Applications and Case Studies in Agriculture and the Environment

The EM and ERT methods have been largely applied in agriculture and environmental studies, from monitoring of saltwater intrusion in coastal areas to the diffusion of pollutants in groundwater; the time-dependent change of soil water content; the surveying of plant root biomass (Amato et al. 2008); the analysis of water–soil–root plant interactions and many other significant applications (Werban et al. 2008; Calamita et al. 2012).

The ARP is increasingly used in precision farming (viticulture) giving contributes for improving the management strategies aimed to improve and enhance the quality of the crop production (Rossi et al. 2013a).

The GPR method can be applied to a wide range of agriculture and environmental problems such as the detection of pollutant leakage in groundwater, the mapping of the root–plant geometry, and the rapid mapping of soil water content. Recently, novel applications in precision farming have been carried out using advanced system of semi-automatic vehicles for GPR data acquisition (Hubbard et al. 2002; Rubin 2006).

A case study where a combination of techniques was used in an agricultural field to highlight variation at different scales regards a study in a wheat field located at Gaudiano-Lavello—Basilicata (41o 60 600 N, 15o 500 5500 E), managed by ALSIA (Agenzia Lucana di Sviluppo ed Innovazione in Agricoltura). The field subjected to two tillage management (conventional tillage at 35 cm and sod seeding) from 3 years.

On-the-go multi-depth resistivity meter (ARP ©, Geocarta, Paris) (Rossi et al. 2013a) was used to measure simultaneously at three different depths that correspond to the distance between receiving wheels (V1 ¼ 0.50 m, V2 ¼ 1 m, V3 ¼ 2 m). Data were real time referenced by DGPS. Data were collected on 2.16 ha along parallel transects 4 m distant between each other. A total number of 59,376 measurements were taken. The entire area was surveyed in about 40 min at an average speed of 3.76 m/s.

A 2D resistivity survey was conducted with a Syscal R2 (Iris Instruments, Orleans, France) resistivity meter with a Wenner–Schlumberger array with 48 electrodes lined up on the soil surface for a total length of 47 m and with an electrode spacing of 1.0 m.

A GPR survey was also carried out on the same survey lines by using an acquisition module GSSI SIR 2000 equipped with a 400 MHz antenna and having survey cart and encoder.

In 2D ERT sections (Fig. 5), resistivity ranged between 30 and 400 Ωm, up to a depth of about 5 m. The highest resistivity values (from 80 to 400 Ωm) can be associated to the presence of resistive structures with values above 100 Ωm. Conversely, the lowest resistivity values (from 30 to 80 Ωm) could be attributed to a higher water and/or clay content. In addition, at about 3.0 m depth, the 2D tomogram shows a low-resistivity feature than can be ascribed to the groundwater table with overlying capillary fringe.

The radargram corresponding to the ERT profile shows a higher attenuation of the signal in the right side, and this can be associated to increased soil water. Conversely, the left side of the radargram shows a continuous series of reflectors and can be related to the presence of discontinuities like stones or compacted soil. The three maps of resistivity from ARP (Fig. 6) show that resistive areas are mostly concentrated in southern part of the field, while an area of low resistivity is discernible in the north-eastern area of the field. Summary statistics of soil electrical resistivity measured at the three consecutive depths are reported in Table 4.

Buvat and coauthors (2014) used the multi-depth resistivity dataset to develop a “geophysical taxonomy” based on the vertical succession of the three apparent resistivity values. They found that the resistivity-based clusters well matched soil pedological profiles and were consistent with soil unit boundaries. Following an approach similar to these authors, we used the vertical succession of resistivity values to map soil layering, based on the difference between the resistivity measured in the first (V1) and in the second layer (V2) values, which were grouped into three classes: D (decreasing values), C (constant values), and I (increasing values). The geophysical taxa show distinct clusters following a north-south gradient. ER increased with depth predominantly in the southern half of the field while it decreases in the northern corner (Fig. 7).

