A Decision Support tool for Potato Blackleg Disease (DeS-BL)

We have used soil microcosms to show that the causal agent of blackleg disease (Pba) can infect potato plants and cause disease directly from the soil, not only tubers. This is exacerbated by the presence of free-living nematodes (FLN) and excess water, providing new possibilities for research and disease control. Our multidisciplinary team of science and stakeholder experts will investigate the role of vectors, irrigation and crop rotation on Pba infection working from field and mesocosm scales to rhizosphere microbial communities. We wish to see how large-scale changes in the field impact on root architecture and exudate production and what influence this has on rhizosphere microbial communities and competition. The latest bacterial genomics, bioinformatics, imaging and GC-MS tools will be used, with potentially far reaching consequences beyond the present study. Our key questions are: Do FLN act as vectors of Pba, how does infection occur and can blackleg be reduced by managing FLN in the soil though irrigation and /or nematicide? Do Pba-carrying insects transmit the pathogen between plants? Does irrigation and the use of cover crops change the microbiome in favour of or away from Pba and relatives, and what role do changes in root architecture and exudates play? Can we identify bacteriocins and their producing strains amongst these microbial communities and patent them for use in disease control? We have unprecedented access to national datasets that have not previously been linked with blackleg disease research. These include over 10,000 national soil samples with FLN data, synthetic aperture radar (SAR) soil moisture data, the National Soil Archive and several other data sets. With these, and data from the project, we will use machine learning, game theory and other modelling approaches to find relationships between these data and blackleg and bring the information together in a decision support tool for the potato industry.