EM38 Sensor Research Articles

Soil salinity and moisture content under non-native Tamarix species

This study investigated, soil salinity and moisture content under the exotic Tamarix in the Olifants River, South Africa, where they predominantly occur. Soil electro-conductivity (EC) was mapped using the electromagnetic induction (EMI) device (EM38 sensor), in three transects laid along the river from as close to the water source outward towards the bank of the River at 50 m apart. This was supported by three soil EC and moisture measurements from each of the three transects at a soil depth of 0–100 cm at intervals of 10 cm using soil EC meter and Amplitude Domain Reflectometry (ADR) sensor, respectively. The highest salt concertation (3,000 mS/m or 19,500 ppm) was found at a depth of 30–40 cm under the dense Tamarix species. The highest soil moisture (20–40%) was also found at the same depth under the Tamarix, suggesting a hydraulic lift of water to the top 30–40 cm, where the Tamarix fine roots for water absorption occur. It also confirms that the distance from water point and Tamarix plant density affect salt leaching depth and amount of litter decomposition, respectively, which is the main source of salt deposition in soil.

A multi-source data fusion approach to assess spatial-temporal variability and delineate homogeneous zones: A use case in a table grape vineyard in Greece

Precision Viticulture requires very fine-scale spatial and temporal resolution to assess quite accurately variation in a vineyard. Many studies have used proximal sensing technology and spatial-temporal data analysis to characterize the local variation of plant vigour over time. The objective of this study was to present the potential of multivariate geostatistical techniques to fuse multi-temporal data from a multi-band radiometer and a geophysical sensor with different support for delineation of a vineyard into homogeneous zones, to be submitted to differential agricultural management. The study was conducted in a commercial table grape vineyard located in southern Greece during the years 2016 and 2017. Soil electrical conductivity was measured using an EM38 sensor, while Crop Circle canopy sensor, with the sensor located at 1.5 m height from the soil surface and 1.2 m horizontally from the vines, was used for scanning the side canopy area at different crop stages. The temporal multi-sensor data were analysed with the geostatistical data fusion techniques of block cokriging, to produce thematic maps, and factorial block cokriging to estimate synthetic scale-dependent regionalized factors. The factor maps at different scales are characterised by random variability with several micro-structures of different plant and soil properties, which leads to difficulties in delineating macro-areas with homogeneous features. In such conditions, high resolution VRA technology should be preferred to management by homogeneous zones for precision viticulture. The results have shown the potential of the proposed approach to deal with multi-source data in precision viticulture. However, further statistical research on data fusion of the outcomes from different sensors is still needed.

Nitrogen replenishment using variable rate application technique in a small hand-harvested pear orchard

Precision agriculture is a management approach for sustainable agriculture. It can be applied even in small fields. It aims to optimize inputs, improve profits, and reduce adverse environmental impacts. In this study, a series of measurements were conducted over three growing seasons to assess variability in a 0.55 ha pear orchard located in central Greece. Soil ECa was measured using EM38 sensor, while soil samples were taken from a grid 17 × 8 m and analysed for texture, pH, P, K, Mg, CaCO3, and organic matter content. Data analysis indicated that most of the nutrients were at sufficient levels. Soil and yield maps showed considerable variability while fruit quality presented small variations across the orchard. Yield fluctuations were observed, possibly due to climatic conditions. Prescription maps were developed for nitrogen variable rate application (VRA) for two years based on the replacement of the nutrients removed by the crop. VRA application resulted in 56% and 50% reduction of N fertiliser compared to uniform application.

Calibration of an electromagnetic induction sensor with time‐domain reflectometry data to monitor rootzone electrical conductivity under saline water irrigation

Management of saline water irrigation at the field scale requires electrical conductivity to be monitored regularly in the generally shallow soil layer explored by plant roots. Non‐invasive electromagnetic induction (EMI) sensors might be valuable for evaluating large‐scale soil salinity. However, obtaining information on soil surface salinity from depth‐integrated EMI measurements requires either an inversion of the electromagnetic signal or an empirical calibration. We opted for an empirical calibration of an EM38 sensor, which requires a reference dataset of local bulk electrical conductivity, σb, for comparison with EMI readings for estimating regression coefficients. We used time‐domain reflectometry (TDR) to replace direct sampling for local σb measurements. With empirical approaches, the different soil volumes involved with the EMI and TDR sensors become problematic. We resolved this issue by analysing large EMI and TDR datasets recorded at several times along three transects irrigated with water at 1, 3 and 6 dS m−1 degrees of salinity. A Fourier filtering technique was applied to remove the high frequency part (at small spatial scales) of the variation in the original data, which was the main source of dissimilarity between the two datasets. Therefore, calibration focused on the lower frequency information only; that is, information at a spatial scale larger than the observation volume of the sensors. We show that information from the TDR observations derives from a combination of local and larger scale heterogeneities, and how it should be managed for calibration of the EMI sensor. Analysis enabled us to identify characteristics of the calibration data that should be included to improve prediction.HighlightsEMI and TDR sensors have different observation volumes and are not immediately comparable. With Fourier filtering the different observation volumes were accounted for in calibration of the EMI data. We improved the empirical calibration between EMI and TDR datasets. We identified criteria to select data series for EMI sensor calibration.

