Wide Range Of Soil Textures Research Articles

Estimation of soil water retention curve using fractal dimension

The soil water retention curve (SWRC) is a fundamental hydraulic property majorly used to study flow transport in soils and calculate plant-available water. Since, direct measurement of SWRC is time-consuming and expensive, different models have been developed to estimate SWRC. In this study, a fractal-based model was developed to predict SWRC. A wide range of soil textures (130 soil samples) was used to determine the fractal dimension of SWRC (DSWRC). Moreover, the SWRC pedotransfer functions were established based on easily available soil properties such as particle size distribution and bulk density by applying multiple linear regression analysis. The measured DSWRC for 110 soil samples was considered for function parameterization and the remaining was used for model validation. The results illustrated that the DSWRC linearly correlates with clay and silt contents and soil bulk density (r2 = 0.909). The SWRC can, therefore, be easily and concisely estimated by the proposed fractal-based functions. Key words: Fractal model; Pedotransfer functions; Regression analysis; Soil water retention curve

Estimation of soil water retention curve using fractal dimension

The soil water retention curve (SWRC) is a fundamental hydraulic property majorly used to study flow transport in soils and calculate plant-available water. Since, direct measurement of SWRC is time-consuming and expensive, different models have been developed to estimate SWRC. In this study, a fractal-based model was developed to predict SWRC. A wide range of soil textures (130 soil samples) was used to determine the fractal dimension of SWRC (D SWRC ). Moreover, the SWRC pedotransfer functions were established based on easily available soil properties such as particle size distribution and bulk density by applying multiple linear regression analysis. The measured D SWRC for 110 soil samples was considered for function parameterization and the remaining was used for model validation. The results illustrated that the D SWRC linearly correlates with clay and silt contents and soil bulk density (r 2 = 0.909). The SWRC can, therefore, be easily and concisely estimated by the proposed fractal-based functions. Keywords: Fractal model; Pedotransfer functions; Regression analysis; Soil water retention curve

Estimating transverse dispersivity based on hydraulic conductivity

Abstract The modeling of depletion timeframe for a non-aqueous phase liquid (NAPL) layer is partially dependent on transverse vertical dispersivity. This paper provides guidance for estimating this parameter over the wide range of soil textures in which NAPL resides at contaminated sites. Transverse dispersivity is re-calculated for several previous NAPL dissolution studies based on a consistent methodology for estimating model input parameters. Through the compilation of a number of case studies from the literature including multiple NAPL dissolution experiments, the trend of transverse vertical dispersivity appears to be inversely proportional to the effective grain diameter when groundwater velocity (v) is below a critical threshold ( v c ). A novel regression equation is derived for estimating transverse dispersivity based on hydraulic conductivity when v v c and the NAPL length is on the order of meters. The critical velocity for NAPL pool dissolution appears to range from 3 to 5 m/d based on a limited number of studies with NAPL pool lengths of 1 m or more. The method derived by Klenk and Grathwohl (2002) is validated for estimating a correction factor for dispersivity when v > v c .

Approximate Explicit Solution to the Green-Ampt Infiltration Model for Estimating Wetting Front Depth

Obtaining reliable information on the wetting front depth of soil is beneficial to the understanding of one-dimensional (1D) vertical infiltration under border irrigation. This paper presents an approximate explicit solution to the Green-Ampt (GA) infiltration model for estimating the wetting front depth. Moreover, the model proposed in this study, the GA model, the Ali model, and the Stone model were validated and evaluated using laboratory experimental data and HYDRUS-1D simulation. Statistical comparisons (root mean square error-RMSE, mean absolute percent relative error-MAPRE, and percent bias-PB) of the estimated data with the measured and simulated data were conducted. The models were ranked on the basis of their overall performance index (OPI). The results demonstrated that all four models can be used to estimate the wetting front depth of 1D vertical infiltration for a wide range of soil textures; the proposed model provided the most accurate result. According to comparisons of the estimated values with the measured and simulated values, the maximum RMSE, MAPRE, and PB were 1.74 cm, 6.92%, and −6.74%, respectively, for the proposed model. On the basis of the OPI, the optimal model was the proposed model, followed by the Ali, GA, and Stone models.

