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Risk factors associated with cows' lying time, stall and cows' own cleanliness in smallholder dairy farms in Kenya

2019· article· en· W2963267483 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueVeterinary World · 2019
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Prince Edward Island
FundersFondation Rideau HallFondations communautaires du CanadaGovernment of Canada
KeywordsUdderMilkingAnimal scienceLyingLogistic regressionDairy cattleStall (fluid mechanics)MedicineVeterinary medicineBiologyMastitisInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND AIM: The welfare of animals kept in livestock production systems has raised concerns around the world. Adult dairy cattle require adequate rest and spend approximately 12 h/day lying down. This cross-sectional study aimed to determine the stall factors and management practices affecting cows' lying time, stall cleanliness, and cows' cleanliness (udder and upper leg), in smallholder dairy cows in Meru County of Kenya. MATERIALS AND METHODS: A total of 106 milking cows from 73 farms were assessed for daily lying time and cleanliness. Data loggers were used to record the lying time of cows for 3 days. Stall, udder, and upper leg cleanliness were assessed using a 5-score system: 1 (very clean) to 5 (very dirty). Management information was acquired using a questionnaire that was administered face-to-face to the farmers in their native Kimeru language. Univariable and multivariable linear and logistic regression models were fit to determine factors associated with cows' lying time and dichotomized stall and cows' own cleanliness, respectively. RESULTS: The mean daily lying time was 10.9±2.2 h, and the mean stall cleanliness score was 2.4±1.0. The mean average cleanliness scores of the udder and upper legs were 1.9±0.7 and 2.5±1.1, respectively. Overall, 35% of the stalls were categorized as dirty (>2.5), whereas 13% and 47% of the cows had udder and leg cleanliness scores >2.5, respectively. From the final multivariable models (p<0.05), daily lying time increased by 1.0 h for cows older than 5.25 years versus younger cows. Conversely, lying time decreased by 1.0 h with stall cleanliness scores >2.5 and by 1.6 h with poorly positioned neck rails. In an interaction term, addition of new bedding at least once a day without removing stall manure at least once a day decreased the daily lying time of the cows by 1.5 h, whereas failure to add new bedding at least once a day but removing stall manure at least once a day decreased the lying time of the cows by 1.2 h. Farm-level risk factors for stall dirtiness (>2.5) included delayed cleaning of the alley (odds ratio [OR]=6.6, p=0.032), lack of bedding (OR=4.9, p=0.008), and standing idle and/or backward in the stall (OR=10.5, p=0.002). Stalls categorized as dirty (OR=2.9, p=0.041) and lack of bedding (OR=2.7, p=0.065) were cow- and farm-level risk factors for dirtiness of the udder (>2.5), respectively, whereas the stall being dirty (OR=2.3, p=0.043) was the only risk factor (cow level) for dirtiness of the upper legs (>2.5). CONCLUSION: It was recommended that farmers should pay attention to the specific factors identified regarding the stall design (e.g., neck rail position) and bedding/manure management that impact the cleanliness of cows and their lying time.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.059
GPT teacher head0.298
Teacher spread0.239 · 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