Stability on Maize Hybrids Based on GGE Biplot Graphical Technique

Shojaei, Seyed Habib; Mostafavi, Khodadad; Bihamta, Mohammad Reza; Omrani, Ali; Mousavi, Seyed Mohammad Nasir; Nagy, J.; Bojtor, Csaba; Nagy, János

doi:10.3390/agronomy12020394

Cited by 17 publications

(22 citation statements)

References 29 publications

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“…The graph was then divided into six sectors, and eight environments were retained in two sectors and partitioned into two mega-environments, probably due to latitudinal and longitudinal differences [42]. Variation in the genotypic performance within environments indicated the strong influence of environments and the existence of a mega-environment [40,43,44].…”

Section: Discussionmentioning

confidence: 99%

Genotype by Environment Interaction Analysis for Grain Yield of Wheat (Triticum aestivum (L.) em.Thell) Genotypes

et al. 2022

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Genotype environment interaction and stability performance were investigated on grain yield per plot in eight environments during Rabi (here, rabi means that a crop has been grown in Rabi season: crops that are sown in winter and harvested in spring in the Indian subcontinent) 2019–2020 and 2020–2021 using 100 diverse wheat genotypes. Research was conducted at Chaudhary Charan Singh Haryana Agricultural University, Hisar, Haryana in India. The analysis of variance revealed that genotype, environment and their interaction had a highly significant effect on the yield as reflected in Eberhart and Russel model and The Eberhart and Russell model indicated the suitability of the genotypes WH 1142, PBW 661, PBW 475 and DBW 17 with high mean, bi > 1 and non-significant deviation from regression to favorable environment, whereas the genotypes UP 2660 and DBW 88 with high mean, bi < 1 and non-significant deviation from regression were found suitable for poor environment. The Additive Main Effects and Multipicative Interaction (AMMI) analysis of variance for grain yield per plot across the environments showed that 26.41% of the total variation was attributed to genotypic effects, 70.22% to environmental effects and 3.37% to genotype × environment interaction effects. AMMI biplot study indicated the genotypes PBW 750, DPW 621-50, WH 542, PBW 486, PBW 661 and WH 1192 stable across the environments as they did not exert strong interactive forces; hence, they were selected as potential candidates for possible release in the study areas. Furthermore, the which-won–where model indicated the adaptation of genotypes PBW 706, PBW 769, DBW 116, WH 1157, WH 789 and WH1186 to first mega-environment and genotypes DBW 16, WH 1152, WH 1105 and PBW 503 in the second. These genotypes could be utilized in breeding programs to improve grain yield in bread wheat and may be used as stable breeding material for commercial cultivation.

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Section: Discussionmentioning

confidence: 99%

Genotype by Environment Interaction Analysis for Grain Yield of Wheat (Triticum aestivum (L.) em.Thell) Genotypes

et al. 2022

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“…Several studies define mega-environments based on the genotypes’ response in a single year ( Singh et al., 2019 ; Mushayi et al., 2020 ; Enyew et al., 2021 ), but since the environmental pattern that defines the genotype response may change significantly across years ( Figure 4 ), this may lead to mistaken recommendations. In most studies that evaluate genotypes across multiple locations and years, the average yield across years is used to fill a two-way table (genotypes x locations) that is further used in AMMI or GGE biplot analysis ( Shojaei et al., 2022 ). Here, we have shown how integrating multi-trait selection for mean performance (within mega-environments) and stability (across years) with detailed environmental typology may be useful to identify specific adaptations (such as tolerance to warmer environments), increasing the sustainability of breed programs mainly under the climate changes in view ( Lopes et al., 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques

Yue

Olivoto

et al. 2022

Front. Plant Sci.

