Spring barley integrated testing for yielding and stability

Abstract

Purpose. To define the regularities of yield level for spring barley varieties in “genotype–environment” interaction when testing in different ecological zones environments of Ukraine and identify genotypes with increased adaptive potential. Methods. As object of the research there were 36 spring barley varieties of domestic and foreign breeding. Varieties were tested at the V. M. Remeslo Myronivka Institute of Wheat of NAAS (MIW) (the Central Forest-Steppe) in 2015–2017, at Nosivka Plant Breeding Experimental Station of the V. M. Remeslo Myronivka Institute of Wheat of NAAS (NPBES) (Polissia) in 2016–2017 and at Kirovohrad State Agricultural Experimental Station of NAAS (KSAES) (the Northern Steppe) in 2016–2017. During three years of the investigation (2015–2017), the results of varieties testing in seven environments have been obtained. Plots with discount area of 10 m2 were laid out with three replications by the method of full randomized blocks, in accordance with conventional methods. Statistical analysis of experimental data was performed using Excel 2010 and Statistica 8.0 software. To interpret visually “genotype-environment” interaction the GGE biplot model was used. Results. The ANOVA of yield data showed reliable contributions into the total variation of environment (64.64%), genotype (14.90%), and their interaction (20.46%). Environmental conditions of MIW in 2016 were characterized with the highest discriminative fineness (informativeness), while KSAES in 2017 were characterized with the lowest one. Environmental conditions of both MIW in 2017 and NPBES in 2016 were the most representative; conditions of KSAES in 2016 were the least representative. The conditions of MIW and KSSGDS in 2016 were the most distant against each other. The GGE biplot “who-won-where” vizualization allowed to divide the environments in two sectors: the first – conditions of MIW 2015–2017 and NPBES 2016–2017, the second – conditions of KSAES 2016–2017. The variety ‘MIP Myrnyi’ had a significant advantage in the first sector, while the variety ‘Skarb’ had it in the second one. The varieties of spring barley ‘MIP Myrnyi’, ‘MIP Bohun’, ‘Talisman Myronivskyi’, ‘MIP Azart’, ‘Dokaz’, ‘Pan’ have been differen­tiated and defined as those with the optimal level of yield in environments being the closest to hypothetical “ideal” genotype of the GGE biplot model. Conclusions. Modelling of integrated variety testing by combining years being contrast in hydrothermal regime and different ecological conditions with interpretation of the investigation results using modern statistical and graphical method contributes to more detailed characterization of the “genotype–environment” interaction, ranking and identifying of prospecting genotypes.