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

Singh, Charan; Gupta, Arun Kumar; Gupta, Vikas; Kumar, Pradeep; Sendhil, R; Tyagi, B. S.; Singh, Gyanendra Pratap; Chatrath, Ravish; Singh, G. P.

doi:10.1590/1984-70332019v19n3a43

Cited by 55 publications

(71 citation statements)

References 17 publications

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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%

“…AMMI and GGE are the two commonly used bi-plots techniques to visualize G×E interactions (Yan et al 2000). The GGE biplot is an effective method based on principal component analysis (PCA) to fully explore multienvironmental data (Rao et al 2011;Singh et al 2019). It has always been challenging to define how new genotypes would respond under different climatic conditions, without graphically presenting the data, when many cultivars are evaluated across many sites, seasons and years (Yan et al 2001).…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

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

Singh

Gupta²,

Kumar³

et al. 2020

Journal of Cereal Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…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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“…The AMMI model came to the attention of agricultural researchers particularly through the publications of Kempton (1984) and Zobel et al (1988). Since then, AMMI has become a popular statistical tool among agricultural researchers for the purposes of understanding the GE and for gaining accuracy in selection if stable jenotypes in many crops, such as wheat (Singh et al 2019), barley (Kiliç 2014), cassava (Morais et al 2017), and oilseed rape (Marjanović-Jeromela et al 2011;Zali et al 2016;Nowosad et al 2016) genotypes.…”

Section: Introductionmentioning

confidence: 99%

Analysis of genotype × environment interaction for seed yield in spring oilseed rape using the AMMI model

Agahi

Ahmadi

Oghan³

et al. 2020

Crop Breed. Appl. Biotechnol.

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In order to assess the genotype by environment interaction (GE) and select genotypes to exploit narrow and broad adaptations, twenty-two spring oilseed rape genotypes were subjected to field surveys at five experimental sites in the 2015-16 and 2016-17 growing seasons. Plant materials were sown in the form of a randomized complete block design with three replicates. The additive main effects and multiplicative interaction (AMMI) model was used to determine the genotype, environment, and GE effects. The sum of squares (SS) for the first three interaction principal components was very close to the SS for the GE signal; therefore, AMMI3 was diagnosed as the most accurate model to optimize predictive accuracy. Hyola 401 had the highest broad adaptability. In total, the chances of increasing yield were 55.

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

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

Cited by 55 publications

References 17 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

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

Analysis of genotype × environment interaction for seed yield in spring oilseed rape using the AMMI model

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