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  • 1
    Language: English
    In: Vadose Zone Journal, 01 March 2018, Vol.17(1)
    Description: Soil structure is a key soil property affecting a soil’s flow and transport behavior. X-ray computed tomography (CT) is increasingly used to quantify soil structure. However, the availability, cost, time, and skills required for processing are still limiting the number of soils studied. Visible near-infrared (vis-NIR) spectroscopy is a rapid analytical technique used successfully to predict various soil properties. In this study, the potential of using vis-NIR spectroscopy to predict X-ray CT derived soil structural properties was investigated. In this study, 127 soil samples from six agricultural fields within Denmark with a wide range of textural properties and organic C (OC) contents were studied. Macroporosity (〉1.2 mm in diameter) and CT (the density of the field-moist soil matrix devoid of large macropores and stones) were determined from X-ray CT scans of undisturbed soil cores (19 by 20 cm). Both macroporosity and CT are soil structural properties that affect the degree of preferential transport. Bulk soils from the 127 sampling locations were scanned with a vis-NIR spectrometer (400–2500 nm). Macroporosity and CT were statistically predicted with partial least squares regression (PLSR) using the vis-NIR data (vis-NIR-PLSR) and multiple linear regression (MLR) based on soil texture and OC. The statistical prediction of macroporosity was poor, with both vis-NIR-PLSR and MLR ( 〈 0.45, ratio of performance to deviation [RPD] 〈 1.4, and ratio of performance to interquartile distance [RPIQ] 〈 1.8). The CT was predicted better ( 〉 0.65, RPD 〉 1.5, and RPIQ 〉 2.0) combining the methods. The results illustrate the potential applicability of vis-NIR spectroscopy for rapid assessment/prediction of CT.
    Keywords: Agriculture
    ISSN: 1539-1663
    E-ISSN: 1539-1663
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  • 2
    Language: English
    In: Scientific reports, 25 July 2018, Vol.8(1), pp.11188
    Description: The intensification of agricultural production to meet the growing demand for agricultural commodities is increasing the use of chemicals. The ability of soils to transport dissolved chemicals depends on both the soil's texture and structure. Assessment of the transport of dissolved chemicals (solutes) through soils is performed using breakthrough curves (BTCs) where the application of a solute at one site and its appearance over time at another are recorded. Obtaining BTCs from laboratory studies is extremely expensive and time- and labour-consuming. Visible-near-infrared (vis-NIR) spectroscopy is well recognized for its measurement speed and for its low data acquisition cost and can be used for quantitative estimation of basic soil properties such as clay and organic matter. In this study, for the first time ever, vis-NIR spectroscopy was used to predict dissolved chemical breakthrough curves obtained from tritium transport experiments on a large variety of intact soil columns. Averaged across the field, BTCs were estimated with a high degree of accuracy. So, with vis-NIR spectroscopy, the mass transport of dissolved chemicals can be measured, paving the way for next-generation measurements and monitoring of dissolved chemical transport by spectroscopy.
