scholarly journals Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods

2012 ◽  
Vol 92 (4) ◽  
pp. 757-770 ◽  
Author(s):  
Reza Mohammadi ◽  
Ahmed Amri
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Environment Interaction ◽  
Parametric Methods ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Yield Data ◽  
Stability Performance ◽  
Genotype Environment Interaction ◽  
Non Parametric

Mohammadi, R. and Amri, A. 2012. Analysis of genotype × environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods. Can. J. Plant Sci. 92: 757–770. Multi-environment trials (MET) are conducted annually throughout the world in order to use the information contained in MET data for genotype evaluation and mega-environment identification. In this study, grain yield data of 13 durum and one bread wheat genotypes grown in 16 diversified environments (differing in winter temperatures and water regimes) were used to analyze genotype by environment (GE) interactions in rain-fed durum MET data in Iran. The main objectives were (i) to investigate the possibility of dividing the test locations representative for rain-fed durum production in Iran into mega-environments using the genotype main effect plus GE interaction (GGE) biplot model and (ii) to compare the effectiveness of the GGE-biplot and several non-parametric stability measures (NPSM), which are not well-documented, for evaluating the stability performance of genotypes tested and the possibility of recommending the best genotype(s) for commercial release in the rain-fed areas of Iran. The results indicate that the grain yield of different genotypes was significantly influenced by environmental effect. The greater GE interaction relative to genotype effect suggested significant environmental groups with different top-yielding genotypes. Warm environments differed from cold environments in the ranking of genotypes, while moderate environments were highly divergent and correlated with both cold and warm environments. Cold and warm environments were better than moderate environments in both discriminating and representativeness, suggesting the efficiency and accuracy of genotype selection would be greatly enhanced in such environments. According to the NPSM, genotypes tend to be classified into groups related to the static and dynamic concepts of stability. Both the GGE-biplot and NPSM methods were found to be useful, and generally gave similar results in identifying high-yielding and stable genotypes. In contrast to NPSM, the GGE-biplot analysis would serve as a better platform to analyze MET data, because it always explicitly indicates the average yield and stability of the genotypes and the discriminating ability and representativeness of the test environments.

Simultaneous selection of yield and yield stability in chickpea genotypes using the GGE biplot technique

2013 ◽  
Vol 61 (3) ◽  
pp. 185-194 ◽  
Author(s):  
E. Farshadfar
Keyword(s):  
Grain Yield ◽  
Principal Component ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Yield Data ◽  
Genotype By Environment ◽  
Growing Seasons ◽  
Genotype Environment Interaction ◽  
The Mean ◽  
Selection Of

GGE biplot analysis is an effective method, based on principal component analysis (PCA), to fully explore multi-environment trials (METs). It allows visual examination of the relationships among the test environments, genotypes and the genotype-by-environment interactions (G×E interaction). The objective of this study was to explore the effect of genotype (G) and the genotype × environment interaction (GEI) on the grain yield of 20 chickpea genotypes under two different rainfed and irrigated environments for 4 consecutive growing seasons (2008–2011). The yield data were analysed using the GGE biplot method. The first mega-environment contained environments E1, E3, E4 and E6, with genotype G17 (X96TH41K4) being the winner; the second mega-environment contained environments E5, E7 and E8, with genotype G12 (X96TH46) being the winner. The E2 environment made up another mega-environment, with G19 (FLIP-82-115) the winner. The mean performance and stability of the genotypes indicated that genotypes G4, G16 and G20 were highly stable with high grain yield.

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.

scholarly journals Genotype environment interaction and stability for yield and its components in advanced breeding lines of red rice (Oryza sativa L.)

