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.

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 Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

2021 ◽  
Vol 81 (01) ◽  
pp. 63-73
Author(s):  
M. V. Nagesh Kumar ◽  
V. Ramya ◽  
C. V. Sameer Kumar ◽  
T. Raju ◽  
N. M. Sunil Kumar ◽  
...  
Keyword(s):  
Cajanus Cajan ◽  
Large Scale ◽  
Interaction Model ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rainfed Agriculture ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Wide Range ◽  
Main Effects

Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important pulse crop grown under Indian rainfed agriculture. Twenty eight pigeonpea genotypes were tested for stability and adaptability across ten rainfed locations in the States of Telangana and Karnataka, India using AMMI (additive main effects and multiplicative interaction) model and GGE (genotype and genotype by environment) biplot method. The grain yields were significantly affected by environment (56.8%) followed by genotype × environment interaction (27.6%) and genotype (18.6%) variances. Two mega environments were identified with several winning genotypes viz., ICPH 2740 (G15), TS 3R (G10), PRG 176 (G8) and ICPL 96058 (G22). E2 (Gulbarga, Karnataka), E3 (Bidar, Karnataka) and E6 (Vikarabad, Telangana) were the most discriminating environments. Genotypes, ICPH 2740, PRG 176 and TS 3R were the best cultivars in all the environments whereas PRG 158 (G9), ICPL 87119 (G12), ICPL 20098 (G19) and ICPL 96058 (G22) were suitable across a wide range of environments. Genotypes, ICPH 2740 and PRG 176 can be recommended on a large scale to the farmers with small holdings to enhance pigeonpea productivity and improve the food security

scholarly journals AMMI and GGE biplots for G×E analysis of wheat genotypes under rain fed conditions in central zone of India

2015 ◽  
Vol 7 (2) ◽  
pp. 656-661 ◽  
Author(s):  
Ajay Verma ◽  
Ravish Chatrath ◽  
Indu Sharma
Keyword(s):  
Maximum Yield ◽  
Central Zone ◽  
Concentric Circle ◽  
Environment Interaction ◽  
Wheat Genotypes ◽  
Ammi Analysis ◽  
Concentric Circles ◽  
Ideal Genotype ◽  
Genotype G1 ◽  
The Ideal

The highly significant environments, genotypes and G×E interaction observed by AMMI analysis of 17 wheat genotypes evaluated at 8 locations in the central zone of the country. Environments(E), genotypes -environment interaction(GE) and genotypes explained 68.8%, 17.6% and 3.2% of the total sum of squares respectively. First four interaction principal components accounted 33.7%, 30.2%, 14.6% and 12.6% of the G×E interaction variation, respectively. The highest positive IPCA1 score of genotype G8 followed by G11 and G10 supported by yield higher than the grand mean 21.8q/ha. Environments E4 (Jabalpur) and E8 (Partapgarh) recorded maximum yield 32.6q/ha and 28.4q/ha while lowest yield was realized in E1 (Arnej). GGE biplot analysis under polygon view indicated that G13 was better in E6 (Sagar), whereas G1 was better in E7 (Bilaspur) and E8 (Partapgarh). The genotype G1, at the centre of concentric circles, was the ideal genotype in terms of yield performance as compared to the other genotypes. In addition, G15 and G12, located on the next consecutive concentric circle, may be regarded as desirable genotypes.

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.

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.

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