Relationship between apparent electrical conductivity and soil physical properties in a Malaysian paddy field

Site-specific crop management, well-established in some developed countries, is now being considered in developing countries such as Malaysia. The apparent electrical conductivity (EC_a) of the soil can be used as an indirect indicator of a number of soil physical properties and even crop yield. Commercially available EC_a sensors can efficiently develop the spatially dense data sets desirable in describing within-field spatial soil variability for precision farming. The main purpose of this study was to generate a variability map of soil EC_a within a Malaysian paddy field using a VerisEC sensor. The EC_a values were then compared with some soil variables within classes after delineation. Measured parameters were mapped using the kriging technique and their correlation with soil EC_a was determined. The study showed that the VerisEC can determine soil spatial variability, and can acquire soil EC_a information quickly. Spatial variability of shallow and deep EC_a showed the same patterns. Estimation of soil properties based on EC_a varied from one soil parameter to another and all could be estimated better by deep EC_a. Cross-validation results showed that shallow and deep EC_a, and also bulk density, gave more accurate estimates compared with other variables.     

World's Oldest Cotton Experiment: Relationships between Soil Chemical and Physical Properties and Apparent Electrical Conductivity

Abstract

Measuring and mapping apparent soil electrical conductivity (EC_a) is a potentially useful tool for delineating soil variability. The “Old Rotation,” the world's oldest continuous cotton (Gossypium hirsutum L.) experiment (ca. 1896), provides a valuable resource for evaluating soil spatial variability. The objectives of this study were to determine the relationship between soil chemical and physical properties and EC_a in the Old Rotation, to determine spatial differences in these properties, and to relate differences in these properties to long‐term management effects. Soils at the site classified as fine, kaolinitic, thermic Typic Kanhapludults. Soil EC_a was measured at 0–30‐ and 0–90‐cm depths (EC_a‐30 and EC_a‐90) using a Veris^® 3100 direct contact sensor with georeferencing. Soils were grid sampled (288 points) at close intervals (1.5×3.0 m) for chemical properties and grid sampled (65 cells, 7.5×6.9 m) for soil texture. Soil organic carbon (SOC) and total nitrogen (N), extractable phosphorus (P), potassium (K), calcium (Ca), pH, buffer pH, and estimated cation exchange capacity (CEC_est) were measured at two depths (0–5‐ and 5–15‐cm). Soil EC_a was highly spatially correlated. The EC_a‐30 was more highly correlated with clay content (r=0.58, P≤0.01) and P(r=0.43, P≤0.01) than other soil properties. Total nitrogen and SOC had little or no relationship with EC_a‐30. Cropping systems affected chemical properties in the Old Rotation, indicating crop rotation and cover crops are beneficial for soil productivity. The relatively poor relationship between soil chemical parameters and EC_a suggest that mapping plant nutrients and SOC using EC_a is problematic because of strong dependence on clay content.     

An application of PCA and fuzzy C-means to delineate management zones and variability analysis of soil

Within-field variability is a well-known phenomenon and its study is at the centre of precision agriculture (PA). In this paper, site-specific spatial variability (SSSV) of apparent Electrical Conductivity (EC_a) and crop yield apart from pH, moisture, temperature and di-electric constant information was analyzed to construct spatial distribution maps. Principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm were then performed to delineate management zones (MZs). Various performance indices such as Normalized Classification Entropy (NCE) and Fuzzy Performance Index (FPI) were calculated to determine the clustering performance. The geo-referenced sensor data was analyzed for within-field classification. Results revealed that the variables could be aggregated into MZs that characterize spatial variability in soil chemical properties and crop productivity. The resulting classified MZs showed favorable agreement between EC_a and crop yield variability pattern. This enables reduction in number of soil analysis needed to create application maps for certain cultivation operations.     

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.     

