Cost-Benefit Analysis of Buffalo Milk Production in India
Bibliographic record
Abstract
Context: The vast resource of Indian livestock played an important role in improving the socio-economic conditions of people in rural areas. Dairying has provided strong support to stabilise the Indian economy by ensuring a certain degree of diversification and flexibility. Aim: The present study aims to analyse the costs and returns from buffalo milk production in the Punjab state of India to know about the viability of the dairy business. Methods: The present study is based on primary data collected through a detailed schedule from 420 dairy farmers belonging to different farm size categories (landless households, large, medium, small, and marginal farmers) from 21 villages situated across three different agro-climatic zones of Punjab state in 2019. A multi-stage sampling technique has been used to select the villages and dairy farmers in the study area. Key Results: The study has revealed that the total costs of buffalo milk production are ₹180.16 per day per milch buffalo. The sale of fluid milk constitutes a major component of gross returns. The net returns are calculated as ₹6.42 per litre from buffalo milk production in rural Punjab. Implications: Economic analysis of dairy farming is very important to know about the economic viability of dairy enterprises. The profitability from dairying depends upon the milk yield of dairy animals, the sale price of milk, and the cost involved in dairying. Adequate knowledge of the cost involved in dairying is important as it can be used for policy-making and also for providing incentives to dairy farmers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".