2013
DOI: 10.1071/rd12132
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Genetic parameter estimates and principal component analysis of breeding values of reproduction and growth traits in female Canchim cattle

Abstract: Phenotypic data from female Canchim beef cattle were used to obtain estimates of genetic parameters for reproduction and growth traits using a linear animal mixed model. In addition, relationships among animal estimated breeding values (EBVs) for these traits were explored using principal component analysis. The traits studied in female Canchim cattle were age at first calving (AFC), age at second calving (ASC), calving interval (CI), and bodyweight at 420 days of age (BW420). The heritability estimates for AF… Show more

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Cited by 42 publications
(36 citation statements)
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“…The reproductive performance of heifers depends on the age at which they calve for the first time; the ones that calve earlier have a more productive life [ 2 ]. In addition to first calving, another important factor is that the cow continues producing calves regularly to maintain its productivity and diminish calving interval [ 3 ]. Studies have reported that indirect selection of females based on the performance of bulls is possible, considering the favorable genetic correlations between scrotal circumference measures and age at calving [ 2 , 4 , 5 ].…”
Section: Introductionmentioning
confidence: 99%
“…The reproductive performance of heifers depends on the age at which they calve for the first time; the ones that calve earlier have a more productive life [ 2 ]. In addition to first calving, another important factor is that the cow continues producing calves regularly to maintain its productivity and diminish calving interval [ 3 ]. Studies have reported that indirect selection of females based on the performance of bulls is possible, considering the favorable genetic correlations between scrotal circumference measures and age at calving [ 2 , 4 , 5 ].…”
Section: Introductionmentioning
confidence: 99%
“…In this context, principal components analysis (PCA) reduces the number of originally correlated variables into a smaller set of uncorrelated variables, maintaining most of the original variability and reducing dimensionality to a new set of variables, under the assumption of losing as little information as possible, which improves the descriptive performance of models [41]. The PCA technique has been successfully incorporated into genetic assessments in horses [42], cattle [43], dairy [44], and for the analysis of reproductive traits in several bovine breeds [45,46]. Principal component methods have commonly been aimed at disentangling the potential redundancies among traits measured rather than directly implemented towards the identification of a common explicative structure underneath genetic effects (whether it is additive, dominance, or epistatic), there have been some attempts to apply such methods in goats' genetic evaluations [47].…”
Section: Discussionmentioning
confidence: 99%
“…Según Buzanskas et al (2013) cuando se utiliza este enfoque, los animales se pueden seleccionar de una manera equilibrada una vez que los puntajes de cada componente principal son combinaciones lineales de todos los valores genéticos de los rasgos evaluados, y no pesos empíricos generalmente utilizados en programas de mejora.…”
Section: Estimación De Parámetros Genéticosunclassified
“…En vacas Holstein, Bignardi et al (2012) apreciaron que solo se requerían dos CP para resumir la variación genética de los toros entre los 10 rasgos evaluados (10 producciones mensuales de leche). También en vacas Canchim, Buzanskas et al (2013) reportaron que el 73.37% de la varianza total de los valores genéticos de tres rasgos reproductivos y uno de crecimiento era explicada por dos CP.…”
Section: Componentesunclassified
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