Introduction

Knowledge of soil characteristics is essential to define the correct doses of fertilizers and correctives to be used in applicator systems. The traditional methodology to characterize the spatial variability of soil attributes is by means of grid sampling, followed by laboratory analysis. However, this technique requires a high sampling density to obtain reliable results, which is expensive and time-consuming (Adamchuk et al., 2004; Queiroz et al., 2017). In this context, sensors can help in the characterization of spatial variability of soil attributes (Adamchuk et al., 2004; Gałuszka et al., 2015).

Soil apparent electrical conductivity (ECa) sensors have been widely used to characterize the spatial variability of soil attributes (Gholizadeh et al., 2012; Machado et al., 2015; Sanches et al., 2018). By using at least two electrodes, those sensors can transmit electric current between them through the soil particles. The total amount of current measured by the sensor will vary according to the soil attributes (Adamchuk & Rossel, 2010). For instance, soils with high water content will allow for higher current values (Misra & Padhi, 2014) since water is a good conductive material.

ECa is strongly correlated with soil water content and can also detect variation in some of the soil attributes such as texture, salinity, cation exchange capacity (Corwin & Lesch, 2003; Fortes et al., 2015; Terrón et al., 2011), pH (Fortes et al., 2015; Sana et al., 2014; Terrón et al., 2011), pore size and distribution, temperature, and organic matter content (Corwin & Lesch, 2003; Fortes et al., 2015). In addition, ECa a may be correlated with crop parameters such as yield (Johnson et al., 2005; Jung et al., 2005; Singh et al., 2016), which in turn is correlated with soil water holding capacity (Grisso et al., 2005; Rudolph et al., 2015).

Different types of soil ECa sensors have been used for soil surveys, in which the most common are based on the contact and non-contact method (Serrano et al., 2014). Sensors that use the contact method, which is based on soil’s electrical resistivity, have been the most used due to their greater simplicity and lower cost of acquisition. The most used sensor array based on this principle is the one that uses a set of four equally spaced electrodes. A low-frequency electrical current is applied to the soil through the external electrodes, and a potential difference is measured in the internal electrodes. Based on the applied electrical current, on the measured potential difference, and on the distance between electrodes, the ECa is calculated (Gunn et al., 2015; Sudduth et al., 2013). The distance between electrodes determines the depth of the soil layer for which the ECa is being determined.

For sensors operating by the electrical resistivity method, the choice of the device’s power source is important for obtaining accurate data. One of the problems associated with this method is the formation of undesirable electrical potentials, due to the polarization of the electrodes caused by the contact between the metal of the bar that forms this electrode and the aqueous electrolyte solution present in the soil. To reduce the effect of this phenomenon, it is recommended that sensors based on the electrical resistivity method apply a low-frequency (< 10 Hz) electric current to the soil (Allred et al., 2008).

In ECa sensors based on the resistivity method, alternating currents have been used within the frequency range below 100 Hz (Allred et al., 2008; Herman, 2001; Telford et al., 1990). Although high frequencies (≥ 187.5 Hz) may result in an increase in the signal-to-noise ratio, the use of low frequencies (< 187.5 Hz) is recommended because the uncontrolled effects on the electrodes, such as capacitive coupling and electromagnetic effects, increase with signal frequency (Lück & Rühlmann, 2010). However, there is still uncertainty about the optimal frequency range to be used for soil surveying, with a study using a frequency of 60 Hz (Abu-Hassanein et al., 1996) and another using a frequency of 20 Hz (Oates et al., 2014).

The ideal frequency range and influence of the frequency of ECa sensors is a subject that has not been clearly defined. Being able to identify a frequency range that better correlates to soil attributes of agronomic interest, such as soil water content and pH, would result in improved farm management and, consequently, lower costs with agricultural inputs, reduced environmental impacts, and optimization on crop yield. Furthermore, the findings could serve as guidelines for researchers and manufacturers willing to develop ECa sensors. Thus, the present study was conducted to evaluate whether the electric current frequency influences the soil ECa value obtained by sensors that use the electrical resistivity method. It was also sought to evaluate whether there is a correlation between the frequency range and the soil chemical and physical attributes of agronomic interest.

Materials and Methods

Experimental Areas

As soil attributes are known to vary among different cropping systems and soil management practices (Tesfahunegn & Gebru, 2020), the study was conducted in four different experimental areas (A1, A2, A3, and A4), which are located in the southeast region of the state of Minas Gerais in Brazil. Table 1 provides locations and information on the characteristics of the four fields.