    Keywords: Biology;
    ISSN: Scientific Reports
    E-ISSN: 2045-2322
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  • 3
    Language: English
    In: Hydrology and Earth System Sciences, Oct 6, 2016, Vol.20(10), p.4017
    Description: Prediction and modeling of localized flow processes in macropores is of crucial importance for sustaining both soil and water quality. However, currently there are no reliable means to predict preferential flow due to its inherently large spatial variability. The aim of this study was to investigate the predictive performance of previously developed empirical models for both water and air flow and to explore the potential applicability of X-ray computed tomography (CT)-derived macropore network characteristics. For this purpose, 65 cylindrical soil columns (6#xE2;#x80;#xAF;cm diameter and 3.5#xE2;#x80;#xAF;cm height) were extracted from the topsoil (5#xE2;#x80;#xAF;cm to 8.5#xE2;#x80;#xAF;cm depth) in a 15#xE2;#x80;#xAF;m#xE2;#x80;#xAF;#xE2;#x80;#x89;#xC3;#x97;#xE2;#x80;#x89;#xE2;#x80;#xAF;15#xE2;#x80;#xAF;m grid from an agricultural field located in Silstrup, Denmark. All soil columns were scanned with an industrial X-ray CT scanner (129#xE2;#x80;#xAF;#xC2;#xB5;m resolution) and later employed for measurement of saturated hydraulic conductivity, air permeability at -30 and -100#xE2;#x80;#xAF;cm matric potential, and gas diffusivity at -30 and -100#xE2;#x80;#xAF;cm matric potential. Distribution maps for saturated hydraulic conductivity, air permeability, and gas diffusivity reflected no autocorrelation irrespective of soil texture and organic matter content. Existing empirical predictive models for saturated hydraulic conductivity and air permeability showed poor performance, as they were not able to realistically capture macropore flow. The tested empirical model for gas diffusivity predicted measurements at -100#xE2;#x80;#xAF;cm matric potential reasonably well, but failed at -30#xE2;#x80;#xAF;cm matric potential, particularly for soil columns with biopore-dominated flow. X-ray CT-derived macroporosity matched the measured air-filled porosity at -30#xE2;#x80;#xAF;cm matric potential well. Many of the CT-derived macropore network characteristics were strongly interrelated. Most of the macropore network characteristics were also significantly correlated with saturated hydraulic conductivity, air permeability, and gas diffusivity. The predictive Ahuja et al.#xC2;#xA0;(1984) model for saturated hydraulic conductivity, air permeability, and gas diffusivity performed reasonably well when parameterized with novel, X-ray CT-derived parameters such as effective percolating macroporosity for biopore-dominated flow and total macroporosity for matrix-dominated flow. The obtained results further indicate that it is crucially important to discern between matrix-dominated and biopore-dominated flow for accurate prediction of macropore flow from X-ray CT-derived macropore network characteristics.
    Keywords: Hydrogeology – Analysis ; Permeability – Analysis ; Porosity – Analysis ; Cat Scans – Analysis
    ISSN: 1027-5606
    E-ISSN: 16077938
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  • 4
    Language: English
    In: Vadose Zone Journal, 01 April 2018, Vol.17(1)
    Description: The soil water retention curve (SWRC) is essential for the modeling of water flow and chemical transport in the vadose zone. The Campbell function and its (pore-size distribution index) parameter fitted to measured data is a simple method to quantify retention under relatively moist conditions. Measuring soil water retention is time consuming, and a method to accurately predict the Campbell relation from either typically available soil parameters such as bulk density, clay-size fraction, and organic matter content (soil fines) or from visible–near-infrared (vis–NIR) spectroscopy may provide a fast and inexpensive alternative. However, the traditional Campbell model has a reference point at saturated water content, and this soil-structure-dependent water content will typically be poorly related to basic texture properties and thus be poorly predicted from vis–NIR spectra. In this study, we anchor the Campbell model at the water content at −1000 cm HO matric potential [log(1000)= pF 3]. Agricultural soil samples with a wide textural range from across Denmark were used. Soil water retention was measured at a number of matric potentials between pF 1 and 3. The soil water content at pF 3 and Campbell were both well predicted using either a soil-fines-based pedotransfer function or vis–NIR spectroscopy. The resulting Campbell function anchored at pF 3 compared closely to measured water retention data for a majority of soils. The ability of the two methods to also predict field average SWRC was evaluated for three fields. Field average, predicted SWRC compared well with field average measured data, with vis–NIR overall performing better.
    Keywords: Agriculture
    ISSN: 1539-1663
    E-ISSN: 1539-1663
    Source: Directory of Open Access Journals (DOAJ)
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  • 5
    Language: English
    In: Vadose Zone Journal, 01 April 2019, Vol.18(1)
    Description: Estimation of soil hydraulic parameters is essential when generating a hydrogeological model for simulating water flow dynamics in an agricultural field. However, estimation of the input parameters through direct measurements is time consuming and costly, and the spatial variability presents an uncertainty. Therefore, we proposed a rapid and inexpensive concept (integration of visible–near-infrared spectroscopy [vis-NIR] and a pedotransfer function [PTF]) to estimate hydraulic properties considering catchment scale. An existing vis-NIR–predicted Campbell retention function was used for estimating the Campbell parameter and the water content at −1000 cm HO soil–water matric potential (log|−1000| = pF 3). A PTF was developed for predicting the saturated hydraulic conductivities using the vis-NIR–predicted Campbell and the effective porosity, defined as the difference in volumetric water contents at pF 0.3 and 3. The concept was evaluated by developing a hydrogeological model in HYDRUS-2D software for simulating the tile drainage discharge from a clayey agricultural subcatchment in Denmark, using as input hydraulic parameters the output from the suggested approach. The suggested approach simulated the main attributes of the flow hydrograph with a reasonable degree of accuracy ( and RMSE values of 0.86 and 1.25 L s, respectively). A sensitivity analysis was performed to determine the response of the model to changes in values of predicted parameters when predicting the drainage discharge, and it showed that small variations (〈10%) would not affect the predictive ability of the model.
