Cross-sectional study of cow comfort and management factors associated with subclinical mastitis in smallholder dairy farms in Kenya
Bibliographic record
Abstract
A number of environmental and contagious factors have been associated with subclinical mastitis (SCM), which is a common and costly problem for smallholder dairy farmers (SDF). We conducted a cross-sectional study on 118 cows in their first two months post-calving on 109 SDF in Kenya. The study objective was to investigate the relationships among various cow and farm management parameters and SCM specific to SDF. The stall floor comfort level was assessed through knee impact and wetness tests, and cleanliness on the leg and udder were also scored. Various mastitis prevention measures were also assessed (e.g., milking protocols, and use of teat dip and dry cow therapy). Individual quarter SCM was assessed on each cow using California Mastitis Test (CMT). Univariable and multivariable logistic regression models were fit to determine management factors associated with cow-level SCM. Farm-level, cow-level and quarter-level prevalence of SCM was 45.9% (50/109), 43.2% (51/118) and 21.9 % (103/471), respectively. The proportion of stalls scored as dirty was 33.1% while 49.1% of cows had dirty legs. Only 10.1% of farms were using either disinfectant teat dip or dry cow therapy (or both) to prevent mastitis. Low parity and poor stall hygiene were significantly associated with occurrence of SCM. At high daily milk yield, the probability of having SCM was higher in cows housed in a shed with a dirty versus clean alleyway, with no significant difference at low daily milk yield. From the study findings, we concluded that certain cow characteristics and comfort measures were associated with SCM and need to be incorporated in education plans for farmers in SDF.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".