Delineation of site-specific management zones by fuzzy clustering of soil and topographic attributes: A case study of East Nile Delta, Egypt

The objective of this study was to define site-specific management zones of 67.2 ha of a wheat pivot field at East of Nile Delta, Egypt for use in precision agriculture based on spatial variability of soil and topographic attributes. The field salinity was analysed by reading the apparent soil electrical conductivity (ECa) with the EM38 sensor horizontally and vertically at 432 locations. The field was sampled for soil attributes systematically with a total of 80 sampling location points. All samples were located using GPS hand held unit. Soil sampling for management zones included soil reaction pH, soil saturation percentage, organic matter, calcium carbonates content, available nitrogen, available phosphorus and available potassium. The field topographic attributes were digital elevation model (DEM), slope, profile curvature, plane curvature, compound topographic index (CTI) and power stream index (PSI). The maps of spatial variability of soil and field topographic attributes were generated using ordinary kriging geostatistical method. Principal component analysis (PCA) was used to determine the most important soil and topographic attributes for representing within-field variability. Principal component analysis of input variables indicated that EM38 horizontal readings (EM38h), soil saturation percentage and digital elevation model were more important attributes for defining field management zones. The fuzzy c-means clustering method was used to divide the field into potential management zones, fuzzy performance index (FPI) and normalized classification entropy (NCE) were used to determine the optimal cluster numbers. Measures of cluster performance indicated no advantage of dividing these fields into more than five management zones. The defined management zones not only provided a better description of the soil properties, but also can direct soil sampling design and provide valuable information for site-specific management in precision agriculture.

Apparent Soil Electrical Conductivity Used to Determine Soil Phosphorus Variability in Poultry Litter-Amended Pastures

The objectives of this research were to determine the relationship between soil apparent electrical conductivity (ECa) and soil P distribution, and to compare the effectiveness of noncontact mobile electromagnetic induction (EM) and direct contact methods for relating ECa to soil P. Studies were conducted at two locations in Southwest Missouri on a longterm forage fertility plot site and three 1 to 1.5 ha sites within beef cattle pasture fields, all having received long-term poultry litter applications. For the long-term plot site, both the direct contact ECa sensor deep reading and the EM-38 (Geonics) sensor in the shallow mode had significant positive correlations with soil test Bray-1 P at both the 0 to 5 and 5 to 15 cm sampling depths. Significant spatial variation in soluble, soil test Bray-1 and total P were observed by landscape position within pasture fields. In general, soil ECa was not significantly correlated with soluble, soil test Bray-1 and total P at each individual pasture site, but when data was combined over all three sites, significant relationships were observed between ECa measured by the EM-38 sensor and soil soluble P, soil test Bray-1 P and total P, especially when the vertical (deep) mode was used. The difference in performance of the two sensors between the two studies was attributed to the proportion of Research Article American Journal of Experimental Agriculture, 3(1): 124-141, 2013 125 coarse fragments contained in the soils and soil water content. These results suggest that soil ECa measurements may provide some useful information for evaluating spatial variation in soil P due to manure applications. However, further research is needed to assess the processes and factors affecting this relationship before it can be recommended for use for improved soil P management in individual farm fields with varying environmental conditions and management practices.

