![](/upload_mm/1/a/7/e3b2b203-af94-4f43-a93e-9c2165b4f0a4_6ff87ce8-5b72-4ee2-a715-35776e2aa7d9_rooien_aardappels_490_220x160_9a18b4f8_750x400.jpg)
Project
Designing disease-resistant cropping landscapes
Designing disease-resistant cropping landscapes in a changing climate using spatial models of epidemics and socio-institutional dynamics – the case of potato (Solanum tuberosum) and late blight (Phytophthora infestans).
Changes in temperature and moisture regimes associated with climate change will affect the occurrence and severity of plant diseases in cropping systems that are vital to food security. A major element in prevention of plant disease is the use of (partially) resistant cultivars. These are developed by private companies that aim for rapid widespread market penetration, but based on their critical importance for effective crop protection and food security, the resistance genes used in breeding can be considered as a scarce and common good. Coordinated gene deployment at regional level can reduce epidemics and prevent early breaking of resistance due to pathogen evolution.
In this project the host-pathogen system of potato (Solanum tuberosum) - late blight (Phytophthora infestans) will be analysed as a model system to study multi-level management and governance of crop-disease interactions. This system is notorious for rapid pathogen evolution and resistance breaking. We aim to identify effective strategies to support the social-institutional dynamics that determine the design of disease-resistant landscapes for potato cultivation. An Agent Based Model of the ecological and social-institutional systems will be developed based on epidemiological models, agronomic data and stakeholder behaviour, derived from social data and participatory processes. Inverse modelling techniques will be applied to identify the most suitable model structure and parameter values, and the model will be further refined in social simulation workshops with stakeholders. Model simulations with alternative climate change scenarios that affect disease dynamics and resistance breaking will elucidate the robustness of alternative decision strategies.