The deployment of resistant cultivars in agricultural landscapes is an effective way to protect crops from plant pathogens. However, resistant cultivars can be quickly overcome by pathogens following their deployment. Several strategies have been proposed to promote more durable deployements of resistant cultivars. These strategies rely on the management of host genetic diversity with the aim to confront pathogens with eco-evolutionary challenges and thus avoid, or delay, their adaptation to plant resistance (evolutionary control), while maintaining effective disease protection (epidemiological control). Resistance genes can be i) combined in the same plant cultivar (pyramiding), ii) deployed in different plants sown in the same field (mixtures) or in different fields (mosaics), or iii) alternated across time (rotations). Previous theoretical studies have explored the evolutionary and epidemiological outcomes associated to different deployment strategies. These studies have focused on pathogens that reproduce purely asexually, althought many plant pathogens includes in their life cycle at least one sexual cycle per growing season (mixed reproduction). At least two mechanisms linked to sexual reproduction could impact on deployment strategies. Genetic recombination may efficiently creates favourable gene combinations that would be accessible only through sequential mutation events in purely asexual reproduction, possibly favouring the emergence of pathogens able to overcome all the deployed resistance genes. Moreover, sexual reproduction often results in the formation of specialized structures that may persist in a state of dormancy and potentially survive in the soil for many years, possibly impacting the efficacy of crop rotation as a means of pathogen control. Here, we improved a spatially-explicit stochastic model to include different systems of pathogen reproduction (purely asexual vs mixed). We used the model to shed light on the effect of genetic recombination and dormancy on the evolutionary and epidemiological outcomes across the four main categories of deployment strategies.