Characterization of field‐scale dryland salinity with depth by quasi‐3d inversion of DUALEM‐1 data

To understand the limitations of saline soil and determine best management practices, simple methods need to be developed to determine the salinity distribution in a soil profile and map this variation across the landscape. Using a field study in southwestern Australia, we describe a method to map this distribution in three dimensions using a DUALEM‐1 instrument and the EM4Soil inversion software. We identified suitable parameters to invert the apparent electrical conductivity (EC_a – mS/m) data acquired with a DUALEM‐1, by comparing the estimates of true electrical conductivity (σ – mS/m) derived from electromagnetic conductivity images (EMCI) to values of soil electrical conductivity of a soil‐paste extract (EC_e) which exhibited large ranges at 0–0.25 (32.4 dS/m), 0.25–0.50 (18.6 dS/m) and 0.50–0.75 m (17.6 dS/m). We developed EMCI using EM4Soil and the quasi‐3d (q‐3d), cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor (λ) of 0.07. Using a cross‐validation approach, where we removed one in 15 of the calibration locations and predicted EC_e, the prediction was shown to have high accuracy (RMSE = 2.24 dS/m), small bias (ME = ?0.03 dS/m) and large Lin's concordance (0.94). The results were similar to those from linear regression models between EC_a and EC_e for each depth of interest but were slightly less accurate (2.26 dS/m). We conclude that the q‐3d inversion was more efficient and allowed for estimates of EC_e to be made at any depth. The method can be applied elsewhere to map soil salinity in three dimensions.     

Establishing soil electrical conductivity ‐ Depth relations from electromagnetic induction measurements

Abstract

Principles of electromagnetic induction (EM) and field calibration approaches are discussed as they pertain to the application of EM to soil systems for the purpose of deriving soil electrical conductivity ‐ depth relations. Evidence is provided to support the utility of EM‐derived estimates of EC_a‐depth relations. Limitations of using electromagnetic induction to determine EC_a for discrete depth intervals through the soil are discussed. Current research designed to increase the accuracy of EC_a‐depth determinations by dealing with the spatial variability problem associated with salinity in soil and by mitigating some of the inherent limitations of the calibration approaches is described.     

Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (EC_p – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R² = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (EC_e – dS/m). Our interpretation based on estimates of EC_e suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.     

Soil salinity and its distribution determined by soil sampling and electromagnetic techniques

Abstract. Diagnosis of soil salinity and its spatial variability is required to establish control measures in irrigated agriculture. This article shows the usefulness of electromagnetic (EM) and soil sampling techniques to map salinity. We analysed the salinity of a 1‐ha plot of surface‐irrigated olive plantation in Aragon, NE Spain, by measuring the electrical conductivity of the saturation extract (EC_e) of soil samples taken at 22 points, and by reading the Geonics EM38 sensor at 141 points in the horizontal (EM_H) and vertical (EM_V) dipole positions. EM_H and EM_V values had asymmetrical bimodal distributions, with most readings in the non‐saline range and a sharp transition to relatively high readings. Most salinity profiles were uniform (i.e. EM_H=EM_V), except in areas with high salinity and concurrent shallow water tables, where the profiles were inverted as shown by EM_H > EM_V, and by EC_e being greater in shallow than in deeper layers. The regressions of EC_e on EM readings predicted EC_e with R² > 84% for the 0–100 to 0–150 cm soil depths. We then produced salinity contour maps from the 141 EC_e values estimated from the electromagnetic readings and the 22 measured values of EC_e. Owing to the high soil sampling density, the maps were similar (i.e. mean surface‐weighted EC_e values between 3.9 dS m^?1 and 4.2 dS m^?1), although the electromagnetically estimated EC_e improved the mapping of details. Whereas soil sampling is preferred for analysing the vertical distribution of soil salinity, the electromagnetic sensor is ideal for mapping the lateral variability of soil salinity.     

