This is most likely due to the fact that, from an edit-tree approach perspective, a large number of trees creates a large number of classes, which leads to higher class imbalance and more sparsity.
edit-tree based approaches do outperform representation learning methods for languages with lower number of trees, which leads to the intuition that the edit-tree formalism does provide a useful inductive bias to the task of lemmatization and it should not be discarded in future work.