SURVEY OF CHARACTERISTICS AND CHALLENGES OF LOCAL MILK PRODUCERS IN DAURA LOCAL GOVERNMENT AREA OF KATSINA STATE
Bibliographic record
Abstract
Milk is essential for human consumption in view of its nutritive value. The study was therefore carried out to characterize milk production based on some socio-economic point of view of the producers and challenges in Daura Local Government area (LGA) of Katsina state. Using a two-stage sampling procedure, ten (10) communities were purposively selected in the first stage based on the high number of milk producers in the LGA while in the second stage, 6 participants were randomly selected from each of the ten (10) villages which totalled 60. The data gathered was analysed using descriptive statistics. The study showed that the respondents’ age group of 31-45 was highest with 60% while the distribution of age and formal education revealed that even the young members of the milk producers and other stakeholders were not well educated with 31-45 age range having the highest percentage of 11% in the primary education level. The female involvement in the milk business was more than that of men (80%). Red Bororo cattle breed were predominantly used (73%) and all the respondents utilized the traditional milking method and fermenting for milk processing and preservation. Poor storage facilities was reported to be the greatest challenge of the respondents while foot and mouth disease was more prevalent (58.3%). A quarter of the respondents received interventions to support the business. It was concluded that milk production, processing and marketing is still well undeveloped despite the inherent potentials. It was then recommended that government and non-governmental organisations should intervene in the area of training and capacity building to develop the milk production industry in the study area.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".