Multivariate analysis of seed yield and morphological characters of Okra (Abelmoschus esculentus L. Moench) accessions

Abstract

Abstract. Enhancement of okra fruit production requires identification of genotypes with promising seed yield attributes, thus multivariate techniques were employed to reveal genetic and morphological attributes of sixteen diverse okra lines during wet and dry seasons in 2019 at Teaching and Research Farm, College of Agriculture, Osun State University, Ejigbo Campus. Seeds were sown in single-row plots of 5 m long, spaced 0.70 m apart and 0.50 m within on sandy loam soil in a randomized complete block design. The dry season crop was supported by the application of 12 mm water weekly. Data collected on growth and seed traits were subjected to the General Linear Model (GLM), principal component analysis (PCA), and cluster analysis using the Statistical Analysis System (SAS, 2018). The petiole color was scored according to IPGRI (1991) square root transformed prior to the analysis. Results showed highly significant lines, season, and their interactions for almost all traits. Four PCAs accounted for 85.77% and the first two PCA captured 51% of the total variations. Both PCA and cluster analysis grouped the lines into four and revealed the potentials of SAHARI F1, NGB01197, and LD-88 for high seed yield. It is concluded that these lines can be incorporated into okra yield improvement program.