Goats are important livestock species in most rural households and were amongst the first species to be domesticated. Despite this, their production is based on extensive systems, exposing them to numerous challenges affecting their productivity. This review examines the applications of precision livestock farming (PLF) and AI-driven technologies in goat management, focusing on their impacts on productivity, welfare, genetic potential, health monitoring, feeding efficiency and sustainability outcomes and identifying challenges for their adoption in smallholder and extensive systems. Unlike previous reviews that focus mainly on cattle raised under intensive systems, this review synthesizes their use in goat production and highlights technological, socio-economic and infrastructural constraints. A conventional literature review approach is used, with studies retrieved from major databases using relevant keywords. The selected studies are evaluated to assess technological applications, benefits and adoption challenges, followed by a SWOT analysis. Engineering aspects of precision livestock farming—including sensors, data connectivity, system integration, automation and scalability—are also discussed. Ideally, these technologies operate as integrated decision-support systems that jointly improve productivity, animal welfare and sustainability, rather than performing isolated tasks. However, many PLF solutions remain at low technology-readiness levels and are constrained by infrastructure gaps, sensor reliability and compatibility issues, which collectively limit adoption in smallholder systems. Future research should focus on the development of cost-effective, reliable PLF systems for smallholder producers, while policy and capacity-building initiatives are needed to enhance infrastructure, training and technology adoption for scalable implementation.
Kichamu et al. (2026) studied this question.