ECa Data Research Articles

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

SummaryThe 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 (ECa). This study describes the generation of electromagnetic conductivity images (EMCIs) by inverting ECa data obtained with the EM38 and EM31 devices along two closely‐spaced transects by the EM inversion approach in the EM4Soil package. The EM38 ECa data are shown to be a more effective predictor of soil ECe. Calibration equations based on calculated true electrical conductivity (σ) and measured electrical conductivity of a saturated soil‐paste extract (ECe) provide reliable estimates of ECa. 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 ECe at various depths in the soil. Improvements in EMCI modelling can also be sought by joint inversion of EM with other geophysical datasets.

Modeling Soil Salinity along a Hillslope in Iran by Inversion of EM38 Data

Electromagnetic (EM) induction has been used to characterize the spatial distribution of salinity. However, most studies have been undertaken to map the areal distribution of the average profile salinity using measurements of the apparent electrical conductivity (ECa, mS m−1). In this study, an EM38 was used to map the distribution of salinity with depth along a 26-km hillslope in central Iran. We generated electromagnetic conductivity images by inverting EM38 ECa data collected at various heights in the EM4Soil software. A number of parameters including forward modeling (cumulative function, CF, and full solution, FS), inversion algorithms (S1 and S2), damping factor (λ), and combinations of different heights were considered to generate calculated soil true electrical conductivity (σ, mS m−1). By comparing different σ against the electrical conductivity of a saturated soil-paste extract (ECe, dS m−1) at various depths, we found that the strongest correlation and smallest modeling error was achieved using the FS, S1, λ = 12, and ECa data collected at 0.4 m alone. We then compared the results achieved by developing a linear regression between σ and ECe at various depths with those achieved using multiple linear regression (MLR) established between ECa and ECe. The inversion method was less time consuming and more robust than the MLR approach. The predicted ECe increased from the crest to the base of the toposequence. The results were consistent with the underlying geology, climate, and local topography. The methodology can be used as guidance for baseline salinity monitoring and management.

Linking satellite derived LAI patterns with subsoil heterogeneity using large-scale ground-based electromagnetic induction measurements

Patterns in crop development and yield are often directly related to lateral and vertical changes in soil texture causing changes in available water and resource supply for plant growth, especially under dry conditions. Relict geomorphologic features, such as old river channels covered by shallow sediments can challenge assumptions of uniformity in precision agriculture, subsurface hydrology, and crop modeling. Hence a better detection of these subsurface structures is of great interest. In this study, the origins of narrow and undulating leaf area index (LAI) patterns showing better crop performance in large scale multi-temporal satellite imagery were for the first time interpreted by proximal soil sensor data. A multi-receiver electromagnetic induction (EMI) sensor measuring soil apparent electrical conductivity (ECa) for six depths of exploration (DOE) ranging from 0–0.25 to 0–1.9m was used as reconnaissance soil survey tool in combination with selected electrical resistivity tomography (ERT) transects, and ground truth texture data to investigate lateral and vertical changes of soil properties at ten arable fields. The moderate to excellent spatial consistency (R2 0.19–0.82) of ECa patterns and LAI crop marks that indicate a higher water storage capacity as well as the increased correlations between large-offset ECa data and the subsoil clay content and soil profile depth, implies that along this buried paleo-river structure the subsoil is mainly responsible for better crop development in drought periods. Furthermore, observed stagnant water in the subsoil indicates that this paleo-river structure still plays an important role in subsurface hydrology. These insights should be considered and implemented in local hydrological as well as crop models.

Three-dimensional digital soil mapping of agricultural fields by integration of multiple proximal sensor data obtained from different sensing methods

The objective of the present study was to evaluate a strategy for three-dimensional (3-D) digital soil mapping on two farms in southwest Sweden. Apparent electrical conductivity (ECa) and gamma radiation data from proximal sensors and laser-scanned elevation data were used as predictors. Depth-integrated ECa measurements from a non-invasive sensor were used directly, but also calibrated against probe sensor ECa measurements to obtain layer-specific values. This allowed the predictive powers of depth-integrated and layer-specific ECa to be compared. Clay and sand fractions, and organic matter content (OM) were modelled for three depth layers by multivariate adaptive regression splines (MARSplines). Clay and sand were consistently better predicted in the topsoil than in the subsoil. MARSplines models based on layer-specific ECa data rather than on depth-integrated ECa data yielded more successful estimations of these soil properties in both subsoil layers (0.4–0.6 and 0.6–0.8 m) on both the farms but this was not always the case in the topsoil. Topsoil OM was better predicted by spatial interpolation of the calibration data than by using MARSplines models with ancillary predictors. In the two subsoil layers, the mapping procedure could not be appropriately tested, because the OM was low and homogeneous. We concluded that a 3-D soil texture map of an agricultural field could be prepared using MARSplines models based on layer-specific ECa values, gamma radiation data and a digital elevation model.

