Relationship between the spatial variability of electrical conductivity and the sodium content of the soil

Precision agriculture uses modern tools capable of facilitating the collection and analysis of georeferenced data. The apparent electrical conductivity (EC a ) of the soil is obtained with remote sensors and is correlated with some soil properties (water storage capacity, organic matter content, salinity and drainage, topography, previous management and textures, among others). Excess salts can be detrimental to plants while high exchangeable sodium content can cause physical and chemical damage to the soil, affecting crop growth. Three plots with an average surface area of ​​80 ha were selected, irrigated with a central pivot, in the southeast of the province of Buenos Aires, Argentina. In each batch, EC was measured atgeoreferenced with a direct measurement sensor. With the data obtained, CE a maps were made using spatial interpolation techniques (Kriging). The fields were divided into zones with the same range of EC a , to take soil samples in which gravimetric moisture (? g ), EC of the saturation extract (EC e ) and soluble cations (Na+, Ca+ 2 and Mg+ 2 ) were measured. ). The sodium adsorption ratio (SAR) was calculated. Data were statistically analyzed using the SAS PROC MIXED procedure. Spatial variability of CE a was observed and an association was detected between CE aand the RAS. There is a significant relationship between the EC a , the EC e and the sodium content of the soil, statistically differentiating the Na+ contents between the different areas of EC a of each plot. Direct EC a measurement sensors are effective tools for spatial estimation of soil sodium content.

Keywords. Georeferencing; Irrigation; Sensor; Precision farming; Sodification.

Relationship between spatial variability of electrical conductivity and soil sodium content


Precision agriculture utilizes modern tools in order to obtain and analyze georreferenced data. Direct measuring sensors of soil apparent electrical conductivity (ECa) are part of these modern tools and are widely used to quantify ECa spatial variability. This variable is correlated with other soil properties (water holding capacity, organic matter content, salinity, drainage, topography, tillage managing and soil texture). Plants are negatively affected by elevated salts amount and elevated exchangeable sodium content, which causes physical and chemical damage of soils, affecting crop’s grown and production. In order to determinate sodium spatial content and distribution, three fields about 80 Has average were selected. All fields are under central pivot irrigation system and are located in the southeast of Buenos Aires province, Argentina. In these fields ECa was measured and georreferenced whit a direct measure sensor. Obtained data was used to create ECa maps in every field using spatial interpolation methods (Kriging). All fields were divided into four different zones, based on its ECa value, where soil samples were taken. Soil samples were laboratory processed in order to determinate gravimetric humidity (?g),, electrical conductivity of soil saturation paste extract (ECe) and soluble cations (Na+, Ca+2 and Mg+2), and sodium adsorption ratio (SAR) was calculated. Experimental data was statistically analyzed using SAS PROC MIXED procedure. We observed CEspatial variability, and associations between ECa and SAR. Significantly relationships between ECa, ECe and soil sodium content were found. Sodium content was statistically differenced between different ECa zones in every field. The ECa direct measuring sensors are accurately tools to estimate soil sodium spatial content.

Key words. Georeferencing; Irrigation; Sensor; Precision Agriculture; Sodicity.



