Soil Science Applications Research Articles

Validation of the percolation‐based effective‐medium approximation model to estimate soil thermal conductivity

AbstractSoil thermal conductivity (λ) has broad applications in soil science, hydrology, and engineering. In this study, we applied the percolation‐based effective‐medium approximation (P‐EMA) to estimate the saturation dependence of thermal conductivity () using data from 38 undisturbed soil samples collected across the state of Kansas. The P‐EMA model has four parameters including a scaling exponent (ts), critical water content (θc), and thermal conductivities at oven‐dry (λdry) and full saturation (λsat) conditions. To estimate the curve, the values of λdry and λsat were measured using a soil thermal properties analyzer and the values of ts and θc were estimated as a function of clay content. Thermal conductivity was also estimated using the Johansen model. By comparison with observations, the P‐EMA model resulted in a root mean square error (RMSE) ranging from 0.029 to 0.158 W m−1 K−1, whereas the Johansen model had an RMSE ranging from 0.021 to 0.173 W m−1 K−1. Our results demonstrate that the P‐EMA model has comparable accuracy to the widely used Johansen model to estimate the saturation‐dependent soil thermal conductivity of undisturbed soils with minimal input parameters. Future studies should focus on better understanding the physical meaning of ts and θc in the P‐EMA model to improve our ability to model thermal conductivity in undisturbed soil from percolation principles.

Determination of soil pore size distribution and water retention curve by internal magnetic field modulation at low field 1H NMR

The determination of the soil pore size distribution, water retention curve, and derived parameters that control important processes in soils, such as water supply for plants; infiltration; water and solute movement in soils; erosion; plant nutrients and contaminants transport, etc, are challenging and the available methods are expensive, time-consuming and prone to bias and errors. The use of 1H Nuclear Magnetic Resonance (NMR) relaxometry to characterise the soil porosity and hydraulic properties through spin-lattice and spin-spin relaxometry results in an ill-posed problem with two correlated unknown quantities: the pore length scales, and surface relaxivity. To overcome this limitation of NMR relaxometry, we propose the use of a method that directly accesses the NMR diffusion modes governed only by the pore size, and therefore, independent of the unknown surface relaxivity. The manuscript describes an unprecedent application in Soil Science of the Decay due to Diffusion in Internal Field (DDIF) method to successfully determine the pore size distribution of undisturbed soil samples, as well as to estimate the water retention curves from the pore size distribution.

Soil moisture effects on predictive VNIR and MIR modeling of soil organic carbon and clay content

Portable visible to near-infrared (VNIR) and mid-infrared (MIR) soil spectroscopy holds great potential to support field applications in soil science and management by complementing conventional soil analytical methods. Under field conditions, however, soil moisture can critically affect the quality of reflectance measurements. In this study, we examined the effects of soil moisture on VNIR and MIR soil spectra and how its magnitude and variation impact the accuracy and robustness of predictive spectral models. We carried out a systematic re-wetting experiment on two soil datasets of different scale and origin that were measured at four gravimetric moisture levels (air-dried, 5 %, 10 %, 15 %) with portable VNIR and MIR instruments. The spectral data of each moisture class, as well as randomized combinations of different moisture contents, were then used to calibrate VNIR, MIR and combined PLSR models to estimate soil organic carbon (SOC) and clay content, where combined models included spectra concatenation (VNMIR) and model output average (MOA). The overall shape of MIR spectra was more significantly distorted by soil moisture than VNIR spectra, while the general impact of soil water content in both spectral domains was texture-dependent. In terms of predictive accuracy, MIR models were generally superior for air-dried sample material, while VNIR models fared better for uniformly moist samples. With increasing soil moisture variability, comparative estimation accuracies between individual VNIR and MIR models were dependent on the underlying dataset. VNMIR and MOA models proved beneficial and yielded the most accurate and robust predictions for SOC and clay content when soil moisture was variable, irrespective of the considered dataset (regional dataset: RMSESOC = 0.22–0.27 %, RMSECLAY = 2.67–3.14 %; field dataset: RMSESOC = 0.09–0.11 %, RMSECLAY = 0.87–1.15 %). Predictive mechanisms, as evaluated by variable importance in the projection (VIP) of PLSR models, changed substantially with variation in soil water content, especially in the MIR, where important absorption bands for SOC and clay minerals could be heavily attenuated or completely masked. Our study highlights the advantages of employing both VNIR and MIR instruments for spectral data collection on soils in field condition and the potential of integrating VNIR and MIR spectra collected at different soil moisture levels into soil spectral libraries.

Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century

AbstractSpectroscopic measurements of soil samples are reliable because they are highly repeatable and reproducible. They characterise the samples' mineral–organic composition. Estimates of concentrations of soil constituents are inevitably less precise than estimates obtained conventionally by chemical analysis. But the cost of each spectroscopic estimate is at most one‐tenth of the cost of a chemical determination. Spectroscopy is cost‐effective when we need many data, despite the costs and errors of calibration. Soil spectroscopists understand the risks of over‐fitting models to highly dimensional multivariate spectra and have command of the mathematical and statistical methods to avoid them. Machine learning has fast become an algorithmic alternative to statistical analysis for estimating concentrations of soil constituents from reflectance spectra. As with any modelling, we need judicious implementation of machine learning as it also carries the risk of over‐fitting predictions to irrelevant elements of the spectra. To use the methods confidently, we need to validate the outcomes with appropriately sampled, independent data sets. Not all machine learning should be considered ‘black boxes’. Their interpretability depends on the algorithm, and some are highly interpretable and explainable. Some are difficult to interpret because of complex transformations or their huge and complicated network of parameters. But there is rapidly advancing research on explainable machine learning, and these methods are finding applications in soil science and spectroscopy. In many parts of the world, soil and environmental scientists recognise the merits of soil spectroscopy. They are building spectral libraries on which they can draw to localise the modelling and derive soil information for new projects within their domains. We hope our article gives readers a more balanced and optimistic perspective of soil spectroscopy and its future.HighlightsSpectroscopy is reliable because it is a highly repeatable and reproducible analytical technique.Spectra are calibrated to estimate concentrations of soil properties with known error.Spectroscopy is cost‐effective for estimating soil properties.Machine learning is becoming ever more powerful for extracting accurate information from spectra, and methods for interpreting the models exist.Large libraries of soil spectra provide information that can be used locally to aid estimates from new samples.

The fundamental of the effects of water, organic matter, and iron forms on the pXRF information in soil analyses

Portable X-ray fluorescence (pXRF) has great potential for numerous applications in soil science. However, the basic knowledge about the effects of soil properties on pXRF spectra are still poorly studied, which may lead users to biased interpretations of mathematical models. The present study aimed to evaluate the outcomes of moisture, soil organic matter content (SOM), and iron forms on pXRF data. The work was conducted with seventeen soil samples from the central region of São Paulo state (Brazil). Three selective dissolution treatments were applied to remove: (i) soil organic matter (−SOM), ii) SOM and poorly crystalline iron forms (−o), iii) SOM and poorly crystalline plus well crystalline iron forms (−d). One additional treatment iv) including water addition (+W) was also carried out. The effects of treatments were evaluated for sandy and clayey samples. Soil particle size distribution and elemental content affected the bremsstrahlung and characteristic peaks counts. In +W, there was a generalized decrease in counts mainly for the light elements (magnesium, aluminum and silicon). Regarding the selective dissolution procedures, alterations were verified, reflecting mainly the removal power of reagents. Generally, the most pronounced alterations occurred for −d and moderate alterations for −SOM and −o. The pXRF data showed high correlation with particle size distribution and mineralogy attributes. The kaolinite, gibbsite, Fe2O3, Al2O3, SiO2, TiO2 and MnO contents were quantified with satisfactory accuracy (0.61 < R2 < 0.97). The pXRF was able to detect changes caused by the selective dissolution treatments and soil particle size distribution. Sources of uncertainty, mainly soil moisture, must be considered. The understanding of the fundamentals of energy interaction with the sample matrix in the X-ray range is the starting point for characterizing the soil through pXRF.

