Selection indexes using principal component analysis for reproductive, beef and milk traits in Simmental cattle

Selection indexes in dual-purpose cattle should include beef, milk and reproductive traits. The principal component analysis is a multivariate technique that allows researchers to explore relationships between explanatory variables and traits of interest. The objective of this study was to construct selection indexes for tropical dual-purpose Simmental cattle based on principal components. The evaluated traits were weight at 8 months of age; age at frst calving; cumulative frst-lactation milk yield at 60, 150, 210 and 305 days; and frst calving interval. The selection indexes were estimated as the sum of the products of the estimated breeding values for the seven traits times their respective eigenvectors for the frst three principal components. The three selection indexes from principal components analysis generated favourable expected genetic progress for all the traits. However, a selection index with a high expected genetic progress for all traits could not be obtained. The principal component analysis allows breeders to have a selection index that simultaneously improves milk, beef and reproductive traits in dual-purpose Simmental cattle. Because a selection index yielding high expected genetic progress for all traits could not be achieved, the decision to use a specifc selection index will depend on the specifc conditions of the market, the local needs and the farmer preference.

• Autores: Amaya, Alejandro; Martínez-Sastre, Rodrigo; Cerón-Muñoz, Mario Fernando
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2021
• Institución: Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
• Repositorio: Repositorio Institucional UDCA
• Idioma: eng
• OAI Identifier: oai:repository.udca.edu.co:11158/4201
• Acceso en línea: https://repository.udca.edu.co/handle/11158/4201
• Palabra clave: Estimated breeding values; Ganado; Vacas lecheras; Mejoramiento animal; Genética animal
• Rights: openAccess
• License: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode.es