Health Constraints and Farm Management Factors Influencing Udder Health of Dairy Cows in Malawi
Bibliographic record
Abstract
The aim of this study was to identify the major health problems and possible farm management practices which could be associated to the prevalence of mastitis in dairy cows kept in smallholder dairy farms in Malawi. A total of 140 randomly selected dairy farms were included in the study. Health problems were assessed using a semi-structured questionnaire and farm records. Physical examination and California Mastitis Tests (CMT) were used to determine the presence of clinical mastitis (CM) and subclinical mastitis (SCM). The most common diseases reported at farm level were mastitis 39.3% (55/140) and East Coast Fever 15.7% (22/140). Mastitis was the major udder disease and 52.0% (93/179) of the cows had at least one case of mastitis in the previous year. The prevalence of mastitis (positive result of physical examination or CMT) was significantly affected (p<0.05) by history of mastitis, floor type, herd size, sanitation of stables and season of the year. Results of the present study suggest a need for targeted control measures against the major diseases identified. In addition, emphasis on management interventions with the aim to improve on the sanitation of stables is recommended in order to alleviate the negative impact of mastitis in dairy farms in Malawi.
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 | 0.000 |
| 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".