Characterising Smallholder Cage fish farmers in the Lake Victoria area of Uganda. A Cluster Analysis Approach
A cluster Analysis Approach
Keywords:Knowledge, Attitude, Perception, Cage fish farming technologies, smallholder farmers, cluster analysis.
A better stewardship of natural water bodies and fishery resources is a crucial factor in realisation of the UN sustainable development goals. Cage fish faming is one of the modern pathways currently designed to enhance fish production to bridge the gap of the declining wild capture. Therefore, it is for policy makers, development partners and researchers to understand what motivates smallholder cage fish farmers. This study aimed at characterising smallholder farmers practicing cage fish farming in the waters of Lake Victoria in Uganda. Descriptive statistics showed that out of 384 smallholder cage fish farmers interviewed, only 29% were females while the rest were males. The difference in gender variable was statistically significant at 5 percent level of significance. The implication is like any commercial agriculture activity, males dominate because they stand an advantage of accessing financial resources they use to invest than they counterparts. Further, a multivariate cluster analysis suggested that cage fish farmers are in fact, diverse in nature. Based on clustering approach, applied on knowledge, attitudes and perceptions of the respondents, three distinct groups were generated using cward linkage technique. The first group of pro-technology cluster comprised of 159 respondents. The second group of pro-environment cluster comprised of 122 respondents and the third cluster of 103 respondents. Characterisation results indicate that all the cage fish farmers interviewed had attained a basic level of formal education. In addition, results also show that experience in fish farming, size of farmers’ association, stocking density, average output of fish in kilograms, location of cages away from the lake shore line and the targeted markets turned out be significant across the three formed clusters.
Albert G. J. Tacon (2020) Trends in Global Aquaculture and Aquafeed Production: 2000–2017, Reviews in Fisheries Science & Aquaculture, 28:1, 43-56, DOI: 10.1080/23308249.2019.1649634
Bacher, K., (2015). Perceptions and Misconceptions of Aquaculture: a Global overview. Globefish Research Programme, Vol. 120, Rome, 35 pp.
Davidson, I. (2002). Understanding K-means non-hierarchical clustering. SUNY Albany Technical Report, 2, 2-14.
Ding, C., & He, X. (2004). K-means Clustering Via Principal Component Analysis. Computational Research Division. Berkeley, CA 94720: Lawrence Berkeley National Laboratory.
FAO. (2006). The State of World Fisheries and Aquaculture 2006. Rome. 162 pp.
FAO. (2016). The State of World Fisheries and Aquaculture (SOFIA): Contributing to food security and nutrition for all, Rome: Food and Agriculture Organization, pp. 200.
FAO. (2018).The State of World Fisheries and Aquaculture. Meeting the sustainable development goals. Rome. License: CC BY-NC-SA 3.0 IGO.
FAO. (2020). The State of World Fisheries and Aquaculture 2020. Sustainability in action. Rome. https://doi.org/10.4060/ca9229en
How to Cite
Copyright (c) 2022 Egerton University International Conference
This work is licensed under a Creative Commons Attribution 4.0 International License.