Economics of production and marketing of fish in Dang district of Nepal
Bibliographic record
Abstract
A study was carried out to analyze the economics of fish production and marketing in Dang district of Nepal. Structured survey was done with 75 respondents (45 producers, 5 wholesalers, 10 retailers, and 15 consumers) in three different sites of Dang district (viz: Lamahi Municipality, Rapti Rural Municipality and Gadhawa Rural Municipality). The initial investment, cost of production and returns, production function, price spread,and marketing margin, and ranking of production and marketing problems of fish were done by index ranking techniques. Financial analysis showed that the total initial investment per hectare of fishpond was found to be NRs. 9, 68,394 with the annual production cost of NRs. 6, 93,483. The total return was found to be NRs. 932,088 and net profit realized per hectare was NRs. 238,604. Out of total cost, about 73.70% was variable cost and the remaining 26.30% was fixed cost. Of the total variable cost, the cost of feed (58.63%) was significantly higher followed by the cost of fingerlings (12.94%), labor (11.37%), manure and fertilizer cost (8.33%) fuel/ energy (4.46%) and maintenance cost (4.27%) respectively. The benefit cost ratio (B/C ratio) was found to be 1.82 which implies that the fish enterprise was found to be profitable in the study area. The research also revealed that the producer’s share was 78.17% with price spread of 71.57 and marketing efficiency of 90.81%. The value sum of the estimated parameters associated with all the inputs is 0.52 which indicates the decreasing return to scale. Lack of technological know-how and unavailability of inputs on time are the major production problems. Inefficient price information system and competition with the Indian fish are the major marketing problems. Thus, for sustainable production and marketing of fish government should focus primarily on technological dissemination and better pricing policy.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".