ARTIFICIAL INTELLIGENCE (AI) IN SUSTAINABLE AGRIBUSINESS IN NIGERIA: A NARRATIVE REVIEW OF INTEGRATED FRAMEWORKS

Abstract

This narrative review evaluates the applications of AI technologies across Nigerian agribusiness, identifies systemic barriers. and synthesises data across the three core dimensions of sustainability: Economic Viability, Environmental Protection, and Social Equity. Utilising thematic synthesis, this study analyses contemporary literature and localized case studies on Agri-AI interventions to establish an integrated socio-technical framework tailored to Nigeria's agricultural infrastructure. The synthesis demonstrates that AI interventions deliver substantial multi-dimensional benefits: AI-driven predictive analytics and computer vision increase crop yields by 12% to 45%, reduce input expenditures by up to 25%, boost net farm profitability by 13% to 21%, and provide alternative credit scoring for unbanked producers. Smart soil sensors and automated precision irrigation achieve water savings of 30% to 50% in open fields (and over 60% in closed-environment agriculture), reduce nitrogen fertiliser leaching by up to 25%, and lower energy consumption while enabling predictive climate adaptation. Multimodal voice/USSD advisories and smartphone image diagnostics democratise agronomic advice for low literacy farmers, expand credit access for female producers, and create technical employment for rural youth. To prevent technology from exacerbating rural inequality or displacing manual labor, the study proposes the Triple P Agric AI Framework. This conceptual model harmonizes Economic Profitability, Environmental Resilience, and Social Inclusion, and serves as a strategic roadmap for policy makers, AgTech developers, and university industry partnerships to drive equitable agricultural transformation in Nigeria.