The apparent soil electrical conductivity (E
The quality of soil data collection for precision agriculture has a very important influence, since it has been found that acquisition of exhaustive information in this phase supports the use of geospatial technologies for the estimation of soil spatial variability and later on assists in the determination of “management units.” However, for assessing the soil spatial variability, a large number of samples are generally needed, which considerably increases costs of sampling and analysis. Notwithstanding, the sampling process can be improved, using soil variables that can be recorded or measured quickly, which can help in enhancing the estimation of other soil properties more difficult to measure.
The measurement of apparent soil electrical conductivity (ECa) allows the collection of information on the field and on the spatial distribution of other properties that are correlated. In accordance with Corwin and Rhoades [
The use of geostatistics has great advantages because it allows the study of the spatial variability of soil properties. Kriging is a geostatistical method that can be used to predict the value of soil properties in unsampled locations, favouring the application of differentiated soil management in precision agriculture. Several authors have devised soil sampling schemes directed by properties that directly or indirectly influence crop yield [
Based on the above rationale, the objectives of this work were as follows:
The experimental field is 6 ha in surface and it is located in Castro Ribeiras de Lea (Lugo, NW Spain). Geographic coordinates are 43°09′49′′N and 7°29′47′′ W, average elevation is 410 m, and mean slope is 2% (Figure
Geographical location of the study area (a). Field digital elevation model (b).
The area where the field is located is considered to be representative of both the topographic patterns and the main soil type of the region “Terra Cha,” which is characterized by an extensive livestock production, on a landscape with seasonal conditions of hydromorphy, due to impeded drainage.
The crop succession of the experimental site was fallow-silage corn (
The soil was classified as a Gleyic Cambisol [
Soil texture data for a representative profile of the study area.
Horizon | Depth (m) | Organic matter | Clay | Silt | Sand | Gravel |
---|---|---|---|---|---|---|
g dm−3 | g kg−1 | |||||
Ap | 0.0–0.35 | 50.50 | 175 | 191 | 634 | 370 |
Bw | 0.35–0.70 | 7.20 | 192 | 207 | 591 | 448 |
Btg | >0.70 | 2.60 | 479 | 280 | 241 | — |
Apparent soil electrical conductivity (ECa) was measured using electromagnetic induction equipment EM38-DD [
To complete continuous record of the apparent soil electrical conductivity in horizontal dipole (ECa-H, mS m−1) and in vertical dipole (ECa-V, mS m−1) (Figures
Scheme showing apparent electrical conductivity (ECa) continuously recorded (line) and the location of 40 soil sampling points (circles) on 23/6/2008 (a) and cart containing the EM38-DD equipment and GPS (b).
Scheme showing apparent electrical conductivity (ECa) recorded during 14/3/2008 (a) and 3/4/2008 (b).
The reference measurements of ECa-H and ECa-V were performed on 23/6/2008 at 1859 sampling points following the scheme presented in Figure
In the 40 points selected during the ECa campaign of 23/6/2008, soil samples were taken at the 0.0–0.3 m depth with a manual soil probe. Soil texture, soil water content, and electrical conductivity of saturated paste extracts were determined using standard methods. Soil texture (clay, silt, and sand, in g kg−1) was determined by the sieve-pipette method, following Camargo et al. [
All the values were statistically analyzed using SPSS package 11.5 at 5% level of SNK (Student-Newman-Keuls) method ANOVA. The test of normality Kolmogorov-Smirnov was used to test the normality of data with probability of error 1% (
The analysis of the spatial variability of soil physical properties was conducted using the experimental variogram; the fitting of variogram model was performed using the method described by Vieira [
Cross-variogram was used to study the spatial correlation between soil variables; when there was a trend in some of these variables, universal cokriging was used [
Statistical analysis of the data (Table
Statistical parameters of the continuously recorded ECa data sets and the soil properties analyzed.
