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Record W3111056195 · doi:10.3168/jds.2020-18916

Associations of freestall design and cleanliness with cow lying behavior, hygiene, lameness, and risk of high somatic cell count

2020· article· en· W3111056195 on OpenAlexaffabout
I. Robles, A. Zambelis, D.F. Kelton, Herman W. Barkema, Greg Keefe, Jean‐Philippe Roy, M.A.G. von Keyserlingk, T.J. DeVries

Bibliographic record

VenueJournal of Dairy Science · 2020
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Prince Edward IslandUniversité de MontréalUniversity of CalgaryUniversity of Guelph
Fundersnot available
KeywordsSomatic cell countLamenessMastitisAnimal scienceHerdBulk tankIce calvingMedicineHygieneVeterinary medicineBiologyLactationSurgeryPregnancy

Abstract

fetched live from OpenAlex

The objective of this study was to investigate associations of freestall design and cleanliness with cow lying behavior, hygiene, lameness, and risk of new high somatic cell count (SCC). Cows from 18 commercial freestall dairy herds (22 ± 15 cows/farm; mean ± SD) in Ontario, Canada, were enrolled in a longitudinal study. Four hundred focal cows that were <120 d in milk, had no mastitis treatment in the last 3 mo, and had an SCC <100,000 cells/mL at their most recent milk test were selected for the study. Data on SCC were collected through Dairy Herd Improvement Association milk testing (at ~5-wk intervals). Each farm was visited 5 ± 3 d (mean ± SD) after each milk test until 3 tests were completed (~105 d), for a total of 3 observation periods per cow. Elevated SCC was used as an indicator of subclinical mastitis. An incident of new high SCC was defined as a cow having SCC >200,000 cells/mL at the end of an observation period, when SCC was <100,000 cells/mL at the beginning of that period. Lying behavior was recorded for 6 d after each milk sampling, using electronic data loggers. Cows were scored during each period for lameness (5-point scale, with scores ≥3 = lame), body condition score (BCS; 5-point scale; 1 = thin to 5 = fat), and hygiene (4-point scale). Stall cleanliness was assessed during each period with a 1.20 × 1.65-m metal grid, containing 88 squares. The grid was centered between stall partitions of every tenth stall on each farm, and the squares containing visible urine or fecal matter (or both) were counted. Cow lying time averaged 10.9 ± 1.9 h/d. On average, cows with low BCS (≤2.5) spent 37 ± 16.6 min/d less time lying down than high-BCS cows (≥4.0). On average, cows tended to spend 36 ± 18.3 min/d more time lying down in deep-bedded versus mattress-based stalls. Mean proportion of soiled squares per stall was 20.1 ± 0.50%. Across farms, cow lying time decreased as the proportion of soiled squares per stall increased. A difference in daily lying time of ~80 more min/d was modeled for cows housed in barns with the cleanest stalls compared with those with the dirtiest stalls. Higher neck rail height [for every 1 SD (10 cm) increase] increased the odds (odds ratio = 1.5) of cows having a dirty upper leg-flank and udder. The odds of a cow having a dirty upper leg-flank, udder, and lower legs were 1.5, 2.0, and 1.9 times greater, respectively, for cows housed with dirtier stalls. Also, cows housed on farms with dirtier stalls had 1.3 times greater odds of being lame at the time of observation. Over the study period, 50 new high-SCC cases were detected, resulting in an incidence rate of 0.45 cases of new high SCC per cow-year at risk. No measured factors were detected to be associated with risk of a new high SCC. Overall, our results confirm that cows lie down longer in cleaner and more comfortable environments. Further, these results highlight the need for improved stall cleanliness to optimize lying time and potentially reduce lameness.

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 score0.075
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.060
GPT teacher head0.302
Teacher spread0.241 · 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

Citations26
Published2020
Admission routes2
Has abstractyes

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