Precision Farming for a Resilient and Sustainable Livestock Production

Precision Livestock Farming (PLF) is reshaping animal agriculture through the integration of sensors, artificial intelligence, automation, robotics, and data-driven decision-support. Livestock systems face growing pressures from climate change, resource scarcity, emerging diseases, environmental impacts, and societal expectations for greater transparency and animal welfare. Although PLF technologies can support earlier disease detection, more precise management, improved productivity, and better welfare outcomes, their adoption remains constrained by challenges involving technology integration, data governance, interoperability, validation, affordability, and farm-level usability. Recent advances in computer vision, wearable and environmental sensors, machine learning, automated monitoring, and digital traceability have demonstrated increasing potential for real-time assessment of animal health, behaviour, welfare, productivity, and emissions. However, important questions remain regarding the reliability and explainability of these systems, their performance across species and production environments, and their ability to generate actionable outcomes under practical farming conditions. Aligned with the ECPLF 2026 Conference in Valencia, this Research Topic addresses the need to connect technological innovation with robust scientific evidence, responsible implementation, and the development of adaptive, transparent, and environmentally sustainable livestock systems.This Research Topic aims to advance Precision Livestock Farming by bringing together research on technologies and approaches that improve animal health, welfare, productivity, resource efficiency, and environmental performance. It will explore how next-generation sensing, artificial intelligence, automation, robotics, and digital decision-support can be validated and implemented in real-world farming systems. Contributions may address questions concerning the accuracy, robustness, interpretability, scalability, and economic feasibility of PLF technologies, as well as their implications for animal-centred management, ethical governance, and farmer decision-making. The Research Topic also seeks to identify strategies for overcoming barriers to adoption, including fragmented data infrastructures, limited interoperability, inadequate validation standards, and insufficient consideration of user needs. By integrating technological, biological, environmental, economic, and societal perspectives, it aims to highlight actionable pathways for deploying PLF solutions that are effective, responsible, and adaptable across species and production systems.To gather further insights into PLF technologies and their application in adaptive, welfare-centred, and environmentally responsible livestock systems, this Research Topic welcomes contributions addressing, but not limited to, the following themes:• Real-time monitoring of animal health, behaviour, welfare, productivity, and environmental conditions.• Novel sensors, wearable technologies, Internet of Things platforms, and edge or cloud-based systems.• Artificial intelligence and machine learning for behaviour recognition, welfare assessment, disease prediction, and decision-support.• Computer vision, acoustics, biosensing, and multimodal data analysis.• Automation and robotics for animal management, precision feeding, milking, health interventions, and farm operations.• Strategies for reducing greenhouse-gas emissions, nutrient losses, water use, and other environmental impacts.• Data governance, privacy, traceability, cybersecurity, interoperability, and digital infrastructure.• Cross-species, cross-system, and regionally adaptable PLF applications.• Validation, benchmarking, explainability, and reliability of PLF technologies in commercial farming conditions.• Economic feasibility, farmer acceptance, skills, and practical pathways for on-farm adoption.• Ethical, societal, regulatory, and animal-centred considerations in the use of digital technologies.• Integration of PLF data into resilient, transparent, and sustainable livestock production systems.Appendix: The Research Topic may include original research articles presenting new technologies or field results, methods papers describing innovative tools or data-processing approaches, and case studies demonstrating on-farm implementation. Topic editors: Xabier Díaz De Otálora Aguirre, Daniela Lovarelli, Javier Plaza, Tomas Norton Source: https://www.frontiersin.org/research-topics/85071/precision-farming-for-a-resilient-and-sustainable-livestock-production