Date | Variable | Unit |
|
Min. | Max. | Mean ± SD | Variance | CV | Skew | Kurt |
|
---|---|---|---|---|---|---|---|---|---|---|---|
14/3/2008 | ECa-V | mS m−1 | 1887 | 5.75 | 18.38 | 10.48 ± 1.19 | 1.42 | 11.35 | 0.527 | 0.124 | 0.045Ln |
14/3/2008 | ECa-H | mS m−1 | 1887 | 9.25 | 19.00 | 14.1 ± 0.77 | 0.60 | 5.46 | 0.065 | 1.810 | 0.040Ln |
3/4/2008 | ECa-V | mS m−1 | 1871 | 9.63 | 20.50 | 14.04 ± 2.15 | 4.64 | 15.31 | 0.662 | 0.083 | 0.073Ln |
3/4/2008 | ECa-H | mS m−1 | 1871 | 6.63 | 19.50 | 14.59 ± 0.77 | 0.60 | 5.28 | 0.160 | 10.51 | 0.095Ln |
23/6/2008 | ECa-V | mS m−1 | 1886 | 4.13 | 20.13 | 11.21 ± 2.47* | 6.12 | 22.03 | 0.485 | −0.243 | 0.071Ln |
23/6/2008 | ECa-H | mS m−1 | 1886 | 6.63 | 20.00 | 12.12 ± 1.79* | 3.22 | 14.77 | 0.839 | 1.285 | 0.092Ln |
23/6/2008 | CEe | mS m−1 | 40 | 7.00 | 28.00 | 13.82 ± 5.09 | 25.94 | 36.83 | 1.200 | 1.008 | 0.159n |
23/6/2008 | Clay | g kg−1 | 40 | 119.00 | 220.00 | 168.37 ± 30.92 | 956.54 | 18.36 | −0.190 | −1.321 | 0.153n |
23/6/2008 | Silt | g kg−1 | 40 | 233.00 | 357.00 | 296.25 ± 35.06 | 1229.73 | 11.83 | 0.149 | −1.041 | 0.098n |
23/6/2008 | Sand | g kg−1 | 40 | 487.00 | 586.00 | 535.37 ± 22.53 | 507.72 | 4.21 | 0.055 | −0.270 | 0.069n |
23/6/2008 |
|
% | 38 | 13.41 | 45.67 | 26.74 ± 6.81 | 49.50 | 25.47 | −2.904 | 6.510 | 0.085n |
Only data ECa-V and ECa-H sampling in 23/6/2008 did not show differentiation by the average test (ANOVA) between the different sampling dates.
The values of the electrical conductivity of the saturation paste extract of the soil (ECe) are higher than the values of ECa-V and ECa-H; this fact is because ECe is a parameter that depends on the content of anions and cations in the soil solution; the water content is homogeneous in all samples, because the sample is saturated with water, and the soil apparent electric conductivity values measured with the equipment EM38-DD (ECa-V and ECa-H) are very influenced by the soil water content [
ECa-V and ECa-H measured on several sampling dates (14/3/2008, 3/4/2008, and 23/6/2008) showed lognormal distribution (Table
In the geostatistical analysis, lognormal transformation was used for properties that showed lognormal distribution. The highest values of coefficient of correlation between ECa variables and clay and silt content are on the first measurement date (14/3/2008); on this date the soil moisture is lower coincided with data of precipitation and evapotranspiration (Table
Precipitation and reference evapotranspiration between successive dates, in which apparent soil electrical conductivity (CEa-V and CEa-H) was recorded.
Period | Precipitation (mm) | Reference evapotranspiration (mm) |
---|---|---|
15/2/2008–14/3/2008 | 52.6 | 38.0 |
14/3/2008–3/4/2008 | 80.4 | 37.1 |
3/4/2008–23/6/2008 | 397.8 | 214.6 |
The coefficient of correlation values between the apparent soil electrical conductivity (ECa-V and ECa-H) measurement on several sampling dates (14/3/2008, 3/4/2008, and 23/6/2008) presented moderate positive correlation coefficient values (0.5 ≤
The coefficient of correlation between
The values of log ECa-V are affected by the groundwater level, so the variogram follows the trend in the ground water level (Figure
Standardized sample semivariogram for log ECa-V (a) and log ECa-H (b) recorded during three successive dates.
Corwin and Lesch [
In order to improve the correlation between the values of ECa-V and ECa-H with clay content, soil water content should be as homogeneous as possible within the study area, better if its value is closer to field capacity and unlike the water table is as low as possible, so the best time to take measurements under these conditions would be in the autumn, when there was heavy rainfall, although under these conditions the water table probably would not have ascended enough to be close to the surface.
The initial geostatistical data analysis showed that the physical properties of the soil (clay, silt, sand, and gravimetric water content) showed no trend, then being possible the estimate of the variable using the original data with ordinary kriging. Moreover, ECe data and apparent electrical conductivity of the soil (ECa-V and ECa-H) on several sampling dates show trend in Figure
The fitted variogram parameters (Table
The spatial variability maps obtained with universal kriging (Figure
Kriging maps of apparent soil electrical conductivity (ECa-V and ECa-H) from the continuous records made in 14/3/2008, 3/4/2008, and 23/6/2008.
It is observed that the maps of ECa-V and ECa-H (Figure
Kriging maps of the soil properties analyzed.
The map of the electrical conductivity of the saturation extract (ECe, Figure
Table
The spatial variability maps constructed using ordinary and universal cokriging (Figure
Map of gravimetric water content,
When taking into account all the soil properties studied, ECa and gravimetric soil water content measured at the same date, that is, 23/6/2008, showed the highest coefficients of correlation Table
Linear correlation matrix between the continuously recorded ECa data sets and the soil properties analyzed.