2020 ◽  
Vol 49 (3) ◽  
pp. 425-435
Author(s):  
BM Dushyantha Kumar ◽  
AP Purushottam ◽  
P Raghavendra ◽  
T Vittal ◽  
KN Shubha ◽  
...  
Keyword(s):  
Grain Yield ◽  
Oryza Sativa L ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Red Rice ◽  
Yield Performance ◽  
Breeding Lines ◽  
Genotype Environment Interaction ◽  
Ammi Analysis ◽  
Total Variability

Effects of genotype, environment and their interaction for grain yield and yield attributing characters in 20 advanced breeding lines of rice across six environments was investigated. Yield stability and adaptability of yield performance were analyzed by Eberhart and Russel model and (GGE) bi-plot. The AMMI analysis of variance indicated that mean squares due to genotypes, location and genotype location contributed per cent 59.08, 5.79 and 21.63, respectively for total variability in grain yield per hectare. Estimates of GGE bi-plot revealed that the lines G1, G3, G11, G13, G15, G12, G16, G7 and G10 were positioned near GGL bi-plot origin indicating wider adaptation for the trait grain yield per hectare. Eberhart and Russel Model and GGE biplot model showed the advanced breeding lines viz., JB 1-11-7 (G1) and JA 6-2 (G15) exhibited wider adaptability across the tested environments for number of productive tillers per plant and yield per hectare.

Agro-morphological diversity and stability of durum wheat lines (Triticum durum Desf.) in Algeria

2013 ◽  
Vol 61 (2) ◽  
pp. 149-159 ◽  
Author(s):  
A. Mekliche ◽  
F. Dahlia ◽  
L. Hanifi-Mekliche
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Morphological Diversity ◽  
Environment Interaction ◽  
The North ◽  
Genotype Environment Interaction ◽  
Genotypic Stability ◽  
Triticum Durum Desf ◽  
Heterogeneity Variance ◽  
Wheat Lines

This study focuses on the genetic potential and genotypic stability of 17 durum wheat genotypes during three crop years under wet conditions in the north of Algeria (Algiers). The results showed highly significant (P<0.001) agro-morphological diversity between the genotypes and a genotype × environment interaction for all the traits except for fertile spikelet number. Wricke’s ecovalance (wi), Shukla's stability variance (σi2), heterogeneity variance (%HV) and the incomplete correlation (%IC) method were used to analyse the genotype × environment interaction on grain yield. The genotypes Ardente/Waha L2, Ardente and Saadi/Simeto L3 exhibited great instability with the highest values of wi, σi2, %HV and %IC. Ardente/Waha L1, Simeto/Vitron L5, Simeto and Ardente/Vitron L1 had the highest grain yield and average stability (wi, %HV and %IC were weak). Significant correlations were found between %HV, Rij2, bi, wi, σi2 and %IC, implying that they were similarly efficient in detecting stable genotypes and in measuring stability.

scholarly journals THE USE OF AMMI MODEL FOR INTERPRETING GENOTYPE × ENVIRONMENT INTERACTION IN DURUM WHEAT

2017 ◽  
Vol 54 (5) ◽  
pp. 670-683 ◽  
Author(s):  
REZA MOHAMMADI ◽  
MOHAMMAD ARMION ◽  
ESMAEIL ZADHASAN ◽  
MALEK MASOUD AHMADI ◽  
AHMED AMRI
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Air Temperature ◽  
Heading Date ◽  
Principal Component ◽  
High Yield ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Breeding Lines ◽  
Ammi Model

SUMMARYDurum wheat (Triticum durum) is one of the most important cereal crops in the Mediterranean region; however, its cultivation suffers from low yield due to environmental constrains. The main objectives of this study were to (i) assess genotype × environment (GE) interaction for grain yield in rainfed durum wheat and to (ii) analyse the relationships of GE interaction with genotypic/meteorological variables by the additive main effects and multiplicative interaction (AMMI) model. Grain yield and some related traits were evaluated in 25 durum wheat genotypes (landrace, breeding line, old and new varieties) in 12 rainfed environments differing in winter air temperature. The AMMI analysis of variance indicated that the environment had highest contribution (84.3% of total variation) to the variation in grain yield. The first interaction principal component axis (IPCA1) explained 77.5% of GE interaction sum of squares (SS), and its effect was 5.5 times greater than the genotype effect, indicating that the IPCA1 contributed remarkably to the total GE interaction. Large GE interaction for grain yield was detected, indicating specific adaptation of genotypes. While the postdictive success method indicated AMMI-4 as the best model, the predictive success one suggested AMMI-1. The AMMI biplot analysis confirmed a rank change interaction among the locations, indicating the presence of strong and unpredictable rank-change location-by-year interactions for locations. In contrast to landraces and old varieties, the breeding lines with high yield performance had high phenotypic plasticity under varying environmental conditions. Results indicated that the GE interaction was associated with the interaction of heading date, plant height, rainfall, air temperature and freezing days.