Electromagnetic Conductivity Imaging of Soil Salinity in an Estuarine–Alluvial Landscape

In coastal–estuarine agricultural landscapes, shallow and often saline water tables are problematic because they limit agricultural productivity. To understand the dynamics of rainfall events and tidal influences, natural resource management baseline data need to be collected to describe the heterogeneous nature of the soil and interactions with fluctuating groundwater tables, particularly in areas characterized by coastal acid sulfate soil. Electromagnetic induction instruments are increasingly being used because they measure the apparent soil electrical conductivity (ECa, mS m−1), which is related to soil salinity and hydrologic properties. Our aim was to demonstrate how the ECa data from a DUALEM‐421 can be used in conjunction with inversion software (EM4Soil) to develop electromagnetic conductivity images, which are two‐dimensional cross‐sections of estimates of the true electrical conductivity (σ, mS m−1). We show how the estimates of σ can be related to the electrical conductivity of a saturated soil paste extract (ECe, dS m−1) across an estuarine landscape and situated within Quaternary fluvial sediments adjacent to Rocky Mouth Creek on the far northern coast of New South Wales, Australia. Our results indicate that a good linear relationship (r2 = 0.70) can be established between σ and ECe, and the results reflect the existing soil landscape units.

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

AbstractIncreasing 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 EC1:5 and pH. For pH, best results were achieved when the EM38 ECa data were log‐transformed. By comparing MLR models using REML analysis, we found that using all ancillary data was optimal for mapping EC1:5, whereas the best predictors for pH were north, log‐EM38v and elevation. Using residual maximum likelihood (REML), the final EC1:5 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.

Delineation of areas with different temporal behavior of soil properties at a landslide affected Alpine hillside using time-lapse electromagnetic data

Landslide activity is largely controlled by changes in soil properties, particularly soil moisture and the corresponding changes in pore pressure within the vadose zone. While knowledge of changes in soil conditions is of utmost importance for the prediction of landslides, it is difficult to obtain reliable information on the field scale. A possibility of filling that information gap is the monitoring of changes in soil properties by time-lapse electromagnetic induction (EMI) data. Given the relative stability of soil properties, changes in apparent electric conductivity (ECa) are mainly related to changes in soil water content and its mineralization. Thus, we use time-lapse ECa data over a nine-month period from different investigation depths (0.75, 1.5, 3, and 6 m) to separate areas with different temporal behavior of soil properties. However, working with time-lapse EMI data raised the comparability problem since the recoded ECa is also affected by several day-specific survey conditions (e.g., instrument temperature, operator). Consequently, the reproducibility of accurate ECa measurements is difficult due to potential dynamic shifts which hinders a direct comparing. We introduce in this study a straightforward method for comparability of ECa values from different time steps by normalization of data ranges assuming that the majority of shifts of measured data originate from field calibration. We identify the intensity of spatial changes by means of the standard deviation (SD) as an indication for the intensity of soil properties variability. To obtain the temporal changes and its progression over time, we separate the dynamic signal from the background. A two-layer system could be identified: one shallow more dynamic layer with an east–west-oriented structure and a deeper, more stationary layer with a north–south-oriented structure. The ECa dynamics of the shallow layer is related to the altitude (R2 = 0.84) while the deeper dynamics follow a different regime. The decreasing of ECa dynamics with depth was consistent with the decreasing of SWC dynamics observed by previous studies.

