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

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

scholarly journals Some aspects of adaptation and adaptability of barley in European conditions.

1985 ◽  
Vol 33 (3) ◽  
pp. 195-213
Author(s):  
L. Jestin
Keyword(s):  
Statistical Models ◽  
Spring Barley ◽  
Winter Barley ◽  
General View ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
Statistical Procedures ◽  
Genotype Environment Interaction ◽  
Nw Europe ◽  
Recent Extension

After a review of different approaches found in the literature to problems of adaptation and adaptability of barley, attention is paid to the ecophysiological reasons which may explain the recent extension of winter barley cultivation in NW Europe. A brief account is given of cooperative trials carried out in Europe to define spring barley varietal adaptability ("ESBAN" and "JESBT" trials). A general view of current statistical procedures to analyse adaptability and genotype environment interaction patterns is presented. Some indications are given of the use that the breeder can make of ecophysiological methodology and statistical models in breeding barley for wider adaptation. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals Statistical models for evaluating the genotype-environment interaction in maize (Zea mays L.)

Phyton ◽  
2010 ◽  
Vol 79 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Kandus M ◽  
D Almorza ◽  
R Boggio Ronceros ◽  
JC Salerno
Keyword(s):  
Zea Mays ◽  
Statistical Models ◽  
Zea Mays L ◽  
Environment Interaction ◽  
Genotype Environment Interaction

Genotype-Environment Interaction in Calf Production. II. Live Measurements and Carcass Traits

1973 ◽  
Vol 36 (3) ◽  
pp. 471-475 ◽  
Author(s):  
T. R. Batra ◽  
W. R. Usborne ◽  
D. G. Grieve ◽  
E. B. Burnside
Keyword(s):  
Carcass Traits ◽  
Environment Interaction ◽  
Genotype Environment Interaction

METHODS FOR EVALUATING THE GENOTYPE-ENVIRONMENT INTERACTION IN BREEDING WINTER WHEAT FOR PRODUCTIVITY

2020 ◽  
Vol 15 (1) ◽  
pp. 56-64
Author(s):  
Irina Manukyan ◽  
◽  
Madina Basieva ◽  
Elena Miroshnikova ◽  
◽  
...  
Keyword(s):  
Winter Wheat ◽  
Environment Interaction ◽  
Genotype Environment Interaction
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