While traditional laboratory methods of determining soil organic carbon (SOC) content are generally simple, this becomes more challenging when carbonates are present in the soil; such is commonly found in semi-arid areas. Additionally, soil inorganic carbon (SIC) content itself is difficult to determine. This study uses visible near infrared (VisNIR) spectra to predict SOC and SIC contents of samples, and the impact of including soil pH and soil total carbon (STC) data as predictor variables was evaluated. The results indicated that combining available soil pH and STC content data with VisNIR spectra dramatically improved prediction accuracy of the Cubist models. Using the full suite of predictor variables, Cubist models trained on the calibration dataset (75%) could predict the validation dataset (25%) for SOC content with a Lin's concordance correlation coefficient (LCCC) of 0.94, and an LCCC of 0.83 for SIC content. This is compared to an LCCC of 0.81 and 0.35 for SOC and SIC content, respectively, when no ancillary soil data was included with VisNIR spectra as predictor variables. These results suggest that there may be promise for using other readily available soil data in combination with VisNIR spectra to improve the predictions of different soil properties. •It can be laborious and expensive to measure soil organic and inorganic carbon content with traditional laboratory methods, and there has been recent focus on using spectroscopic techniques to overcome this.•This study demonstrates that combining ancillary soil data (pH and total carbon content) with these spectroscopic techniques can considerably improve predictions of SOC and SIC content.
Keywords: Ancillary soil data; Carbonate; Cubist; Inorganic carbon; Organic carbon; Soil carbon measurement with visible near infrared spectroscopy; Visible near-infrared spectroscopy.