scholarly journals Stability of the expression of the maize productivity parameters by AMMI models and GGE-biplot analysis

2020 ◽  
Vol 48 (3) ◽  
pp. 1387-1397 ◽  
Author(s):  
Dragan BOŽOVIĆ ◽  
Vera POPOVIĆ ◽  
Vera RAJIČIĆ ◽  
Marko KOSTIĆ ◽  
Vladimir FILIPOVIĆ ◽  
...  
Keyword(s):  
Total Variation ◽  
Phenotypic Variance ◽  
Gge Biplot ◽  
Total Phenotypic Variance ◽  
Maize Productivity ◽  
Biplot Analysis ◽  
Growing Seasons ◽  
Maize Genotypes ◽  
Ammi Model

The objective of this study was to estimate genotype by locality, by year, by treatments (G×LxYxT) interaction using AMMI model, to identify maize genotypes with stable number of rows of grains performance in different growing seasons. The trials conducted with seven maize lines/genotypes, four treatments, two years and at the two locations. The results showed that the influence of genotype (G), year (Y), locality (L), and G×L, G×T, G×L×T, G×Y×T, G×Y×L×T interaction on maize number of rows of grains were significant (p<0.01). The genotype share in the total phenotypic variance for the grains number rows of was 53.50%, and the interaction was 21.15%. The results also show that the sums of the squares of the first and second major components (PC1 and PC2) constitute 100% of the sum of the squares of the interaction G×L. The first PC1 axis belongs to all 100%, which points to the significance of the genotype in the total variation and significance of the genotype for overall interaction with other observed sources of variability. The highest stability in terms of expression of the grains number of rows had the genotype L-6, followed by the genotypes L-4, L-5 and L-3. The lowest stability was demonstrated by the genotypes L-2 and L-1, which confirmed that these genotypes are not important for further selection in terms of this trait.

GGE biplot analysis of genotype × environment interaction and identification of mega-environment for baby corn hybrids evaluation in India

2020 ◽  
Vol 79 (04) ◽  
Author(s):  
Mukesh Choudhary ◽  
Bhupender Kumar ◽  
Pardeep Kumar ◽  
S. K. Guleria ◽  
N. K. Singh ◽  
...  
Keyword(s):  
Total Variation ◽  
Value Added ◽  
Corn Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Corn Hybrids ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Green Fodder ◽  
Fodder Yield

Baby corn has emerged as one of the most important sources to augment the farmer’s income in peri-urban areas. It has diverse uses as vegetables, snacks, value-added products and assured supply of green fodder for livestock. The multilocation varietal trials mainly emphasize on the identification of new superior cultivars over commercial checks, while genotype×environment interaction (GEI) is ignored. In the current study, 13 baby corn hybrids were evaluated for green ear yield, baby corn yield and green fodder yield over eight locations (environments) in kharif seasons of 2015 and 2016 using GGE biplot analysis. The results revealed a higher proportion of the variation in the data is attributable to the environment (72.4-87.0%), while genotype contributed only 2.5-7.3% of the total variation. GEI contributed 10.5-24.1% of the total variation. Superior stable hybrids for green ear yield, baby corn yield and green fodder yield could be identified using a biplot graphical approach effectively. ‘Which won where’ plot for each of the traits partitioned testing locations into three mega-environments with different winning genotypes for different traits in respective mega-environments. Thus it can be concluded that similar inferences can be drawn from one or two representatives of each mega-environment instead of using several locations. Hence, the presence of extensive crossover GEI in baby corn multi-location trials clearly suggests the need to emphasize on smaller zonation of testing locations and location-specific breeding. Particularly in baby corn, this is the first study on GGE biplot analysis to identify mega-environments for effective evaluation of baby corn trials.

Grain yield stability of barley genotypes in uniform regional yield trails in warm and semi warm dry land area

2017 ◽  
Author(s):  
Hassan Khanzadeh ◽  
Behroz Vaezi ◽  
Rahmatolah Mohammadi ◽  
Asghar Mehraban1 ◽  
Tahmaseb Hosseinpor ◽  
...  
Keyword(s):  
Grain Yield ◽  
Agricultural Research ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Dry Land ◽  
Biplot Analysis ◽  
Ammi Model ◽  
Cropping Seasons ◽  
Ideal Genotype ◽  
Better Than

The aim of this study was to assess the effect of GEI on grain yield of barley advanced lines and exploit the positive GEI effect using AMMI and SREG GGE biplot analysis. Therefore, 18 lines were evaluated at five research stations (Ghachsaran, Mogan, Lorestan, Gonbad and Ilam) of Dryland Agricultural Research Institute (DARI), in the semi-warm regions in Iran, in 2012, 2013 and 2014 cropping seasons under rain-fed conditions. Analysis of variance showed that grain yield variation due to the environments, genotypes and GE interaction were highly significant (p>0.01), which accounted for 68.9%, 9.3% and 22.7% of the treatment combination sum of squares, respectively. To determine the effects of GEI on yields, the data were subjected to AMMI and GGE biplot analysis. The first five AMMI model terms were highly significant (p>0.01) and the first two terms explained 59.56% of the GEI. There were two mega-environments according to the SREG GGE model. The best genotype in one location was not always the best in other test locations. According to AMMI1 biplot, G2, G4, G5 and G6 were better than all other genotypes across environments. G2 was the ideal genotype to plant in Gachsaran. It seems that Ghachsaran is the stable environment between the environments studied and next in rank was Gonbad. In finally, the ATC method indicated that G1, G3, G4 and G6 were more stable as well as high yielding.

