SELECTING THE SUPERIOR GENOTYPE OF SUMMER MAIZE HYBRIDS IN MEGA-ENVIRONMENTS USING AMMI MODEL AND GGE BIPLOT IN CHINA

S.Q. WANG, Q. GUO, S.D. WANG, Z.Y. CHEN
2020 Applied Ecology and Environmental Research  
Wang et al.: Selecting the superior genotype of summer maize hybrids in mega-environments using AMMI model and GGE biplot in China -3593 -APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18(2):3593-3614. Abstract. Effective analysis of genotype by environment interactions (GEI) is helpful to screen stable genotypes in a variety of environments. Therefore, the purpose of this study is to test the stability and adaptability of the agronomic traits of maize hybrids from different ecological
more » ... ogical environments. Thirteen maize hybrids in twenty-six locations over two years (2017)(2018) in Huanghuaihai region was conducted to compare the performance and stability of six agronomic traits using AMMI (additive main effect and multiplicative interaction) model and GGE (genotype, genotype × environment) biplot. The analysis of variance through AMMI model showed that genotype (G), environment (E), and GEI had significant effects on agronomic traits. E explains a larger portion of the total variation in grain yield (GY), ear weight (EW) and 100-grain weight (100-GW), to a much higher degree than GEI and G. However, compared to E and G, GEI contributed more to total variation in ear length (EL), kernel row number (KRN) and bald tip length (BTL). Comprehensive analysis of the AMMI model and the GGE biplot results showed that genotypes G2, G3, and G4 had better agronomic performance and stability than other genotypes and are ideal for planting. Wang et al.: Selecting the superior genotype of summer maize hybrids in mega-environments using AMMI model and GGE biplot in China -3594 -APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18 (2) :3593-3614. ). There is a wide difference in maize yield between the Huanghuaihai region, with an average yield of 5.3 tons/ha, a large area with a high yield of 7.5-9 tons, and a small area with a high yield of 15 tons. For most regions, there is a huge potential for increased production (Huang et al., 2019) . High yielding and stable yielding of summer maize has always been one of the research goals of scientists all over the world, and it is also one of the difficulties. Analysis of the high yielding and stability of the representative hybrids, as well as the contribution of various yield components to yield, can effectively guide the production and breeding of maize in the Huanghuaihai region. Screening and identifying good genotypes are very difficult due to the genotype (G) by environment (E) interaction (GEI). But assessing this interaction is important because it is the primary factor in genotypic performance changes under different environments. The GEI can weaken the association between maize phenotypes and genotype values, and leading to bias in the terms of genotypic effect assessment (Farshadfar et al., 2011; Mohamed, 2013) . The GEI can make summer maize genotypes behave differently in different environments, especially in the Huanghuaihai region where the climate is complex and variable (Yue et al., 2019). The selection and breeding of important traits of maize genotypes is complicated by the cross-interaction between hybrids in different environments, and the result is that high-yielding and stable genotypes are easily overlooked (Mulema et al., 2008; Nzuve et al., 2013) . In order to breed hybrids that meet people's living needs, it is necessary to systematically evaluate the yield, resistance and quality of the tested genotypes, and obtain basic data on adaptability, high yielding and stability in different ecological regions, comprehensive evaluation and screen out hybrids with excellent yield traits. At the same time, the discriminative power and representativeness of each testing site are evaluated, and the basis for selecting the ideal site for resource and hybrid screening is provided. Multi-environment trials (Mets) is a well-established method for identifying the high yielding and adaptability of different crop varieties. In the Mets, the newly bred varieties were tested according to uniform specifications, and their important characteristics such as high yield, stability, adaptability, stress resistance and quality were comprehensively identified (Navas-Lopeza et al., 2019) . There were many statistical methods for evaluating the GEI, such as, scientists have earlier proposed the coefficient of variation (CV) (Döring and Reckling, 2018), analysis of variance (ANOVA) (Fry, 1992) , principal component analysis (PCA) (Perkins, 1972) and linear regression analysis (LRA) (Kang, 1993), but each method has its shortcomings. The coefficient of variation method describes the stability of genotypes, this method can only explain the difference in genotype effects, and does not explain the environmental effects and the interaction between genotypes and the environment. The study of the stability of the variety by linear regression does not reflect the adaptability of the genotype to the environment. The application of analysis of variance to evaluate the adaptability of genotypes cannot analyze the relationship between environmental effects and interaction effects (Blouin et al., 2015) . In recent years, with the deepening of research methods, scientists have proposed two analysis methods of the additive main effects and multiplicative interaction (AMMI) model (Gauch, 1988) and the genotype plus genotype by environment (GGE) biplot (Yan et al., 2000) for GEI research. The AMMI model is a graphically unique linear-bilinear model based on a biplot, combining both the ANOVA and the multiplicative model (Yan et al., 2007) . The AMMI model is an effective tool for studying genotypic Wang et al.: Selecting the superior genotype of summer maize hybrids in mega-environments using AMMI model and GGE biplot in China -3595 -APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18(2):3593-3614. Wang et al.: Selecting the superior genotype of summer maize hybrids in mega-environments using AMMI model and GGE biplot in China -3598 -APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18(2):3593-3614. APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18(2):3593-3614. APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18(2):3593-3614. Figure 3. The mean performance and stability of the tested 13 maize hybrids evaluated in 26 environments using GGE biplot Wang et al.: Selecting the superior genotype of summer maize hybrids in mega-environments using AMMI model and GGE biplot in China -3606 -APPLIED ECOLOGY AND ENVIRONMENTAL RESEARCH 18(2):3593-3614.
doi:10.15666/aeer/1802_35933614 fatcat:5ewdfvsisne6vl452vvv7cb7bu