School of Bioscience
All over the world, soilborne bacteria and fungi cause severe crop diseases which lead to substantial problems in agriculture due to serious yield losses of great economic impact.
In Sweden, for example, there are demands for the expansion of domestic production of certain crops like oilseed rape, sugar beets, and peas grown for food, feed and biofuels. However, the occurrence of soilborne plant pathogens like bacteria and fungi restricts the possibility to grow these crops with high frequency.
Often the infestation level has to exceed a certain threshold before it reduces the yield of a crop, but it is usually difficult to accurately estimate the yield reduction caused by a specific disease. Current plant growth strategy for most farmers is therefore to cultivate susceptible crops in long intervals to keep the infestation level at a minimum. By monitoring the infestation level of critical soilborne pathogens and predicting the risk of severe diseases, the potential to optimize crop rotation and to allow the efficient use of break crops to obtain secure time-laps between susceptible and profitable crops would be greatly improved.
The aim of this project is to develop accurate tools for prediction of severe soilborne crop diseases in order to support a more efficient decision-making in crop management. This will be achieved by refining existing DNA-based qPCR methods to obtain specific and quantitative detection of very low levels of certain plant pathogens in naturally infested soil samples, and by developing of computational prediction models.