The aquaculture business has expanded over the years in Malaysia. It provides a source of income and self-employment for many small-time players besides providing the protein requirement and a contributor to food security. However, the sustenance of this important sector is at stake as many small-timers are not making sufficient profits, citing increasing costs and dumbing from neighbouring countries. Hence, the focus of the study was to investigate various factors that influence this sector and identify important factors to help make this sector a viable and profitable venture in Malaysia. The various factors for the investigation were identified from the literature as influencers, and through the use of machine learning techniques on logit regression, significant factors were identified in the local context. The responses for the factors were adduced from a questionnaire survey directed at 268 farm operators and owners of aquaculture. The data collected from this survey form the primary input for the analysis. The important factors identified as drivers of Profitability are the Provision of Extension Services, Climate Change, Innovative Technologies, Farming Practices, Institutional Influences, Environmental, Learning and Development, and Economics. However, Societal, Supply Chain, Risk Management Culture, and Feed are not significant drivers in the local context.
This study is limited in scope as it involves only the farm operators and owners and no other stakeholders and uses a single model in the machine learning process namely logit regression. However, findings are not diminished in the sense that the factors identified can be a source of input for policymakers in the local context for any future blueprint for this sector.
Chen, S. C., Huang, C. C., & Chiang, H. C. (2010). Factors affecting the participation of the aquaculture industry in university-industry co-operation. World Transactions on Engineering and Technologies Education, 8(4), 510-516.
Daudpota, A. M., Kalhoro, H., Abbas, G., Kalhoro, I. B., & Shah, S. S. A. (2016). Effect of Feeding Frequency on Growth Performance, Feed Utilization and Body Composition of Juvenile Nile Tilapia, Oreochromis niloticus (L.) Reared in Low Salinity Water. Pak J Zool (48), 171-177.
Dube, S., & Mabika, N. (2022). Factors affecting the adoption of modern fish farming technologies in Gokwe South District, Zimbabwe. International Journal of Scientific Reports. (9), 11-16.
El?Gayar, O. F. (1997). The use of information technologies in aquaculture management. Aquaculture Economics & Management, 1(1-2), 109-128.
Galappaththi, E. K., Galappaththi, C. J., Aubrac, S. T., Ichien, A. A., & Hyman, J. D. (2020). Climate change adaptation in aquaculture. Reviews in Aquaculture (12), 2160-2176.
Jegede, T., & Olorunfemi, O. T. (2013). Effects of Feeding Frequency on Growth and Nutrient Utilization of (O. niloticus) fingerlings. Global Journal of Science Frontier Research Agriculture and Veterinary (13).
Kumar, G., Engle, C., & Tucker, C. (2018). Factors Driving Aquaculture Technologies Adoption. Journal of World Aquaculture Society, 49(3), 447-476.
Ludson, G. M., Marcos, A. S., Érika, R. A., Gabriel, F. O., & Nadille, H. F. (2020). Effects of different stocking densities on Nile tilapia performance and profitability of a bio-floc system with a minimum water exchange. Aquaculture, (530), Article 735814.
Miyata, S., & Manatunge, J. (2004). Knowledge Sharing and Other Decision Factors Influencing Adoption of Aquaculture in Indonesia. International Journal of Water Resources Development, 20(4), 523-536.
Department of Fisheries Malaysia (2024). Official Portal, Department of Fisheries Malaysia.
Olivier, M. J., Laurens, K., Malcolm, D., & Marc, V. (2017). How is innovation in aquaculture conceptualized and managed? A systematic literature review and reflection framework to inform analysis and action. Aquaculture (470), 129–148.
RAS aquaculture. (2023). Aquaculture Industry In Malaysia: Challenges And Issues. Interview.
Rowan, N. J. (2023). The role of digital technologies in supporting and improving fishery and aquaculture across the supply chain – Quo Vadis? Aquaculture and Fisheries, 8(3), 365-374.
Wang, P., & Mendes, I. (2022). Assessment of Changes in Environmental Factors Affecting Aquaculture Production and Fisherfolk Incomes in China between 2010 and 2020. Fishes, 7(4), article 192. https://doi.org/10.3390/fishes7040192
Zhou, Z., Cui, Y., Xie, S., Zhu, W. & Lei, W. (2003). Effect of feeding frequency on growth, feed utilization, and size variation of juvenile gibel carp (C. auratus gibelio). J Appl Ichthyol 19, 244-249.
Nair, S., Gopal, S., & Mavani, B. (2024). Using Machine Learning to Identify Factors of Importance in Aquaculture Management in Malaysia. International Journal of Academic Research in Accounting, Finance and Management Sciences, 13(4), 407–416.
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