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 Expected genetic advance for thousand grain weight and grain number per spike of bread wheat and durum wheat

2016 ◽  
Vol 61 (2) ◽  
pp. 113-125
Author(s):  
Gordana Brankovic ◽  
Dejan Dodig ◽  
Desimir Knezevic ◽  
Vesna Kandic ◽  
Jovan Pavlov
Keyword(s):  
Durum Wheat ◽  
Bread Wheat ◽  
Yield Component ◽  
Grain Weight ◽  
Phenotypic Variance ◽  
Environment Interaction ◽  
Genetic Advance ◽  
Thousand Grain Weight ◽  
Grain Number ◽  
Grain Number Per Spike

The research was aimed at examining variability, variance components, broadsense heritability (h2), expected genetic advance of thousand grain weight (TGW) and grain number per spike (GNS) of 15 genotypes of bread wheat and 15 genotypes of durum wheat. Field trials were carried out during 2010-2011 and 2011-2012 growing seasons at the three sites: Rimski Sancevi, Zemun Polje and Padinska Skela. Results of this investigation showed that the genetic component of variance (?2 g) was predominant for TGW of bread and durum wheat and for GNS of bread wheat. The genotype ? environment interaction (?2 ge) component of phenotypic variance was 8.72 times higher than ?2 g for GNS of durum wheat and pointed to the greater instability of durum wheat genotypes. h2 was very high (>90%) for TGW and GNS of bread wheat, high for TGW of durum wheat - 87.3% and low for GNS of durum wheat - 39.5%. Considering the high values obtained for h2 - 96.4% and the highest value for expected genetic advance as percent of mean (GAM) - 19.3% for TGW of bread wheat, the success of selection for desired values of this yield component can be anticipated. The success of selection cannot be predicted for GNS of durum wheat due to low values obtained for h2 and GAM of 39.5% and 2.8%, respectively.

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.

scholarly journals Genotype x environment interaction of wheat genotypes under salinity environments

2017 ◽  
Vol 3 (1) ◽  
pp. 38-43
Author(s):  
Md Saleh Uddin ◽  
Md Sultan Alam ◽  
Nasrin Jahan ◽  
Kazi Md Wayaz Hossain ◽  
Md Ali Newaz
Keyword(s):  
Grain Yield ◽  
Grain Weight ◽  
Environment Interaction ◽  
Stability Parameters ◽  
Wheat Genotypes ◽  
Genotype X Environment ◽  
Genotype Environment Interaction ◽  
Days To Heading ◽  
Salinity Levels ◽  
1000 Grain Weight

Genotypes x environment interaction as well as stability of performance were determined for grain yield and yield contributes of 12 wheat genotypes under four salinity levels of environments (control, 8, 12, 16 dS/m). Significant genotype-environment interaction (linear) for days to heading, plant height, number of spikes per plant and grains per spikes, 1000-grain weight and grain yield per plant at 1% level of probability when tested against pooled deviation. Both the environment (linear) and genotype x environment (linear) components of variation for stability were also significant indicating that prediction of the genotypes on the environment appeared feasible for all the characters. The variance due to pooled deviation was significant for only days to heading. Considering all the three stability parameter, genotype G11 was found most stable among all the genotypes for grain weight of wheat. Among the genotypes G11, G22, G24, G33 and G40 were most desirable for yield per plant. The genotype G32 showed more responsiveness to changing environment and was suited only for highly favorable environments. Based on three stability parameters, G11, G22 and G37 were the most stable and desirable genotypes with reasonable good yield among the all.Asian J. Med. Biol. Res. March 2017, 3(1): 38-43

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.

A study of genotype × environment interaction in three chickpea triple test crosses

1986 ◽  
Vol 107 (3) ◽  
pp. 549-554
Author(s):  
S. Singh ◽  
I. S. Pawar ◽  
I. P. Singh
Keyword(s):  
Grain Yield ◽  
Plant Height ◽  
Grain Weight ◽  
Environment Interaction ◽  
Gene Effects ◽  
Genotype Environment Interaction ◽  
Triple Test ◽  
Sowing Dates ◽  
Additive Gene ◽  
Number Of Branches

SUMMARYThe analysis of Perkins & Jinks (1971) was applied to 360 progeny families of three chickpea F2 triple test crosses, namely, F 378 × ICCC 1, P 1198–1 × ICCC 1 and US 613 × BG 203, to detect and measure the interaction of additive, dominance and epistatic gene effects with sowing dates. The families were grown in completely randomized blocks in three replications with two sowing dates, and data were recorded for plant height, number of branches per plant, number of days from sowing to flowering, number of days from sowing to maturity, number of pods per plant, number of grains per plant, 100-grain weight and grain yield per plant. The i type epistasis and additive genetic component were relatively more important than j and l type epistasis and dominance component, respectively. The j and l type epistasis and additive gene effects were more sensitive to environmental differences than the i type epistasis and dominance gene effects, respectively.

scholarly journals Analysis of Genotype-Environment Interaction and Yield Stability of Introduced Upland Rice in the Groundnut Basin Agroclimatic Zone of Senegal

