AMMI and GGE biplot analysis of multi environment yield trial of soybean in North Western Himalayan state Uttarakhand of India

Bhartiya, Anuradha; Aditya, J. P.; Singh, Kulveer; Pushpendra, .; Purwar, J.P.; Agarwal, Anjuli

doi:10.18805/lr.v0iof.3548

Cited by 26 publications

(34 citation statements)

References 15 publications

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“…The results of this study imply that environment and GEI are important in controling the expression of yield trait (Gedif et al, 2014). Similar findings in the same crop were also obtained in various studies (Asfaw et al, 2009;Gurmu et al, 2009;Bueno et al, 2013;Atnaf et al, 2013;Jandong et al, 2011;Bhartiya et al, 2017).…”

Section: Discussionsupporting

confidence: 89%

“…GGE has been recognized and applied as a popular method to analyse and visualize the pattern of GEI in multi-environment yield trials in various crops (Brar et al, 2010;Jandong et al, 2011;Ullah et al, 2012;Bhartiya et al, 2017). However, the use of GGE biplot in Indonesia have not very much implemented, particularly as a tool for determining the mega environments, the best genotype in each location, and stability of soybean genotypes.…”

Section: Introductionmentioning

confidence: 99%

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GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

Krisnawati

Adie

2019

ipas

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Soybean in Indonesia is grown in diverse agro-ecological environments. The performance of soybean yield often varies due to significant genotype × environment interaction (GEI), therefore the yield stability of performance is an important consideration in the breeding program. The aim of the research was to exploring the GEI pattern and yield stability of soybean promising lines in the tropics using GGE (Genotype and Genotype by Environment Interaction) biplot method. A total of 16 soybean promising lines were evaluated in ten environments during 2016 growing season. The experiment was arranged in a randomized completely block design with four replicates. The analysis of variance revealed that environments (E) explained the highest percentage of variation (51.45%), meanwhile the genotypes (G) and genotype × environment interactions (GEI) contributed for 3.24%, and 14.59% of the total variation, respectively. Seed yield of 16 soybean promising lines ranged from 2.41 to 2.83 t.ha-1 with an average of 2.74 t.ha-1. Joint effects of genotype and interaction (G+GE) which was partitioned using GGE biplot analysis showed that the first two components were significant, explaining 60.88% (37.89% PC1 and 22.98% PC2) of the GGE sum of squares. Indonesia can be divided into at least four putative mega environments for soybean production. The GGE biplot identified G10 as high yielding and stable promising line, thus recommended to be developed in multi-environment in tropical regions of Indonesia.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

Krisnawati

Adie

2019

ipas

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“…Soybean genotype Himso1685 was found winner at Palampur and VLS 86 was found winner at Almora, Bajaura and Majhera and confirming the earlier study based on one year data in which both VLS 86 and Himso 1685 were found winner at Almora and Majhera location (Bhartiya et al 2016). Environments within the same sector share the same winning genotype or vice-versa (Farshadfar and Sadeghi 2014).…”

Section: Which-won-where and Mega-environment Identificationsupporting

confidence: 86%

Stability analysis of soybean [Glycine max (L.) Merrill] genotypes under multi-environments rainfed condition of North Western Himalayan hills

Bhartiya¹,

Aditya²,

Kumari³

et al. 2018

IJGPB

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In the present study, performance of five promising soybean genotypes over 4 locations during kharif 2013, 2014 and 2015 were investigated using GGE biplot analysis. Location attributed the highest proportion of the variation for all the traits except 100 seed weight ranging from 26.97-86.81% whereas, genotype contributed only 3.01-60.51% and genotype x location interaction contributed 6.01-31.42% of total variation. For 100 seed weight genotype has contributed major proportion of variation (66.26%) than location (31.08%) and genotype x location interaction (2.65%). Superior genotypes for key traits viz., grain yield (VLS 86) and 100 seed weight (Himso 1685) were effectively identified using GGE biplot graphical approach. It may be stated from present study that, VLS 86 was the closest to ideal genotype with stability for high grain yield as well as earliness. ‘Which-won-where’ study partitioned the testing locations into two mega-environments: first with three locations with VLS 86 as the winning genotype; second mega environment encompassed only one location with Himso 1685 as the winning genotype. Existence mega environments was found correlated with the rainfall pattern and clearly suggested that different entries need to be selected and deployed for realising maximum grain yield in hill zone.

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“…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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AMMI and GGE biplot analysis of multi environment yield trial of soybean in North Western Himalayan state Uttarakhand of India

Cited by 26 publications

References 15 publications

GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

Stability analysis of soybean [Glycine max (L.) Merrill] genotypes under multi-environments rainfed condition of North Western Himalayan hills

Yield stability of soybean promising lines across environments

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