Evaluation of spring barley performance by biplot analysis

Abstract

Unpredictable environmental conditions lead to occurrence of large genotype by environment (G × E) interaction. It reduces the correlation between genotypic and phenotypic values and complicates selection of superior genotypes. The objective of this study was to estimate genotype by year (G × Y) interaction using AMMI model, to identify spring barley genotypes with stable and high yield performance and to observe association of different meteorological variables with tested growing seasons. The trials with 15 spring barley genotypes were conducted during seven years (1999)(2000)(2001)(2002)(2003)(2004)(2005) at the location of Rimski Šančevi. The results showed that the influence of year (Y), genotype (G) and G × Y interaction on barley grain yield were significant (p < 0.01). Meteorological variables varied significantly from year to year and Y explained the highest percent of treatment variation (81%). The first three IPCA were significant and explained 83% of interaction variation. According to this study, it could be concluded that AMMI analysis provided an enhanced understanding of G × Y interaction in barley multi-years trials. Among the tested genotypes, LAV and NS 477 could be separated as highest yielding genotypes, however LAV could be recommended for further breeding program and large-scale production due to its stable and high yielding performance. It also provided better insight in specific association between spring barley grain yield and meteorological variables.

The variation of agronomic traits associated with breeding progress in winter barley cultivars

“…These results are in agreement with reports by other authors for spring and winter barley (Pržulj et al, 2014;Mirosavljević et al, 2015 a). In our study, the ('Novosadski 595').…”

“…The Pannonian zone is characterized by high year-to-year climate variability, frequent water deficit and high temperature during grain filling (Pržulj et al, 2015). Furthermore, the effect of climate change is expected to additionally increase the occurrence of undesirable seasons for cereal production (Olesen et al, 2011).…”

Dry matter accumulation of winter wheat and barley at different sowing dates

“…Under conditions of mild temperatures and higher level of precipitation during the winter and grain filling period in 2014/15 growing season, plants had more vigorous growth in spring leading to higher biomass accumulation and grain yield than in 2013/14. As previously reported by Pržulj et al (2015) and Dimitrijević et al (2010), under conditions of the Pannonian plain growing season had significant influence on barley and wheat grain yield and other agronomic traits. Also, the difference between growing seasons, mainly in precipitation level and temperature conditions, during the pre-anthesis and post-anthesis period, led to significant variation in two main grain yield components -grain weight and grain number (Mirosavljević et al, 2017).…”

Yield stability of soybean promising lines across environments

“…In AMMI2 biplot, which created using scores of genotypec and environment of the first two AMMI components (IPCA1 and IPCA2), explain the magnitude of interaction of each genotype and environment [4,26,29]. Genotypes which located near biplot origin were more stable (less interaction) compared to genotypes positioned further away (30]. In Figure 2, based on distribution of genotype points in the AMMI2 biplot, it revealed that soybean promising lines G2 and G6 scattered close to the origin, indicating less interaction (showed high stability) of these genotypes with environments.…”