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.

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).

Drought stress is currently a major constraint limiting crop production worldwide and is expected to become an even more severe challenge for agricultural production in the future. Breeding and growing of adapted plant varieties that are drought stress tolerant is thereby a major pillar for buffering the effects of the on-going changes of regional and global climatic conditions. Predictive breeding methods have opened-up new avenues for wheat research and breeding for complex traits like drought stress tolerance. The usage of genome-wide distributed markers for conducting a genomic selection has thereby gained large popularity in applied breeding programs. Phenomic selection is on the other hand a recently developed method, which is similar to genomic selection but replaces molecular markers with information from near-infrared spectroscopy (NIRS). NIRS is already routinely used by cereal breeders to predict quality traits such as grain protein content, and since NIR spectra are influenced by the molecular composition of the analyzed samples they are furthermore able to capture genetic similarity between genotypes. Hence, they have the potential to be a high throughput and low-cost complement or alternative to molecular markers. In this project, we will develop an omics-based predictive breeding approach for breeding superior yielding drought stress tolerant bread wheat varieties using SNP marker and NIRS fingerprints. For this purpose, we will conduct dedicated drought stress trials in multiple locations and years, which will be used to develop multi-kernel omics-based prediction models, which interconnect the information from NIRS spectra with genotyping information from SNP Arrays as well as meteorological data for environtyping experimental sites. This will facilitate a more directed selection towards drought stress tolerance and consequently trait stability by predictive breeding technologies after completion of the project.