ECa Patterns Research Articles

Soil Salinity Patterns in an Olive Grove Irrigated with Reclaimed Table Olive Processing Wastewater

The agricultural use of saline table olive processing wastewater enables the implementation of closed water cycles in this socioeconomically important industry for rural southern Spain and relieves environmental, economic, and legal burdens. To allow growers to evaluate and guarantee adequate long-term soil and plant conditions when irrigating with such regenerated wastewaters, efficient soil monitoring strategies are needed. Field-scale monitoring with electromagnetic induction sensing, after one (2013) and five years (2017) of irrigation with regenerated wastewater with average electrical conductivity (EC) near 6 dS m−1 in an olive orchard in southern Spain, showed accumulation of highly conductive material in the subsoil in relation to local topography and soil characteristics. Laboratory analysis of the soil water revealed strongly varying patterns of EC during the growing season and across the olive grove, which were attributed to dilution and concentration effects due to rainfall and evaporation, respectively. Visual inspection and leaf analyses revealed no negative effects on the olive trees. Apparent electrical conductivity (ECa), measured in between the tree rows in 2013, showed a linear relationship with surface soil EC1:5 under the drippers and allowed identification of areas with high ECa in the low elevation zones of the farm, due to the presence of shallow perched saline water tables. A second ECa measurement in 2017 showed similar spatial ECa patterns and was used to estimate the distribution of soil EC across the soil profile using inversion software, although no unique field-wide relationships with soil properties could be inferred, possibly as a consequence of spatially variable soil clay and water contents, due to the influence of the topography. Despite the implementation of a more conservative irrigation strategy since 2015, results showed that the salinity has increased since 2013 in about 15% of the study area, with the largest increments in the deepest horizons.

Depth-Specific Soil Electrical Conductivity and NDVI Elucidate Salinity Effects on Crop Development in Reclaimed Marsh Soils

Agricultural management decision-making in salinization-prone environments requires efficient soil salinity monitoring methods. This is the case in the B-XII irrigation district in SW Spain, a heavy clay reclaimed marsh area where a shallow saline water table and intensively irrigated agriculture create a fragile balance between salt accumulation and leaching in the root zone, which might be disrupted by the introduction of new crops and increasing climate variability. We evaluated the potential of electromagnetic induction (EMI) tomography for field-scale soil salinity assessment in this hyper-conductive environment, using EMI and limited analytical soil data measured in 2017 and 2020 under a processing tomato–cotton–sugar beet crop rotation. Salinity effects on crop development were assessed by comparing Sentinel 2 NDVI imagery with inverted depth-specific electrical conductivity (EC). Average apparent electrical conductivity (ECa) for the 1-m depth signal was 20% smaller in 2020 than in 2017, although the spatial ECa pattern was similar for both years. Inverted depth-specific EC showed a strong correlation (R ≈ 0.90) with saturated paste extract EC (ECe), [Na+] and sodium absorption ratio (SAR), resulting in linear calibration equations with R2 ≈ 0.8 for both years and leave-one-out cross validation Nash–Sutcliffe Efficiency Coefficient, ranging from 0.57 to 0.74. Overall, the chemical parameter estimation improved with depth and soil wetness (2017), yielding 0.83 < R <0.98 at 0.9 m. The observed spatial EC distributions showed a steadily increasing inverse correlation with NDVI during the growing season, particularly for processing tomato and cotton, reaching R values of −0.71 and −0.85, respectively. These results confirm the potential of EMI tomography for mapping and monitoring soil salinity in the B-XII irrigation district, while it allows, in combination with NDVI imagery, a detailed spatial assessment of soil salinity impacts on crop development throughout the growing season. Contrary to the popular belief among farmers in the area, and despite non-saline topsoil conditions, spatial EC and subsoil salinity patterns were found to affect crop development negatively in the studied field.

