Retraction notice to ‘Use of HA-GGE biplot for interpretation of genotype × environment interaction when assessing sources of durable resistance against powdery mildew in mungbean (Vigna radiata)’ [Crop & Pasture Science (2020) doi:10.1071/CP20114]

2020 ◽  
Vol 71 (8) ◽  
pp. 794
Author(s):  
Arpita Das ◽  
Sanjeev Gupta ◽  
A. K. Parihar ◽  
K. P. Kushwaha ◽  
Sudip Bhattacharya
Keyword(s):  
Powdery Mildew ◽  
Vigna Radiata ◽  
Durable Resistance ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype Environment Interaction ◽  
Retraction Notice

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.

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 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 AMMI and GGE Biplot Analysis for Stability of Yield in Mid-early Pigeonpea [Cajanus cajan (L.) Millspaugh] Genotypes

2022 ◽  
Author(s):  
P. Jagan Mohan Rao ◽  
N. Sandhyakishore ◽  
S. Sandeep ◽  
G. Neelima ◽  
A. Saritha ◽  
...  
Keyword(s):  
Block Design ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Single Location ◽  
Randomized Complete Block Design ◽  
Advanced Stages ◽  
Superior Genotypes

Background: The genotype × environment interaction greatly influences the success of breeding and in multi-location trials complicates the identification of superior genotypes for a single location, due to magnitude of genotype by location interaction are often greater than genotype by year interaction. This necessitates genotype evaluation in multi environments trials in the advanced stages of selection. Methods: Nine elite pigeonpea genotypes of mid-early duration were evaluated in six diverse locations in randomized complete block design with three replications during kharif, 2019 to ascertain the stable genotypes, environments discrimination and genotype by environment crossovers using AMMI and GGE biplot stability models. Result: The results in the present investigation revealed that first two principal components explained 73.4% of variation interaction, while, 80.50% in GGE biplot. Both the models identified WRGE-126 (G6) as stable performer with high yield (1733 kg ha-1) and among the locations Tandur (E1) measured as the ideal environment. Whereas, the environments, Adilabad (E3) and Warangal (E4) were observed representative with better discriminating ability.

scholarly journals A study on genotype–environment interaction based on GGE biplot graphical method in sunflower genotypes ( Helianthus annuus L.)

2020 ◽  
Vol 8 (7) ◽  
pp. 3327-3334
Author(s):  
Isa Ansarifard ◽  
Khodadad Mostafavi ◽  
Mahmood Khosroshahli ◽  
Mohammad Reza Bihamta ◽  
Hosein Ramshini
Keyword(s):  
Helianthus Annuus ◽  
Graphical Method ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Helianthus Annuus L ◽  
Genotype Environment Interaction

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.

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