At the adopted imaging resolution and at this time of the year (about 6 months after tillage) we didn't distinguish any spatial pattern related to tillage type, which splits the field longitudinally in two blocks; instead the presence of a structured spatial pattern underlies the necessity of accounting for soil spatial variability in evaluating tillage effects. ER varied from 18 to peak values of about 200 Ωm; highest values were found in the third layer. This range of values can be attributed to different soil features (Samouelian et al. 2005) hence, as in all geophysical exploration, map interpretation requires ground-truth calibration.

Fig. 5 (a) 2D tomogram of electrical resistivity measured in the experimental field, P1 and P2 arrows point the management systems, respectively, P1 ¼ No Tillage and P2 conventional tillage.

(b) GPR Radargram carried out in the same direction of resistivity profile in the two management systems (NT no tillage and CT conventional tillage)

Other studies where a combination of geophysical techniques have been applied are related to coastal areas as shown in the work of Nowroozi et al. (1999), Abdul Nassir et al. (2000), Choudhury and Saha (2004), Sherif et al. (2006), Khalil (2006), Cimino et al. (2008). In the characterization of the coastal saltwater intrusion in the Metapontum forest reserve, Satriani et al. (2012a) used ER tomography alone to highlight the spatial distribution of saline water in the pine forest (Fig. 8)

Geophysical prospecting has an important field of application in archaeology. Loperte et al. (2011) used an integrated geophysical approach based on magnetic, Ground-Penetrating Radar, and geoelectrical survey to investigate a construction work site in the Greek and Roman settlement of Paestum, southern Italy (Fig. 9). The survey showed features that could be ascribed to archaeological remains, as was confirmed by subsequent excavations where walls, canals, and tombs were found.

The high potential of geophysical survey in agriculture has been now recognized; over the last decade geophysical sensors based on the nondestructive measurement of soil electrical conductivity (or its inverse resistivity) have been extensively used in precision agriculture, alone or coupled with terrain information, to help delineating uniform management zones (Peralta et al. 2013; Moral et al. 2010; Kitchen et al. 2005). Using such techniques, we are able to visualize soil features related to their electrical behavior; as current flux in soil is mostly electrolytic, resistivity is very sensitive to the two components that are mainly involved in charge transfer: the degree of pore water saturation and salinity (Lesch 2005) and the specific surfaces associated to the presence of clay particles (Tabbagh et al. 2000). Resistivity is even sensitive to the microstructure of clays,

Fig. 6 ARP multi-depth apparent resistivity maps and relative frequency distribution (red shade indicate high values and blue shade depict low values): top V1 ¼ 0–0.5 m layer, middle V2 ¼ 0–0.1 m layer, bottom V3 ¼ 0–2 m layer

based on lab measurements of worldwide collected clay samples; a first database of clays resistivity was compiled by Giao and coauthors (Giao et al. 2003). A soil conductivity survey conducted across different soils showed strong and consistent correlations with clay (Sudduth et al. 2005). This sensitivity is very useful in agricultural soil mapping, since many relevant properties are heavily influenced by and covariate with clay content, such as: water holding capacity, organic matter content, soil structure, temperature, and cation exchange capacity. For the opposite reason resistivity readings can also be used to localize resistive features, that act as

Table 4 Descriptive statistic of the multi-depth apparent resistivity layers












Standard deviation




















Number of observations




Fig. 7 Bottom left: map of apparent resistivity taxa, based on the difference between the resistivity measured in the first (V1) and in the second layer (V2) values were grouped in three classes: D (decreasing values ¼ blue), C (constant values ¼ green), and I (increasing values ¼ red). Bottom right: bar plot of V1 (solid gray bar) and V2 (solid black bar) resistivity average values (and relative standard error bars) of the three geophysical taxa (D, C, I)

barriers to current flux, such as gravel lenses (Tetegan et al. 2012; Rossi et al. 2013b); this is of great value, because of the strong influence that rock fragments exert on soil hydrology, workability, thermal regime, and nutrients pools (Poesen and Lavee 1994) but also because these techniques help filling the well-known methodological gap of quantitative research in stony soils (Eriksson and Holmgren 1996). This extraordinary sensitivity of the technique to the presence of insulating materials constituted the base for the use of the technique for imaging woody plant root system (Amato et al. 2008; Rossi et al. 2011). Plant roots are the key component of plant survivorship and ecology but at the same time are

