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.     

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.     

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.     

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.     

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.     

Modelling soil salinity across a gilgai landscape by inversion of EM38 and EM31 data

The soil in arid and semi‐arid areas is often markedly saline, which can severely limit agricultural productivity. Increasingly, geophysical methods are being implemented to map the levels and areal extent of soil salinity. One of the most effective methods is electromagnetic (EM) induction with instruments designed to measure apparent soil electrical conductivity (EC_a). This study describes the generation of electromagnetic conductivity images (EMCIs) by inverting EC_a data obtained with the EM38 and EM31 devices along two closely‐spaced transects by the EM inversion approach in the EM4Soil package. The EM38 EC_a data are shown to be a more effective predictor of soil EC_e. Calibration equations based on calculated true electrical conductivity (σ) and measured electrical conductivity of a saturated soil‐paste extract (EC_e) provide reliable estimates of EC_a. The patterns of σ in a test of the method in soil developed over thick alluvium on a clay plain in central New South Wales, Australia, compare favourably with existing pedological mapping; the mounds and depressions of gilgai were strongly differentiated from the more sandy alluvial sediments that characterize prior stream channels. The overall approach is potentially useful for generating a single calibration equation that can be used to predict EC_e at various depths in the soil. Improvements in EMCI modelling can also be sought by joint inversion of EM with other geophysical datasets.     

Spatial evaluation of soil salinity using the WET sensor in the irrigated area of the Segura river lowland

The electrical conductivity of the water within the soil pores (EC_p) measured with the WET sensor, appears to be a reliable estimate of soil salinity. A methodology combining the use of the WET sensor along with geostatistics was developed to delimit and evaluate soil salinity within an irrigated area under arid to semiarid Mediterranean climate in SE Spain. A systematic random sampling of 104 points was carried out. The association between EC_p and the saturation‐extract electrical conductivity (EC_se) was assessed by means of correlation analysis. The semivariograms for EC_p were obtained at three different soil depths. Interpolation techniques, such as ordinary kriging and cokriging, were applied to obtain EC_p levels in the unknown places. For each one of the soil depths, a model able to predict EC_se from EC_p was developed by means of ordinary least squares regression analysis. A good correlation (r = 0.818, p < 0.001) between EC_p and EC_se was found. Spherical spatial distribution was the best model to fit to experimental semivariograms of EC_p at 10, 30, and 50 cm soil depths. Nevertheless, cokriging using the EC_p of an adjacent soil depth as an auxiliary variable provided the best results, compared to ordinary kriging. An analytical propagation‐error methodology was found to be useful to ascertain the contribution of the spatial interpolation and ordinary least squares analysis to the uncertainty of the EC_se mapping. This methodology allowed us to identify 98% of the study area as affected by salinity problems within a rooting depth of 50 cm, with the threshold of EC_se value at 2 dS m^–1. However, considering the crops actually grown and 10% potential reduction yield, the soil‐salinity‐affected area decreased to 83%. The use of sensors to measure soil salinity in combination with geostatistics is a cost‐effective way to draw maps of soil salinity at regional scale. This methodology is applicable to other agricultural irrigated areas under risk of salinization.     

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.     

ECe prediction from EC1:5 in inland salt-affected soils collected from Khorat and Sakhon Nakhon basins,Thailand

ABSTRACT

We estimate the electrical conductivity of saturated soil paste extract (ECe) from electrical conductivity of a 1:5 soil-water dilution ratio (EC_1:5) in Northeastern Thailand. Soil samples of various textures and salinity collected from Sakhon Nakhon basin were used to develop multiple regression models, from which the linear model was chosen and was validated on soil samples from the Khorat basin. Comparison with previous models indicated that most linear models gave a good fit, but the non-linear models either over or underestimated the measured values. The models performed very well for low values of EC_e (<5 dS m^?1), while the prediction errors increased significantly for EC_e levels >35 dS m^?1. The present model performed well at various EC_e levels and can be used to predict salinity levels for soils weathered from salt deposits in sedimentary rocks with similar rock formation in countries like Malaysia, Vietnam, Cambodia, and Laos.     

