From simple to complex: Evaluating methods for estimating soil organic carbon changes in croplands

The importance of agricultural soils as a potential carbon sink has been extensively discussed as a key step towards climate neutrality and sustainable land use. Typical measures that could enhance soil organic carbon (SOC) stocks include cover cropping, perennial crops, or establishing hedgerows or agroforestry systems. Targeted advisory services and financial incentives, including subsidies or carbon credit trading systems, could encourage farmers to establish these measures. However, accurately assessing their contribution to climate protection via enhanced SOC stocks remains challenging. Taking soil samples every few years can fulfil this purpose, but they are costly, labour-intensive, require a careful sampling regime and usually a period of at least ten years to detect significant differences in SOC stocks. As a result, various estimation methods are discussed as an alternative. However, there is currently no consensus as to which approach best balances accuracy, feasibility and practicality. This study evaluates five different methods and models of varying complexity to estimate SOC stock changes, using data from 46 German permanent soil monitoring sites. Included in the assessment is the VDLUFA humus balance method as well as the process-based model RothC, which is run with two variants regionally averaged and site-specific. Our results confirmed previous conclusions, that simple carbon balance methods perform poorly if no site-specific pedoclimatic information is considered in the methodology. By comparison, the RothC model achieved significantly better predictive performance, especially if executed with site-specific information. A hybrid approach integrating properties of the RothC model with the simplicity of the VDLUFA method achieved a comparable predictive performance while reducing methodological complexity. Our findings provide insights into the trade-offs between model complexity and prediction accuracy, offering recommendations on their applicability for climate policy and agricultural decision-making.