14/03/2008 | 03/04/2008 | 23/06/2008 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|
|
ECe | Clay | Silt | Sand |
|
||
14/03/2008 |
|
1.000 | ||||||||||
14/03/2008 |
|
0.780 | 1.000 | |||||||||
03/04/2008 |
|
0.972 | 0.759 | 1.000 | ||||||||
03/04/2008 |
|
0.724 | 0.796 | 0.792 | 1.000 | |||||||
23/06/2008 |
|
0.861 | 0.729 | 0.855 | 0.688 | 1.000 | ||||||
23/06/2008 |
|
0.541 | 0.644 | 0.515 | 0.569 | 0.751 | 1.000 | |||||
23/06/2008 | ECe | 0.127 | 0.185 | −0.012 | −0.141 | 0.156 | 0.224 | 1.000 | ||||
23/06/2008 | Clay | 0.344 | 0.495 | 0.252 | 0.197 | 0.253 | 0.346 | 0.221 | 1.000 | |||
23/06/2008 | Silt | −0.247 | −0.423 | −0.172 | −0.216 | −0.137 | −0.228 | −0.145 | −0.773 | 1.000 | ||
23/06/2008 | Sand | −0.086 | −0.012 | −0.076 | 0.065 | −0.133 | −0.119 | −0.076 | −0.170 | −0.494 | 1.000 | |
23/06/2008 |
|
* | * | * | * | 0.685 | 0.648 | 0.005 | 0.221 | −0.145 | −0.075 | 1.000 |
**To correlate the measurement of
Fitted semivariogram parameters and respective models of the continuously recorded ECa data sets and the soil properties analyzed.
Date | Variable | Geostatistical method | Model |
|
|
|
SD |
---|---|---|---|---|---|---|---|
14/03/2009 |
|
UK | Spherical | 0.0001 | 3.14 | 105.00 | 0.00 |
|
UK | Spherical | 0.14 | 0.302 | 44.00 | 31.67 | |
|
|||||||
03/04/2008 |
|
UK | Spherical | 0.00 | 5.10 | 145.00 | 0.00 |
|
UK | Spherical | 0.10 | 0.32 | 40.00 | 23.80 | |
|
|||||||
23/06/2008 |
|
UK | Spherical | 0.001 | 0.01 | 130.00 | 9.09 |
|
UK | Spherical | 0.001 | 0.005 | 130.00 | 1.96 | |
ECe residual | UK | Spherical | 0.00025 | 0.0018 | 100.00 | 9.09 | |
Clay | OK | Spherical | 0.001 | 1060.00 | 130.00 | 0.00 | |
Silt | OK | Spherical | 0.00 | 1400.00 | 130.00 | 0.00 | |
Sand | OK | Spherical | 0.00 | 510.00 | 70.00 | 0.00 | |
|
OK | Spherical | 0.001 | 60.00 | 130.00 | 0.00 |
UK: universal kriging; OK: ordinary kriging;
Fitted cross-semivariogram models and respective parameters between gravimetric water content (principal variable) and
Variable | Geostatistical method | Model |
|
|
|
|
---|---|---|---|---|---|---|
23/6/2008 |
|
Universal cokriging | Spherical | 1.00 | 20.00 | 130.00 |
23/6/2008 |
|
Ordinary cokriging | Spherical | 0.00 | 15.00 | 130.00 |
Correlation coefficients between measured gravimetric water content and data estimated by kriging and cokriging.
|
0.637 |
|
0.746 |
|
0.756 |
The spatial patterns of spatial variability of the logarithmic values of apparent soil electrical conductivity (ECa) and the electrical conductivity of the soil saturated paste (ECe) were modeled by universal kriging, whereas those of sand, clay, silt, and gravimetric water content were modeled by ordinary kriging. The use of cokriging with ECa data as secondary variable improved the estimation of the gravimetric soil water content with respect to the use of kriging.
Apparent soil electrical conductivity
CEa in vertical dipole
CEa in horizontal dipole
Electrical conductivity of soil saturation extract.
The authors declare that there is no conflict of interests regarding the publication of this paper. The referencing brands of commercial products in the paper do not imply that the authors recommend the equipment utilized in this study. The support funds presented by the Development Agencies do not show conflict of interests regarding the publication of this paper.
The authors are grateful to the Ministerial de Asuntos Exteriors y de Cooperation (MAEC-AECID) from Spain for the granting of scholarships for Ph.D. studies. This work has been funded by Ministerial de Education y Ciencia, within the framework of research Project CGL2005-08219-C02-02, and cofunded by Xunta de Galicia, within the framework of research Project PGIDIT06PXIC291062PN and by the European Regional Development Fund (ERDF). The authors acknowledge the provincial farm Gayoso Castro of the Deputation of Lugo for allowing the use of their facilities to carry out this work. The authors thank the CNPq (National Council for Scientific and Technological Development (Brazil)) and FACEPE (Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco (Brazil)) and they also thank CNPq for the scholarship DCR—Regional Scientific Development awarded to the first author. Also thanks are given to FAPEMA, MA, Brazil, for funding the publication of this paper. The authors would like to thank two anonymous reviewers for the comments that undoubtedly improved the quality of this paper.