Time‐lapse monitoring of soil water content using electromagnetic conductivity imaging

The volumetric soil water content (θ) is fundamental to agriculture because its spatiotemporal variation in soil affects the growth of plants. Unfortunately, the universally accepted thermogravimetric method for estimating volumetric soil water content is very labour intensive and time‐consuming for use in field‐scale monitoring. Electromagnetic (EM) induction instruments have proven to be useful in mapping the spatiotemporal variation of θ. However, depth‐specific variation in θ, which is important for irrigation management, has been little explored. The objective of this study was to develop a relationship between θ and estimates of true electrical conductivity (σ) and to use this relationship to develop time‐lapse images of soil θ beneath a centre‐pivot irrigated alfalfa (Medicago sativa L.) crop in San Jacinto, California, USA. We first measured the bulk apparent electrical conductivity (EC_a – mS/m) using a DUALEM‐421 over a period of 12 days after an irrigation event (i.e. days 1, 2, 3, 4, 6, 8 and 12). We used EM4Soil to generate EM conductivity images (EMCIs). We used a physical model to estimate θ from σ, accounting for soil tortuosity and pore water salinity, with a cross‐validation RMSE of 0.04 cm³/cm³. Testing the scenario where no soil information is available, we used a three‐parameter exponential model to relate θ to σ and then to map θ along the transect on different days. The results allowed us to monitor the spatiotemporal variations of θ across the surveyed area, over the 12‐day period. In this regard, we were able to map the soil close to field capacity (0.27 cm³/cm³) and approaching permanent wilting point (0.03 cm³/cm³). The time‐lapse θ monitoring approach, developed using EMCI, has implications for soil and water use and management and will potentially allow farmers and consultants to identify inefficiencies in water application rates and use. It can also be used as a research tool to potentially assist precision irrigation practices and to test the efficacy of different methods of irrigation in terms of water delivery and efficiency in water use in near real time.     

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.     

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.     

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.     

An empirical equation to calculate soil solution electrical conductivity at 25°C from major ion concentrations

The electrical conductivity at 25°C (EC₂₅) of soil solutions or irrigation waters is the standard property for assessing salinity. Many models for soil salinity prediction calculate the major ion composition of the soil solution. The electrical conductivity of a solution can be determined from its composition through several different empirical equations. An assessment of these equations is necessary to incorporate the most accurate and precise equations in such models. Twelve different equations for the EC₂₅ calculation were calibrated by means of regression analyses with data from 133 saturation extracts and another 135 1:5 soil‐to‐water extracts from a salt‐affected agricultural irrigated area. The equations with better calibration parameters were tested with another data set of 153 soil solutions covering a wide range of salt concentrations and compositions. The testing was conducted using the standardized difference t‐test, which is a rigorous validation test used in this study for the first time. The equations based on the ionic conductivity decrement given by Kohlrausch's law presented the poorest calibration parameters. The equations founded on the hypothesis that EC₂₅ is proportional to analytical concentrations had worse calibration and validation parameters than their counterparts based on free‐ion concentrations and ionic activities. The equations founded on simpler mathematical relationships generally gave improved validation parameters. The three equations based on the specific electrical conductivity definition presented a mean standardized difference between observations and predictions indistinguishable from zero at the 95% confidence level. The inclusion of the charged ion‐pair concentrations in the equation based on free‐ion concentrations improved its predictions, particularly at large electrical conductivities. This equation can be reliably used in conjunction with chemical speciation software to assess EC₂₅ from the ion composition of soil solutions.     

Evaluating soil salinity status from bulk electrical conductivity and permittivity

In the range of volumetric water content, θ, from about 0.12 cm³ cm^–3 to saturation the relation between bulk electrical conductivity, C_b, and bulk electrical permittivity, ε, of mineral soils was observed to be linear. The partial derivative ?C_b/?ε appeared independent of the moisture content and directly proportional to soil salinity. We found that the variable X_s = ?C_b/?ε determined from in situ measurements of C_b(θ > 0.2) and ε(θ > 0.2) can be considered as an index of soil salinity, and we call it the ‘salinity index’. Knowing the index and sand content for a given soil we could calculate the electrical conductivity of the soil water, C_w, which is a widely accepted measure of soil salinity. The two variables from which the salinity index can be calculated, i.e. C_b and ε, can be read simultaneously from the same sensor by time-domain reflectometry. Quantities and symbols a constant /dS m^–1 b constant c constant /dS m^–1 C _b electrical conductivity of bulk soil /dS m^–1 C _b′ constant equal to 0.08 dS m^–1 C _s electrical conductivity of a solution used to moisten soil samples /dS m^–1 C _w electrical conductivity of soil water defined as the soil salinity /dS m^–1 C _wref reference salinity (that truly existing) resulting from the procedure of moistening samples, expressed as C_s + C_r/dS m^–1 C _r baseline value of C_s due to residual soluble salts present in the soil /dS m^–1 d constant D dry soil bulk density /g cm^–3 l slope r ratio S sand content /% by weight t time /s X _s salinity index /dS m^–1 X _si initial salinity index when distilled water is used to moisten soil samples /dS m^–1 Y a moisture-independent salinity-dependent variable /dS m^–1 z coordinate along direction of flow of the soil solution ε′ constant equal to 6.2 ε relative bulk electrical permittivity (dielectric constant) of the soil θ volumetric water content determined thermogravimetrically using oven-drying /cm³ cm^–3     

