Identification of suitable mungbean genotypes for hilly terrains of India using GGE and AMMI biplot approaches

Muniyandi, Samuel Jeberson; Parihar, A. K.; Shashidhar, K.S.; Dev, Jai; Dar, Shabir Ahmad; Gupta, Shalani

doi:10.22438/jeb/40/6/mrn-974

Cited by 2 publications

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“…Among different methods employed in understanding GE, GGE biplot analysis is most effective (Yan, 2002Dias et al, 2003and Ma et al, 2004. GGE biplot analysis is being exploited in different crops such as soybean (Yan and Rajcan, 2002;Bhartiya et al, 2018), mungbean (Jeberson et al, 2019), rice (Samonte et al, 2005), wheat (Kaya et al, 2006;Singh et al, 2019b), barley (Dehgani et al, 2006, lentil (Sabaghnia et al, 2008), sorghum (Rakshit et al, 2012) in multi-environmental trials. Although, Chen et al (2007) .…”

Section: Introductionmentioning

confidence: 99%

GGE biplot analysis of vegetable type soybean genotypes under multi-environmental conditions in India

Nataraj¹,

Pandey²,

Ramteke³

et al. 2021

JEB

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Aim: To understand the magnitude and pattern of genotype-environment interaction in vegetable type soybeans and to identify mega environment(s) and best performing genotype(s) across environments. Methodology: Five vegetable type soybean genotypes were evaluated across five geographical locations viz., Indore, Parbhani, Adilabad, Bengaluru and Pune, during rainy season of 2018. Genotypes were grown in a plot size of 1.35 x 3 m2 in three replications in randomized block design. Data on green pod yield, green seed test weight, days to 50% flowering, days to maturity and plant height were recorded using standard methods. GGE biplot analysis was performed using software “GGE Biplot version 7.0”. Results: In the present investigation, except in case of green seed test weight, in remaining four traits, major portion of variation was contributed by location (52.95-79.4%) followed by genotype (17.7-42.7%) and genotype x location interaction (2.21-4.29%). Through GGE biplot analysis, Bengaluru was found to be near ideal environment and genotypes Karune and Harasoya were found to be the best performers across the locations with respect to green pod yield. Interpretation: Bengaluru was found to be near ideal environment for vegetable type soybean evaluation. Selection for genotypes having wider adaptability can be conducted at this location. Genotypes Karune and Harasoya were found to be the best performers with respect to green pod yield. These two genotypes can be included as parents for breeding as vegetable type soybean. Key words: GEE biplot, Multienvironmental analyses, Soybean genotypes

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Section: Introductionmentioning

confidence: 99%

GGE biplot analysis of vegetable type soybean genotypes under multi-environmental conditions in India

Nataraj¹,

Pandey²,

Ramteke³

et al. 2021

JEB

Self Cite

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“…Thus, GGE biplot provides a better graphical representation of true worth of an accession as it simultaneously assesses stability of genotypes, mean performance, discriminating ability, representativeness of environment, etc. (Jeberson et al, 2019). GGE biplot was constructed by the two principle components (PC1 and PC2) derived from subjecting environment centered wilt incidence data, i.e., Fusarium wilt variation due to GGE to singular value decomposition.…”

Section: Resultsmentioning

confidence: 99%

Evaluation of chickpea (Cicer arietinum L.) accessions for Fusarium wilt resistance

Srivastava¹,

Dixit²,

Singh³

et al. 2021

JEB

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Aim: The present study was conducted to evaluate chickpea breeding lines and popular varieties for Fusarium wilt resistance through multi-location field evaluation in major chickpea growing states of India. Methodology: Forty-five chickpea accessions were evaluated for Fusarium wilt resistance in field sick plots at Kanpur, Junagadh, Sehore and Rahuri in India during 2016 and 2017. Each accession was planted in three replications in a randomized block design. The data was subjected to analysis of variance (ANOVA) for testing the significance of variation due to accessions, environments and their interaction. GGE biplots analysis were constructed from the first two principal components (PC1 and PC2) using 45 genotypes and 8 environments using statistical software R, versions 2.15. Results: The effects of genotype, environment and G x E interaction for wilt incidence were highly significant with maximum variation caused by G x E interaction effect (50.42%), followed by genotypic (46.92 %) and environmental effect (2.24%). GGE biplot analysis revealed that Rahuri and Junagadh locations were most discriminating locations and could differentiate the wilt resistant and susceptible chickpea accession, while Kanpur was least discriminating. Junagadh was most representative followed by Sehore and Kanpur while Rahuri was least representative of the average environment. On an average, the most severe wilt incidence was observed at Junagadh, followed by Sehore, Kanpur and Rahuri over the years. Interpretation: Elite chickpea accessions possessing high level of fusarium wilt resistant at each location can be utilized for region specific breeding.The accessions IPC 2008-11, H 2010-05, GNG 1581, JG 24, SCGP-WR 28, H 2010-01 and IPC 2008-69 exhibited stable resistance over locations. These possessed resistance against multiple races of Fusarium wilt prevailing in the country and can be utilized as donors for disease resistance breeding. Key words: Chickpea, Fusarium wilt, GGE biplot, Multi-location evaluation, Stable resistance

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Identification of suitable mungbean genotypes for hilly terrains of India using GGE and AMMI biplot approaches

Cited by 2 publications

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GGE biplot analysis of vegetable type soybean genotypes under multi-environmental conditions in India

GGE biplot analysis of vegetable type soybean genotypes under multi-environmental conditions in India

Evaluation of chickpea (Cicer arietinum L.) accessions for Fusarium wilt resistance

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