Developing a Parameterization Approach for Soil Erodibility for the Rangeland Hydrology and Erosion Model (RHEM)

Soil erodibility is a key factor for estimating soil erosion using physically based models. In this study, a new parameterization approach for estimating erodibility was developed for the Rangeland Hydrology and Erosion Model (RHEM). The approach uses empirical equations that were developed by applying piecewise regression analysis to predict the differences of erodibility before and after disturbance (i.e., wildfire, prescribed fire, and tree encroachment) and across a wide range of soil textures as a function of vegetation cover and surface slope angle. The approach combines rain splash, sheet flow, and concentrated flow erodibilities into a single parameter for modeling erodibility in most cases. We evaluated the new approach for sites representing different degrees of disturbance associated with burning and tree encroachment. Our results show that the new erodibility approach in RHEM predicts erosion at the plot scale with a satisfactory range of error in all cases. The new approach extends the applications of RHEM to a greater scope of landscapes and soil texture.

Connecting crop models with highly resolved sensor observations to improve site-specific fertilisation

High spatial variability of soil properties restricts the benefits of process-oriented modelling for management recommendations on field scale due to rare information about the soil inventory and its distribution. Geo-electrical mapping provides with a certain spatial pattern, but results are influenced by various factors. The model HERMES was applied to 60 of altogether 80 soil sampling points of a well-documented field in North-Rhine Westphalia characterised by a wide range of soil texture. Validation of HERMES resulted in satisfactory root mean square errors (RMSE) for yield (0.76 t ha-1), water (34.70 mm) and nitrogen in soil (51.44 kg ha-1) over the whole simulation period. At the same field, values of conducted electrical conductivity (ECa) mapping ranged from 20 to 90 mS m-1 and have been assigned to the soil sampling points to proof statistical relations. Clay and sand contents of three soil layers were highly correlated with measured ECa values. Derived regression models show R2 values between 0.73 and 0.89. The subsequent calculation of soil texture at points of ECa mapping produced an improved resolution of this key value to initialize model simulation.

Soil texture and water retention as spatial predictors of denitrification in urban wetlands

Urban wetlands potentially play an important role in nitrate (NO3−) removal from stormwater, but nitrogen loading from the atmosphere and surface water must intersect with soil properties optimal for NO3− removal for this potential to be realized. We examined predictors of NO3− removal via the microbial process of denitrification in an urban wetland system in New Jersey, USA with highly heterogeneous soils. Soil cores representing the wide range of soil textures at the site were collected to examine relationships between intact core denitrification rates, denitrification enzyme activity (DEA), available inorganic nitrogen, and soil water retention characteristics. Water retention curves were characterized for pressure potentials ranging from −1 to −5000 cm and used to estimate pore size distribution parameters. The highest intact core denitrification rates occurred in soils located at low elevations, with high macroporosity, and low variability in soil pore radius. High DEA corresponded with high available soil NO3− and high elevation. Soil samples collected at 118 points from the site and analyzed for soil organic matter and texture fractions were used to create interpolated raster layers of properties related to high denitrification rates (“hot spots”). Weighted estimations of whole-site NO3− removal based on denitrification hot spots were higher than site estimations based on average denitrification rates, suggesting that studies using the latter approach may be underestimating NO3− removal at the landscape level. Stormwater channels at the site intersected with denitrification hot spots over 20% of total channel area, indicating that soils may be at least partially reducing total NO3− loads to the adjacent creek. These results show that soil physical properties that are relatively immutable can be used for predicting the location of potential hot spots of microbial activity at the landscape scale.

Evidence of a union between organic carbon and water content in soil

Soil organic matter, comprising ∼58% soil organic carbon (SOC), is attributed with increased water holding capacity in the surface horizon of agricultural soil. This paper addresses the role of SOC as a component of a common functional unit in soil from analysis within a single field and over multiple fields. Soil data measured on the fields during the SMAPVEX12 satellite prelaunch algorithm development campaign exhibited high correlation among SOC, field-mean soil water content (SWC), bulk density, and soil texture. The analysis extended over a wide range of soil texture and wetness in the top 5 cm of soil over 50 agricultural fields covering ∼400 km2 of southern Manitoba. Data collected over a much smaller area from Ontario silt loam soils at the Elora Research Centre demonstrated a similar correlation between SOC and SWC in intensive field sampling. This intercorrelation of SOC and SWC is examined with partial least-squares regression, principal component analysis, and geostatistical semivariograms. A model is proposed to interpret the feedback process between SOC and SWC to explain the persistent correlation. Further work to substantiate the strengths and limits of the relationship between SOC and SWC may be beneficial for estimating SWC for remote sensing, agriculture, hydrology, and ecosystem function.

