Data/hora: 10/06/2024 18:07:19

Provedor de dados: 	124	
País: 	Brazil	
Título: 	Principal component and cluster analyses to evaluate production and milk quality traits	
Autores: 	Abreu,Bueno da Silva; Barbosa,Severino Benone Paes; Silva,Elizabete Cristina da; Santoro,Kleber Régis; Batista,Ângela Maria Vieira; Martinez,Rafael Leonardo Vargas.	
Data: 	2020-01-01	
Ano: 	2020	
Palavras-chave: 	Multivariate analysis; Dairy cattle; Milk composition.	
Resumo: 	ABSTRACT Using multivariate analyses, this study attempted to identify important traits explaining the relationship between milk production and quality produced by Holstein cows. Monthly milk records from three commercial dairy farms located in the Agreste region of Pernambuco, Brazil, collected in the period from 2007 to 2017, were used. A total of 5,872 observations regarding milk production, milk components and somatic cell score (SCS) were analyzed using principal component analysis (PCA) and cluster analysis. According to the former analysis, the first three principal components explained 79.69% of the total variation. Total solids content contributed 29.66% of the variation in the first principal component, while lactose content contributed 49.43% of the variation in the second principal component. According to the latter analysis, three clusters differed for all characteristics (p&lt;0.001) and cluster 2 concentrated 43.15% (2,534) of the information with lower SCS and higher lactose content and milk production. Total solids, lactose and fat were considered the most representative traits explaining the variability of the data set. The multivariate techniques used in this study proved useful in obtaining effective characteristics, with three factors considered important in explaining the relationship between Holstein cows’ milk production and quality.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000300417	
Editor: 	Universidade Federal do Ceará	
Relação: 	10.5935/1806-6690.20200060	
Formato: 	text/html	
Fonte: 	Revista Ciência Agronômica  v.51 n.3 2020	
Fonte: 	info:eu-repo/semantics/openAccess