Genetic analysis of novel resilience indicators for heat stress-induced milk loss in Indian Sahiwal cattle.

Climate change significantly threatens livestock production, affecting animal productivity, feed availability, and disease dynamics. In India’s hot arid zones, which cover approximately 12% of the country’s land area, livestock are exposed to erratic rainfall and extreme thermal stress, necessitating improved adaptive capabilities. Genetic improvement for climate resilience has gained momentum, with a focus on identifying SNPs and phenotypic traits linked to heat tolerance. In this context, the present study aimed to develop and analyse climate resilience indicator traits using 132,547 daily milk yield records from 500 Sahiwal dairy cattle. The Temperature-Humidity Index (THI) was calculated to quantify thermal discomfort, and unperturbed lactation curves were fitted using four different nonlinear lactation curve models viz., Wilmink’s model, Exponential decline function, Parabolic exponential function and Quadratic function to estimate milk losses due to heat stress. Nine resilience indicator traits were derived: (i) three direct deviation indicators—number of days in heat stress (NDHS), total milk loss (TML), and average milk loss per day (AML); (ii) four variance-based indicators—log-transformed variance (Var), natural log-transformed variance (LnVar), lag-1 autocorrelation (r-auto), and skewness of yield deviations (Skew); and (iii) two fixed regression indicators—intercept and slope of milk yield deviations with respect to THI. Seasonal and stage-wise analyses revealed that milk loss was highest during the monsoon, followed by the pre-monsoon season, and was most pronounced in early lactation. On average, first-lactation Sahiwal cows experienced 54.07 days of heat stress in a lactation, resulting in a total milk loss of 70.12 kg. Heritability estimates for the resilience traits ranged from 0.07 to 0.22, indicating low to moderate genetic control. The genetic correlation was positive and high between NDHS and TML and Var and LnVar whereas Skew had a strong negative correlation with TML, AML, Var and LnVar. The average estimates of r-auto, Skew, Var, and LnVar indicated poor resilience of Sahiwal cows to heat stress. Thus, these resilience indicators can serve as valuable tools for identifying heat-tolerant animals and form the basis for genetic selection strategies to improve climate resilience in dairy cattle.