Andrea Ficke
Research Scientist
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CV Andrea FickeBiography
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.
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.
Authors
Andrea FickeAbstract
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Authors
Björn Andersson Annika Djurle Jens Erik Ørum Marja Jalli Antanas Ronis Andrea Ficke Lise Nistrup JørgensenAbstract
Validation of models for plant disease management is a crucial part in the development of decision support systems in plant protection. Bespoke field trials are usually conducted to determine the performance of a model under practical conditions. However, field trials are very resource-demanding, and the use of already existing field trial data could significantly reduce costs for model validation. In this study, we took this novel approach to verify the performance of models for determining the need of fungicide applications against leaf blotch diseases in wheat by utilising historical weather data and yield data available from fungicide efficacy field trials. Two models based on humidity factors were used in the study. To estimate how specific humidity settings in the two models affect the number of recommended fungicide treatments per season, historical weather data from a 5-year period from weather stations in Denmark, Sweden, Norway, Finland, and Lithuania was used. The model output shows major differences between seasons and regions, typically recommending between one and three treatments per season. To determine the prediction potential of the models, data on yield gains from either one or two fungicide applications in fungicide efficacy trials conducted in wheat over a 5-year period in the five countries was utilised. The yield responses from fungicide treatments in the efficacy trials varied considerably between years and countries, as did the proportion of predictions of profitable treatments. In general, there was a tendency for the models to overestimate the need to apply fungicides (low specificity), but they rarely failed to recommend an application that was needed (high sensitivity). Despite the importance of having specific trials across regions in order to adjust models to local cropping and weather conditions, our study shows that historical weather data and existing field trial data have the potential to be used in model validation.
