Comparison of GGE biplot and AMMI analysis of multi-environment trial (MET) data to assess adaptability and stability of rice genotypes

Abstract

Genotype × Environment (G×E) interaction and stability performance were investigated on paddy yield of eighteen rice genotypes and twelve locations using two well renowned statistical models; genotype main effect and G×E Biplot analysis (GGE) and additive main effects and multiplicative interaction (AMMI) analysis. The aim of this study was to elucidate the performance of some advance rice lines/genotypes at multiple locations in multi environment trials (METs) using GGE biplot and AMMI analyses. The results of GGE biplot and AMMI analyses performed over the data of paddy yield at multiple locations of two years 2014 and 2015 indicated that G×E interaction plays a crucial role in determining the performance of genetic material in METs. The results declare that GGE and AMMI not only provide easy and affective evaluation of genotypes into environment interactions in a number of locations but also a comprehensive understanding of the variability of the target locations. AMMI analyses for data of both years indicated that RRI 7 was the highest priority selected genotype for six locations, NIAB 1175 for four and RRI 3 for three locations. Dhokri and Kala Shah Kaku were the highest yielding, while Faisalabad and Dhokri were the most stable environments in 2014. Likewise, Faisalabad and PARC Islamabad were the highest yielding as well as most stable environments in 2015. Basmati 515 and PS 2 were the most favorable genotypes in 2014 and 2015, respectively for their high paddy yield and stability at all locations. The results further suggested that both models were useful and presented similar interpretations about MET data. Key words: Genotype main effect and G × E biplot analysis (GGE), additive main effects and multiplicative interaction (AMMI) analysis, rice, fine type, multiple locations.

Genotype x environment interaction for yield of rice hybrids and inbred varieties in Venezuela

“…The GGE biplot analysis showed high variability as compared to that reported by Ponnuswamy et al (2018) and Haider et al (2017). Additionally, both components IPCA1 and IPCA2 showed high significance.…”

“…Recent studies describe the predicting efficiency of AMMI and GGE biplots by using the genotype average yield, the relationship between genotypes and the evaluation of the environments (Acevedo et al, 2021;Acevedo et al, 2019;Rebolledo et al, 2018;Haider et al, 2017;Akter et al, 2015). Nevertheless, few publications are available in Venezuela and Latin America on rice hybrid GEI research.…”

Importance of mega-environments in evaluation and identification of climate resilient maize hybrids (Zea mays L.)

“…To identify stable genotypes and investigate the GE interaction among crops, numerous statistical methods have been proposed since the 19 th century, ranging from univariate methods such as the regression method [ 18 , 19 ] and stability method [ 20 ] to multivariate methods like factor analytic models, AMMI and GGE. For evaluating multi-location trials, biplot approaches such as GGE, AMMI and WAASB are widely used [ 21 , 22 ] as they provide information on stable genotypes and types of GE interactions and also assist in the identification of mega-environments.…”

Rendimiento y estabilidad de genotipos de soya para el trópico de México

“…La relación que presentaron los años dependió del coseno de ángulo de los vectores el cual se aproxima al coeficiente de correlación, donde el ángulo agudo indicó una relación positiva, sin embargo el ángulo obtuso reveló una asociación negativa y los ángulos rectos significó que no existió ninguna (Yan, 2002;Haider et al, 2017), pudiéndose formar dos grupos de megaambientes: el primer grupo estuvo conformado por los años 2015, 2016 y 2017, asimismo el segundo grupo se conformó por el año 2014. Este último presentó un ángulo de recto a obtuso con respecto a los años del primer grupo.…”