Research

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.

The seeds of the Styrian oil pumpkin (Curcurbita pepo var. styriaca) are used for the production of pumpkin seed oil. The seed reacts very sensitively to rainfall and excessively cool soil temperatures during the emergence phase. In addition, the hull-less seed provides a good attack surface for soil-borne diseases such as Pythium, Rhizoctonia, Fusarium and other plant pathogens. It is not fully known which soil-borne diseases have severely impaired seed germination in recent years. A research project therefore aims to investigate various ways of ensuring the emergence and juvenile development of oil pumpkin seedlings in order to help stabilise yields. As part of a research project led by the Steirisches Kürbiskernöl g.g.A. association, the Institute of Plant Protection at BOKU will analyse soil and plant samples to determine which pathogens are responsible for poor emergence in oil pumpkin cultivation.

Dogs, as reliable partners for humans, are increasingly used as species detection dogs in nature conservation or for searching for invasive species. Thanks to their extraordinary sense of smell, trained sniffer dogs are able to detect even the slightest traces of odor molecules such as volatile organic substances from plants. In this project, we use this ability to search for native and invasive poisonous plants with pyrrolizidine alkaloids (PAs) in herb and spice fields. Regular consumption of PAs can be carcinogenic even in relatively small quantities and, in higher doses, can lead to irreversible liver damage. Even extremely low levels of contamination with PAs from poisonous plants can mean that herbs or spices are no longer allowed to be sold in the EU according to updated EU PA limits for those products. Despite the most careful control, contamination with PAs can occur. In addition to native PA poisonous plants such as common groundsel (Senecio vulgaris), there are also invasive species such as the eastern groundsel (S. vernalis) or the narrow-leaved ragwort (S. inaequidens), which have been introduced from Eastern Europe and Southern Africa, respectively. The changed environmental conditions caused by climate change could mean that these invasive species can increasingly establish themselves in arable crops and could thereby endanger food security. Experienced search dogs are trained to detect these poisonous plants using fresh and dried plant material at different stages of development from multiple origins. At the same time, the volatile organic compounds of these samples are also analyzed in order to be able to estimate how the content and composition of essential oils influences the detection success. In the second year of the project, searches will be carried out in different herb and spice fields in order to determine whether specially trained poisonous plant detection dogs are more efficient in searching for Senecio species than human experts and which environmental factors have the greatest influence on precision, sensitivity and efficiency. The use of detection dogs represents a particularly innovative approach to detect potential poisonous plants as early as possible in order to reduce the use of manpower and the use of weed control measures.