From sensors to solutions: harnessing big data for early intervention in calf health

Healthy calf rearing is central to dairy herd renewal but remains economically demanding due to high early-life morbidity and mortality. Although an increasing number of digital sensors generate vast data streams, their fragmentation limits real-time health insights. The InnoKalb project integrates feeding, health, and climate data into a unified framework for real-time calf health assessment. An automated R-based pipeline standardizes multi-farm datasets, applies z-score standardization for daily calf ranking, and uses a quantile-based leave-one-out benchmarking approach to compare herd performance. The framework is a prototype that converts complex data into actionable insights for early risk identification but is not yet validated due to limited health outcome data.