Precision agriculture is the use of modern tools capable of facilitating the obtaining and analysis of georeferenced data. Likewise, it allows the preparation of crop yield maps, facilitating a clearer visualization of the spatial variability that they present. However, these maps do not indicate which are the probable sources of variation, nor the relative importance of each of them. To solve this problem it is necessary to study the soil and know the spatial and temporal variability of its physical and chemical properties as well as other characteristics of the terrain (Ruffo et al,,2006). With this information, site-specific management of the natural resource could be carried out, subdividing the lots into homogeneous areas, which would increase efficiency in the use of inputs, improve the sustainability of the company, protect the environment, and economically benefit the producer (Dinnes et al, 2002).
Complex relationships between geological and pedological processes determine the variability in soil properties, adding that resulting from erosive processes and management history (Bouma & Finke, 1993; Mallarino, 1996; Young et al ,1999). According to Bouma & Finke (1993), management has caused multiple differences in soils of the same series due to changes in depth, fertility and natural structure. This variability produces biases in the frequency distributions of the physical or chemical variables, preventing the use of statistical techniques that are sensitive to the assumption of normality (Young et al, 1999). To solve this drawback, the theory of regionalized variables can be used, which establishes that the values ​​of most soil properties are spatially correlated, that is, they have spatial dependence (Isaaks & Srivastava, 1989; Webster & Oliver, 1990; Timlin et al, 1998).
Interest in managing spatial variability has increased with the adoption of precision agriculture tools and technology. For this reason, it is necessary to develop methods for a better characterization of the soil.
Spatial characterization includes the collection of georeferenced data on variables such as soil nutrients, yield, and crop status. Using remote sensing, differences in vegetation greenness at 10-meter pixel sizes can be identified from thousands of kilometers away, and yield monitors allow productivity measurements to be taken every second. While these tools allow increasing the spatial resolution of the information, the soil analyzes are measured at a relatively low resolution compared to the aforementioned tools. One way to obtain more information from soil sampling is to take a larger number of samples, however this option is often limited by cost and al, 2005).
The electrical conductivity (EC) of the soil is the ability to conduct electrical current, which depends on the amount of positive and negative ions found in the soil solution, therefore the EC of the soil solution is an indicator of the salt content. EC is commonly measured in the laboratory, in the saturation paste extract (Warrick & Nielsen, 1981). The apparent electrical conductivity (EC a ) of a soil is that measured in situ and presents correlations with some properties such as water storage capacity, the presence of contrasting lithological layers, soil types, organic carbon content, salinity and drainage, topography, previous management, and textures (Doolittle et al.,1994). Consequently, by measuring EC a we can indirectly know other chemical and physical properties of the soil under study (Rhoades et al, 1976). There are a variety of methods to characterize the spatio-temporal variability of the ground, including ground penetrating radar (GPR), aerial photography, multispectral imaging, and CE a ; the latter has been the most researched (Corwin & Lesch, 2005).
In precision agriculture, efficient methods to measure soil variability within plots are important (Bullock & Bullock, 2000). EC toit has become one of the most reliable and frequent measures to characterize variability for its application in precision agriculture, due to its reliability and ease of measurement (Rhoades et al., 1992a, 1992b; Corwin & Lesch, 2003). In order to establish a correct relationship between EC a readings and a certain variable, calibration is necessary, clearly establishing the way to perform it and the intention in data collection (Sudduth et al ., 2001).
Excess salts can be detrimental to plants while high exchangeable sodium content can cause physical and chemical damage to the soil, affecting crop growth. Alkaline soils (PSI>15) present pH levels between 8.5 to 10 or more; In this situation, the dispersion of soil colloids is favored, causing rupture of aggregates with the consequent loss of structure and obstruction of the pores, for which reason the hydraulic properties deteriorate: infiltration and saturated hydraulic conductivity decrease (Brady & Weil, 1999).
Salinity can be induced by irrigation. If the irrigation water has a significant amount of Na+ compared to Ca+ 2 and Mg+ 2, and especially if bicarbonate is present, the colloidal complex can become saturated with sodium and an unproductive sodium soil is generated (Brady & Weil, 1999).
The most common source of water for irrigation in the southeast of the province of Buenos Aires is of underground origin, and most of this water has a high content of sodium bicarbonate. The use of this water causes increases in the sodium content of the soil, measured through the sodium adsorption ratio (RAS) and the percentage of exchangeable sodium (PSI) (Costa, 1995).
Soil EC is a measure affected by the combination of soil water content, dissolved salt content, clay content, mineralogy and soil temperature (Tarr et al., 2005 ).
The traditional method to measure the EC of the soil solution is in the saturation paste extract, when it is required to estimate the EC, an alternative is to measure the EC a (Friedman, 2005), which can be done in the field using sensors. of direct measurement by electrodes (SMD). These sensors can collect large amounts of georeferenced data, showing important advantages over traditional methods. These include low cost, increased efficiency, as well as immediate results. In addition, they allow the measurement of the EC a of the soil in the fallow period, without the need for previous information (Sudduth et al ., 2001).
The scarce information existing in the southeast of Buenos Aires on the use of georeferenced automatic sensors that measure the EC a of the soil and allow its characterization, makes it necessary to carry out studies of this measurement and its relationship with other soil properties. In addition, it is necessary to focus the monitoring in areas with mild to moderate affectation by salts, where the soils are at potential risk of becoming salinized (Farifteh, 2006).
The incorporation of agronomic management of sodium within precision agriculture practices is a tool that could allow a more careful use of irrigation, in order to avoid soil deterioration. In order to carry out a spatial management of sodium, we must first obtain an effective method to be able to estimate it with the level of detail necessary for its inclusion in the precision agriculture approach.
The objectives of the present investigation were: i) Quantify the spatial variability of the EC a of the soil in the selected plots of the Southeast of Buenos Aires; ii) Identify and quantify the possible relationships between EC a and the variables analyzed by soil sampling: soluble Na+, soluble Ca+ 2 , Mg+ 2soluble, RAS, electrical conductivity in the saturation extract (CE e ) and ? g .


