scholarly journals AMMI and GGE Biplot Analysis for Seed Yield and Oil Content of Sesame (Sesamum indicum L.) Genotypes in Tigray Northern Ethiopia

2020 ◽  
pp. 58-67 ◽  
Author(s):  
Yirga Belay Kindeya ◽  
Firew Mekbib ◽  
Eyasu Abraha Alle
Keyword(s):  
Seed Yield ◽  
Oil Content ◽  
Northern Ethiopia ◽  
Block Designs ◽  
Gge Biplot ◽  
Local Varieties ◽  
Biplot Analysis ◽  
Cropping Seasons ◽  
Ideal Genotype ◽  
Sesame Genotypes

Seventeen sesame genotypes were tested at ten environments in Tigray, Northern Ethiopia during 2014-2015 cropping seasons. Randomized Complete Block Designs (RCBD) with three replications was used in the study. According to the GGE bi-plot different sesame growing environments grouped into two mega-environments: The first mega-environment contained the favorable environments Dansha area with a vertex G4 and Sheraro area with winner G3 and the second environment included medium to low environments E2 (Humera-2), E4 (Dansha-2), E5 (Sheraro-1), E7 (Wargiba-1), E8 (Wargiba-2) and E9 (Maykadra) for seed yield. Three mega-environments identified for oil content: The 1st environment contained G12, G7 and G2 in the mega-environment group of Humera, Dansha and Gendawuha, The 2nd environment, Sheraro location contained G9 and the 3rd environment Wargiba, was containing G17. G1 (HuRC-4) identified as an “ideal” genotype and E1 (Humera-1) also identified as an ideal environment the most representative of the overall environments and the most powerful to discriminate genotypes. The multivariate approaches AMMI and GGEbi-plot were better for partitioning the GEI into the causes of variation. According to different stability models, G1, G7, and G3 were high yielder and the most stable both in terms of seed yield and oil content. Moreover, showed yield advantages over the released and local varieties. The stable genotypes recommended for wider areas while G14 and G4 were for specific favorable environments Sheraro and Dansha, respectively.

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 THE ANALYSIS OF GENOTYPE × ENVIRONMENT INTERACTION USING RAPESEED (BRASSICA NAPUS L.) BY GGE BIPLOT METHOD

2020 ◽  
Vol 51 (5) ◽  
pp. 1337-1349
Author(s):  
Motahhari & et al.
Keyword(s):  
Seed Yield ◽  
Agricultural Research ◽  
Environment Interaction ◽  
Block Designs ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Brassica Napus L ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Selection Of

This study was aimed to asses seed yield performances of 16 rapeseed genotypes  in randomized complete block designs (RCBD) with three replications at four Agricultural Research Stations of cold and mid-cold regions over two years in Iran (2015-2017). GGE biplot analysis indicated that the first two components explained 83% of seed yield variations. Genotype, location and their interaction explained 18%, 52% and 30%of the total GE variation, respectively. In this research, a graphically represented GGE biplot analysis enabled selection of stable and high-yielding genotypes for all investigated locations, as well as genotypes with specific adaptability. The GGE biplot analysis was adequate in explaining GE interaction for seed yield in rapeseed. It can be concluded that genotypes G2, G4 and G13 had the highest mean seed yield and stability in four investigated locations. For specific adaptability, G13 was recommended for Isfahan, Karaj and Kermanshah and G4 for Mashhad.

scholarly journals Biplot analysis of seed yield and oil content combining ability in rapeseed (Brassica napus L.)

2016 ◽  
Vol 10 (9) ◽  
pp. 1238-1243
Author(s):  
Gul Ghani ◽  
◽  
Raziuddin ◽  
Antonio Teixeira do Amaral Júnior ◽  
Ibni Amin Khalil ◽  
...  
Keyword(s):  
Brassica Napus ◽  
Seed Yield ◽  
Combining Ability ◽  
Oil Content ◽  
Brassica Napus L ◽  
Biplot Analysis

ADAPTIVE PROPERTIES OF NEW OAT VARIETIES IN THE MIDDLE VOLGA REGION

2020 ◽  
pp. 100-107
Author(s):  
V.G. Zakharov ◽  
◽  
О.G. Mishenkina ◽  
Keyword(s):  
Stability Index ◽  
Volga Region ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Average Yield ◽  
Middle Volga Region ◽  
Biplot Analysis ◽  
Middle Volga ◽  
Ideal Genotype ◽  
The Ideal

The research was conducted in 2016-2020 in the Ulyanovsk region. The aim was to assess the yield and genotype-environment interaction of varieties and promising lines of spring oats in the Middle Volga region. The source material was 9 varieties and 4 promising lines of oats created in the Ulyanovsk RAS. Contrasting moisture and temperature conditions provided differentiation of the studied material by yield and level of adaptability. Two-factor dispersion analysis revealed significant differences between genotypes in yield, media, and their interaction. The highest average yield among filmy varieties was formed by the Dragun variety (42.7 c/ha), the lowest by Vsadnik (37.0 c/ha). Naked varieties Azil and Griva showed the same yield (24.3 c / ha). The share of influence of environmental conditions (years) was 51.6%, varieties-33.8%. According to GGE biplot analysis, 2016, 2017, and 2020 were characterized by a high differentiating ability, while 2018 was the most representative. A rank assessment based on six adaptive criteria (regression coefficient (bi), stability index (S2 j), coefficient of variation (Vc), Martynov ultrastability (Hi), ultrastability (Hom) and selection value of the variety (Sc) V.V. Khangildin revealed the advantage of Grum (17), Dragun (22), Konkur (18), and Kenter (24) varieties, while Troika (64) had the lowest rank. Evaluation and ranking of genotypes by average yield and stability in different environments using GGE biplot analysis relative to the “ideal” genotype showed that the highest average yield was in the Dragun variety, which also has high stability, and practically corresponds to the «ideal» genotype. Next are the lines 479/11, 549/15, and the varieties Grum and Konkur, which are close to the « ideal» genotype. Less stable is the 537/15 line, which produced yields less than expected in 2016, 2018 and 2019 environments and more in 2017 and 2020. Biplot analysis of the yield of film varieties confirmed the results of the rank assessment for adaptability parameters, adjusting the location in the group of the best varieties.

Relationship between Seed Yield and Yield Chracteristics in Faba Bean (Vicia faba L.) by GGE-Biplot Analysis

2018 ◽  
Vol 12 (2) ◽  
Author(s):  
Sinem Koç ◽  
Adnan Orak ◽  
Hazım Serkan Tenikecier ◽  
Nezihi Sağlam
Keyword(s):  
Vicia Faba ◽  
Seed Yield ◽  
Faba Bean ◽  
Gge Biplot ◽  
Biplot Analysis ◽  
Vicia Faba L

AMMI and GGE biplot analysis of multi environment yield trial of soybeanin North Western Himalayan state Uttarakhand of India

2016 ◽  
Author(s):  
Anuradha Bhartiya ◽  
J. P. Aditya ◽  
Kamendra Singh ◽  
Pushpendra Pushpendra ◽  
J. P. Purwar ◽  
...  
Keyword(s):  
Seed Yield ◽  
Block Design ◽  
Principal Component ◽  
High Yield ◽  
Gge Biplot ◽  
Soybean Genotype ◽  
Genotype By Environment ◽  
Yield Trial ◽  
Soybean Genotypes ◽  
Ideal Genotype

The investigation was carried out to study Genotype × Environment (G×E) interaction for seed yield in 36 soybean genotypes including check PS1092 over 3 diverse environments represented by different altitudes in Uttarakhand. Grain yield performances of soybean genotypes were evaluated during Kharif 2013 season using a randomized complete block design. The AMMI analysis indicated that environment, genotypes and genotype by environment interactions had significantly affected seed yield and accounted for 9.76, 28.97 and 47.55% of the total variation, respectively. GGE biplot clearly displayed interrelationships between test locations as well as genotypes and facilitated visual comparisons based on Principal Component Analysis (PCA). The first two principal components PCI and PCII were used to create a two-dimensional GGE biplot that accounted for 45.68 and 38.88% variations respectively and based on discriminating and representative ability, E2 (Majhera) was most suitable location for selecting generally adapted genotypes. Soybean genotype C1 (PS1539) was identified as ideal genotype with high yield and low G×E interaction i.e. high stability.

