A Predictive Analytics Framework for Proposed Decision Support System and Market Validation for Aquaculture Disease Management in Delta State, Nigeria

Abstract Aquaculture is a critical contributor to food security and livelihoods in Nigeria, yet the sector faces significant production losses due to unmonitored water quality parameters and consequent fish mortality. This study investigates stakeholder readiness for a predictive analytics-driven early warning system for disease detection in catfish farms across Delta State, Nigeria, and proposes a comprehensive framework for its implementation. A cross-sectional survey was administered to 137 stakeholders across six segments: End Users (small-scale farmers, n = 42), Buyers/Payers (farm owners/investors, n = 38), Distributors (cooperatives and ag-tech vendors, n = 26), Suppliers (feed and sensor vendors, n = 13), Regulators (government agriculture officials, n = 11), and Community Leaders/Influencers (n = 7). The weighted average criticality score across all segments was 8.76/10, indicating widespread recognition of fish mortality as a systemic crisis. Total documented financial losses exceeded ₦36 million, with average monthly "Crisis Spend" of approximately ₦55,610 per respondent. Pilot commitment rates averaged 83.1%, with Buyers demonstrating 94.7% commitment to on-farm trials. Based on these findings, this paper proposes a novel Predictive Analytics Framework for Aquaculture Disease Management (PAFADM) that integrates market validation data, water quality parameters, and economic indicators into a decision support system. The framework employs logistic regression for disease risk prediction, threshold-based alerting mechanisms, and an economic dashboard for cost-benefit visualization. If fully developed, the proposed framework will provide fish farmers, SMEs, and agricultural extension officers with timely, actionable intelligence to reduce fish mortality rates by an estimated 40–60% within Delta metropolis. The framework represents an ongoing research initiative transitioning from market validation to technical implementation.