02299naa a2200289 a 450000100080000000500110000800800410001902400520006010000170011224501640012926000090029352013440030265000170164665000370166365000270170065000220172765000260174965000240177570000250179970000190182470000190184370000260186270000200188870000230190870000220193177300560195321295992021-01-27       2020    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1007/s11295-020-01431-52DOI1 aALVES, R. S.  aOptimization of Eucalyptus breeding through random regression models allowing for reaction norms in response to environmental gradients.h[electronic resource]  c2020  aReaction norms fitted through random regression models are a powerful tool to identify and quantify the genotype × environment (G × E) interaction and they represent a promising alternative in forest tree breeding for analysis of multi-environment trials. Thus, the objective of this study was to compare random regression models with the compound symmetry model in Eucalyptus breeding for analysis of multi-environment trials. To this end, a data set with 215 Eucalyptus clones of different species and hybrids evaluated in four environments for diameter at breast height and Pilodyn penetration was used. The random regression models provided a better fit for both traits. Results showed that there was genotypic variability among Eucalyptus clones and that the reaction norms over the environmental gradients identified the G × E interaction. The compound symmetry model and the random regression models are highly correlated in terms of genotype ranking for both traits. The main advantage of random regression models over the compound symmetry model is the ability to predict genotypic performance in environments where a genotype has not been evaluated. Thus, our results suggest that reaction norms fitted through random regression models can be successfully used in forest tree breeding for analysis of multi-environment trials.  aForest trees  aGenotype-environment interaction  aPlant selection guides  aÁrvore Florestal  aInteração Genética  aSeleção Genótipa1 aRESENDE, M. D. V. de1 aAZEVEDO, C. F.1 aSILVA, F. F. e1 aROCHA, J. R. A. S. C.1 aNUNES, A. C. P.1 aCARNEIRO, A. P. S.1 aSANTOS, G. A. dos  tTree Genetics & Genomesgv. 16, n. 2, p. 1-8, 2020.