Using Factor Analytic Models for Joining Environments and Genotypes without Crossover Genotype × Environment Interaction

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Crop Yield Prediction Using Deep Neural Networks (2019, background): “[9] proposed an integrated approach of factor analytic (FA) and linear ∗Corresponding author ar X iv :1 90 2.”
Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, methodology): “As clearly explained by Burgueño et al. (2008), SREG2 can be perceived as consisting of a set of multiple regression equations, one for each environment, each regression equation consisting of an environmental mean or environmental eff ect as intercept plus two terms for regression on two genotypic ”
Genomic Prediction of Breeding Values when Modeling Genotype × Environment Interaction using Pedigree and Dense Molecular Markers (2012, methodology): “…interaction can be incorporated into additive infi nitesimal models (e.g., Fisher, 1918; Wright, 1921) by means of genetic and environmental covariances (e.g., Piepho, 1997, 1998, 2009; Smith et al., 2001; Crossa et al., 2004, 2006; Oakey et al., 2006; Burgueño et al., 2007, 2008, 2011).”
Genotype-by-environment interaction and yield stability analysis of biomass sorghum hybrids using factor analytic models and environmental covariates (2020, background): “This information is critical for the release of new cultivars showing yield stability across a set of environments, or that are specifically adapted to a given environment (Bornhofen et al., 2018; Burgueño et al., 2008; Dias et al., 2018b).”