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Record W4401032887 · doi:10.3168/jds.2024-25138

Awareness and perceived barriers to the adoption of best management practices for the transportation of lactating cull dairy cows of dairy producers in Ontario

2024· article· en· W4401032887 on OpenAlexafffundabout
Michael W. Brunt, Caroline Ritter, D.L. Renaud, S.J. LeBlanc, D.F. Kelton

Bibliographic record

VenueJournal of Dairy Science · 2024
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Prince Edward IslandUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsBusinessDairy industryDairy cattleAgricultural scienceAnimal scienceFood scienceEnvironmental scienceChemistryBiology

Abstract

fetched live from OpenAlex

The objective of this study was to understand dairy farmers' awareness of and perceived barriers to the implementation of best management practices (BMP) for the transportation of lactating culled cows being moved to auction or abattoir. An invitation to participate was emailed to all dairy farms in Ontario, Canada. Responses to this invitation were used to recruit additional participants through criterion purposive snowball sampling. In total, 28 dairy producers participated in 1 of 5 semistructured focus groups. All focus groups were audio recorded, transcribed verbatim, and analyzed with applied thematic analysis. Four themes were identified, including (1) transfer of responsibility, (2) interpersonal relationships, (3) juggling priorities, and (4) complexities of long-term planning. Individual participant knowledge varied, and participants also described misconceptions that other producers held surrounding transport duration and distances or the number of possible sales points for lactating culled cows. Participants did not agree on whether the producer's responsibility ended once the cow left their property or if all stakeholders from the farm to the final destination shared responsibility. Participants discussed the importance of existing trusted relationships with local cattle transporters and veterinarians to ensure information on best practices is shared. Participants also discussed how business pressures (e.g., production demands, space limitations) often challenge their ability to dry off lactating cows before transport; however, they mentioned that the degree of milk production may influence the destination of their animal (e.g., direct to slaughter for animals with high milk production). Some participants described a dynamic balance between business pressures and the potential for declining welfare of the animals under consideration for culling during lactation. Participants posited that producers who did not prioritize proactive herd management and producers nearing retirement had limited long-term planning for culling individual cows, which might lead to the transport of vulnerable or unfit animals. Finally, the lack of access to direct transportation to local slaughter was identified as an important barrier to adherence with the BMP for cull cows. In summary, many participants did not have an accurate understanding of what happens to cull cows after departing the farm, and they were unsure whether they retained a level of responsibility for an animal after it was transported from their farm. The priority placed by participants on trusted interpersonal relationships, the greater availability of peer-to-peer farmer training, and the professional accreditation of animal transporters, in addition to structural changes to increase local slaughter capacity, could potentially be leveraged to increase implementation of BMP and enhance lactating cull cow welfare.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.063
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.073
GPT teacher head0.361
Teacher spread0.288 · 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

Citations5
Published2024
Admission routes3
Has abstractyes

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