The model comprises 70 data points organised into seven broad categories:

1. Farm and location: farm-related information, geographical location and relevant climatic conditions.

2. Field and soil properties: soil texture, moisture, soil organic matter, soil organic carbon, drainage and pH.

3. Crop cycle: crop identity, harvested quantity, farm-gate-ready quantity, crop residues, residue management and co-products.

4. Operations and agricultural inputs: fertilisers, manure, plant protection products, energy use and relevant operational information.

5. Irrigation: information related to irrigation activities and water use.

6. Transport: information relevant to agricultural transport activities.

7. Carbon and biomass changes: information associated with changes in carbon stocks and biomass.

The model provides a common conceptual representation of information that may otherwise be organised differently by individual tools. It is intended to support the identification of corresponding data requirements across systems and to provide a reference structure for subsequent data mapping. The model does not imply that all assessment tools have identical data requirements or calculation methods. Rather, it provides a structured basis for identifying and representing the information they require. Differences in calculation methods, system boundaries and assumptions may remain even when tools use similar agricultural input data. Such methodological choices are important considerations in carbon-footprint assessment [OECD25].

Agricultural data sources and interoperability requirements

The information required for GHG assessment may originate from different agricultural and environmental information systems. Potential sources include:

• Farm management information systems (FMIS) and digital field records.

• Machinery platforms and telematics systems.

• Agricultural administration systems and registers.

• Climate services and meteorological datasets.

• Soil databases and environmental information services.

• Input-product databases and other agricultural information resources.

This diversity makes it difficult to rely on a single source or file format. A practical interoperability approach must therefore accommodate information from different sources while retaining the context needed to interpret each record correctly. The data model defines the agricultural information to be recognised and organised. The interoperability prototype explores how information from heterogeneous files can be extracted and associated with the corresponding data points in this model.

Prototype for agricultural data interoperability

To explore the practical challenges of agricultural data interoperability, Digi-NaSA developed a prototype that processes heterogeneous agricultural information and relates it to the data points defined in the standardised model. It represents an initial step towards supporting the structured reuse of agricultural information across assessment systems (Figure 2).

intermediate development step rather than a complete, production-ready integration of all potential data sources and assessment tools.

Further development and reseacrch directions

The ongoing development of Digi-NaSA focuses on improving the processing and mapping of heterogeneous agricultural information. Particular attention is given to differences in file formats, terminology and data organisation, as well as to preserving the relationships between labels, values and units.

Further work will address the evaluation of the approach with real-world agricultural data, the handling of additional source formats and layouts, and improvements to the mapping and review processes. The transformation of units and the preparation and validation of information for downstream assessment systems are also relevant areas for further development.

An additional challenge concerns the distinction between data interoperability and methodological harmonisation. Different GHG assessment tools may apply different system boundaries, emission factors, assumptions and calculation procedures, even when they use the same farm information. A common data model can support the reuse and organisation of information, but it does not by itself make the underlying calculation methods equivalent. Understanding these differences is important for the longer-term objective of supporting more transparent and comparable agricultural sustainability assessments.

Conclusion

Digi-NaSA investigates how agricultural data interoperability can support sustainability assessment in digital farming, with a particular focus on GHG assessment in arable farming. The project analysed existing agricultural sustainability assessment tools, developed a conceptual data model comprising 70 data points and explored how heterogeneous agricultural information can be processed and mapped through an interoperability prototype.

By providing a common conceptual representation of agricultural information and investigating approaches for mapping data from heterogeneous sources, Digi-NaSA establishes a foundation for improving agricultural data reuse across assessment systems. The work also highlights that data interoperability is only one part of the broader challenge. Meaningful comparison of GHG assessment results requires attention not only to the information used but also to the methodologies, assumptions and system boundaries underlying the calculations.

Digi-NaSA therefore contributes to the ongoing development of more interoperable and sustainable digital farming systems and provides a basis for further research into agricultural data integration and sustainability assessment.

References

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[OECD25] OECD (2025). Measuring Carbon Footprints of Agri-Food Products: Eight Building Blocks. OECD Publishing, Paris. https://doi.org/10.1787/8eb75706-en

[Wo17] Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M.-J. (2017). Big Data in Smart Farming – A review. Agricultural Systems, 153, 69–80. https://doi.org/10.1016/j.agsy.2017.01.023