Fig. 8 Electrical resistivity tomograms from the Metapontum forest reserve

Fig. 9 Map of processed magnetometric results (left) and 3D visualization of GPR prospecting (right) at a construction work site in Paestum (SA). The main electromagnetic anomalies are marked by capital letters while the black arrows indicate the travertine bank

considered the most elusive aspect of belowground studies; this is mainly related to the lack of methodologies to study root systems at the appropriate spatiotemporal scale without interfering with their growth and development (Amato 2004). Quantitative research on the use of resistivity tomography for mapping root system spatial variability has shown that lignified coarse plant roots exhibit a strong electrical response, that rooted soil resistivity can increase several hundred Ohm meter (Amato et al. 2008), and that their effect can be dominating in agricultural soil (Rossi et al. 2011). First research on herbaceous roots (Amato et al., 2009) has shown that even at very low density they increase resistivity, but that their resistivity values overlap those of other common soil materials; thus fine roots could only be discerned and quantified keeping the other sources of variability low and unstructured.

A combination of Ground-Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) has been used by Satriani et al. (2010) to produce high resolution images that were obtained in laboratory measurements, and they have clearly shown the presence of soil volumes with a high density of fine and woody roots.

Several research reports have shown that resistivity could be used to map permanent soil properties at farm scale (Andre´ et al. 2012; Buvat et al. 2014). In some cases, soil texture can dominate the resistivity pattern overshadowing soil structure and water-related properties (Banton et al. 1997). For a given texture, though, soil structural state variation, by altering the proportions between water and air filled porosity, can exert a strong effect on resistivity; this is at the base of the successful use of high resolution resistivity tomography for mapping soil alterations induced by tillage (Besson et al. 2004; Basso et al. 2010). Basso and coauthors (2010) found that resistivity mapping allowed to discern between tilled, freshly tilled, and untilled soils better than penetrometry. Time lapsed resistivity tomography was later used to evaluate soil structural recovery after compaction (Besson et al. 2013). Satriani et al. (2012b) monitored water content and distribution in drybean using resistivity tomography and time-domain reflectometry in two different irrigation treatments with applications for the reduction of water use without reducing yield.

Repeated resistivity measures were also used to infer within-field spatiotemporal organization of soil water, discounting this way the effect of soil texture (Besson et al. 2010). Whether resistivity is going to be used to discern permanent or transient soil properties, some baseline conditions must be satisfied: the target soil property variation must be large enough and must have a sufficient degree of contrast with the surrounding matrix (Banton et al. 1997), and of course the scale of measurements must be proportional to the target. Once these prerequisites are met, map interpretation requires ground-truth calibration, since several soil constituents show overlapping resistivity ranges (Samouelian et al 2005) or offsetting resistive behavior (i.e., rock fragments coated in clay; sandy saline layers) which can lead to ambiguous interpretation. The choice of the sampling strategy is crucial since the high costs of destructive sampling can rapidly counterweight the benefits of using a low-cost ancillary information instead of traditional expensive and labordemanding soil survey methods. The issue of geophysical sensor data groundtruth sampling schemes has been addressed by Lesch (2005) that suggests the use of a model-based sampling strategy as an alternative to probability-based sampling. Model-based or directed sampling instead of relying on randomization principle is focused toward the estimation of a regression model; hence sampling locations are explicitly chosen to cover the full range of the target variable (feature space). Directed sampling strategies typically allow to reduce the number of samples for an efficient model parameter estimation (Fitzgerald et al. 2006). Additional spatial optimization criteria can be included to maximize the spread of data to minimize the autocorrelation between observations (Lesch 2005), to reduce the costs of measurements (Minasny and McBratney 2006), or to intensify the number of samples where the variation is large (Minasny et al. 2007).

Acknowledgments This work was partly financed by the Project psr BASILICATA 2007–2013. MISURA 124 “Approcci innovativi per il miglioramento delle performances ambientali e produttive dei sistemi cerealicoli no-Tillage BIO-TILLAGE” CUP C32I14000080006.

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