Location of lots

For this work, 3 lots located in producer fields were selected. Two of them are located close to the town of Pieres: called P1 (110 ha), 38°23’3.78″S, 58°37’19.33″W, and P2 (64 ha), 38°20’45.00″S, 58°39’11.28″W. The third lot is located near the town of Ta-mangueyú, it is called T1 (70 ha), at 38°17’48.85″S, 58°50’3.57″W.

Batch history

The three plots have a history of extensive agricultural crops in Wheat – Sunflower or Soybean – Corn rotations, which at the time of the sampling were in the fallow period. All the lots are managed with sprinkler irrigation through the electromechanical central pivot system with a groundwater pump, and for more than five years they have been irrigated with water of doubtful quality with respect to the RAS, with values ​​between 16 and 18, and of safe quality with respect to CE, with values ​​of 1.7 dS m -1. Irrigations are generally complementary to rainfall and during the summer season, and have the objective of covering the demands of the crops in periods when the rains are insufficient for their growth. The amount of irrigation applied varies according to the year and the crop, irrigating no more than 160-180 mm per year.
The types of soils found in the analyzed plots make up a mosaic of typical argiudols (Semillero Buck series, fine illitic and thermal; gently undulating landscape and normal relief), petrocalcic paleudols (La Alianza series, fine silty, mixed, shallow thermal, landscape of hills and slopes, undulating relief) and vertical argiacuol (Chocorí Series, fine, illithic, thermal, flat landscape, subnormal to concave relief) (Soil Chart of the Argentine Republic).

Sampling date

The EC determinations and the soil sampling were carried out during the year 2008, in the month of June in P1; in July in T1 and in October in P2. Each opportunity for EC determination and soil sampling was selected based on fundamentally soil and climate conditions. At the time of the EC measurements, the soil had between 0.25 and 0.28 m 3 m -3 of 9 g , which indicates that it was wet enough to carry out the measurement since the manufacturer of the SMD indicates a minimum of 0 .10 m 3 m -3 to measure EC a .

