Challenge 1: Environmental Performance Dashboard

Two teams worked on developing an interactive KPI dashboard that allows farmers to see their environmental performance on a set of indicators (such as biodiversity, carbon and nitrogen). The big idea is that such a tool allows farmers to track their environmental performance, set goals and benchmark with other farmers.

One team worked on a dashboard to help farmers visualise the environmental effect of carbon farming and creating an opportunity to grow a sustainable carbon farming community. They used the existing Carbon Model Flux for their data model.

The second team, consisting of a collaboration of several WUR scientists from team Earth Informatics, worked on an existing concept of a dashboard that shows results in terms of a set of KPI’s in relation to circular agriculture, using open data.

You can find the pitch, photo's and the recordings of both their pitches here:

Challenge 2: Data for Carbon Sequestration

​The team, that consisted of representatives of MyEasyFarm, Trekkerdata.nl and WUR had three objectives:

1. To test the assessment of carbon sequestration (and emissions) by MyEasyCarbon with Dutch data at plot level​
2. To show the impact of better quality and more complete (machine) data on the assessment of carbon footprint​
3. To show the advantage of a more accurate assessment (and potentially of more = less discounted, carbon credits), to farmers and other stakeholders

You can find their pitch, photo's and the recordings of their pitch here:

Challenge 3: AI and geotagged photos

The goal that this team set themselves was to “identify and analyze data captured in geotagged photos that can be used to enhance the satellite data which will allow further refinement of the satellite data for decision making purposes”. In a very impressive and inspirational collaborative effort by the team, they designed an API based solution for smart use of geotagged photo’s and AI to better distinguish different types of land covers.

You can find their pitch, photo's and the recordings of their pitch here:

Challenge 4: Data for Strip Cropping

The team focussed on the design of a system that gathers data from difference sources in order to detect narrow strips in fields:

• a geo-photo app to collect photo data
• algorithms to make sense of the combination of photos
• a user interface to handle the application process at the PA

You can find their pitch, photo's and the recordings of their pitch here:

NIVA

The hackathon built on top of results of the NIVA4CAP program, a collaboration of Paying Agencies from 9 EU Member States to develop new tools to improve their Administration and Control Systems, and  offer farmers opportunities to improve their farming practices.