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.

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 GGE Biplot Analysis of Multi-Environment Yield Trials in Soybean Promising Lines

2019 ◽  
Vol 3 (2) ◽  
pp. 72
Author(s):  
Ayda Krisnawati ◽  
M. Muchlish Adie
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Tropical Regions ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Promising Line ◽  
The Tropics

Soybean in Indonesia is grown in diverse agro-ecological environments. The performance of soybean yield often varies due to significant genotype × environment interaction (GEI), therefore the yield stability of performance is an important consideration in the breeding program. The aim of the research was to exploring the GEI pattern and yield stability of soybean promising lines in the tropics using GGE (Genotype and Genotype by Environment Interaction) biplot method. A total of 16 soybean promising lines were evaluated in ten environments during 2016 growing season. The experiment was arranged in a randomized completely block design with four replicates. The analysis of variance revealed that environments (E) explained the highest percentage of variation (51.45%), meanwhile the genotypes (G) and genotype × environment interactions (GEI) contributed for 3.24%, and 14.59% of the total variation, respectively. Seed yield of 16 soybean promising lines ranged from 2.41 to 2.83 t.ha-1 with an average of 2.74 t.ha-1. Joint effects of genotype and interaction (G+GE) which was partitioned using GGE biplot analysis showed that the first two components were significant, explaining 60.88% (37.89% PC1 and 22.98% PC2) of the GGE sum of squares. Indonesia can be divided into at least four putative mega environments for soybean production. The GGE biplot identified G10 as high yielding and stable promising line, thus recommended to be developed in multi-environment in tropical regions of Indonesia.

scholarly journals GGE biplot analysis of genotype × environment interaction in spring barley varieties

2014 ◽  
Vol 0 (106) ◽  
pp. 93-102
Author(s):  
П. М. Солонечний ◽  
М. Р. Козаченко ◽  
Н. І. Васько ◽  
О. Г. Наумов ◽  
О. Є. Важеніна ◽  
...  
Keyword(s):  
Spring Barley ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Biplot Analysis ◽  
Genotype Environment Interaction

scholarly journals Interpretation of Genotype × Environment Interaction of Sesame Yield Using GGE Biplot Analysis

2015 ◽  
Vol 60 (3) ◽  
pp. 349-354 ◽  
Author(s):  
Kang-Bo Shim ◽  
Seong-Hyu Shin ◽  
Ji-Young Shon ◽  
Shin-Gu Kang ◽  
Woon-Ho Yang ◽  
...  
Keyword(s):  
Environment Interaction ◽  
Gge Biplot ◽  
Biplot Analysis ◽  
Genotype Environment Interaction

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.

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