Unraveling grazing dairy cows' response to weather through respiration rate and drooling

Heat stress in dairy cows poses a significant challenge, particularly on pasture, where cows are exposed to variable climates. The study objective was to use weather variables and established heat load indices to predict grazing dairy cow respiration rate (RR) and drooling. Data from 483 Friesian × Jersey crossbred cows across 7 New Zealand farms were collected over 2 consecutive 5-d periods per farm. Respiration rate and drooling were observed up to 5 times per cow daily across 3 time periods (0900-1100 h, 1200-1400 h, and 1700-1800 h) and matched to 15-min weather data. Although farmers were asked to minimize heat mitigation, the observational nature of the study meant that shade, sprinklers, or both were available periodically on 6 farms as part of their standard management. Weather variables (temperature, humidity, wind speed, solar radiation), temperature-humidity index (THI), and 2 grazing heat load indices (GHLI 2023; GHLI 2025) were used to model RR and drooling. Data subsets (1200-1500 h and 0900-1800 h) were analyzed separately, with the former explaining more RR variance (65.4 vs. 61.3%). We found that GHLI 2025, GHLI 2023, and THI explained 59.8%, 59.7%, and 58.7% of RR variance, respectively. Nonlinear relationships between RR and individual weather variables were detected, and temperature, solar radiation, and wind speed together explained ~9% more RR variation than indices. Respiration rate was stable to ~19°C, with a sharper increase above 23°C and 0.99 MJ/m² 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 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.