Investigation of genotype × production environment interaction for weaning weight in the Santa Gertrudis breed in Australia

Bradfield, M.J.; Graser, H. U.; Johnston, David

doi:10.1071/a96009

Cited by 12 publications

(17 citation statements)

References 9 publications

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“…Mean age at weaning was slightly lower in favorable environments. In agreement with Bradfield et al (1997), these findings indicate differences in rearing conditions between environments related to feeding and management practices, among other factors. There was no pattern of variation in the age of dam at calving between environments.…”

Section: Resultssupporting

confidence: 84%

“…Therefore, the genotype × production environment interaction may be important for genetic evaluations of favorable and other environments. The genetic correlations between direct effects were low as also reported by Bradfield et al (1997) for WW of Australian Santa Gertrudis cattle (0.64), by Lopes et al (2008) for the BW and WW of Nelore cattle raised in southern Brazil (−0.04 to 0.79), and by Santana et al (2012) for the BW and WW of composite beef cattle raised in different regions of Brazil (−0.02 to 0.92).…”

Section: Resultsmentioning

confidence: 60%

“…Bradfield et al (1997) reported a correlation of 0.38 between maternal genetic effects in extreme environments and concluded that changes in the ranking of animals across production environments can be expected. The correlations between direct and maternal genetic effects for all traits were always low and negative (analyses A, C and E).…”

Section: Resultsmentioning

confidence: 99%

“…As proposed by Bradfield et al (1997), using the least square contemporary group solutions separately for each trait and each farm, those above farm average were assigned to trait 1 (favorable production environment) and those below farm average to trait 2 (unfavorable production environment), respectively. In another definition, those contemporary group solutions above one standard deviation of the farm average were assigned to trait 1 (favorable production environment), those between one standard deviation below and above the farm average to trait 2 (intermediate production environment) and those below one standard deviation of the farm average to trait 3 (unfavorable production environment), respectively.…”

Section: Models and Definition Of Production Environmentsmentioning

confidence: 99%

“…The most common manifestations of G × E are sire × herd, sire × season of birth, and sire × contemporary group interactions. In this respect, Bradfield et al (1997) defined favorable and unfavorable production environments based on contemporary groups and demonstrated the importance of including sire × contemporary group interaction in the genetic evaluation of Santa Gertrudis cattle in Australia. Diaz et al (2011) and Santana et al (2012) observed G × E in beef cattle raised in different parts of Brazil.…”

Section: Introductionmentioning

confidence: 98%

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Genotype by production environment interaction for birth and weaning weights in a population of composite beef cattle

Júnior

Eler

Bignardi

et al. 2014

Animal

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The objectives of the present study were: (1) to evaluate the importance of genotype × production environment interaction for the genetic evaluation of birth weight (BW) and weaning weight (WW) in a population of composite beef cattle in Brazil, and (2) to investigate the importance of sire × contemporary group interaction (S × CG) to model G × E and improve the accuracy of prediction in routine genetic evaluations of this population. Analyses were performed with one, two (favorable and unfavorable) or three (favorable, intermediate, unfavorable) different definitions of production environments. Thus, BW and WW records of animals in a favorable environment were assigned to either trait 1, in an intermediate environment to trait 2 or in an unfavorable environment to trait 3. The (co)variance components were estimated using Gibbs sampling in single-, bi-or three-trait animal models according to the definition of number of production environments. In general, the estimates of genetic parameters for BW and WW were similar between environments. The additive genetic correlations between production environments were close to unity for BW; however, when examining the highest posterior density intervals, the correlation between favorable and unfavorable environments reached a value of only 0.70, a fact that may lead to changes in the ranking of sires across environments. The posterior mean genetic correlation between direct effects was 0.63 in favorable and unfavorable environments for WW. When S × CG was included in two-or three-trait analyses, all direct genetic correlations were close to unity, suggesting that there was no evidence of a genotype × production environment interaction. Furthermore, the model including S × CG contributed to prevent overestimation of the accuracy of breeding values of sires, provided a lower error of prediction for both direct and maternal breeding values, lower squared bias, residual variance and deviance information criterion than the model omitting S × CG. Thus, the model that included S × CG can therefore be considered the best model on the basis of these criteria. The genotype × production environment interaction should not be neglected in the genetic evaluation of BW and WW in the present population of beef cattle. The inclusion of S × CG in the model is a feasible and plausible alternative to model the effects of G × E in the genetic evaluations.

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Section: Resultssupporting

confidence: 84%

Section: Resultsmentioning

confidence: 60%

Section: Resultsmentioning

confidence: 99%

Section: Models and Definition Of Production Environmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

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