| Abstrak/Abstract |
Ensuring robust and context-appropriate welfare assessments in laying hen production systems requires the useof protocols that align with diverse operational conditions, user capabilities, and regulatory or market expec-tations. This study developed a structured decision-making framework in the form of a decision tree to guidestakeholders in identifying welfare assessment protocols most appropriate for their specific needs. The decisiontree was constructed by synthesizing criteria and decision points identified from a comprehensive review ofexisting assessment protocols. A systematic literature search conducted across 24 languages identified 12 eligibleprotocols, which were subsequently analyzed based on the type of assessment (whether the protocol involvedself-assessment by farmers or external assessment by independent parties), inclusion of animal handling,benchmarking capacity, and implementation feasibility. Results indicate that self-assessment protocols providepractical advantages in terms of accessibility and frequency of use, particularly when supported by mobiletechnologies and visual guidance. However, the effectiveness of self-assessment protocols may be constrained byvariability in observer expertise and the absence of objective benchmarking, particularly in cases where adequatetraining and support are lacking. External assessment protocols, which frequently include animal handlingprocedures, 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 sys-tematic tool to direct users toward protocols that best match their operational realities and assessment goals. Byoffering 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 toimprove flexibility and digital integration is recommended to support broader implementation across varyingproduction systems |
