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KMID : 1098420230310040222
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Korean Journal of Medicinal Crop Science
2023 Volume.31 No. 4 p.222 ~ p.234
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Applying Multivariate Statistical Analysis for Agronomic Characteristics to Evaluate Leaf Availability in Perilla Germplasms
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Lee Myoung-Hee
Lee Eun-Soo
Kim Jung-In
Yoo Eun-Ae
Kim Sang-Woo
Kim Sung-Up
Oh Eun-Young
Kim Min-Young
Lee Jeong-Eun
Sung Jung-Sook
Cho Kwang-Soo
Kim Chun-Song
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Abstract
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Background : This study was performed to analyze the agronomic characteristics of 300 perilla germplasms to develop a perilla cultivar for leaves using principal component (PC) and cluster analyses.
Methods and Results : In total, 300 perilla germplasms were analyzed in this study. The morphological diversity and relationships among 300 perilla germplasms were assessed using principal component and cluster analyses. The perilla cultivar for leaf use requires resources with a flowering date later than 105 days and a short node length; we selected 74 resources with a flowering date later than 105 days and a length of less than l00 ㎝. The coefficients of variation of the perilla germplasms were the highest for the days to flowering and days to maturation, whereas they were the lowest for the stem and flower colors. PC analysis revealed the eigenvalues and contributions respective to each PC as follows: PC1, 4.592 and 41.75%; PC2, 1.774 and 16.13%; and PC3, 1.280 and 11.64%. According to cluster analysis, the genetic resources were divided into four groups, comprising 118, 110, 69, and 3 resources, respectively.
Conclusions : Clusters I and Ⅱ were found the most suitable for breeding materials, considering flowering days, initial shape, and leaf size, which are relevant traits for developing leaf-only varieties.
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KEYWORD
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Perilla frutescens (L.) Britt, Cluster Analysis, Germplasm, Multivariate Analysis
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