Quantification of Soil Permanganate Oxidizable C (POXC) Using Infrared Spectroscopy. Issue 2 (20th April 2017) - Calderón, Francisco J.; Culman, Steve; Six, Johan; Franzluebbers, Alan J.; Schipanski, Meagan; Beniston, Joshua; Grandy, Stuart; Kong, Angela Y. Y.
Abstract : Core Ideas: Infrared spectroscopy and chemometrics can be used to quantify soil labile C. Soils amended with organic materials need to be taken into account during calibration development. Selection of spectral regions can give parsimonious predictions of soil total and labile C. Labile soil carbon is an important component of soil organic matter because it embodies the mineralizable material that is associated with short‐term fertility. Permanganate‐oxidizable C (POXC) is a widely used method for the study of labile C dynamics in soils. Rapid methods are needed to measure labile C, and better understand how this pool varies with soil C at regional scales. Infrared spectroscopy is an inexpensive way to quantify SOC and observe fluctuations in C functional groups. Using a sample set that encompassed several soil types and plant communities (seven different research projects, n = 496), soils were analyzed via diffuse reflectance Fourier transformed mid‐infrared (MidIR, 4000–400 cm –1 ) and near‐infrared (NIR, 10000–4000 cm –1 ) spectroscopy. Spectral data were used to develop calibrations for POXC, soil organic C (SOC), and total N (TN) using partial least squares (PLS) regression. The MidIR predicted POXC slightly better than the NIR, with calibration and/or validation R 2 values ranging from 0.77 to 0.81 depending on spectral pretreatments. Predictions for POXC were better than SOC and TN, but site variability influenced the calibration quality for SOC and TN. Using a selected MidIR region, which included bands correlated to POXC (3225–2270 cm –1 ), reduced the calibration quality, but still gave acceptable R 2 values of 0.76 to 0.77 for the calibration and validation sets. We show that POXC can be predicted using NIR and MidIR spectra. Selecting informative spectral bands offers an alternative to using full spectra for PLS regressions.