Implementing FAIR principles in data management systems: A multi-case study in precision farming

Over the past few years, the agricultural sector has witnessed a significant transformation, increasingly adopting a data-driven approach. Adopting an advanced data management system becomes imperative to effectively manage and govern the vast amounts of data generated in this context. Within the realm of data management, the FAIR principles provide valuable guidance. These principles aim to make data Findable, Accessible, Interoperable, and Reusable (FAIR), ensuring that data can be effectively managed, shared, and reused across different domains and disciplines. However, implementing the FAIR principles is not a straightforward task, requiring careful consideration of various factors such as data organization, metadata standards, interoperability protocols, and accessibility mechanisms. To address these challenges, this paper focuses on presenting a systematic approach for implementing the FAIR data principles within the data management system of an interdisciplinary agricultural project. In this paper, each FAIR principle is analyzed in detail, delving into the specific requirements and considerations for achieving them in the context of agricultural data. The current implementation approaches for each principle are identified and synthesized, taking into account both common practices and variant approaches that may be applicable to different scenarios. To provide practical insights, a multi-case study approach is applied to an interdisciplinary project involving dairy and fish farming. This research underscores the importance of metadata, secure data access protocols, semantic interoperability, and comprehensive documentation for implementing FAIR principles in agricultural data management systems, offering valuable insights applicable to dairy and fish farming domains.