Factors Determining the Profitability of Catfish Production in Ibadan, Oyo State, Nigeria
Bibliographic record
Abstract
This study evaluated the socioeconomic factors influencing the profitability of catfish production in the city of Ibadan. Multistage sampling method was used to collect data from 120 fish farmers. Descriptive statistics, budgetary analysis and the multiple regression model were used to analyse the data obtained. The results showed that catfish production in Ibadan was male dominated as 80% of the fish farmers were men. The mean age of fish farmers was 44.3±12.0 years while as many as 78.3% of the farmers had post-secondary education. The mean family size was 5.2±1.9 while fish farmers were small operators with a mean farm size of 0.3±0.2 hectares. Fish farming is very recent as farmers had a mean farm experience of 6.9±6.5 years. Eighty per cent of the fish farmers got involved in fish farming for commercial reasons. The gross margin to catfish farming was N197,520.25 (US$ 987.60)/ha with a net income of N182, 573.04 (US$912.87)/ha. The budgetary analysis revealed that fish feed which constituted 79.18% of the total operating cost was the major cost item in catfish production. The regression analysis showed that fish farming experience, amount of labour used and quantity of feed used were significant determinants of net income in catfish production. The study concluded that there is the need to access fish farmers to substantially cheaper feed inputs to ensure the use of adequate quantity and quality of feed in catfish production. This will enhance output, productivity and net income in catfish enterprises.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".