Sensor data fusion to predict multiple soil properties

The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources. In this study, a data fusion was performed of a Vis–NIR spectrometer and an EM38 sensor for multiple soil properties. Stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and principal components analysis combined with stepwise multiple linear regression (PCA + SMLR) methods were used in three different fields. Soil properties investigated for data fusion included soil texture (clay, silt and sand), EC, pH, total organic carbon (TOC), total nitrogen (TN) and carbon to nitrogen ratio (CN). It was found that soil property models based on fusion methods significantly improved the accuracy of predictions of soil properties measureable by both sensors, such as clay, silt, sand, EC and pH from those based on either of the individual sensors. The accuracy of predictions of TOC, TN and CN was also improved in some cases, but was not consistent in all fields. Among data fusion methods, PLSR outperformed both SMLR and PCA + SMLR methods because it proved to have a better ability to deal with the multi-collinearity among the predictor variables of both sensors. The best data fusion results were found in a clayey field and the worst in a sandy field. It is concluded that sensor data fusion can enhance the quality of soil sensing in precision agriculture once a proper set of sensors has been selected for fusion to estimate desired soil properties. More efficient statistical data analysis methods are needed to handle a large volume of data effectively from multiple sensors for sensor data fusion.

The effect of soil heterogeneity on the spatial distribution of Heterodera schachtii within sugar beet fields

The beet cyst nematode, Heterodera schachtii, causes severe yield losses in sugar beet production worldwide. It tends to occur in heterogeneously distributed patches within fields. Soil texture is often regarded as the dominant soil property affecting the population density of H. schachtii. This study was based on the hypothesis that a relationship between soil texture and related soil properties and the population density of H. schachtii can be quantified with measurements of the soil's apparent electrical conductivity (EC a). The EC a, which is known to be strongly related to soil texture, was measured in four agricultural fields using the non-invasive EM38 sensor. Two of the fields showed little spatial variation in soil texture, whereas the other two were more spatially heterogeneous. Soil samples to determine the nematode population were taken either along transects or following a nested sampling design. Geostatistical methods were used to analyse the spatial distribution of EC a and nematodes. Correlations between EC a and nematode population density were moderate ( R 2 = 0.47) and strong ( R 2 = 0.74) in heterogeneous fields, indicating that H. schachtii prefers deep soil with medium to light textures and non-stagnic water conditions. Management maps based on EC a and soil taxation maps indicate areas with different soil-related living conditions for H. schachtii. These maps could enable farmers to improve site-specific management strategies on nematode-infested fields.

Five-year growth and yield response of two young olive cultivars (Olea europaea L., cvs. Arbequina and Empeltre) to soil salinity

Olive is considered moderately tolerant to salinity, but most studies are short in duration and report its tolerance on the basis of targeted irrigation water salinity (ECiw) rather than on measured root zone soil salinity (ECe). We evaluated the growth (tree height and trunk diameter) and yield (fruit and oil weights) response of two drip-irrigated young olive cultivars (Arbequina and Empeltre) subject to three ECiw treatments (2, 4 and 10 dS m−1). The ECe of 23 Arbequina and 20 Empeltre trees was intensively monitored during the 2003–2007 study period using an EM38 sensor and converting its readings into ECe through sensor calibration. For growth, the slopes of the linear regression equations (−3% to −5%) and the ECe50 estimates (12.5–14.0 dS m−1) were similar in both cultivars and vegetative traits. Likewise, for yield the slopes of the linear regression equations (−4% to −7%) and the ECe50 estimates (7.9–9.6 dS m−1) were similar in both cultivars and yield traits. Based on mean ECe50 estimates, olive vegetative growth was ranked as moderately tolerant (mean ECe50 = 13.2 dS m−1), whereas olive and oil yields were ranked as moderately sensitive (mean ECe50 = 8.7 dS m−1).

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.

Soil heterogeneity at the field scale: a challenge for precision crop protection

Crop protection seldom takes into account soil heterogeneity at the field scale. Yet, variable site characteristics affect the incidence of pests as well as the efficacy and fate of pesticides in soil. This article reviews crucial starting points for incorporating soil information into precision crop protection (PCP). At present, the lack of adequate field maps is a major drawback. Conventional soil analyses are too expensive to capture soil heterogeneity at the field scale with the required spatial resolution. Therefore, we discuss alternative procedures exemplified by our own results concerning (i) minimally and non-invasive sensor techniques for the estimation of soil properties, (ii) the evidence of soil heterogeneity with respect to PCP, and (iii) current possibilities for incorporation of high resolution soil information into crop protection decisions. Soil organic carbon (SOC) and soil texture are extremely interesting for PCP. Their determination with minimally invasive techniques requires the sampling of soils, because the sensors must be used in the laboratory. However, this technique delivers precise information at low cost. We accurately determined SOC in the near-infrared. In the mid-infrared, texture and lime content were also exactly quantified. Non-invasive sensors require less effort. The airborne HyMap sensor was suitable for the detection of variability in SOC at high resolution, thus promising further progress regarding SOC data acquisition from bare soil. The apparent electrical conductivity as measured by an EM38 sensor was shown to be a suitable proxy for soil texture and layering. A survey of arable fields near Bonn (Germany) revealed widespread within-field heterogeneity of texture-related ECa, SOC and other characteristics. Maps of herbicide sorption and application rate were derived from sensor data, showing that optimal herbicide dosage is strongly governed by soil variability. A phytoassay with isoproturon confirmed the reliability of spatially varied herbicide application rates. Mapping areas with an enhanced leaching risk within fields allows them to be kept free of pesticides with related regulatory restrictions. We conclude that the use of information on soil heterogeneity within the concept of PCP is beneficial, both economically and ecologically.