scholarly journals Stability analysis of bread wheat landraces and lines using biometrical genetic models

Genetika ◽  
2018 ◽  
Vol 50 (2) ◽  
pp. 449-464
Author(s):  
Fatemeh Bavandpori ◽  
Jafar Ahmadi ◽  
Sayyed Hossaini
Keyword(s):  
Bread Wheat ◽  
Block Design ◽  
Gge Biplot ◽  
Breeding Programs ◽  
Biplot Analysis ◽  
Genotype Effect ◽  
Randomized Complete Block Design ◽  
Main Effect ◽  
Ammi Analysis ◽  
Ammi Model

In order to evaluate yield stability of twenty genotypes of bread wheat, an experiment was conducted in randomized complete block design (RCBD) with three replications under irrigated and rainfed conditions in Razi University of Kermanshah for three years (2011-2013). Combined analysis of variance showed highly significant differences for the GEI. Stability determined by AMMI analysis indicated that the first two AMMI model (AMMI1-AMMI2) were highly significant (P<0.01). The GEI was three times higher than that of the genotype effect. The results of Biplot AMMI2 showed that, genotypes WC-47359, WC-47472, WC-4611, WC-47388 and WC-47403 had general adaptability. Based on the ASV and GSI, the genotypes number WC-47403 and WC-47472 revealed the highest stability. GGE biplot analysis of yield displaying main effect G and GEI justified 57.5 percent of the total variation. The first two principal components (PC1 and PC2) were used to create a 2-dimensional GGE biplot and explained 34.3, 23.2 of GGE sum of squares (SS), respectively. Genotypes WC-47403, PISHGAM2 exhibited the highest mean yield and stability. Based on the results obtained the best genotypes were WC-47403, PISHGAM2, WC-4968, WC-47472 and WC-47528 for breeding programs.

scholarly journals Evaluation of genotype x environment interactions in maize hybrids using GGE biplot analysis

2011 ◽  
Vol 11 (1) ◽  
pp. 01-09 ◽  
Author(s):  
Fatma Aykut Tonk ◽  
Emre Ilker ◽  
Muzaffer Tosun
Keyword(s):  
Grain Yield ◽  
Gge Biplot ◽  
Maize Breeding ◽  
Private Companies ◽  
Breeding Programs ◽  
Maize Hybrids ◽  
Biplot Analysis ◽  
Maize Genotypes ◽  
Cropping Seasons ◽  
Hybrid Maize

Seventeen hybrid maize genotypes were evaluated at four different locations in 2005 and 2006 cropping seasons under irrigated conditions in Turkey. The analysis of variance showed that mean squares of environments (E), genotypes (G) and GE interactions (GEI) were highly significant and accounted for 74, 7 and 19 % of treatment combination sum squares, respectively. To determine the effects of GEI on grain yield, the data were subjected to the GGE biplot analysis. Maize hybrid G16 can be proposed as reliably growing in test locations for high grain yield. Also, only the Yenisehir location could be best representative of overall, locations for deciding about which experimental hybrids can be recommended for grain yield in this study. Consequently, using of grain yield per plant instead of grain yield per plot in hybrid maize breeding programs could be preferred by private companies due to some advantages.

scholarly journals AMMI and GGE Biplot Analysis for G×E Interaction of Wheat Genotypes under Different Irrigation and Sowing Condition

2021 ◽  
Author(s):  
Ragini Dolhey ◽  
V.S. Kandalkar
Keyword(s):  
Concentric Circle ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Wheat Genotypes ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Significant Difference ◽  
Ammi Model ◽  
Ideal Genotype ◽  
Main Effects