Spatial variability and temporal stability of apparent soil electrical conductivity in a Mediterranean pasture

The general objectives of this study were to evaluate (i) the specificity of the spatial and temporal dynamics of apparent soil electrical conductivity (ECa) measured by a electromagnetic induction (EMI) sensor, over 7 years, in variable conditions (of soil moisture content (SMC), soil vegetation cover and grazing management) and, consequently, (ii) the potential for implementing site-specific management (SSM). The DUALEM 1S sensor was used to measure the ECa in a 6 ha pasture experimental field four times between June 2007 and February of 2013. Soil spatial variability was characterized by 76 samples, geo-referenced with the global positioning system (GPS). The soil was characterized in terms of texture, moisture content, pH, organic matter content, nitrogen, phosphorus and potassium. This study shows a significant temporal stability of the ECa patterns under several conditions, behavior that is an excellent indicator of reliability of this tool to survey spatial soil variability and to delineate potential site-specific management zones (SSMZ). Significant correlations were obtained in this work between the ECa and relative field elevation, pH, silt and soil moisture content. These results open perspectives for using the EMI sensor as an indicator of SMC in irrigation management and of needs of limestone correction in Mediterranean pastures. However, it is interesting to extend the findings to other types of soil to verify the origin of the lack of correlation between the ECa data measured by DUALEM sensor and properties such as the clay, organic matter or phosphorus soil content, fundamental parameters for establishment of pasture SSM projects.

Apparent electrical conductivity measurements in an olive orchard under wet and dry soil conditions: significance for clay and soil water content mapping

Mediterranean olive trees traditionally grow under rainfed conditions, on poor soils with steep slopes. Rainfall is mainly concentrated during autumn and winter and is characterized by intense rain pulses, separated by dry periods. The use of electromagnetic induction (EMI) techniques in these olive orchards might be questioned since EMI surveys are generally recommended to be performed under moist soil conditions. A 6.7 ha olive orchard was surveyed for EMI-based apparent electrical conductivity (ECa), both under wet and dry soil conditions. In addition, 48 soil samples were analyzed for soil texture and for soil water content (SWC) under both soil conditions. The relationships between ECa, soil texture and SWC, under both soil conditions were evaluated. Despite the significantly larger ECa values measured during the wet survey as compared to the dry survey, a similar spatial correlation structure was found, indicating temporally stable ECa patterns. Significant correlations (r) were found between both surveys for ECa (r = 0.67) and for SWC (r = 0.63). The correlation between SWC and clay content exceeded 0.60 for both surveys, and the correlation between ECa and clay content was twice as high under wet soil conditions as compared to dry soil. In both situations, the ECa surveys revealed the same patterns of soil texture, indicating that moist soil conditions are not an absolute prerequisite for the use of EMI to map the spatial variability of these soil properties. Nonetheless, measuring the ECa under different moisture conditions can provide additional information about soil moisture dynamics.

Quantifying the effects of soil variability on crop growth using apparent soil electrical conductivity measurements

Spatial heterogeneity of crop growth within fields is rarely quantified but essential for estimating yield and optimizing crop management. Relationships in fields between crop growth and soil physical characteristics have been described before but an unrealistically high number of invasive measurements have to be made to obtain spatially continuous soil information. Alternatively, non-invasive methods are available for characterizing soil heterogeneity but relationships to growth characteristics have rarely been investigated. Here, we use an electromagnetic induction (EMI) sensor to measure the apparent electromagnetic conductivity of the soil (ECa), which can be used as a proxy for the relative spatial variability of the prevailing soil properties. We evaluate relationships between ECa and soil and crop characteristics assuming that measured ECa patterns relate to observed growth patterns in the field. The test fields were located in Western Germany where different crops (winter wheat, winter barley, and sugar beet) were grown between 2011 and 2013. Measurements include soil texture, soil moisture and crop growth characteristics taken frequently throughout the vegetation periods for plant height, leaf area index (LAI), dry matter of plants and selected organs (green leaves and storage organs). Spatial variability was observed for soil and crop characteristics that differed among fields, crops and years. Good correlations between ECa and soil texture and soil moisture confirmed that ECa measurements are suitable for characterizing spatial differences in soil properties for our test sites. Averaged over all sampling dates of a vegetation period the differences in the spatial variability of crop characteristics were small between the years and crops considered. However, the within-field crop growth heterogeneity changed throughout the growing period depending on the crop development stage. Correlations were found between ECa and the crop characteristics that varied with time and were most pronounced in the main growth phase when LAI approached its maximum. Crop height correlated better with ECa than yield, LAI, and dry matter but differences were observed between fields, years and crops. Our results suggest that in dry years soil patterns have a stronger influence on the crop growth patterns than in wetter years when water limitation is less severe. We conclude that ECa measurements are suitable for detecting spatial patterns in soil characteristics that influence the spatial crop growth patterns for the region, years and crops considered. However, relationships between patterns in crop growth and soil characteristics within fields are more complex and require further investigation.

