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Record W2778012291 · doi:10.3168/jds.2017-13462

Prevalence of lameness and leg lesions of lactating dairy cows housed in southern Brazil: Effects of housing systems

2017· article· en· W2778012291 on OpenAlexafffund
J.H.C. Costa, T.A. Burnett, M.A.G. von Keyserlingk, María José Hötzel

Bibliographic record

VenueJournal of Dairy Science · 2017
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of British Columbia
FundersCiência sem FronteirasUniversidade Federal de Santa CatarinaConselho Nacional de Desenvolvimento Científico e TecnológicoMinistério da EducaçãoMitacsCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsLamenessMilkingHerdDairy cattleAnimal scienceMedicineHygieneBarnVeterinary medicineGeographyBiologySurgery

Abstract

fetched live from OpenAlex

Within the last few decades, the North American and European dairy industries have been collecting information about lameness and leg injury prevalence on dairy farms and have tried to develop solutions to mitigate these ailments. Few published articles report the prevalence of lameness and leg lesions in areas outside of those 2 regions, or how alternative housing systems, such as compost-bedded packs, affect the prevalence of these maladies. The objectives of this study were to compare the prevalence of lameness and leg lesions on confined dairies that used freestall, compost-bedded packs, or a combination of these 2 systems in Brazil. Data were collected in the autumn and winter of 2016 from 50 dairy farms located in Paraná state, including 12 compost-bedded pack dairies (CB), 23 freestall dairies (FS), and 15 freestall dairies that used compost-bedded packs for vulnerable cows (FS+C). A visit to the farm consisted of a management questionnaire, an inspection of the housing areas as well as the milking parlor, and an evaluation of all lactating cows as they exited the parlor for lameness (score 1-5), hygiene (score 0-2), body condition score (score 1-5), and hock and knee lesions (score 0-1). Median 1-way chi-squared test was used to compare production systems. We found no difference between farm types in management practices related to hoof health management or average daily milk production per cow [31 (29-33.9) kg/d; median (quartile 1-3)], percentage of Holstein cattle in the herd [100% (90-100%)], conception rate [35.8% (30.2-38%)], or pregnancy rate [15% (13.7-18%)]. The CB farms were smaller [85 (49.5-146.5) milking cows] than both the FS [270 (178-327.5) milking cows] and FS+C farms [360 (150-541.5) milking cows). The overall prevalence of severe lameness (score 4 and 5) across all farms was 21.2% (15.2-28.5%) but was lower on the CB farms [14.2% (8.45-15.5%)] in comparison to the FS [22.2% (16.8-26.7%)] and the FS+C farms [22.2% (17.4-32.8%)]. Less than 1% of all cows scored on CB farms were observed with swollen or wounded knees (or both), which was lower than either the FS or FS+C farms [7.4% (3.6-11.9%) and 6.4% (2.6-11.8%) of all cows scored, respectively]. The same pattern was found for hock lesions, where the farm-level prevalence within the 3 different housing types was 0.5% (0-0.9%), 9.9% (0.8-15.3%), and 5.7% (2.6-10.9%) for CB, FS, and FS+C farms, respectively. No differences between farm systems were observed for hygiene or body condition score. On average, 2.7% (0.8-10.9%) of lactating cows had a soiled side, 15.4% (2.1-37.4%) had dirty legs and 1.7% (0-9.3%) had dirty udders. The average herd-level body condition score across farms was 2.9 (2.9-3), with 0.86% of the all cows scored having a body condition score <2.5. These results indicate that lameness prevalence on confined dairies in Brazil is high and highlight the need for remedial changes in environmental design and management practices. We found that CB farms in this region had reduced lameness and lesions in relation to FS or FS+C dairies.

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)0.001
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 score0.450
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.051
GPT teacher head0.348
Teacher spread0.298 · 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

Citations54
Published2017
Admission routes2
Has abstractyes

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