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Foi realizado um levantamento dos dados de contagem de células somáticas (CCS), porcentagens de gordura, proteína, lactose e sólidos totais de amostras de leite de tanques recebidas no período de dezembro de 1996 a julho de 1998, com o objetivo de se caracterizar a composição do leite segundo sua contagem de células somáticas. Os 4785 dados de análise foram classificados segundo sua CCS, sendo formados quatro grupos (grupo 1, CCS < 500 mil cél./mL; grupo 2, 500 < CCS < 1000 mil cél./mL; grupo 3, 1000 < CCS < 1500 mil cél./mL; grupo 4, CCS > 1.500 mil cél./mL). Aos grupos foram aplicadass técnicas de estatística descritiva, análise de variância e comparações múltiplas de médias. O aumento da ordem das classes acarretou acréscimo da porcentagem de gordura e dos desvios-padrão e redução nas porcentagens de proteína e lactose. A concentração de sólidos totais, apesar de não-significativa, apresentou tendência de redução. Concluiu-se que leite de tanques com CCS mais altas apresentaram maior porcentagem de gordura, menor porcentagem de proteína e lactose e igual porcentagem de sólido totais. As mudanças significativas nas concentrações do componentes do leite ocorrem a partir de 1.000.000 cél./mL para gordura e 500 mil cél./mL para proteína e lactose. Grupos de tanques com maiores CCS apresentaram maior variabilidade nas concentrações dos constituintes do leite.

Information about genetic parameters is essential for selection decisions and genetic evaluation. These estimates are population specific; however, there are few studies with dairy cattle populations reared under tropical and sub-tropical conditions. Thus, the aim was to obtain estimates of heritability and genetic correlations for milk yield and quality traits using pedigree and genomic information from a Holstein population maintained in a tropical environment. Phenotypic records (n = 36 457) of 4203 cows as well as the genotypes for 57 368 single nucleotide polymorphisms from 755 of these cows were used. Covariance components were estimated using the restricted maximum likelihood method under a mixed animal model, considering a pedigree-based relationship matrix or a combined pedigree-genomic matrix. High heritabilities (around 0.30) were estimated for lactose and protein content in milk whereas moderate values (between 0.19 and 0.26) were obtained for percentages of fat, saturated fatty acids and palmitic acid in milk. Genetic correlations ranging from -0.38 to -0.13 were determined between milk yield and composition traits. The smaller estimates compared to other similar studies can be due to poor environmental conditions, which may reduce genetic variability. These results highlight the importance in using genetic parameters estimated in the population under evaluation for selection decisions.

ABSTRACT. We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.

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