Research

Soybean production in Europe is continuously increasing. Over the past 15 years, the soybean acreage in Austria has tripled, making it the fourth most cultivated crop in the country. Austrian soybeans are particularly important for human consumption and seed production. In the EU, seed certification is granted only if Diaporthe infestation remains below the legally stipulated threshold of 15%. Infestation with Diaporthe disease complex significantly reduces germination capacity and decreases quality, rendering infected soybeans unsuitable for human consumption. Therefore, the project “Unraveling the Epidemiology of Diaporthe: Survival, Transmission, and Host Dynamics in Austrian Soybean Fields”, aims to investigate the impact of Diaporthe on Austrian soybean production. This issue has gained increasingly importance due to expanding soybean cultivation and more favorable climatic conditions for the pathogen. Although Diaporthe has been under study for a long time, many aspects of its epidemiology remain unsolved. Diaporthe is a fungal complex composed of various species that cause different disease patterns in soybean including Phomopsis seed decay, pod and stem blight, and stem canker. The composition of this complex varies geographically, highlighting the necessity of working with the dominant Diaporthe species in specific regions. Disease development is favored by high humidity, rainfall, and elevated temperatures. With climate change, weather conditions conducive to disease outbreaks are becoming more frequent. The primary objective of this project is to examine the epidemiological aspects of the Diaporthe disease complex by studying the survival stages of Diaporthe species present in Austria. In particular, investigating these species in different soybean organs (stem, pod, seed) and their survival structures will help to assess their significance as the initial inoculum for the following year. Furthermore, this project includes a comprehensive examination of the prevalence of Diaporthe species in other host plants, such as crops in crop rotation or weeds, to evaluate the potential of alternative hosts in increasing Diaporthe infection levels within a region or field. Additionally, the mechanisms of transmission between plants and the spatial-temporal distribution patterns within the environment will be examined to gain better understanding of fungal dispersal. Lastly, the behavior of Diaporthe species within the plant remains unknown due to the long latent period. Therefore, this study will also assess the colonization process of soybeans. The results of this project will provide significant insights into the disease cycle of Diaporthe species in soybean. Investigating the role of other crops and weeds may underscore the need to adapt current crop rotation and other agricultural practices to reduce the available inoculum, especially in light of increasing soybean acreage. This comprehensive approach will deepen our understanding of the disease and pave the way for more effective Diaporthe management and sustainable agricultural practices.

The detection and quantification of plant pathogens in field samples are critical for monitoring and controlling plant diseases in agricultural environments. This project focuses on the development and optimization of digital PCR (dPCR) assays for the precise and sensitive detection of plant pathogens from field-collected samples. Digital PCR offers several advantages over traditional PCR methods, including absolute quantification without the need for standard curves, increased sensitivity and specificity and greater tolerance to PCR inhibitors. By leveraging these benefits, our assays aim to provide reliable and rapid diagnostics for a range of plant pathogens relevant in the agricultural context.

The primary diseases in viticulture are downy mildew (Plasmopara viticola) and powdery mildew (Erysiphe necator). To control both pathogens, winegrowers must regularly spray fungicides and use forecast models for timing of applications. Prognosis for disease development is typically based on large-scale weather data as input parameters, but these do not reflect small-scale differences in disease appearance. This often leads to unnecessary applications in many vineyards. Furthermore, weather parameters are usually measured outside the vineyards, which does not reflect the actual microclimate in the canopy. As a result, disease predictions that are not adapted to the local conditions in a vineyard. The objective of our project is to train artificial neural network models, namely sequence models, to predict downy and powdery mildew. We use not only site-specific disease level and weather parameters as training data but also the parameters vine development, vine management and vegetation cover. The training data consists of values determined in 5 years before the start of the project and newly in 15 vineyards collected data. Unlike sensors used in other studies, this approach records leaf wetness on both the lower and upper sides of the leaf, which is an important parameter for both pathogens. Our models forecast will be tailored to the specific location and will predict disease progression up to 10 days in advance. By adapting forecasts to the location, fungicide use can be reduced. The output of the models can provide valuable information for winegrowers on when to apply fungicides, thereby contributing to a sustainable and environmentally friendly use of fungicides.