Comparative analysis of thermal indices for modeling cold and heat stress in US dairy systems.

Quantifying the effect of thermal stress on milk yields is essential to effectively manage present and future risks in dairy systems. Despite the existence of numerous heat indices designed to communicate stress thresholds, little information is available regarding the accuracy of different indices in estimating milk yield losses from both cold and heat stress at large spatiotemporal scales. To address this gap, we comparatively analyzed the performance of existing thermal indices in capturing US milk yield response to both cold and heat stress at the national scale. We selected 4 commonly used thermal indices: the temperature-humidity index (THI), black globe humidity index (BGHI), adjusted temperature-humidity index (THIadj), and comprehensive climate index (CCI). Using a statistical panel regression model with observational and reanalysis weather data from 1981 to 2020, we systematically compared the patterns of yield sensitivities and statistical performance of the 4 indices. We found that the US state-level milk yield variability was better explained by the THIadj and CCI, which combine the effects of temperature, humidity, wind, and solar radiation. Our analysis also reveals continuous and nonlinear responses of milk yields to a range of cold to heat stresses across all 4 indices. This implies that solely relying on fixed thresholds of these indices to model milk yield changes may be insufficient to capture cumulative thermal stress. Cold extremes reduced milk yields comparably to those affected by heat extremes on the national scale. Additionally, we found large spatial variability in milk yield sensitivities, implying further limitations to the use of fixed thresholds across locations. Moreover, we found decreased yield sensitivity to thermal stress in the most recent 2 decades, suggesting adaptive changes in management to reduce weather-related risks.