Digital Transformation of Livestock Production in Africa: A Critical Appraisal of AI Application with Special Emphasis on Nigeria

Abstract

Livestock production underpins food security, income generation and cultural identity across Africa, yet the sector remains constrained by disease burden, low productivity, weak infrastructure and limited access to modern management tools. Artificial intelligence (AI), encompassing machine learning, computer vision, and Internet of Things (IoT)-enabled sensing, has begun to reshape livestock husbandry in high-income countries and is increasingly being tested and adapted for African production systems, including Nigeria's mixed crop-livestock and pastoral economies. This review synthesises the peer-reviewed literature on AI applications across animal health diagnostics, disease surveillance, poultry monitoring, dairy and cattle management, genomic selection, feed optimisation and climate-resilience planning, with particular attention to Nigerian case studies and the wider sub-Saharan African context. The review finds that AI tools have demonstrated technical feasibility for disease detection, individual animal identification, oestrus monitoring, and methane emission prediction, and that mobile-phone-based advisory platforms have delivered measurable productivity gains in East African dairy systems. However, adoption in Nigeria and across much of the continent remains constrained by fragmented connectivity, high hardware costs, scarce training data representative of indigenous breeds and agro-ecologies, low digital literacy among smallholders, and underdeveloped data governance frameworks. The review argues that the translation of AI from proof-of-concept studies to routine farm practice depends on locally trained models, affordable low-bandwidth solutions, gender-responsive design, and coordinated policy support, rather than the direct transplantation of technologies developed for intensive, capital-rich systems elsewhere. The paper concludes with directions for future research and practice that could narrow the gap between the technical promise of AI and its realised benefit for African livestock keepers.