scholarly journals RGxE: An R Program for Genotype x Environment Interaction Analysis

2017 ◽  
Vol 08 (07) ◽  
pp. 1672-1698 ◽  
Author(s):  
Mahendra Dia ◽  
Todd C. Wehner ◽  
Consuelo Arellano
Keyword(s):  
Interaction Analysis ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
R Program

scholarly journals Genotype x environment interaction and stability of indigenous coriander (Coriandrum sativum L.) genotypes for seed yield in different agro-climatic zones of Chhattisgarh

1970 ◽  
pp. 122-130
Author(s):  
S L Sawargaonkar ◽  
A K Singh ◽  
J Tiwari ◽  
K P Singh ◽  
A Sao ◽  
...  
Keyword(s):  
Seed Yield ◽  
Interaction Analysis ◽  
Block Design ◽  
Principal Component ◽  
Genotype X Environment Interaction ◽  
Coriandrum Sativum ◽  
Environment Interaction ◽  
Climatic Zones ◽  
Genotype X Environment ◽  
Testing Environments

The present study was conducted to find out the stability and yield performances of 13 genotypes of indigenous coriander (Coriandrum sativum L.) evaluated in different agro climatic zones of Chhattisgarh. The trials were laid out in a Randomized Block Design (RBD) with three replications at three locations for three years resulting in nine environments (Genotype × year interactions). The genotypes and G × E interactions revealed significant differences at p <0.01 for seed yield indicating varieties and testing environments were distinct from each other. Additive main effects and multiplicative interaction analysis (AMMI-biplot) indicated that the yield performances of indigenous coriander genotypes were highly affected by the environments. The first two principal component axes (PCA 1 and PCA 2) were significant and they explained 67% of the total genotype x environment interaction of which 42.4% and 24.6% were represented by PCA 1 and PCA 2, respectively. A biplot generated using genotypic and environmental scores of the first two AMMI components demonstrated that genotype with larger PCA 1 and lower PCA 2 scores were high yielding and stable genotypes and genotypes with lower PCA 1 and larger PCA 2 scores were low yielding and unstable cultivars in tested locations. The genotype GC 5 C-101 (ICS 4) showed higher grain yields (16.35 q ha-1) over grand mean (13.03 q ha-1) and also had the minimum PCA 1 score, minimum AMMI stability value (ASV) and yield stability index (YSI). Therefore genotype ICS 4 (Chhattisgarh Shri Chandrahasini Dhaniya -2) showed wider stability across different agro climatic environments of Chhattisgarh.

scholarly journals Genotype x environment interaction analysis in Wheat (Triticum aestivum L.)

2020 ◽  
Vol 5 (01) ◽  
pp. 128-132
Author(s):  
Dipendra Rawal ◽  
Amit Tomar ◽  
Mahak Singh
Keyword(s):  
Interaction Analysis ◽  
Triticum Aestivum L ◽  
Sensitivity Coefficient ◽  
Genotype X Environment Interaction ◽  
Linear Component ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
G X E Interaction ◽  
Days To Heading

The results of genotype x environment interaction analysis revealed highly significant mean squares due to genotypes as well as environments for all the characters which indicated presence of substantial differences among the genotypes as well as environments for all the eleven characters. The environment linear (E-L) component was also significant for all the characters indicating that the six environments can be graded linearly for their differences in influencing the expression of characters of wheat genotypes. The mean squares due to genotype x environment interaction were also highly significant for all the characters except days to heading, plant height, effective tillers per plant and total number of tillers per plant to suggest important role of g x e interaction in expression of most of the characters in wheat. The linear component of g x e interaction was significant for all the characters which indicated good possibility of predicting linear responses of genotypes for all the characters under study. Thus, there would be ample possibility of discriminating the genotypes for above average, below average and average linear responses to predict their performances in changing environments under study. The significance of non-linear component (pooled deviation) of g x e interactions for all characters except ear length suggested that considerable number of genotypes may exhibit unpredictable and unstable mean performance for different characters across environments even defying their prediction on the basis of linear sensitivity coefficient.

scholarly journals Genotype X Environment Interaction of Food Barley

2015 ◽  
Vol 21 ◽  
pp. 41-48
Author(s):  
Gebremedhin Welu
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Genotype Effect ◽  
Environment Effects ◽  
Randomized Complete Block Design ◽  
Barley Genotypes

The objective of this experiment was to estimate the magnitude of genotype X environment interaction on grain yield and yield related traits. Twelve varieties of food barley were included in the study planted in randomized complete block design with three replications. The ANOVA of combined and individual location revealed significant differences among the food barley genotypes for grain yield and other traits. The results of ANOVA for grain yield showed highly significant (p≤0.01) differences among genotypes evaluated for grain yield at Maychew and significant (p≤0.05) differences in Korem, Alage and Mugulat. The ANOVA over locations showed a highly significant (p≤0.01) variation for the genotype effect, environment effects, genotype X environment interaction (GEI) effect and significant (p≤0.05) variation for GEI effect of yield and for most of the yield related traits of food barley genotypes. Haftysene, Yidogit, Estayish and Basso were the genotypes with relatively high mean grain yield across all locations and they are highly performing genotypes to the area. Among locations, the highest mean grain yield was recorded at Korem and it was a suited environment to all the genotypes whereas Mugulat is unfavoured one. ECOPRINT 21: 41-48, 2014DOI: http://dx.doi.org/10.3126/eco.v21i0.11903

scholarly journals Genotype x Environment Interaction and Yield-Stability Analyses of Rice Grown in Tropical Inland Swamp

2011 ◽  
Vol 39 (1) ◽  
pp. 220 ◽  
Author(s):  
Adesola L. NASSIR ◽  
Omolayo J. ARIYO
Keyword(s):  
Total Variation ◽  
Yield Stability ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Rice Varieties ◽  
Environmental Indices ◽  
Genotype X Environment ◽  
Additional Information ◽  
Gxe Interaction ◽  
The Stability

Twelve rice varieties were cultivated in inland hydromorphic lowland over a four year-season period in tropical rainforest ecology to study the genotype x environment (GxE) interaction and yield stability and to determine the agronomic and environmental factors responsible for the interaction. Data on yield and agronomic characters and environmental variables were analyzed using the Additive Main Effect and Multiplicative Interaction (AMMI), Genotype and Genotype x Environment Interaction, GGE and the yield stability using the modified rank-sum statistic (YSi). AMMI analysis revealed environmental differences as accounting for 47.6% of the total variation. The genotype and GxE interaction accounted for 28.5% and 24% respectively. The first and second interaction axes captured 57% and 30% of the total variation due to GXE interaction. The analysis identified ‘TOX 3107’ as having a combination of stable and average yield. The GGE captured 85.8%of the total GxE. ‘TOX 3226-53-2-2-2’ and ‘ITA 230’ were high yielding but adjudged unstable by AMMI. These two varieties along with ‘WITA 1’ and ‘TOX 3180-32-2-1-3-5’ were identified with good inland swamp environment, which is essentially moisture based. The two varieties (‘TOX 3226-53-2-2-2’ and ‘ITA 230’), which were equally considered unstable in yield by the stability variance, ?2i, were selected by YSi in addition to ‘TOX 3107’, ‘WITA 1’, ‘IR 8’ and ‘M 55’. The statistic may positively complement AMMI and GGE in selecting varieties suited to specific locations with peculiar fluctuations in environmental indices. Correlation of PC scores with environmental and agronomic variables identified total rainfall up to the reproductive stage, variation in tillering ability and plant height as the most important factors underlying the GxE interaction. Additional information from the models can be positively utilized in varietal development for different ecologies.

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