Buffalo Healthcare Management Practices in India
Bibliographic record
Abstract
Background and Aim: Dairy farming is one of the most important sub-sectors of the Indian farming system. Healthcare management practices play a crucial role in realising the full potential of dairying. Hence, the present study aims to analyse the adaptation of healthcare management practices by the buffalo farmers in the Punjab state of India. Materials and Methods: A sample size of 397buffalo farmers from three different agro-climatic zones —i.e., Shivalik Foothills, South-West Dry, and Central Plains—was selected using a multistage sampling technique for the year 2019. Descriptive statistics and the Chi-Square test are used for analysis. Results: Most buffalo farmers adopt general healthcare management practices such as vaccinating their buffaloes against Foot and Mouth Disease and use of anti-parasites for tick eradication, but they are not disinfecting the dairy shed at all. The farmers follow calf and udder healthcare management practices, such as providing bedding material to newborn calves, deworming calves, and udder cleaning. The chi-square test indicates a significant difference across categories regarding the adaptation or non-adoption of certain healthcare practices, such as the source of vaccination, tick solution, bedding material for calves, and deworming of calves. Conclusion: Buffalo health is not only a veterinary concern but also a socio-economic imperative. While certain healthcare management practices are universally embedded among the farmers, others are constrained by access, awareness, and resource availability, thereby introducing important equity considerations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".