Stability some sunflower genotypes across divergent environments

Helia ◽  
2020 ◽  
Vol 43 (72) ◽  
pp. 33-49
Author(s):  
Mohamed Ali Abdelsatar ◽  
Tamer Hassan Ali Hassan ◽  
Mahrous Abd El-Baset Attia
Keyword(s):  
Seed Yield ◽  
Oil Content ◽  
Seed Oil ◽  
Agricultural Research ◽  
Block Designs ◽  
Seed Oil Content ◽  
High Performing ◽  
Important Target ◽  
Wide Range ◽  
North Sinai

AbstractSimultaneously identify superior performing in terms of seed yield and seed oil content and broad adaptation across a wide range of different environments is an important target for sunflower breeder. So, 10 sunflower genotypes were evaluated across the eight various environments created by sowing at four locations i. e. Kafr El Hamam/ Sharkia, Shandaweel /Sohag, Tag El Ezz/ Dakahlia and Al Arish/ North Sinai Agricultural Research Stations, Agricultural Research Center (ARC), Egypt during the two successive summer seasons 2018 and 2019 using randomized complete block designs with four replications in each environment. Results showed that mean squares due to environments, genotypes and their interaction were highly significant for seed yield and seed oil content. Most stability approaches revealed that high performing stable genotypes were L240 for seed yield and Sakha 53, L110 and L235 for seed oil content under divergent environments. Hence, these four stable sunflower genotypes could be behaved as good breeding materials stock for sunflower improvement.

scholarly journals Response and yield stability of canola (Brassica napus L.) genotypes to multi-environments using GGE biplot analysis

2021 ◽  
Vol 33 (2) ◽  
pp. 105-114
Author(s):  
Mehmet Sincik ◽  
Abdurrahim T. Goksoy ◽  
Emre Senyigit ◽  
Yahya Ulusoy ◽  
Mustafa Acar ◽  
...  
Keyword(s):  
Seed Yield ◽  
Block Design ◽  
Data Matrix ◽  
Gge Biplot ◽  
Brassica Napus L ◽  
Yield Data ◽  
Essential Information ◽  
Biplot Analysis ◽  
Gxe Interaction ◽  
Main Effects

he GxE interaction (GEI) provides essential information for selecting and recommending cultivars in multi-environment trials. This study aimed to evaluate genotype (G) and environment (E) main effects and GxE interaction of 15 canola genotypes (10 canola lines and 5 check varieties) over 8 environments and to examine the existence of different mega environments. Canola yield performances were evaluated during 2015/16 and 2016/17 production season in three different locations (Southern Marmara, Thrace side of Marmara, and Black Sea regions) of Turkey. The trial in each location was arranged in a randomized complete block design with four replications. The seed yield data were analyzed using GGE biplot and the yield components data were analyzed using ANOVA. The agronomical traits revealed that environments, genotypes, and GEI were significant at 1 % probability for all of the characters. The variance analysis exhibited that genotypes, environments, and GEI explained 21.6, 21.7, and 25.7 % of the total sum of squares for seed yield, respectively. The GGE biplot analysis showed that the first and second principal components explained 57.3 and 18.3 % of the total variation in the data matrix, respectively. GGE biplot analysis showed that the polygon view of a biplot is an excellent way to visualize the interactions between genotypes and environments.

Sign in / Sign up

Export Citation Format

Share Document