RELEVANCE OF DATA ANALYTICS IN SUSTAINABLE FISHERIES MANAGEMENT: AN EVIDENCE-BASED STUDY

Author:

Lukambagire IsaacORCID, ,Matovu Baker,Rao R. Bhavani,Meltem Yildiz Alkoyak,S.N. Remya,Sarfo IsaacORCID, , , , ,

Abstract

Aim of the study: The value of fish resources to socioeconomic development is immense. However, there is growing evidence that loss of quality in the marine environments e.g. marine waters at sea and coastal freshwater zones is threatening the availability of livelihood resources e.g. fish resources evidenced by the dwindling fisheries resources that promote sustainable livelihood. Cognizant of this, several fisheries institutions, governments, and local users have come up with several strategies to boost marine water environment management and promote sustainable fishing. Unfortunately, most of the initiatives have not fully managed to repulse unsustainable practices e.g. marine water pollution that leads to loss of wild catches and leads to unsustainable fishing practices. Material and methods: Owing to this we use a mixed research methods approach involving a systematic literature review and field survey in Alappad to argue and present a case for governments and fishery management organizations to invest in data analytics as a new pathway that can give a comprehensive, near real-time view of both ocean resources and coastal fishing activities in light of the declining fish stocks and escalating environmental problems e.g. marine water quality loss e.g. due to pollution. Results and conclusions: Findings documented that by using data analytics, governments, and fisheries management organizations/individuals may respond more swiftly to external forces like climate change, and marine water quality loss and implement new policies and regulations thus promoting sustainable fisheries management. Local perspectives of coastal communities could also guide the best data analytics tool or method to be used.

Publisher

The University of Agriculture in Krakow

Subject

Nature and Landscape Conservation,Water Science and Technology,Ecological Modeling,Environmental Engineering

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