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Precision livestock farming data sharing, integration and exploitation

This use case is run by the Universitat de Lleida (UdL) in cooperation with the experimental pig farm Centre of Swine Studies of Catalonia (CEP). The role of the CEP is mainly as a data originator, willing to share through the data space the data generated as a result of the different experiments carried out on the pig farm. For the CEP, it is crucial that they can maintain control of the data they provide, mainly when it is generated by third parties, such as manufacturers of automatic feeding machines testing their products at the experimental farm.

Precision feeding machines by Exafan in a pig farm.

Some examples of use case datasets available through AgrospAI are:

Data is made available in its original format and schema, usually in custom tabular form, following a "Pay-as-you-go" approach, as detailed in the Interoperability of agri-food data spaces good practices document. Instead of requiring publishers to integrate data based on existing schemas, which causes a significant upfront overhead, this favors an incremental approach. In this way, entry barriers are reduced and data exchange is facilitated.

Later, when data integration is required, AgrospAI features data mapping and integration services. Furthermore, data sovereignty is guaranteed by design through the "Data Room" approach. The mapped data does not leave the room; it is processed and then stored in a Knowledge Graph that remains within the room. In this way, it remains under the control of the data originator, CEP. An example of a data mapping service implementing the RDF Mapping language (RML), capable of mapping from CSV, TSV, XML, and JSON data sources to Knowledge Graphs based on RDF, is the:

Later, the CEP can decide to grant access to trusted algorithms to visit the Data Room and cut the Knowledge Graph to extract the semantically integrated data relevant to their calculations. Also, in this case, data sovereignty is guaranteed since only the computation results, such as aggregations or AI-trained models, can leave the room, not the original data or subsets thereof. Examples of AI services available through the AgrospAI portal include: