Selection of morpho-agronomic descriptors for characterization of papaya cultivars

Abstract

This study was conducted to define a list of sufficient minimum descriptors to distinguish between papaya genotypes quickly and precisely. To this end, 30 quantitative and 21 multi-category descriptors related to plant characteristics, such as leaves, flowers, fruit and seeds were evaluated in 27 genotypes of papaya, including crops, local varieties and improved lines. The quantitative descriptors were subjected to principal components analyses using the Singh and direct selection methods, whereas a correlation analysis was conducted for the qualitative descriptors. Eighteen and fifteen quantitative descriptors were discarded by the Singh and direct selection methods, respectively. However, considering the simultaneous analyses of these methodologies, 60% of the descriptors were selected to maximize the total variation of the genotypes. Six of the multi-category descriptors were monomorphic, and two were highly correlated with other characteristics and were discarded. The minimum descriptors that were selected had high discrimination potentials when they were analyzed together. Thus, for the purposes of the protection of varieties and the classification of the genotypes of papaya, there were found to be 18 quantitative and 13 multi-category minimum descriptors that contributed significantly to the total variation and possessed low correlation with each other. The elimination of descriptors did not entail a loss of information. Those descriptors that contributed most significantly to the first three principal components were the stem diameters, fruit lengths and widths, inflorescence peduncle lengths, thickness of fruit skin, leaf widths, dry and fresh seed weights and ratio between fruit lengths and widths and between total soluble solids and total titratable acidities.

Phenotypic and molecular characterization of apple (Malus × domestica Borkh) genetic resources in Greece

“…Traditionally, genetic diversity in apple species or varieties was assessed with morphological descriptors (Reig et al, 2015), commonly produced by the International Board for Plant Genetic Resources (IBPGR, 1983) and later by the International Union for the Protection of New Varieties of Plants (UPOV, 2005). The multivariate data analysis is an efficient approach to analyze large data of apple qualitative phenotypic traits and is considered the most suitable to identify patterns and relationships among powerful statistical techniques, such as the principal component analysis (PCA) and the cluster analysis (Oliveira et al, 2012;Furones-Pérez and Fernández-López, 2009;Ganopoulos et al, 2015;Mehmood et al, 2014).…”

Evaluation of Elite Open-Pollinated Maize Lines in Two Contrasting Environments

“…Multivariate analysis is preferred for analyzing data derived from both quality and quantity characteristics (de Oliveira, Dias, & Dantas, 2012). Multivariate techniques such as Principal Component Analysis (PCA) and Agglomerative Hierarchical Clustering (AHC) analysis have been previously used for morphological assessment of genetic variability in sweet cherry (Prunus avium L.) cultivars (Ganopoulos et al, 2015) and in maize (Alves, Cargnelutti Filho, Toebe, Burin, & Silva, 2014).…”

Phenotypic Characterization of Selected Kenyan Purple and Yellow Passion Fruit Genotypes Based on Morpho-Agronomic Descriptors

“…Morphological and agronomic characterization of germplasm as well as new varieties using descriptors is a key consideration in breeding programs. The term descriptor is used to refer to a character or attribute that is used to discriminate between varieties, with redundant descriptors being seen during evaluation of many traits and thus many descriptors are judged accordingly as unnecessary due to their low contribution to variability [18][19][20]. Elimination of redundant descriptors is an important strategy in that it ensures reduction of the work required to collect data without causing significant losses in genotype discrimination [20,21].…”