Research

RISE-SOY aims to provide knowledge on the plasticity of physiological and biochemical adjustments of soybean to water-limiting conditions associated with yield and quality stability, and to identify digital physiological marker as well as enzymatic signatures indicative of high metabolic plasticity under stressful conditions. This will contribute to healthy food (nutrition) by increasing sustainable, climate-resilient plant protein production, particularly GMO-free, domestic soybean-based protein production under changing environmental conditions. To achieve its goals, RISE-SOY will pursue a novel and innovative approach of functional trait evaluation (drought tolerance) of high-quality food grade soybean including specialty lines such as those with increased levels of spermidine or hypo-allergenicity. RISE-SOY will combine next-generation physiological markers obtained from hyperspectral reflectance imaging and biochemical analyses of enzyme activity signatures to assess metabolic responses important/indicative for drought tolerance with quality and agronomic traits from field and controlled trials. This will significantly advance technologies/approaches to enable a climate-resilient plant-based food production for healthy nutrition in Lower Austria and beyond.

Global food security is seriously threatened by plant diseases, with one of the most dangerous epidemic fungal diseases in cereal production being Fusarium head blight. On the one hand, the yield is reduced, on the other hand, the quality of the harvest is drastically damaged. In the worst case, there is a serious health risk from the mycotoxins in the grain resulting from the fungal infestation. The aim is therefore to develop resistant varieties in order to avert the problems caused by this disease. In plant breeding, those varieties and new breeding lines that are resistant to the disease must be identified and selected, a task that is usually carried out by trained personnel. Since classical selection is very time-consuming, expensive and prone to error due to the human factor, such a procedure is enormously time-consuming in practice for larger selection programs: an automated approach is required. To avoid these problems, the aim of the project is to use a drone to automatically capture high-resolution images of the field plots and classify the different test lines. As there is no suitable technology for the specific task (high-resolution images, extreme flight altitude, oblique images), a new system for image acquisition must be developed. In addition, existing methods for image analysis are not suitable in this setup due to the highly variable environmental conditions. A new robust and generally applicable approach is required. In addition, the effort for labeling the data must be reduced in order to ensure a practically applicable system that is accepted in plant breeding. This requires close cooperation between breeders (from a plant science perspective) and computer scientists (from a technical perspective).

The overall goal of this innovation action is to improve the competitiveness of European legume crops. This will be achieved by establishing focused innovation partnerships between research- and industry-based players who together will increase the availability of well-adapted and productive cultivars of key legumes species. The partnership framework is designed to be sustained after the project ends with the ability to expand into other species. There are twelve objectives. Six of these relate to cross-cutting (generic) matters that arise from the call topic. Six are focused on the improvement of specific species, or groups of similar species: Soya bean Lupin Pea Common bean Lentil Clovers BOKU engagement in WPs: Soybean (J. Vollmann, R. Hood-Nowotny) Lupin (J. Strauss)