Factors associated with leg cleanliness of smallholder dairy cows in Kenya
Bibliographic record
Abstract
Dairy cow cleanliness provides information about animal welfare, along with risk of diseases and quality of housing environments. This study determined animal- and farm-level factors associated with upper hind leg cleanliness in smallholder dairy cows. All lactating cows (n=234) on 118 randomly selected zero-grazing fams participated in this cross-sectional study between May to August 2015 in the Naari area of Meru County, Kenya. Cleanliness scores of hind legs were assessed visually on a 1-4 ordinal scale (clean to very soiled). Potential risk factors for poor leg cleanliness were evaluated by inspection of cows and their housing, along with a questionnaire about herd management. Descriptive statistics, and univariable and multivariable logistic regression were used to determine factors associated with soiled legs (cleanliness score>2) in the analyses. Prevalence of soiled legs was 59.0% (137/234). In the final model, factors positively associated with soiled legs included failure of the knee wetness test on the stall floor (OR=11.2; 95%CI: 5.1, 24.7), animal restlessness in the stall (OR=4.9; 95%CI: 1.8, 13.5), and milk production in kg/cow/day (OR=1.09; 95%CI: 1.02, 1.16). Protective factors for soiled legs included having stalls without excessive space (OR=0.25; 95%CI: 0.11, 0.57), and having an intact stall roof (OR=0.34; 95%CI; 0.15, 0.76). Our results suggest that farmers should address both housing design (especially the roof and stall size) and management issues (especially stall cleanliness) to enhance leg cleanliness and animal welfare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".