“LINKDAPA”: Linking multi-source data for adoption of precision agriculture

This project will use multi-source data to co-create with farmers and their advisers, crop management zones for precision agriculture (PA) solutions. The zones will be based on synergistic integrations of historical, current and innovative spatial data sources. Algorithms will be used to predict maps of potential yield and grain quality (protein/seed moisture) as well as probabilities that yield/quality will exceed farmer-specified thresholds in individual wheat fields. Failure by farmers to implement PA solutions is, however, a recognized barrier to sustainable intensification.

As part of the co-creation of innovative precision agriculture solutions for management zones, the project will, therefore, explore:

how accuracy increases with the number of data sources;

farmers’ willingness to pay for more data (e.g. higher resolution image capture by drone/satellite, soil electrical conductivity/compaction scans); and enduser confidence based on probability maps.

Co-created novel solutions will not only include spatially variable inputs, but also harvesting for higher grain quality. Research in 2020, will focus on a sample of 15 fields with historical big data for current wheat crops and with links to John Deere or Agricolus in Germany, Italy or the UK. In 2021, we will upscale to more fields and farms to validate algorithms and field management options. These will link to a new commercial software platform.

Project Leadership: Alistair Murdoch, University of Reading

Contact details: a.j.murdoch@reading.ac.uk

Participating organisations: