Reaction 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.

通过随机回归模型拟合的反应规范是确定和量化基因型×环境（G×E）相互作用的强大工具，它们代表了林木育种中用于多环境试验分析的有希望的替代方法。因此，本研究的目的是比较桉树育种中的随机回归模型与复合对称模型，以进行多环境试验的分析。为此，使用了包含215个不同物种的桉树无性系和杂种的数据集，在四个环境中评估了它们的胸高直径和Pilodyn穿透力。随机回归模型为两种性状提供了更好的拟合。结果表明，桉树之间存在基因型差异克隆，并且在环境梯度上的反应范数确定了G×E相互作用。复合对称模型和随机回归模型在两个性状的基因型排名方面高度相关。相对于复合对称模型，随机回归模型的主要优点是能够预测尚未评估基因型的环境中的基因型表现。因此，我们的结果表明，通过随机回归模型拟合的反应规范可以成功地用于林木育种中，以进行多环境试验的分析。