Research

Insect-borne cereal viruses are considered the 'winners' of climate change. Winter cereals, especially winter barley and winter wheat, are under increased pressure for infection with Wheat dwarf virus (WDV), Barley yellow dwarf virus (BYDV) and Cereal yellow dwarf virus (CYDV). Cereal plants are most susceptible to these viruses at the juvenile stage. The viruses are transmitted by sucking insects (vectors): WDV is transmitted by a dwarf cicada (Psammotettix alienus), BYDY and CYDV by several aphid species. The activity of the vectors is dependent on temperature and thus weather conditions. Rising temperatures increase the mobility of the vectors. In particular, longer periods of warm temperatures in the fall, in some years into early winter, which are increasingly common, increase the risk of viruses to our cereal crops. The extent of damage varies depending on the degree of infestation; heavily infested crops can lead to total failure. In the project, the necessary preliminary work (pre-breeding) for breeding 1) new resistant breeding lines will be carried out and 2) effective selection methods will be developed, with a focus on resistance to WDV, because WDV is of increasing importance in wheat in Central Europe. In work package 1, the genetic variation in the current breeding material will be examined in multi-site field trials and selection markers for quantitative resistance will be sought. In work package 2, a highly effective resistance factor on chromosome 6A recently discovered by us in an old Eastern European variety will be introduced into regionally adapted winter wheat variety candidates. Overall, the expected new findings on the inheritance of virus resistance and the newly developed pre-breeding material with improved virus resistance represent an essential step towards future-fit wheat varieties and the sustainable safeguarding of wheat cultivation in Austria.

Durum wheat is a food crop with increasing importance. Durum wheat suffers from increasing stresses partly due to global changes. Two relevant constraints are the fungal disease Fusarium head blight (FHB) and the viral disease wheat dwarf virus (WDV). In this project we will accelerate breeding for genetic resistance against these two diseases and thus make durum wheat more stress resilient by: i) Genetic analysis of a new four parent MAGIC population descending from a combination of modern cultivars with WDV resistance crossed with FHB resistant experimental lines. We intend to identify and map relevant QTL for the traits WDV resistance and FHB resistance. ii) Selection of climate fit durum wheat cultivars by starting with large segregating populations in the F3 generation which will be rigorously phenotypically selected or increase FHB resistance, and subsequently selected for improved WDV resistance to identify new cultivar candidates that will enter the cultivar development program. Selected lines will be DNA fingerprinted and used to explore on ‘reverse’ predictions, means checking for genome regions under selection in response to improved resistance.

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).