Investigation of soil surface organic and inorganic carbon contents in a low-intensity farming system using laboratory visible and near-infrared spectroscopy

In some regions of Italy, low-intensity farming systems, together with variable climate conditions, have lowered soil organic carbon (SOC) content and soil quality attributes. This work aims to investigate on some aspects of (1) total organic carbon (TOC) prediction using Vis-NIR reflectance spectroscopy in combination with partial least squares regression (PLSR); (2) the most appropriate pre-processing techniques of Vis-NIR absorbance spectra; (3) the composition of organic carbon using variable importance of prediction (VIP). The study area was an olive grove, located at Montecorvino Rovella (Salerno, southwestern Italy), characterized by a calcaric soil (Leptic Calcisols) and (Luvic Phaeozem), with a low content of TOC (mean 2.03 g kg(?1)), caused by a low-intensity farming. Results of univariate PLSR analyses showed a good agreement between measured and predicted values both for TOC (R-2: 0.66) and total carbonate content (R-2: 0.93), when pH, electrical conductivity (EC) and absorbance spectra were used as predictors. The best results were obtained using as pre-treatments of the spectral data: 1) standard normal variate (SNV); 2) Savitzky-Golay algorithm; 3) first derivative. Variable Importance for Prediction (VIP) statistics showed to be a good tool to gain insights in TOC composition also when its content is low and influenced by carbonate.