Abstract
Spectroscopy is a powerful means of increasing the availability of soil data necessary for understanding carbon cycling in a changing world. Here, we develop a calibration transfer methodology to appropriately apply an existing mid infrared (MIR) spectral library with analyte data on the distribution of soil organic carbon (SOC) into particulate (POC), mineral-associated (MAOC), and pyrogenic (PyC) forms to nearly 8000 soil samples collected in the Great Plains ecoregion of the United States. We then use this SOC fraction database in combination with a machine learning-based predictive soil mapping approach to explore the controls on the distribution of fractions through soil profiles and across the region. The relative abundance of each fraction had unique depth distribution profiles with POC fraction dropping exponentially with depth, the MAOC fraction having a broad distribution with a maxima at 35–50 cm, and the PyC fraction showed a slight subsurface maxima (10–20 cm) and then a steady decline with increasing depth. Within the Great Plains ecoregion, clay content was a strong control on the total amount and relative proportion of each fraction in both the surface and subsoil horizons. Sandy soils and soils in cool semi-arid regions contained significantly more POC relative to the MAOC and PyC fractions. Cultivated soils had significantly less SOC than grassland soils with losses following a predictable pattern: POC > MAOC ≫ PyC. This SOC fraction database and resulting maps can now form the basis for improved representation of SOC dynamics in biogeochemical models.
Highlights
Soil organic carbon (SOC) is a large and not very well constrained pool in carbon cycle models
As part of a larger investigation into land use and climate impacts on carbon cycling in the Great Plains ecoregion of the United States, here we present an application of efficient knowledge transfer using mid infrared (MIR) spectroscopy and digital soil mapping in order to explore trends in the fractional allocation of SOC into particulate, mineral-associated and pyrogenic carbon pools across this broad geographic area
Power functions best described the decrease in POC (R2 = 0.42) and fPOC (R2 = 0.19) with depth, while log-lin functions best described the relationship between MAOC (R2 = 0.32), fMAOC (R2 = 0.11) and PyC (R2 = 0.21) with depth
Summary
Soil organic carbon (SOC) is a large and not very well constrained pool in carbon cycle models. Lavallee et al (2020) proposed that the division of SOC into particulate and mineral-associated organic carbon (POC and MAOC) results in two fundamentally different pools of SOC. POC is composed primarily of recognizable fragments of plant detritus with typical residence times of \ 5 years and can vary from only a few percent to upwards of 50% of total SOM depending on soil texture and management history (Cambardella and Elliott 1992; Baldock et al 2013a). MAOC typically makes up the majority of SOC (von Lutzow et al 2007; Baldock et al 2013b) and is composed primarily of highly decomposed material, often microbial in origin (Miltner et al 2012) and is stabilized by association with reactive mineral surfaces (Schmidt et al 2011). MAOC with typical low carbon-to-nutrient ratios represents the major reservoir of potentially plant-available nutrients (Cambardella and Elliott 1994), contributes the majority of the negative charge attributable to SOC (Kleber and Johnson 2010), and can be an important binding agent between clay particles
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