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In large-scale, multi-national research projects on agricultural cropping systems such as SoilCare (Horizon 2020), ensuring consistency, comparability, and timely reporting of the (meta)data of the agricultural experiments across diverse partners has been a persistent challenge. To address these concerns, the SoilCare project developed a comprehensive data management system centered around a standardized template for the collection, organization, and sharing of experimental data and metadata from cropping systems. This template, designed to support harmonized sharing, analysis, and documentation through a common structure and terminology, meets the interdisciplinary requirements of modern agricultural research. Experimental data and metadata were structured into five core pools: 1. basic information, 2. field information, 3. experimental setup, 4. agricultural management data and 5. measured data/results. The Excel-based template was carefully structured to support integration into a relational database, enabling robust monitoring, analysis, and traceability of experimental processes and outcomes. The database schema and template, together with the (meta)data collected using this system during the SoilCare projects, were made openly available via Zenodo. The standardized approach ultimately enabled unified analyses and harmonized reporting across all experimental sites, demonstrating the system’s effectiveness in facilitating collaborative agricultural research at scale.

Background: Ecosystem services (ES) mapping is becoming mainstream in many sustainability assessments, but its impact on real world decision-making is still limited. Robustness, enduser relevance and transparency have been identified as key attributes needed for effective ES mapping. However, these requirements are not always met due to multiple challenges, referred to here as bottlenecks, that scientists, practitioners, policy makers and users from other public and private sectors encounter along the mapping process. New information: A selection of commonly encountered ES mapping bottlenecks that relate to seven themes: i) map-maker map-user interaction; ii) nomenclature and ontologies; iii) skills and background; iv) data and maps availability; v) methods-selection; vi) technical difficulties; and vii) over-simplification of mapping process/output. The authors synthesise the variety of solutions already applied by map-makers and map-users to mitigate or cope with these bottlenecks and discuss the emerging trade-offs amongst different solutions. Tackling the bottlenecks described here is a crucial first step towards more effective ES mapping, which can in turn ensure the adequate impact of ES mapping in decision-making.