Please note: The algorithm descriptions in English have been automatically translated. Errors may have been introduced in this process. For the original descriptions, go to the Dutch version of the Algorithm Register.
Cutting algorithm
- Publication category
- Impactful algorithms
- Impact assessment
- DPIA
- Status
- In use
General information
Theme
Begin date
Contact information
Link to publication website
Responsible use
Goal and impact
The algorithm helps make the annual CAP grant application better. It checks whether the applicant's information is correct. If something is incorrect, the applicant is notified.
Considerations
Since 2023, the Area Monitoring System (AMS) has been mandatory for monitoring land-related schemes. This is stated in Article 7 of a European law (EU 2022/1173). Using satellite images is the most efficient way to do this automatically. The applicant can also provide their own evidence and may later object if they disagree with something.
Human intervention
If the algorithm finds a discrepancy, an employee first looks at the result. If the clerk agrees with the deviation, he will let the requester know. The applicant can then send evidence via a special app (the geotag photo app) to show that it might be right after all.
Risk management
The quality of the algorithm is tested annually. This is done on the basis of samples. And a mandatory quality test that has to be reported to the European Commission.
Legal basis
Since 2023, the Area Monitoring System (AMS) has been a mandatory component for monitoring land-based schemes, as stipulated in Article 7 of Implementing Regulation (EU) 2022/1173.
Links to legal bases
Impact assessment
Operations
Data
Functional_id, declared crop code, geometry, application number (declaration of relationship data for a CAP grant application, Satellite data).
Links to data sources
- BRP Gewaspercelen : https://www.pdok.nl/-/brp-gewaspercelen
- Copernicus Data Space Ecosystem : https://dataspace.copernicus.eu/
Technical design
The mowing marker detects mowing activity. Detection is based on the decline and subsequent recovery of the normalised vegetation index (NDVI) value in the observed time series. It is not a self-learning algorithm.
External provider
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