This use case pilot deals with strategically offering knowledge to join food security and biodiversity conservation goals in arable management and providing FAIR data therefor.

Background

WeeDive focuses on weeds in arable farming, the occurrence of species affected by management, and possible contributions to biodiversity. Data sources on different time and spatial scales are required. WeeDive strives to make three types of data available for open access. The data types vary by nature due to the sampling method used. WeeDive addresses RDM in agrosystem research and develops and tests a service to overcome the challenges of no access, buried data and scattered knowledge.

We will (1) add to a European monitoring data format, “Arable Weeds and Management in Europe” (AWME), and (2) develop a format for long-term experimental (LTE) data on weeds under management options that allow weed survival in crops, (3) offer a reference inventory to modify weed management for biodiversity goals. A (4) comprehensive example will be derived of how these data can enable and assist scientific exercises, e.g., about changes in weeds on time scales relevant to climate change or on spatial scales relevant to IPM decisions in farming.

Thus, WeeDive strategically offers knowledge to join food security and biodiversity conservation goals in arable management. Besides adapting data format and management to FAIR principles, data curation will be safeguarded by hand-shake activities of the project partners.

Access to the three data collections will help to address future questions in agriculture. Moreover, all data types used in WeeDive can be applied to related issues, e.g., monitoring herbicide-resistant weeds, accessing more LTE on arable management, or linking up with other services (e.g., soil erosion and soil structure).

Progress & next steps

UC Update: weeDive: Making Long-Term Arable Vegetation and Monitoring Data FAIR

The UC10 team led by Bärbel Gerowitt  presents a blueprint for making long-term arable vegetation and monitoring data FAIR (Findable, Accessible, Interoperable, Reusable). It highlights challenges in harmonizing heterogeneous agricultural data and demonstrates solutions through case studies, such as crop rotation experiments and biodiversity databases. The work aims to unlock the full potential of agricultural data by creating standardized, reusable workflows for complex and diverse datasets.