Towards a dynamic soil survey: Identifying and delineating soil horizons in-situ using deep learning

Soil horizons are the manifestation of pedogenesis and contain the basic morphologic indicators of soil formation. Accurate and quantitative tools for delineating soil horizons in-situ can assist soil scientists towards rapid and dynamic soil survey information. The objective of this study was to develop a deep-learning-based, soil-profile-imaging method for identifying and delineating soil master horizons to assist in digital soil descriptions. A total of 160 soil profile images from four soil orders (Alfisols, Entisols, Inceptisols, and Mollisols) were collected from north China in the Inner Mongolia and Liaoning regions. The 160 profile images were amplified to 2400 individual images for model building using data-augmentation procedures. The augmented profile image dataset was divided into training (70%), validation (15%), and test (15%) datasets. The training and validation datasets were imported into a nested, U-net network for model building. The proposed deep-learning (DL) model classified soil profiles into A, B, and C horizons. The mean pixel accuracy of the DL model was 0.86 with the training dataset, 0.82 with the validation dataset, and 0.83 for the test dataset. Results showed that the DL model was judged to be accurate enough for identifying and delineating soil master horizons from profile images in practice. Based on the proposed DL model, a smartphone application was subsequently developed to digitalize the soil profile and assist field evaluation of soil profile horizonation. The smartphone application, for the Android operating system (version 5.0 and greater), featured a response time of < 5 s. This study demonstrated that the proposed DL model and smartphone application could be a simple, fast, and digital tool for quickly identifying and delineating in-situ soil horizons. These tools allow for rapid data collection, which can be used for future artificial intelligence development and application in soil science towards digital soil profile description and dynamic soil survey.

Does the limited use of orthogonal signal correction pre-treatment approach to improve the prediction accuracy of soil organic carbon need attention?

Visible-Near-infrared (Vis-NIR) spectroscopy is a relatively modern method that can be used to predict soil properties such as soil organic carbon (SOC). Predictions of soil properties with Vis-NIR requires pre-processing algorithms. Applying a wrong type or applying too extreme pre-processing algorithms may result in the removal of valuable information or may even introduce unwanted variations. This can negatively affect the prediction accuracy of the property being studied. Orthogonal signal correction (OSC) pre-processing method has been used in other fields for visible and near infrared spectra improvement. However, the application of OSC application in soil science remains limited. The main idea behind OSC is the removal of only unwanted variation from the spectrum unlike some other pre-treatment methods which are believed to remove valuable information in the process of removing undesirable variation. This study verifies the effectiveness of the OSC against nine commonly used pre-treatment methods across three different agricultural fields for both lab-dry and in-field spectra. For the prediction, partial least square regression (PLSR) and support vector machine regression (SVMR) algorithms were used. In this study, the OSC method overall improved prediction accuracy the most (e.g. with OSC the best result was R2CV = 0.79, without OSC the best result was R2CV = 0.62) and is therefore a promising tool that should be included in further studies on different soils and other soil properties.

Applications of synchrotron in soil science

Synchrotron radiations (SR) are emerging as a real-time probing tool for the wide range of applied sciences. Since the beginning of 1990s, synchrotron-based techniques have become increasingly employed in various fields of life science. The unique properties of Synchrotron radiations break the limits to characterize the material properties than previous laboratory based techniques. The use of SR in soil sciences also has increased dramatically in the last decade. SR techniques are used to assess soil physical, chemical and biological properties. Besides that SR techniques are also used in soil pollution studies and rhizosphere science. So this paper intends to explain about the instrument synchrotron, its techniques used in soil science and applications in soil science. Furthermore the paper tries to elucidate a few relevant researches in soil science which involves SR techniques.

Optimizing acquisition parameters in diffuse reflectance infrared Fourier transform spectroscopy of soils