Application of digital soil mapping methods for identifying salinity management classes based on a study on coastal central China

In coastal China, there is an urgent need to increase land for agriculture. One solution is land reclamation from coastal tidelands, but soil salinization poses a problem. Thus, there is need to map saline areas and identify appropriate management strategies. One approach is the use of digital soil mapping. At the first stage, auxiliary data such as remotely sensed multispectral imagery can be used to identify areas of low agricultural productivity due to salinity. Similarly, proximal sensing instruments can provide data on the distribution of soil salinity. In this study, we first used multispectral QuickBird imagery (Bands 1–4) to provide information about crop growth and then EM38 data to indicate relative salt content using measurements of apparent soil electrical conductivity (EC_a) in the horizontal (EC_h) and vertical (EC_v) modes of operation. Second, we used a fuzzy k‐means (FKM) algorithm to identify three salinity management zones using the normalized difference vegetation index (NDVI), EC_h and EC_v/EC_h. The three identified classes were statistically different in terms of auxiliary and topsoil properties (e.g. soil organic matter) and more importantly in terms of the distribution of soil salinity (EC_e) with depth. The resultant three classes were mapped to demonstrate that remote and proximally sensed auxiliary data can be used as surrogates for identifying soil salinity management zones.     

Detecting a leachate plume in an aeolian sand landscape using a DUALEM‐421 induction probe to measure electrical conductivity followed by inversion modelling

Solid waste poses a serious health risk when it is disposed of inadequately because water‐based solutions derived from the decomposition of solid waste products (leachate) can enter groundwater systems via plumes. To assess the public health risk and potential ecological impacts, we require knowledge on the pedological and hydrogeological settings in which waste is disposed. This is particularly the case in coarse textured highly permeable soil. To rapidly collect data, geophysical methods such as direct current (dc) resistivity techniques have been used. Moreover, non‐contact electromagnetic (EM) induction instruments have also been employed. The aim of this research was to demonstrate how the inversion using a 1‐dimensional inversion algorithm with lateral constraints of the apparent electrical conductivity (σ_a) measured in the horizontal coplanar (HCP) and perpendicular co‐planar arrays (PRP) of a DUALEM‐421 EM induction probe can be used to develop a two‐dimensional model of the true electrical conductivity (σ) within a Quaternary aeolian sand in the Tuggerah Soil Landscape southeast of Sydney in Australia. Our results from 2D models of σ accord with estimates of bulk electrical conductivity (σ_b) of a leachate plume and uncontaminated groundwater, the stratigraphy of the Tuggerah soil landscape unit and the depth of sand used to landscape the decommissioned landfill. Further research is needed to determine the origin of the plume and a quasi‐3D modelling approach is applicable.     

SOIL SALINITY AND SODICITY APPRAISAL BY ELECTROMAGNETIC INDUCTION IN SOILS IRRIGATED TO GROW COTTON

In the Far West Texas region in the USA, long‐term irrigation of fine‐textured valley soils with saline Rio Grande River water has led to soil salinity and sodicity problems. Soil salinity [measured by saturated paste electrical conductivity (EC_e)] and sodicity [measured by sodium adsorption ratio (SAR)] in the irrigated areas have resulted in poor growing conditions, reduced crop yields, and declining farm profitability. Understanding the spatial distribution of EC_e and SAR within the affected areas is necessary for developing management practices. Conventional methods of assessing EC_e and SAR distribution at a high spatial resolution are expensive and time consuming. This study evaluated the accuracy of electromagnetic induction (EMI), which measures apparent electrical conductivity (EC_a), to delineate EC_e and SAR distribution in two cotton fields located in the Hudspeth and El Paso Counties of Texas, USA. Calibration equations for converting EC_a into EC_e and SAR were derived using the multiple linear regression (MLR) model included in the EC_e Sampling Assessment and Prediction program package developed by the US Salinity Laboratory. Correlations between EC_a and soil variables (clay content, EC_e, SAR) were highly significant (p ≤ 0·05). This was further confirmed by significant (p ≤ 0·05) MLRs used for estimating EC_e and SAR. The EC_e and SAR determined by EC_a closely matched the measured EC_e and SAR values of the study site soils, which ranged from 0·47 to 9·87 dS m⁻¹ and 2·27 to 27·4 mmol^1/2 L^−1/2, respectively. High R² values between estimated and measured soil EC_e and SAR values validated the MLR model results. Results of this study indicated that the EMI method can be used for rapid and accurate delineation of salinity and sodicity distribution within the affected area. Copyright © 2012 John Wiley & Sons, Ltd.     

