Supplemental Material to “Genetic analysis of resilience indicators derived from lactation curve deviations and their relationships with milk production, composition, and udder health in dairy sheep” by Arribas-Gonzalo et al.
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Supplemental Material to “Genetic analysis of resilience indicators derived from lactation curve deviations and their relationships with milk production, composition, and udder health in dairy sheep” by Arribas-Gonzalo et al. 1) Supplemental Material File S1: Description of lactation curve fitting models tested for first-lactation ewes. 2) Supplemental Tables S1 to S7.
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This study aimed to evaluate resilience indicators in dairy sheep, estimate their genetic parameters, and assess their genetic relationships with production and functional traits. Milk records from 1,604 first-lactation Assaf ewes belonging to 4 flocks were used to fit individual lactation curves. We compared 4 lactation curve models to identify the best fit: Wilmink’s model (wil), fourth-order Legendre polynomials (leg4th), quantile regression (rq), and natural cubic spline with 5 percentiles (spline5). 3 resilience indicators were derived from deviations between observed and expected milk yield: the natural logarithm of the variance (LnVar), the autocorrelation lag-1 coefficient (rauto) and the skewness of the deviations (Skew). These indicators reflect the magnitude of the deviations, the recovery and the direction of the deviations. For these indicators we explored the genetic correlations with other traits of interest, including average daily milk yield (ADMY), test-day traits, such as milk fat and protein percentages (FP, PP), fat to protein ratio (FTP) and milk urea content (MU), as well as the lactational lactational somatic cell score (LSCS). Variance components and genetic correlations were obtained using Bayesian animal models. Parameter estimates are reported as posterior means ± posterior standard deviations (PSD).