Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada

Abstract

Xie, H. T., Yang, X. M., Drury, C. F., Yang, J. Y. and Zhang, X. D. 2011. Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada. Can. J. Soil Sci. 91: 53–63. Mid-infrared (MIR) and near-infrared (NIR) spectroscopy of soils have been tested to estimate soil organic carbon (SOC) and total N (TN) concentrations at local, regional and national scales. However, these methods have rarely been used to assess SOC and TN concentrations of the same soil under different management practices. The objective of this study was to determine if models developed from infrared spectra of Brookston clay loam soils under different management practices could be used to estimate SOC, and TN concentrations and the C:N ratio. Soils used for model calibration included 217 samples from a long-term fertilization and crop rotation study and a long-term compost study, whereas 78 soil samples from a long-term tillage study on the same soil type were used for model validation. Soil organic carbon and TN concentrations of all samples were also analyzed using dry combustion techniques. Soil samples were scanned from 4000 to 400 cm−1 (2500–25 000 nm) for MIR spectra and from 8000 to 4000 cm−1 (1250–2500 nm) for NIR spectra. Partial least squares regression (PLSR) analysis was used for the calibration dataset to build prediction models for SOC, TN and C:N ratio. The SOC and TN concentrations determined using dry combustion techniques were compared with the prediction from the models using the calibration datasets. The predictions of SOC and TN concentrations by the PLSR method using infrared spectra were statistically sound, with high coefficient of determination with the calibration dataset (R2cal, SOCMIR=0.99 and SOCNIR=0.97, TNMIR=0.98 and TNNIR=0.97) and the validation dataset (R2val, SOCMIR=0.96 and SOCNIR=0.95, TNMIR=0.96 and TNNIR=0.95) and low root mean square error (RMSEPcal, SOCMIR=0.93 and SOCNIR=1.60, TNMIR=0.08 and TNNIR=0.12; RMSEPval, SOCMIR=1.40 and SOCNIR=1.75, TNMIR=0.11 and TNNIR=0.12). The predictions of SOC and TN concentrations in the 5 to 30 cm depth were better than the predictions for either the surface (0 to 5 cm) soils or for soils from lower depths (>30 cm). The models could be used as an alternative method for determining SOC and TN concentrations of Brookston clay loam soils; however, larger sample populations and improved model algorithms could further improve predictions.