EC measurement

EC a was measured using the Veris 3100 (Veris 3100, Division of Geoprobe Systems, Salina, KS). It is an SMD, since the electrodes come into contact with the ground at the time of measurement. This equipment measures the EC a of the soil without the need for any calibration, since it comes calibrated from the factory with built-in software and gathers the data in its memory. The SMD used consists of 6 disc-shaped metal electrodes that penetrate approximately 6 cm into the ground. The two intermediate discs emit an electrical current and simultaneously the other two pairs of electrodes measure the change in voltage, and in this way estimate the EC at. The two central discs measure from 0 to 30 cm while the two extreme discs measure from 0 to 90 cm deep respectively. The depth of the measurement is given by the distance that separates the emitter discs from the receivers, which are mounted on a frame with wheels and a hydraulic lift, which can be dragged across the ground with a vehicle. The implement also has a GPS that makes it possible to make a georeferenced map of EC measurements carried out in the field. The passes of the SMD were made in parallel at an average speed of 15 km h -1 and a distance between passes of 20 meters, obtaining approximately 120 data per hectare.
The EC measurement was georeferenced by coupling a Trimble Geoexplorer 2005 global positioning system (GPS) to the SMD data-logger, so that the EC data at both depths and the exact position ( latitude and length) at the time of measurement. For this research, only the data from CE to
superficial (0-30 cm) were considered , since in soils under supplementary irrigation, the effect of salts and sodium is observed in the first centimeters of soil (Costa, JL, 1999).

Map of CE a

With the data obtained in each batch, the corresponding EC a maps were made using the Surfer software (Surfer version 8, Surface Mapping System, Golden Software Inc., Golden, Colorado). This program makes an experimental semivariogram with the entered data.
The semivariograms express the variance in the EC increments atas the distance between the points increases, which is a way of characterizing the spatial continuity of the data in descriptive spatial statistics. With traditional descriptive statistics it is not possible to describe this type of variability since the spatial location of the data is not considered (Isaaks & Srivastava, 1989). Then, a mathematical model was fitted to the calculated experimental semi-variogram. The model was chosen from a series of mathematical functions that describe spatial relationships, matching the curve of the experimental semivariogram with the curve of the mathematical function. Finally, a cross-validation of each selected and adjusted model was carried out, the adjustment was made in all directions.
The mathematical definition of the semivariogram is presented in the following equation [1]:

where Z(x,y) is the value of the variable of interest at location (x, y), and e is the expected statistical value. Note that the semi-variogram ?(?x, ?y ) is a function of the separation between points (x + ?x, y + ?y ) and not a function of the specific location (x, y). Based on the selected mathematical model, the corresponding map was made by spatial interpolation (Kriging).
The maps obtained were processed, dividing them into 4 ranges of EC a since previous investigations in different soils recommended the division into three to four zones (Fleming et al., 2000; McMillan et al.,1998), since a larger number of zones had few additional advantages (Fraisse et al., 2001). The values ​​and amplitude of the ranges of CE a for each batch were determined based on the distribution observed in the generated histograms.
In each lot, the soil sampling points were defined considering that the 4 CE ranges would be sampled separately , in 4 different places within each range (4 repetitions) and 3 subsamples would be taken for each repetition in a distance that did not exceed the zone bounded by the range from CE to . This way of determining the sampling aims to obtain soil analysis data that encompasses the full range of spatial variability of the EC.a , in order to obtain reliable correlations between variables.

soil sampling

The sampling was carried out with a hydraulic sampler and a GPS. Once the sampler was located on the point to be sampled, a soil cylinder 5 cm in diameter by 30 cm deep was extracted, coinciding with the surface measurement of CE a of the SMD.

Laboratory analysis

Each soil sample was quartered, moisture was determined in a fraction by the gravimetric method (by difference of wet weight and dry weight obtained by drying the sample in an oven at 105 °C) and the other fraction was dried in an oven at 30 °C. C and ground to pass through a 2 mm sieve. Once in the laboratory, the soil extract was obtained. In this extract, the electrical conductivity (EC e ) and the content of soluble Na+, Ca+ 2 and Mg+ 2 were determined (Rhoades, 1982).
EC e determination was performed with an electrode (Thermo Orion model 150 Aplus). The determination of soluble Na+ was carried out by direct reading in the saturation extract with a Corning Photometer 410 flame photometer and Ca+ 2and soluble Mg+ 2 were performed directly on the saturation extract using a Shimadzu AA-6200 atomic absorption spectrophotometer.
With the data for soluble Na+, Ca+ 2 and Mg+ 2 , the sodium adsorption ratio (SAR) of each sample was calculated, as described in equation [2]:

where Na + , Ca +2 and Mg +2 are the concentrations of these cations in the saturation extract.

