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TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|July 23, 2024
Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trialsIgor K Fernandes, Caio C Vieira, Kaio O G Dias, et al.TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|December 9, 2017
Efficiency of multi-trait, indirect, and trait-assisted genomic selection for improvement of biomass sorghumSamuel B Fernandes, Kaio O G Dias, Daniel F Ferreira, et al.TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|November 21, 2023
Models to estimate genetic gain of soybean seed yield from annual multi-environment field trialsMatheus D Krause, Hans-Peter Piepho, Kaio O G Dias, et al.G3 (Bethesda, Md.)|January 20, 2024
ProbBreed: a novel tool for calculating the risk of cultivar recommendation in multienvironment trialsSaulo F S Chaves, Matheus D Krause, Luiz A S Dias, et al.Scientific Reports|October 31, 2025
Enhancing enviromics based predictions in common bean multi-environment trialsGabriel M Blasques, Luiz A S Dias, Mauricio S Araújo, et al.TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|February 22, 2022
Leveraging probability concepts for cultivar recommendation in multi-environment trialsKaio O G Dias, Jhonathan P R Dos Santos, Matheus D Krause, et al.TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|March 13, 2024
GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targetingMaurício S Araújo, Saulo F S Chaves, Luiz A S Dias, et al.Pageof 1