Les J. Janik

The Performance of Visible, Near-, and Mid-Infrared Reflectance Spectroscopy for Prediction of Soil Physical, Chemical, and Biological Properties

Abstract This review addresses the applicability of visible (Vis), near-infrared (NIR), and mid-infrared (MIR) reflectance spectroscopy for the prediction of soil properties. We address (1) the properties that can be predicted and the accuracy of the predictions, (2) the most suitable spectral regions for specific soil properties, (3) the number of predictions reported for each property, and (4) in-field versus laboratory spectral techniques. We found the following properties to be successfully predicted: soil water content, texture, soil carbon (C), cation exchange capacity, calcium and magnesium (exchangeable), total nitrogen (N), pH, concentration of metals/metalloids, microbial size, and activity. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties (e.g., biological). An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for clay, water, total organic C, extractable phosphorus, total C and N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness. The nature of the variable being predicted, the quality and consistency of the reference laboratory methods, and the adequate representation of unknowns by the calibration set must be considered when predicting soil properties using reflectance spectroscopy.

Influence of the nature of soil organic matter on the sorption behaviour of pentadecane as determined by PLS analysis of mid-infrared DRIFT and solid-state 13C NMR spectra

The nature of soil organic matter (SOM) functional groups associated with sorption processes was determined by correlating partitioning coefficients with solid-state (13)C nuclear magnetic resonance (NMR) and diffuse reflectance mid-infrared (DRIFT) spectral features using partial least squares (PLS) regression analysis. Partitioning sorption coefficients for n-pentadecane (n-C(15)) were determined for three alternative models: the Langmuir model, the dual distributed reactive domain model (DRDM) and the Freundlich model, where the latter was found to be the most appropriate. NMR-derived constitutional descriptors did not correlate with Freundlich model parameters. By contrast, PLS analysis revealed the most likely nature of the functional groups in SOM associated with n-C(15) sorption coefficients (K(F)) to be aromatic, possibly porous soil char, rather than aliphatic organic components for the presently investigated soils. High PLS cross-validation correlation suggested that the model was robust for the purpose of characterising the functional group chemistry important for n-C(15) sorption.

Effect of organic matter removal by low-temperature ashing on dispersion of undisturbed aggregates from a tropical crusting soil

Abstract Low-temperature ashing (LTA) can be used to remove organic matter (OM) from undisturbed soil aggregates with minimal disturbance and damage to soil microstructure. A combination of LTA with FTIR photoacoustic spectroscopy PAS; which enables the study of sample surfaces) and appropriate soil dispersion tests was employed to assess the influence of OM on aggregate dispersion and to characterize the organic fractions involved in destabilizing or maintaining microstructure in a tropical soil that suffers severe crusting under cultivation. For this purpose, the cultivated soil was also compared with uncultivated soil from an adjacent field. This study suggested that the observed decrease in dispersion after LTA treatment of the aggregates of cultivated soil could be ascribed to the removal of negatively charged, low molecular weight humic substances. These substances, formed by the degradation of OM due to cultivation, may destabilize the microstructure of the soil under specific physico-chemical conditions. Conversely, the increase in dispersion, after LTA treatment, of the uncultivated soil, appeared to be caused by the removal of aliphatic hydrophobic compounds. These aliphatic compounds which were more abundant in the uncultivated soil, protect aggregates from the action of water (slaking and dispersion). The combination of LTA with soil dispersion test enabled to demonstrate, in this study, the contrasting roles of soil OM. These roles were related to the OM quality and its interaction with soil minerals.

