Oral Presentation Science Protecting Plant Health 2017


Agata Kaczmarek 1 , Matthew Dickinson 1 , Jon West 2 , Neil Crout 1 , Stuart Wili 3 , Patrick Jarvis 4 , Debbie Sparkes 1 , Mark Stevens 5
  1. University of Nottingham, Loughborough, Leics, LE12 5RD, United Kingdom
  2. Rothamsted Research, Harpenden, Herts, AL5 2JQ, United Kingdom
  3. Burkard Scientific Ltd., Uxbridge, Middx, UB8 2RT, United Kingdom
  4. AB Sugar, Sugar Way, Peterboruogh, PE2 9AY, United Kingdom
  5. British Beet Research Organisation (BBRO), Innovation Centre, Norwich Research Park, Colney Lane, Norwich, NR4 7GJ, United Kingdom

Current advances in technology allow us to use novel agricultural solutions, such as robots, on farm integrated weather stations, and GPS for better crop management to improve sustainability and enhance environmental benefits whilst ensuring profitability for the grower.

The aim of this project is to develop a unique disease monitoring system that would be fast, robust and cost effective. Novel diagnostic tools, crop disease modelling and yield forecasting have been linked to underpin grower decision making, and investigate the potential impact of emerging disease on sugar beet.

SporeID is designed to minimise the impact of foliar diseases on the yield of the sugar beet crop including powdery mildew, rust and other potential threats. Currently, potential yield reduction due to these pathogens is estimated to be 12, 20 or 50% for rust, mildew or cercospora respectively and, whilst current practices prevent losses of this magnitude, it is calculated that up to 10% of yield in the UK is still lost each season to foliar diseases. Climate change may also lead to increased pressure, both from existing and ‘new’ emerging diseases, which will require increased crop protection. Improved knowledge and decision making will optimise chemical input and offer environmental benefits through improved resistance management in future climates.

SporeID involves a fully automated technology to trap, recognise and quantify possible fungal pathogens before the disease development in the sugar beet field. Results are generated by a high volume air sampler incorporated with a molecular system, performing Loop-mediated isothermal Amplification (LAMP) and results, together with weather data, are sent out to a server, where the calculations of the risk and predictions for the disease development and consequently yield losses are estimated. Growers receive an alert of the disease pressure, supported by risk analysis, in order to take appropriate action, if needed, within the crop.