Nonparametric multivariate analysis of phenotypic traits of sorghum genotypes evaluated under non-irrigated and irrigated conditions

Abstract

Abstract Sorghum (Sorghum bicolor) is a major cereal crop cultivated primarily in arid and semi-arid regions because of its drought tolerance and adaptability to low-moisture environments. It serves multiple roles, including food, fodder, bioenergy, and industrial uses. This study aimed to assess phenotypic variability among sorghum genotypes under irrigated and non-irrigated conditions and performed genotype selection with superior performance in major traits of sorghum using nonparametric multivariate techniques, which are suitable for datasets that violate parametric assumptions. The analysis employed nonparametric multivariate analysis of variance, nonmetric multidimensional scaling, nonparametric principal component analysis, and exploratory factor analysis. PERANOVA revealed that there were significant differences in phenotypic traits among the genotypes, explaining 91.6% of the phenotypic traits variance by genotypes and between treatments, accounting for 4.6% of the traits variances. The result of nonmetric multidimensional scaling, nonparametric principal component analysis, and nonparametric factor analysis found that the phenotypic traits were grouped into three clusters such as days of 50% maturity, above-ground dry matter, grain-filling period, and days of 50% flowering as the first cluster; panicle height and seedling vigor as the second cluster; and the third cluster, labeled as major phenotypic traits, including grain yield, panicle yield, number of green leaves, thousand seeds weight, panicle width, panicle length, harvest index, and panicle exertion. The selection of high-performing genotypes was based on major yield components. Genotypes identified as best performers in major phenotypic traits were G190, G149, and G145, which comprised Cluster 3 of the classification based on nonparametric multivariate analysis. When phenotypic traits are non-normally distributed, nonparametric multivariate analysis is advised to select the best-performing genotype for subsequent breeding and to generate reliable results. The future work will focus on genomic prediction of the genotypes performance in major traits of sorghum and validation of the genotypes under different environments.