Table 1 Location of experimental areas
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Area A1 was an area cultivated with Tifton (Cynodon spp.) pasture. The experimental field A2 was traditionally cultivated with different crops, such as sorghum and maize, and using different soil tillage systems. During the experiment, area A2 was being cultivated with common bean, using both conventional tillage and no-tillage. The area A3 had been cultivated with maize and had the presence of straw on the soil. The area A4 was a coffee plantation (Table 1). Areas A1, A2, and A3 have flat relief with an average slope of around 3.5% and altitudes of approximately 677, 660, and 655 m, respectively, and A4 has mountainous relief (average slope of 18.3%), with an average altitude of 750 m. The region’s climate is classified as Cwa type (humid subtropical, with dry winter and rainy summer) according to the Köppen classification, with an average annual rainfall of 1229 mm, average relative humidity of 80%, and average annual temperature of 20.6 °C (Peel et al., 2007). The predominant soil in the four experimental areas is clayey soil, classified as a dystrophic Red-Yellow Oxisol (EMBRAPA, 2011).

Determination of Soil Apparent Electrical Conductivity

ECa was determined using a portable sensor developed by Queiroz et al. (2020). The sensor design was based on the Wenner matrix arrangement (Fig. 1), in which the four sensor electrodes are equally spaced and arranged linearly. The distance between electrodes was 30 cm on each sensor. The determinations of ECa were performed using six different frequencies of electric current: 1, 5, 10, 20, 30, and 40 Hz. Soil apparent electrical conductivity for electrodes arranged according to the Wenner matrix is calculated using Eq. 1 (Queiroz et al., 2020).

$${EC}_a=\frac{1000\ i}{2\ \pi\ l\Delta {U}_{CD}}$$
(1)
Fig. 1
figure 1

Scheme of operation of the soil apparent electrical conductivity sensor by the electrical resistivity method, using a sensor of four equally spaced electrodes. Source: Adapted from Corwin and Lesch (2005)

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where

ECa = soil apparent electrical conductivity (mS.m-1);

∆UCD = difference in electrical potential determined between electrodes C and D (V);

i = applied electric current (A);

l = spacing between electrodes (m).

Data Collection

Five points were sampled in each area (A1, A2, A3, and A4), as shown in Fig. 2. For each point sampled, soil ECa was determined using the six frequencies of electric current. For each frequency, 10 determinations of electrical conductivity were performed, and in each determination, the sensor was placed in a random position within a radius of 3 m around the measuring point. The sampling radius was based on the accuracy of the sensor’s GNSS module, which was approximately ± 1.8 m. Data collections in the experimental areas were carried out immediately after a rainy period (minimum precipitation of 30 mm within the past 2 h), over a period of 8 days, to ensure the experiment standardization.

Fig. 2
figure 2

Data collection points in the areas under pasture (A1), common bean (A2), maize (A3), and coffee cultivation (A4)

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At each sampling point, four soil samples were randomly collected, within a radius of up to 3 m around the sensor collection point. Then, these were homogenized and sealed in a labeled plastic container. The samples were collected using a Dutch auger at 0.00–0.30 m depth, since this was the soil layer depth measured by the ECa sensor. The samples were sent for laboratory analysis to determine soil attributes. The analyzed soil attributes, both physical and chemical, were soil water content (SWC), soil water holding capacity, up to the matric potential of 10 kPa (WHC), exchangeable calcium content in the soil (Ca2+), exchangeable magnesium content in the soil (Mg2+), exchangeable aluminum content in the soil (Al3+), potential acidity (H+Al), hydrogen potential (pH), remaining phosphorus (P-Rem), silt content (SLT), clay content (CLA), coarse sand content (CS), fine sand content (FS), organic matter content (OM), phosphorus content (P), and potassium content (K). Table 2 shows the laboratory techniques used to analyze the soil characteristics under study.