Changes of arbuscular mycorrhizal traits and community structure with respect to soil salinity in a coastal reclamation land

A comprehensive knowledge on the relationship between soil salinity and arbuscular mycorrhizal fungi (AMF) is vital for a deeper understanding of ecosystem functioning under salt stress conditions. The objective of this study was to determine the effects of soil salinity on AMF root colonization, spore count, glomalin related soil protein (GRSP) and community structure in Saemangeum reclaimed land, South Korea. Soil samples were collected and grouped into five distinct salt classes based on the electrical conductivity of soil saturation extracts (EC_se). Mycorrhizal root colonization, spore count and GRSP were measured under different salinity levels. AMF community structure was studied through three complementary methods; spore morphology, terminal restriction fragment length polymorphism (T-RFLP) and denaturing gradient gel electrophoresis (DGGE). Results revealed that root colonization (P < 0.01), spore count (P < 0.01) and GRSP (P < 0.01) were affected negatively by soil salinity. Spore morphology and T-RFLP data showed predominance of AMF genus Glomus in Saemangeum reclaimed land. T-RFLP and DGGE analysis revealed significant changes in diversity indices between non (EC_se < 2 dS/m) and extremely (EC_se > 16 dS/m) saline soil and confirmed dominance of Glomus caledonium only in soils with EC_se < 8 dS/m. However, ribotypes of Glomus mosseae and Glomus proliferum were ubiquitous in all salt classes. Combining spore morphology, T-RFLP and DGGE analysis, we could show a pronounced effect in AMF community across salt classes. The result of this study improve our understanding on AMF activity and dominant species present in different salt classes and will substantially expand our knowledge on AMF diversity in reclaimed lands.     

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.     

INTERACTIVE EFFECTS OF SALINITY AND N ON PEPPER (CAPSICUM ANNUUM L.) YIELD,WATER USE EFFICIENCY AND ROOT ZONE AND DRAINAGE SALINITY

The aim of this study was to determine the salt tolerance of pepper (Capsicum annuum L.) under greenhouse conditions and to examine the interactive effects of salinity and nitrogen (N) fertilizer levels on yield. The present study shows the effects of optimal and suboptimal N fertilizer levels (270 kg ha^?1 and 135 kg ha^?1) in combination with five different irrigation waters of varying electrical conductivity (EC) (EC_iw = 0.25, 1.0, 1.5, 2.0, 4.0, and 6.0 dS m^?1) and three replicates per treatment. At optimal N level, yield decreased when the irrigation water salinity was above EC_iw 2 dS m^?1. At the suboptimal N level, a significant decrease in yield occurred only above EC_iw 4 dS m^?1. At high salinity levels the salinity stress was dominant with respect to yield and response was similar for both N levels. Based on the results it can also be concluded that under saline conditions (higher than threshold salinity for a given crop) there is a lesser need for N fertilization relative to the optimal levels established in the absence of other significant stresses.     

Soil texture affects the conversion factor of electrical conductivity from 1:5 soil-water to saturated paste extracts

Electrical conductivity (EC) of soil-water extracts is commonly used to assess soil salinity. However, its conversion to the EC of saturated soil paste extracts (EC_e), the standard measure of soil salinity, is currently required for practical applications. Although many regression models can be used to obtain EC_e from the EC of soil-water extracts, the application of a site-specific model to different sites is not straightforward due to confounding soil factors such as soil texture. This study was conducted to develop a universal regression model to estimate a conversion factor (CF) for predicting EC_e from EC of soil-water extracts at a 1:5 ratio (EC_1:5), by employing a site-specific soil texture (i.e., sand content). A regression model, CF=8.910 5e^{0.010 6sand}/1.298 4 (r²=0.97, P < 0.001), was developed based on the results of coastal saline soil surveys (n=173) and laboratory experiments using artificial saline soils with different textures (n=6, sand content=10%-65%) and salinity levels (n=7, salinity=1-24 dS m^-1). Model performance was validated using an independent dataset and demonstrated that EC_e prediction using the developed model is more suitable for highly saline soils than for low saline soils. The feasibility of the regression model should be tested at other sites. Other soil factors affecting EC conversion factor also need to be explored to revise and improve the model through further studies.