Three‐dimensional imaging of subsurface structural patterns using quantitative large‐scale multiconfiguration electromagnetic induction data

AbstractElectromagnetic induction (EMI) systems measure the soil apparent electrical conductivity (ECa), which is related to the soil water content, texture, and salinity changes. Large‐scale EMI measurements often show relevant areal ECa patterns, but only few researchers have attempted to resolve vertical changes in electrical conductivity that in principle can be obtained using multiconfiguration EMI devices. In this work, we show that EMI measurements can be used to determine the lateral and vertical distribution of the electrical conductivity at the field scale and beyond. Processed ECa data for six coil configurations measured at the Selhausen (Germany) test site were calibrated using inverted electrical resistivity tomography (ERT) data from a short transect with a high ECa range, and regridded using a nearest neighbor interpolation. The quantitative ECa data at each grid node were inverted using a novel three‐layer inversion that uses the shuffled complex evolution (SCE) optimization and a Maxwell‐based electromagnetic forward model. The obtained 1‐D results were stitched together to form a 3‐D subsurface electrical conductivity model that showed smoothly varying electrical conductivities and layer thicknesses, indicating the stability of the inversion. The obtained electrical conductivity distributions were validated with low‐resolution grain size distribution maps and two 120 m long ERT transects that confirmed the obtained lateral and vertical large‐scale electrical conductivity patterns. Observed differences in the EMI and ERT inversion results were attributed to differences in soil water content between acquisition days. These findings indicate that EMI inversions can be used to infer hydrologically active layers.

Characterizing Compaction Variability with an Electromagnetic Induction Sensor in a Puddled Paddy Rice Field

Paddy rice (Oryza sativa L.) fields are repeatedly puddled at water saturation to loosen the top soil and to form a uniformly compacted plow pan required to reduce losses of water and nutrients. In paddy growing conditions however, the non-invasive detection of compaction variation within the plow pan layer is challenging. This paper evaluates a proximal soil sensing based methodology to identify areas compacted by different intensities of puddling to support precise land preparation of paddy rice fields. Therefore, a 1.6-ha alluvial paddy field of silt loam texture (Aeric Haplaquepts) located in the Brahmaputra floodplain of Bangladesh was selected. Three areas within the field were variably puddled and characterized by soil properties and soil apparent electrical conductivity (ECa). The Floating Soil Sensing System (FLoSSY) employing the EM38 electromagnetic induction sensor was used to record the ECa of the soil for each of the puddling intensities. The effects of plow pan compaction on water percolation losses and paddy yield were assessed. The results show that the variably puddled areas had significant differences in soil bulk density which was linked to the soil penetration resistance. Spatial comparison of ECa data sets showed that the ECa values increased systematically under increased puddling. Sufficient soil compaction could be identified when percolation losses were significantly the lowest and paddy yield was significantly the highest. It was concluded that a non-invasive ECa survey using electromagnetic induction based soil sensing allowed the detection of soil heterogeneity linked to soil compaction caused by puddling, which offers an interesting potential for precision puddling and land preparation in paddy rice fields.

Using multivariate geostatistics to assess patterns of spatial dependence of apparent soil electrical conductivity and selected soil properties.

The apparent soil electrical conductivity (ECa) was continuously recorded in three successive dates using electromagnetic induction in horizontal (ECa-H) and vertical (ECa-V) dipole modes at a 6 ha plot located in Northwestern Spain. One of the ECa data sets was used to devise an optimized sampling scheme consisting of 40 points. Soil was sampled at the 0.0–0.3 m depth, in these 40 points, and analyzed for sand, silt, and clay content; gravimetric water content; and electrical conductivity of saturated soil paste. Coefficients of correlation between ECa and gravimetric soil water content (0.685 for ECa-V and 0.649 for ECa-H) were higher than those between ECa and clay content (ranging from 0.197 to 0.495, when different ECa recording dates were taken into account). Ordinary and universal kriging have been used to assess the patterns of spatial variability of the ECa data sets recorded at successive dates and the analyzed soil properties. Ordinary and universal cokriging methods have improved the estimation of gravimetric soil water content using the data of ECa as secondary variable with respect to the use of ordinary kriging.

Design of Electrode Disk of Electrical Resistivity to Measure the Variations of Apparent Electrical Conductivity on Agricultural Land

A design of electrode disk device of Electrical Resistivity (ER) has been tested its stability of the data and its relationship closeness with the electrodes stick devices. The designed electrode device was easy to use and recorded data quickly and accurately. The design of this device consisted of a sleeve electrode, a diskshaped electrode, and shockbreaker; it was also equipped with wheels and a tool box. A testing process of the disk electrode device was done by measurements of apperant Electrical Conductivity (ECa) repeatedly to see the consistency of data measurement, with a correlation value of 0.988. The testing was also done by correlating the ECa data between the use of the disk electrode and the use of the stick electrode, with a correlation of 0.985 . The results of the testing showed that the disk electrode could be applied quickly and accurately to the ECa measurement of agricultural land with uneven topography and levees lied on rice fields.

