  We study the problem of optimal control for mean-field stochastic partial
differential equations (stochastic evolution equations) driven by a Brownian
motion and an independent Poisson random measure, in the case of
\textit{partial information} control. One important novelty of our problem is
represented by the introduction of \textit{general mean-field} operators,
acting on both the controlled state process and the control process. We first
formulate a sufficient and a necessary maximum principle for this type of
control. We then prove existence and uniqueness of the solution of such general
forward and backward mean-field stochastic partial differential equations. We
finally apply our results to find the explicit optimal control for an optimal
harvesting problem.
