Data/hora: 03/06/2024 16:45:15

Provedor de dados: 	75	
País: 	Brazil	
Título: 	Combinations of distance measures and clustering algorithms in pepper germplasm characterization	
Autores: 	Gomes,Gisely Paula; Baba,Viviane Yumi; Santos,Odair P dos; Sudré,Cláudia P; Bento,Cintia dos S; Rodrigues,Rosana; Gonçalves,Leandro SA.	
Data: 	2019-06-01	
Ano: 	2019	
Palavras-chave: 	Capsicum spp.; Multivariate analysis; Clustering methods; Genetic diversity; Qualitative and quantitative descriptors.	
Resumo: 	ABSTRACT Characterization and evaluation of genotypes conserved in the germplasm banks have become of great importance due to gradual loss of genetic variability and search for more adapted and productive genotypes. This can be obtained through several ways, generating quantitative and qualitative data. Joint analysis of those variables may be considered a strategy for an accurate germplasm characterization. In this study we aimed to evaluate different clustering techniques for characterization and evaluation of Capsicum spp. accessions using combinations of specific measures for quantitative and qualitative variables. A collection of 56 Capsicum spp. accessions was characterized based on 25 morphoagronomic descriptors. Six quantitative distances were used [A1) average of the range-standardized absolute difference (Gower), A2) Pearson correlation, A3) Kulczynski, A4) Canberra, A5) Bray-Curtis, and A6) Morisita] combined with distance for qualitative data [Simple Coincidence (B1)]. Clustering analyses were performed using agglomerative hierarchical methods (Ward, the nearest neighbor, the farthest neighbor, UPGMA and WPGMA). All combined distances were highly correlated. UPGMA clustering was the most efficient through cophenetic correlation and 2-norm analyses, showing a concordance between the two methods. Six clusters were considered an ideal number by UPGMA clustering, in which Gower distance showed a better adjustment for clustering. Most combined distances using UPGMA clustering allowed the separation of the accessions in relation to species, using both quantitative and qualitative data, which could be an alternative for simultaneous joint analysis, aiming to compare different clusters.	
Tipo: 	Info:eu-repo/semantics/article	
Idioma: 	Inglês	
Identificador: 	http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-05362019000200172	
Editor: 	Associação Brasileira de Horticultura	
Relação: 	10.1590/s0102-053620190207	
Formato: 	text/html	
Fonte: 	Horticultura Brasileira  v.37 n.2 2019	
Fonte: 	info:eu-repo/semantics/openAccess