Archive | 2019
QUANTIFICATION OF G × E INTERACTION FOR FEED BARLEY GENOTYPES BY PARAMETRIC AND NON-PARAMETRIC MEASURES
Abstract
Genotype × environment (G × E) interaction of 28 feed barley genotypes in 12 environments was quantified by the parametric and non-parametric measures. Significant differences among G × E, environments and genotypes were observed as 42.3% of the total variance accounted for interaction effect. Interaction Principal Component Axes (IPCA1, IPCA2, IPCA3 and IPCA4) contributed 32.2, 20.3, 15.6 and 10.5% of the interaction sum of squares. Crossover interaction among genotypes and environments was confirmed by positive and negative values IPCAs. RD2786 followed by RD2876 had large negative IPCA1 score along with positive IPCA3 and IPCA4 values. Desirable genotypes were arranged in ascending order by D values as G23 (1.32) < G2 (1.42) < G20 (1.47) < G21 (1.63). The least AMMI Stability Value (ASV) score was observed for KB1367 followed by JB290 for yield performance. Smallest Pi was satisfied by BH 946, HUB 113 and RD2552. Environmental variance and CV identified non-stable performance of RD2874 and NDB1578 along with RD2876. Wricke’s ecovalence showed UPB1040 and UPB1042 as promising genotypes. Nonparametric measures (Si, Si, Si, Si) pointed towards UPB1040 and PL881 for stable and unstable genotypes, however, Si, Si selected UPB1040 and UPB1042 as of stable yield. More or less similar results were observed by parametric as well as non-parametric measures. Introduction Barley (Hordeum vulgare L. ssp. vulgare) has been cultivated as the world’s fourth important cereal crop owing to broader environmental adaptation as compared to other cereals. Multifarious uses of barley as a feed, food and malt for brewing industries have been well known in world wide. Barley is popularly grown as feed in many parts of the world including Indian subcontinent. Feed barley genotypes are evaluated in multi-environment trials (MET) to select the promising genotypes for specific environments. G × E interaction in MET helps to evaluate stable performance of genotypes (Sisay and Sharma 2016). Large numbers of stability measures were been observed (Mohammadi et al. 2016). Crop improvement programs incorporate both parametric and non-parametric approaches (Mohammadi and Ahmed 2008). Several parametric methods including univariate and multivariate are the environmental variance (Syi) (Lin et al. 1986), Wricke’s ecovalence (Wi) (Wricke 1962) and the coefficient of variability (CVi) (Francis and Kanenberg 1978), AMMI stability value (ASV) (Purchase et al. (2000). Ranks of genotypes as per their yield performance across environments used to calculate non-parametric measures as suggested by Huehn (1990), Nassar and Huehn (1987), Kang and Pham (1991) and Thennarasu (1995). The genotypes with similar ranking across environments were considered as of stable yield performance (Farshadfar et al. 2014). Hence, this study was conducted to quantify the magnitude of genotype × environment interaction by parametric and non parametric measures for feed barley genotypes evaluated under multi-location trials. The prime objectives of this study were to (i) interpret genotype-environment interaction by latest analysis procedures and (ii) association analysis among different measures as per the various statistics. \uf02aAuthor for correspondence: .