Artificial Intelligence Techniques in Enhancing Livestock Adaptation to Climate Change: A Systematic Literature Review

Abstract

Climate change is increasingly affecting livestock systems worldwide by exacerbating heat stress, water shortages, and disease prevalence, which are critical challenges that can jeopardize the productivity and sustainability of this sector, particularly in vulnerable areas. In this context, this research aims to conduct a systematic literature review of 60 reviewed articles to examine the contribution of artificial intelligence (AI) in enhancing the adaptation of the livestock sector to climate change. The literature review has synthesized the major opportunity domains, factors of adoption, barriers to adoption, areas of application, and theory base. The research has found that AI is mainly used in heat stress detection, disease monitoring, and productivity optimization. However, its adoption is significantly influenced by technological and economic feasibility. On the other hand, the major barriers to adopting AI are high implementation costs and lack of infrastructure. A critical gap in research is that most literature has not adopted a comprehensive theory base to explain the complex factors affecting AI adoption in the livestock sector. To this end, a new conceptual framework is introduced that incorporates AI attributes, climate stressors, adaptation mechanisms, production results, and adoption factors. This framework is envisioned to serve as a holistic window for forthcoming studies and practice, recognizing that successful AI implementation is not only about technological advancements but also about facilitating infrastructure, capacity development, and policy support. This paper makes a theoretical and practical contribution to the field by synthesizing information in an organized manner and informing future studies that can deliver a more comprehensive AI-driven solution.