Soil organic carbon fractions in the Great Plains of the United States: an application of mid-infrared spectroscopy

Abstract Spectroscopy is a powerful means of increasing the availability soil data necessary for understanding carbon cycling in changing world. Here, we develop calibration transfer methodology to appropriately apply an existing mid infrared (MIR) spectral library with analyte on distribution organic (SOC) into particulate (POC), mineral-associated (MAOC), and pyrogenic (PyC) forms nearly 8000 samples collected Great Plains ecoregion United States. We then use this SOC fraction database combination machine learning-based predictive mapping approach explore controls fractions through profiles across region. The relative abundance each had unique depth POC dropping exponentially depth, MAOC having broad maxima at 35–50 cm, PyC showed slight subsurface (10–20 cm) steady decline depth. Within ecoregion, clay content was strong control total amount proportion both surface subsoil horizons. Sandy soils cool semi-arid regions contained significantly more fractions. Cultivated less than grassland losses following predictable pattern: > ? PyC. This resulting maps can now form basis improved representation dynamics biogeochemical models.