Journal of Pioneering Artificial Intelligence Research

Abstract

Technology adoption in livestock agriculture remains uneven globally, with smallholder and mid-scale cattle producers facing significant barriers to accessing digital tools and data-driven decision support. Limited research compares how these barriers and opportunities differ across developed and developing-country contexts, particularly in the cattle sector. This capstone investigates cattle producer challenges and technology adoption patterns in the United States and Zimbabwe to identify shared pain points and context-specific entry points for scalable, AI-enabled solutions.

Using a mixed-methods design, the study combines producer surveys with secondary research. Primary data are collected from U.S. producers via online survey and from Zimbabwean producers through extension officer-led fieldwork, implemented in partnership with the Agricultural Research Council of Zimbabwe. Secondary data synthesize industry reports, peer-reviewed literature, and government statistics on market structure, animal health, extension systems, and digital connectivity.

Analysis reveals persistent pain points in herd health management, market access, and labor constraints, alongside distinct adoption barriers such as cost, connectivity, digital literacy, and trust in AI-based tools. Findings include a comparative analysis of these patterns and practical recommendations for agritech developers, extension services, and policymakers seeking to design context-fit digital and AI solutions that improve decision-making, productivity, and resilience among cattle producers in diverse production systems.