Predicting the relative density from on-the-go horizontal penetrometer measurements at some arable top soils in Northern Switzerland

This study sought to develop empirical models to predict soil relative density (ρrel) from measurements of horizontal penetrometer resistance (PR) and soil water content (θg) in a wide range of soil textures. This permits the comparison of the state of soil compactness in different soil textures. It was hypothesised that model coefficients would be texture-dependent when soil compactness was expressed as bulk density (ρd) and that a model with constant coefficients could be obtained when soil compactness was expressed in terms of ρrel (obtained as the ratio of ρd to reference bulk density (ρref)). Field measurements were conducted in 2014 using a horizontal penetrometer at 0.25m depth in 10 fields in Switzerland with a wide range of soil textures covering sandy loam, silt loam, loam, clay loam and clay (clay concentration, (CC)=153–585gkg−1 and organic matter concentration, (OM)=9–168gkg−1). At selected locations along the penetrometer measurement transects, cylindrical soil cores were sampled for determination of soil texture, OM, θg and ρd. Soil water potential and effective stress (σ') were also estimated for each location. Standard Proctor tests were performed on eight soils with variable textures. Proctor density was well described as a function of CC and OM (R2adj=0.97, RMSE=0.046Mgm−3) and was used as reference density to obtain ρrel. From this we developed a model for prediction of ρrel from PR and σ′ that allows comparisons between soils without changes in model coefficients. However, σ' cannot be obtained from on-the-go measurements and the model is therefore of limited value for soil compaction mapping. A model for estimating ρrel from PR and θg yielded satisfactory predictions (R2adj=0.66, RMSE=3.3%), although θg is a texture-dependent measure of soil water that cannot be compared across soils. Moreover, ρd was well predicted from PR and θg (R2adj=0.93, RMSE=0.05Mgm−3), possibly because all our measurements were carried out at similar soil water potential, which implies that θg carries soil textural information. Future research should test the proposed equations for a wide range of soil water potential values. The findings presented can be of use in developing measurement systems for mapping soil compactness that combine the proposed prediction functions with horizontal penetrometer and water content sensor systems.

Using the double-exponential water retention equation to determine how soil pore-size distribution is linked to soil texture

The link between soil particle arrangement and pore structures and its role in soil water retention is important. However, it is not completely determined. In this study, based on similarities and synchronizations between the three terms, i.e., residual, matrix, and structural porosities in a double-exponential water retention equation (DE) and the three soil separates (clay, silt and sand), we aimed to explore the link between soil particles and pores and to determine the causes of mono- and bi-modality in DE differential functions. For this purpose, 78 soil samples were selected from the Unsaturated Soil Hydraulic Database with a wide range of soil texture. Results showed that the DE fitted with the measured soil water retention curves well. Soil residual and structural pores could be reasonably predicted with clay and sand contents, respectively, via the derived linear functions. Soil texture reasonably influenced the modality of DE differential functions, particularly in the following situation. (1) When a soil separate (e.g., silt) occupied the absolute majority in soil particle size distribution, the DE differential function may tend to show a mono-peak, whereas the others may tend to be bi-peaked. (2) On the contrary, the matrix peak was significantly influenced by clay content, and the structural peak was greatly affected by sand content. (3) When the sand content was greater than 0.5, the curve of the DE differential function may tend to be bi-peaked, i.e., a high structural peak together with a low matrix peak. Further analyses, e.g., the technologies of X-ray and original SEM are needed to confirm the proposed soil structural model.

Critical evaluation of particle size distribution models using soil data obtained with a laser diffraction method.

Mathematical descriptions of classical particle size distribution (PSD) data are often used to estimate soil hydraulic properties. Laser diffraction methods (LDM) now provide more detailed PSD measurements, but deriving a function to characterize the entire range of particle sizes is a major challenge. The aim of this study was to compare the performance of eighteen PSD functions for fitting LDM data sets from a wide range of soil textures. These models include five lognormal models, five logistic models, four van Genuchten models, two Fredlund models, a logarithmic model, and an Andersson model. The fits were evaluated using Akaike’s information criterion (AIC), adjusted R2, and root-mean-square error (RMSE). The results indicated that the Fredlund models (FRED3 and FRED4) had the best performance for most of the soils studied, followed by one logistic growth function extension model (MLOG3) and three lognormal models (ONLG3, ORLG3, and SHCA3). The performance of most PSD models was better for soils with higher silt content and poorer for soils with higher clay and sand content. The FRED4 model best described the PSD of clay, silty clay, clay loam, silty clay loam, silty loam, loam, and sandy loam, whereas FRED3, MLOG3, ONLG3, ORLG3, and SHCA3 showed better performance for most soils studied.

