Genetic Evaluation for Functional Longevity in Nellore Cattle

ABSTRACT

Stayability (STAY) presents limitations in capturing genetic variability among animals with varying calving records up to 76 months of age. Furthermore, because STAY is measured late in life (typically at 6 years of age), it inherently prolongs generation intervals. To address this limitation, we evaluated an alternative approach using random regression models, which incorporate multiple phenotypes recorded throughout an animal's lifespan to generate predictive curves for breeding values. Using calving records from 179,001 Nellore cows, we compared two phenotypic expressions of longevity and aimed to identify the optimal random regression model for predicting breeding values. Ten specific ages were defined for functional longevity expressed as stayability (FLSTAY). At each age, a binary phenotype (1 or 0) was assigned based on whether the cow remained in the herd. Alternatively, for functional longevity associated with the number of calvings (FLNC), the cumulative number of calvings recorded up to each specific age was calculated. Models fitting Legendre orthogonal polynomials ranging from linear to cubic degrees were compared. The cubic polynomial proved most suitable for modelling FLSTAY, whereas the quadratic polynomial provided the best fit for FLNC. Predictive ability metrics of estimated breeding values for FLNC were superior to those for FLSTAY. Heritability estimates for FLNC (0.025–0.074) were consistently higher than those for FLSTAY (0.004–0.045). Similarly, genetic correlations across ages were generally stronger for FLNC, reaching 0.88 between 39 and 75 months (the traditional age for selection). These findings suggest the feasibility of applying random regression models to FLNC, allowing its use as an early selection strategy to improve functional longevity.