Biography

I am a cereal plant pathologist that is working with leaf diseases in barley and wheat, as well as with leaf and stem diseases in oilseed crops. My main task is to develop integrated pest management (IPM) strategies based on the pathogen’s and the plant’s biology and development. This includes disease epidemiology and host phenology. We are developing mathematical models and disease thresholds to better align the use of fungicides with the actual or predicted need in the field. These decision tools are then accessible to the extension specialists and farmers via our decision support system VIPS (Varsling Innen PlanteSkadegjørere). Our national and international collaborations focuse on sustainable disease management for current and future food demands and includes research on fungicide resistance, precision agriculture, early disease diagnostics and closing the yield gap.

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Abstract

Leaf blotch diseases (LBD), such as Septoria nodorum bloch (Parastagnospora nodorum), Septoria tritici blotch (Zymoseptoria tritici) and Tan spot (Pyrenophora tritici-repentis) can cause severe yield losses (up to 50%) in Norwegian spring wheat (Triticum aestivum) and are mainly controlled by fungicide applications. A forecasting model to predict disease risk can be an important tool to optimize disease control. The association between specific weather variables and the development of LBD differs between wheat growth stages. In this study, a mathematical model to estimate phenological development of spring wheat was derived based on sowing date, air temperature and photoperiod. Weather factors associated with LBD severity were then identified for selected phenological growth stages by a correlation study of LBD severity data (17 years). Although information regarding host resistance and previous crop were added to the identified weather factors, two purely weather-based risk prediction models (CART, classification and regression tree algorithm) and one black box model (KNN, based on K nearest neighbor algorithm) were most accurate to predict moderate to high LBD severity (>5% infection). The predictive accuracy of these models (76–83%) was compared to that of two existing models used in Norway and Denmark (60 and 61% accuracy, respectively). The newly developed models performed better than the existing models, but still had the tendency to overestimate disease risk. Specificity of the new models varied between 49 and 74% compared to 40 and 37% for the existing models. These new models are promising decision tools to improve integrated LBD management of spring wheat in Norway.

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Abstract

The necrotrophic fungal pathogen Parastagonospora nodorum causes Septoria nodorum blotch (SNB), which is one of the dominating leaf blotch diseases of wheat in Norway. A total of 165 P. nodorum isolates were collected from three wheat growing regions in Norway from 2015 to 2017. These isolates, as well as nine isolates from other countries, were analyzed for genetic variation using 20 simple sequence repeat (SSR) markers. Genetic analysis of the isolate collection indicated that the P. nodorum pathogen population infecting Norwegian spring and winter wheat underwent regular sexual reproduction and exhibited a high level of genetic diversity, with no genetic subdivisions between sampled locations, years or host cultivars. A high frequency of the presence of necrotrophic effector (NE) gene SnToxA was found in Norwegian P. nodorum isolates compared to other parts of Europe, and we hypothesize that the SnToxA gene is the major virulence factor among the three known P. nodorum NE genes (SnToxA, SnTox1, and SnTox3) in the Norwegian pathogen population. While the importance of SNB has declined in much of Europe, Norway has remained as a P. nodorum hotspot, likely due at least in part to local adaptation of the pathogen population to ToxA sensitive Norwegian spring wheat cultivars.