Unravelling grazing dairy cows’ response to weather through respiration rate and drooling

solar radiation; wind speed had a mitigating effect. Despite both behaviors being linked to heat stress, RR explained just 30% of drooling variance. Drooling probability reached 50% at a GHLI (2025) of 82; however, < 20% drooling variance was explained, likely due to between-farm variability. These limitations aside, results illustrate the nonlinear effects of weather variables on heat load responses of grazing dairy cows; given large between-farm variation, individual weather variables may be more accurate predictors than linear indices. However, farmers require clear guidelines for decision making, and indices are straightforward; thus, continued research should refine nonlinear predictive models that could be used in conjunction with on-cow devices. Models should be validated across different climatic regions, farming systems, breeds, and individual animal characteristics, as doing so will support sustainability and resilience in dairy farming in the face of climate change.