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Record W4414831414 · doi:10.1093/jas/skaf300.150

79 Maximizing the utilization of wheat straw in finishing beef cattle diets with canola or flax screenings supplementation: Growth performance, carcass characteristics and economic analysis.

2025· article· en· W4414831414 on OpenAlexaff
Beatriz J Montenegro, G.B. Penner, H.A. Lardner, Kathy Larson, J. J. McKinnon, D. J. Gibb, Tim A. McAllister, Gabriel O Ribeiro

Bibliographic record

VenueJournal of Animal Science · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Saskatchewan
Fundersnot available
KeywordsCanolaFeedlotSilageStrawBeef cattleDistillers grainsCattle feedingCarcass weight

Abstract

fetched live from OpenAlex

Abstract Inclusion of crop residues and byproducts in feedlot diets can be an attractive alternative during feed shortages, potentially decreasing feed costs and enhancing beef production sustainability. However, limited data is available evaluating the use of wheat straw (WS) supplemented with oilseed screenings in finishing beef cattle diets. This study’s objective was to evaluate the effect of increasing WS inclusion, supplemented with canola or flax screenings, in finishing feedlot cattle diets compared to a standard dry-rolled barley grain (DRB):barley silage diet on growth performance, carcass characteristics and cost of gain. In a completely randomized design study, 300 steers (initial body weight [BW]: 366 ± 32 kg) were stratified by weight, randomly allocated to 20 pens (15 steers/pen) and fed for 148 d. Each pen was randomly assigned to one of five diet treatments (n=4/treatment). The diets contained (DM basis): 1) 85.64% DRB, 10.00% barley silage (CTL); 2) 78.14% DRB, 12.50% canola screenings, 5.00% WS, (LSC); 3) 78.14% DRB, 12.50% flax screenings, 5.00% WS (LSF); 4) 73.14% DRB, 12.50% canola screenings, 10.00% WS (HSC); and 5) 73.14% DRB, 12.50% flax screenings, 10.00% WS (HSF). Supplement (4.36% of dietary DM) was included in all diets. Data were analyzed using the MIXED procedure of SAS with diet as fixed effect and pen the experimental unit. Diet did not affect (P=0.47) DMI (11.7 kg/d). Steer final BW, average daily gain (ADG) and hot carcass weight (HCW) decreased (P<0.05) as dietary WS inclusion increased; however, final BW and HCW did not differ between steers fed CTL and low WS inclusion diets (P>0.05). There was a tendency (P=0.05) for gain:feed to decrease with increasing WS inclusion. The proportion of AAA carcasses was greatest (P=0.02) for CTL and lowest for steers fed diets with high WS inclusion; however, carcasses from steers fed low WS inclusion diets did not differ from CTL (P=0.47). The proportion of yield grade 2 (Y2) carcasses tended to increase with increasing WS inclusion (P=0.09). Supplementing WS diets with canola compared to flax screenings did not affect any performance metrics (P>0.05) except carcass quality grade distribution. Flax screenings inclusion promoted a decrease (P=0.04) in AA-classified carcasses and tended (P=0.07) to increase AAA grades (P< 0.05). Diet cost ($/steer/d) decreased (P=0.01) with higher WS inclusion. No differences were observed across treatments (P >0.33) for backfat thickness, lean meat yield, and cost:gain. Increasing WS inclusion in finishing diets (5 to 10% of diet DM) negatively affected some carcass characteristics and live performance metrics; however, overall performance of steers fed low WS inclusion diets was comparable to steers fed the CTL diet. These findings suggest that feeding finishing steers diets with low WS inclusion (5%) supplemented with oilseed screenings may be an attractive feeding strategy during feed shortages.

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 score0.004
Threshold uncertainty score0.008

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 head0.017
GPT teacher head0.272
Teacher spread0.255 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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