Key soil and topographic properties to delineate potential management classes for precision agriculture in the European loess area

Recent advances in on-the-go soil sensing, terrain modelling and yield mapping have made available large quantities of information about the within-field variability of soil and crop properties. But the selection of the key variables for an identification of management zones, required for precision agriculture, is not straightforward. To investigate a procedure for this selection, an 8 ha agricultural field in the Loess belt of Belgium was considered for this study. The available information consisted of: (i) top- and subsoil samples taken at 110 locations, on which soil properties: textural fractions, organic carbon (OC), CaCO₃ and pH were analysed, (ii) soil apparent electrical conductivity (EC_a) obtained through an electromagnetic induction based sensor, and (iii) wetness index, stream power index and steepest slope angle derived from a detailed digital elevation model (DEM). A principal component analysis, involving 12 soil and topographic properties and two EC_a variables, identified three components explaining 67.4% of the total variability. These three components were best represented by pH, EC_a that strongly associated with texture and OC. However, OC was closely related to some more readily obtainable topographic properties, and therefore elevation was preferred. A fuzzy k-means classification of these three variables produced four potential management classes. Three-year average standardized yield maps of grain and straw showed productivity differences across these classes, but mainly linked to their landscape position. In the loess area with complex soil-landscape interactions pH, EC_a and elevation can be considered as key properties to delineate potential management classes.     

Identifying management zones in agricultural fields using spatially constrained classification of soil and ancillary data

Site‐specific management requires accurate knowledge of the spatial variation in a range of soil properties within fields. This involves considerable sampling effort, which is costly. Ancillary data, such as crop yield, elevation and apparent electrical conductivity (EC_a) of the soil, can provide insight into the spatial variation of some soil properties. A multivariate classification with spatial constraint imposed by the variogram was used to classify data from two arable crop fields. The yield data comprised 5 years of crop yield, and the ancillary data 3 years of yield data, elevation and EC_a. Information on soil chemical and physical properties was provided by intensive surveys of the soil. Multivariate variograms computed from these data were used to constrain sites spatially within classes to increase their contiguity. The constrained classifications resulted in coherent classes, and those based on the ancillary data were similar to those from the soil properties. The ancillary data seemed to identify areas in the field where the soil is reasonably homogeneous. The results of targeted sampling showed that these classes could be used as a basis for management and to guide future sampling of the soil.     

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.     

Spatial scaling of soil salinity indices along a temporal coastal reclamation area transect in China using wavelet analysis

High spatial variability of soil salinity in coastal reclamation regions makes it difficult to obtain accurate scale-dependent information. The objectives of this study were to describe the spatial patterns of saline-sodic soil properties (using soil pH, electrical conductivity (EC_1:5) and sodium ion content (SIC) as indicators) and to gain knowledge of the scaling relationships between those variables. The soil pH, EC_1:5 and SIC data were measured at intervals of 285 m along a 13,965-m temporal transect in a coastal region of China. The spatial variability of soil pH was weak but it was strong for soil EC_1:5 and SIC at the measurement scale. There was a significant positive correlation between soil EC_1:5 and SIC, while correlations between soil pH and either EC_1:5 or SIC were weak and negative. For each saline-sodic soil parameter, the variability changed with the decomposition scales. The high-variance area at the larger scales (≥570 m) occupied less than 10% of the total area in the local wavelet spectrum, which meant that the spatial variations of the salinity indicators were insignificant at these scales. For local wavelet coherency, at a scale of 1500–2800 m and a sampling distance of 0–4500 m, the covariance was statistically significant between any two of the saline-sodic soil parameters.     

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.     

Comparison of spatial interpolation techniques for mapping soil pH and salinity in agricultural coastal areas,northern Iran

This study attempted to characterize the spatial distributions of soil pH and electrical conductivity (EC_e) of coastal fields in the Miyandoroud region, northern Iran, for three soil layer depths by assessing spatial variability and comparing different interpolation techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and conditional simulations (CS). Three soil composite samples were collected from 0–50, 50–100, and 100–150 cm depths at 105 sampling sites. At all three soil depths, pH and EC_e were best fitted by exponential and spherical models, respectively. Nugget effects were higher for soil EC_e data sets compared with soil pH at all three soil depths showing soil EC_e had a spatial variability in small distances. The prediction accuracy of the interpolation methods indicated that the minimum error for all data sets was achieved with the OK method, except for pH at 50–100 cm depth, and the CS technique revealed the largest error. The effect of different numbers of simulations (100, 500 and 1000) in the CS interpolation method resulted not in a realistic mapping for the soil EC_e and pH. Considering the high importance of irrigated agriculture in the Caspian Sea coastal areas, more subsoil salinity build-up and groundwater salinity monitoring plans are needed as a prerequisite for sustainable agricultural production systems of the future.     

