Parametric and Non-Parametric Measures to Identify Stable and Adaptable Cotton (Gossypium Hirsutum L.) Genotypes

Abstract

ABSTRACTThe study, comprised seventeen genotypes, was conducted during 2016 to 2019 growing seasons. The design was Randomized Complete Block Design (RCBD) with three replications to identify adaptable cotton genotypes. Parametric and non-parametric measures, principal component analysis (PCA) and correlation among the ranks of the parameters computed using R software. The environment, genotype and genotype-environment interaction contributed 79.58, 3.2 and 15.46% of the variation, respectively, and were significant (p < 0.001). The highest (1510.7 kg/ha) and lowest (1080.1 kg/ha) seed cotton yield was recorded from G12 and G8 genotypes respectively. Coefficients of determination (R2) and superiority index (Pi) recognized G13, G11, G10 and G11, G12, G7 as the top three stable genotypes correspondingly. G10 was selected three times as superior by stability variance (σ2), wricke’s ecovalence (Wi) and deviation from regression (S2di). Seed cotton yield positively correlated with GAI, Kang’s rank-sum (KRS), Huhn and Nassar S(1), S(6) S(3) while negatively correlated with Thennarasu stability measures NP(2), NP(3) and NP(4 . The first, second and third clusters of the PCA biplot comprised, 4, 4 and 10 parameters respectively. G12, with highest seed cotton yield and good stability, was the best genotype and recommended for variety verification.

Keywords

• Cotton
• genotype
• genotype-environment interaction
• parametric and non-parametric
• seed cotton yield
• stability