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Record W7005067856

Performance and economic comparison of conventional and non-conventional post-weaning calf management systems

2022· dissertation· en· W7005067856 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
Fundersnot available
KeywordsTylosinFeed conversion ratioMonensinCarcass weightWeight gainCattle feeding
DOInot available

Abstract

fetched live from OpenAlex

A two-year study was conducted to evaluate three feeding management systems based on starting weight at weaning: Heavy (HV) = direct-entry finish, 295 kg average, Medium (MD) = short background to finish, 250 kg average, Light (LT) long background to finish for weaned steers, 205 kg average, (n=4 in year 1 and 2, respectively) using conventional (CONV) or natural (NAT) management. The usage of performance enhancing technology (PET) including hormone implants, monensin and tylosin was included in CONV treatments, but not NAT. Each year of the study, 240 steers were allocated into three weight brackets and randomly allocated to CONV or NAT management and raised to a target weight of 635 kg.. In the backgrounding phase, the ADG of MD and LT-CONV steers was 19-22% better than MD and LT-NAT steers (P<0.01). Dry matter intake was 12% higher for CONV steers among all groups (P<0.01). Feed efficiency (G:F) was not different among treatments for MD calves, but G:F was 25% better for LT-CONV steers (P<0.01). The cost of gain (COG) for raising MD/LT-CONV animals in winter backgrounding was 11-15% lower than MD/LT-NAT. In the finishing phase, there were 25% more DOF for NAT steers taken to the same end weight as CONV counterparts (P<0.01). The Heavy, MD and LT CONV treatments had 19%, 34% and 40% better ADG than their NAT counterparts (P<0.01). There was no difference in DMI across all treatments (P=0.12). There was a 25% improvement in G:F for CONV animals in finishing compared to NAT (P<0.01). The MD weight group exhibited the poorest feed efficiency of all weight groups (P<0.05). For overall performance, Heavy, MD and LT NAT groups had a 25%, 20% and 17% more DOF from weaning to slaughter (P<0.01). The three CONV treatment groups had equal cost/kg gain overall from weaning to slaughter (P<0.01). The Heavy CONV had equal cost/kg gain as their NAT counterparts, but the MD and LT CONV groups had 17% and 13% reduced cost/kg gain than their NAT counterparts, respectively (P<0.01). The economic and production disparity between CONV and NAT treatments in the Heavy weight class was less pronounced than the backgrounded weight groups. For carcass characteristics, the Heavy CONV group had the highest HCW and REA, and the other two CONV groups did not grade better than NAT in this area (P<0.01). Marbling, backfat thickness and yield scores were higher for NAT management. Natural animals had 29% more AAA grades (P<0.01) and CONV cattle had more AA grade (P<0.01). Overall, NAT cattle exhibited a higher proportion of liver abscesses (P<0.01). In the backgrounding period the MD and LT-CONV steers had 11% and 15% lower COG than their NAT counterparts (P<0.01). In the finishing period the H, MD and LT-CONV groups had 7%, 29% and 28% better COG than their NAT counterparts (P<0.01). Overall, raising NAT beef under western Canadian conditions will have lower ADG and more DOF to reach a target finish weight, resulting in higher overall COG. Animals under a NAT long-backgrounding system that are grazed previous to fall feedlot entry will be most efficient when compared to the H or MD management systems. The results of this study indicate that natural beef production in Canada will require an 8-11% premium to break even on a cost basis if calves are slaughtered at similar live body weights as conventionally raised calves.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.993
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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