Application of GGE biplot to analyse stability of Iranian tall fescue (Lolium arundinaceum) genotypes

2015 ◽  
Vol 66 (9) ◽  
pp. 963 ◽  
Author(s):  
M. R. Dehghani ◽  
M. M. Majidi ◽  
G. Saeidi ◽  
A. Mirlohi ◽  
R. Amiri ◽  
...  
Keyword(s):  
Tall Fescue ◽  
Festuca Arundinacea ◽  
Forage Yield ◽  
Superior Performance ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Lolium Arundinaceum ◽  
Isfahan Province ◽  
Genotype Environment Interaction ◽  
Moisture Conditions

This research was carried out to determine stable genotypes and investigate genotype × environment interaction (GE) effects on the forage yields of 24 tall fescue genotypes (Lolium arundinaceum, syn. Festuca arundinacea Schreb.) across 14 test environments (combination of year, location and moisture conditions). The GGE biplot method was used to evaluate the phenotypic stability of forage yield in the studied genotypes. The GGE biplot analysis accounted for 75% of the G + GE variation. According to GGE biplot, in terms of performance, the genotypes were divided into two groups. The first group, with more than the average yield, included G20, G24, G04, G01, G22, G14, G10, G17 and G02. The second group included the remaining genotypes with below-average performance. From the seven foreign genotypes evaluated, G10 and G22 fell in the first group and the rest were clustered in the second group. In the first group, the performance of G24 (from Semnan province) was the most variable (the least stable), whereas the G20 and G14 (both from Isfahan province) were highly stable. In the second group, except for G08 and G16, the performance of genotypes was highly stable. The genotype G20 (from Isfahan province) had superior performance under all of the test environments, suggesting that it has a broad adaptation to the diverse environments. The results obtained in this study demonstrated the efficiency of the GGE biplot technique for selecting genotypes that are stable, high yielding, and responsive.

scholarly journals Delineation of Genotype × Environment Interaction for Identification of Stable Genotypes to Grain Yield in Mungbean

2020 ◽  
Vol 2 ◽  
Author(s):  
Santhi Madhavan Samyuktha ◽  
Devarajan Malarvizhi ◽  
Adhimoolam Karthikeyan ◽  
Manickam Dhasarathan ◽  
Arumugam Thanga Hemavathy ◽  
...  
Keyword(s):  
Grain Yield ◽  
Tamil Nadu ◽  
Stability Index ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype Environment Interaction ◽  
South Indian ◽  
Indian State ◽  
Wide Adaptation ◽  
High Yields

In the present study, fifty-two mungbean (Vigna radiata) genotypes were evaluated for seven morphological traits at three different environments in South Indian state Tamil Nadu, namely Virinjipuram (E1), Eachangkottai (E2), and Bhavanisagar (E3) during Kharif 2017, 2018, and 2019, respectively. The data collected were subjected to variability and correlation analyses, followed by stability analysis using additive main effects and multiplicative interaction (AMMI) model, genotype and genotype × environment interaction effects (GGE) biplot. Variablility was observed among the genotypes for the following traits viz., plant height, days to fifty per cent flowering, number of pods per plant, pod length, number of seeds per pod, hundred seed weight and grain yield. Correlation analysis showed that the trait number of pods per plant was significantly associated with grain yield. The G × E was smaller than the genetic variation of grain yield as it portrayed the maximum contribution of genotypic effects (61.07%). GGE biplot showed E3 as a highly discriminating and representative environment. It also identified environment-specific genotypes viz., EC 396111 for E1, EC 396125 for E2 and EC 396101 for E3 environments. The genotypes with minimum genotype stability index (GSI) viz., V2802BG (7), HG 22 (13), and EC 396098 (13) were observed with wide adaptation and high yields across all the three environments. In summary, we identified stable genotypes adapted across environments for grain yield. These genotypes can be used as parent/pre-breeding materials in future mungbean breeding programs.

Evaluation of sugarcane genotypes with respect to sucrose yield across three crop cycles using GGE biplot analysis

2021 ◽  
pp. 1-13
Author(s):  
Aliya Momotaz ◽  
Per H. McCord ◽  
R. Wayne Davidson ◽  
Duli Zhao ◽  
Miguel Baltazar ◽  
...  
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Area 5 ◽  
Randomized Complete Block Design ◽  
Main Effects

Summary The experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

scholarly journals GENOTYPE BY ENVIRONMENT INTERACTION IN COWPEA LINES USING GGE BIPLOT METHOD

2018 ◽  
Vol 31 (1) ◽  
pp. 64-71 ◽  
Author(s):  
MASSAINE BANDEIRA E SOUSA ◽  
KAESEL JACKSON DAMASCENO-SILVA ◽  
MAURISRAEL DE MOURA ROCHA ◽  
JOSÉ ÂNGELO NOGUEIRA DE MENEZES JÚNIOR ◽  
LAÍZE RAPHAELLE LEMOS LIMA
Keyword(s):  
Maximum Yield ◽  
Genotype By Environment Interaction ◽  
Superior Performance ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Brazilian Cerrado ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Adjusted Means ◽  
Selection Of