Mapping the three-dimensional variation of soil salinity in a rice-paddy soil

Soil salinity is widespread in a variety of environments, and land managers need to map its severity and extent both laterally and vertically. In this research we explore the inversion of apparent electrical conductivity (ECa) measured with an EM38 using a linear model and Tikhonov regularization to model electrical conductivity (σ) profiles in a saline paddy field in the Yangtze delta of China. The modelled σ matched closely the directly measured bulk electrical conductivity (σb) in the topsoil within our calibration field. Discrepancies were greatest between 0.4 and 0.8m, below which they converged again, and were judged small enough to map soil salinity. Equivalent ECa data, recorded in an adjacent field, was similarly inverted with the modelled σ analysed geostatistically. In this regard, the σ data at 10 depths were treated as 10 correlated variates, and experimental auto-and cross-variograms were computed by the method of moments from them. A linear model of coregionalization fitted well, and it was used to cokrige σ on 5m×5m blocks on a fine grid. The kriging errors, computed as the square roots of the cokriging variances, were typically about 5% of the kriged estimates. Estimates of σ were then converted into the universal standard of soil salinity measurement (i.e. electrical conductivity of a saturated soil paste extract — ECe). The results indicate that an irregularly shaped patch of strongly saline topsoil (i.e. 8–12dSm−1) and subsoil salinity (i.e. >16dSm−1) at the southern end of the field was consistent with a yield reduction of some 33%; and as compared with the weakly saline conditions evident at the northern end of the field (e.g. topsoil ECe 2–4dSm−1) where yield was much larger. We conclude that the approach has merit and might be useful in providing a baseline set of data and a method that can used to monitor and evaluate the management of salinity.

Sensor data fusion for topsoil clay mapping

The study investigated proximal sensor data fusion for topsoil clay mapping on a 22hectare agricultural field in southwest Sweden. Eight different predictor sets and two different prediction methods were tested in an orthogonal design. The predictor sets were different combinations of proximally measured gamma (γ) ray spectrometry and apparent electrical conductivity (ECa), four terrain attributes (elevation, slope and the cosine and the sine of the aspect) and the digital numbers (DNs) of an aerial photo. The two prediction methods were partial least squares regression (PLS-R) and k nearest neighbor prediction (kNN). It was found that the γ ray spectrometry variables (232Th, 40K and total count of decays) were good predictors of topsoil clay content (mean absolute error of about 1.5% clay) and predictions were neither much improved nor deteriorated by addition of any of the other predictors. The ECa measurements, which are affected also by the subsoil, did not perform as well. Predictions were improved when the ECa data were integrated with the aerial photo DN but were deteriorated by addition of elevation data. The kNN method yielded slightly better predictions than the PLS-R method but overall it was more important which input data were used than how the predictions were made. It was observed that even though dense soil sampling was used for calibration (three samples per hectare), use of proximal soil sensor data was almost always better than mere interpolation of the calibration samples.

Modeling soil electrical conductivity–depth relationships with data from proximal and penetrating ECa sensors

Apparent soil electrical conductivity (ECa), a widely used proximal soil sensing technology, is related to several important soil properties, including salinity, clay content, and bulk density. Particularly in layered soils, interpretation of ECa variations would be enhanced with better calibrations to depth-wise variations in these soil properties. Thus, the objective of this research was to combine point measurements of layer conductivity obtained using ECa-equipped penetrometers with mapped ECa data from proximal sensors for improved quantification of conductivity–depth relationships in layered soils. Data were collected from a set of large plots on a claypan-soil landscape in central Missouri which had been managed in grain and perennial grass cropping systems for 20years. Soil ECa variation with depth was represented by both two- and three-layer models that were visualized and parameterized with the use of penetrometer ECa data. The three-layer model provided a more realistic representation of the soil profiles in the study area and provided similar topsoil depth estimation accuracy as the commonly used two layer model. Penetrometer ECa data also provided efficient and accurate (r2=0.92) estimation of calibration-point topsoil depth as an alternative to soil coring and manual determination. A key to accurate model calibration was selection of points from areas of spatially-homogeneous proximal ECa. Combining penetrometer ECa with proximal ECa data improved modeling of conductivity–depth relationships in terms of model selection, model parameterization, and model calibration.

