Adaptability and stability of conventional soybean by GGE biplot analysis

Carvalho, Marcos Paulo; Nunes, José Airton Rodrigues; Carmo, Eduardo Lima do; Simon, Gustavo André; Moraes, Rânia Nunes Oliveira

doi:10.1590/1983-40632021v5167995

Cited by 3 publications

(3 citation statements)

References 16 publications

(20 reference statements)

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“…(2020) (93.59%), Amira et al. (2013) (86.6%); Carvalho et al. (2021) (80%); Mulugeta et al (2013) (63.4%), Krisnawati and Adie (2018a) (60.87%) and Bhartiya et al.…”

Section: Discussionmentioning

confidence: 96%

Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia

Habtegebriel

2022

Front. Plant Sci.

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Genotype by environment interaction (GEI) is a phenomenon that occurs in heterogeneous environments that slows breeding progress by preventing the selection of superior cultivars for breeding and commercialization. Therefore, the objectives of this study were to find out how GEI impacts soybean output and to identify the most adapted and stable genotypes. Moreover, to look at the possibility of other mega environments for testing in the future. The experiments were grown for two years in a four-replicated randomized block design at each environment. Over the course of several harvests, yield components, days to flowering, days to maturity, plant height, the number of pods per plants, the number of seeds per plant, hundred seed weight and grain yield per hectare were evaluated in the main for 2018 and 2019.To analyze the stability performance of the genotypes, general linear method, GGE and Additive main effect and multiplicative interaction effects analysis (AMMI) and ASV rank analysis were applied. The GGE biplot revealed that the GGE biplots explained 74.29% of the total variation distributed as,56.69% and 17.62% of sum of squares between principal component PC1 and PC2, respectively whereas, AMMI model, the first two interaction principal component axes (IPCA1 and IPCA2) explained 47.74% and 26.62% of the variation due to GEI, respectively, exposed genotypes identified the five as best performer. The results from the four distinct stability statistics AMMI biplot (G8, G2, G1, G11), ASV (G1, G11; (GSI; G9, G1, G11) and (GGE: G2, G8, G9) are taken into account together with the genotypes` grand mean. The genotypes JM-CLK/CRFD-15-SD (G8) and 5002T (G1), which rank among the best and have the highest seed output, are suitable for hybridization as a parent and commercial production. Therefore, genotypes JM-CLK/CRFD-15-SD (G8) and 5002T(G1) have the highest seed output were among the best and thus could be recommended for release as a new soybean varieties cultivation across.

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“…(2020) (93.59%), Amira et al. (2013) (86.6%); Carvalho et al. (2021) (80%); Mulugeta et al (2013) (63.4%), Krisnawati and Adie (2018a) (60.87%) and Bhartiya et al.…”

Section: Discussionmentioning

confidence: 96%

Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia

Habtegebriel

2022

Front. Plant Sci.

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“…Similar results were reported by many researchers ( Kumar et al, 2014 ; Bhartiya et al, 2017 ). Carvalho et al (2021) also reported the epistatic effect on yield. Results obtained by GGE biplot analysis recommended that E3 was the most suitable environment for the selection of high-yielding genotypes and general adaptability ( Figure 7D ).…”

Section: Discussionmentioning

confidence: 97%

“…In the past, many studies have been conducted to check the stability of soybean across environments. GGE biplots have been used to check the stability and adaptability of soybean genotypes that were cultivated in multiple environments and select the varieties that were highly stable and performed better across the environments ( Mukuze et al, 2020 ; Carvalho et al, 2021 ). Other than soybean, GGE biplots were used in oat, sugarcane, rice, wheat, and maize for screening the stable genotypes in mega-environmental trials.…”

Section: Introductionmentioning

confidence: 99%

Analysis of genotype × environment interactions for agronomic traits of soybean (Glycine max [L.] Merr.) using association mapping

et al. 2023

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The soybean yield is a complex quantitative trait that is significantly influenced by environmental factors. G × E interaction (GEI), which derives the performance of soybean genotypes differentially in various environmental conditions, is one of the main obstacles to increasing the net production. The primary goal of this study is to identify the outperforming genotypes in different latitudes, which can then be used in future breeding programs. A total of 96 soybean genotypes were examined in two different ecological regions: Faisalabad and Tando Jam in Pakistan. The evaluation of genotypes in different environmental conditions showed a substantial amount of genetic diversity for grain yield. We identified 13 environment-specific genotypes showing their maximum grain yield in each environment. Genotype G69 was found to be an ideal genotype with higher grain yield than other genotypes tested in this study and is broadly adapted for environments E1 and E2 and also included in top-yielding genotypes in E3, E4, and E5. G92 is another genotype that is broadly adapted in E1, E3, and E4. In the case of environments, E3 is suggested to be a more ideal environment as it is plotted near the concentric circle and is very informative for the selection of genotypes with high yield. Despite the presence of GEI, advances in DNA technology provided very useful tools to investigate the insight of advanced genotypes. Association mapping is a useful method for swiftly and efficiently investigating the genetic basis of significant plant traits. A total of 26 marker–trait associations were found for six agronomic traits in five environments, with the highest significance (p-value = 2.48 × 10–08) for plant height and the lowest significance (1.03 × 10–03) for hundred-grain weight. Soybean genotypes identified in the present study could be a valuable source for future breeding programs as they are adaptable to a wide range of environments. Genetic selection of genotypes with the best yields can be used for gross grain production in a wide range of climatic conditions, and it would give an essential reference in terms of soybean variety selection.

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Identification of superior genotypes for leaf architecture traits in Sorghum bicolor through GGE biplot analysis

Wang,

Zhao,

Wang

et al. 2024

Crop & Pasture Science

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Context Well-organised leaf architecture produces compact canopies and allows for greater sunlight penetration, higher photosynthetic rates, and thus greater yields. Breeding for enhanced leaf architecture of sorghum (Sorghum bicolor L.), a key food source in semi-arid regions, benefits its overall production. Aims The study focuses on selecting useful genotypes with excellent leaf architecture for grain sorghum improvement. Methods In total, 185 sorghum genotypes were subjected to multi-environment trials. Leaf flagging-point length, leaf length, leaf width, leaf angle and leaf orientation value (LOV) were characterised under field conditions. Genotype + genotype × environment interaction (GGE) biplot analysis was used to identify the most stable genotypes with the highest LOV. Key results Statistical analysis showed significant effects of genotype × environment interaction (P < 0.001), and high broad-sense heritability for the traits. Correlation analysis demonstrated negative correlations (P < 0.001) between LOV and its components. Singular value decomposition of LOVs in the first two principal components explained 89.19% of the total variation. GGE biplot analysis identified G55 as the ideotype with the highest and most stable LOV. Conclusions Leaf architecture optimisation should be given greater attention. This study has identified a genotype with optimal and stable leaf architecture, laying the foundation for improvement in breeding to increase overall yields of sorghum. Implications Genotype G55 can be utilised as a parent with other parents that display economically important characteristics in breeding programs to produce offspring that can be planted densely to increase population yields. Genotypes identified with loose leaf architecture are useful in dissecting genes controlling leaf architecture by crossing with G55 to construct genetic mapping populations.

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Adaptability and stability of conventional soybean by GGE biplot analysis

Cited by 3 publications

References 16 publications

Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia

Adaptability and stability for soybean yield by AMMI and GGE models in Ethiopia

Analysis of genotype × environment interactions for agronomic traits of soybean (Glycine max [L.] Merr.) using association mapping

Identification of superior genotypes for leaf architecture traits in Sorghum bicolor through GGE biplot analysis

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