Spatial heterogeneity of soil properties and its mapping with apparent electrical conductivity

The site‐specific cultivation as part of the precision‐agriculture concept is more and more introduced into practical farming. However, soil information is often not available in a spatial resolution intrinsically needed for precision farming or other site‐specific soil use and management purposes. One approach to obtain spatially high‐resolution soil data is the non‐invasive measurement of the apparent electrical conductivity (EC_a). In this study, we recorded the EC_a on three fields with an EM38 (Geonics, Canada). The EC_a data were compared with (1) ground truth data obtained by conventional drilling, (2) traditional soil maps (large scale, ≤1:5,000), (3) the growth and yield of corn. The temporal variability of the EC_a due to varying soil moisture and temperature was taken into account by repeated measurements of the same fields and subsequent averaging of the EC_a values. Significant correlations (r² = 0.76) were found between the mean weighted clay content (0–1.5 m) and the EC_a. Furthermore, in soils with differently textured layers, EC_a was used to estimate the thickness of the uppermost loess layer. A comparison of EC_a and large‐scale soil maps reveals some pros and cons of EC_a measurements. The main advantages of EC_a recordings are the high spatial resolution in combination with low efforts. Yet, the EC_a signal is no direct measure for a soil type or unit. Depending on the variability of substrates and layering, the EC_a pattern can be a precise indicator for the spatial distribution of different soils. A strong conformity of the spatial variability of plant growth (derived from orthophotos and yield maps) and EC_a patterns within a field indicates that the EC_a signal per se—without conversion to traditional soil parameters—integrates the effects of various soil variables that govern soil fertility. Altogether, EC_a surveys can be a powerful tool to facilitate and improve conventional soil mapping.     

Investigating soil physical properties and yield response in a grassland field using a dual‐sensor penetrometer and EM38

Precision‐farming applications are mainly based on site‐specific information of soil properties at the field scale. For this purpose, a number of novel sensor techniques have been developed but not intensively tested under different field conditions. This study presents a combined application of a self‐developed dual‐sensor vertical penetrometer (DVP) for measuring volumetric soil water content (VSWC) and cone index (CI), and an EM38 for soil apparent electrical conductivity (EC_a) in a pasture (1.4 ha). To verify the feasibility of the DVP for interpreting the depth‐specific information in the field, not only the soil physical properties and their geographical coordinates were measured, but also geo‐referenced yield data were collected. We found that the yield pattern was quite similar to the soil water‐content pattern of each layer (layer‐1: 5–15 cm; layer‐2: 15–25 cm, layer‐3: 25–35 cm) and EC_a pattern. Using the map‐based comparisons in conjunction with the statistical analyses, the effect of each measured soil physical property (VSWC, CI, and EC_a) on the yield was investigated. The regression between the yield and VSWC at each layer fitted a quadratic equation (R² = 0.515 at 5–15 cm; R² = 0.623, at 15–25 cm; R² = 0.406 at 25–35 cm). The negative correlation between yield and CI at each layer fitted a linear model with R² ≥ 0.510.     

Field level digital mapping of soil mineralogy using proximal and remote‐sensed data

Primary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (EC_a – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.     

Spatial prediction of the exchangeable sodium percentage at multiple depths using electromagnetic inversion modelling

Agriculture in the semi‐arid and arid areas of the world requires irrigation. However, in these areas, soils naturally contain large amounts of sodium (sodic) which can cause amongst other things, surface crusting on the topsoil or structural instability in the subsoil. The exchangeable sodium percentage (ESP) needs to be mapped to guide the application of gypsum. Whilst geostatistical techniques, such as ordinary, co‐ and 3‐D kriging have been used, they have often been criticized because they are unable to take into account soil knowledge concerning distribution, processes and factors of formation. The use of digital soil mapping methods which couple remote or proximally sensed data with soil information is increasingly becoming useful because of the production of high‐resolution ancillary data. In this study, we first invert (using EM4Soil software) the electrical conductivity (σ_a –mS/m) of DUALEM‐421 data collected along a single transect. In doing this, we generate a 2‐dimensional electromagnetic conductivity image (EMCI). We couple the estimates of electrical conductivity (σ – mS/m) at 0.30 m depth increments down to 1.5 m with measured soil ESP. We compare the results of inversion using various possible coil array configurations of the DUALEM‐421 to determine a suitable set of data. We conclude that the use of the DUALEM‐41 is optimal (r² = 0.70). We use the calibration to estimate ESP along adjacent transects where we also generate EMCI. We are thus able to estimate ESP at various depths across a clay plain and an associated prior stream channel. We conclude that the collection of additional transects of DUALEM‐421 data as well as the use of a quasi‐3‐D inversion modelling approach would improve prediction.     