Evaluation of N environmental risks on Andosols from an intensive dairy farming watershed using DNDC

Manure nitrogen (N) in the livestock sector has become a key driver of environmental change. The denitrification–decomposition (DNDC) model was used to evaluate N pollution strengths on Andosols with intensive dairy manure application in Upper Naka River Watershed, Japan. The calibrated model was capable of predicting Andosol N flows because the simulated soil mineral N content, soil nitrogen oxide (N2O) fluxes, denitrification rate, and crop N uptake matched the patterns and magnitudes of the field observations from a wide range of soil textures, as well as manure management and cropping systems. The simulations showed that current intensive manure application systems caused low crop N use efficiency and a large amount of NO3−-N leaching and N2O emission. The crop N use efficiency was 27%–42% and 37%–55% of input N for uplands and paddy rice, respectively. The uplands showed much more serious N environmental pollution risks with N leaching 123–362kgNha−1yr−1 and N2O emissions 6.53–11.8kgNha−1yr−1 than that in the lowland paddy rice with N leaching 17.4–103kgNha−1yr−1 and N2O emissions 0.59–2.77kgNha−1yr−1. Forage rice/barley crop systems have high N cleaning capability due to the greater crop N uptake which reached to 304kgNha−1yr−1. High precipitation stimulated more NO3−-N leaching. Sandy soil also showed higher N leaching and was unsuitable for paddy rice. Slurry application stimulated more N2O emission than compost manure. To mitigate the current high N pollution, the critical N application rate was recommended to be approximately 380, 470, 640, and 390kgNha−1yr−1 for loam sand planted with maize/grass, loam soil with maize/grass, forage rice/barley, and rice/fallow with winter manure application, respectively.

Performance evaluation of four-parameter models of the soil-water characteristic curve.

Soil-water characteristic curves (SWCCs) are important in terms of groundwater recharge, agriculture, and soil chemistry. These relationships are also of considerable value in geotechnical and geoenvironmental engineering. Their measurement, however, is difficult, expensive, and time-consuming. Many empirical models have been developed to describe the SWCC. Statistical assessment of soil-water characteristic curve models found that exponential-based model equations were the most difficult to fit and generally provided the poorest fit to the soil-water characteristic data. In this paper, an exponential-based model is devised to describe the SWCC. The modified equation is similar to those previously reported by Gardner (1956) but includes exponential variable. Verification was performed with 24 independent data sets for a wide range of soil textures. Prediction results were compared with the most widely used models to assess the model's performance. It was proven that the exponential-based equation of the modified model provided greater flexibility and a better fit to data on various types of soil.

High-order averaging method of hydraulic conductivity for accurate soil moisture modeling

Summary Richards’ equation (RE) is the most common mathematical expression for soil water movement in a porous medium. Despite advancements in numerical schemes and high-performance computing, the requirements of iterative computations and fine grids hinder further extension of the RE to multi-dimensional and large-scale applications. Averaging methods of hydraulic conductivity have been known to be one of the significant factors affecting the accuracy of numerical solutions of the RE, especially when coarse grids are used. In this study, we developed a high-order averaging method of hydraulic conductivity for accurate numerical modeling of the RE, which has a straightforward formula regardless of the soil conditions and produces high simulation accuracy when used on coarse grids. The developed method is based on the high-order upwind scheme, which is widely used for hyperbolic partial differential equations within a finite volume framework in order to prevent numerical oscillations near a discontinuity while preserving high-order accuracy. Numerical simulations of several one- and two-dimensional cases performed in the study indicate that the proposed method outperforms existing simple averaging methods and is also superior, or at least equivalent, to complex averaging methods over a wide range of soil textures, especially on coarse grids. In addition, the proposed method is straightforwardly extended to nonorthogonal grids by being combined with the coordinate transformation method and the extension is verified through multi-dimensional test cases as well as tests on a heterogeneous soil domain.

