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Record W4386570268 · doi:10.3168/jds.2023-23796

Associations of cow- and herd-level factors during the dry period with indicators of udder health in early-lactation cows milked by automated milking systems

2023· article· en· W4386570268 on OpenAlexaffabout
Catalina Wagemann-Fluxá, D.F. Kelton, T.J. DeVries

Bibliographic record

VenueJournal of Dairy Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsUdderMilkingHerdIce calvingSomatic cell countLactationAnimal scienceMastitisBiologyVeterinary medicineMedicinePregnancy

Abstract

fetched live from OpenAlex

This observational study aimed to determine the association of cow-level factors and herd-level housing and management practices during the dry period with indicators of udder health in early-lactation cows in automated milking system (AMS) herds. Data were collected from 166 commercial AMS dairy farms (mean ± standard deviation = 116 ± 111 milking cows; range = 39 to 1,200) across Canada between October 2018 and September 2020. Information on herd demographics, housing, and management practices was obtained on each farm using 2 surveys. On each farm, we selected all cows that had available Dairy Herd Improvement (DHI) somatic cell count (SCC) data for their last milk test before dry-off (>250 d in milk) and their first milk test after calving (5-45 d in milk). Data from 14,007 cows were included after excluding cows with a dry period of <30 d and >120 d. Using the SCC data, we calculated for each cow the somatic cell score (SCS) for the last milk test before dry-off (PreSCS) and the first milk test after calving (PostSCS), which we then averaged per herd at a test-day level. Intramammary infection (IMI) was estimated using cow SCC data. Each cow was classified as not infected (SCC <200,000 cells/mL) or infected (SCC ≥200,000 cells/mL) at her last milk test before dry-off and her first milk test after calving. Based on this classification, cows were further categorized as never infected, always infected, new IMI, or cured IMI. At the cow level, a higher PostSCS was associated with longer dry periods. The odds of having a new IMI were higher for cows of higher parity and that had lower 305-d milk yield before dry-off. Cows with lower parity were more likely to cure an IMI. At the herd level, a higher 305-d milk yield before dry-off was associated with a lower incidence of new IMI and a higher incidence of cured IMI. Separating cows into a different pen as preparation for dry-off tended to be associated with a lower PostSCS and incidence of new IMI. At dry-off, herds that used teat sealants and blanket antibiotic dry cow therapy also had lower PostSCS. During the dry period, housing cows in different groups was associated with a higher PostSCS and a lower incidence of cured IMI, while housing cows in both pack pens and stalls compared with only pack pens was associated with a lower incidence of new IMI. Finally, placing cows onto the AMS to be milked one or more days after calving tended to be associated with a lower PostSCS compared with placing them in the AMS within the first day postpartum. In summary, indicators of udder health in early-lactation cows in AMS herds were associated with several cow-level factors and herd-level housing and management practices before dry-off, at dry-off, during the dry period, and at the beginning of lactation. Thus, if some of the associations identified are causal, AMS producers may be able to improve udder health through modifications of housing and management practices.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.275
Teacher spread0.242 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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