AbstractThe number of co‐added scans and spectral resolution are two fundamental acquisition parameters in diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). However, systematic studies evaluating these parameters in soil science applications are lacking, especially when using mid‐infrared frequencies (mid‐DRIFTS). The objective of this study was to evaluate how numbers of co‐added scans and spectral resolution affect both qualitative and predictive applications of high‐throughput mid‐DRIFTS of soils. We first explored the literature with a systematic review to better understand variability in acquisition parameters and their hypothesized relationships with mid‐DRIFTS predictive performance (accuracy) of soil organic and/or total carbon (as a reference variable), but no evident relationship could be established across studies. Second, we experimentally evaluated how spectral resolutions (4, 8, 16, and 32 cm−1), and number of co‐added scans (8, 16, and 24 scans) affected (i) specific spectral peaks representing mineral and organic functional groups, and (ii) predictive performance of soil variables clay, sand, pH, total organic carbon, and permanganate oxidizable carbon (POXC). Decreasing the number of co‐added scans from 24 to 8 increased wavenumber‐specific spectral variability and decreased both the predictive performance and the ability to characterize smaller peaks of mineral and organic functional groups. In contrast, broadening spectral resolution from 4 to 32 cm−1 reduced multivariate scores dispersion and had a positive effect on the predictive performance, even though some smaller peaks disappeared in resolutions wider than 8 cm−1. Acquisition parameters can be set to reduce both scanning time and computational demand while maintaining qualitative and predictive applications of mid‐DRIFTS in soil analysis.

Machine learning and soil sciences: a review aided by machine learning tools

Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last 10 years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely available open-source algorithms, have led to an accelerated adoption of ML techniques to analyse soil data. Given the large number of publications, it is an impossible task to manually review all papers on the application of ML in soil science without narrowing down a narrative of ML application in a specific research question. This paper aims to provide a comprehensive review of the application of ML techniques in soil science aided by a ML algorithm (latent Dirichlet allocation) to find patterns in a large collection of text corpora. The objective is to gain insight into publications of ML applications in soil science and to discuss the research gaps in this topic. We found that (a) there is an increasing usage of ML methods in soil sciences, mostly concentrated in developed countries, (b) the reviewed publications can be grouped into 12 topics, namely remote sensing, soil organic carbon, water, contamination, methods (ensembles), erosion and parent material, methods (NN, neural networks, SVM, support vector machines), spectroscopy, modelling (classes), crops, physical, and modelling (continuous), and (c) advanced ML methods usually perform better than simpler approaches thanks to their capability to capture non-linear relationships. From these findings, we found research gaps, in particular, about the precautions that should be taken (parsimony) to avoid overfitting, and that the interpretability of the ML models is an important aspect to consider when applying advanced ML methods in order to improve our knowledge and understanding of soil. We foresee that a large number of studies will focus on the latter topic.

Quantifying multiple electromagnetic properties in EMI surveys: A case study of hydromorphic soils in a volcanic context – The Lac du Puy (France)

We used two different loop-loop electromagnetic induction (EMI) devices to determine the 3D geometry and morphology of the pedo-sedimentary filling and underlying basaltic bedrock of a former wetland in a volcanic soil area, the Lac du Puy depression (Auvergne, France). Electrical conductivity (or resistivity) is usually sufficient for environmental and soil science applications, but the local volcanic context of the survey area results in high values of magnetic susceptibility and possible electrical polarization effects. Therefore we investigated the roles of the four properties: electrical conductivity, magnetic susceptibility, magnetic viscosity and dielectric permittivity. We created models using these four properties for the two coil configurations of each device in order to assess the degree to which each of the properties contributed to the recorded electromagnetic signal. The results show that electrical conductivity controls the quadrature component of the secondary field response but that it can be affected by high values of magnetic viscosity, while magnetic susceptibility controls the in-phase component. Moreover, the low frequencies imply a limited contribution of dielectric permittivity to the in-phase component, except in the cases of higher permittivity or frequency values or greater inter-coil separation. Based on these observations, we propose a way to map the apparent properties from field measurements. We then carried out a 1D inversion, first by considering the electrical conductivity alone and secondly by taking all the electromagnetic properties into account. The results show that there is a marked contrast in the complex magnetic susceptibility between the sedimentary in-fill and the border of the Lac de Puy depression, (stronger than for the electrical conductivity), and that permittivity is unlikely to have a significant influence. The shape and nature of the sedimentary in-fill was thus considerably refined by the second inversion results based on the three other properties. These data, combined with litho-stratigraphic observations from a previous study, allowed the lateral continuity and geometry of the in-fill to be assessed across the whole basin. Results are also consistent with previous interpretations of the depression as a pseudo-sinkhole, a relatively common morphology in volcanic plateaus. Analysis of the magnetic properties also made it possible to characterize the spatial variation of some key features related to hydromorphic processes, such as clayey granularity and the development of iron oxides/hydroxides. This opens up the possibility for using new methods for rapid spatial and pedological characterization of hydromorphic soils and palaeosoils.