Analysis of the information

Analysis of variance of the data obtained was made using the PROC MIXED procedure (SAS Institute, 2002), the comparison of means was carried out with the LSMeans method (Mean Least Square Differences).
Each batch was considered as a different locality and the EC a ranges of each batch were considered as treatments for the analysis. The three batches were analyzed as a single data set.
For the organization, manipulation and graphic visualization of the results, geographic information systems (ArcGIS, 2001 and Arc view, 1996) and Google Earth were used. Contour maps (Surfer version 8) were made for each of the evaluated soil properties.


spatial variability

Based on the experimental semivariograms obtained for each batch, combined exponential and linear mathematical models were fitted. Figures 1 , 2 and 3 show the semivariograms with their respective models and the fit of the curves. In all three cases, it was a combination of exponential and linear models that allowed us to obtain the best fit. This information is essential when generating the CE a maps , since the higher the precision in the fit, the better the estimate of the CE a .

Figure 1 . Experimental semivariogram (calculated with the measured data, line and black dots) and fit of the linear exponential theoretical model (blue line) for the apparent electrical conductivity variable in lot P1.
Figure 1 . Experimental semivariogram (calculated with the measured data, line and black dots) and theoretical fit linear exponential model (blue line) for variable apparent electrical conductivity in lot P1.

Figure 2
 . Experimental semivariogram (calculated with the measured data, line and black dots) and fit of the linear exponential theoretical model (blue line) for the variable apparent electrical conductivity in batch T1.
Figure 2 . Experimental semivariogram (calculated with the measured data, line and black dots) and theoretical fit linear exponential model (blue line) for variable apparent electrical conductivity in lot T1.

Figure 3 . Experimental semivariogram (calculated with the measured data, line and black dots) and fit of the linear exponential theoretical model (blue line) for the variable apparent electrical conductivity in lot P2.
Figure 3 . Experimental semivariogram (calculated with the measured data, line and black dots) and theoretical fit linear exponential model (blue line) for variable apparent electrical conductivity in lot P2.

By representing the CE a measurements obtained and interpolated to non-sampled areas with the fitted semivariograms, the contour maps were obtained ( Fig. 4 , 5 and 6 ). In these maps a heterogeneous and skewed distribution was observed, which is spatially dependent. Thus, the usefulness of CE a to detect variability was confirmed, as has already been widely verified (Corwin & Lesch, 2005; Dinnes et al., 2002; Peralta et al., 2012; Herber, 2012; Castro Franco et al., 2012 ). The possibility of classifying the soil using CE toprovides an effective basis for delineating interrelated soil attributes, it also offers a very useful framework for soil sampling, reflecting spatial heterogeneity (Johnson et al, 2001).

Figure 4
 Map of spatial distribution of EC a (mS m -1 ) in lot P1. The circle represents the area irrigated by the central pivot.
Figure 4 . Map of spatial distribution of EC a (mS m -1 ) in the batch P1. The circle represents the area irrigated by the center pivot.

Figure 5
 . Spatial distribution map of the CE a (mS m -1 ) in batch T1. The circle represents the area irrigated by the central pivot.
Figure 5 . Map of spatial distribution of EC a (mS m -1 ) in the batch T1. The circle represents the area irrigated by the center pivot.

Figure 6
 . Spatial distribution map of the CE a (mS m -1 ) in batch P2. The circle represents the area irrigated by the central pivot.
Figure 6 . Map of spatial distribution of EC a (mS m -1 ) in the batch P2. The circle represents the area irrigated by the center pivot.

