Investigating spatially varying relationships between total organic carbon contents and pH values in European agricultural soil using geographically weighted regression

Highlights: • Spatially varying relationships between TOC and pH are revealed by GWR. • Positive correlations between TOC and pH cluster in central-eastern Europe. • Negative and mixed correlations are observed in northern and southern Europe. • Quartz-rich soil is the main contributing factor to the positive relationship. Total organic carbon (TOC) has received increased attention in recent years, not only as an important indicator in soil fertility, but also due to its close relationship with the atmosphere. Generally, soil TOC and pH values follow a negative correlation, which was revealed by traditional statistical methods. However, the conventional global models lack the ability to capture the spatial variation locally. In this study, spatially varying local relationships between TOC and pH values are studied by geographically weighted regression (GWR) on continental-scale data of European agricultural soil from the project 'Geochemical Mapping of Agricultural and Grazing land Soil' (GEMAS). In this study, TOC is the dependent and pH the independent variable. Both negative and positive local correlation coefficients are observed, showing the existence of 'special' spatially varying relationships between TOC and pH values. Original negative relationships change to positive values in more than 50% of the study area. Novel finding of significant positive correlations is observed in central-eastern Europe, while negative correlations are found mainly in northern Europe. Mixed relationships occur in southern Europe. These special patterns are strongly associated with specific natural factors, especially the extensive occurrence of quartz-rich soil in the central-eastern part of Europe. Anthropogenic inputs may have also played a role in the mixed southern European areas. The GWR technique is powerful and effective for revealing spatially varying relationships at the local level. Thus, it provides a new way to further explore the related influencing factors on the TOC and pH spatial distribution.