Guiding carbon farming using interdisciplinary mixed methods mapping

Abstract In recognition of the need to address complex environmental problems, some ecological studies have adopted social research methods to better understand the complexity of social‐ecological systems management. The overwhelming majority of these studies stop short of fully embracing qualitative methodologies. The lack of integrative social and natural science data for a topic such as soil carbon farming is problematic as theoretical carbon sequestration opportunities identified through soil mapping and process‐based models can fail to deliver the sequestration levels promised when introduced on‐the‐ground. Such mapping needs to account for the human factors involved in delivering increased soil carbon on‐farm. Here, we develop a mixed methods mapping approach to explore the potential for increasing soil carbon stocks on upland farms in the UK. Our approach considers ecological and social complexity through application of soil science, ecology, participant observation, interviews and a focus group. Our maps revealed landscapes that are full of carbon farming opportunity, but contain previously hidden barriers to the delivery of increased soil carbon. For example, they revealed that carbon farming can be considered by farmers to work in opposition to perceived ‘good farming’ practices and be correlated with increased incidents of livestock disease. We also discovered that the use of maps in research can be problematic as they can close down discussion and exclude local representation of an area. Trialling an interdisciplinary mixed methods approach produced new, deeper and more richly‐textured understandings about how soil carbon management is produced socially as well as ecologically on upland livestock farms. Our findings have potential to improve the success of future carbon farming initiatives by incorporating farmer knowledge and social drivers of implementation. A plain language summary is available for this article.

English

Sustainable reuse of pre-treated drill cuttings (a hazardous waste) as part substitute for fine aggregate in concrete for construction purposes is becoming increasingly attractive; however, issues remain. With recent studies focusing on the use of near-infrared spectroscopic technique for non-invasive determination of chloride concentration in concrete structures, this review examines the applicability of this new technique in the rapid determination of other equally important physicochemical characteristics of concrete produced with this hazardous waste. The nature, source, composition, ecological effects of, and management options for drill cuttings are reviewed. Furthermore, the principles of near-infrared diffuse reflectance spectroscopy are highlighted and lessons from its practical applications in soil science and petrochemical, environmental, and civil engineering are discussed. A framework for a rapid near-infrared analysis of concrete produced with pre-treated drill cuttings for enhanced sustainability as a construction material is also proposed. Key words: Characterization, concrete, drill cuttings, diffuse reflectance spectroscopy.

Development of an intelligent system based on ANFIS model for predicting soil erosion

This study explores capability of adaptive neuro-fuzzy interface system (ANFIS) model for soil erosion assessment of which various parameters are involved and the parameters interaction is highly nonlinear. Soft computing technique has been applied widely in various fields, but its application in soil science is limited. A number of models have been established in predicting soil erosion such as a revised universal soil loss equation (RUSLE) model whereby parameters of the RUSLE model are employed in testing the ANFIS model in this study. Besides, a new parameter of biomass microbe (Mf) is introduced and its effectiveness in controlling erosion is demonstrated. A hilly area near the Guthrie Corridor Expressway, Malaysia, is chosen as a study area whereby five experimental plots size of 8 × 8 m and 5 × 5 m are developed. These five plots are characterized by either natural or planted vegetation or either with or without microbe bio-fertilizer. Input parameters of RUSLE, i.e. rainfall erosivity, soil erodibility, slope length and steepness, vegetation and support practice, are measured, and observed erosion is monitored for 1 year. The microbe factor presented as Mf is examined by pairing the plot with and without microbe while keeping other similar characteristic for those plots. Result shows a satisfactory achievement between observed and predicted model by ANFIS with R2, RMSE and MAE being 0.8275, 1.4276 and − 0.0165, respectively. In terms of a relationship between the Mf factor and soil erosion, it is noticed that the soil erosion is inversely proportional to the Mf when the entire erosion conservation practices are unchanged for a given period. Therefore, the application of microbes could be an alternative or additional erosion conservation technique in soil conservation works.

