Animal health constraints in dairy goats kept under smallholder farming systems in Kongwa and Mvomero Districts, Tanzania
Bibliographic record
Abstract
This study was conducted to determine animal health constraints for dairy goats kept by small-scale farmers in Kongwa and Mvomero districts, Tanzania. A total of 129 dairy goats belonging to 108 farmers were screened for gastrointestinal nematode (GIN) infection, coccidiosis, haemoparasites, brucellosis and contagious caprine pleuropneumonia (CCPP) over a period of 11 months. Other clinical diseases and mortalities were recorded. The goats used were Norwegian crosses and Toggenburg crosses. The mean prevalence of GIN infection and coccidiosis in all goats were 54.8 and 57.4%, respectively. Prevalence of GIN infection was higher (P ≤ 0.05) during the rainy months than in the dry months, but the prevalence of coccidiosis did not differ (P > 0.05) between the dry and rainy seasons. The EPG in goats did not differ (P > 0.05) between Kongwa (169.79 ± 0.03 EPG) and Mvomero (171.51 ± 0.04 EPG) districts, but the OPG differed significantly (P ≤ 0.05) with values of 793.15 ± 0.04 (Kongwa) and 364.02 ± 0.05 (Mvomero). The prevalence of CCPP in the goats was 26.4%. Other clinical diseases included respiratory diseases, infectious keratoconjunctivitis and orf (scabby lesions around mouth and nostrils). Both tests for haemoparasites and brucellosis indicated negative results for all goats tested. Mortality rate during the study period was 15.5% and the major causes of deaths were respiratory diseases, bloat and food poisoning. In conclusion, gastrointestinal nematodes are prevalent in both districts, but the burdens are relatively low to justify mass treatment. The Norwegian goats are more susceptible to GIN infection and coccidiosis compared to Toggenburg goats. Key words: Coccidiosis, diseases, gastrointestinal nematodes, mortality, Norwegian goats, Toggenburg goats.
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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 | 0.000 |
| 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) | 0.001 | 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".