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.     

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.     

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.     

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.     

Apparent Electrical Conductivity in Correspondence to Soil Chemical Properties and Plant Nutrients in Soil

Spatial variability and relationship between soil apparent electrical conductivity (EC_a), soil chemical properties, and plant nutrients in soil have not been well documented in Malaysian paddy fields. For this reason precision farming has been used for assessing field conditions. EC_a technique for describing soil spatial variability is used for soil data acquisition. Soil sampling provides the data used to make maps of the spatial patterns in soil properties. Maps are then used to make recommendations on the variation of application rates. The main purpose of the authors in this study was to generate variability map of soil EC_a within a Malaysian rice cultivation area using VerisEC sensor. The EC_a values were compared to some soil properties after delineation. Measured parameters were mapped using kriging technique and their correlation with soil EC_a was determined. Through this study the authors showed that the EC sensor can determine soil spatial variability, where it can acquire the soil information quickly.     

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.     

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.     

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.     

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.     

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.     

Relationships of barley biomass and grain yields to soil properties within a field in the arid region: Use of factor analysis

Abstract

Understanding the variability of soil properties and their effects on crop yield is a critical component of site-specific management systems. The objective of this study was to employ factor and multiple regression analyses to determine major soil physical and chemical properties that influence barely biomass and grain yield within a field in the arid region of northern Iran. For this purpose, soil samples and crop-yield data were collected from 108 sites, at regular intervals (20×30 m) in a 5.6 ha field. Soil samples were analysed for total nitrogen (TN), available phosphorus (P_ava), available potassium (K_ava), cation-exchange capacity(CEC), electrical conductivity (EC), pH, mean weight diameter of aggregates (MWD), water-stable aggregates (WSA), field capacity volumetric (FC), available water-holding capacity (AWHC), bulk density (BD), and calcium carbonate equivalent (CCE). Results of the factor analysis, followed by regression of biomass and grain yield of barley with soil properties, showed that the regression equations developed accounted for 78 and 73% of the total variance in biomass and grain yield, respectively. Study of covariance analysis among soil variables using factor analysis indicated that some of the variation measured could be grouped to indicate a number of underlying common factors influencing barley biomass and grain yields. These common factors were salinity and sodicity, soil fertility, and water availability. The most effective soil variables to barley production in the study area identified as EC, SAR, pH, TN, P_ava, AWHC, and FC. In this study, factor analysis was effective to identify the groups of correlated soil variables that were significantly correlated with the within field variability in the yield of the barley crop. Our results also suggest that the approach can be applied to other crops under similar soil and agroclimatic conditions.     

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.     

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.     

Cotton lint yield variability in a heterogeneous soil at a landscape scale

Landscape variability associated with topographic features affects the spatial pattern of soil water and N redistribution, and thus N uptake and crop yield. A landscape-scale study was conducted in a center pivot irrigated field on the southern High Plains of Texas in 1999 to assess soil water, soil NO₃-N, cotton (Gossypium hirsutum L.) lint yield, and N uptake variability in the landscape, and to determine the spatial correlation between these landscape variables using a state-space approach. The treatments were irrigation at 50 and 75% cotton potential evapotranspiration (ET). Neutron access tubes were placed at a 15-m interval along a 710 m (50% ET) and 820 m (75% ET) transect across the field. Soil NO₃-N in early spring was autocorrelated at a distance varying between 60 and 80 m. Measured soil volumetric water content (WC), total N uptake, and lint yield were generally higher on lower landscape positions. Cotton lint yield was significantly correlated to soil WC (r=0.76), soil NO₃-N (r=0.35), and site elevation (r=−0.54). Differences of site elevation between local neighboring points explained the soil water, NO₃-N and lint yield variability at the micro-scale level in the landscape. Soil WC, cotton lint yield, N uptake, and clay content were crosscorrelated with site elevation across a lag distance of ±30–40 m. The state-space analysis showed that cotton lint yield was positively weighted on soil WC availability and negatively weighted on site elevation. Cotton lint yield state-space models give insights on the association of soil physical and chemical properties, lint yield, and landscape processes, and have the potential to improve water and N management at the landscape-scale.     

