Effect of inclusion or non-inclusion of short lactations and cow and/or dam genetic group on genetic evaluation of Girolando dairy cattle.

The objective of this study was to evaluate the effects of inclusion or non-inclusion of short lactations and cow (CGG) and/or dam (DGG) genetic group on the genetic evaluation of 305-day milk yield (MY305), age at first calving (AFC), and first calving interval (FCI) of Girolando cows. Covariance components were estimated by the restricted maximum likelihood method in an animal model of single trait analyses. The heritability estimates for MY305, AFC, and FCI ranged from 0.23 to 0.29, 0.40 to 0.44, and 0.13 to 0.14, respectively, when short lactations were not included, and from 0.23 to 0.28, 0.39 to 0.43, and 0.13 to 0.14, respectively, when short lactations were included. The inclusion of short lactations caused little variation in the variance components and heritability estimates of traits, but their non-inclusion resulted in the re-ranking of animals. Models with CGG or DGG fixed effects had higher heritability estimates for all traits compared with models that consider these two effects simultaneously. We recommend using the model with fixed effects of CGG and inclusion of short lactations for the genetic evaluation of Girolando cattle.

Prediction of alpaca fibre quality by near-infrared reflectance spectroscopy.

Rapid and efficient methods to evaluate variables associated with fibre quality are essential in animal breeding programs and fibre trade. Near-infrared reflectance spectroscopy (NIRS) combined with multivariate analysis was evaluated to predict textile quality attributes of alpaca fibre. Raw samples of fibres taken from male and female Huacaya alpacas (n = 291) of different ages and colours were scanned and their visible-near-infrared (NIR; 400 to 2500 nm) reflectance spectra were collected and analysed. Reference analysis of the samples included mean fibre diameter (MFD), standard deviation of fibre diameter (SDFD), coefficient of variation of fibre diameter (CVFD), mean fibre curvature (MFC), standard deviation of fibre curvature (SDFC), comfort factor (CF), spinning fineness (SF) and staple length (SL). Patterns of spectral variation (loadings) were explored by principal component analysis (PCA), where the first four PC's explained 99.97% and the first PC alone 95.58% of spectral variability. Calibration models were developed by modified partial least squares regression, testing different mathematical treatments (derivative order, subtraction gap, smoothing segment) of the spectra, with or without applying spectral correction algorithms (standard normal variate and detrend). Equations were selected through one-out cross-validation according to the proportion of explained variance (R 2CV), root mean square error in cross-validation (RMSECV) and the residual predictive deviation (RPD), which relates the standard deviation of the reference data to RMSECV. The best calibration models were accomplished when using the NIR region (1100 to 2500 nm) for the prediction of MFD and SF, with R 2CV = 0.90 and 0.87; RMSECV = 1.01 and 1.08 µm and RPD = 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R 2CV < 0.65 and RPD < 1.5. External validation performed for MFD and SF on 91 samples was slightly poorer than cross-validation, with R 2 of 0.86 and 0.82, and standard error of prediction of 1.21 and 1.33 µm, for MFD and SF, respectively. It is concluded that NIRS can be used as an effective technique to select alpacas according to some important textile quality traits such as MFD and SF.

Semiparametric animal models via penalized splines as alternatives to models with contemporary groups.

Contemporary groups (CG) are used in genetic evaluation to account for systematic environmental effects of management, nutritional level, or any other differentially expressed group effect; however, because the functional form of the distribution of those effects is unknown, CG serve as an approximation to a time-varying mean. Conversely, in semiparametric models, there is no need to assume any functional form for the time-varying effects. In this research, we present a semiparametric animal model (AMS) using the covariate day of birth (DOB) by means of penalized splines (P-splines), as an alternative to fitting CG. In the AMS, the functionality of the data on DOB is expressed by means of a Basic segmented polynomial line (B-spline) basis, and proper covariance matrices are used to reflect the serial correlation among the points of support (or knots) at different times. Three different covariance matrices that reflect either short- or long-range dependences among knots are discussed. Different models were fitted to birth weight data from Polled Hereford calves. Models compared were an animal model with CG, an animal model with CG and the covariate DOB nested within CG (CG + DOB), and P-splines with the first difference penalty matrix and three different AMS with 20, 40, 60, 80, or 120 knots. Models were compared using a modified Akaike information criterion (AICC), which was calculated as a byproduct of the estimation of variance components by REML using the expectation maximization algorithm. All three AMS had smaller (better) values of AICC than the regular model with CG, while producing almost the same ranking of predicted breeding values and similar average predicted error variance. In all AMS, the inference and all measures of comparison were similar when the number of knots was equal > or = 40. The model CG + DOB had analogous performance to the AMS, but at the expense of using more parameters. It is concluded that the use of penalized regression splines using a B-spline basis with proper covariance matrices is a competitive method to the fitting of CG into animal models for genetic evaluation, without having to assume any functional form for the covariate DOB.