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.     

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.     

Exploring the predictability of soil texture and organic matter content with a commercial integrated soil profiling tool

In soil mapping, combining information from conceptually different proximal soil sensors can increase the accuracy of prediction and robustness of the model when compared with using individual sensors. In this study the predictability of soil texture (clay, silt and sand fractions) and soil organic matter (SOM) content was tested with a commercial integrated soil profiling tool that included sensors for measuring apparent electrical conductivity (EC_a), reflectance in the visible and near‐infrared (vis‐NIR) parts of the electromagnetic spectrum and insertion force (IF). The measurements were made at 20 locations on each of two Swedish farms. At every location, sensor measurements were made at 1.5‐cm intervals from the soil surface to a depth of 0.8 m. Soil samples were collected close to the sensor measurement points and analysed for texture and SOM content. Farm‐specific calibrations were developed for texture and SOM with each sensor separately and with combinations of all three sensors. The calibrations were made using both partial least squares regression (PLSR) and simple linear regression. The results for the two farms were quite consistent in terms of rank in prediction performance between the individual sensors and the sensor combinations. The vis‐NIR spectrometer was the best individual sensor for predicting the soil properties tested on both farms, with root mean square error of cross‐validation (RMSECV) of 0.3–0.5% for SOM, about 6% for clay and silt and 10–11% for sand. The inclusion of IF reduced the RMSECV for predictions of SOM content by about 10%. For soil texture, including EC_a reduced the RMSECV on average for all particle size fractions by 5–10%. However, the small improvements obtained by combining sensors do not provide strong support for combining vis‐NIR sensor measurements with measurements of EC_a and or IF.     

Assessing the Value of Using a Remote Sensing-Based Evapotranspiration Map in Site-Specific Management

ABSTRACT

In the glaciated regions of the northern Great Plains, water - either too much or too little - influences soil development, carbon storage, and plant productivity. Integrating site-specific water variability information directly into management is difficult. Simulation models that employ remotely sensed data can generate hard to measure values such as evapotranspiration (ET). This information can be used to identify management zones. The objective of this study was to determine if the METRIC (Mapping Evapotranspiration at High Resolution and with Internalized Calibration) model, which uses weather station and remote sensing data can be used as a tool in site-specific management. This study was conducted on a 65 ha corn (Zea mays L.) field located in east central South Dakota. The METRIC model used Landsat 7 data collected on August 4, 2001 to calculate ET values with spatial resolution of 30 m. ET values were correlated with corn yield (r = 0.85??), apparent electrical conductivity (EC_a; r = 0.71??), soil organic carbon (SOC; r = 0.32?), and pH (r = 0.28?). In the footslope positions, high ET values were associated with high corn yields, SOC, EC _a , and pH values, while in the summit/shoulder areas low ET values were associated with low yields, SOC, EC_a, and pH values. The strong relationship between ET and productivity was attributed to landscape processes that influenced plant available water, which in turn influenced productivity. Cluster analysis of the ET and EC data showed that these data bases complimented each other. Remote sensing-based ET data was most successful in identifying areas where water stress reduced corn yields, while EC_a was most successful in identifying high yielding management zones. Findings from this study suggest that remote sensing-based ET estimates can be used to improve management zone delineation.     

VNIR Soil Spectroscopy for Field Soil Analysis

The advent of affordable, ground-based, global positioning information (GPS)–enabled sensor technologies provides a new method to rapidly acquire georeferenced soil datasets in situ for high-resolution soil attribute mapping. Our research deployed vehicle-mounted electromagnetic sensor survey equipment to map and quantify soil variability (?50 ha per day) using apparent electrical conductivity as an indirect measure of soil texture and moisture differences. A portable visible–near infrared (VNIR) spectrometer (350–2500 nm) was then used in the field to acquire hyperspectral data from the side of soil cores to a specified depth at optimized sampling locations. The sampling locations were derived by statistical analysis of the electromagnetic survey dataset, to proportionally sample the full range of spatial variability. The VNIR spectra were used to predict soil organic carbon (prediction model using field-moist spectra: R² = 0.39; RPD = 1.28; and air-dry spectra: R² = 0.80; RPD = 2.25). These point values were combined with the electromagnetic survey data to produce a soil organic carbon map, using a random forest data mining approach (validation model: R² = 0.52; RMSE = 3.21 Mg C/ha to 30 cm soil depth; prediction model: R² = 0.92; RMSE = 1.53 Mg C/ha to 30 cm soil depth). This spatial modeling method, using high-resolution sensor data, enables prediction of soil carbon stocks, and their spatial variability, at a resolution previously impractical using a solely laboratory-based approach.     

