This paper studies average age of information (AoI) minimization in cognitive radio energy harvesting communications. The secondary user is an energy harvesting sensor that harvests ambient energy with which it performs spectrum sensing and status updates. Status-update data is sent by opportunistically accessing the primary spectrum. Specifically, the secondary user aims to minimize the average AoI by adaptively making sensing and update decisions based on its energy availability and the availability of the primary spectrum. The sequential decision problem is formulated as a partially observable Markov decision process and solved by dynamic programming. The properties of the optimal sensing and updating policies are investigated and shown to have threshold structure. Numerical results confirm the analytical findings.