Modeling within field variation of the compaction layer in a paddy rice field using a proximal soil sensing system

A key characteristic of flooded paddy fields is the plough pan. This is a sub‐soil layer of greater compaction and bulk density, which restricts water losses through percolation. However, the thickness of this compacted layer can be inconsistent, with consequences such as variable percolation and leaching losses of nutrients, which therefore requires precision management of soil water. Our objective was to evaluate a methodology to model the thickness of the compacted soil layer using a non‐invasive electromagnetic induction sensor (EM38‐MK2). A 2.7 ha alluvial non‐saline paddy rice field was measured with a proximal soil sensing system using the EM38‐MK2 and the apparent electrical conductivity (EC_a) of the wet paddy soil was recorded at a high‐resolution (1.0 × 0.5 m). Soil bulk density (n = 10) was measured using undisturbed soil cores, which covered locations with large and small EC_a values. At the same locations (within 1 m²) the depth of the different soil layers was determined by penetrometer. Then a fitting procedure was used to model the EC_a – depth response functions of the EM38‐MK2, which involved solving a system of non‐linear equations and a R² value of 0.89 was found. These predictions were evaluated using independent observations (n = 18) where a Pearson correlation coefficient of 0.87 with an RMSEE value of 0.03 m was found. The EC_a measurements allowed the detail estimation of the compacted layer thickness. The link between water percolation losses and thickness of the compacted layer was confirmed by independent observations with an inverse relationship having a Pearson correlation coefficient of 0.89. This rapid, non‐invasive and cost‐effective technique offers new opportunities to measure differences in the thickness of compacted layers in water‐saturated soils. This has potential for site‐specific soil management in paddy rice fields.     

Identifying potential management zones in a layered soil using several sources of ancillary information

Variation in soil texture has a profound effect on soil management, especially in texturally complex soils such as the polder soils of Belgium. The conventional point sampling approach requires high sampling intensity to take into account such spatial variation. In this study we investigated the use of two ancillary variables for the detailed mapping of soil texture and subsequent delineation of potential management zones for site‐specific management. In an 11.5 ha arable field in the polder area, the apparent electrical conductivity (EC_a) was measured with an EM38DD electromagnetic induction instrument. The geometric mean values of the EC_a measured in both vertical and horizontal orientations strongly correlated with the more heterogeneous subsoil clay content (r = 0.83), but the correlation was weaker with the homogenous topsoil clay content (r = 0.40). The gravimetric water content at wilting point (θg_{(?1.5 MPa)}) correlated very well (r = 0.96) with the topsoil clay content. Thus maps of topsoil and subsoil clay contents were obtained from 63 clay analyses supplemented with 117θg_{(?1.5 MPa)} and 4048EC_a measurements, respectively, using standardized ordinary cokriging. Three potential management zones were identified based on the spatial variation of both top and subsoil clay contents. The influence of subsoil textural variation on crop behaviour was illustrated by an aerial image, confirming the reliability of the results from the small number of primary samples.     

Mapping soil salinity and pH across an estuarine and alluvial plain using electromagnetic and digital elevation model data

Increasing pressures from agriculture and urbanization have resulted in drainage of many floodplains along the eastern Australian coastline, which are underlain by sulphidic sediments, to lower water tables and reduce soil salinity. This leads to oxidation of the sediments with a rapid decline in pH and an increase in salinity. Accurately mapping soil salinity and pH in coastal acid sulphate soil (CASS) landscapes is therefore important. One required map is the extent of highly acidic (i.e. pH < 4.5) areas, so that the application of alkaline amendments (e.g. lime) to neutralize the acid produced can be specifically targeted to the variation in pH. One approach is to use digital soil mapping (DSM) using ancillary information, such as an EM38, digital elevation models (DEM – elevation) and trend surface parameters (east and north). We used an EM38 in the horizontal (EM38h) and vertical (EM38v) modes together with elevation data to develop multiple linear regressions (MLR) for predicting EC_1:5 and pH. For pH, best results were achieved when the EM38 EC_a data were log‐transformed. By comparing MLR models using REML analysis, we found that using all ancillary data was optimal for mapping EC_1:5, whereas the best predictors for pH were north, log‐EM38v and elevation. Using residual maximum likelihood (REML), the final EC_1:5 and pH maps produced were consistent with previously defined soil landscape units, particularly CASS. The DSM approach used is amenable for mapping saline soils and identifying areas requiring the application of lime to manage acidic soil conditions in CASS landscape.     

