2026/02/20 by Ryan E. Champiny, Katerina Georgiou, Yang Lin · 1 voice
Agricultural and Biological Sciences · Environmental Science · #Soil Carbon and Nitrogen Dynamics #Soil Geostatistics and Mapping #Soil and Water Nutrient Dynamics
paper · doi:10.1016/j.geoderma.2026.117732
openalex publication_date 2026/02/20 · openalex created_date 2026/02/21 · openalex updated_date 2026/05/06
• 95th quantile regression of mineral-associated organic carbon saturation provides clear effective saturation capacity. • Effective MAOC capacity of USA soils was 54 ± 1 g C kg −1 mineral. • Effective MAOC capacity of European soils was 65 ± 4 g C kg −1 mineral at the continent scale. • Using 95th quantile regression all datasets adhered to global saturation limit. Identifying organic carbon (C) saturation behavior in large soil datasets will broaden the scientific community’s understanding of C sequestration potential and enable targeted effort toward efficient C sequestration. Recently, there has been debate on whether mineral-associated organic carbon (MAOC) saturates at a certain limit – here we seek to compare methods for analysis of MAOC saturation in three large datasets from Europe and the United States of America (USA). Using 95th quantile regression of the MAOC fraction to the percent clay and silt of the soil, and assessment of the contribution of MAOC to total soil organic carbon (SOC) content, we show the interpretation of apparent saturation limits differs depending on the method used. Assessment of the MAOC fraction to total SOC provides inconsistent results, particularly in one European dataset where MAOC did not appear to saturate. In contrast, under quantile regression, all three datasets show saturation limits between 54 to 88 g MAOC kg −1 mineral. Here we demonstrate that the quantile regression method minimizes ambiguity and is the more useful tool for identifying and quantifying MAOC saturation limits in large datasets. Our analysis also highlights the importance of sampling design and fractionation methods in identifying the MAOC saturation within these datasets.