Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods

Sousa, Larissa Barbosa de; Hamawaki, Osvaldo Toshiyuki; Nogueira, Ana Paula Oliveira; Batista, R. O.; Oliveira, Valécia Martins de; Hamawaki, Raphael Lemes

doi:10.4238/2015.october.19.10

Cited by 24 publications

(34 citation statements)

References 20 publications

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“…In contrast, the genotypes Delta Opal (G7) and BRS BURITI (G2) were the most unstable. Importantly, we note that the classification of genotypes based on phenotypic stability was similar from both methods used in this study, corroborating previous results (Balestre et al, 2009;Miranda et al 2009;Camargo-Buitrago et al, 2011;Mattos et al, 2013;Sousa et al, 2015). An ideal genotype should have a mean seed cotton yield that is consistently high over all environments of interest.…”

Section: Resultssupporting

confidence: 89%

Biplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso

Farias¹,

Carvalho²,

Filho³

et al. 2016

Genet. Mol. Res.

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ABSTRACT. Seed cotton yield is a trait governed by multiple genes that cause changes in the performance of genotypes depending on the cultivation environment. Breeding programs examine the genotype x environment interaction (GE) using precise statistical methods, such as AMMI (additive main effects and multiplicative interaction) and GGE biplot (genotype main effects + genotype x environment interaction). The AMMI method combines the analysis of variance and principal components, to adjust the main effects (genotypes and environments) and the effects of GE interaction, respectively. The GGE biplot groups the genotype additive effect together with the multiplicative effect of the GE interaction, and submits both of these to the principal components analysis. The aim of this study was to investigate the association between the AMMI and GGE biplot methods and select cotton genotypes that simultaneously showed high productivity of seed cotton and stability in Mato Grosso environments. cotton productivity were analyzed by AMMI and GGE biplot methods. Both methods were concordant in the discrimination of environments and genotypes for phenotypic stability. The genotypes BRS ARAÇÁ and LD 05 CV had high seed cotton productivity and phenotypic stability, and could be grown in all environments across Mato Grosso State.

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Section: Resultssupporting

confidence: 89%

Biplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso

Farias¹,

Carvalho²,

Filho³

et al. 2016

Genet. Mol. Res.

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“…Estimates of decomposition of the complex G × E interaction greater than 50% represented a predominance of complex interactions (Sousa et al, 2015), indicating the changes in the magnitude of the differences among genotypes in different environments or from changes in their relative ranking (Sivcev et al, 2011). The major challenge of plant breeders was to find the useful information hidden within the multi-environment data, and then interpret and use it effectively.…”

Section: Discussionmentioning

confidence: 99%

“…Soybean yield is a complex quantitative trait controlled by many genes, and it is determined by multiple interactions between genes and environment (Li et al, 2008), greatly affected by environmental conditions especially day length of different latitude (Singh and Vatsa, 2009;Zhang et al, 2015;Bhartiya and Aditya, 2016;AbdulHamid et al, 2017). Photoperiod is the leading climatic factor in determining soybean adaptation to different eco-regions (Câmara et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Identification of Traits Contributing to High and Stable Yields in Different Soybean Varieties Across Three Chinese Latitudes

Liu

Chun-sheng

et al. 2020

Front. Plant Sci.

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Soybean yield is a complex quantitative trait, which is greatly affected by environmental conditions. The main objective of this study is not only to identify specific traits contributing to yield in different latitudes, which can be further used in breeding, but also to identify the outperforming varieties, as this can help to select new lines with these traits. One hundred and seventy-three soybean genotypes were tested in three different ecological environments, including Harbin, Changchun, and Shenyang in China during 2015-2016 cropping seasons. The evaluation on the different agronomic and physiological traits indicated that the soybean varieties with higher plant height, more nodes of main stem, branches, pods, grains, and 100-grain weight, or longer growth periods may have higher yield. Pods, grains and 100-grain weight can be used as direct selection criteria for yield increase, and likewise the other traits such as plant height, nodes of main stem, branches, growth periods indirectly affected yield by affecting the three traits above. The effect of genotype × environment (G × E) interaction on different agronomic traits was significant. The representativeness and discriminability for grains yield per plant was the most significant in Harbin, which could be used to screen varieties with high yield and wider adaptability. Genotype "Suinong 1" was considered stable with higher value of grain yield per plant than other genotypes used in this study. As the yield of certain soybean cultivars may be significantly reduced if they are grown in a region as little as 2°N beyond its normal cultivation latitudes, therefore, the identification and analysis on the stable and widely adaptive soybean genotypes would be very important, and it would provide the significant reference accordance of soybean variety selection for the soybean breeders.

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“…The seed yield of nine genotypes were significantly affected by environment, genotype, and GEI. The coefficient of variation (CV) for seed yield was 12.34%, indicating the precision of experimental research and control of environmental factors [8]. The highly significant genotype indicates the different performance of genotypes across environments [5].…”

Section: Resultsmentioning

confidence: 99%

“…Currently, the additive main effects and multiplicative interaction analysis (AMMI) have been widely used to quantify the genotypic effects of the GEI [6,7,8,9,10]. AMMI biplot analysis is defined as a useful tool to explore the GEI patterns [11] with simple graphic representation of genotypes and environments in a multivariate biplot [12].…”

Section: Introductionmentioning

confidence: 99%

Yield stability of soybean promising lines across environments

Krisnawati¹,

Adie²

2018

IOP Conf. Ser.: Earth Environ. Sci.

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Abstract. Soybean is grown in the wide range of environments in Indonesia, hence the genotype and environment interaction is become one of major complication in the breeding programs. The objective of this study was to evaluate the stability performance of soybean promising lines in eight locations during the dry season 2015. A completely randomized block design with four replicates was used to arrange the experiment. An additive main effects and multiplicative interaction (AMMI) model was used to examine the performance of yield stability. Soybean seed yield was affected by genotype (9.23%), environment (45.23%) and their interaction (45.62%). The first two principal component axes (PCA 1 and PCA 2) were significant (p < 0.01) and cumulatively contributed to 77.41% of the total variation. Based on AMMI multivariate method, two genotypes (G511H/Anj-1-6 and G511H/Anj-1-4) with seed yield of 2.67 and 2.74 t/ha, respectively, were identified as high yielding and stable promising lines across environments.

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Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods

Cited by 24 publications

References 20 publications

Biplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso

Biplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso

Identification of Traits Contributing to High and Stable Yields in Different Soybean Varieties Across Three Chinese Latitudes

Yield stability of soybean promising lines across environments

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