Gilberto Romeiro de Oliveira Menezes

Estudo de características de produção de matrizes de corte por meio da análise de componentes principais

Records of 270 meat-type chickens from three lines, 90 of each one, were used to discard variables in a principal component analysis. Data were obtained from meat-type chicken lines of the genetic breeding program of the Universidade Federal de Vicosa. The following traits were evaluated: days at first egg (DFE), egg production rate (EPR) from 22nd to 56th week, body weights at 32nd (BW1), 40th (BW2), 48th (BW3), 56th (BW4), and 64th weeks of age (BW5), and average of three egg weights, at 32nd (EW1), 40th (EW2), 48th (EW3), 56th (EW4) and at 64th weeks (EW5). From the 12 principal components, seven showed variance lower than 0,7 (eigenvalue lower than 0,7), suggesting seven variables to be discarded. Variables which showed the highest coefficients, in absolute value, in the last principal component were discarded. Highly correlated variables with the smaller principal components variance explain a small part of the whole variation. In addition, discarded variables in function of the significant simple linear correlation with the nondiscorded variable, were considered redundants. The variables DFE, EPR, BW4, EW1, and EW4 are recommended for principal component analysis of broiler matrix.

Modelos de regressão aleatória na avaliação da produção de leite em cabras da raça Saanen

It was used 10,238 weekly test day records from 388 first lactations of Saanen goats with the objective of comparing random regression models (RRM). Firstly, it was compared five models, whose exponential terms of Wilmink function assumed the following values: -0.0350; -0.0500; -0.0565; -0.0680 and -0.1000 (W0350, W0500, W0565, W0680 and W1000, respectively) by considering homogeneity of residual variance over the lactation period. The value -0.0500 was kept in the model W0500 whereas models W0350, W0565, W0680 and W1000 used values -0.0350; -0.0565; -0.0680 and -0.1000, respectively, replacing the value -0.0500, proposed by the original model used in dairy cattle. After choosing the best model according to ln L, homogeneity and heterogeneity for residual variance: homogeneity, two classes, three classes, four classes, five classes and six classes along the lactation were evaluated by using AIC, BIC and ln L criteria. According to criterion used, W0350 presents the best fit among the evaluated models. With regard to residual variance, the use of six classes over lactation is indicated by AIC, BIC, ln L and likelihood ratio test. Heritability estimates over lactation, for the best model, ranges from 0.07 (2nd lactation week) to 0.25 (20th lactation week).

Evaluation of persistency lactation measures of Saanen goats under random regression model

It was used 10,238 weekly milk production records on the control day from the first 388 lactations of Saanen goats on the evalution of six lactation persistency measures in order to find out which was the best fitted for using in genetic evaluations on this trait. These six evaluated measures are adaptations from those used on dairy cattle, obtained by replacing, in the formula, bovine reference values by the goat ones. The values used in the calculations were obtained from random regression models. Heritability estimates for persistency measures ranged from 0.03 to 0.09. Genetic correlations between persistency measures and milk production until 268 days ranged from -0.64 to 0.67. Because it presented the lowest genetic correlation with production at 268 days (0.14), the persistency measure PS4 obtained by the sum of the genetic values from the 41st to the 240th day of lactation as deviations of production at 40 days of lactation, is the best recommended for genetic evaluations of lactation persistence on Saanen goats. Thus, the selection of the goats with the best persistency of lactation does not change the production at 268 days. Due to the low heritability of this measure (0.03), few responses to selection are expected on this herd.

