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Public Funding for Crop Performance Ltd

Registration Number 08559609

SporeID (Innovative disease monitoring and diagnostics for improved efficiency of crop production)

33,725
2015-04-01 to 2018-09-30
Collaborative R&D
The aim of this project is to minimise the impact of disease on yield of the UK sugar beet crop which is worth approximately £240M per year. Yield potential of the UK sugar beet crop is c.130 t/ha compared to an average yield of 70t/ha. One of the factors responsible for this yield gap is foliar diseases which can reduce yield by more than 50% and, whilst current practices prevent yield losses of this magnitude, it is estimated that 10% yield is lost to foliar diseases, representing £24M per year. Climate change may lead to increasing pressure from existing diseases and 'new' emerging diseases, which require increased crop protection. This project will bring together novel diagnostic tools, crop disease modelling and yield forecasting to underpin grower decision making and investigate the potential impact of emerging diseases on the crop.

Optimised Detection and Control of Potato Blight: Sensing Pathogens to Inform Smart Spray Decisions

31,003
2015-04-01 to 2018-09-30
Collaborative R&D
The maintenance of global food security, mediated by sustainable intensification of agriculture, is a recognised global issue and the effective management of plant disease is critical to productive cropping of agricultural land. Potato is the third most important food crop globally, with late blight control being a major challenge estimated to cost £3.5billion in losses per annum. In the UK, disease control alone costs £55M per annum on average to the industry. This project seeks to demonstrate a new prototype device that will sample airborne spores of P. infestans (the cause of late blight) and Alternaria species (the cause of early blight) in the field, automatically process the sample, quantify DNA by fluorescence and relay results by mobile phone text message. The aim is to improve current weather-based disease risk models and predictions for late blight, resulting in enhanced decision making ability for growers with respect to fungicide choice and application and therefore more efficient resource use.

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