Toward the Ultimate Soil Survey: Sensing Multiple Soil and Landscape Properties in One Pass

Many studies have found the range of spatial dependence is shorter than the distances used in most grid sampling, and government soil surveys have limited utility in many precision applications. A new multi‐sensor platform was developed that records apparent soil electrical conductivity (ECa), optical reflectance readings with red and near‐infrared light‐emitting diodes and pH along with topography data. The objective of this study was to evaluate its performance for estimation of soil cation exchange capacity (CEC), organic matter (OM), and pH on eight fields in four states, comparing results with lab‐analyzed samples, USDA soil surveys, and 1‐ha grid maps. Proximal soil sensor measurements correlated well with lab‐analyzed soil samples. The OM calibrations using ECa, optical, and/or topographic data showed good performance with R2 of 0.8 or higher and ratio of prediction to deviation (RPD) of 2.5 or greater in all fields. In CEC calibrations, five of six fields had higher than 0.86 for R2 and greater than 2.8 for RPD. The pH calibration results showed RPD of 2.1 or greater and R2 of 0.76 or higher in seven fields. The sensor maps showed small‐scale variability not detected at conventional grid sample scales or with USDA soil surveys. Using the proximal soil sensors, the average root mean square error of prediction for OM was 2.78 g kg−1, CEC 1.20 cmolc kg−1, and pH 0.26 for the project fields. These values are significantly lower than the soil property ranges found in the soil surveys. This is a promising development for improving farm practices and management.

Manganiferous Soils in Maryland: Regional Extent and Field‐Scale Electromagnetic Induction Survey

There are some unique soils in central Maryland with extremely high quantities of Mn and Fe oxides, up to 141 g kg−1 Mn and 169 g kg−1 Fe (extracted by dithionite–citrate–bicarbonate). Such high quantities of Mn oxides have pigmented the entire soil matrix black in subsoil horizons. Previously, little was known about the extent of these manganiferous soils in Maryland. Pedons were observed in transects across several fields containing manganiferous soils to better understand their spatial variability and possible relationship to landscape and geology. Manganiferous soils are heterogeneous; some pedons show modest to moderate Mn enrichment in the form of black nodules or coatings, while other pedons show extreme Mn enrichment in the form of black, loamy material up to 8.5 m deep. The soils seem to occur in relation to areas mapped as having marble bedrock in the geologic survey. The utility of electromagnetic induction (EMI) for delineating manganiferous soil units was tested for the first time. Apparent electrical conductivity (ECa) readings from the EM38DD EMI sensor had a significant empirical relationship to Mn content of the studied soils (R2 = 0.88), so maps generated from ECa data correspond reasonably well to observations made during transect work. Electromagnetic induction is therefore a useful tool for delineating manganiferous soils, which occurred in units of approximately 1 to 2 ha in each of the three fields observed.

Depth slicing of multi-receiver EMI measurements to enhance the delineation of contrasting subsoil features

Mobile multi-receiver electromagnetic induction sensors provide simultaneous readings of the apparent electrical conductivity (ECa) from overlapping soil volumes. Therefore, small contrasting features can be difficult to identify because they have a limited contribution to the bulk measurement, especially if they are present in the subsoil (i.e. beneath the topsoil). Integrating ECa data from simultaneous measurements with multiple coil configurations has the potential to elucidate the variability within the soil profile as it enables modelling the electrical conductivity (EC) for distinct depth intervals. Therefore, it was our objective to develop a methodology to enhance the delineation of contrasting subsoil features, such as in-filled gullies and archaeological features. We selected a 3.5ha study site where contrasting features were expected. A three-layered build-up was taken as the initial EC-slicing model. After varying the interface depths, the shallowest and deepest EC-depth slice showed a clear minimum of their combined variances at interface depths of 0.36m, which corresponded to the ploughing depth, and 0.86m. This implies that the EC-depth slice in-between these depths, contained a demonstrably higher variability. A sub-area of 0.85ha was completely excavated to a depth of 0.7m, revealing the subsoil features and the host material. An automated edge detection algorithm showed that the EC-depth slice was superior to any individual ECa measurement for delineating the contrasting subsoil features. Therefore, we concluded that depth slicing by integrating simultaneous ECa signals from a multi-receiver EMI sensor clearly improved the identification of subsoil features.

