An exponential change decline function to estimate soil organic carbon stocks and their changes from topsoil measurements

Soil organic carbon ( SOC ) stocks and their changes are important indicators in ecosystem service assessments. Routine soil inventories are often limited to the topsoil, even though a non‐negligible fraction of SOC is known to be stored in deeper horizons. To assess SOC stocks and their changes in the upper metre of the soil profile, vertical extrapolation of topsoil SOC measurements is necessary. The commonly used exponential decline function is not valid, however, for soil types in which subsurface horizons with a larger SOC content (‘anomalies’) occur. Here, we propose an exponential change decline function to account for these profile anomalies. Therefore, we applied the exponential decline function to the difference between the recent (2008–11) and historical (1947–74) SOC contents in the topsoil and compared the results with those derived by the original method. We applied the exponential change decline function to 54 041 agricultural land units (7159 km 2 ) in F landers ( B elgium) and were able to model specific profile characteristics such as spodic horizons, plaggic topsoil and peat substrates. For these particular land units, the exponential decline function underestimated SOC stocks; therefore, it compromised an in‐depth assessment of changes in SOC stocks over time. This study shows that the exponential change decline function is promising for certain soil types and will contribute to the more accurate assessment of ecosystem service indicators. In addition, we emphasize the need for more detailed descriptions of subsoil reference profiles, sampled by pedogenetic horizon rather than by fixed depth interval to optimize calibration of the decline functions. Highlights When recent soil sampling is limited to the topsoil, extrapolation is needed to assess subsoil SOC stocks. We modified the exponential decline function to model SOC ‐rich subsurface horizons with the integration of legacy data. An appropriate extrapolation approach is essential for in‐depth SOC assessments. Detailed subsoil data are needed to optimize the calibration of the decline functions.