Understanding the biology of fungicide resistance is crucial for ensuring sustainable disease control with fungicides, prolonging the life of the fungicides as well as developing integrated pest managements systems. Recently there has been concern due to a gradual decline in the field performance of some azole fungicides against Septoria tritici blotch caused by the pathogen Mycophaerella graminicola, which is the most serious disease in wheat production in northern Europe. Control of this disease relies on only few actives, which makes the control very vulnerable. In recent years the reduction in field performance from triazoles has been linked to specific mutations in the cytochrome P450 CYP 51 gene along with other mechanisms, which remain to be clarified (Leroux & Walker 2011, Cools & Fraaije 2012). Resistant pathogens have proven to be relatively stable in the populations also when selection pressure have stopped indicating that a fitness penalty is not always to be expected. Population studies in the area of fungicide resistance management have been limited, and the strategies devised have been based on expected responses of a pathogen population to selection pressure. Population studies reflecting different farming practices can help to understand the dynamics of the changes which are taking place. Recently modeling has been an approach to predict the risk for development of resistance, but the lack of comprehensive datasets on which to base the modeling has been an obstacle (Hobbelen et al 2011).
