stationed in one of the pens for enteric methane measurement. For measurement of rumen fermentation products, three ruminally canulated steers (BW = 350 ± 20 kg) were allocated to the three treatments using a 3 x 3 Latin Square Design with 21-d periods so that one animal received all the treatments over the three experimental periods. Animals were adapted to the treatments for 17 d and rumen samples were collected between d 19 and 21 at 0700 h, 1000h, 1300 h, 1600 h and 1900 h. Data were analyzed using the Mixed procedure of SAS. Relative to the control treatment (9.9 kg/d), DM intake was 4 to 5% greater (P < 0.001) following CMP supplementation regardless of supplementation frequency. Similarly, average daily gain was greater (7 to 11%, P = 0.01) for CPM supplemented treatments as compared with control (1.04 kg/d) irrespective of supplantation frequency. Rumen fermentation was affected by treatments. Total VFA concentration was greater (P = 0.01) for the every other day supplementation of CMP as compared with control and daily supplementation treatments. Conversely, rumen ammonia concentration was not affected by treatments. The control group had greater acetate and reduced propionate molar proportions that resulted in a 5 to 6% greater acetate:propionate ratio (P < 0.01) as compared with the CMP supplemented groups. Enteric methane production (g/d) was greater for the daily CMP supplementation (P = 0.02) while yield (g/kg daily weight gain) was not affected by treatment. Our results indicated that reduction of protein supplementation frequency improved animal performance without negatively impacting rumen fermentation.

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 score0.005
Threshold uncertainty score0.010

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 head0.023
GPT teacher head0.285
Teacher spread0.262 · 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
Published2024
Admission routes1
Has abstractyes

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