Integrating Physiological Biomarkers into Precision Livestock Farming for Laying Hens: Methodological Challenges and Future Research Directions.

Summary

This narrative review examines how physiological biomarkers can be integrated into precision livestock farming (PLF) systems for laying hens, moving beyond sensor validation against subjective human labels. The authors identify critical methodological gaps: only seven studies directly compared sensors to physiological reference states, none developed and externally validated composite biomarker labels, and significant mismatches exist between sensor timescales and biomarker sampling intervals. The review proposes that future PLF research must incorporate individual animal identification, biomarker-specific temporal alignment, multimodal composite labelling, and independent farm-level validation to translate sensor data into biologically grounded management decisions.

Regional applicability

The methodological framework and recommendations are applicable to United Kingdom poultry farming operations using or considering precision livestock farming technologies. Given the UK's substantial laying hen sector and increasing adoption of automated monitoring systems, these findings directly address current validation challenges in domestic farm contexts.

Key measures

Blood biochemistry, corticosterone concentration, heterophil-to-lymphocyte ratio, body temperature, immune and disease indicators; sensor data types (video, thermal, acoustic); temporal alignment between sensor and biomarker measurements; individual versus flock-level units of analysis.

Outcomes reported

This narrative review examined methodological requirements for integrating physiological indicators (blood biochemistry, corticosterone, heterophil-to-lymphocyte ratio, body temperature, immune indicators) as reference labels in precision livestock farming models for laying hens. It identified only seven direct sensor-to-reference comparison studies and highlights critical gaps in composite biomarker validation and sensor-biomarker alignment.

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