Predicting contents of carbon and its component fractions in Australian soils from diffuse reflectance mid-infrared spectra

Abstract

Quantifying the content and composition of soil carbon in the laboratory is time-consuming, requires specialised equipment and is therefore expensive. Rapid, simple and low-cost accurate methods of analysis are required to support current interests in carbon accounting. This study was completed to develop national and state-based models capable of predicting soil carbon content and composition by coupling diffuse reflectance mid-infrared (MIR) spectra with partial least-squares regression (PLSR) analyses. Total, organic and inorganic carbon contents were determined and MIR spectra acquired for 20 495 soil samples collected from 4526 locations from soil depths to 1 m within Australia’s agricultural regions. However, all subsequent MIR/PLSR models were developed using soils only collected from the 0–10, 10–20 and 20–30 cm depth layers. The extent of grinding applied to air-dried soil samples was found to be an important determinant of the variability in acquired MIR spectra. After standardisation of the grinding time, national MIR/PLSR models were developed using an independent test-set validation approach to predict the square-root transformed contents of total, organic and inorganic carbon and total nitrogen. Laboratory fractionation of soil organic carbon into particulate, humus and resistant forms was completed on 312 soil samples. Reliable national MIR/PLSR models were developed using cross-validation to predict the contents of these soil organic carbon fractions; however, further work is required to enhance the representation of soils with significant contents of inorganic carbon. Regional MIR/PLSR models developed for total, organic and inorganic carbon and total nitrogen contents were found to produce more reliable and accurate predictions than the national models. The MIR/PLSR approach offers a more rapid and more cost effective method, relative to traditional laboratory methods, to derive estimates of the content and composition of soil carbon and total nitrogen content provided that the soils are well represented by the calibration samples used to build the predictive models.

Microbial community structure mediates response of soil C decomposition to litter addition and warming

“…Spectra were obtained using a 3.2 ms 195 w 90 pulse with a contact time of 1 ms and a recycle delay of 1 s, set after determining the T1H value on these samples. Details of operating conditions can be found in Baldock et al (2013). Between 1000 (litters) and 60,000 (soil) scans were used and line broadening of 50 Hz was applied to the soil spectra.…”

Post-wildfire Erosion in Mountainous Terrain Leads to Rapid and Major Redistribution of Soil Organic Carbon

“…The data derived from 13 C NMR and MIR along with partial least squares regression analysis (MIR/PLSR) was then used to determine the proportion of PyC in the soil samples. This NMR and MIR/PLSR technique reliably estimates the concentration of PyC across a range of soil types and concentrations of bulk C and PyC (Skjemstad et al, 2004;Janik et al, 2007;Baldock et al, 2013a) and is also the most cost and time-effective technique for quantifying the amount of PyC in soil to date.…”

“…This model has proven to be a reliable and time-effective method for predicting soil fractions and has been demonstrated a reasonable predictor across different land uses and vegetation types in and out of Australia (Baldock et al, 2013b;Ahmed et al, 2017;Jauss et al, 2017). The SCaRP model is based on 312 soils collected from agricultural soils across Australia, and it independently predicts organic carbon originating from three fractions without the need for mechanical processing of samples (Baldock et al, 2013a). Due to the independent predictions of the three fractions within this model, the sum of C in each fraction does not necessarily add up to 100%, and the error of the total C predictions averaged 108 ± 11% of the measured total C concentration.…”

“…Principal component analysis of the MIR spectra confirmed that most of the variability in the soil chemical properties was in the depositional soil samples. Samples for NMR analysis were packed into a 7 mm zirconia rotor, and a standard cross polarization experiment was performed using a pulse of 3.2 µs, 195 W, 90 • , a contact time of 1 ms and a recycle delay of 1 s (Sanderman et al, 2011;Baldock et al, 2013a).…”

Sediment and carbon deposition vary among vegetation assemblages in a coastal salt marsh

“…MIR spectroscopy characterises the bulk composition and is therefore inclusive of both mineral and organic components. Spectra were acquired using a Nicolet 6700 Fourier transform infrared (FTIR) spectrometer (Thermo Fisher Scientific Inc., Waltham, MA, USA) following the specifications and procedures outlined by Baldock et al (2013a). Spectra were acquired over 8000-400 cm −1 with a resolution of 8 cm −1 , but were truncated to 6000-600 cm −1 .…”