To develop a parser for a language with no syntactically annotated data, we either have to develop a (small) treebank for the target language or rely on cross-lingual learning or projection, or possibly use some combination of these methods. In this paper, we compare the usefulness of cross-lingual model transfer and target language annotation for three different languages, with varying support from closely related high-resource languages. The results show that annotating even a very small amount of data in the target language is superior to any cross-lingual setup and that accuracy can be further improved by adding training data from related languages in a multilingual model.