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One of the most important aspects of genetic evaluation (GE) is the definition of contemporary groups (CG), commonly defined as animals of the same sex born in the same herd, year and season. The objective of this study was to use an aridity index (AI) to classify season and evaluate the implications on the GE of Braunvieh cattle. A data set with 32 777 and 22 448 birth weight (BW) and weaning weight adjusted to 240 days (WW) records, respectively, was used to compare two methods of classification of climatic seasons to be used in the definition of CG for GE models. The first method considered rain season criterion (RC), and the second method is a proposed classification using an AI. Both methods were compared using two approaches. The first approach examined differences in mixed models using the RC and AI season to select the best model for BW and WW, evaluated by different goodness of fit measures. The second approach considered fitting a GE model including the season classifications into the CG structure. Lower probability values for season effect and better goodness of fit measures were obtained when the season was classified according to the AI. Results showed that although differences are small, the AI allows a better model fitting for live-weight traits than RC and revealed a re-ranking effect on expected progeny differences data. Further analysis with other traits would demonstrate the extended utility of AI indicators to be considered for fitting models under a climatic change environment.

Live weight traits are economically important for beef cattle production systems. Genetic analysis of live weight traits frequently presents a problem due to animal records, in that matter, not all the animals have complete records as many young animals leave the herd because of sale, transfer to another herd, or culling reasons. Therefore, the use of multi-trait genetic analysis might be of assistance to help overcome any possible loss of information for those animals with incomplete records. In this study, genetic variances and covariances were obtained to estimate genetic parameters for birth (BW), weaning (WW), and yearling (YW) live weights in a registered Charolais beef cattle population using a multivariate model, where a considerable reduction of data from birth weight to year weight was observed. Direct and maternal heritabilities for BW, WW, and YW were 0.50, 0.30, and 0.28, and 0.31, 0.25, and 0.14, respectively. Direct and maternal genetic correlations were negative in all live weight traits. Genetic correlations among direct BW with direct WW and YW were low, while genetic correlations among maternal traits were medium or high (r>0.39). Comparison between univariate and multi-trait models with substantial reduction of information revealed important differences, implying that multi-trait analysis is better for the structure of data allowing a better fitting of genetic effects by covariance among evaluated traits. Results support multi-trait analysis implementation for genetic evaluations for live weight traits of Charolais cattle.