Validation of the ANOCOVA model for regional‐scale ECa to ECe calibration

Two approaches have emerged as the preferred means for assessing salinity at regional scale: (i) vegetative indices from satellite imagery (e.g., MODIS enhanced vegetative index, NDVI) and (ii) analysis of covariance (ANOCOVA) calibration of apparent soil electrical conductivity (EC_a) to salinity. The later approach is most recent and least extensively validated. It is the objective of this study to provide extensive validation of the ANOCOVA approach. The validation comprised 77 fields in California's Coachella Valley, ranging from 1.25 to 30.0 ha in size with an average size of 12.8 ha. Mobile electromagnetic induction (EMI) equipment surveyed the fields obtaining geospatial measurements of EC_a. Soil sample sites selected following EC_a‐directed soil sampling protocols characterized the range and spatial variation in EC_a across the field. From the data, a regional ANOCOVA model was developed. The regional ANOCOVA model successfully reduced cross‐validated, average log salinity prediction error (variance) estimate by more than 30% across the 77 fields and improved the depth‐averaged prediction accuracy in 58 of the 77 fields. The results show that the ANOCOVA modelling approach improves soil salinity predictions from EMI signal data in most of the surveys conducted, particularly fields where only a limited number of calibration sampling locations were available. The establishment of ANOCOVA models at each depth increment for a representative set of fields within a regional‐scale study area provides slope coefficients applicable to all future fields within the region, significantly reducing ground‐truth soil samples at future fields.     

应用电磁感应和遥感的新疆绿洲区域尺度盐渍土识别

针对干旱区土壤盐渍化问题,以新疆渭干河-库车河绿洲为研究区,探讨电磁感应技术和光谱角分类法(SAM)相结合来识别典型干旱区盐渍土的可行性。以重度盐渍土为例,利用电磁感应仪EM38和采样数据,结合光谱角分类法和回归分析法,反演土壤电导率(EC1:5)的空间分布,识别重度盐渍土。结果表明:土壤表观电导率(ECa)与土壤电导率(EC1:5)具有较好的非线性相关性;垂直模式电导率(EMv)对土壤电导率(EC1:5)的解译精度优于水平模式电导率(ECh);研究区土壤盐分含量在区域尺度呈中等强度变异;电磁感应技术和光谱角分类法结合可以较好识别符合条件的盐渍土。该研究方法能够较好识别典型干旱区的盐渍土,为盐渍化评估、预防和治理提供了新的途径。     

Mapping soil salinity in 3‐dimensions using an EM38 and EM4Soil inversion modelling at the reconnaissance scale in central Morocco

Large areas of Morocco require irrigation and although good quality water is available in dams, farmers augment river water with poorer quality ground water, resulting in salt build‐up without a sufficient leaching fraction. Implementation of management plans requires baseline reconnaissance maps of salinity. We developed a method to map the distribution of salinity profiles by establishing a linear regression (LR) between calculated true electrical conductivity (σ, mS/m) and electrical conductivity of the saturated soil‐paste extract (ECe, dS/m). Estimates of σ were obtained by inverting the apparent electrical conductivity (ECa, mS/m) collected from a 500‐m grid survey using an EM38. Spherical variograms were developed to interpolate ECa data onto a 100 m grid using residual maximum likelihood. Inversion was carried out on kriged ECa data using a quasi‐3d model (EM4Soil software), selecting the cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor of 3.0. Using a ‘leave‐one‐out cross‐validation' (LOOCV), of one in 12 of the calibration sites, the use of the q‐3d model yielded a high accuracy (RMSE = 0.42 dS/m), small bias (ME = ?0.02 dS/m) and Lin's concordance (0.91). Slightly worse results were obtained using individual LR established at each depth increment overall (i.e. RMSE = 0.45 dS/m; ME = 0.00 dS/m; Lin's = 0.89) with the raw EM38 ECa. Inversion required a single LR (ECe = 0.679 + 0.041 × σ), enabling efficiencies in estimating ECe at any depth across the irrigation district. Final maps of ECe, along with information on water used for irrigation (ECw) and the characterization of properties of the two main soil types, enabled better understanding of causes of secondary soil salinity. The approach can be applied to problematic saline areas with saline water tables.     

