In agricultural field mapping missions, a collision risk occurs when the UAVs deviate from their planned trajectories due to the wind or uncertainties in the model, but also in case of intersecting paths of the UAVs, e.g. when the battery level is not sufficient and a UAV needs to change its initially planned path and return to the base unexpectedly.In this paper, three nonlinear model predictive control (NMPC) trajectory tracking strategies for collision avoidance are compared for multi-UAV mapping of an agricultural field: incorporating collision avoidance as a nonlinear constraint, applying it as a penalty cost, and employing a safe flight corridor approach. All the presented strategies consider passing priority allocation of the UAVs involved in the mission, where only a UAV with a lower-level priority handles collision avoidance. Control strategies are compared regarding robustness to external disturbances, such as wind, and model uncertainties through Monte Carlo simulations. The performance is evaluated with respect to the resulting tracking errors, the ability to avoid collision, and the computational time needed to solve the optimal control problem. The objective is to determine, among these three approaches, the one that exhibits the best trade-off between performance and computational burden.