GGE biplot analysis of genotype × environment interaction in<i>rabi</i>grain sorghum [<i>Sorghum bicolor</i>(L.) Moench]

Rakshit, Sujay; Ganapathy, K. N.; Gomashe, S. S.; Swapna, M.; More, A. W.; Gadakh, S. R.; Ghorade, R. B.; Kajjidoni, S. T.; Solanki, B. G.; Biradar, B. D.; Prabhakar, .

doi:10.5958/0975-6906.2014.00889.x

Cited by 16 publications

(14 citation statements)

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“…No environment fall into sectors with G34 and G44 as the vertices, indicating that these genotypes were not the best in any environments. Similar findings were reported earlier (Rakshit et al 2012;2014;Singh et al 2019 ). The present investigation also showed that instead of conducting multi-location trials (MLTs) across closely related locations, near similar conclusion could be drown from fewer locations clustered within a mega-environment (ME).…”

Section: Which-won-where and Mega Environment Identificationsupporting

confidence: 90%

“…The distance between two environments indicates their dissimilarity in discriminating ability among genotypes. If two test locations are consistently highly correlated across the years, one location can be eliminated from the analysis without losing much genotypic information (Farshadfar et al 2013;Rakshit et al 2014). Close relationship between the test environments may be removed from multilocation testing of cultivars as they will generate the similar information.…”

Section: Discriminating Ability and Representativeness Of Environmentsmentioning

confidence: 99%

“…The GGE biplot Which-Won-Where (Fig.4), which is based on a tester-centered (G + GE) table, without any scaling and it is row metric preserving. Several researchers (Yan et al 2000;Yan and Timker, 2006;Rakshit et al 2012;2014) also reported that the most attractive feature of GGE biplot is which-won-where analysis in which crossover genotype × environment interactions (GEI), mega-environment differentiation and specific adaptation of genotypes are graphically represented. The polygon (biplot of which-won-where) is formed by joining the markers of the genotypes that are farthest from the biplot origin, such that all other genotypes are placed within polygon and also a set of equality and/or perpendicular lines drawn from biplot origin to each side of the polygon.…”

Section: Which-won-where and Mega Environment Identificationmentioning

confidence: 99%

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Multi-environment analysis of grain yield in a diverse set of bread wheat genotypes

Singh

Gupta²,

Kumar³

et al. 2020

Journal of Cereal Research

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A diverse set of 50 bread wheat genotypes was evaluated at 16 different locations belonging to four major wheat growing zones of India during 2016-17. The yield data was analyzed using Eberhart and Russel (1966), AMMI and GGE biplot stability models. Genotypes G14 and G26 were identified as the most stable genotypes based on Eberhart and Russel (1966) model, whereas genotypes G10, G14, G18, G23, G26, G30, and G47 were identified stable as per AMMI model whereas five genotypes G26, G23, G10, G43 and G14 were found stable as per GGE biplot analysis. Genotypes G14 and G26 were turned out to be the most stable genotypes based on these three stability models. The G48 ranked best for (Durgapura and Pantnagar); G29 (Powarkheda) and G35 (Hisar, Delhi and Ludhiana) based on grain yield performance. The GGE bi-plot indicated Durgapura to be an ideal environment followed by Hisar, Ludhiana, Delhi, Pantnagar, and Powarkheda that could be recommended for the future wheat breeding programmes aiming towards yield enhancement.

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Section: Which-won-where and Mega Environment Identificationsupporting

confidence: 90%

Section: Discriminating Ability and Representativeness Of Environmentsmentioning

confidence: 99%

Section: Which-won-where and Mega Environment Identificationmentioning

confidence: 99%

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Multi-environment analysis of grain yield in a diverse set of bread wheat genotypes

Singh

Gupta²,

Kumar³

et al. 2020

Journal of Cereal Research

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“…In both years (particularly in 2009/10), it was found that a majority of locations were closely related, which has also been observed in other sorghum MLT data (Rakshit et al . 2012 a , 2014). Non-discriminating and closely related locations may be dropped in future from the programme as they will not generate any additional information.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Indian post-rainy sorghum multi-location trial data reveals complexity of genotype × environment interaction

et al. 2016

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SUMMARYSorghum [Sorghum bicolor (L.) Moench] grown in India is of two adaptive types: rainy and post-rainy. The post-rainy sorghum is predominantly consumed by humans. While releasing new cultivars through multi-location testing, major emphasis is given to the superiority of new cultivars over existing cultivars, with very little emphasis on the genotype × environment interaction (GEI). To understand the complexity of GEI in post-rainy sorghum testing location trials, the multi-location evaluation data of two post-rainy seasons (2009/10 and 2010/11) under the All India Coordinated Sorghum Improvement Project were analysed. In both years, location explained the highest proportion of total sum of squares followed by the GEI effect and main effect of genotype. Additive main effects and multiplicative interaction (AMMI), stability values (ASV) and genotype + genotype × environment interaction (GGE) instability values recorded high correlation resulting in identification of the best performing cultivars. However, the rank correlations were lower, though still significant. A mixture of crossover and non-crossover GEI was a common occurrence in both years. ‘Which-won-where’ analysis suggested the existence of four possible mega-environments (ME) among post-rainy testing locations, with a few non-informative locations within ME. Mega-environments are characterized by soil type, rainfall pattern and moisture conservation practices. The present study indicated the possibility of reducing the number of test locations by eliminating non-representative highly correlated locations and suggested the need to breed for location-specific genotypes rather than genotypes with wider adaptability.

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“…The GGE biplot analysis was used to identify the best line of each environment and assess the stability of the lines. The most attractive feature of GGE biplots is the 'which-won-where' analysis, in which crossover GE interaction, mega-environment differentiation and specific genotype adaptation are graphically represented (Rakshit et al 2014, Oral et al 2018. The visualization of a 'which-won-where' pattern in multi-environment trials is essential to study the possible existence of different mega-environments in a region (Yan and Tinker 2006).…”

Section: Gge Biplot Analysismentioning

confidence: 99%

Genotype x environment interaction analysis of multi-environment wheat trials in India using AMMI and GGE biplot models

Singh

Gupta

et al. 2019

Crop Breed. Appl. Biotechnol.

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GGE biplot analysis of genotype × environment interaction inrabigrain sorghum [Sorghum bicolor(L.) Moench]

Cited by 16 publications

References 0 publications

Multi-environment analysis of grain yield in a diverse set of bread wheat genotypes

Multi-environment analysis of grain yield in a diverse set of bread wheat genotypes

Analysis of Indian post-rainy sorghum multi-location trial data reveals complexity of genotype × environment interaction

Genotype x environment interaction analysis of multi-environment wheat trials in India using AMMI and GGE biplot models

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