Editorial ‐ December 2017

Seventy years ago in April 1947, the British Society of Soil Science had its inaugural ordinary meeting. The eminent soil physical chemist, R.K. Schofield, gave a presentation entitled ‘A New Approach to Problems of Soil’. However, one chooses to place the emphasis, it is clear that Schofield considered soils provided problems, which required resolution. In the first three issues of the Society's journal, then aptly named ‘Journal of Soil Science’, soil-based problems were identified under crop transpiration (H.L. Penman); fresh groundwater laying over saline water (E.C. Childs); soil aggregation, soil organic matter and the influence of earthworms (R.J. Swaby); and the evaluation of soils and the definition of quality classes from studies of the physical properties of the soil profile in the field (G.R. Clarke). In this issue, we highlight two invited papers that both help celebrate the 70th anniversary of the Society and provide an update on the state of the art of soil science understanding and application in solving practical problems. The first by Hallett et al. focuses on developments in the capability of land information systems (LIS). Combining database and metadata allows modern LIS to offer management options at a variety of scales. In the second paper, McDowell poses and answers the question ‘Does variable rate irrigation decrease nutrient leaching losses…?’. Benefits are reported in terms of N species and reactive and total P downstream of areas under variable rate irrigation compared with flood or uniform rate irrigation. Included in this issue are the last three papers of the series looking at the application electromagnetic techniques to digital soil mapping, with John Triantafilis and Denis Corwin as Guest Editors. The studies described look at the potential for use of these techniques to provide maps of differing scales and report on a web-based geographic information system for producing maps and making them available to interested parties. We will continue to publish new studies in this field of research under the same subheading in future issues, but I would like to thank John and Denis for the wide-ranging scope of the papers they have provided. We currently have a call for papers on ‘Soil information sharing and knowledge building’ (see our website). These papers are intended to provide a single issue dedicated to this subject and be published early in 2019. Lisa Lobry de Bruyn (University of New England, Armadale) and Julie Ingram (University of Gloucester) will be the Guest Editors for this special issue. I would also like to draw attention to the papers and the correspondence that has resulted from the publication of the paper by Huijsmans et al. in the special supplement dedicated to the late Brian Chambers in 2016. I would like to think that Soil Use and Management has provided a forum for a proper scientific evaluation of a storm that has raged in the Netherlands over the past couple of years. I have been delighted with the broad range of topics and the significant developments reported in the journal over this anniversary year. The next five years, culminating in the British Society of Soil Science celebrating 75 years of service by hosting the 2022 International Union of Soil Science (IUSS) meeting in Glasgow is likely to see more major changes in the publication of scientific advances. Soil Use and Management will continue to provide a reliable platform for the peer assessment of reports on the application of soil science to the management of land, and we expect to be publishing many of the papers presented at the meeting in Rio de Janeiro next year as well as those from other contributors across the globe.

Validation of Sensor-Directed Spatial Simulated Annealing Soil Sampling Strategy.

Soil spatial variability has a profound influence on most agronomic and environmental processes at field and landscape scales, including site-specific management, vadose zone hydrology and transport, and soil quality. Mobile sensors are a practical means of mapping spatial variability because their measurements serve as a proxy for many soil properties, provided a sensor-soil calibration is conducted. A viable means of calibrating sensor measurements over soil properties is through linear regression modeling of sensor and target property data. In the present study, two sensor-directed, model-based, sampling scheme delineation methods were compared to validate recent applications of soil apparent electrical conductivity (EC)-directed spatial simulated annealing against the more established EC-directed response surface sampling design (RSSD) approach. A 6.8-ha study area near San Jacinto, CA, was surveyed for EC, and 30 soil sampling locations per sampling strategy were selected. Spatial simulated annealing and RSSD were compared for sensor calibration to a target soil property (i.e., salinity) and for evenness of spatial coverage of the study area, which is beneficial for mapping nontarget soil properties (i.e., those not correlated with EC). The results indicate that the linear modeling EC-salinity calibrations obtained from the two sampling schemes provided salinity maps characterized by similar errors. The maps of nontarget soil properties show similar errors across sampling strategies. The Spatial Simulated Annealing methodology is, therefore, validated, and its use in agronomic and environmental soil science applications is justified.