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.

Mapping impaired olive tree development using electromagnetic induction surveys

Future success of olive cropping in the Mediterranean depends critically on improving yield, reducing production costs, and preventing infestation by soil-borne pathogens. In order to put forward adequate soil management practices accurate knowledge of the spatial distribution of soil properties is required. The aims of this study were to delimit areas with constrained tree development in an olive orchard using electromagnetic induction (EMI), and to identify the causal relationships between apparent electrical conductivity (ECa) and soil properties. The experimental field was exhaustively sampled for different soil properties and ECa was measured in 2011 and 2012 under dry and wetter soil conditions, respectively. The spatial ECa distribution matched the observed canopy coverage pattern well. Three zones were delimited according to ECa values from 0 to 27.5, from 27.5 to 57.5, and greater than 57.5 mS m−1. All ECa signals, regardless of soil-water status, exhibited a common dominant ECa pattern. The area with the lowest ECa values (0–27.5 mS m−1) showed optimal tree growth (45 % canopy coverage) and presented significantly lower average clay contents than the other two areas. Intermediate ECa values (27.5–57.5 mS m−1) identified accurately the area with deficient tree development and tree die-off (12 % canopy coverage), and corresponded with an area along the drainage pathway where profile-averaged soil-water, clay, stone and organic matter content were highest. EMI surveys detected subtle differences in soil properties and provided useful information to delimit areas with constrained tree development. The approach can be used as a screening technique before installing tree plantations.

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.

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

AbstractPrecision‐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 (ECa) 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 ECa pattern. Using the map‐based comparisons in conjunction with the statistical analyses, the effect of each measured soil physical property (VSWC, CI, and ECa) 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.

Apparent electrical conductivity in dry versus wet soil conditions in a shallow soil

The general objective of this study was to evaluate the stability of patterns of apparent soil electrical conductivity (ECa) in dry versus wet soil conditions in a shallow soil typically used for pastures in Mediterranean conditions of the southern region of Portugal. A 6 ha experimental field of permanent bio-diverse pasture was divided into 76 squares of 28 × 28 m. The soil electrical conductivity was measured using a Dualem 1S sensor under dry conditions (June 2007) and under wet conditions during the rainy season (March 2010). Soil samples, geo-referenced with GPS, were collected in a depth range of 0–0.30 m. The soil was characterized in terms of bedrock depth, moisture content, texture, pH, organic matter content, and macronutrients (nitrogen, phosphorus, and potassium). Pasture samples, also geo-referenced with GPS, were collected to measure the pasture dry matter yield. The statistical analysis of apparent electrical conductivity between dry and wet soil conditions resulted in a linear significant correlation coefficient (R = 0.88). The results also showed a significant correlation between apparent electrical conductivity and the relative field elevation (R = −0.64 and R = −0.66), the pasture dry matter yield (R = 0.42 and R = 0.48), the bedrock depth (R = 0.40 and R = 0.27), the pH (R = 0.50 and R = 0.49), the silt (R = 0.27 and R = 0.38) and soil moisture content (R = 0.48 and R = 0.45), in dry and wet conditions, respectively. A multi-variate regression was carried out using the following soil parameters that showed significant correlation with ECa and that did not present multi-collinearity: pH, bedrock depth, silt and moisture content. The results showed, in dry and wet conditions, that the analysis was significant (R = 0.75 and R = 0.84, respectively). Overall, these results indicate the temporal stability of ECa patterns under different soil moisture contents, which is relevant with respect to the time when a field should be surveyed and is important for using the electrical conductivity sensor, as a decision support tool for management zones in precision agriculture.

