Predicting soil carbon saturation deficit and related properties of New Zealand soils using infrared spectroscopy

Conversion of soils supporting native vegetation to agricultural production has led to a loss of soil carbon stocks. Replacing a portion of the lost stocks will sequester atmospheric carbon with the concurrent benefit of enhancing soil sustainability. The ability of the fine fraction of soils (≤50-µm fraction) to adsorb organic carbon (OC) is considered a key mechanism capable of stabilising soil OC against loss. The difference between the current and maximum concentrations of OC in the soil fine fraction (FFC) has been termed the ‘saturation deficit’ (SatDef) and used to define the potential for a soil to sequester carbon. For New Zealand surface 0–15 cm soil layers, pedotransfer functions have been derived to quantify the soil carbon SatDef. The ability of combining infrared spectroscopy (IR) with partial least squares regression (PLSR) to derive predictive algorithms for soil properties included in these pedotransfer functions, the capacity of the soil fine fraction to stabilise carbon and the SatDef of the soil fine fraction were assessed in this study. A total of 168 air-dried and finely ground New Zealand surface soils representative of the major soil orders used for agricultural production were included. Principal components analysis of IR spectra showed a grouping by soil order that was related to mineralogy. Predictive IR/PLSR algorithms were derived for specific surface area, pyrophosphate-extractable aluminium, the FFC content, the 90th quantile regression of FFC and the SatDef of the fine fraction (R2 values ≥0.85; ratio of performance to interquartile range values ≥2.9). The results indicate that IR/PLSR provides a rapid and cost-effective mechanism for deriving information related to the amount of FFC in soils and the SatDef of the fine fraction. The IR/PLSR approach could be used to define the potential of soils to sequester carbon and identify the soil types to target for carbon sequestration technologies. The approach would also generate valuable data for soil carbon in national inventories or national soil condition monitoring programs.