Interpretation of Genotype × Environment Interaction in Agronomic Traits in 24 Potato Cultivars Using AMMI and GGE Biplot Analysis

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).

Download Full-text

GGE biplot analysis of genotype × environment interaction inrabigrain sorghum [Sorghum bicolor(L.) Moench]

Indian Journal of Genetics and Plant Breeding (The) ◽

10.5958/0975-6906.2014.00889.x ◽

2014 ◽

Vol 74 (4s) ◽

pp. 558 ◽

Cited By ~ 3

Author(s):

Sujay Rakshit ◽

K. N. Ganapathy ◽

S. S. Gomashe ◽

M. Swapna ◽

A. More ◽

...

Keyword(s):

Sorghum Bicolor ◽

Environment Interaction ◽

Gge Biplot ◽

Biplot Analysis ◽

Genotype Environment Interaction ◽

Sorghum Bicolor L

Download Full-text

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

Indian Journal of Genetics and Plant Breeding (The) ◽

10.31742/ijgpb.79.4.3 ◽

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.

Download Full-text

Evaluation of the performance of sorghum genotypes using GGE biplot

Canadian Journal of Plant Science ◽

10.4141/cjps-2015-119 ◽

2015 ◽

Vol 95 (6) ◽

pp. 1205-1214 ◽

Cited By ~ 8

Author(s):

Edmore Gasura ◽

Peter S. Setimela ◽

Caleb M. Souta

Keyword(s):

Grain Yield ◽

Agronomic Traits ◽

Abiotic Factors ◽

Research Station ◽

Environment Interaction ◽

Gge Biplot ◽

Ecological Zones ◽

Genotype Environment Interaction ◽

Sorghum Production ◽

Sorghum Genotypes

Gasura, E., Setimela, P. S. and Souta, C. M. 2015. Evaluation of the performance of sorghum genotypes using GGE biplot. Can. J. Plant Sci. 95: 1205–1214. In spite of sorghum's drought tolerance, it is largely affected by genotype×environment interaction (GE), making it difficult and expensive to select and recommend new sorghum genotypes for different environments. The objectives of this study were to examine the nature of GE for sorghum grain yield, to identify superior sorghum genotypes for sorghum production environments and determine ideal testing locations for future breeding activities in Zimbabwe. The grain yield of 20 sorghum genotypes from Seed Co. Pvt. Ltd. were evaluated for 2 yr (2011/2012 and 2012/2013 cropping seasons) at five locations in different agro-ecological zones of Zimbabwe. Combined analyses of variance showed significant differences for genotypes (P<0.01), environments (P<0.001) and genotype×location (P<0.01). Genotype×environment variance component was seven times greater than that of genotypes. Genotype×environment interaction was attributed to the variability in the predictable biotic and abiotic factors associated with the different locations. The genotype main effect plus GE biplot showed that the experimental sorghum genotypes W07, W09, W05, G06 and OP46 were high yielding and stable, and possessed other desirable agronomic traits. The most discriminating and representative location was Rattray Arnold Research Station.

Download Full-text

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

Ilmu Pertanian (Agricultural Science) ◽

10.22146/ipas.33593 ◽

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.

Download Full-text