Profitability Analysis of Small Scale Fishery Enterprise in Nigeria
Bibliographic record
Abstract
Segmented markets of sub-optimal size existing in fishery value chain do not ensure sizeable private investment in the different stages of the value chain in Nigeria. Supply-demand gaps are increasingly being filled by imports, thus dampening prospects for increased revenue generation by actors in the chain. Market failures in the fish value chain limited capability and performance of small scale fish enterprises at the various stages of the chain. As such this study is prompted by the need to determine and compare profitability of actors along the fish value chain with the use of survey data collected from fishery farmers, marketers and processors. Budgetary framework was used to estimate cost and returns to actors while regression framework was used to estimate determinants of profit at the small scale farm level. The results showed that lowest level of profitability was associated with the producers of fish at farm level. Across all stages, profitability was affected by changes in the cost of labour more than any other costs. In addition, the results showed that profit level declined by 0.04%, 0.51%, 0.01%, and 0.13%, respectively, for every one percent increase in the cost of labour, fertilizer and liming, feed and pond construction at the small scale farm level. Findings suggest that emphasis of new agricultural promotion policy should be on strengthening linkage and access of small scale operators in fishery subsector to adequate inputs, information, and innovation at reduced costs so as to drive increased investments and profitability in fishery value chain.
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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 | 0.000 | 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".