Estimating Topsoil Water Content of Clay Soils With Data From Time-Lapse Electrical Conductivity Surveys

Spatial estimation of soil-water content (θ) at the field, hillslope, or catchment scale is required in numerous applications. Time-lapse electrical resistivity and apparent electrical conductivity (ECa) surveys are recognized as a useful source of information about both spatial variations in θ and spatial differences in soil properties. The objective of this work was to test the hypothesis that the accuracy of the regression relationships between θ and collocated ECa survey data can be improved for any given time if several time-lapse ECa surveys rather than a single ECa survey are used. Vertisol plots under conventional tillage and direct drilling were surveyed for gravimetric θ (θg) in the top 0.3-m layer at 17 times and for topsoil ECa at 13 times in 2008 through 2010. Both dry and wet periods were covered by the surveys. On four occasions, θg and ECa surveys were done on the same day. Only weak correlations (with R < 0.21) were found between ECa and θg measured on the same day. The accuracy of regression predictions of θg substantially improved when data of several ECa surveys, rather than a single survey, were used. Therefore, the knowledge about the temporal variability in soil properties, as captured by the time-lapse ECa data, can improve the estimation of spatial variability in soil properties affecting soil-water content.

STOX1 overexpression in mice induces severe preeclampsia-like symptoms prevented by aspirin at low doses

Soil salinity is widespread in a variety of environments, and land managers need to map its severity and extent both laterally and vertically. In this research we explore the inversion of apparent electrical conductivity (ECa) measured with an EM38 using a linear model and Tikhonov regularization to model electrical conductivity (σ) profiles in a saline paddy field in the Yangtze delta of China. The modelled σ matched closely the directly measured bulk electrical conductivity (σb) in the topsoil within our calibration field. Discrepancies were greatest between 0.4 and 0.8 m, below which they converged again, and were judged small enough to map soil salinity. Equivalent ECa data, recorded in an adjacent field, was similarly inverted with the modelled σ analysed geostatistically. In this regard, the σ data at 10 depths were treated as 10 correlated variates, and experimental auto-and cross-variograms were computed by the method of moments from them. A linear model of coregionalization fitted well, and it was used to cokrige σ on 5 m × 5 m blocks on a fine grid. The kriging errors, computed as the square roots of the cokriging variances, were typically about 5% of the kriged estimates. Estimates of σ were then converted into the universal standard of soil salinity measurement (i.e. electrical conductivity of a saturated soil paste extract — ECe). The results indicate that an irregularly shaped patch of strongly saline topsoil (i.e. 8–12 dS m− 1) and subsoil salinity (i.e. > 16 dS m− 1) at the southern end of the field was consistent with a yield reduction of some 33%; and as compared with the weakly saline conditions evident at the northern end of the field (e.g. topsoil ECe 2–4 dS m− 1) where yield was much larger. We conclude that the approach has merit and might be useful in providing a baseline set of data and a method that can used to monitor and evaluate the management of salinity.

Evaluation of a local regression kriging approach for mapping apparent electrical conductivity of soil (ECa) at high resolution

AbstractApparent electrical conductivity of soil (ECa) is a property frequently used as a diagnostic tool in precision agriculture, and is measured using vehicle‐mounted proximal sensors. Crop‐yield data, which is measured by harvester‐mounted sensors, is usually collected at a higher spatial density compared to ECa. ECa and crop‐yield maps frequently exhibit similar spatial patterns because ECa is primarily controlled by the soil clay content and the interrelated soil moisture content, which are often significant contributors to crop‐yield potential. By quantifying the spatial relationship between soil ECa and crop yield, it is possible to estimate the value of ECa at the spatial resolution of the crop‐yield data. This is achieved through the use of a local regression kriging approach which uses the higher‐resolution crop‐yield data as a covariate to predict ECa at a higher spatial resolution than would be prudent with the original ECa data alone. The accuracy of the local regression kriging (LRK) method is evaluated against local kriging (LK) and local regression (LR) to predict ECa. The results indicate that the performance of LRK is dependent on the performance of the inherent local regression. Over a range of ECa transect survey densities, LRK provides greater accuracy than LK and LR, except at very low density. Maps of the regression coefficients demonstrated that the relationship between ECa and crop yield varies from year to year, and across a field. The application of LRK to commercial scale ECa survey data, using crop yield as a covariate, should improve the accuracy of the resultant maps. This has implications for employing the maps in crop‐management decisions and building more robust calibrations between field‐gathered soil ECa and primary soil properties such as clay content.