The right fit: A decision tree to select a protocol for assessing the welfare of laying hens

Ensuring robust and context-appropriate welfare assessments in laying hen production systems requires the use of protocols that align with diverse operational conditions, user capabilities, and regulatory or market expectations. This study developed a structured decision-making framework in the form of a decision tree to guide stakeholders in identifying welfare assessment protocols most appropriate for their specific needs. The decision tree was constructed by synthesizing criteria and decision points identified from a comprehensive review of existing assessment protocols. A systematic literature search conducted across 24 languages identified 12 eligible protocols, which were subsequently analyzed based on the type of assessment (whether the protocol involved self-assessment by farmers or external assessment by independent parties), inclusion of animal handling, benchmarking capacity, and implementation feasibility. Results indicate that self-assessment protocols provide practical advantages in terms of accessibility and frequency of use, particularly when supported by mobile technologies and visual guidance. However, the effectiveness of self-assessment protocols may be constrained by variability in observer expertise and the absence of objective benchmarking, particularly in cases where adequate training and support are lacking. External assessment protocols, which frequently include animal handling procedures, enable more rigorous and standardized evaluations but may present challenges related to cost, logistical complexity, and producer acceptance. The proposed decision tree provides a transparent and systematic tool to direct users toward protocols that best match their operational realities and assessment goals. By offering tailored guidance rather than prescriptive choices, the framework enhances the consistency, usability, and real-world impact of welfare evaluations in the laying hen sector. Further refinement of existing protocols to improve flexibility and digital integration is recommended to support broader implementation across varying production systems. © 2025