Reconstructing pH of Paleosols Using Geochemical Proxies

Soil pH is essential for understanding weathering, nutrient availability, and biological-edaphic relationships. However, standard pH measurement on soils requires friable material, thereby excluding most paleosols. In this article, bulk geochemical proxies for pH are developed for soil B horizons using indices that track pH-dependent mineralogical transformations. Geochemical relationships within a continental-scale modern soil data set (n = 619) reveal a close association between pH and log-transformed CaO and little influence of refractory oxides on pH. These results guided the formulation of three geochemical indices that consist of ratios of Fe2O3, TiO2, and Al2O3 to CaO, herein referred to as FeCa, TiCa, and AlCa. After careful screening for anthropogenic influence, pedotransfer functions relating each index to pH were derived using sigmoidal regressions on a calibration data set (n = 305). Each index has similar predictive capacity for pH (r² = 0.70–0.74, root mean square error = 0.83–0.88). The models were cross-validated on an external testing set (n = 130), which returned root mean square prediction errors (RMSPEs) similar to regression results (RMSPE = 0.81–0.86). While soil pH shows a significant correlation with mean annual precipitation, partial correlation analysis of FeCa, TiCa, AlCa, and a number of widely used paleosol weathering indices revealed that the relationship between B horizon composition and pH is significant, even when climate is held constant. This finding implies that bulk geochemical indices used in pedotransfer functions for climate primarily track pH, which in turn responds to climate state. A case study is examined, where the pH transfer functions were applied on a succession of Triassic alluvial paleosols that experienced a large range of soil-forming conditions. Reconstructed pH values closely track interpreted vegetation, climate, and pedogenesis. These pedotransfer functions offer a new pathway to estimate an ecologically significant parameter in deep-time Critical Zones.