Field‐Scale Soil Moisture Pattern Mapping using Electromagnetic Induction

Soil apparent electrical conductivity (ECa) responds to time‐variable soil properties, such as soil water content (θ), and can therefore be used to characterize the spatial and temporal dynamics of θ at the field scale. When clay content is high and uniform and the θ range small, however, it is not clear whether ECa maps can be used for this purpose. A soil management experiment established in a Vertisol in 1982 was surveyed for ECa on 13 occasions to capture changing soil conditions and to determine the sources of this variability. Less variation with time was found in subsoil than in topsoil ECa patterns, especially within the conventional tillage (CT) plots, in areas with shallow soil, and along the drainage network. Using the 13 ECa relative difference data sets as variables, principal component (PC) analysis showed that the first three PCs explained 90% of their total variance. The time‐stable or mean ECa pattern was significantly correlated with PC1 and could also be associated with topography, soil depth, and soil structure but could not be related to a single survey. Topography and soil management could be associated with PC2 and PC3, respectively. Time‐stable θ patterns, inferred from 26 surveys, revealed topographical and management characteristics and showed significant relationships (P < 0.001) with ECa–derived patterns like soil porosity and infiltration caused by soil management, topography, and rainfall. Electromagnetic induction sensors were useful for mapping soil spatial variability and changing soil conditions due to management effects and external forcing in uniform clay soils.

Distribution and causes of intricate saline - sodic soil patterns in an upland South Australian hillslope

We describe a soil–landscape investigation conducted in a South Australian upland hillslope (128 ha) to understand the distribution and causes of saline–sodic soil patterns using convenient, ground-based geophysical surveys of the hillslope. These surveys included: (i) EM31 for deep (~6 m) apparent electrical conductivity (ECa) patterns, (ii) EM38 for shallow (>1.5 m) ECa patterns, and (iii) Bartington MS2-D loop sensor for surface volume magnetic susceptibility (κ) patterns. From these surveys we inferred hillslope distributions of: (i) deep (~6 m) concentrations of salinity associated with deep groundwater systems and deposits of magnetic gravels (dominated by maghemite and hematite) (EM31 sensor); (ii) shallow (<1.5 m) soil salinity (EM38 sensor); and (iii) preservation of pedogenic magnetic materials (e.g. maghemite and hematite) (MS2-D loop sensor). We also describe terrain analysis to locate near-surface hydropedological patterns using topographic wetness index. When combined in 3D geographic information system, strong visual matches were identified between patterns in: (i) geophysical surveys, (ii) terrain, and (iii) soil survey data, thus allowing integrated interpretations of soil–landscape pedogenic processes to be made on a whole-of-landscape basis. Such mechanistic interpretations of soil–landscape processes reveal and map intricate saline and sodic soil–regolith patterns and groundwater and fresh surface water flow paths that were not revealed during a previous traditional soil survey.

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

AbstractThe 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 (ECa). In this study, we recorded the ECa on three fields with an EM38 (Geonics, Canada). The ECa 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 ECa due to varying soil moisture and temperature was taken into account by repeated measurements of the same fields and subsequent averaging of the ECa values. Significant correlations (r² = 0.76) were found between the mean weighted clay content (0–1.5 m) and the ECa. Furthermore, in soils with differently textured layers, ECa was used to estimate the thickness of the uppermost loess layer. A comparison of ECa and large‐scale soil maps reveals some pros and cons of ECa measurements. The main advantages of ECa recordings are the high spatial resolution in combination with low efforts. Yet, the ECa signal is no direct measure for a soil type or unit. Depending on the variability of substrates and layering, the ECa 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 ECa patterns within a field indicates that the ECa signal per se—without conversion to traditional soil parameters—integrates the effects of various soil variables that govern soil fertility. Altogether, ECa surveys can be a powerful tool to facilitate and improve conventional soil mapping.

Soil Scientists Revisit a High-Intensity Soil Survey in Northwest Illinois with Electromagnetic Induction and Traditional Methods

Electromagnetic induction (EMI) and global positioning system (GPS) data were used with geographical information system (GIS) to improve a high intensity soil survey that was completed with traditional methods in northwest Illinois. Apparent conductivity (ECa) maps provided additional layers of information, which improved knowledge of soils and directed soil sampling. The information provided by ECa maps and additional soil sampling led soil scientists to recognize different soils and modify mapping concepts. Within the site, ECa maps assisted the identification and delineation of soil polygons and improved the quality of the high-intensity soil map. White spatial ECa patterns influenced the judgments of soil scientists, ECa maps were not accepted as a substitute for a high-intensity soil map.

