Climate change intensifies agricultural risks, requiring an integrated analysis of climatic, hydrological, and crop data to support resilient farming. Despite advances in remote sensing, in-field sensors, and artificial intelligence, fragmented data silos hinder spatiotemporal risk assessments by requiring labor-intensive data handling. We present agriclima, a federated, cloud-native, FAIR-by-design platform that unifies heterogeneous agricultural and environmental datasets under consistent identity, policy, and metadata governance. Its scalable open-source architecture, compliance with INSPIRE and RNDT standards, and privacy-preserving access enable researchers and decision-makers to perform comprehensive analyses with minimal coding, accelerating data-driven agricultural risk management. Developed and tested in a research project by a consortium of stakeholders in agricultural risk management, the platform was evaluated via: (1) FAIR assessment of 26 datasets using F-UJI, (2) system performance monitoring on Kubernetes, and (3) a demonstrative spatiotemporal aggregation use case. It achieved 80% average FAIR compliance, with perfect accessibility (7.00/7.00), while findability and reusability remain key areas for improvement. Performance showed stable operation (CPU 17.24%, memory 49.89%) with capacity headroom. The demonstrative use case validated that researchers can conduct spatiotemporal analyses with minimal coding effort through the abstracted data access components. Beyond technical evaluation, we share lessons learned to guide future platform development and metadata standardization, highlighting the platform’s effectiveness as a foundation for data-driven agricultural decision-making.
Pincheira et al. (Wed,) studied this question.
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