Buffalo Milk Yield, Quality, and Marketing in Different Agro-Climatic Districts of Bangladesh
Bibliographic record
Abstract
The study was aimed at assessing the productive performance of dairy buffalo and milk marketing approaches in different agro-climatic districts of Bangladesh. Three (03) districts of Bangladesh viz. Bhola, Mymensingh, and Dinajpur were chosen from the coastal, river basin and semi-arid region, respectively. A triangulation method of survey was used to collect the data and the components of the triangle were buffalo farms, buffalo farmers and buffalo herdsmen. The investigation duration was twelve (12) months. The study revealed that the highest milk yield (5 L/h/d; p=0.010) was found in the river basin and semi-arid region. Lactation yield was also recorded double in the river basin and semi-arid districts compared to coastal districts (p=0.000). In the case of lactation length, the river basin buffaloes possessed 33 and 36% longer than coastal and semi-arid districts, respectively. All the chemical components were found significantly different (p≤0.050) but fat. Among different agro-climatic districts, about 92% of milk was traded in the coastal region after meeting the household's need but it was noted that the farmers from the semi-arid region kept more than 21% of milk for family consumption (p=0.000). The highest unit price (BDT 72/L) of milk was observed in the river basin district (p=0.011). In conclusion, the current situations of buffalo farming and milk marketing approaches in Bangladesh, varies considerably.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".