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Record W4293661180 · doi:10.3389/fvets.2022.974061

A survey of dairy cattle farmers' management practices for cull cows in Ontario, Canada

2022· article· en· W4293661180 on OpenAlexafffundabout
J. A. Marshall, Derek B. Haley, Lena Levison, D.F. Kelton, Cynthia Miltenburg, S.M. Roche, T.F. Duffield

Bibliographic record

VenueFrontiers in Veterinary Science · 2022
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsMinistry of Agriculture, Food and Rural AffairsUniversity of Guelph
FundersOntario Agri-Food Innovation AllianceOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsCullingAgricultural scienceHerdDairy cattleOddsBusinessDairy industryAnimal scienceMedicineBiology

Abstract

fetched live from OpenAlex

Removal of cows from dairy cattle production is a routine and unavoidable practice of the dairy industry and is often referred to as culling. The objectives of this study were to use a survey to describe current on-farm cull cow management, farmers' perception of cull cows' journeys to slaughter, and the adoption of current recommendations and regulations by Ontario dairy farmers. All Ontario dairy farmers were invited to complete a cull cow management survey between December 2020 and March 2021 that included 44 questions covering farmer demographic information, farm characteristics, and cull cow management. The survey response rate was 7.4% ( n = 248); a total of 226 of the responses were included in this study for analysis. Most respondents indicated they have a written standard operating procedure (SOP) for cull cows (62%), and 48, 13, and 15% of those identified they use their cull cow SOP “always,” “sometimes,” and “never,” respectively. The more confident respondents were that cull cows arrived at slaughter in the condition they left the farm the less likely they were to have a cull cow SOP [odds ratio (OR) 0.83]. The most important sources of information for the management of cull cows were the herd veterinarian (64%) and members of the marketing/regulatory organization the Dairy Farmers of Ontario (44%). Drug withdrawal time was the only factor most respondents (73%) considered “very important” for the assessment of cull cows prior to transport. Most farmers believe cull cows journey from the farm to slaughter is three or less days (55%), and the confidence of farmers that cull cows arrive at slaughter in the condition they left their farm was generally high. Lastly, most farmers (66%) identified they were familiar with recent regulatory changes around the fitness, duration of transport, and lactation status for cull cows. These results highlight farmers' perceptions of the impacts and durations of the journey of cull cows differs from reality, and there are misunderstandings of the requirements for cull cow management. Further research should investigate how different strategies for training farmers may lead to improved cull cow welfare and regulatory compliance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.122
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.086
GPT teacher head0.335
Teacher spread0.248 · 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 teacher head, 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

Citations9
Published2022
Admission routes3
Has abstractyes

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