Evaluation of the performance of sorghum genotypes using GGE biplot

2015 ◽  
Vol 95 (6) ◽  
pp. 1205-1214 ◽  
Author(s):  
Edmore Gasura ◽  
Peter S. Setimela ◽  
Caleb M. Souta
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Abiotic Factors ◽  
Research Station ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Ecological Zones ◽  
Genotype Environment Interaction ◽  
Sorghum Production ◽  
Sorghum Genotypes

Gasura, E., Setimela, P. S. and Souta, C. M. 2015. Evaluation of the performance of sorghum genotypes using GGE biplot. Can. J. Plant Sci. 95: 1205–1214. In spite of sorghum's drought tolerance, it is largely affected by genotype×environment interaction (GE), making it difficult and expensive to select and recommend new sorghum genotypes for different environments. The objectives of this study were to examine the nature of GE for sorghum grain yield, to identify superior sorghum genotypes for sorghum production environments and determine ideal testing locations for future breeding activities in Zimbabwe. The grain yield of 20 sorghum genotypes from Seed Co. Pvt. Ltd. were evaluated for 2 yr (2011/2012 and 2012/2013 cropping seasons) at five locations in different agro-ecological zones of Zimbabwe. Combined analyses of variance showed significant differences for genotypes (P<0.01), environments (P<0.001) and genotype×location (P<0.01). Genotype×environment variance component was seven times greater than that of genotypes. Genotype×environment interaction was attributed to the variability in the predictable biotic and abiotic factors associated with the different locations. The genotype main effect plus GE biplot showed that the experimental sorghum genotypes W07, W09, W05, G06 and OP46 were high yielding and stable, and possessed other desirable agronomic traits. The most discriminating and representative location was Rattray Arnold Research Station.

Selection of environments using simultaneous clustering based on genotype × environment interaction

1994 ◽  
Vol 74 (2) ◽  
pp. 311-317 ◽  
Author(s):  
C. P. Baril ◽  
J-B. Denis ◽  
P. Brabant
Keyword(s):  
Cluster Analysis ◽  
Wheat Yield ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Yield Data ◽  
Cluster Procedure ◽  
Genotype Environment Interaction ◽  
Series Of Experiments ◽  
The Common ◽  
Selection Of

Cluster analysis is used to classify genotypes and environments to decompose and interpret genotype × environment (GE) interaction. A simultaneous clustering method is applied to wheat-yield data collected over 8 yr in seven locations, with two agronomic treatments per location. This approach evidenced redundancies among the used environments constituting the Institut National de la Recherche Agronomique series of experiments in northern France. The aim is to reduce the number of environments without losing GE interaction. A graphical method based on the decreasing mean square of GE interaction is proposed to provide a cutting criterion of the cluster procedure. The comparison of groupings made independently for successive years suggested the removal of some environments, hence providing rational savings in the breeding program. Lastly, the simultaneous two-way clustering procedure is compared with the common one-way clustering procedure. Key words: Cluster analysis, genotype × environment interaction, pattern analysis, series of experiments, wheat

scholarly journals Using of GGE biplot in determination of genetic structure and heterotic groups in wheat (Triticum aestivum L.)

