Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat

2020 ◽  
Author(s):  
Reza Mohammadi ◽  
Behzad Sadeghzadeh ◽  
Mohammad Mehdi Poursiahbidi ◽  
Malak Masoud Ahmadi
Keyword(s):  
Durum Wheat ◽  
Statistical Models ◽  
Environment Interaction ◽  
Multivariate Statistical ◽  
Genotype Environment Interaction

scholarly journals Analysis of yield stability of strawberry varieties and hybrids

2020 ◽  
Vol 25 ◽  
pp. 02014
Author(s):  
Vadim Lapshin ◽  
Valentina Yakovenko ◽  
Sergey Shcheglov
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster ◽  
Yield Stability ◽  
Environment Interaction ◽  
Multivariate Statistical ◽  
Genotype Environment Interaction ◽  
Yield Structure ◽  
Biological Yield ◽  
Factor Of Influence

The profitability of strawberry cultivation is largely determined by the capacity and quality of the yield, depended on the features of the variety genotype. The aim of this work was to estimate the yield stability of varieties and hybrids by the methods of multivariate statistical analysis and identify the best genotypes. To solve this problem, we have used the two-factor analysis of variance and hierarchical cluster analysis according to the Ward’s method as well as the integral estimate of the differences between the values of yield. The results of the studies have shown that the genotype of the variety (hybrid) are makes a decisive factor of influence for variability of the yield structure signs from 17,1% (number of inflorescences) to 32,2% (number of berries). The «genotype × environment» interaction is comparable with the genotype influence, the share of influence of the year conditions of the year is insignificant. Cluster analysis according to complex of economic valuable signs allows us to identify the eight forms that the most adapted to the conditions of the Krasnodar Territory as 13-1-15, Florence, Roxana, 18-1-15, Asia, Onda, Kemia, Nelli from which the Roxana, Florence, 18-1-15, 13-1-15 have a high and steadily rising biological yield.

Agro-morphological diversity and stability of durum wheat lines (Triticum durum Desf.) in Algeria

2013 ◽  
Vol 61 (2) ◽  
pp. 149-159 ◽  
Author(s):  
A. Mekliche ◽  
F. Dahlia ◽  
L. Hanifi-Mekliche
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Morphological Diversity ◽  
Environment Interaction ◽  
The North ◽  
Genotype Environment Interaction ◽  
Genotypic Stability ◽  
Triticum Durum Desf ◽  
Heterogeneity Variance ◽  
Wheat Lines

This study focuses on the genetic potential and genotypic stability of 17 durum wheat genotypes during three crop years under wet conditions in the north of Algeria (Algiers). The results showed highly significant (P<0.001) agro-morphological diversity between the genotypes and a genotype × environment interaction for all the traits except for fertile spikelet number. Wricke’s ecovalance (wi), Shukla's stability variance (σi2), heterogeneity variance (%HV) and the incomplete correlation (%IC) method were used to analyse the genotype × environment interaction on grain yield. The genotypes Ardente/Waha L2, Ardente and Saadi/Simeto L3 exhibited great instability with the highest values of wi, σi2, %HV and %IC. Ardente/Waha L1, Simeto/Vitron L5, Simeto and Ardente/Vitron L1 had the highest grain yield and average stability (wi, %HV and %IC were weak). Significant correlations were found between %HV, Rij2, bi, wi, σi2 and %IC, implying that they were similarly efficient in detecting stable genotypes and in measuring stability.

Comparative Analysis of Statistical Models for Evaluating Genotype × Environment Interaction in Rainfed Safflower

2017 ◽  
Vol 6 (4) ◽  
pp. 455-465 ◽  
Author(s):  
Khoshnood Alizadeh ◽  
Reza Mohammadi ◽  
Abdollah Shariati ◽  
Masoud Eskandari
Keyword(s):  
Comparative Analysis ◽  
Statistical Models ◽  
Environment Interaction ◽  
Genotype Environment Interaction

scholarly journals Genomic‐enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat

2018 ◽  
Vol 11 (2) ◽  
pp. 170112 ◽  
Author(s):  
Sivakumar Sukumaran ◽  
Diego Jarquin ◽  
Jose Crossa ◽  
Matthew Reynolds
Keyword(s):  
Durum Wheat ◽  
Environment Interaction ◽  
Genotype Environment Interaction

Biological interpretation of genotype × environment interaction in rainfed durum wheat

2020 ◽  
Vol 48 (4) ◽  
pp. 547-554
Author(s):  
R. Mohammadi ◽  
B. Sadeghzadeh ◽  
M. M. Ahmadi ◽  
A. Amri
Keyword(s):  
Durum Wheat ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Biological Interpretation

Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat

1992 ◽  
Vol 83 (5) ◽  
pp. 597-601 ◽  
Author(s):  
M. M. Nachit ◽  
G. Nachit ◽  
H. Ketata ◽  
H. G. Gauch ◽  
R. W. Zobel
Keyword(s):  
Linear Regression ◽  
Durum Wheat ◽  
Regression Models ◽  
Environment Interaction ◽  
Linear Regression Models ◽  
Genotype Environment Interaction

Statistical models for genotype by environment data: from conventional ANOVA models to eco-physiological QTL models

2005 ◽  
Vol 56 (9) ◽  
pp. 883 ◽  
Author(s):  
Fred A. van Eeuwijk ◽  
Marcos Malosetti ◽  
Xinyou Yin ◽  
Paul C. Struik ◽  
Piet Stam
Keyword(s):  
Statistical Models ◽  
Regression Models ◽  
Physiological Model ◽  
Environment Interaction ◽  
Response Curves ◽  
Physiological Models ◽  
Factorial Regression ◽  
Multiple Quantitative Trait Locus ◽  
Bilinear Models ◽  
Genotype Environment Interaction

To study the performance of genotypes under different growing conditions, plant breeders evaluate their germplasm in multi-environment trials. These trials produce genotype × environment data. We present statistical models for the analysis of such data that differ in the extent to which additional genetic, physiological, and environmental information is incorporated into the model formulation. The simplest model in our exposition is the additive 2-way analysis of variance model, without genotype × environment interaction, and with parameters whose interpretation depends strongly on the set of included genotypes and environments. The most complicated model is a synthesis of a multiple quantitative trait locus (QTL) model and an eco-physiological model to describe a collection of genotypic response curves. Between those extremes, we discuss linear-bilinear models, whose parameters can only indirectly be related to genetic and physiological information, and factorial regression models that allow direct incorporation of explicit genetic, physiological, and environmental covariables on the levels of the genotypic and environmental factors. Factorial regression models are also very suitable for the modelling of QTL main effects and QTL × environment interaction. Our conclusion is that statistical and physiological models can be fruitfully combined for the study of genotype × environment interaction.

Linear, bilinear, and linear-bilinear fixed and mixed models for analyzing genotype × environment interaction in plant breeding and agronomy

2010 ◽  
Vol 90 (5) ◽  
pp. 561-574 ◽  
Author(s):  
J. Crossa ◽  
M. Vargas ◽  
A K Joshi
Keyword(s):  
Plant Breeding ◽  
Least Squares ◽  
Statistical Models ◽  
Fixed Effects ◽  
Linear Models ◽  
Environment Interaction ◽  
Bilinear Models ◽  
Genotype Environment Interaction ◽  
Effect Model ◽  
Value Decomposition

The purpose of this manuscript is to review various statistical models for analyzing genotype × environment interaction (GE). The objective is to present parsimonious approaches other than the standard analysis of variance of the two-way effect model. Some fixed effects linear-bilinear models such as the sites regression model (SREG) are discussed, and a mixed effects counterpart such as the factorial analytic (FA) model is explained. The role of these linear-bilinear models for assessing crossover interaction (COI) is explained. One class of linear models, namely factorial regression (FR) models, and one class of bilinear models, namely partial least squares (PLS) regression, allows incorporating external environmental and genotypic covariables directly into the model. Examples illustrating the use of various statistical models for analyzing GE in the context of plant breeding and agronomy are given. Key words: Least squares, singular value decomposition, environmental and genotypic covariables

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