SOCIO-ECONOMIC IMPACT AND PROFITABILITY ANALYSIS OF FISH FARMING IN LAGOS STATE, NIGERIA

The study evaluates the socio-economic impact and profitability analysis of fish farming in Lagos State, Nigeria. A total of 120 fish farmers were surveyed using a structured questionnaire, with data analyzed through descriptive statistics. The results reveal that 91.7% of respondents identified income generation as the primary benefit of fish farming, followed closely by employment creation (90%) and improved protein intake (87.5%). Fish farming contributes significantly to the socio-economic status of 66.7% of respondents, with a mean socio-economic score of 22.45 (SD = 7.94). However, 33.3% reported a low contribution, highlighting disparities linked to farm size and access to resources. Profitability analysis shows an average return of ₦2.27 for every ₦1 invested, with total revenue per production cycle amounting to ₦5,040,000 and a net farm income of ₦3,497,300. Feed costs constitute the largest portion of expenses, accounting for 78.41% of total variable costs. Despite the profitability, challenges such as high feed costs (Findings) and limited access to finance persist (findings), impacting the sector’s growth potential. The findings suggest that fish farming plays a crucial role in economic empowerment and food security in coastal Lagos but requires policy interventions to address key challenges.

Authors

Publication Details

Journal
Nigerian Journal of Agriculture and Agricultural Technology
Published
2024-12-31
DOI
https://doi.org/10.59331/njaat.v4i4b.912
Citations
1
Primary Topic
Natural Resources and Economic Development
Type
article
Field-Weighted Citation Impact
0.83
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SOCIO-ECONOMIC IMPACT AND PROFITABILITY ANALYSIS OF FISH FARMING IN LAGOS STATE, NIGERIA

A. S. Oyegunwa, Musa A. Sanni, A. M. Mebude
1 citations
Nigerian Journal of Agriculture and Agricultural Technology
Natural Resources and Economic Development
0.83
article

SOCIO-ECONOMIC IMPACT AND PROFITABILITY ANALYSIS OF FISH FARMING IN LAGOS STATE, NIGERIA

A. S. Oyegunwa, Musa A. Sanni, A. M. Mebude
article en
1 citations

Abstract

The study evaluates the socio-economic impact and profitability analysis of fish farming in Lagos State, Nigeria. A total of 120 fish farmers were surveyed using a structured questionnaire, with data analyzed through descriptive statistics. The results reveal that 91.7% of respondents identified income generation as the primary benefit of fish farming, followed closely by employment creation (90%) and improved protein intake (87.5%). Fish farming contributes significantly to the socio-economic status of 66.7% of respondents, with a mean socio-economic score of 22.45 (SD = 7.94). However, 33.3% reported a low contribution, highlighting disparities linked to farm size and access to resources. Profitability analysis shows an average return of ₦2.27 for every ₦1 invested, with total revenue per production cycle amounting to ₦5,040,000 and a net farm income of ₦3,497,300. Feed costs constitute the largest portion of expenses, accounting for 78.41% of total variable costs. Despite the profitability, challenges such as high feed costs (Findings) and limited access to finance persist (findings), impacting the sector’s growth potential. The findings suggest that fish farming plays a crucial role in economic empowerment and food security in coastal Lagos but requires policy interventions to address key challenges.

Nigerian Journal of Agriculture and Agricultural TechnologyVol. 4(4B)
Openalex Percentile: Top 18%
Natural Resources and Economic Development
0.83
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.