Soil salinity assessment using directed soil sampling from a geophysical survey with electromagnetic technology: a case study

Spatial characterization of soil salinity is required for establishing salt control measurements in irrigated agriculture. For that, cost-effective, specific, rapid, and reliable methodologies for determining soil salinity in-situ and processing those data are required. This paper shows the usefulness of an integrated methodology involving a hand-held electromagnetic sensor (Geonics EM38), and the ESAP (Electrical conductivity or salinity, Sampling, Assessment and Prediction) software package to assess, predict and map soil salinity at field scale. The salinity of a 1.74 ha plot of a surface-irrigated field of Navarre, northern Spain, was analyzed by reading the bulk soil electrical conductivity (ECa) with the EM38 sensor at 180 locations. At 20 of those sites, soil core samples were taken at 0.3 m intervals to a depth of 0.9 m, and electrical conductivity of the saturation extract (ECe), saturation percentage (SP) and water content (WC) were measured. Salinity was the dominant factor influencing the EM38 readings. The multiple linear regression (MLR) calibration model predicted ECe from EM38 readings with squar R ranging from 0.38 to 0.90 for the multiple-depth profile. The ESAP software also provided field range average estimates of soil salinity. Eighty-one percent of the field had ECe values above 4 dS mE-1. The obtained salinity map was helpful to display the spatial patterns of soil salinity and identify sources/causes of salt loading.

An integrated methodology for assessing soil salinization, a pre-condition for land desertification

Soil salinization is mainly an arid-zone problem leading to land desertification. It reduces soil quality and limits the growing of crops. The control of this problem involves inventorying, mapping, and monitoring soil salinity, which requires cost-effective, rapid, and reliable methods for determining soil salinity in the field, and rapid, specific data-processing methods. This paper shows the usefulness of an integrated methodology involving a hand-held electromagnetic sensor (Geonics-EM38) and the ESAP ( Electrical conductivity or salinity, Sampling, Assessment and Prediction) software for assessing, predicting, and mapping soil salinity. The salinity of a 0.45-ha surface-irrigated plot was analysed by reading the EM38 at 161 locations, and by employing the ESAP software for calibrating the sensor, and predicting and mapping soil salinity at multiple depths. To calibrate the EM38 sensor, the electrical conductivity of the saturation extract (ECe) of 57 soil samples taken at 19 points was measured. The multiple linear regression (MLR) calibration model predicted ECe from EM38 readings with R 2 ranging from 0.71 to 0.95 for the multiple-depth profile. Furthermore, the MLR calibration model provided field range average estimates of soil salinity. Fifty-seven percent of the field had ECe values above 4 dS m −1. The salinity levels and distribution in the root zone identified areas with inverted profiles, which revealed drainage problems. The integrated method presented is a breakthrough in the ability to accurately and rapidly assess soil salinity in agricultural lands.

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 (ECe) of soil samples taken at 22 points, and by reading the Geonics EM38 sensor at 141 points in the horizontal (EMH) and vertical (EMV) dipole positions. EMH and EMV 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. EMH=EMV), except in areas with high salinity and concurrent shallow water tables, where the profiles were inverted as shown by EMH > EMV, and by ECe being greater in shallow than in deeper layers. The regressions of ECe on EM readings predicted ECe with R2 > 84% for the 0–100 to 0–150 cm soil depths. We then produced salinity contour maps from the 141 ECe values estimated from the electromagnetic readings and the 22 measured values of ECe. Owing to the high soil sampling density, the maps were similar (i.e. mean surface‐weighted ECe values between 3.9 dS m−1 and 4.2 dS m−1), although the electromagnetically estimated ECe 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.