In the EC a maps of the three plots analyzed (Figs. 4, 5 and 6), a distribution of EC a with higher ranks in the center was observed , which coincides with the irrigated area in each plot. In these zones that are under the central irrigation pivots, the two upper ranges of CE a are concentrated mostly .

Relationship between EC a and concentration of soluble Na+

Each batch was divided into 4 zones using the CE values ​​a , each one with an amplitude of 5 mS m -1 but with a different initial and final value since in each batch the distribution had the same amplitude (20 mS m -1 / 4 ranges = 5 mS m -1 each) ( Table 1 ). The ranges indicated were the same used to delineate the CE a maps ( Figs. 4 , 5 and 6 ).

Table 1 . Apparent electrical conductivity ranges in the lots studied, defined by equal intervals.
Table 1 . Apparent electrical conductivity ranges in lots studied, defined by two equal intervals.

The AIC test (Akaike, 1973) was used to evaluate the different models and the values ​​obtained for each of them were: i.- Composite Symmetry (AIC = 377.7), ii.- Auto-regressive (AIC= 377, 3) and iii.- Simple (AIC = 375.7), with the simple model being the one that best adjusted both soluble Na+ and the rest of the variables analyzed.
There were statistically significant differences (p<0.05) in EC e and soluble Na+ contents between the different ranges of EC a for all the batches studied and no interaction was detected between EC a and batches ( Table 2 ).

Table 2 . Comparison of means between electrical conductivity of the saturation extract (EC e ), soluble Na+, Ca++ and Mg++ for each of the apparent electrical conductivity ranges (EC a ) ‘different letters indicate a significant difference (p>0.05).
Table 2 . Mean comparison between electrical conductivity of the saturation extract (ECe), Na +, Ca + + and Mg + + soluble for each range of apparent electrical conductivity (ECa)

Soluble Na+ contents presented a positive correlation (r=0.61) with EC a in the lots analyzed. In addition, there were statistically significant differences between all CE ranges except between the medium ranges (medium low and medium high) ( Table 2 ). This type of observation has led various authors to make classifications in only three ranges (Fraisse et al., 2001). Another procedure to define the number of ranges, not used in this work, is the cluster analysis carried out by the MZA program ( Kitchen et al.,2005). In our work, it was statistically proven that, both in the analysis of the three batches as a whole and in each batch separately (data not shown) with three ranges is sufficient to classify the areas with different CE a . In addition to the statistical significance, the classification into three ranges has physical significance and the different areas cover a greater proportion of the land, avoiding unnecessary separate sampling of two similar areas (the 2 middle ranges). Reducing the number of zones also reduces sampling cost and labor time, which represents valuable advantages for those wishing to spatially quantify the soil.
The use of maps from CE toit allowed to identify the amplitude of the spatial variability of the lots and guided the soil sampling covering a wide range of variability and quantifying the sodium content with greater certainty than in a traditional grid-type sampling. If, due to the increase in Na+ in the exchange complex, it is necessary to apply an amendment, for example gypsum, knowing the concentration of Na+ and its spatial distribution we can, with appropriate machinery, apply the doses of gypsum appropriate to the Na+ content it has. soil.

Relationship between CE and 6

The moisture content of the soil is one of the main factors that influence the EC a , since the conduction of electricity is carried out in the liquid phase present in the soil (Friedman, 2005).
When finding significant interaction (p<0.05) between ranges and batches for the content of ? g ( Table 3 ), the batches were analyzed with respect to ? g separately. As in the case of sodium, EC a only has a direct and significant correlation with ? g for P1 and T1. In the ANOVA we did not find differences between means in P2 and yes in P1 and T1 ( Table 3 ). However, the results found indicated that the effect of ? gsoil is less than the effect produced by Na+ since the statistically significant differences between treatments found for sodium were not found for ? g ( Table 3 ). In this way, it was demonstrated that the increase in EC a , especially for the high range, is due to sodium and not to the content of ? soil g .

