Genetic Analysis of Growth Traits in Polled Nellore Cattle Raised on Pasture in Tropical Region Using Bayesian Approaches

Abstract

Components of (co)variance and genetic parameters were estimated for adjusted weights at ages 120 (W120), 240 (W240), 365 (W365) and 450 (W450) days of Polled Nellore cattle raised on pasture and born between 1987 and 2010. Analyses were performed using an animal model, considering fixed effects: herd-year-season of birth and calf sex as contemporary groups and the age of cow as a covariate. Gibbs Samplers were used to estimate (co)variance components, genetic parameters and additive genetic effects, which accounted for great proportion of total variation in these traits. High direct heritability estimates for the growth traits were revealed and presented mean 0.43, 0.61, 0.72 and 0.67 for W120, W240, W365 and W450, respectively. Maternal heritabilities were 0.07 and 0.08 for W120 and W240, respectively. Direct additive genetic correlations between the weight at 120, 240, 365 and 450 days old were strong and positive. These estimates ranged from 0.68 to 0.98. Direct-maternal genetic correlations were negative for W120 and W240. The estimates ranged from −0.31 to −0.54. Estimates of maternal heritability ranged from 0.056 to 0.092 for W120 and from 0.064 to 0.096 for W240. This study showed that genetic progress is possible for the growth traits we studied, which is a novel and favorable indicator for an upcoming and promising Polled Zebu breed in Tropical regions. Maternal effects influenced the performance of weight at 120 and 240 days old. These effects should be taken into account in genetic analyses of growth traits by fitting them as a genetic or a permanent environmental effect, or even both. In general, due to a medium-high estimate of environmental (co)variance components, management and feeding conditions for Polled Nellore raised at pasture in tropical regions of Brazil needs improvement and growth performance can be enhanced.

Bodyweight performance, estimated carcass traits and methane emissions of beef-cattle categories grazing Andropogon gayanus, Melinis minutiflora and Stylosanthes capitata mixed swards and Brachiaria humidicola pasture

“…To the authors knowledge, there is no comparable experimental information about the performance of adult animals in the study region, but it supports, the limited number of available on ranch observations. Thus, given the fact that irrespective of the year, all studied cattle were subject to a similar commercial management considerations, differences in LW performance most likely reflects a variability and/or interaction amongst environmental conditions (Domínguez et al 2003;; growth traits, including compensatory gains (Hernández-Hernández et al 2015); genetic parameters and their interacting networks (Ceacero et al 2016;Lopes et al 2013;Pereira et al 2016); hormones secretion (Kasuya 2016; Widmann et al 2013); maternal effects (Neidhardt et al 1979); grazing management (Vera and Ramírez-Restrepo 2017); diet selection (O'Neill et al 2013); nutritive and metabolic trigger factors in the forage resources (Tedeschi et al 2014); and the adaptive capacity of Brahman and Belmont Red Composite to respond to those triggers within a climate change environment (Ramírez-Restrepo and Charmley 2015). In parallel, a particularly relevant aspect is that putting weight on cull cows in thin to medium condition has been found to be more profitable than cows with higher body scores (Amadou et al 2014).…”

Bayesian estimates of genetic parameters for reproductive traits in Nellore cows raised on pasture in tropical regions

“…Gunawan et al (2011b) considered that this might be partly a correlated response to selection for increased daily gain. A possible cause of the undesirable trend in gestation length and of the marked fluctuation of values observed for AFC, CI, and DO could be the intense selection to increase productive traits (Lopes et al, 2011;Santos et al, 2012;Lopes et al, 2013), without giving attention to reproductive traits.…”

Estimating the genetic parameters for liver fat traits in broiler lines divergently selected for abdominal fat

“…In many cases, GS has been reported to be more suitable than REML analyses for the analysis of complicated models, as it can obtain and update all information from the posterior probability distribution without solving the mixed model equations (Gianola and Fernando, 1986;Van Tassell et al, 1995). Therefore, the GS method is increasingly used to estimate the genetic parameters of quantitative traits (Waldmann and Ericsson, 2006;Pardo et al, 2013), such as BW in cattle (Lundgren et al, 2014), feed intake and litter weight in sows (Lopes et al, 2013), and egg production in chicken (Luo et al, 2007). In this study, a comparison of the estimates obtained using the REML and GS methods were determined to be similar; this was consistent with the results obtained in other studies (Van Tassell and Van Vleck, 1996;Andersen-Ranberg et al, 2005;Stock et al, 2007).…”