01993naa a2200289 a 450000100080000000500110000800800410001902400520006010000190011224501160013126000090024752010960025665000140135265000350136665000140140165300210141565300170143665300240145365300290147770000250150670000180153170000180154970000240156770000190159170000210161077300720163121410632022-07-12       2022    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1007/s00122-022-04041-y2DOI1 aDIAS, K. O. G.  aLeveraging probability concepts for cultivar recommendation in multi?environment trials.h[electronic resource]  c2022  aStatistical models that capture the phenotypic plasticity of a genotype across environments are crucial in plant breeding programs to potentially identify parents, generate ofspring, and obtain highly productive genotypes for target environments. In this study, our aim is to leverage concepts of Bayesian models and probability methods of stability analysis to untangle genotype-by-environment interaction (GEI). The proposed method employs the posterior distribution obtained with the NoU-Turn sampler algorithm to get Hamiltonian Monte Carlo estimates of adaptation and stability probabilities. We applied the proposed models in two empirical tropical datasets. Our fndings provide a basis to enhance our ability to consider the uncertainty of cultivar recommendation for global or specifc adaptation. We further demonstrate that probability methods of stability analysis in a Bayesian framework are a powerful tool for unraveling GEI given a defned intensity of selection that results in a more informed decision-making process toward cultivar recommendation in multi-environment trials.  aGenótipo  aMelhoramento Genético Vegetal  aVariedade  aModelo Bayesiano  aModelo misto  aPrevisão genômica  aRegressão de parâmetro1 aSANTOS, J. P. R. dos1 aKRAUSE, M. D.1 aPIEPHO, H.-P.1 aGUIMARAES, L. J. M.1 aPASTINA, M. M.1 aGARCIA, A. A. F.  tTheoretical and Applied Geneticsgv. 135, n. 4, p. 1385-1399, 2022.