Nonlinear spatial series analysis from unidirectional transects of soil physical properties

Soil is usually presented as a complex dynamical system. Nevertheless, evidences based on the theoretical background of complex system physics are still scarce. The main objective of this work was to search for chaotic parameters using some basic concepts of nonlinear dynamical system theory with spatial series of soil properties. Three spatial series consisting of 1000 data point transects were used. We selected horizontal and vertical electrical conductivity (EC_h and EC_v, respectively) and gravimetric water content from a Vertisol (Typic Hapludert) under rice cropping. Each spatial transect was oriented from South to North with 1-m spacing. It was used the TISEAN Software Package (a public domain software available at http://www.mpipks-dresdren.mpg.de/~tisean) for deriving nonlinear parameters from spatial series. We found interesting evidences of chaotic behaviour as maximal Lyapunov exponents were all positive. They ranged from λ_m = 0.129 for water content to λ_m = 0.219 for EC_v (filtered series in each case). Original (unfiltered), filtered, and surrogate spatial series confirmed these findings as they also showed positive Lyapunov exponents. All the spatial series showed a higher deterministic component (|κ|> 0.9 in most cases). The Lyapunov range of correlation (ρ) was within the limits 4.56 m (EC_v) to 7.75 m (gravimetric water content) as usually reported from geostatistical investigations. Future works based on dynamical system theory will advance our knowledge on spatial variability of important soil properties and the emergence of deterministic and/or stochastic components.     

Carbon Dioxide in Arable Soil Profiles: A Comparison of Automated and Manual Measuring Systems

Carbon dioxide (CO₂) concentrations in arable soil profiles are influenced by autotrophic and heterotrophic respiration as well as soil physical properties that regulate gas transport. Although different methods have been used to assess dynamics of soil CO₂ concentrations, our understanding of the comparability of results obtained using different methods is limited. We therefore aimed to compare the dynamics in soil CO₂ concentrations obtained from an automated system (GMP343 sensors) to those from a manually operated measurement system (i.e., soil gas sampled using stainless steel needles and rods). In a winter wheat field in Denmark, soil CO₂ concentrations were measured from 29 November 2011 to 14 June 2012 at upslope and footslope positions of a short catena (25 m). Carbon dioxide was measured at 20- and 40-cm soil depths (i.e., within and below the nominal plow layer) using the two measurement systems. Within the measurement range for the GMP343 sensors (0–20,000 ppm), mean results from the two systems were similar within the plow layer at the upslope (P = 0.060) and footslope (P = 0.139) position, and also below the plow layer at the upslope position (P = 0.795). However, results from the two systems deviated for the soil from the footslope position below the plow layer (P = 0.001). These results were partly attributed to larger variation in soil parameters below than within the nominal plow layer. The data suggested that generally the application of either system may be adequate; however, differences may occur in response to soil spatial variability. A better coverage of spatial variability is more easily addressed using manually operated systems, whereas temporal variability can be covered using the automated system. Depending on the aim of the study, the two systems may be used in combination to enhance both spatial and temporal data coverage.     

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.     

Predicting Soil Electrical Conductivity of the Saturation Extract from a 1:1 Soil to Water Ratio

Since 1954, the electrical conductivity of the saturated paste extract (ECe) has been the preferred index for soil salinity. Based on this value, remediation strategies were developed and widely used but this approach is time consuming and not routinely offered by many soil testing facilities. However, many laboratories determine the EC_1:1 value of a 1:1 soil to solution ratio extract. The objective of this study was to identify the relationship between ECe and EC_1:1 and determine if EC_1:1 can be used as a proxy in the northern Great Plains for ECe. Samples were collected across five studies and from AGVISE Laboratory. The samples were analyzed for EC_1:1 and ECe. The relationship between the ECe and EC_1:1 showed that soil parent materials need to be considered in the conversion of EC_1:1 values to ECe values. A failure to consider parent materials in this conversion may have short and long-term sustainability ramifications.