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Record W4308008858 · doi:10.3168/jds.2022-22238

A survey of male and female dairy calf care practices and opportunities for change

2022· article· en· W4308008858 on OpenAlexaffabout
Devon J. Wilson, Jessica A. Pempek, Ting‐Yu Cheng, Gregory Habing, Kathryn L. Proudfoot, Charlotte B. Winder, D.L. Renaud

Bibliographic record

VenueJournal of Dairy Science · 2022
Typearticle
Languageen
FieldVeterinary
TopicAnimal health and immunology
Canadian institutionsUniversity of Prince Edward IslandUniversity of Guelph
Fundersnot available
KeywordsColostrumLogistic regressionAnimal scienceBiologyDemographyMedicine

Abstract

fetched live from OpenAlex

The primary objective of this study was to compare male and female dairy calf management practices and evaluate risk factors associated with differences in care. Secondary objectives were to understand surplus calf transportation and marketing practices and investigate incentives to motivate calf care improvements. An online survey was distributed to all dairy producers in Ontario (n = 3,367) from November 2020 to March 2021 and Atlantic Canada (n = 557) from April to June 2021. Dairy producers were identified through provincial dairy associations and contacted via e-mail and social media. Descriptive statistics were computed, and a logistic regression model was created to evaluate factors associated with using discrepant feeding practices (i.e., fed less colostrum, fed colostrum later, or fed raw, unsalable milk) for male calves compared with females. The survey had a 7.4% response rate (n = 289/3,924) and was primarily filled out by farm owners (76%). Although colostrum and milk feeding practices were similar between male and female calves, male calves received less milk while still on the dairy farm of origin compared with females. Male calves were also more likely to be fed a higher proportion of raw, unsalable milk. Female producers and those that kept their male calves beyond 10 d of age had lower odds of using poorer feeding practices for male calves. Male calves were mostly sold between 1 and 10 d (64%), primarily through direct sales to a calf-rearing facility (45%), with auctions being the next most common method (35%). A small but notable proportion of producers (18%) agreed that euthanizing male calves is a reasonable alternative when their sale price is very low; however, few producers (13%) reported that financial costs limited their male calf care. The largest proportion (43%) of producers reported that a price premium for more vigorous calves would motivate them to take better care of their male calves. Conversely, only 28% of producers reported that a price discount for calves in poor condition would be motivating. Producers placed importance on the opinion of their calf buyer, their herd veterinarian, and the Canadian Code of Practice for the Care and Handling of Dairy Cattle when considering their calf care practices, and they highly valued practices that promote calf health. Respondents to this survey reported a lower proportion of tiestall barn use and higher milk productivity compared with typical dairy herds in the region, suggesting selection bias for more progressive dairy producers. Nevertheless, our results suggest that dairy producers provide similar care between male and female calves, but some male calves experience challenges due to milk feeding and marketing practices. Feedback from calf buyers along with continued support and guidance from herd veterinarians and the Code of Practice may motivate dairy producers to improve male calf care.

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 score0.181
Threshold uncertainty score0.361

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.389
GPT teacher head0.424
Teacher spread0.036 · 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

Citations23
Published2022
Admission routes2
Has abstractyes

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