Properties of soils from the Swedish long-term fertility experiments: VI. Mapping soil electrical conductivity with different geophysical methods

Abstract

Swedish long-term soil fertility experiments were used to investigate the effect of texture and fertilization regime on soil electrical conductivity. In one geophysical approach, fields were mapped to characterize the horizontal variability in apparent electrical conductivity down to 1.5 m soil depth using an electromagnetic induction meter (EM38 device). The data obtained were geo-referenced by dGPS. The other approach consisted of measuring the vertical variability in electrical conductivity along transects using a multi-electrode apparatus for electrical resistivity tomography (GeoTom RES/IP device) down to 2 m depth. Geophysical field work was complemented by soil analyses. The results showed that despite 40 years of different fertilization regimes, treatments had no significant effects on the apparent electrical conductivity. Instead, the comparison of sites revealed high and low conductivity soils, with gradual differences explained by soil texture. A significant, linear relationship found between apparent electrical conductivity and soil clay content explained 80% of the variability measured. In terms of soil depth, both low and high electrical conductivity values were measured. Abrupt changes in electrical conductivity within a field revealed the presence of ‘deviating areas’. Higher values corresponded well with layers with a high clay content, while local inclusions of coarse-textured materials caused a high variability in conductivity in some fields. The geophysical methods tested provided useful information on the variability in soil texture at the experimental sites. The use of spatial EC variability as a co-variable in statistical analysis could be a complementary tool in the evaluation of experimental results.     

Gypsum and Texture Effects on the Estimation of Saturated Paste Electrical Conductivity by Two Extraction Methods

Abstract

Texture and salt type can influence the relationships between saturated paste electrical conductivity (EC_e) and EC of other soil/water ratios. The objectives of this study were to develop and validate relationships among EC_e, EC_1∶2, and EC_1∶5 for soils in Yazd Province, and evaluate the effects of texture and gypsum on those relationships. Two hundred thirty‐six soil samples were collected, of which 200 were used to develop and 36 were used to validate the models. The soils were divided into two textural categories, coarse and fine, and two categories, with and without gypsum. The 1∶2 procedure predicted EC_e of slightly more than 1∶5. Gypsum content had stronger impact on the accuracy of models in predicting EC_e than texture. The EC_e=4.0723EC_1∶2?5.7135 and EC_e=3.5142EC_1∶2+0.7615 models are recommended for soil with and without gypsum, respectively. The methodology can be implemented in any other region, particularly if gypsum is present.     

基于电磁感应的典型干旱区土壤盐分空间异质性

为研究干旱区土壤盐分空间异质性,指导农业生产实践,运用大地电导率仪（EM38、EM31）对研究区域进行移动式磁感调查,获取表观电导率（ECa）。同时,通过27个校准点的采样和ECa测量,建立土壤盐分的电磁感应解译模型。干旱区土壤盐分质量分数与EM38、EM31水平模式读数（H38、H31）显示出良好的相关性（R=0.935）,可以利用ECa结合GIS和地统计学知识研究土壤盐分的空间分布。采用两种方法进行研究：一种是先利用解译模型获取磁感调查点的土壤盐分质量分数,然后进行地统计分析研究其空间分布;另一种是先利用地统计分析研究H38和H31的空间分布,然后利用解译模型通过栅格运算计算盐分质量分数,精度检验显示前者预测值与实测值之间的相关性更好（R2, 0.888＞0.873）;标准差较低（std. 0.414＜0.426）,具有更高的预测精度。研究结果表明,基于电磁感应研究干旱区土壤盐分空间异质性是切实可行的,这对于土壤盐渍化的快速诊断,指导农业生产和促进精准农业的发展具有重要的意义。