Dynamics of soil carbon following destruction of tropical rainforest and the subsequent establishment of Imperata grassland in Indonesian Borneo using stable carbon isotopes

AbstractSoutheast Asia has the highest rate of tropical rainforest deforestation worldwide, and large deforested areas have been replaced ultimately by the highly invasive grass Imperata cylindrica. However, information on the carbon (C) budget with such land transition is very scarce. This study presents the dynamics of soil C following rainforest destruction and the subsequent establishment of Imperata grassland in the lowland humid tropics of Indonesian Borneo using stable C isotopes. To evaluate the relative contribution of organic matter originating from primary forest (C3) and grasslands (C4), we compared soil C stock and natural 13C abundance from six sites to a depth of 100 cm using samples with a wide range of soil textures. Twelve years after the first soil sampling in the grasslands, we re‐sampled to examine temporal changes in soil organic matter. The grassland topsoil (0–5 cm) is an active layer with rapid decomposition and incorporation of fresh C (mean residence time: 7.5 year) and a substantial proportion of the stable C pool (37%). The decline in forest‐derived C was slight, even at 5–10 cm depths, and subsoil (20–100 cm depth) forest‐derived C did not change along the forest‐to‐grassland chronosequence. Grassland‐derived C stock increased significantly in the subsurface and subsoils (5–100 cm). Simulation indicated that total soil C stock (0–100 cm) increased by 18.6 Mg ha−1 from initial primary forest (58.0 Mg ha−1) to a new equilibrium state of the grassland (76.6 Mg ha−1) after 30–50 years of grassland establishment. This research indicates that the soil did not function as a CO2 source when the deforested area was replaced by Imperata grassland on the Ultisols of the Asian humid tropics. Instead, increased soil C stocks offset CO2 emissions, with the C offset accounting for 6.6–7.4% of the loss of biomass C stock.

A new model for predicting relative nonwetting phase permeability from soil water retention curves

Relative permeability of the nonwetting phase in a multiphase flow in porous media is a function of phase saturation. Specific expressions of this function are commonly determined by combining soil water retention curves with relative nonwetting phase permeability models. Experimental evidence suggests that the relative permeability of the nonwetting phase can be significantly overestimated by the existing relative permeability models. A new model for the prediction of relative nonwetting phase permeability from soil water retention curves is proposed in this paper. A closed form expression can be obtained in combination with soil water retention curves. The model is mathematically simple and can easily and efficiently be implemented in numerical models of multiphase flow processes in porous media. The predicting capability of the proposed model is contrasted with well‐supported models by comparing the measured and predicted relative air permeability data for 11 soils, representing a wide range of soil textures, from sand to silty clay loam. In most of the cases the proposed model improves the agreement between the predicted relative air permeability and the measured data.

Does no‐till farming induce water repellency to soils?

AbstractSoil water repellency (SWR) is an intrinsic and dynamic soil property that can influence soil hydrology and crop production. Although several land use systems have been shown to induce water repellency in soil, the specific effects of no‐till cropping on SWR are poorly understood. This article reviews the impacts of no‐till on SWR and identifies research needs. No‐till cropping generally induces 1.5 to 40 times more SWR than conventional tillage, depending on soil type. This may result from near‐surface accumulation of hydrophobic organic C compounds derived from crop residues, microbial activity and reduced soil disturbance. While large SWR may have adverse impacts on soil hydrology and crop production, the level of SWR under no‐till relative to conventional tillage may contribute to aggregate stabilization and intra‐aggregate C sequestration. More research is needed to discern the extent and relevance of no‐till induced SWR. This includes: (1) further assessment of SWR under different tillage systems across a wide range of soil textures and climates, (2) comparison of the various methods for measuring SWR over a range of water contents, (3) inclusion of SWR in routine soil analysis and its use as a parameter to evaluate management impacts, (4) assessment of the temporal and spatial changes in SWR under field conditions, (5) further assessment of the impacts of the small differences in SWR between no‐till and conventionally tilled soils on crop production, soil hydrology and soil C sequestration, and (6) development of models to predict SWR for different tillage systems and soils.

