Estimates of genetic parameters and cluster analysis of the lactation curve of dairy Gyr cattle

• Published in: Livestock science Vol. 244; p. 104337
• Main Authors: Pereira, M.A., Faro, L El, Savegnago, R.P., Costa, E.V., Filho, A.E. Vercesi, Faria, C.U.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-02-2021
• Subjects: Breeding value; Milk production; Multivariate analysis; Persistency; Breeding value; Multivariate analysis; Persistency; Milk production
• ISSN: 1871-1413; 1878-0490
• DOI: 10.1016/j.livsci.2020.104337

•Persistency is important for dairy Zebu because they have short lactations.•Random regression is a suitable tool to model longitudinal traits like milk yield.•Cluster analysis identified group of animals suitable to attend the breeding goals.
The aim of this study was to estimate genetic parameters for month test-day milk yields of dairy Gyr cattle and to explore the additive genetic pattern of the milk production curve using cluster analysis in order to choose subgroups of animals that can result in genetic gain for milk production. The dataset of dairy Gyr cows was provided by the Brazilian Association of Zebu Breeders (ABCZ) and contained 123,035 first-lactation test-day records from 16,318 cows and calvings between 1983 and 2014. Random regression under an animal model was used to predict the breeding values of animals for monthly milk yields from 30 to 305 days, as the sum of breeding values of each day in milk, for coefficients of the lactation curve (intercept, linear, quadratic, and cubic), for peak yield, persistency of lactation, and total milk yield. Two cluster analyses were used to describe the additive genetic structure of the population: the hierarchical (Ward method) and non-hierarchical (k-means method) cluster analyses using the predicted breeding values of the above traits. Cluster analysis were used to figure out the patterns of the additive genetic lactation curves within the population that matched up with pre-defined breeding goals (improve the milk production) to suggest potential selection candidates. The estimates of heritability for test day milk yields ranged from 0.14 ± 0.02 to 0.20 ± 0.02. The intercept of the random regression had strong genetic correlation with peak yield and total milk yield while the linear random regression coefficient had strong genetic correlation with the persistency, as defined in this study, of the lactation curve. Cluster analysis resulted in four groups. The animals from Cluster 1 could be used as selection candidates because their additive genetic pattern presented EBVs above the mean population for all the traits. So, those animals could help to increase the milk production, and the persistency of lactation. Cluster analysis was effective to identify groups of animals suitable to attend the breeding goals, i.e. increase the persistency of lactation and the milk production along the lactation curve in dairy Gyr cattle.