scholarly journals A General Bayesian Estimation Method of Linear–Bilinear Models Applied to Plant Breeding Trials With Genotype × Environment Interaction

2011 ◽  
Vol 17 (1) ◽  
pp. 15-37 ◽  
Author(s):  
Sergio Perez-Elizalde ◽  
Diego Jarquin ◽  
Jose Crossa
Keyword(s):  
Plant Breeding ◽  
Bayesian Estimation ◽  
Estimation Method ◽  
Environment Interaction ◽  
Bilinear Models ◽  
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 Cultivating response: Peasant seed and plant–human collaboration in an agro-industrial heartland

2021 ◽  
pp. 251484862110224
Author(s):  
Leila Rezvani
Keyword(s):  
Plant Breeding ◽  
Environmental Changes ◽  
F1 Hybrids ◽  
Vegetable Production ◽  
Environment Interaction ◽  
Peasant Farmer ◽  
Genotype Environment Interaction ◽  
Seed Saving ◽  
Peasant Farmers ◽  
Ways Of Being

Using Donna Haraway’s notion of “response-ability”, or the cultivation of the capacity for response, this paper seeks to understand seed saving and plant breeding as politically and ethically charged modes of interspecies communication. In Brittany, France, a region known for its industrial-scale fresh vegetable production, peasant farmers and organic plant breeders question the modernist plant breeding and agro-industrial paradigm, cross-pollinating ideas to produce new understandings of genotype-environment interaction, biodiversity and heredity. Plant liveliness is understood as politically transformative, constitutive of an agriculture that supports peasant farmer and crop plant creativity and self-determination. In contrast to F1 hybrids, open-pollinated semences paysannes (peasant seed) retain the ability to respond to environmental changes, adapt and evolve over (human and plant) generations. Farmers must in turn engage specific modes of attention, interpreting plant expressions and shaping future generations through rouging and crossing, selecting and saving, watching and learning from their crops. Mutual response is the foundation of interdependence, in which nonconspecific partners adjust to one another’s ways of being and doing in order to labor together. In remaining response-able, farmers reckon with the liveliness and agential capacities of plants, qualities that work against their subsumption into factory-like methods of cultivation. These communicative practices hint at the radical potential for interspecies resistance to monoculture within plant breeding and cultivation, practices that are so often molded by the interests of agro-industrial capital.

Genotype-environment interaction – a challenge for plant breeding

1975 ◽  
Vol 85 (3) ◽  
pp. 477-493 ◽  
Author(s):  
J. Hill
Keyword(s):  
Plant Breeding ◽  
Environment Interaction ◽  
Genotype Environment Interaction

Much has been written and said about genotype-environment (GE) interactions and the particular problems which they pose for plant breeders. It is not the purpose of this article to dwell upon every aspect of this story, but rather to discuss how these problems came to be recognized, to comment upon the various techniques which have been employed in seeking a solution to them and to suggest what developments might lie ahead.

scholarly journals Classification of environments based on correlations of yield in spring bread wheat

2021 ◽  
Vol 181 (4) ◽  
pp. 14-21
Author(s):  
S. B. Lepekhov
Keyword(s):  
Correlation Analysis ◽  
Plant Breeding ◽  
Bread Wheat ◽  
Environment Interaction ◽  
Spring Bread Wheat ◽  
Additional Information ◽  
Breeding Material ◽  
Genotype Environment Interaction ◽  
Correlation Parameters

Background. Genotype–environment interaction complicates selection of lines in plant breeding. Researchers have developed different ways to classify environments to mitigate its effect. The use of correlation analysis between yields of cultivars grown in different environments was earlier proposed for classification of these environments.The aim of this research was to classify years on the basis of correlations of the yields in a specially selected set of spring bread wheat cultivars and to verify the application of such classification to breeding material in different nurseries.Materials and methods. The material for the experiment included cultivars, lines and breeding samples from the collection nursery, competitive variety trials, and the nursery for segregating populations, respectively. The experiments were conducted from 2010 through 2017. The correlation analysis between the yields of 19 marker cultivars of different ecogeographic origin was used as the basis for the classification of years. The calculated correlation parameters for the yields of marker cultivars and those of the breeding material in nurseries for the same pairs of years were compared using the Mann–Whitney U-test.Results. The years under consideration were classified into three groups: 1) 2010 and 2013; 2) 2011, 2012 and 2014; 3) 2015, 2016 and 2017. Correlations between the yields of the marker cultivars showed no significant differences from those of the genotypes from other nurseries across the analyzed years. Consequently, the classification of years based on the reactions of marker cultivars can be justifiably extended onto other breeding material.Conclusion. It is suggested to select and use a set of marker cultivars in multi-environment trials to obtain additional information about target environments and make more informed decisions on culling plant breeding materials. 

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.

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