Limitations and Potential of Spectral Subtractions in Fourier‐Transform Infrared Spectroscopy of Soil Samples

Core Ideas Spectral subtractions are a tool for improving FTIR spectroscopy. Minor components of soil spectra such as SOM can be resolved by subtracting interfering backgrounds (e.g., minerals). Limitations to spectral subtractions are specific to both FTIR spectroscopy and soils. There is strong potential to improve accuracy and applications of subtractions in soil spectra. Soil science research is increasingly applying Fourier transform infrared (FTIR) spectroscopy for analysis of soil organic matter (SOM). However, the compositional complexity of soils and the dominance of the mineral component can limit spectroscopic resolution of SOM and other minor components. The use of spectral subtraction is an established technique for isolating specific components of multicomponent spectra, and as such is a powerful tool to improve and expand the potential of spectroscopy of soil samples. To maximize the utility of spectral subtraction and avoid its misuse, consideration must be given to the general and soil‐specific limitations of FTIR spectroscopy and subtractions, as well as specific experimental objectives. This review summarizes the history of subtractions in FTIR spectroscopy of soil samples, identifies and qualifies misconceptions and limitations on its use, and highlights trade‐offs, recent developments, and future directions on the potential of spectral subtractions to improve and expand FTIR applications in soil science.

Comparison of Three Commercial Carrier Ampholytes for Soil Science Applications in Isoelectric Focusing

ABSTRACTIsoelectric focusing (IEF) has been used in soil science for identification of different matrices in organic fertilizers and assessment of stabilization of organic matter of soil and composts. Fundamental components for IEF analysis are carrier ampholytes; those used in soil science, ampholines, have not been produced since 2008, and IEF is not available for soil science any longer. We tested commercial ampholytes Pharmalyte and Bio-Lyte on extracts of organic matter of different soils and composts, compared the IEF profiles acquired with those of ampholines, and checked the quality of the information. Results showed that the IEF profiles acquired by Pharmalyte are poorly differentiated and of no use, whereas those produced by Bio-Lyte are well differentiated and provide information that can be interpreted like that of ampholines. Bio-Lyte appears to be the IEF carrier ampholyte that could replace ampholines and enable continued research in soil science through IEF.

Analytical pyrolysis as a tool to probe soil organic matter

The environmental importance of soil organic matter (SOM) in the ecosystems and in the C biogeochemical cycle is well established. Indeed, it represents the main terrestrial carbon pool and due to its vulnerability, it plays a key role in the global carbon cycle. However, as SOM is mainly composed of products resulting from microbial and physicochemical transformations of vegetal, microbial and animal biomass, it results in a heterogeneous mixture. This complexity, along with organo-mineral interactions, makes challenging the characterization of SOM composition at the molecular scale. Nevertheless, its precise characterization is essential to determine its fate in the environment and eventually to provide recommendations on sustainable practices. Among the available techniques to analyse SOM, thermal degradations appear as especially efficient as they are less selective than some chemical ones, leading to a larger view of the SOM chemical structure. Analytical pyrolysis was thus used in a wide range of soil science fields including studies on pedogenesis and anthropic effects. It allows to characterize SOM at the molecular level, including identification of biomarkers, and to compare different soils and/or different horizons in a given soil profile under various impacts (land use, evolution, etc.). The review of recent developments in data acquisition and/or processing leads us to provide guidelines to select the most appropriate method and to avoid possible pitfalls. Examples will illustrate the wide range of soil science applications and show the potential and limitations of this approach.

Application of TDR Method for Moisture Profiles Measurement in Cellular Concrete

Possible applicability of TDR method for continuous monitoring of moisture content in porous building materials is studied in the paper. TDR method, originally proposed for application in soil science, represents prospective tool for long time monitoring of moisture content in laboratory as well as in-situ conditions neglecting the effect of salts presence in researched material on accuracy of measured moisture content. The basic experimental setup of the TDR method is adjusted to the measurement of water content in building materials. The practical application of the method is presented on the measurement of moisture profiles in rod-shaped sample of cellular concrete.