Analysis of stability and adaptation of cotton genotypes using gge biplot method

Abstract

This research was conducted to study effects of G × E interaction on 38 selected genotypes of cotton with two commercial cultivars Golestan and Sepid (control) in a randomized complete block design with three replications at three locations in Golestan Province in 2014-15. The measured characteristics were included: plant height, sympodial length, sympodial number, boll number, boll weight, seed cotton yield and earliness. Analysis of variance showed that genotype effect is significant in 1 or 5% probability levels on measured traits except for boll number and earliness. A significant interaction effect between genotype × locations in yield showed different variation trends in various locations. So that genotype 29 had the best performance in Hashemabad station but genotypes 24 and 18 showed their best performances in Gonbad and Anbarolum station respectively. Stability parameters were calculated and genotype No. 8 was defined according to regression slope close to 1 as the most stable genotypes among. This genotype had a smaller share in genotype and environment interaction according to Rick ecovalans and Shukla stability variance parameters and 10 and 33 were the most unstable genotypes in terms of performance. GGE biplot method showed that the first two principal components regression model explained 74% of the observed changes. GGE biplot graph plotted by software reflected the superior genotypes TJ82, ER26, DB29, DB19, DB25 and ER36 respectively. Also Hashemabad has been identified as appropriate region for ER26 genotype and TJ82 was identified as the best and most stable genotype.

Stability Study of Cotton Genotypes Cultivated in Different Environments

“…Linear) was highly significant for all traits, and this indicates that the response to different environments is genetically controlled. This was confirmed by [4] and [15].…”

Stability analysis of the new cotton lines

“…Many researchers used these methods to evaluate the stability and adaptability of different cotton genotypes (commercial cultivars, new varieties, promising lines, hybrids and their parents) across environments to select the superior and adaptable ones (Balakrishna et al, 2016;Chinchane еt al., 2018;Deho et al, 2021;Dewdar, 2013;Fathi et al, 2018;Güvercİn et al, 2017;Iqbal еt al., 2018;Khalifa et al, 2010;Patil et al, 2017;Shashibhushan & Patel, 2020). Vavdiya et al (2021) used the regression parameters (bi) (S 2 di) to evaluate the stability of 50 "line × tester" (10 × 5) crosses, at three different sowing dates.…”

“…The AMMI method and the GGE bi-plot model were often used to analyze experimental data from different ecological experiments and compare the results (Farias et al, 2016;Maleia et al, 2017;Moiana et al, 2014;Orawu et al, 2017;Pretorius et al, 2015;Riaz et al, 2019). The biplot analysis was used to examine the genotype (G) and the genotype × environment interaction (GE) (Farias et al, 2016;Fathi Sadabadi et al, 2018;Yan and Kang, 2003) and to test environments and megaenvironments (Xu et al, 2014). Shahzad et al (2019) tested 41 genotypes (11 inbred lines and 30 intraspecific cotton hybrids) in 6 different environments using the AMMI method to analyze and compare the results.…”

Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI

“…In a study by Omrani, et al 38 the first four components explained 83% of the GEI. Fathi, et al 39 reported the contribution of the first and second PCs of the GEI as 49.49% and 22.50%, respectively, and both accounted for 71.60% of the variations in the GEI. In another study using AMMI model, the first IPC was significant and explained about 63% of GEI variation 34 .…”