Table 2 Methods for determining the soil attributes studied
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Other soil attributes were estimated from the attributes described in Table 2. The sum of exchangeable bases (SB) corresponds to the sum of Ca2+ and Mg2+ contents. The effective cation exchange capacity (t) corresponds to the sum of SB with the Al3+ content. The total cation exchange capacity at pH 7.0 (T) corresponds to the sum of SB with potential acidity (H+Al). The percentage of base saturation (V) expresses the percentage of negatively charged soil sites that are neutralized with basic cations, such as Ca2+ and Mg2+ (Weil & Brady, 2016), and was calculated using Eq. 2 (EMBRAPA, 2017). The percentage of aluminum saturation (m) expresses the percentage of negatively charged soil sites that are neutralized with Al3+ ions (Weil & Brady, 2016) and was calculated using Eq. 3 (EMBRAPA, 2017).

$$V=\frac{100\ SB}{T}$$
(2)

where

V = percentage of base saturation (%);

SB = sum of exchangeable bases (cmolc/dm3);

T = total cation exchange capacity (cmolc/dm3).

$$m=\frac{100\ {Al}^{3+}}{SB+{Al}^{3+}}$$
(3)

where

m = aluminum saturation percentage (%);

SB = sum of exchangeable bases (cmolc/dm3);

Al3+ = exchangeable aluminum in soil (cmolc/dm3).

Statistical Analysis

The influence of the frequency of the electric current signal on the value obtained by the sensor was analyzed using linear regressions followed by the F-test (α = 0.05). For this, linear regressions were performed, for each point and in all areas, between the frequency of the signal (independent variable) and the mean ECa (dependent variable). The mean ECa is equivalent to the arithmetic mean of the ten measurements of ECa for their respective experimental point and signal frequency. In this sense, the F-test sought to assess whether the slopes of the regression lines obtained were equal (there is no influence of frequency) or different (there is an influence of frequency) from zero.

Pearson’s correlations (r) (α = 0.05) were calculated to quantify the relationships between ECa and the soil’s physical and chemical attributes, for each frequency of the sensor’s electrical current signal. For this, the mean values of ECa, for all frequencies at all experimental points, were calculated. Based on the significance and number of correlations found, the electric current frequencies most correlated with the soil attributes were evaluated. The chi-square test (p ≤ 0.05) was used to evaluate whether the frequency of significant correlations was homogeneous at all electrical current frequencies tested. Specifically, it was sought to identify the frequency of electric current with the most correlations with soil attributes.

Results and Discussion

Evaluation of the Influence of Frequency on ECa Values

Tables 3 and 4 present summary statistics of soil physical and chemical attributes for the experimental areas A1 and A2 and for A3 and A4, respectively. Al3+ contents and m in A2 and P contents in A4 showed high variability, with coefficients of variation (CV) greater than 50%. The other soil attributes showed low or medium variability, with CV lower than 50%, in all areas.

Table 3 Descriptive statistics of the soil attributes studied in the areas under pasture and common bean cultivation
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Table 4 Descriptive statistics of soil attributes studied in the areas under maize and coffee cultivation
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Results of the soil texture analysis pointed out the predominancy of clayey soil throughout the four experimental areas (Fig. 3). The clay content was more than 45% in all the experimental points. Although all the areas presented the same soil texture, the soil texture in the area under pasture (A1) was observed to be more homogeneous.

Fig. 3
figure 3

Overview of the soil texture determined for each experimental point of the areas under pasture (1), common bean (2), maize (3), and coffee cultivation (4)

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Areas A2 and A3 had similar mean values of ECa (Tables 3 and 4). The greatest difference between mean values of ECa was observed between the areas A1 (ECa < 11 mS/m) and A4 (ECa > 15 mS/m) (Tables 3 and 4). In addition, the areas showed different values of CV for ECa, and the lowest CV values were observed in A3 and A4. However, no trend between sensor signal frequencies and CV values was observed. This is mainly due to the fact that the frequencies under study are located in a range below 100 Hz (Lück & Rühlmann, 2010; Lueck & Ruehlmann, 2013). Lueck and Ruehlmann (2013), using a multi-frequency sensor that operates by the electrical resistivity method, found that the use of frequencies in the range from 62.5 to 562.5 Hz did not affect soil resistivity. The tests they did show that the noise in the signal of the sensor was higher at lower frequencies (< 187 Hz). We did not find any effect of the signal frequency on the coefficient of variation; this means that noise in the sensor signal may be similar for all used frequencies. We used frequencies in a range that is lower than the one used by Lueck and Ruehlmann (2013); also, their sensor is arranged in a dipole–dipole array, and our sensor uses the Wenner array arrangement of the electrodes. The differences in sensor design and in the soil conditions may be the reason for different results.

