The objective of this study was to estimate the heritability and genetic, phenotypic, and environmental correlations between 305-day milk yield (305MY), age at first calving (AFC), calving interval (CI) and dry period length (DPL), as well as to compare the productive and reproductive performances of several groups of Girolando cows during their first, second and third parturitions in Brazil. Genetic parameters were estimated using uni-and bi-trait analyses via restricted maximum likelihood (REML). Heritability estimates were similar between uni-and bi-trait analyses and varied from 0.20 to 0.28, 0.00 to 0.08 and 0.07 to 0.14 for 305MY, CI and DPL, respectively, whereas AFC was 0.20. Genetic correlations between 305MY and AFC (-0.49) or DPL (-0.40 to -0.79) show a favourable association. However, an unfavourable genetic association was verified between 305MY and CI (0.59) in the first calving. Environmental and phenotypic correlations between 305MY and CI or DPL showed a favourable association (-0.25 to -0.42 and -0.14 to -0.44). Concerning the cow breed group, a decrease in 305MY was observed as the fraction of the Gyr breed increased, whereas animals from the 1/2 Holstein þ1/2 Gyr crossbreeding group yielded better performances for AFC and CI. These results reveal large genetic, phenotypic and environmental variations that could be used in selection programmes.

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 5 291) 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 2 CV), 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 2 CV 5 0.90 and 0.87; RMSECV 5 1.01 and 1.08 mm and RPD 5 3.13 and 2.73, respectively. Models for SDFD, CVFD, MFC, SDFC, CF and SL had lower predictive quality with R 2 CV , 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 mm, 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.Keywords: alpaca, fibre quality, diameter, near-infrared spectroscopy, NIRS Implications Near-infrared reflectance spectroscopy (NIRS) was evaluated as a technique to assess alpaca fibre diameter, staple length and other related traits, measured in greasy samples. The best calibration models, developed by a multivariate regression method, were able to obtain useful predictions for fibre diameter and spinning fineness. Other variables were poorly predicted. As diameter of fibre is one of the most important variables related to textile quality, NIRS can be employed as an effective technique to evaluate the product and to assist alpaca breeders in the identification of superior animals.

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A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre’s polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield (PSi) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre’s polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from −0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from −0.98 to 1.00, respectively. The use of PS7 would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits.

RESUMO O objetivo neste estudo foi avaliar a estrutura genética da população de bovinos da raça Girolando no