Sleep and Drowsiness in Cattle: A Narrative Review of Physiology, Measurement, and Methodological Challenges

Sleep in cattle is a complex physiological state comprising non-rapid eye movement (NREM) and rapid eye movement (REM) sleep, while drowsing represents an intermediate vigilance state between wakefulness and sleep. Research has progressed from invasive electrophysiological recordings to noninvasive polysomnography (PSG), behavioral assessment, accelerometry, and automated classification approaches. However, accurately identifying and quantifying vigilance states under production conditions remains challenging. This narrative review, based on 22 primary studies identified through searches of Web of Science, Scopus and Google Scholar, critically synthesizes current knowledge of sleep physiology in cattle, the approaches used to assess it, and the principal methodological limitations affecting interpretation of the available evidence. Cattle exhibit a polyphasic sleep pattern, with NREM generally accounting for the largest proportion of total sleep. However, reported sleep and drowsing durations vary considerably among studies, and these differences may reflect both biological variation and methodological factors. Drowsing is particularly difficult to characterize because its behavioral and physiological features overlap with quiet wakefulness and light sleep. Rumination represents an additional challenge because associated muscular activity can interfere with electrophysiological recordings and complicate vigilance-state classification. Behavioral observations and accelerometry provide scalable measures of resting and sleep-like behavior but cannot independently establish physiological sleep, whereas PSG provides greater physiological resolution while remaining susceptible to artifacts and practical constraints. Current evidence is insufficient to establish causal relationships between sleep variation and cattle health, welfare, or productive performance. Future research should improve physiological classification during overlapping behavioral states, better validate scalable monitoring approaches, and evaluate the functional consequences of sleep variation across diverse cattle populations and production conditions.