GLOBAL JOURNAL OF FISHERIES SCIENCE
Integrity Research Journals
ISSN: 2782-750X
Model: Open Access/Peer Reviewed
DOI: 10.31248/GJFS
Start Year: 2018
Email: gjfs@integrityresjournals.org
A novel Body Mass Index (sfBMI) approach to fish health assessment in fisheries and aquaculture: Prototype framework and species-specific considerations
https://doi.org/10.31248/GJFS2025.091 | Article Number: 0F8C761B1 | Vol.7 (5) - December 2025
Received Date: 05 September 2025 | Accepted Date: 14 November 2025 | Published Date: 30 December 2025
Author: Mathew Olatunji Awotunde
Keywords: Body Mass Index, fisheries, aquaculture, environmental condition, fish condition, Fulton’s Condition Factor (K), Relative Condition Factor (Kn).
Assessing the health and condition of fish is essential in Fisheries and Aquaculture, as it reflects nutritional status, ecological well-being, and environmental quality. Traditional indices such as Fulton’s condition factor (K) and relative condition factor (Kn) have been widely used by fisheries scientists and environmentalists. Still, these often assume isometric growth and may be less insightful for stakeholders. This paper proposes a novel Body Mass Index (BMI) framework, adapted from the human BMI concept, which incorporates fish length–weight parameters. The method produces a standardised fish BMI (sfBMI) and a classification system for scientific interpretation. This approach bridges the gap between biological precision and stakeholder (farmers, extension agents, and policymakers)-friendly communication, enabling more effective decision-making in fish health management. As a prototype, the sfBMI framework establishes preliminary condition thresholds (<90%, 90–97%, 98–102%, >110%) that can be used to distinguish between under-conditioned, healthy, and over-conditioned or obese fish. Compared with Fulton’s K and Kn, the sfBMI shows potential for providing more consistent and simple classification across species and various environmental conditions. Looking ahead, this prototype index could be further validated and applied in both aquaculture and wild fisheries, with future integration into an image-based system to support real-time decision-making in fish management.
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