We are delighted to invite you to our online webinar event “Exploiting farm-level big data to increase economic and environmental efficiency in crop management” showcasing the results from the EIT Food project LINKDAPA. The LINKDAPA project has been running for three years and has developed algorithms to integrate historical and current data from individual fields with the aim of enhancing within-field level management by farmers. A core part of the projects work has been the co-creation with farmers and identifying opportunities to increase adoption of more precise and sustainable farming systems. The online webinar event will take place on Wednesday 14th December at 10.00 – 11.00 GMT as follows:
- 10:00-10:30. Presentation: Exploiting farm-level big data to increase economic and environmental efficiency in crop management” by Dr Lindsay Todman, University of Reading
- 10:30-11:00. Q & A with the research team.
If you would like to join us please Click here to join the meeting
Please feel free to share the invitation within your professional networks.
Abstract: The LINKDAPA project aims to promote sustainable agriculture by working with farmers to co-create crop management zones for precision agriculture solutions.
Farmers increasingly generate a large amount of data and collect information about their fields and crops. This has the potential to be used to support crop management decisions, making farming more economically and environmentally efficient. Precision agriculture solutions gather, process and analyse this data and information, however so far there has been low uptake of this methodology by the farming community.
This project is working with farmers and their advisers, using multi-source data to co-create crop management zones to provide precision agriculture solutions. The zones will be based on integrating historical and current data taken from individual fields. Algorithms developed as part of the project are being used to map wheat crops. These maps provide information about the potential yield and grain quality variation in fields, as well as probabilities that yield and quality will exceed farmer specified thresholds.