Figure 4 shows the Box-plot graphs of the ECa at the five experimental points, for each of the four areas in which the determinations were performed using the six different frequencies of electrical current applied to the soil by the sensor. For most experimental points, it was observed that the ECa values showed similar median values at the six frequencies of electric current studied. However, a large dispersion was observed in the ECa readings for the experimental point two of area A2 (common bean cultivation) (Fig. 4b). To better understand the dispersion observed in Fig. 4, Table 5 presents the average values of SWC and SB for all experimental points. These variables were chosen due to their direct relationship with ECa (Allred et al., 2008; Corwin & Lesch, 2005). Both SB and SWC presented a moderate to strong correlation (r ≥ 0.67) with ECa, as illustrated in Fig. 6. In this study, we define the following correlation scale: 0.0 ≤ | r | < 0.40: relatively weak correlation; 0.40 ≤ | r | < 0.70: relatively moderate correlation; and | r | ≥ 0.70: relatively strong correlation.

Fig. 4
figure 4

Box-plot of apparent electrical conductivity of the soil measurements and their respective frequencies for experimental points 1, 2, 3, 4, and 5 of areas under pasture (a), common bean (b), maize (c), and coffee cultivation (d)

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Due to the fact that ECa and SWC generally present a strong correlation (r > 0.88) (Misra & Padhi, 2014; Molin & Faulin, 2013; Robinson et al., 2009), the dispersion observed in Fig. 4b may be due to the observed difference in SWC between points in area A2 (Table 5). Furthermore, the presence of different soil tillage systems (no tillage and conventional tillage) in area A2 and the variation of the soil cover layer had a direct influence on the variation of SWC, which in turn may have contributed to the observed dispersion in the ECa values. Differences in the amplitude of the ECa values, caused mainly by the variation of SWC, were also observed in other studies (Lesch et al., 2005; Stadler et al., 2015).

Table 5 Average values of soil water content and the sum of exchangeable bases for all experimental points
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Table 6 presents the results of the regression analysis between the frequency of the electric current and the sensor ECa at each of the sampled points in each of the areas (A1, A2, A3, and A4). Based on the F-test (α ≤ 0.05), it is possible to conclude that the frequency of an ECa sensor does not significantly affect the measured conductivity value, as most of the experimental points presented non-significant results for the F-test. It was found that the frequency of the electric current had a significant effect on ECa only for the experimental point P3 in area A3 (maize cultivation) and for the experimental point P3 in area A4 (coffee cultivation). These significant results can be attributed to the interference of uncontrolled factors, such as the difference in soil temperature (Corwin & Lesch, 2005) between the experimental points, the heterogeneity of soil attributes within the same experimental point, or experimental errors. Since there was no variation in SWC within the experimental points, its interference on the values (Lesch et al., 2005; Stadler et al., 2015) can be ruled out.

Table 6 Regression analysis between the frequency of the electric current and the sensor ECa at each of the sampled points in each of the experimental areas
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To the best of our knowledge, there are no studies with a deep analysis of the existence of the relationship between low frequencies of electric current and ECa, in sensors that operate by the electrical resistivity method. For sensors that operate by the electromagnetic induction method, contrary to what was observed in the present study, studies point to the existence of this relationship (Calamita et al., 2015; Tromp-van Meerveld & McDonnell, 2009).

Correlation Between ECa and Soil Physical and Chemical Attributes for Different Frequencies

Figure 5 illustrates the matrix of Pearson’s significant correlations between the chemical and physical attributes of the soil. Figure 6 shows the matrix of Pearson’s significant correlations between the ECa measurements and the attributes of the soil. The correlation matrices were calculated considering the values of the variables at all experimental points, for all areas. Significant correlations were found between ECa and Ca2+, Mg2+, SB, t, OM, CLA, WHC, SWC, and CS, for all frequencies (1, 5, 10, 20, 30, and 40 Hz) (Fig. 5).

Fig. 5
figure 5

Pearson’s correlation matrix calculated for the soil chemical and physical attributes. SWC, soil water content; WHC, soil water holding capacity, up to the matric potential of 10 kPa; Ca2+, exchangeable calcium content in the soil; Mg2+, exchangeable magnesium content in the soil; Al3+, exchangeable aluminum content in the soil; H+Al, potential acidity; pH, hydrogen potential; P-Rem, remaining phosphorus; SLT, silt content; CLA, clay content; CS, coarse sand content; FS, fine sand content; OM, organic matter content; P, phosphorus content; K, potassium content; SB, sum of exchangeable bases; t, effective cation exchange capacity; T, total cation exchange capacity; V, percentage of base saturation; m, percentage of aluminum saturation. Correlation is significant at a 5% significance level

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Fig. 6
figure 6

Pearson correlation matrix calculated for the soil apparent electrical conductivity and the attributes of the soil. ECa1, ECa5, ECa10, ECa20, ECa30, and ECa40, soil apparent electrical conductivity for the frequencies of electric current of 1, 5, 10, 20, 30, and 40 Hz, respectively; SWC, soil water content; WHC, soil water holding capacity, up to the matric potential of 10 kPa; Ca2+, exchangeable calcium content in the soil; Mg2+, exchangeable magnesium content in the soil; Al3+, exchangeable aluminum content in the soil; H+Al, potential acidity; pH, hydrogen potential; P-Rem, remaining phosphorus; SLT, silt content; CLA, clay content; CS, coarse sand content; FS, fine sand content; OM, organic matter content; P, phosphorus content; K, potassium content; SB, sum of exchangeable bases; t, effective cation exchange capacity; T, total cation exchange capacity; V, percentage of base saturation; m, percentage of aluminum saturation. Correlation is significant at a 5% significance level

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Correlations Between ECa and Exchangeable Cations

Strongly positive and significant correlations (r ≥ 0.7) were found between ECa and Ca2+, SB and t, using frequencies of 1, 10, 30, and 40 Hz. Between ECa and Mg2+, significant correlations (0.5 < r < 0.67) were found for all frequencies. Between ECa and T, there was no significant correlation only for the frequency of 20 Hz (Fig. 5). These results corroborate studies in the literature that reported a significant correlation between ECa and exchangeable cations (Ca2+ and Mg2+) (Costa et al., 2014; Sana et al., 2014). This correlation is mainly due to the fact that exchangeable cations, located in surface soil, conduct electrical current (Corassa et al., 2016).

Correlations Between ECa and WHC and Soil Water Content

The variables ECa and WHC showed significant correlations with each other for all frequencies. However, strongly positive correlations (r ≥ 0.7) were found only at frequencies of 1, 10, and 40 Hz. The variables ECa and soil water content showed strongly positive and significant correlations with each other (r > 0.7), for all frequencies studied (Fig. 6).

Some reports also attested to the existence of a significant correlation between WHC and ECa (Lo et al., 2017; Martínez-Casasnovas et al., 2017). This is due to the fact that WHC is related to clay content in the soil (Rawls et al., 2003), which in turn is associated with exchangeable cations. Exchangeable cations are directly related to ECa (Corassa et al., 2016; Corwin & Lesch, 2003).

ECa is a response function of the soil water content (Misra & Padhi, 2014; Molin & Faulin, 2013; Robinson et al., 2009), which explains the significance of the correlations found in the present study and in previous reports (Costa et al., 2014; Käthner et al., 2017; Tsoulias & Zude-Sasse, 2018).

Correlations Between ECa and Organic Matter

The organic matter content present in the soil and the ECa values showed significant correlations (between 0.64 and 0.77, p < 0.05) for all frequencies studied. Only for the frequencies of 1, 10, 30, and 40 Hz, these variables were strongly correlated (r > 0.7) (Fig. 6). This can be explained by the indirect relationship between ECa and organic matter, where the organic matter content influences the degree of soil saturation, in addition to providing a conductance path mainly through exchangeable cations (Allred et al., 2008; Fontana et al., 2014; Minasny & McBratney, 2018). The results presented in Fig. 4 prove this relationship, with the organic matter being strongly correlated with exchangeable cations (Ca2+ and Mg2+) and SWC (r > 0.7). Both exchangeable cations and SWC showed significant correlations with ECa values (between 0.66 and 0.86, p < 0.05).

Studies in the literature diverge in relation to the existence of a significant correlation between ECa and organic matter, with some reporting its existence (Machado et al., 2015; Valente et al., 2012) and others reporting the lack of significance (Sana et al., 2014; Sanches et al., 2018). However, due to the indirect relationship of the organic matter content with ECa, the variation of other factors that have a direct relationship with ECa may have been the cause of the difference between these results.

Correlations between ECa and soil texture

Negative and significant correlations (r ≤ −0.47) were found between ECa and coarse sand content in the soil for all frequencies studied (Fig. 6). Between ECa and fine sand content, negative and significant correlations (r ≤ −0.36) were found for the frequencies of 1, 10, and 30 Hz. The other frequencies did not show significant correlations (Fig. 5). Between ECa and clay content, positive and significant correlations (r ≥ 0.51) were found for all frequencies studied (Fig. 6). Those correlations can be explained by the indirect relationship between ECa and soil texture. The clay content influences the degree of soil saturation. Furthermore, it serves as a conductance pathway, primarily through exchangeable cations, in the solid-liquid phase (Allred et al., 2008), and the sand content is inversely related to soil water content (Korsaeth, 2008). The results presented in Fig. 5 prove this relationship, where the clay content showed positive and significant correlations with SWC and exchangeable cations, and the sand content showed negative and significant correlations with SWC. In turn, ECa showed significant and positive correlations with exchangeable cations and SWC.

These results confirm findings from previous studies that reported the existence of significant correlations between ECa and sand content (Gholizadeh et al., 2012; Siri-Prieto et al., 2006), and between ECa and clay content (Corwin & Lesch, 2013; Gholizadeh et al., 2012; Sanches et al., 2018). However, correlations between ECa and fine sand content were not significant for the frequencies of 5, 20, and 40 Hz. This is mainly due to the fact that the sampling points have a similar soil composition, resulting in a low range of fine sand content data present in the soil (Tables 3 and 4). A similar situation was reported by Stadler et al. (2015), who found no significant correlation between soil clay content and ECa in two of their experimental fields.

Homogeneity of the Relationship Between Electrical Current Frequency and Soil Properties

Figure 7 presents a bar graph with the observed and expected frequencies of the number of significant correlations between ECa and soil attributes at each of the frequencies of electric current studied. For the electric current frequencies of 1, 5, 10, 20, 30, and 40 Hz, it was observed 11, 10, 11, 9, 11, and 10 significant correlations between ECa and the studied soil attributes, respectively (Fig. 6). To calculate the expected frequency of the number of significant correlations, it was considered that the number of significant correlations does not depend on the frequency of electrical current used to determine the ECa. For this, the expected frequency of the number of correlations for each frequency of electric current was equivalent to the arithmetic mean of the six observed frequencies (Fig. 7).

Fig. 7
figure 7

Bar graph with the frequency of the number of observed and expected correlations of soil apparent electrical conductivity, related to their respective frequencies of sensor electrical current, calculated for the chi-square statistical test (p ≤ 0.05)

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According to the chi-square test, the number of significant correlations was homogeneous (p ≤ 0.05) at all frequencies of electric current. This result corroborates that obtained by the F-test, which indicates the absence of the effect of electric current frequency on the ECa determined by the sensor. As a result, it is not possible to determine an ideal frequency range (below 40 Hz) within which the value read by the sensor is correlated with chemical and physical attributes of the soil.

Strengths and Limitations

Strengths of this study include its novelty and its direct application to the establishment of evidence-based recommendations for farm management. To the best of our knowledge, there are no in-depth studies that analyze the influence of low-frequency ranges of electric current and ECa values obtained by sensors. The present study focused on analyzing whether different frequencies, below 40 Hz, influence the soil ECa value obtained by sensors. In addition, this study evaluated whether there is a correlation between the frequency range and the soil attributes of agronomic interest. In sum, those findings will serve as guidelines for the operation of ECa sensors for soil surveys. Moreover, it should provide important information to researchers and sensors’ manufacturers about the influence of electric current frequencies, below 40 Hz, on ECa values obtained by sensors.

There are a few limitations of this study that need to be considered. First, the lack of variability of types of soils under study restricts the findings to clayey soils. Although the experimental fields were being used to grow different crops, all of them presented the same type of soil, which is prevalent in the region where the study was conducted. Due to logistic and funding constraints, different types of soils were not considered in the study.

Lastly, the frequency range studied is limited. This study focused on low frequencies of electric current due to the fact that signal frequency increases the uncontrolled effects on the electrodes (Lück & Rühlmann, 2010). Moreover, the effects of high frequencies on ECa values have already been studied. Tromp-van Meerveld and McDonnell (2009) found the existence of strong linear relationships (R2 > 0.75) between the ECa values measured by the different frequencies, within the range from 7.29 to 14.01 kHz.

Conclusions

This study showed that the frequency between 1 and 40 Hz of an ECa sensor does not significantly affect the measured conductivity value. Furthermore, it was observed that the number of significant correlations between ECa and the chemical and physical attributes of the soil was statistically similar in all frequencies of electric current. Therefore, all frequencies in the range of 1 to 40 Hz can be used to correlate ECa values with chemical and physical soil attributes.