Table 3 . Comparison of means of the sodium adsorption ratio (SAR) and gravimetric moisture (? g ) for each of the apparent electrical conductivity ranges (EC a ) and batches. f ‘ different letters indicate significant difference (p>0.05).
Table 3 . Mean comparison of sodium adsorption ratio (RAS) and gravimetric moisture (qg) for each range of apparent conductivity (CEA) and batches.

Relationship between CE and CE

There were statistically significant differences between ranges of CE a for CE e The differences between ranges of CE a were significant except between the two medium treatments (medium low and medium high) ( Table 3 ).
The similar behavior in the comparison of treatment of soluble Na+ and CE e between ranges of CE a was an expected result since the electrical conductivity depends directly on the concentration of salts in solution.

Relationship between CE a and Mg+ 2 , Ca+ 2 and RAS

There were no statistically significant differences in the contents of soluble Ca+ 2 and Mg+ 2 between ranges of CE a ( Table 2 ). These results coincide with those reported by Corwin and Lesch (2005) who found that Ca+ and Mg+ 2 in saturation extract did not correlate well with EC measurements.
In RAS there was interaction between CE a and batches, for which reason the analysis was carried out from the interaction ( Table 3 ). Table 3 shows the means of the SAR values ​​for the different ranges and batches.
Contrary to what occurs in the case of soluble Na+, statistically significant differences in RAS were only observed between the low range and the rest of the CE a ranges ( Table 3 ) and there were no significant differences for the P1 lot. . This behavior of the RAS can be explained if the distribution of the cations that give rise to it is analyzed. If we remember the RAS calculation equation (Equation [2]), the effect of Na+ is greater than that of Ca+ 2 and Mg+ 2 because they are below the square root. Precisely, it coincides that between the ranges of EC to low and medium low there is the greatest comparative difference with respect to the other continuous ranges and since no differences were found in Ca+ 2and soluble Mg+ 2 between ranges, the variation in the soluble Na+ content would be explaining part of what was observed in the RAS.
Both in the case of Na+ and RAS, the lowest values, corresponding to the range from low EC to, are located almost entirely in the areas that are outside the reach of the central irrigation pivot (Figs. 4, 5 and 6). The fact that this type of distribution occurs proves the effect associated with the quality of irrigation water. In previous analyzes it was determined that the irrigation water used in these plots was of doubtful quality for irrigation. The effect that this water has produced on each lot is clear: a general increase in the RAS of the soil. This is an important fact to take into account when using complementary irrigation, in order not to deteriorate the quality of the soil (Costa, 1995). In the particular case of the batches analyzed, the RAS values ​​found in the medium low, medium high and high ranges from CE tothey were high in T1 and P2 where more years of irrigation accumulate, instead they are lower for P1. The sodification process in these batches is underway and must be followed carefully, especially it is necessary to determine if constant RAS values ​​were reached or if the sodification process is still continuing. In contrast, the EC e values ​​were low, indicating a low salinization potential.


The soils of all the lots analyzed presented spatial variability from EC to quantifiable by means of SMD by electrodes. The sensors that measure the EC a of the soil allow estimating the spatial variability of the sodium content in the irrigated production lots studied.
There was a relationship between the CE measured with SMD and the soluble sodium contents in the extensive production lots of crops under complementary irrigation studied. However, there was no evidence of a relationship between the contents of Ca+ 2 and Mg+ 2 with EC a .
The RAS values ​​found showed that the areas that are under irrigation are in the process of sodification, careful use of irrigation must be made since they are in the tolerance limit values.


The authors of this work thank the agricultural producers who have provided their facilities for carrying out this research, as well as INTA for providing the necessary budget to carry out the corresponding field and laboratory activities.
We also wish to thank Mr. Luis Alonso for his collaboration in the experimental part…


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