A study on genotype–environment interaction based on GGE biplot graphical method in sunflower genotypes (Helianthus annuus L.)

Abstract

GGE biplot technique is one of the appropriate methods for investigating the genotype–environment interaction. An experiment was conducted to examine and evaluate the stability and adaptability of grain yield of 12 sunflower genotypes using the randomized complete block design (RCBD) with three replications in five regions including Karaj, Birjand, Firooz‐Abad, Kashmar, and Arak within two agricultural years. Analysis of variance indicated that the effect of location, year, location × year, genotype, and genotype × location was significant at 1% probability level. Results of biplot analysis showed that the first and second principle components accounted 50.6% and 22.8%, respectively, and in total 73.4% of grain yield variance. In this study, genotype, location, year, year × location, genotype × location, genotype × year, and genotype × year × location explained 2.75%, 17.36%, 5.47%, 17%, 10.8%, 1.04%, and 7.48% of total variance, respectively. Investigating the polygon view led to the identification of three top genotypes and also three mega‐environment. The first mega‐environment included Karaj, Birjand, and Kashmar. The second was Arak, and the third was Firooz‐Abad. To study the kernel yield and stability of genotypes simultaneously, average coordinate view of environments was used and it was determined that genotype Zaria with the highest grain yield has high yield stability also. Ranking the cultivars based on the ideal genotype introduced the genotype Zaria as the best genotype. The highest grain yield belonged to Zaria cultivar at 3.34 t/ha followed by Favorite with 3.23 t/ha. Results obtained from ranking the environments based on the ideal environment introduced Kashmar and Birjand regions as the best environments. Examining the biplot figure for testing environments correlation confirms the positive correlation among Karaj, Birjand, and Kashmar. Correlation between Karaj with Arak, Karaj with Firooz‐Abad, and Arak with Firooz‐Abad was negative. Arak and Firooz‐Abad were highly discriminating and representative and would be used to identification of superior genotypes.

Deciphering genotype‐by‐environment interaction in new soybean lines based on multiple traits using different adaptability and stability methods

“…In our study, soybean lines 2, 5, 6, and 7 were identified as desirable due to their proximity to the ideal genotype. This finding aligns with the work of Ansarifard et al (2020) and Stansluos et al (2023), who employed the GGE biplot for evaluating and selecting outstanding genotypes based on various agronomic traits. While the outcomes of parametric stability analysis methods largely concurred with the GGE biplot analysis, additional stability approaches were necessary to categorize stable lines and reinforce our results.…”

Mega-environment analysis to assess adaptability, stability, and genomic predictions in grain sorghum hybrids

“…Although MET primarily focus on identifying superior genotypes for target environments, established breeding programs also apply MET to generate relevant information about tested environments (Gauch and Zobel, 1997;Yan et al, 2000;Yan and Kang, 2002;Laffont et al, 2013;Gauch, 2013;Yan, 2015Yan, , 2016. This analysis can be useful to identify mega-environments (ME) across a wider production area (Rakshit et al 2012;Nielsen and Vigil 2018;Dalló et al 2019;Sharma et al 2020;Ansarifard et al 2020) A common breeding approach for multinational commercial breeding programs is to cross elite germplasm adapted to diverse target environments (Cooper et al, 2014). Studies have demonstrated the potential advantages of utilizing this strategy in hybrid crops (Podlich and Cooper, 1998;Technow et al, 2020).…”

Multi-environment Evaluations Across Ecological Regions Reveal That the Kernel Oil Content of Cottonseed Is Equally Determined by Genotype and Environment

“…It can interpret the interaction between different traits of a cultivar and is often used for analyzing the interaction effect of multi-environment tests 13,21 in wheat, 22 maize, 23 rice, 24 bean, 25 oats, 26 Papaver somniferum L., 27 Pisum sativum L., 28 sorghum, 7,29 and sunflower. 30 In cotton, the GGE biplot has been used to assort the cotton production areas into different ecological regions in Spain 31 and to differentiate the ecological regions of cotton fiber quality in Northwest Inland (NWI) in China. 32 It was also used to analyze the lint yield and fiber traits of cultivars and evaluate the performance of experimental stations in a regional experiment in the Yangzi River Valley (YaRV).…”

“…GGE biplot is a novel tool for studying the effect of genotype, environment, and G×E that displays the optimum planting area of a cultivar simply and intuitively via a two-dimensional plot. It can interpret the interaction between different traits of a cultivar and is often used for analyzing the interaction effect of multi-environment tests , in wheat, maize, rice, bean, oats, Papaver somniferum L., Pisum sativum L., sorghum, , and sunflower . In cotton, the GGE biplot has been used to assort the cotton production areas into different ecological regions in Spain and to differentiate the ecological regions of cotton fiber quality in Northwest Inland (NWI) in China .…”