Brown rot (Ralstonia solanacearum) comprises a major threat to the Dutch potato production chain. Eradication of the disease has not been achieved thus far, due to insufficient knowledge of the relative importance of possible risk factors with respect to brown rot prevalence and dispersal in the potato production chain. To study the relationship between brown rot infections in potatoes and possible risk factors, we evaluated two epidemiological models, i.e. a compartmental state-variable model, and a spatial individual-based model (IBM). Our approaches differ from most existing ecological applications of the two modelling techniques in that they focus on disease epidemiology within the industrially defined dynamics of the brown rot pathogen in the potato production chain. The state-variable model proved useful for obtaining insight into the basic principles of brown rot dispersal. It showed that the dynamics of the fraction of infected seed lots in the total potato lot population forms the key to a general understanding of brown rot epidemics. However, this model was unable to reflect the large fluctuation in yearly number of infections that is inherent to brown rot epidemics. To give a more detailed and realistic representation of the fraction of infected seed lots, a conceptual IBM was developed. As in this IBM a specific location is assigned to each individual potato lot, it becomes straightforward to include spatial heterogeneities based on detailed data on the potato production sector. In contrast to the state-variable model, the IBM enables us to study the effects of specific brown rot control policies in spatially defined areas. Moreover, the inherent high level of detail makes the IBM a convenient technique for policy application. The IBM will be further developed and extended to a bio-economic model for application in brown rot control strategy analysis.
Breukers, A., Hagenaars, T. H. J., van der Werf, W., & Oude Lansink, A. G. J. M. (2005). Modelling of brown rot prevalence in the Dutch potato production chain over time: from state variable to individual-base models. Nonlinear Analysis: Real World Applications, 6(4), 797-815. https://doi.org/10.1016/j.nonrwa.2004.12.006