Changes in soil carbon stock after deforestation and subsequent establishment of “Imperata” grassland in the Asian humid tropics

Globally, tropical deforestation is often followed by the establishment of fire-prone grassland. In Southeast Asia, Imperata cylindrica grassland is the dominant land cover after deforestation. No quantitative data are available on the changes in soil carbon (C) stock upon such land conversion. We aimed to elucidate changes in soil C stock after deforestation followed by the occurrence and persistence of I. cylindrica grassland in the Asian humid tropics. We compared soil C stock between primary forests (n = 20) and grasslands (n = 14) with a wide range of soil textures in East Kalimantan (Indonesian Borneo). We also assessed the temporal change in soil C stock in the grassland sites between 1992 and 2004 by comparing identical soil pits (n = 7). Soil C stock (0–100 cm deep) increased by 23% following transition from primary forest to grassland during about 10 years. Over 12 years at the grassland sites, however, soil C stock did not change in the 0–100 cm depth, but we observed increased blackness of soil, especially coarse-textured soils. The increase in C stock following the transition was largely attributed to the organic matter supply by grass roots, rhizomes, and charred materials from wildfires to the subsurface soils and subsoils (5–100 cm). The unchanged soil C stock (0–100 cm) over 12 years at the grassland sites suggests that the soil C stock level there has nearly reached a new equilibrium state. However, the increased blackness of the soil suggests changes in the quality of soil organic matter by higher subsoil root input and deeper bioturbation by earthworms.

Predicting daily soil temperature profiles in arable soils in cold temperate regions from air temperature and leaf area index

Modelling of ecosystem processes often requires soil temperature as a driving variable. Since soil temperature measurements are seldom available for regional applications, they have to be estimated from standard meteorological data. The objective of this paper is to present a general, simple empirical approach for estimating daily depth profiles of soil temperature from air temperature and a surface cover index (LAI; leaf area index) mainly focusing on agricultural soils in cold temperate regions. Air and soil temperature data measured daily or every fifth day at one to six different depths were acquired from all meteorological stations in Sweden where such records are available. The stations cover latitudes from 55.65° to 68.42° N and mean annual air temperatures from +8.6 to −0.6 °C. The time series spanned between two and ten years. The soils at the stations cover a wide range of soil textures, including two organic soils. We calibrated the model first for each station and then for all stations together and the general parameterization only slightly decreased the goodness of fit. This general model then was applied to two treatments in a field experiment: bare soil and a winter rape crop. The parameters governing the influence of LAI on heat fluxes were optimized using this experiment. Finally, the model was validated using soil temperature data from two barley treatments differing in LAI taken from another field experiment. In general, the model predicted daily soil temperature profiles well. For all soils and depths at the meteorological stations, 95% of the simulated daily soil temperatures differed by less than 2.8 °C from measurements. The corresponding differences were somewhat higher for the validation data set (3.9 °C), but bias was still low. The model explained 95% of the variation in the validation data. Since no site-specific adjustments were made in the validation simulations, we conclude that the application of the general model presented here will result in good estimates of soil temperatures under cold temperate conditions. The very limited input requirements (only air temperature and LAI) that are easily obtainable from weather stations and from satellites make this model suitable for spatial applications at catchment or regional scales.

Calibrating Access‐tube Time Domain Reflectometry Soil Water Measurements in Deep Heterogeneous Soils

Hydrologic characterization of the vadose zone requires accurate measurements of soil water in a variety of soil textures. Time domain reflectometry (TDR) instruments are commonly used to obtain soil moisture. Although there are advantages to using TDR, certain types of instruments pose challenges, including proper installation and calibration, which must be overcome. The principal objective of this study was to develop a new method for obtaining and calibrating access‐tube TDR measurements in deep heterogeneous soils. Using two regression techniques, physical and chemical property‐based calibrations were developed to predict soil water and explain large differences between TDR and gravimetric soil water values. Differences between TDR soil water and gravimetric soil water were almost entirely controlled by physical and chemical soil properties; indicating that our access‐tube TDR probe likely produced an accurate measurement of ‘free’ but not bound water content. The bound fraction is controlled by physical and chemical properties and is a nearly static parameter at the depths and time scales considered in this study. Changes in TDR measurements after the initial calibration represent changes in the ‘free’ water content. Our calibration equations used easily obtained parameters to predict accurate soil water values in a wide range of soil textures. Approximately 95% of the soil water values predicted using our calibration equations had residuals of less than ± 10% soil water. We also developed a cost‐effective method for installing access tubes which minimized problems related to air gaps and soil structure disturbances in deep soil profiles.