2021 ◽  
Vol 65 (1) ◽  
pp. 17-27
Author(s):  
Ensieh Es'haghi Shamsabadi ◽  
Hossein Sabouri ◽  
Habibollah Soughi ◽  
Seyed Javad Sajadi ◽  
Ahmad Reza Dadras
Keyword(s):  
Grain Yield ◽  
Bread Wheat ◽  
Combining Ability ◽  
Grain Weight ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Heterotic Groups ◽  
Plant Seed ◽  
Genotype Environment Interaction ◽  
Grain Number Per Spike

The present study was undertaken to analyze diallel data using GGE biplot model to gather information about genetic interrelationships among parents and identification of heterotic combinations for yield and yield components in bread wheat varieties. For this purpose, 8 bread wheat genotypes tested across in half-diallel crosses design, GGE biplot technique was used. Parents included the genotypes of Kouhdasht, Karim, Ehsan, Mehregan, N-92-9, Line 17, N80-19 and Atrak. The hybrids obtained from the one-way cross (28 hybrids) in agricultural years of 2016-17 were evaluated as randomized complete block design in two replications on the research farm of Gonbad Kavous University. The evaluated traits included the grain yield, weight of spike grains, number of grains in spike and number of spikes. Additive main effects and genotype × environment interaction (GGE) were employed in the evaluation of genotypes; analyses showed significant (P< 0.01) G × E, (genotype × environment interaction) with respect to plant seed yield. GGE biplot analysis showed that Karim was as the best general combiners for grain yield, number grain per spike and grain weight per spike, whereas Ehsan had the highest GCA effects for number of spikes. Ehsan and Karim had higher specific combining ability than other genotypes. The studied genotypes for this trait were divided into two heterotic groups where the first group included the genotypes of Kouhdasht, N-92-9, N80-19 and Atrak and the second group contained the genotypes of Line 17, Mehregan and Karim. Mehregan line had a weak combining ability with all testers and N-92-9 had also more power than others. Based on the biplot, the Karim genotype with high general adaptation was introduced as the ideal genotype in terms of grain yield, spike number, grain number per spike and grain weight, so the Karim genotype can be adapted to obtain high yield hybrids.

scholarly journals Estimation of adaptability and stability of early strains of ‘Carioca’ and black beans (Phaseolus vulgaris) in Pernambuco State, Brazil

2020 ◽  
pp. 263-270 ◽  
Author(s):  
Richardson Sales Rocha ◽  
Paulo Ricardo dos Santos ◽  
Tâmara Rebecca Albuquerque de Oliveira ◽  
Rogério Figueiredo Daher ◽  
Maxwel Rodrigues Nascimento ◽  
...  
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Yield Data ◽  
Black Beans ◽  
Black Bean ◽  
Randomized Block Design ◽  
Yield Trait ◽  
Selection For

Low yields of early ‘Carioca’ and black bean varieties are a consequence of instability and low yield adaptability to specific growing environments. The goal of this work was to evaluate the genotype x environment interaction of early ‘Carioca’ and black bean new lines aiming at obtaining genotypes with high grain yield and good adaptability and stability. In (2012), 15 genotypes of the black group were evaluated in Ibimirim, Belém do São Francisco, and Petrolina municipalities, in Pernambuco State, Brazil; and, in (2016), 11 genotypes of early ‘Carioca’ bean were evaluated in Caruaru, Arcoverde, and Araripina municipalities, also in Pernambuco State. The experiment was conducted in a randomized block design with three replicates, and grain yield data were submitted to analysis of variance, in which means were compared by the Tukey’s test and analysis via GGE Biplot. In Arcoverde, the grain yield was greater for all early ‘Carioca’ bean genotypes, whereas, in the environment of Petrolina, there was the worst grain yield in kg ha-1 for all black bean genotypes, individually. The GGE Biplot methodology was found to be efficient in identifying genotypes with good adaptability, stability, and yield. Differences presented by the genotypes for the grain yield trait give the basis to recommend the genotypes and express the possibility of selection for genetic improvement of the species.

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