Electrical Conductivity of a Failed Septic System Soil Absorption Field

Locating existing septic systems and determining the extent of soil contamination after septic system failure can be destructive, time consuming, and a nuisance to homeowners. The objective of this study was to determine the effectiveness of noninvasive electromagnetic induction (EMI) for locating a failed septic system in fine‐textured glacial‐till‐derived soils. Components of a failed septic system were located with a push probe, georeferenced with a theodolite, and surveyed with a dual receiver EMI sensor (DUALEM‐2) in December 2001 (wet soil moisture condition) and July 2002 (dry soil moisture condition). Three transects located perpendicular to the soil absorption field trenches were sampled to a depth of 1.2 m and used to ground reference the EMI survey. Near‐surface (1‐m depth) apparent conductivity (ECa) was significantly correlated to unweighted average electrical conductivity from soil saturated paste extracts (ECsat; r = 0.79). The ECa below the soil absorption field was higher than the surrounding native soil under both dry and wet soil moisture conditions. Individual soil absorption trenches had a higher ECa than background ECa under both soil moisture conditions. A higher ECa pattern that was apparent in December 2001 associated with discharge of wastewater at shallow depths was not evident in July 2002 after the system had been abandoned for 6 mo. While more research is warranted, results from this study suggest that electromagnetic induction is a promising technique to identify the location of septic system components, failed septic systems, and their associated effluent plumes.

COMPARISON OF ELECTROMAGNETIC INDUCTION, CAPACITIVELY-COUPLED RESISTIVITY, AND GALVANIC CONTACT RESISTIVITY METHODS FOR SOIL ELECTRICAL CONDUCTIVITY MEASUREMENT

In situ measurement of apparent soil electrical conductivity (ECa) is an important precision agriculture tool usefulfor determining spatial changes in soil properties. Three near-surface geophysical methods are available for rapid,continuous measurement of ECa in agricultural fields. These methods are electromagnetic induction (EMI), capacitivelycoupled resistivity (CCR), and galvanic contact resistivity (GCR). Acceptance for using geophysical methods to gauge spatialchanges in soil properties hinges to a significant degree on there being consistency of the measured ECa spatial patternbetween geophysical methods. Testing of all three methods was conducted on two adjacent test plots having fine-grained soilsand during two time periods with dissimilar shallow hydrologic conditions. Different operational modes for each of the threegeophysical methods were evaluated, including three primary electromagnetic field frequencies (8190, 14610, and 20010 Hz)used for the EMI method, four spacing distances (0.625, 1.25, 2.5, and 5.0 m) between the two dipoles employed with the CCRmethod, and two different Schlumberger electrode array lengths (0.7 and 2.1 m) utilized for the GCR method. Therefore, atotal of nine geophysical method - operational mode combinations were tested.<br><br>Based on spatial correlation analysis, the areal ECa patterns measured by the nine method - operational modecombinations showed substantial similarity to each other, with one exception. The exception was the short electrode arraymode of the GCR method that when paired with the various modes of the other two geophysical methods, exhibited an averagecorrelation coefficient, r, that ranged between only 0.30 and 0.45. All other average r values for pairs of different geophysicalmethod - operational mode combinations ranged between 0.62 and 0.97. Spatial correlation coefficients, for both test plots,between the same method - operational mode combination, but at two different times in which hydrologic conditions varied,ranged from 0.55 to 0.95 for eight of the nine method - operational mode combinations, with the GCR short electrode arrayhaving values of 0.32 and 0.58. Regarding the test plot EC average or median, there were substantial differences in valuesobtained by the three geophysical methods. Electromagnetic vertical sounding measurements along with results obtained byCCR, and GCR surveying, when combined, indicate that for both test plots, from the surface to a depth of a little over 2 m,soil electrical conductivity generally increased first and then decreased. Most importantly, although the measured ECamagnitudes vary between the three geophysical methods, results show that EMI, CCR, and GCR all provide useful andconsistent information on soil electrical conductivity spatial patterns.

THE IMPACT OF TEMPERATURE AND SHALLOW HYDROLOGIC CONDITIONS ON THE MAGNITUDE AND SPATIAL PATTERN CONSISTENCY OF ELECTROMAGNETIC INDUCTION MEASURED SOIL ELECTRICAL CONDUCTIVITY

