Sub-clinical mastitis and associated risk factors on lactating cows in the Savannah Region of Nigeria
Bibliographic record
Abstract
BACKGROUND: Sub-clinical mastitis limits milk production and represents an important barrier to profitable livestock economics worldwide. Milk production from cows in Nigeria is not at optimum levels in view of many factors including sub-clinical mastitis. RESULTS: The overall herd-level prevalence rate for SCM was 85.33% (256/300 heads of cows) while the quarter-level prevalence rate of SCM was 43.25% (519/1,200 quarters). The prevalence of SCM was 50.67%, 43.67%, 39.67% and 39.13% for the left fore-quarter, right hind-quarter, left hind-quarter and right fore-quarter, respectively. The Rahaji breed had the highest prevalence of SCM with 65.91% (29/44), while the White Fulani breed had the least with 32.39% (57/176). A total of 32.33% (97/300) had only one mammary quarter affected, 30.33% (91/300) had two quarters affected, 16.00% (48/300) had three quarters affected while 6.67% (20/300) had all the four quarters affected. A total of 53.00% had SCM in multiple quarters (159/300). The risk of SCM decreased significantly among young lactating cows compared to older animals (OR = 0.283; P < 0.001; 95%CI = 0.155; 0.516). The Rahaji breed had significantly higher risk compared with the White Fulani breed (OR = 8.205; P = 0.013; 95% CI = 1.557; 43.226). Improved sanitation (washing hands before milking) will decrease the risk of SCM (OR = 0.173; P = 0.003; 95% CI = 0.054; 0.554). CONCLUSION: SCM is prevalent among lactating cows in the Nigerian Savannah; and this is associated with both animal characteristics (age, breed and individual milk quarters) and milking practices (hand washing).Good knowledge of the environment and careful management of the identified risk factors with improved sanitation should assist farm managers and veterinarians in implementing preventative programmes to reduce the incidence of SCM.
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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".