A Hierarchical Interpretative Framework for Broiler Welfare Indicators in Intensive Production Systems

The assessment of broiler welfare in intensive production systems is based on a variety of behavioral, health, physiological, and production indicators. Despite the growing amount of available data, its interpretation remains a significant challenge, as individual indicators vary in sensitivity, biological specificity, and dynamics over time, and their significance depends on the environmental context and management practices. This issue becomes particularly relevant in light of the rapid development of precision livestock farming (PLF) technologies, which generate ever-increasing amounts of data requiring biologically sound interpretation. The aim of this study was to propose and justify a hierarchical interpretative framework to support the assessment of broiler welfare by organizing indicators according to their primary interpretative function and temporal characteristics. The proposed framework was developed based on a structured, integrative, and conceptual literature synthesis covering current approaches to welfare assessment, animal-based indicators, environmental and management factors, and PLF technologies. Literature synthesis allowed us to identify three fundamental limitations of current welfare assessment systems: a fragmented interpretation of indicators, the limited biological specificity of many parameters, and insufficient consideration of their temporal dynamics. In response to these limitations, the early–confirmatory–outcome (E–C–O) framework was proposed, in which indicators are classified as early warning signals, confirmatory indicators, and outcome indicators. These categories do not imply a mandatory chronological sequence or a cause-and-effect relationship, but rather represent complementary interpretative functions of the indicators used in assessing welfare. This framework treats welfare assessment as an ordered process of diagnostic interpretation rather than a simple aggregation of heterogeneous indicators. The presented framework does not replace existing welfare assessment systems but complements them by introducing a biologically sound logic for data interpretation that takes into account the primary interpretative function of indicators, their temporal characteristics, and the production context. The framework is conceptual in nature and requires empirical validation but can serve as a basis for further empirical validation and the development of decision support systems utilizing PLF technologies and artificial intelligence.