A seminar with Ozias Hounkpatin (Biogeochemistry of Forest Soils) in the series Soil Science Talks.
The status of the soil organic carbon (SOC) stock at any position in the landscape is subject to a complex interplay of soil state factors operating at different scales and regulating multiple processes resulting either in soils acting as a net sink or net source of carbon. Forest landscapes are characterized by high spatial variability, and key drivers of SOC stock might be specific for sub-areas compared to those influencing the whole landscape. Consequently, separately calibrating models for sub-areas that collectively cover a target area can result in different prediction accuracy and SOC stock drivers compared to a single model that covers the whole area.
We investigated (1) how global and local models differ in predicting the humus layer, mineral soil, and total SOC stock in Swedish forests and (2) evaluated how a group of covariates (mostly remote sensing data) associated to these models compared to site data from the Swedish National Forest Soil Inventory (NFSI). We showed that local calibration has the potential to improve prediction accuracy, especially when associated with site-specific covariates, which showed better explanatory strength for SOC stocks compared to the group of covariates. However, improving prediction require both types of covariates.