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We propose a new class of models for the estimation of Genotype by Environment (GxE) interactions in plantbased genetics. Our approach, named AMBARTI, uses semiparametric Bayesian Additive Regression Trees to accurately capture marginal genotypic and environment effects along with their interaction in a fully Bayesian model. We demonstrate that our approach is competitive or superior to the traditional AMMI models widely used in the literature via both simulation and a real world data set.doi:10.1101/2021.05.07.442731 fatcat:zt4pzeak45dknjqyrdgndjgg3q