Soil Healt...
Cultivating Soil Connectivity: Empowering Farmers and Youth Community as Citizen Scientists to Monitor Soil Health and Biodiversity
- Project start date:
- January 2024
- Project end date:
- December 2025
- Project Contacts:
- Jelena Jovic - jelena.jovic@biosense.rs
- Project cost:
- 35000 EUR
- Project URL:
- https://cuvarizemljista.com/
- Impact Assesment progress:
- 100% complete
Project Description
The Soil Health Guards project (2024–2025) in Vojvodina, Serbia, is a two-year citizen science initiative addressing soil degradation exacerbated by climate change and unsustainable farming. It empowers farmers and youth to monitor soil health indicators—organic matter decomposition, earthworm biodiversity, soil pH, nutrients (NPK), and physical properties—through practical toolkits and training workshops. Methods are scientifically validated, cost-effective, and adapted from established European citizen science protocols, ensuring data comparability. Participants analyze their soil data using a user-friendly “traffic light” system and receive tailored management recommendations to promote sustainable agriculture and reduce chemical fertilizer use. Data validation includes laboratory analyses and soil microbial community metagenomics, enhancing reliability. The project aligns with EU and global sustainability goals, including the Green Deal and SDGs 2, 13, and 15, fostering local stewardship and climate-resilient practices. Through inclusive outreach, involving diverse age groups and education levels, it bridges scientific research with community engagement, improving soil health monitoring where comprehensive data were previously limited. Soil Health Guards also contributes to increasing public trust in science, supports open data sharing, and has produced educational resources, positioning itself as a model within European citizen science initiatives focused on sustainable soil management and climate adaptation.
Impact Assesment Tools
Current Project Impact
The following scores were calculated using a statistically-driven machine-learning approach, a type of AI that learns to perform a task by analysing patterns in data. This is an experimental approach to citizen-science impact assessment, and the exact reasoning behind the scores is not explainable. The scores represent a best guess of the impact the project is having in each domain. Scores are recalculated and updated when “View impact report” is clicked.
Domain Progress
Proportion of questions answered in each domain.
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