In situ measurement of apparent soil electrical conductivity (ECa) is an important precision agriculture tool fordetermining spatial changes in the soil properties affecting soil fertility. However, dynamic temperature and shallowhydrologic conditions also influence the measured ECa and need to be considered. Therefore, the impact of temperature (air,soil) and shallow hydrologic conditions (soil moisture content, water table depth) on the magnitude and spatial patterns ofECa was evaluated. Eighty-eight ECa mapping surveys were conducted at a single test plot over an interval of two years. Soilelectrical conductivity was measured by electromagnetic induction (EMI) at primary field frequencies of 8190, 14610, and20010 Hz. Because results were similar at all three frequencies, analyses focused on the 14610 Hz data. The ECa surveys weregrouped into four time periods for analysis based upon factory recalibration of the sensor, climate changes, and a shift ininstrument response. Reduced values of measured ECa occurred with air temperatures at or below 12C and/or soiltemperatures at or below 8C. Correlation analysis within each of the four data groups showed that the average ECa, withinthe test plot, was most strongly affected by near-surface volumetric moisture content (rMC-ECa: Grp. Avg. = 0.73), followed bywater table depth (rWTD-ECa: Grp. Avg. = -0.42), soil temperature (rST-ECa: Grp. Avg. = 0.14), and ambient air temperature(rAT-ECa: Grp. Avg. = -0.10). Correlation analysis of spatial ECa patterns between pairs of EMI surveys, conducted under arange of temperature and shallow hydrologic conditions, produced rSpatial: ECa vs. ECa values averaging 0.63 (standarddeviation equaled 0.17), indicating that spatial ECa patterns remain relatively consistent over time. These ECa results indicatethat the spatial ECa response is governed to a large extent by the spatial changes in soil properties and less by changes inshallow hydrologic conditions.

TEMPORAL STABILITY OF SOIL ELECTRICAL CONDUCTIVITY IN IRRIGATED SANDY FIELDS IN COLORADO

The utility of apparent soil electrical conductivity (ECa) remains elusive because of its complex interactions withsoil properties. Nonetheless, a significant interest is emerging to utilize the spatial variability in ECa to guide direct soilsampling and develop varying site-specific management. While the spatial variability of ECa is of significant importance,understanding its temporal variability is equally important. That is particularly true if delineated ECa zones are to be usedto manage agricultural inputs across the field for multiple years. We present multi-year (1998 to 2002) measurements ofshallow (top 0.3 m of soil) and deep (top 0.9 m of soil) ECa from three irrigated sandy fields in eastern Colorado and quantifytheir degree of temporal change. During the study period, the fields were managed uniformly, including all farm operationsand the applied inputs. For each field, soil ECa values were highly correlated between measurement days (for periods of afew days to four years between measurements) with coefficients ranging between 0.71 and 0.92 for shallow and 0.87 and 0.96for deep ECa, but significant deviations from the 1:1 line (indicative of temporal variability) were exhibited by shallow ECa.In these non-saline and sandy fields, delineating spatial patterns of ECa into low, medium, and high zones was highly stableover time, mainly because they reflect the stable soil properties. Grid-by-grid comparison of the low, medium, and high ECapatterns from different measurement days showed mean matching percentages of 87, 74, and 78 for shallow and 93, 85, and89 for deep ECa patterns, respectively, for the three fields. When salt concentration and buildup are low (as was the caseherein), results suggest that single ECa mapping should suffice to delineate stable low, medium, and high ECa zones withouta need for remapping.

INFERRING SUBSURFACE MORPHOLOGY FROM TRANSIENT SOIL MOISTURE PATTERNS USING ELECTRICAL CONDUCTIVITY

A non–intrusive survey method was developed that examined the relationships among spatial and temporal variations of soil water content, soil texture, and bulk electrical conductivity (ECa) in a 7.5 ha research watershed in southwestern Tennessee. Our goal was to identify areas that may exhibit rapid movement of subsurface moisture, as this is a precursor of offsite agrochemical migration. The survey protocol identified similar and dissimilar temporal variations in ECa patterns. Repeated spatial measurements of ECa, starting at near field capacity and then progressing through the draining and drying process, supplied visually shifting ECa patterns that correspond to dynamic soil moisture variations and subsurface morphology transitions. We noted that spatial ECa patterns for a field remained somewhat analogous across data gathering events, shifting in relative amplitude along with seasonal moisture levels. For this study, we considered soil morphology constant over the short data acquisition interval, with short–term subsurface moisture variations as the parameter primarily influencing ECa changes. We inferred soil morphology as the major factor for ECa pattern similarity across time. Follow–up soil coring analysis along two separate low–to–high ECa transects supported this assumption for this site.