Precision phenotyping of reproductive events in dairy cattle under heat stress: epidemiological validity and...

Heat stress in tropical and subtropical dairy systems invariably affects fertility because it reduces the rate of conception and increases the calving period. Although traditional visual techniques of reproductive event detection are insensitive and subject to errors, precision livestock farming (PLF) technologies such as accelerometers, rumination sensors, infrared thermography, and in-line progesterone assays can provide automated, continuous reproductive event detection. However, heat stress suppresses key behavioral signs such as activity, reducing the sensitivity of many automated systems and introducing heat-dependent misclassification. A critical gap remains in systematically linking sensor performance across temperature-humidity index (THI) strata to the design of genetic selection programs for heat resilience. This review, therefore, (1) evaluates validation evidence for sensor-based phenotyping of estrus, pregnancy, and calving; (2) assesses how rising THI degrades diagnostic accuracy through signal suppression; and (3) proposes a framework for standardizing sensor-derived phenotypes into genetically robust fertility and resilience traits. We emphasize that without explicit modeling of heat exposure and repeated phenotyping, environmental variance increases, heritability declines, and the reliability of estimated breeding values is consequently undermined. The integration of multi-sensor data with THI-indexed phenotypes enables more stable genetic selection for improved fertility in heat-stressed dairy herds.