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Record W3158397719 · doi:10.82308/46514

Effect of positioning the tie-rail to follow the natural neck line of cows when eating and rising on the welfare of dairy cows housed in tie-stall barns

2019· article· en· W3158397719 on OpenAlexfundaboutno aff
Jessica St. John

Bibliographic record

VenueeScholarship@McGill (McGill) · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNovalaitCanadian Dairy CommissionDairy Farmers of Canada
KeywordsWelfareDairy cattleTie lineAnimal scienceStall (fluid mechanics)Animal welfareBiologyEngineeringEcologyEconomicsMarket economy

Abstract

fetched live from OpenAlex

A majority of dairy farms in Canada are tie-stall barns, but few experimental studies have investigated ways to improve cow comfort through tie-stall design. Epidemiological studies have found that tie-stall design may have an effect on dairy cow welfare. Our objective was to investigate tie-rail positions and develop new recommendations that combine both height and forward positions to improve dairy cow welfare. Four treatments were tested: two new tie-rail positions that follow the natural neck line of cows (Neckline1, Neckline2), Current Recommendation, and the tie-rail position commonly found on farm. Forty-eight cows blocked by parity and stage of lactation were divided between two start dates and randomly allocated to a treatment for 10 weeks. Live scoring was performed weekly to evaluate: injury, cow and stall cleanliness, bedding quantity, and body condition. Lameness scoring was performed weekly through video observation. Milk yields were recorded at each milking and milk samples were collected weekly to evaluate milk components. Feeding/rumination time was recorded continuously using ear-mounted activity data loggers. Resting behaviours were continuously recorded using leg mounted accelerometers. Cows were recorded 1 d/wk by overhead cameras and 6 lying and rising events were evaluated per recording. The tie-rail positions tested did not have an effect on cow and stall cleanliness, bedding quantity, body condition, lameness, milk yield and components, feeding/rumination time, rising and lying ability, and resting behaviour. However, Current Recommendation (difference from wk 0: +0.9 ± 0.16) had an increase in proximal neck injuries compared to Neckline2 (+0.1 ± 0.15). Neckline2 (+0.8 ± 0.16) and Neckline1 (+0.5 ± 0.16) had an increase in medial neck injuries compared to the Current Recommendation (-0.1 ± 0.18). All treatments showed a decrease over time in average lying intention time (-5.9 s/event), lying-down time (-1.1 s/event), contact with stall (-32.5 %) and slipping (-9.4 %) during lying motion. All treatments showed a decrease over time in average backwards movement on knees (-10.8 %;) and contact with tie-rail (-14.3 %) during rising motion and overall abnormal rising (-15.7 %). Although, lying and rising ability improved over time, the prevalence of abnormal lying and rising was still high in the long-term for all treatments; for example, in the long-term, cows still came in contact with the stall dividers or tie-rail 42.3% of the time during lying motion. Results suggest that the injury location on the neck shifts based on tie-rail placement. Further research is needed to improve stall design to reduce injuries and abnormal lying and rising behaviours. For instance, studies investigating alternative options to stall design such as a different material apposed to metal bars (e.g., a flexible bar or chain) and/or increasing the tie-rail forward position even further may reduce contacts between the bar and the cows. Alternatively, housing options providing fewer obstacles in the cow's environment through the elimination of stall hardware should be investigated such as a deep-bedded pack or compost pack.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 integrity0.0010.000
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 head0.007
GPT teacher head0.219
Teacher spread0.211 · 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
Published2019
Admission routes2
Has abstractyes

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