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Record W7037940243

Evaluating long term benefits of genomic selection programs in beef cattle breeding programs in Western Canada

2024· dissertation· en· W7037940243 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2024
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsSireBreedIce calvingBeef cattleSelection (genetic algorithm)Animal breedingProgeny testing
DOInot available

Abstract

fetched live from OpenAlex

A study was conducted to evaluate long-term benefits of genomic selection programs in natural service multi-sire breeding programs in Western Canada. A total of 24 breeding groups and 117 sires (some repeated) were followed over 6 breeding and calving years to determine the association between sire performance over multiple years, number of calves sired, calf performance, and replacement heifer performance over multiple years. To evaluate sire performance and account for the different numbers of cows and sires in each breeding group, bull prolificacy indexes (BPI) were calculated. BPI ranged from 0-4 and sires were categorized based on BPI for analysis with groups representing bottom 25% of sires BPI, middle 50%, and top 25%. Age of sires influenced sire prolificacy between yearlings, 2-year-olds, and mature sires. Twenty sires were used for 3 or more years of breeding and performance was not found to be repeatable across years. \nGenomic testing (EnVigour HX™, Delta Genomics, Edmonton, Alberta, Canada) was performed in two years of the six year study. EnVigour HX™ testing provided vigour scores and breed composition of the animals tested. The 2018 heifers from the cooperating producer had an average vigour score (VS) of 69% with a standard deviation of 10.5% and range of 66% with most calves having 5 breeds detected. The 2019 heifers from the cooperating producer had an average VS of 75% with a standard deviation of 11.5% and range of 58 percent.\nReplacement heifers produced from targeted sires in 2015 were evaluated for 1 to 3 breeding and calving seasons to evaluate grand-calf performance. A total of 16 sires produced the 74 heifers selected for replacement in 2015. A total of 171 calves were produced from the 2015-born replacement heifers between the 2018-2020 calving seasons. The top 25% of sires, based on BPI, had a higher proportion of replacement heifers retained in 2015, and therefore more grand-calves attributed to them. The bottom 25% of sires had a greater number of heifers retained over multiple calving seasons compared to the middle and top groups. Heifers born in the first 21d of the calving season tended to have more calves also born in the first cycle (R2 = 0.5239). Calving interval tended to decrease as a heifer matured across three calving cycles. There is a benefit of informed sire selection using DNA parentage. The data can help improve overall calf numbers and improve replacement heifer performance in the herd. \nA two-year net return analysis of using EnvigourHXTM testing in the herd was performed based on differences between low vigour score and high vigour score heifers. Total kg weaned over two parturitions was valued based on CanFax reported average market prices for 182kg steers in October 2020 and 205kg steers in October 2021. Based on cost of testing and total return there was a loss of $3,409 for the operation. Longer term analysis will need to be performed to see if costs can be recouped for genomic testing on a commercial operation.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.208
Teacher spread0.190 · 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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