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Under global climate changes, understanding climate variables that are most associated with environmental kinships can contribute to improving the success of hybrid selection, mainly in environments with high climate variations. The main goal of this study is to integrate envirotyping techniques and multi-trait selection for mean performance and the stability of maize genotypes growing in the Huanghuaihai plain in China. A panel of 26 maize hybrids growing in 10 locations in two crop seasons was evaluated for 9 traits. Considering 20 years of climate information and 19 environmental covariables, we identified four mega-environments (ME) in the Huanghuaihai plain which grouped locations that share similar long-term weather patterns. All the studied traits were significantly affected by the genotype × mega-environment × year interaction, suggesting that evaluating maize stability using single-year, multi-environment trials may provide misleading recommendations. Counterintuitively, the highest yields were not observed in the locations with higher accumulated rainfall, leading to the hypothesis that lower vapor pressure deficit, minimum temperatures, and high relative humidity are climate variables that –under no water restriction– reduce plant transpiration and consequently the yield. Utilizing the multi-trait mean performance and stability index (MTMPS) prominent hybrids with satisfactory mean performance and stability across cultivation years were identified. G23 and G25 were selected within three out of the four mega-environments, being considered the most stable and widely adapted hybrids from the panel. The G5 showed satisfactory yield and stability across contrasting years in the drier, warmer, and with higher vapor pressure deficit mega-environment, which included locations in the Hubei province. Overall, this study opens the door to a more systematic and dynamic characterization of the environment to better understand the genotype-by-environment interaction in multi-environment trials.

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“…In that perspective, a novel multi-trait genotype-ideotype distance index (MGIDI) was proposed to select genotypes with desirable mean performances of multiple traits that overcome the fragility of classical linear indices (Olivoto and Nardino, 2021). A few previous attempts at multiple traits in the selection of maize hybrids with multi-environment data have been reported (Langner et al, 2019;Olivoto et al, 2021;Oliveira et al, 2020;Singamsetti et al, 2021;Peixoto et al, 2021;Shojaei et al, 2022;Yue et al, 2022c). The purpose of this research was mainly to select the promising maize hybrids based on multiple traits suitable for different moisture regimes including drought, waterlogging, and optimal conditions and across all moisture conditions.…”

Section: Introductionmentioning

confidence: 99%

Genetic gains in tropical maize hybrids across moisture regimes with multi-trait-based index selection

et al. 2023

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Unpredictable weather vagaries in the Asian tropics often increase the risk of a series of abiotic stresses in maize-growing areas, hindering the efforts to reach the projected demands. Breeding climate-resilient maize hybrids with a cross-tolerance to drought and waterlogging is necessary yet challenging because of the presence of genotype-by-environment interaction (GEI) and the lack of an efficient multi-trait-based selection technique. The present study aimed at estimating the variance components, genetic parameters, inter-trait relations, and expected selection gains (SGs) across the soil moisture regimes through genotype selection obtained based on the novel multi-trait genotype–ideotype distance index (MGIDI) for a set of 75 tropical pre-released maize hybrids. Twelve traits including grain yield and other secondary characteristics for experimental maize hybrids were studied at two locations. Positive and negative SGs were estimated across moisture regimes, including drought, waterlogging, and optimal moisture conditions. Hybrid, moisture condition, and hybrid-by-moisture condition interaction effects were significant (p ≤ 0.001) for most of the traits studied. Eleven genotypes were selected in each moisture condition through MGIDI by assuming 15% selection intensity where two hybrids, viz., ZH161289 and ZH161303, were found to be common across all the moisture regimes, indicating their moisture stress resilience, a unique potential for broader adaptation in rainfed stress-vulnerable ecologies. The selected hybrids showed desired genetic gains such as positive gains for grain yield (almost 11% in optimal and drought; 22% in waterlogging) and negative gains in flowering traits. The view on strengths and weaknesses as depicted by the MGIDI assists the breeders to develop maize hybrids with desired traits, such as grain yield and other yield contributors under specific stress conditions. The MGIDI would be a robust and easy-to-handle multi-trait selection process under various test environments with minimal multicollinearity issues. It was found to be a powerful tool in developing better selection strategies and optimizing the breeding scheme, thus contributing to the development of climate-resilient maize hybrids.

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Stability on Maize Hybrids Based on GGE Biplot Graphical Technique

Cited by 17 publications

References 29 publications

Genotype by Environment Interaction Analysis for Grain Yield of Wheat (Triticum aestivum (L.) em.Thell) Genotypes

Genotype by Environment Interaction Analysis for Grain Yield of Wheat (Triticum aestivum (L.) em.Thell) Genotypes

Multi-trait selection for mean performance and stability of maize hybrids in mega-environments delineated using envirotyping techniques

Genetic gains in tropical maize hybrids across moisture regimes with multi-trait-based index selection

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