Long-term monitoring data for Carbon Farming

This report aims to provide clear guidance on how long‑term monitoring (LTM) sites can improve the FAIRification (Findable, Accessible, Interoperable, and Reusable) of their data to support emerging data needs in the context of Carbon Farming (CF). As part of the CREDIBLE project, Focus Group (FG) 3.4—one of 11 FGs addressing key challenges in European carbon farming—consolidates insights from existing long-term monitoring (LTM) networks and LTM data users in public and private sectors. We developed practical recommendations for managing and reusing LTM data efficiently, improving accuracy while reducing costs for Monitoring, Reporting and Verification (MRV) to support the EU Carbon Removal and Carbon Faming (CRCF) regulation. We target carbon modellers and CF project developers who rely on LTM data to calibrate/validate models and verify monitoring approaches. It is also relevant for LTM site owners, network coordinators, and researchers who need to understand the data requirements of CF projects and adopt good practices for data FAIRification. In addition, the report targets policy makers involved in land‑use, climate, and agricultural policy who require insights into the role and usefulness of LTMs and the opportunities for integrating these existing datasets into regional CF strategies and the development of CRCF‑aligned methodologies. This report is based on a literature review, the feedback from FG3.4 discussions and two breakout sessions at the European Carbon Farming Summits (ECFS) involving LTM data providers and data users in both public and private sectors. We outline the role of LTMs in CF and highlight key challenges and opportunities in using LTM data (Section 3). We review FAIR data‑management practices across existing LTM networks and provide guidance to help site owners to improve data FAIRification (Section 4). Finally, we discuss broader data‑sharing needs between public and private actors (Section 5) to reduce administrative burdens for land managers and enhance the cost‑effectiveness of MRV in CF projects. LTM sites are critical for robust MRV systems, providing essential data for calibrating, validating, and simplifying models that simulate SOC changes and GHG emissions. · LTM data support long‑term assessment of CF practices, climate‑change impacts, and the wider digitalisation of CF monitoring. · Broader representativeness of LTM networks is needed, including new sites in underrepresented regions, environmental conditions, and (a combination of) emerging management practices. EU Soil Mission living labs, Lighthouses and on-farm monitoring networks — provided that they comply with FAIR data principles and apply consistent, harmonised data collection and data management methodologies — can help fill existing gaps. · Improving the FAIRness of LTM (meta)data should be prioritised, through standardisation, harmonised protocols, shared vocabularies, and wider use of the principle of ‘as open as possible, as close as necessary’. Address technical and organisational barriers by investing in data-management tools, training and capacity building for LTM owners. · Recognition and incentivisation of data publication—such as supplementary datasets—can help valorise data‑generation efforts, promote best practices in data sharing, and cover the cost for maintaining LTMs. To establish new LTM sites or maintain existing ones, applying FAIR principles is essential, and existing LTM networks already offer strong examples of good practice. · Findability: Assign persistent identifiers (e.g., DOIs) to all datasets, including versioned datasets. Use standardised metadata templates such as: EJP SOIL LTE metadata template, ICOS flux metadata standards and ICP Forests and eLTER metadata standards for forestry and ecosystem data. Deposit datasets in trusted repositories (e.g., BonaRes for long-term field experiment (LTE) (meta) data, Zenodo for general datasets). · Accessibility: Ensure data are retrievable through standardised access manners. Apply appropriate access restrictions when needed, while keeping metadata publicly accessible to support MRV purpose and transparency. · Interoperability: Use common, open data formats (e.g., CSV) and shared standards. Adopt controlled vocabularies from networks such as ICOS, ICP Forests, and eLTER—preferably in machine‑readable form. Provide tools to facilitate data integration (e.g., ReLTER R packages). · Reusability: Provide comprehensive metadata and clear documentation of data provenance. Apply open licensing: CC0 for metadata and CC BY 4.0 (or equivalent) for datasets to ensure credit and maximise reuse. Beyond LTM data, there is an urgent need for increased transparency and stronger collaboration between public and private sectors to enable effective data sharing across the entire carbon farming value chain. · Adopt the “Collect Once, Use Many Times” principle by utilising user-friendly software connected to data-sharing platforms to reduce the administrative burden for landowners and improve data accuracy. · Implement clear data sharing agreements & incentivize data exchange by creating standard data sharing agreement templates (e.g., dual licensing for commercial & non-commercial use), offering federated modelling without sharing raw data, introducing transparent review procedures with a ‘research commenting period’, and enhancing trust and incentivise data exchange for LTM maintenance. · Extend LTM networks to on-farm monitoring networks, including commercial farms, to capture real-life conditions for carbon farming and verify monitoring approaches at field scale and improve overall accuracy. Looking ahead, implementing data-sharing agreements and incentive mechanisms could strengthen trust and enable more effective collaboration along the value chain. At the 3rd ECFS, we will explore practical examples and success stories of data sharing, bringing together LTM data owners, private companies, and other actors along the value chain to discuss their respective needs and identify workable solutions.