Drivers of Soil Organic Matter Stabilization across Ohio

Preserving and increasing soil organic matter (SOM) has been identified a key strategy for climate change mitigation. Plant residues, fungal and bacterial necromass, and other detritus accumulates in the soil and a portion of this SOM is protected or stabilized from further microbial mineralization. In addition to providing numerous agronomic benefits, it is thought greenhouse gas emissions could be offset through net gains in SOM and the upward climb of global temperatures could be stalled or even reversed. However, storage of atmospheric carbon as SOM is only effective as a climate change mitigation strategy if SOM associated C remains in the soil long-term, i.e. more than 100 years.Soil organic matter stabilization is known to be controlled by several key mechanisms, i.e. physical occlusion, polyvalent cation bridging, weak interactions like hydrogen bonding and Van der Waals forces, and ligand exchange. These physical and chemical stabilization mechanisms vary in the protection they provide SOM; physical occluded SOM is weakly protected as compared to SOM protected via ligand exchange. While these stabilization mechanisms and the affects they have on SOM have been known for some time, much regarding the soil properties associated with these mechanisms and SOM they stabilize remain a mystery.Measurements of SOM and other soil properties important for SOM stabilization are necessary for modeling changing soil C levels over time. However, conventional laboratory methods for assessing these soil properties are time consuming and expensive for large numbers of samples. Visible near-infrared spectroscopy (Vis-NIRS) is a rapid method and inexpensive technique for predicting soil properties. Multivariate statistical approaches are used in conjunction with reference soil data representative of the study area to calibrate and validate models, which if a sufficient level of accuracy is achieved can be used to predict unknown samples only with the VNIR spectra. Therefore, the objective of this research is to 1) elucidate which soil properties influence C stabilization in Ohio soils and 2) assess the ability of portable Vis-NIRS to predict SOC and other soil properties thought to impact SOC stabilization using two multivariate statistical calibration approaches: partial least squares regression (PLSR) (linear) and support vector machines (SVM) (non-linear). Soils evaluated in this study were part of an archival collection of soils sampled from across Ohio as part of the National Cooperative Soil Survey. Soils were sampled by the Soil Survey Staff from 1957-1994 and, after being dried and sieved 2 = excellent, RPD > 1.4 = fair, and RPD < 1.4 = not reliable ). Based on these categories, excellent models were found for Ca2+ (SVM), base saturation (PLSR), base saturation (SVM), CEC (SVM), clay (SVM). fine clay (SVM), SOC (SVM), and POXC (SVM). Fair models were found for Ca2+ (PLSR), Mg2+ (PLSR), Mg2+ (SVM), K+ (SVM), extractable acidity (PLSR), extractable acidity (SVM), CEC (PLSR), sand (SVM), clay (PLSR), fine clay (PLSR), SOC (PLSR), and POXC (PLSR). Not reliable models were found for K+ (PLSR), sand (PLSR), and silt (PLSR). All soil properties were estimated with an R2 > 0.29 and RPD > 1.19. The best performing model was the SVM Ca2+ model (RMSEv = 2.14 cmolc kg-1, RPDv = 2.62, and R2v = 0.85). The poorest performing model was developed with PLSR to predict K+ (RMSEv = 0.11 cmolc kg-1, RPDv = 1.19, R2v = 0.29). Except in the case of base saturation, all soil property predictions improved with SVM models as compared PLSR models.