An Integrative Data-Driven Precision Livestock Farming Model to Improve the Efficiency and Sustainability of Livestock Production Systems

Abstract

This study aims to explore how an integrative data-driven Precision Livestock Farming (PLF) model can improve the efficiency and sustainability of livestock production systems. The study addresses the growing need for livestock farms to integrate digital data, animal monitoring technologies, and managerial decision-making in response to productivity, welfare, and environmental challenges. A qualitative exploratory case study design was used to examine PLF implementation in livestock production units that had adopted or tested digital tools, such as animal health sensors, feed recording applications, barn monitoring systems, and production databases. Data were collected through semi-structured interviews, field observation, and documentation involving farmers, farm managers, veterinarians, livestock extension workers, technology operators, and digital system developers. The data were analyzed using thematic analysis to identify patterns of meaning related to data use, adoption barriers, and sustainability practices. The findings reveal three main patterns: data as a new basis for decision-making, socio-technical barriers in PLF adoption, and PLF as a pathway to efficiency and sustainability. The study shows that PLF becomes effective when digital data are combined with farmers’ experiential knowledge, supported by trust, user capacity, reliable infrastructure, and clear data governance. These findings contribute to PLF literature by framing digital livestock management as a socio-technical process rather than a purely technological intervention. The study implies that policy and practice should support inclusive, practical, and context-sensitive PLF adoption. Future research should test the proposed model across different livestock systems and farm scales.