The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.

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Despite disease control management, each year part of crop harvest is lost due to plant diseases. Yellow spot is an important foliar wheat disease throughout the world. The fungus that causes the disease survives on wheat stubble and this is most commonly the source of primary infection (by ascospores) in a crop canopy in the next season. On infected leaves, lesions are formed, surrounded by yellow halos. After a latency period, conidia, the cause of secondary infection, are produced on lesions and are spread over long distances by wind. The secondary cycle can repeat several times through the season and results in the progression of the disease in the canopy. Weather conditions and the developmental stage of the crop play an important role in the progression and severity of disease in the crop canopy. To study the interactions between pathogen, climatic conditions and growing host crop, we developed an epidemiological model of Pyrenophora tritici-repentis, the fungal pathogen that causes yellow spot, and coupled it with an existing functional-structural plant model (FSPM) for cereal crops. An FSPM simulates mutual interactions between plant architecture (structure) and physiological processes (function) in plants at a (sub)organ scale, affected by environmental conditions. In our model, light interception and temperature determine the development and the growth of the cereal crop. Temperature, rainfall, relative humidity and wind data control the development of yellow spot. The pathogen submodel predicts maturation of ascospores and simulates production and wind dispersal of conidia across the canopy. Conidia are transported inside a virtual cone starting from a sporulating lesion and with the axis following the wind direction. Simulations demonstrated horizontal and vertical progression of the disease in the growing crop canopy. However, the upper leaves grew often away from the disease after the begin of stem elongation. In the future we will perform enhanced sensitivity analysis that should help us to identify the most (least) important parameters and so help in the process of model parameterisation. Epidemiological models coupled to models for plant architecture and growth under different climatic conditions are a promising tool to study the dynamics of plant-pathogen-environment interactions and their effect on crop yield. Furthermore, the coupled model can be used as a simulation tool to study the impact of different disease management approaches and lead to improved disease control. We will test the applicability of the model against field data on disease progression in spring wheat.