Arla's Soil Organic Carbon Sequestration Methodology

Soil organic carbon (SOC) is a central component of the Earth’s carbon cycle and a key lever for climate mitigation in agricultural systems. For Arla—spanning roughly 1.5 million hectares across seven countries—this represents both a scientific and operational challenge. Our methodology adopts a rigorous, data-driven approach: we establish SOC baselines using GIS, the EU LUCAS database, ESDAC resources, and pedotransfer functions to produce spatially coherent, farm-level estimates across pedoclimatic contexts. We then quantify carbon inputs from crops, cover crops, grasslands, and organic fertilisers with literature-calibrated allocation and rhizodeposition factors, scale belowground inputs by rooting depth, and fractionate inputs into chemically defined pools (AWENH) to capture decomposition and stabilisation pathways.Process-based modelling with Yasso20 integrates these inputs to simulate SOC stocks, stock changes, and CO2-C emissions using climate drivers (temperature, precipitation) and chemically explicit pool dynamics. Results are reported as averages using the atmospheric sign convention, supported by a transparent uncertainty protocol: component-level SE/SD where available, primary farm data treated as given, and IQR-based outlier detection with sense checks prior to aggregation. Whole-profile accounting (up to 100 cm, or shallower if bedrock-limited) improves representativeness, especially for grasslands with deep rooting. Land-use-change reporting is constrained to relevant categories and applies conservative assumptions where primary conversion data are unavailable; peat-specific emission factors reflect historical drainage. Potential carbon reversals due to changes in farm affiliation are addressed via a conservative buffer on reported removals.Planned extensions, contingent on data access and research progress, include incorporating additional MRV elements (e.g., eddy covariance with rigorous QA/QC, Earth observation indicators, enhanced weather/PAR products) and trait- or spectroscopy-informed litter characterisation. These developments are expected to reduce uncertainty, strengthen verification, and further align reporting with frameworks such as the CSRD, ESRS, the GHG Protocol, and the SBTi—providing a robust scientific foundation for sustainable, climate-aligned land management.