Abstract Precision livestock technology lies at the intersection of engineering, data science, artificial intelligence (AI), and animal science. These emerging tools have the potential to improve livestock production efficiency, enhance animal welfare, and reduce labor needs. While each technology requires specific steps to collect, process, and interpret data, the greatest opportunity for advancing precision livestock systems, particularly on extensive rangelands, will come from integrating multiple technologies, data streams, AI model outputs, and biological models into unified decision frameworks. From a land-grant university perspective, it is critical to consider how AI can be incorporated into the three-part mission of research, teaching, and Extension to educate the next generation of animal and range scientists and support livestock production. Ongoing research at the South Dakota State University Cottonwood Field Station is aimed at evaluating precision livestock technologies for beef production on extensive grassland systems. A central focus of this effort has been developing robust, automated data pipelines that streamline data acquisition and processing to feed into biological models. These machine learning and animal nutrition models frameworks translate raw data into actionable management decisions. Examples of ongoing work include models to estimate net energy for maintenance activity (NEmₐct) using in-pasture weigh systems and GPS tracking; prediction of dry matter intake for grazing steers based on remotely sensed forage quality and body weight data; and a dynamic supplementation model that uses real-time weight data and precision feeding systems to adjust individual supplement allotments. Together, these models demonstrate how data science, AI, and animal nutrition can be integrated to provide management-relevant insights for beef producers operating in rangeland environments. While these technologies present promising opportunities, their adoption often comes with a steep learning curve, particularly in managing and interpreting the large volumes of data they produce. AI has taken the news by storm with its potential to transform everything from how we live and work to the way our food is produced. One prominent example is large language models (LLMs) such as ChatGPT. Within the teaching context, LLMs can add tremendous value in graduate education, enabling students to more quickly process and analyze precision livestock data through AI-assisted coding. For Extension, LLMs are already being used as advisory chatbots to synthesize and deliver information to farmers and ranchers, with some universities piloting chatbot plug-ins on Extension websites. Looking ahead to the next wave, agentic AI will enable autonomous systems that understand natural language and operate with minimal human oversight, providing new opportunities to improve real-time decision-making in livestock production. It is not inconceivable that within the next few years a ‘digital ranch hand’ will become available that analyzes complex data streams and transforms data into management recommendations with minimal human oversight.
Brennan et al. (2026) studied this question.