Prediction of Atrazine Sorption Coefficients in Soils Using Mid-Infrared Spectroscopy and Partial Least-Squares Analysis

This study explored the potential of mid-infrared spectroscopy (MIR) with partial least-squares (PLS) analysis to predict sorption coefficients (Kd) of pesticides in soil. The MIR technique has the advantage of being sensitive to both the content and the chemistry of soil organic matter and mineralogy, the important factors in the sorption of nonionic pesticides. MIR spectra and batch Kd values of atrazine were determined on a set of 31 soil samples as reference data for PLS calibration. The samples, with high variability in soil organic carbon content (SOC), were chosen from 10 southern Australian soil profiles (A1, A2, B, and C in one case). PLS calibrations, developed for the prediction of Kd from the MIR spectra and reference Kd data, were compared with predictions from Koc-based indirect estimation using SOC content. The reference Kd data for the 31 samples ranged from 0.31 to 5.48 L/kg, whereas Koc ranged from 30 to 680 L/kg. Both coefficients generally increased with total SOC content but showed a relatively poor coefficient of determination (R2 = 0.53; P > 0.0001) and a high standard error of prediction (SEP =1.22) for the prediction of Kd from Koc. This poor prediction suggested that total SOC content alone could explain only half of the variation in Kd. In contrast, the regression plot of PLS predicted versus measured Kd resulted in an improved correlation, with R2 = 0.72 ( P > 0.0001) and standard error of cross-validation (SECV) = 0.63 for three PLS factors. With the advantages of MIR-PLS in mind, (i) more accurate prediction of Kd, (ii) an ability to reflect the nature and content of SOC as well as mineralogy, and (iii) high repeatability and throughput, it is proposed that MIR-PLS has the potential for an improved and rapid assessment of pesticide sorption in soils.

Complexation of silver and dissolved organic matter in soil water extracts

An important aspect of the behaviour and fate of silver (Ag) in soils is the interaction with dissolved organic matter (DOM). The complexation and strength of binding of Ag(+) with DOM in soil water extracts was examined and modelled based on a range of chemical and quality DOM measurements. Silver ion binding measured by addition of the (110m)Ag radioisotope in addition to a cation exchange resin technique were used to determine strongly complexed Ag in solutions. Silver was found to be up to 70% strongly complexed. The variability in Ag(+) binding by DOM across different soils was closely related (R(2) = 0.8) to the mid-infrared spectra of these extracts. The affinity of Ag(+) for DOM was stronger in solutions containing a greater content of humic and aromatic structures. The ability of Ag(+) to complex with DOM could result in increased mobilisation of this metal in the soil environment.

Use of handheld mid-infrared spectroscopy and partial least-squares regression for the prediction of the phosphorus buffering index in Australian soils

The development of techniques for the rapid, inexpensive and accurate determination of the phosphorus (P) buffer index (PBI) in soils is important in terms of increasing the efficiency of P application for optimum crop requirements and preventing environmental pollution due to excessive use of P fertilisers. This paper describes the successful implementation of partial least-squares regression (PLSR) from spectra obtained with bench-top and handheld mid-infrared (MIR) spectrometers for the prediction of PBI on 601 representative Australian agricultural soils. By contrast, poor predictions were obtained for available (Colwell) P. Regression models were successfully derived for PBI ranges of 0–800 and 0–150, the latter range resulting in the optimum model considering the dominance of low PBI soils in the sample set. Concentrations of some major soil minerals (mainly kaolinite and gibbsite content for high PBI, and smectites or illites for low PBI), quartz (representative of low surface area of soils) and, to a lesser extent, carbonate and soil organic matter were identified as the main drivers of the PBI models. Models developed with soils sieved to <2u2009mm presented an accuracy similar to those developed using fine-ground material. The accuracy of the PLSR for the prediction of PBI by using bench-top and handheld instruments was also similar. Our results confirm the possibility of using MIR spectroscopy for the onsite prediction of PBI.

Some advantages of mid-IR for soil analysis

t:l lo<: c tel = c :.: Q) o E ~ (J) Because of the high sensitivity of MIA to a wide range of soil constituents, high prediction accuracy is routinely achieved for many properties. Some examples are illustrated in Table 1. Often, prediction error is due more to a lack of precision of the primary laboratory method rather than the MIA-PLS method. Properties of many soils are dependent on the chemistry and surface area of soil microaggregates (2-53 urn), Clay and organic coatings on the larger particles, usually quartz grains, affect the surface area and therefore the cation exchange properties and reactivity of the micro-aggregate surfaces. Clay, sand, CEC and OM content will therefore be amenable to prediction by IA and particularly MIA.