Precocidade sexual, eficiência reprodutiva e desempenho produtivo de matrizes jovens Nelore e cruzadas

Com o objetivo de avaliar matrizes da raca Nelore (NE) e cruzadas ½ Angus + ½ Nelore (AN), ½ Caracu + ½ Nelore (CN) e ½ Senepol + ½ Caracu (SC) quanto a precocidade sexual em sistemas de recria a pasto, eficiencia reprodutiva e desempenho produtivo das matrizes em cada grupo genetico (GG), matrizes desses grupos foram produzidas por tres safras. Foram avaliadas 40 matrizes AN, 37 CN, 51 NE e 43 SC. Para prenhez precoce, matrizes AN apresentaram taxa de prenhez de 92,2% contra 29,1% para CN, 22,6% para SC e 1,1% para NE. Na prenhez convencional, matrizes AN obtiveram 99,4%, 98,8% para CN, 84,4% para SC e 80,0% para NE. A reconcepcao das matrizes AN foi 86,3%, 75,1% de CN, 49,6% de NE e 43,6% de SC. Matrizes AN tiveram menor media de idade ao primeiro parto em meses, 26,36±0,79, contra 31,33±0,86 para CN, 33,51±0,98 para SC e 38,08±0,74 para NE. Para peso ao desmame, crias three-cross das AN pesaram mais que as F1 das NE, por volta de 19%. Para relacao de peso ao desmame, nao houve diferencas estatisticas entre GG. Matrizes AN foram superiores as demais nos aspectos reprodutivos e produtivos, seguidas das matrizes CN.

Modelling lactation curves of dairy goats by fitting random regression models using Legendre polynomials or B-splines

Abstract: A total of 17 356 test-day milk yield (TDMY) records from 642 first lactations of Alpine goats were used to model variations in lactation curve using random regression models (RRM). Orthogonal Legendre polynomials and B-splines were evaluated to obtain adequate and parsimonious models for the estimation of genetic parameters. The analysis was performed using a single-trait RRM, including the additive genetic, permanent environmental, and residual effects. We estimated the mean trend of milk yield, and the additive genetic and permanent environmental covariance functions through random regression using different orders of orthogonal Legendre polynomial (three to six) and B-spline functions (linear, quadratic, and cubic, with three to six knots). This study further evaluated different number of classes of residual variances. The covariance components and the genetic parameters were estimated using the restricted maximum likelihood method. Heritability estimates presented similar trends for both functions. The RRM with a higher number of parameters better described the genetic variation of TDMY throughout the lactation. The most suitable RRM for genetic evaluation of TDMY of Alpine goats is a quadratic B-spline function with six knots, for the mean trend, curves of additive genetic and permanent environmental effects, and five classes of residual variance.

Estimation of genetic parameters for growth traits in Tabapuã cattle using a multi-trait model

To provide information that can contribute to the genetic evaluation and selection in Tabapua cattle, genetic parameters were estimated for growth traits using a multi-trait animal model. (Co)variance components were estimated by a Bayesian approach using the Gibbs Sampler. Moderate and similar responses to selection are expected when selecting for weights at the four ages evaluated, since the direct heritability estimates were similar and of average magnitude (0.18 to 0.19). The direct and maternal additive genetic correlations between all pairs of weights were higher than 0.70, indicating a high degree of association between the four traits. This suggests that using any of them as selection criteria will result in correlated response in the others and that multi-trait analysis are recommended for the genetic evaluation of growth traits in beef cattle for exploiting this association.

Efeito da autocorrelação residual na avaliação genética de cabras para a produção de leite e para o formato da curva de lactação

The objective of this study was to evaluate the residual autocorrelation effect on the estimates of genetic parameters for total milk yield (MY) and the coefficients a, b and c of Woods model and, consequently, the animals classification for these traits. Woods model was fitted to the lactation of goats considering three cases of residual structure: (IE) - independent errors, (AR1) - first order autoregressive errors, and (IE - AR1) - AR1 errors only for lactations that had significant residual autocorrelation and IE for others. Estimates of the coefficients a, b and c and PL were used as dependent variables in a multivariate animal model, which included the random effects of animal and permanent environmental and fixed effects of contemporary group, number of foals per birth, genetic groups and order of calving, besides the covariates of lactation length (linear) and age at parturition (linear and quadratic). The different residual structures affected the estimates of genetic parameters and the classification of animals for the traits studied. Therefore, the selection of animals can be modified with the presence of residual autocorrelation and the structure IE-AR1 is presented as an alternative to solve this problem.