Characterization of Spatial Variability of Soil Parameters in Apple Orchards of Himalayan Region Using Geostatistical Analysis

ABSTRACT

Knowledge of spatial variability in soil properties is critical for effective nutrient management plan in crop production. Poor productivity of apple orchards in Indian Himalayan Region (IHR) is due to lack of information on the variability of soil properties important for enhancing productivity in the region. The study was conducted in apple orchards with the hypothesis that spatial variability of soil properties is high due to adoption of varied management and passive soil factors. The major objectives of the study were to assess the spatial variability of soil parameters, viz. soil pH, electrical conductivity, soil organic carbon along with plant available soil nitrogen (N), phosphorus (P), potassium (K), exchangeable calcium (Ca), and exchangeable magnesium (Mg) at a regional scale through geostatistical methods. Coefficient of variation (CV) indicated that the fertility parameters varied from medium to high in heterogeneity (CV > 20%). Available N was found critical/medium in 69.6% of soil samples and might be one of the limiting nutrients for crop growth, P and K were in high, and OC in very high range. Significant correlation was found between OC with N; pH with K, Ca and Mg and EC with OC, P and K. The semivariogram parameters indicated that the spatial distribution of soil fertility parameters were inconsistent and showed strong to weak degree of spatial dependence for all parameters. The study highlighted the importance of delineation of soil fertility management zones in the apple growing region as a guide for precise and site-specific nutrient.     

Determining and mapping some soil physico-chemical properties using geostatistical and GIS techniques in the Naqade region,Iran

The distribution variability of soil electrical conductivity (EC), pH, clay, sand, CaCO₃, organic carbon (OC) and available potassium (K) in the Naqade region was investigated using a geostatistical method and Geographical Information System (GIS) technique. Two hundred and eighty-two topsoil (0–30 cm) samples were randomly collected and analyzed. pH and clay followed a normal distribution, whereas sand EC, CaCO₃, OC and K were log-transformed. The highest variation was observed for soil EC, and the lowest for soil pH. In the variography analysis, spherical, exponential and gaussian models were best fit on experimental semivariograms. The minimum effective spatial autocorrelation was 1500 m for OC and the maximum effective spatial autocorrelation was 4000 m for sand and K. Strong spatial correlations were noted with sand and CaCO₃ (<25%), whereas values were moderate for clay, EC, OC and K (25–75%). Ordinary kriging was utilized for the interpolation of estimated variable determinations in unsampled sites. It was found that soil properties in this study area were strongly influenced by both environmental and natural factors. The results can be used as a source of information for the development and implementation of any further land management and soil and water conservation plans.     

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.     

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.     

Soil‐quality‐index model for assessing the impact of groundwater on soil in an intensively farmed coastal area of E China

Soil quality is important in measuring sustainable land‐use and soil‐management practices. It is usually assessed by evaluating important physical, chemical, and biological soil properties. For this study, a site‐specific 22 variables representing pertinent soil (0–10 cm) and groundwater properties were selected as potential soil‐quality indicators in a coastal salt‐affected farmland of E China. To investigate the role of groundwater in soil‐quality assessment, we designed two sets of minimum data sets (MDSs). Minimum data set 1 (MDS1) had inputs of the 19 soil chemical and physical properties whereas MDS2 was based on the 22 soil and groundwater properties. Using principal‐component analysis, discriminant analysis, and soil‐quality‐index (SQI) model, we demonstrated the procedures of MDS selection, indicator normalization, and integration of MDS into SQI value for soils used for the two cropping systems. Results indicated selection of SOCD, AK, and ρ_b as MDS1 indicators but MDS2 indicators included SOM, SOCD, Cl, Na, WT_g, and EC_g. These were found to be the most effective discriminators between the two cropping systems. Available K (AK) made greatest contribution to SQI using MDS1 indicators, however, WT_g, EC_g, and Cl were the greatest contributors to the SQI for MDS2. Contribution of SOCD to SQI was severely inhibited in cotton–barley rotation system while EC_g and WT_g contributions to SQI were inhibited in rice–rape rotation system. In general, cotton–barley rotation system had a better soil quality over rice–rape rotation system as the former had higher SQI values than the latter for both MDSs. Crop parameters did also exhibit significant relationship with the SQI values using MDS2 but it was not significant for MDS1. Our results suggest that in addition to soil chemical, physical, and biological indicators, groundwater properties particularly the WT_g and EC_g are also important for assessing soil quality in an intensively farmed coastal area.