Machine data can be a rich source of information for farmers and Paying Agencies, but in many occasions it stays in the tractor terminal, unused. Objective of NIVA Use Case ‘Machine Data’ is to showcase that machine data can support validation of farmers actions.

This was done for the measures ‘sowing catch crops’ and ‘wildlife saving mowing’. In the first test, as-applied data were collected for a convincing overview of field covering application of a crop that prevents fertilizers to leach from the cultivated soil layer. The fact that the tractor was driving all over the parcel on an accepted date, is circumstantial evidence that the crop is sown on that date. Later coverage images by satellite show that the crop is really present.

In the mowing test, the combination of mower data and tractor location – a tramline on the same distance as the mower with – shows the exact timing of the mowing. Here also, images show that the grass is harvested between 2 passages of satellites. The machine data tell more about the exact date, which should be after young birds could leave the nests.

In the same way evidence on fertilizer application or measures taken near trees (not visible for satellite) can be registered and transferred to the Paying Agency.

At the end, farmers are at stake, they should see the advantage of collecting and transmitting these data. The farmers inventory in the Use Case gave a good driver for farmers to do this effort: get more information for better management decisions. So: if farmers can take better decisions, Paying Agencies can do more tailored observations.

The Use Case was developed by a cross-national team: RVO, ZLTO and WUR from the Netherlands and SEGES from Denmark. The Use Case was tested with different agricultural activities and types of machines. The dataflows were tested in the Netherlands and Denmark, but also in a later stage in Greece and Spain.

More information can be found on the NIVA website: and the source code is available on GitLab: