Soil ECa measurements were made using the Veris 3100® sensor system (Fig. 2), at a low soil moisture content. The Veris 3100 device has six disc-shaped metal electrodes (coulter), which penetrate approximately 6 cm into the soil. One pair of electrodes passes electrical current into the soil, while the other two pairs measure the voltage drop. The measurement depth is based on the distance between the emitting and receiving coulter-electrodes. The system can be set up to work in configuration A (0-0.30 m) or B (0-0.90 m). Configuration A comprises the inside coulters (2, 3, 4, 5) and voltage is measured between the innermost ones (3 and 4). In configuration B, the four outside coulters (1, 2, 5, 6) include the 0-0.90 m deep measurement, and the voltage gradient is measured between coulters 2 and 5. Output from the Veris data logger reflects the conversion of resistance to conductivity (1/Resistivity = Conductivity). In this work, the ECa was measured at 0-0.30 m because 80% of the pasture roots are found at this depth (Doll & Deregibus, 1986). The Veris 3100 sensor was pulled across each field behind a pick-up truck (Fig. 2), taking simultaneous and geo-referenced ECa measurements in real-time with a differential GPS with sub-meter measurement accuracy and configured to take a satellite position once per second. The differential GPS was installed over the Veris 3100. On average, travel speeds through the field mapping ranged between 7 and 11 km/h, corresponding to about 2–3 m spacing between measurements in the direction of travel. For ease of maneuvering, the field was traversed in a series of parallel transects spaced from 15 to 30 m intervals, because a spacing greater than 30 m generates measurement errors and information loss (Farahani & Flynn, 2007). Elevation data were collected at the same time as the ECa data, using a differential GPS (vertical accuracy of 3-5 cm).

The structure of the ECa and elevation were quantified using geostatistics analysis, which were estimated as Isaaks & Srivastava (1989):

Based on geophysical surveys a grid composed by 12 points was sampled at a depth of 0-0.30 m. Each point represents the spatial variability of the plot. Each composite soil sample (three subsamples) was stored in a plastic bag and air dried in the laboratory. The following soil parameters were obtained: i) soil organic matter content (OM), using the Walkley & Black (1934) method; ii) pH, using a glass electrode at a 1:2.5 soil/water ratio suspension; iii) the electrical conductivity of the saturation extract (EC_ext), following the Chapman (1965) method; iv) available phosphorous (P) was determined according to the Bray & Kurtz (1945) method; and v) anaerobically incubated nitrogen (Nan), following the Echeverría et al. (2000) method.

Descriptive statistics were determined for elevation, ECa, and soil properties. Georeferenced buffers of 15 m (Peralta & Costa, 2013) were created around each soil sampling point using ArcGIS 9.3.1 (ArcGIS v9.3.1, ESRI, Redlands, CA, USA) and the ECa mean was calculated within the buffer areas. Linear regressions were computed between this ECa mean and soil properties with PROC REG (SAS Inst., 2007).

Principal component analysis (PCA) was used to examine the relationship between soil properties and to estimate which of these exert a greater influence on ECa. Principal components (PCs) become new, independent and random variables that can be used to identify which studied soil properties influence ECa. Any PCs with an eigenvalue > 1 explains a significant soil property variance (Peralta & Costa, 2013) and therefore were used in a stepwise-regression procedure (SAS Institute, 2007) to determine if there was a significant relationship between the PCs and ECa. The stepwise-regression procedure repeatedly alters the model by adding or removing the PCs predictor until the significance level of the last one is above 0.15. When the PCs remaining in the regression model accounted for >50% of the ECa measurement variability, the eigenvectors (loading factors) were examined and the soil properties in the PCs ranked according to the amount of variability explained by the PCs. Soil properties with loading factors <0.4 were not considered key latent variables because they did not substantially influence the relationship between the PC groups and the ECa.

The relationships between soil properties and ECa are shown in Table 2. The pH and EC_ext were positively associated with ECa. These high correlations are expected because they reflect the influence of salts and pH on the measured ECa and because these properties are highly correlated (Corwin et al., 2003; Peralta & Costa, 2013). Salts concentration and pH increased soil solution conductivity and is consistent with findings in previous studies (Rhoades et al., 1989; Kaffka et al., 2005). These results also agree with those reported by Peralta et al. (2013) in agricultural soils of the Argentinean pampas.

Our results showed that significant and negative correlation coefficients were found between ECa and OM (Table 2). This may be due to the fact that in these soils, the areas with thin superficial horizon, and in consequence with lower OM content, also have the highest solute concentrations (Batista et al., 2005), which increases the measured ECa. On the contrary, in agricultural soils of the Argentinean pampas, a direct association was established between ECa and OM (Peralta et al., 2013).

The P showed a weak association with ECa (Table 2). This nutrient is less positively correlated, but still significant (p<0.05). Jung et al. (2005) mentioned that the low association between ECa and P is attributable to the influence of the fertilization method (band application) usually used in the Argentinean pampas (Simón et al., 2013). On the contrary, no association was established between ECa and Nan (Table 2). This behavior may be explained by variation and low concentrations of N. These results agree with those reported by Peralta & Costa (2013).

A significant relation was found between some soil properties (pH, OM, EC_ext and P) and ECa (Table 2). However, due to the co-linearity of the independent variables, mutivariate statistical methods that include PC analysis are more appropriate to evaluate the relation between soils properties and ECa (Moral et al., 2010; Peralta & Costa, 2013).

Table 3 shows the three first PCs. These PCs had a cumulative variance of more than 95%. The first PC (PC1) explained 69% of the total variance and was positively influenced by pH and EC_ext, and negatively by OM (loading factors>0.4) (Table 3). On the other hand, the second PC (PC2) and third PC (PC3) only explained 20 and 10% of the total variance respectively. PC2 was highly related to Nan, whereas PC3· was related to P (Table 3).

PCs with an eigenvalue greater than 1 explain a significant amount of the variance present in the soil properties (Peralta & Costa, 2013). In this case only PC1 had an eigenvalue greater than 1 (Table 3). Confirming this, the PC-stepwise regression analysis only retained PC1 (Table 3). Therefore, EC_ext, pH and OM were considered key latent variables because they substantially influence the relationship between the PC1 and the ECa (loading factors>0.4) (Table 3). Conversely, as previously mentioned, PC2 and PC3 showed a more intense relationship with Nan and P (Table 3). Nevertheless, these PCs were not retained in the PC-regression model. Figure 5 shows the spatial distribution of PC1. The sites with lower values of PC1 correspond to sectors of the field where the ECext and the pH are low and the OM is high.

As conclusion, identification of regression models that were able to account for a large portion (50%) of the variability in soil ECa would indicate situations where this parameter could be used successfully to measure soil properties (Heiniger et al., 2003). Our model explained slightly more than 69% of the total variation of the ECa measured. Therefore, our results provide evidence that soil ECa is useful in identifying sites with different pH, EC_ext and OM (loading factors>0.4) in soils used for livestock. Thus, ECa can be used as a support tool to implement site-specific management in permanent pastures.

This study shows that soil pH, OM and EC_ext have a reasonably strong spatial correlation with the ECa of the soil. The use of geo-electric sensors in the particular type of soil of the studied site can be promising for the nutritional management of pastures. This will enable increased economic, environmental and energy efficiency. It also allows mapping the soil at field scale with a low input of resources.