Background: AMMI analysis showed that genotype, environment and genotype-environment interaction had a highly significant variation for 20 wheat genotypes analyzed over four environments. ASV ranking revealed G15 (RVW-4275) as a stable genotype while G3 (RVW-4263) and G9 (RVW-4269) as unstable genotypes. GGE biplot analysis for environment interrelationship revealed that E1 (Irrigated timely sown), E2 (Restricted irrigation timely sown) were correlated forming one group and E3 (Irrigated late sown), E4 (Restricted irrigation late sown) were correlated forming another group. Polygon view showed that G9 (RVW-4269) was found stable and better performing in E1, G12 (RVW-4272) was stable under the E2 environment and G3 (RVW-4263) was stable in E3. Ideal genotype graph with concentric circles having ideal genotype at the center and genotypes G12(RVW-4272), G18(RVW-4278), G13(RVW-4273), G11(RVW-4271), G10 (RVW4270) present in a concentric circle close to the center can to considered as stable and desirable genotypes.Methods: In the present study the plant material comprised of 20 wheat genotypes. These genotypes were randomly allocated in different replication under different environmental condition. The field trial was evaluated at four different environments viz., E1- Irrigated timely sown, E2- Restricted irrigation is timely sown (RI- 2 irrigation), E3- Irrigated late sown, E4- Restricted irrigation late sown during Rabi season of 2016-2017 at research farms, college of agriculture, Gwalior, MP. The genotype main effects and genotype × environment interaction effects (GGE) model and additive main effects and multiplicative interaction (AMMI) model were two statistical approaches used to determine stable genotype in R software.Result: Highly significant difference was seen for genotype and G×E interaction in our study, revealing that genotype yield output was highly impacted by G×E. In all four environments and G3, G9 as unstable genotypes in all four environments, ASV ranking revealed G15 as a stable genotype. For further breeding, these genotypes G12, G18, G13, G11, G2, G10 and G15 may be used to grow genotypes adapted to conditions of partial irrigation or drought stress.

scholarly journals Stability and G × E Analysis of Oil Yield of Sunflower Single Cross Hybrids In Diverse Environments

2021 ◽  
Author(s):  
Mehdi Ghaffari ◽  
Amir Gholizadeh ◽  
Seyyed Abbasali Andarkhor ◽  
َAsadolah Zareei Siahbidi ◽  
Seyed Ahmad Kalantar Ahmadi ◽  
...  
Keyword(s):  
Graphical Method ◽  
Block Design ◽  
Oil Yield ◽  
Gge Biplot ◽  
Biplot Analysis ◽  
Growing Seasons ◽  
Superior Genotypes ◽  
Ideal Genotype ◽  
Selection Of ◽  
The Ideal

Abstract Multi-environment trials have a fundamental role in selection of the best genotypes across different environments before its commercial release. This study was carried out to identify high-yielding stable sunflower genotypes using the graphical method of the GGE biplot. For this purpose, 11 new hybrids along with four cultivars were evaluated in a randomized complete block design with four replications across 8 environments (combination of years and locations) during 2018–2020 growing seasons. The results indicated that genotype (G), environment (E) and genotype × environment (G×E) effects were significant for oil yield. The G, E and G×E interaction effects accounted for 51.94, 9.50 and 18.67% of the total variation, respectively. Results of biplot analysis showed that the first and second principle components accounted 45.9% and 20.4%, respectively, and in total 66.3% of oil yield variance. GGE biplot analysis indicated two major mega-environments of sunflower testing locations in Iran. Based on the hypothetical ideal genotype biplot, the genotypes G3 and G5 were better than the other genotypes for oil yield and stability, and had the high general adaptation to all environments. Ranking of genotypes based on the ideal genotype from the most appropriate to most inappropriate genotypes is as follows: G5 ˃ G3 ˃ G8 ˃ G14 ˃ G6 ˃ G2 ˃ G13 ˃ G12 ˃ G10 ˃ G11 ˃ G1 ˃ G7 ˃ G4 ˃ G15 ˃ G9. Furthermore, ranking the environments based on the ideal environment introduced Sari location as the best environment. Therefore, the Sari location can be used as suitable test location for selecting superior genotypes of sunflower in Iran. Generally, our results showed the efficiency of the graphical method of the GGE biplot for selection of the genotypes that are stable, high yielding, and responsive.

scholarly journals Evaluation of genotype × environment relationship in silage maize genotypes using biplot analysis and stability index

2020 ◽  
pp. 50-61
Author(s):  
Erkan Ozata
Keyword(s):  
Total Variation ◽  
Analysis Of Variance ◽  
Dry Matter ◽  
Block Design ◽  
Dry Matter Yield ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Biplot Analysis ◽  
Silage Maize ◽  
Herbage Yield

This study was conducted to evaluate the adaptability and stability of silage maize hybrids determing herbage and dry matter yield using Biplot analysis and some stability indexes. The studies were carried out using five registered corn varieties under irrigated conditions for six years (2013-2018) in Çarşamba plain of Samsun province, Turkey. The experimental layout was a Randomized Complete Block Design with four replications. Finlay and Wilkenson's regression and Eberhart and Russel's deviation from regression (S2d) coefficients were used in statistical analysis. Genotype (G) x environment (E) interactions were studied using the additive main effects and multiplicative interaction (AMMI) and G + GE (GGE) biplot models. The combined analysis of variance revealed significant (P<0.01) effects of G, E and G × E interaction on herbage and dry matter yields. The analysis of variance indicated that 62.70% of variation in the herbage yield explained by E, 29.79% by the differences in G and 7.49% by the GE interaction. The analysis of variance indicated that 62.80% of the total variation in the dry matter yield accounted for E, 30.20% of the total variation by G and G × E interaction explained only 7.00% of the total variation in the data. The results of AMMI and GGE biplot models and stability analyses (R2, bi and S2di) revealed that PR31Y43 and Burak hybrids were stable in both herbage yield and dry matter yield.

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