Non-Invasive Monitoring of Stress and Welfare in Sustainable Ruminant Systems

Livestock production is increasingly oriented toward systems that integrate animal welfare, sustainability, and ethical management. However, welfare assessment in ruminants still relies largely on invasive, labor-intensive, or retrospective indicators that provide limited support for real-time management and decision-making.

This Research Topic focuses on the development and application of non-invasive approaches to monitor stress, emotional state, and adaptive responses in domestic ruminants. By integrating physiology, neuroscience, ethology, and Precision Livestock Farming (PLF), we aim to establish a coherent framework linking biological mechanisms, multimodal data, and decision-support tools for welfare-oriented management.

The Research Topic will bring together experimental and applied research that improves the reliability, interpretation, and implementation of non-invasive indicators, including biomarkers of allostatic load and machine learning-based systems, supporting a transition from reactive welfare assessment toward more preventive, predictive, and management-relevant strategies in sustainable ruminant systems.

This Research Topic will be of interest to researchers in physiology, neuroscience, animal welfare, and precision livestock systems.

We welcome original research articles, reviews, and methodological contributions addressing, but not limited to, the following areas:

Mechanisms and biological interpretation:

• Non-invasive physiological indicators of acute and chronic stress (e.g., heart rate, heart rate variability, thermal profiles).

• Biomarkers of allostatic load and long-term stress adaptation.

Neurobiological and behavioral markers:

• Infrared thermography in relation to autonomic and emotional regulation.

• Pupillometry and eye-based metrics of arousal, attention, and cognitive load.

• Behavioral lateralization and brain–body asymmetry in coping and adaptation.

Systems, analytics, and management:

• Integration of multimodal sensors in Precision Livestock Farming systems.

• Data analysis and machine learning approaches for welfare monitoring and decision support.

Translation and responsibility:

• Translational and ethical aspects of continuous, non-invasive welfare monitoring.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• Hypothesis and Theory
• Methods
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Study Protocol
• Systematic Review

Keywords: animal welfare, monitoring, sustainable ruminant systems, acute stress, chronic stress, stress adaptation, thermography, pupillometry, multimodal sensors, welfare monitoring

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.