More About rmAgro

About the rmAgro Reference Model

Innovations and developments in the agrifood sector are hindered by data interoperability due to the need for many custom solutions to share data. By implementing standardised reference data models like rmAgro, we help to optimise data flows and make information systems work better together.

Our team at Wageningen Research provides expert guidance in data modelling and implements reference models that align with industry standards, such as ISO/TC 347, and tests the reference models against various use cases. This helps agricultural organisations to improve how they handle and share data across the entire food value chain.

Services and Implementation

Our work results in a service that provides more efficient data sharing between different systems and organisations, reducing data integration challenges and improving operational efficiency. The service, which can be found on the AgriFoodTEF catalogue, is particularly valuable for organisations looking to modernise their data infrastructure or needing to share data more effectively with partners in the agri-food sector.

This service provides an assessment of interoperability for AI-driven solutions within the agri-food sector, facilitating conformance testing and verification of whether related IT systems comply with relevant standards, guidelines, data space regulations, and other interoperability requirements.

Wageningen Research leverages its expertise to evaluate the overall AI solution on its quality, performance, and how well it aligns with industry standards, offering insights and recommendations for improvement. This service supports organisations in ensuring their solutions are interoperable, compliant, and ready for seamless integration within the agri-food value chain.

Summarised, services around rmAgro are there to optimise data flows across the agri-food value chain through the implementation of reference architectures and data modelling, enhancing organisational efficiency and innovation in data sharing.

Success Stories

Some exemplary cases from national and international projects:

NxtGen HighTech - Hollandse data linie

Biomonitoring Akkerbouw (BMA)

• Objective: To be able to measure different indicators for monitoring biodiversity for the arable domain. Different data sources will be integrated to reach this objective.
• Method: The reference model supports in providing a shared vocabulary to be able to communicate with other systems and organisations.
• Outcome: In development

ReGeNL

• Objective: To define and advance the implementation of regenerative farming by emphasizing soil health and ecological restoration.
• Method: The reference model supports in developing a semantic strategy by identifying information needs and defining key data concepts.
• Expected Outcome: A semantic strategy that includes a model mapping and linking data from WUR and research initiatives to practical applications in farmers' systems.

PL 4.0

Biocertificering

• Objective: To digitize and automate the mass balance as used by the national organic certification body.
• Method: The reference model is used to describe the semantics of the organic value chain to indicate the information need, data availability and the desired design of the system.
• Outcome: A sub model that represents the organic value chain.

Onkruidrobot

• Objective: To come to a set of architectural principles for precision agriculture.
• Method: A case study on autonomous weeding robot including data designs for which the reference model is used.
• Outcome: A sub model that is aligned with the investigated standards and supports the design of the so-called FAIR Data Ecosystem: Published Interoperable Data and Algorithms. The report can be found here.

IoF2020

• Objective: To be able to integrate data from disconnected subdomains for Plant Protection Products with the purpose to support farmers-decision making for more sustainability when using Farm Management Information Systems.
• Method: The reference model supported in modeling the data within the domain of Plant Protection Products data with a selected use case Within Field Management Zoning.
• Outcome: A sub model that describes the Plant Protection Products domain as well as derived design patterns, heuristics and recommendations for developing knowledge graphs. The report can be found here.

PPS DataFair

• Objective: To develop a blueprint that should support farmers in their framework for action and the abattoir to calculate the carbon footprint of pigs meat. The blueprint should also support the abattoir in developing their sustainability strategy further.
• Method: The data model that describes the carbon footprint calculation for pig breeders was an essential building block of the blueprint, and was developed by using the reference model. The model was aligned with the LCA-process as described within the case.
• Outcome: A metamodel including, among others, the business process steps, the data model, and recommendations. The report can be found here.