Turkish is an agglutinative language with rich morphology-syntax interactions. As an extension of this property, the Turkish Treebank is designed to represent sublexical dependencies, which brings extra challenges to parsing raw text. In this work, we use a joint POS tagging and parsing approach to parse Turkish raw text, and we show it outperforms a pipeline approach. Then we experiment with incorporating morphological feature prediction into the joint system. Our results show statistically significant improvements with the joint systems and achieve the state-ofthe-art accuracy for Turkish dependency parsing.