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ghislain Kanfany ◽  
Mathieu Anatole Tele Ayenan ◽  
Yedomon Ange Bovys Zoclanclounon ◽  
Talla Kane ◽  
Malick Ndiaye ◽  
...  
Keyword(s):  
Grain Yield ◽  
Upland Rice ◽  
Block Design ◽  
Principal Component ◽  
Stability Index ◽  
Rice Production ◽  
Yield Stability ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype Environment Interaction

Identification of highly performing varieties under Senegalese environment is crucial to sustain rice production. Genotype-environment interaction and stability performance on the grain yield of ten upland rice genotypes were investigated across 11 environments in Senegal during the rainy seasons of 2016 and 2017 to identify adapted varieties. The experiment was conducted using a randomized complete block design with three replications at each environment. Data on grain yield were recorded and analyzed using the additive main effects and multiplicative interaction (AMMI) model. The combined analysis of variance revealed that the grain yield was significantly affected by environment (67.9%), followed by genotype × environment (G × E) interaction (23.6%) and genotype (8.5%). The first two principal component axes were highly significant with 37.5 and 26% of the total observed G × E interaction variation, respectively. GGE biplot grouped the environments into four potential megaenvironments. Based on the yield stability index parameter and ranking GGE biplot, NERICA 8 and ART3-7-L9P8-1-B-B-1 were stable and high-yielding varieties compared to the local check NERICA 6. These varieties should be proposed for cultivation in order to sustain the rice production in the southern part of the groundnut basin of Senegal and used as parental lines in rice breeding program for grain yield improvement.

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.

Genotype, genotype × environmental and environmental effects on grain yield and related characters of cowpea (Vigna unguiculata (L.) Walp.)

1977 ◽  
Vol 28 (4) ◽  
pp. 609 ◽  
Author(s):  
W Erskine ◽  
TN Khan
Keyword(s):  
Grain Yield ◽  
Environmental Effects ◽  
Soil Conditions ◽  
Environment Interaction ◽  
Plant Seed ◽  
Genotype Environment Interaction ◽  
Two Factors ◽  
Tropical Areas ◽  
Joint Regression Analysis ◽  
Pod Length

Genotypic, environmental and genotype x environmental interaction variances of grain yield and related characters were estimated in six diverse lowland environments in Papua New Guinea. The genotypic variance of grain yield for six diverse genotypes accounted for only 0.2% of overall variance and was not significant against genotype x environment interaction. The relative merits of alternative selection criteria, viz. pod number per plant, seed number per pod, mean seed weight, pod length and main stem height were assessed. Environmental effects of grain yield accounted for 82% of total variance, and two factors associated with moisture and soil conditions were isolated as the major causes of this variation. Genotype × environment interaction effects were analysed by joint regression analysis, and the applicability of the analysis to tropical areas with low levels of management is discussed.

scholarly journals GGE Biplot Analysis of Genotype by Environment Interaction and Grain Yield Stability of Bread Wheat Genotypes in Central Ethiopia

2019 ◽  
Vol 7 (2) ◽  
pp. 75-85
Author(s):  
Gadisa A. Wardofa ◽  
Dawit Asnake ◽  
Hussein Mohammed
Keyword(s):  
Grain Yield ◽  
Bread Wheat ◽  
Principal Component ◽  
Moisture Stress ◽  
Genotype By Environment Interaction ◽  
Large Contribution ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Wheat Genotypes ◽  
Wide Range

GGE biplot is an effective method based on principal component analysis to fully explore mega-environments trials data. The study conducted was to identify the best performing, high yielding stable advanced bread wheat genotype for selection environments, the identification of mega-environments and analysis of the ideal genotype and environment by GGE biplot method. Fifteen bread wheat genotypes were evaluated using RCBD with four replications at six locations in Ethiopia. The results of combined analysis of variance for grain yield of fifteen bread wheat genotypes indicated that genotype, environment and GEI were highly significant (P0.001). The factors explained showed bread wheat genotypes grain yield was affected by environment (35.28%), genotype (33.46%) and GEI (31.45%). The first two PC axes of GGE explained 88.7% of G+GEI and divided the six locations into three major groups: Group1 included Asasa, Kulumsa and Arsi Robe (moderately discriminating locations); Group2 had the highland wheat producing locations Holeta and Bekoji (most discriminating locations), while Group3 contain Dhera (least discriminating location), a moisture stress location in the rift valley. Locations within the same group were closely correlated and provided redundant information about the genotypes. Testing can be performed in any one of the locations within a group. Genotype ETBW8078 and ETBW8459 were more stable as well as low yielding. Considering simultaneously yield and stability, genotype ETBW9045 and Hiddase showed the best performances suggesting their adaptation to a wide range of environments. Lemu, ETBW8084 and ETBW8065 were considered as desirable. Genotype ETBW8075 was the least stable with low yield and had a large contribution to the GEI, having the longest distance from the average environment. ETBW9470 was specifically adapted to Group1 locations while ETBW8070 was adapted to Group2 environments. Based on yield performance advanced lines ETBW9470 and ETBW8070 are recommended to be included in variety verification trials for further release.

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