    Keywords: Agriculture
    ISSN: 1539-1663
    E-ISSN: 1539-1663
    Source: Directory of Open Access Journals (DOAJ)
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  • 6
    Language: English
    In: Vadose Zone Journal, 01 April 2019, Vol.18(1)
    Description: The soil-water retention curve (SWRC) at the dry end, also known as soil water vapor sorption isotherms, is essential for the modeling of water vapor transport, microbial activity, and biological processes such as plant water uptake in the vadose zone. Measurement of detailed soil water vapor sorption isotherms (WSIs) can be time consuming. Therefore, we propose rapid, inexpensive methodologies (visible–near-infrared spectroscopy [vis–NIRS] and a pedotransfer function [PTF]) to predict the Campbell–Shiozawa (CS) model parameters to obtain the WSIs. Water vapor sorption isotherms were measured on 144 soil samples with a vapor sorption analyzer. The CS semi-logarithmic-linear function anchored at a soil-water matric potential of −10 cm HO (log|−10| = pF 6) was fitted to the measured data because it accurately characterizes the WSIs. Thereafter, a vis–NIRS calibration model and a PTF, based on clay and organic C contents, were developed and used to predict the two reference CS model parameters (α and ). Both parameters were predicted with a reasonable degree of accuracy using vis–NIRS and the PTF (for α, RMSE values of 0.0041 and 0.0025, and for , RMSE values of 0.0042 and 0.0034 for vis–NIRS and the PTF, respectively). Based on the predicted α and values, the predicted WSIs compared closely with the measured isotherms for individual soil samples from each field. At the field scale, the vis–NIRS model performed marginally better than the PTF. Thus, it is evident that the use of vis–NIRS or PTFs provides a relatively inexpensive approach to predicting soil water sorption isotherms.
    Keywords: Agriculture
    ISSN: 1539-1663
    E-ISSN: 1539-1663
    Source: Directory of Open Access Journals (DOAJ)
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  • 7
    Language: English
    In: Vadose Zone Journal, 01 June 2019, Vol.18(1)
    Description: Knowledge of the cation exchange capacity (CEC) for soils or other porous media is very important for civil engineering and agricultural applications. However, the standard laboratory methods to measure CEC are costly and laborious. The aim of this research was to develop a visible–near-infrared spectroscopy (Vis–NIRS, 400–2500 nm) calibration model to predict CEC based on multivariate analysis and to compare the predictive ability of Vis–NIRS with that of a pedotransfer function (PTF). For this purpose, reference CEC was measured by the ammonium acetate method for 235 soil samples, collected from 21 countries. Diffuse spectral reflectance data were also collected by using a NIRSTM DS2500 spectrometer. The model was constructed on a calibration subset (80%) and evaluated with a validation subset (20%) using partial least squares regression. The Vis–NIRS calibration model was sufficiently robust based on the cross-validation results [ = 0.79, RMSE of cross-validation values of 7.9 cmol kg and bias = −0.14]. The independent validation of the Vis–NIRS model showed good prediction accuracy, regardless of sample origin (RMSE of prediction value of 5.0 cmol kg and ratio of performance to interquartile distance value of 4.5). Moreover, the Vis–NIRS prediction performance was superior to that of the PTF, which was influenced by the sample origin (RMSE values of 11.5 cmol kg). The better prediction of CEC by the Vis–NIRS calibration model suggests that it is due to the co-variation of CEC with clay (type and content) and organic C content.
    Keywords: Agriculture
    ISSN: 1539-1663
    E-ISSN: 1539-1663
    Source: Directory of Open Access Journals (DOAJ)
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