The NBSOIL Health Index Tool supports soil advisors and farmers by integrating data from laboratories, digital tools and satellites into actionable soil health insights. It assesses soil threats at the field level, using the Soil Management Assessment Framework (SMAF) to generate scaled indicator scores (0-1) based on benchmarking datasets. These scores are combined into soil function scores for climate regulation, nutrient cycling, biodiversity and water retention, which are visualised using radar plots.
The soil health assessment starts with a soil consultant selecting relevant indicators and determining the texture class of the soil. As many soil indicators are texture dependent, soils are classified as coarse (<15% clay), medium (15-25% clay) or fine (>25% clay). This classification refines the benchmarking by comparing the soil with soils under similar conditions.
Next, the tool applies the Soil Management Assessment Framework (SMAF) methodology to calculate scaled indicator scores (0-1) based on the benchmarking dataset. These scores represent the health of the soil in relation to the reference benchmarks.
The individual scores are then combined into soil function scores representing key soil functions such as climate regulation, nutrient cycling, biodiversity and water retention. The results are visualised using radar plots, providing an intuitive overview of soil health.
The tool available here is accompanied by a comprehensive guidebook to ensure its effective use. Both the tool and the guidebook are integrated into the educational offering of the NBSOIL Academy, which provides structured courses to promote the adoption and dissemination of the tool.
Project coordinator
Grzegorz Siebielec,
IUNG
gs@iung.pulawy.pl
Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them.
This work has received funding from UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee grant number 10061997.
This work has received funding from the Swiss State Secretariat for Education, Research and Innovation (SERI).
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