ABSTRACT The GGE Biplot method is efficien to identify favorable genotypes and ideal environments for evaluation. Therefore, the objective of this work was to evaluate the genotype by environment interaction (G×E) and select elite lines of cowpea from genotypes, which are part of the cultivation and use value tests of the Embrapa Meio-Norte Breeding Program, for regions of the Brazilian Cerrado, by the GGE-Biplot method. The grain yield of 40 cowpea genotypes, 30 lines and 10 cultivars, was evaluated during three years (2010, 2011 and 2012) in three locations: Balsas (BAL), São Raimundo das Mangabeiras (SRM) and Primavera do Leste (PRL). The data were subjected to analysis of variance, and adjusted means were obtained to perform the GGE-Biplot analysis. The graphic results showed variation in the performance of the genotypes in the locations evaluated over the years. The performance of the lines MNC02-675F-4-9 and MNC02-675F-4-10 were considered ideal, with maximum yield and good stability in the locations evaluated. There mega-environments were formed, encompassing environments correlated positively. The lines MNC02-675F-4-9, MNC02-675F-9-3 and MNC02-701F-2 had the best performance within each mega-environment. The environment PRL10 and lines near this environment, such as MNC02-677F-2, MNC02-677F-5 and the control cultivar (BRS-Marataoã) could be classified as those of greater reliability, determined basically by the genotypic effects, with reduced G×E. Most of the environments evaluated were ideal for evaluation of G×E, since the genotypes were well discriminated on them. Therefore, the selection of genotypes with adaptability and superior performance for specific environments through the GGE-Biplot analysis was possible.

scholarly journals Genotype-environment interaction, genetic trends, and performance dissimilarity of Nellore herds raised in three different environmental gradients

2018 ◽  
Vol 39 (1) ◽  
pp. 349
Author(s):  
Julio Cesar de Souza ◽  
Fabio Rafael Leão Fialho ◽  
Marcos Paulo Gonçalves Rezende ◽  
Carlos Henrique Cavallari Machado ◽  
Mariana Pereira Alencar ◽  
...  
Keyword(s):  
Weight Gain ◽  
Fixed Effects ◽  
Genetic Parameters ◽  
Environmental Gradients ◽  
Superior Performance ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
And Performance ◽  
Main Components ◽  
Genetic Trends

The objectives of this work were to evaluate the genotype-environment interaction, and estimate genetic parameters, genetic trends, and performance dissimilarity-weight gain from birth to weaning (WGBW), adjusted weight to 205 days (W205), weight gain from weaning to 18 months of age (WG18), and adjusted weight to 550 days (W550)-in Nellore animals born between 1986 and 2012, and raised in pasture-based system in three different environmental gradients in Brazil. Data of 62,001 animals-11,729 raised in the Alto Taquari/Bolsão region (ATBR), 21,143 raised in the Campo Grande/Dourados region (CGDR) and 29,129 raised in the western São Paulo/Paraná region (SPPR) in Brazil-were used. The contemporary groups were defined by sex, location, and birth year and season, with at least nine individuals, two different environments, and breeding bulls with at least five progenies. The statistical model contained the direct additive and residual genetic effects (random effects), and environmental and contemporary group effects (fixed effects). Genetic parameters, genotype-environment interaction and genetic trends were estimates using animal model (uni- and/or bi- traits). The level of similarity between regions was evaluated using principal components. The animals raised in the CGDR had superior performance regarding the traits evaluated. The direct heritability estimates ranged from 0.39 to 0.44 (WGBW), 0.41 to 0.45 (W205), 0.42 to 0.55 (WG18) and 0.60 to 0.62 (W550). The maternal heritability of the traits ranged from 0.20 (WGBW), 0.12 to 0.18 (W205), 0.00 to 0.06 (WG18) and 0.02 to 0.22 (W550). According to the Spearman correlation, the ranking of the breeding bulls in the regions evaluated were different. The mean of Euclidean distance indicated low similarity between ATBR and CGDR (43.20), and ATBR and SPPR (29.24). CGDR and SPPR presented similarity of 17.84. The breed values increased over the years in the traits evaluated. The cumulative variance percentage of the first two main components explained 99.99% variation among the regions, and the weight gains of the animals were the most important to differentiate the regions. A genotype-environment interaction was found for the traits evaluated, thus, the breeding bull selected with superior genetic merit for one region might not be the best for others.

Genotype–environment interaction of lovegrass forage yield in the semi-arid region of Argentina

2001 ◽  
Vol 137 (3) ◽  
pp. 329-336 ◽  
Author(s):  
M. A. IBAÑEZ ◽  
M. A. DI RENZO ◽  
S. S. SAMAME ◽  
N. C. BONAMICO ◽  
M. M. POVERENE
Keyword(s):  
Regression Analysis ◽  
Arid Region ◽  
Interaction Effects ◽  
Sum Of Squares ◽  
Forage Yield ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Ammi Model ◽  
Semi Arid Region ◽  
Semi Arid

Genotype–environment interaction and yield stability were evaluated for 19 genotypes of lovegrass (Eragrostis curvula). The study was conducted in the central semi-arid region of Argentina. Three locations and two growing seasons in combination generated six environments. Genotypic responses and stability of yield under variable environments were investigated. The genotype–environment interaction was analysed by three methods: regression analysis, AMMI and principal coordinates analysis (PCO). Analysis of variance showed that effects of genotype, environment and genotype–environment interaction were highly significant (P < 0·01). The genotypes accounted for 20% of the treatment sum of squares, with environment responsible for 65% and interaction for 14·5%. The biplot indicated that there was partial agreement between the AMMI and regression model. However the scatter point diagrams obtained from PCO analysis revealed only limited agreement with the results obtained by the regression analysis and the AMMI model. The results show that the AMMI model as a whole explained twice as much of the interaction sum of squares as did regression analysis and was more adequate than PCO analysis in quantifying environment and genotype effects for forage yield. AMMI analysis of the genotype–environment interaction effects showed that there were responses characteristic of a particular location. This type of association implies some predictability of genotype–environment interaction effects on forage yield production when differential responses across genotypes are associated with locations. Environmental factors may contribute to the interpretations of genotype–environment interaction. However in the semi-arid region, where fluctuations in growing conditions are unpredictable, additional research is required to obtain an integration of interaction analysis with external environmental (or genotypic) variables.

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