Electrical resistivity tomography to delineate greenhouse soil variability

2013 ◽  
Vol 27 (2) ◽  
pp. 211-218 ◽  
Author(s):  
R. Rossi ◽  
M. Amato ◽  
G. Bitella ◽  
R. Bochicchio
Keyword(s):  
Electrical Resistivity ◽  
Spatial Variability ◽  
Electrical Resistivity Tomography ◽  
Clay Content ◽  
Soil Bulk Density ◽  
Soil Variability ◽  
Greenhouse Soil ◽  
Resistivity Tomography ◽  
Yield And Quality ◽  
Soil Spatial Variability

Abstract Appropriate management of soil spatial variability is an important tool for optimizing farming inputs, with the result of yield increase and reduction of the environmental impact in field crops. Under greenhouses, several factors such as non-uniform irrigation and localized soil compaction can severely affect yield and quality. Additionally, if soil spatial variability is not taken into account, yield deficiencies are often compensated by extra-volumes of crop inputs; as a result, over-irrigation and overfertilization in some parts of the field may occur. Technology for spatially sound management of greenhouse crops is therefore needed to increase yield and quality and to address sustainability. In this experiment, 2D-electrical resistivity tomography was used as an exploratory tool to characterize greenhouse soil variability and its relations to wild rocket yield. Soil resistivity well matched biomass variation (R2=0.70), and was linked to differences in soil bulk density (R2=0.90), and clay content (R2=0.77). Electrical resistivity tomography shows a great potential in horticulture where there is a growing demand of sustainability coupled with the necessity of stabilizing yield and product quality.

scholarly journals Effects of machinery trafficking in an agricultural soil assessed by Electrical Resistivity Tomography (ERT)

2018 ◽  
Vol 3 (1) ◽  
pp. 378-385 ◽  
Author(s):  
Aitor García-Tomillo ◽  
Tomás de Figueiredo ◽  
Jorge Dafonte Dafonte ◽  
Arlindo Almeida ◽  
Antonio Paz-González
Keyword(s):  
Electrical Resistivity ◽  
Hydraulic Conductivity ◽  
Bulk Density ◽  
Soil Compaction ◽  
Electrical Resistivity Tomography ◽  
Soil Bulk Density ◽  
Saturated Hydraulic Conductivity ◽  
Soil Horizon ◽  
Resistivity Tomography ◽  
Before And After

Abstract Soil compaction is a serious problem, which is aggravated due to its difficulty to locate and reverse. Electrical resistivity tomography (ERT) is a non-invasive geophysical method that can be used to identify compacted areas, soil horizon thickness and assess soil physical properties. This study assesses the relationship between ERT and soil compaction. Data were collected on a 4-m transect in a fallow plot located at Braganca (Portugal). Measurements were performed before and after tillage and tractor passage. Soil samples at different depths (0-0.05, 0.05-0.1 and 0.1-0.2 m depth) were taken to determine: soil bulk density, porosity, saturated hydraulic conductivity and soil water content. The effect of tillage and tractor passage was more significant on the first 0.05 m depth. In the wheel track areas, ERT suffered a reduction of about 40%, saturated hydraulic conductivity decreased by 70% and bulk density increased by 24%. These results proved that ERT can be a useful tool for assessing soil compaction.

scholarly journals Constrained electrical resistivity tomography Bayesian inversion using inverse Matérn covariance matrix

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. E129-E141 ◽  
Author(s):  
Abderrezak Bouchedda ◽  
Giroux Bernard ◽  
Erwan Gloaguen
Keyword(s):  
Electrical Resistivity ◽  
Electrical Resistivity Tomography ◽  
Clay Content ◽  
Real Data ◽  
Bayesian Inversion ◽  
Resistivity Tomography ◽  
Statistical Homogeneity ◽  
Matérn Covariance ◽  
Matern Covariance ◽  
Structure Orientation

Bayesian inversion using maximum a posteriori estimator is a quantitative approach that has been successfully applied to the electrical resistivity tomography inverse problem. In most approaches, model covariance parameters are generally chosen as stationary and isotropic, which assumes a statistical homogeneity of the studied field. However, the statistical properties of resistivity within the earth are, in reality, location dependent due to spatially varying processes that control the bulk resistivity of rocks, such as water content, porosity, clay content, etc. Taking into account the spatial variability of the resistivity field, we use the nonstationary Matérn covariance family, which is defined through linear stochastic partial differential equations. Two types of prior information are considered: structure orientation and spatially increasing the range with increasing depth. The latter is applied successfully on the first synthetic model, which aims at retrieving the depth of bedrock and the shape of the conductive lens. In the second synthetic example, a conductive dike model embedded into four layers is used to study the performance of structure orientation. Finally, the proposed approach is used to invert real data measured over an extensively characterized sandy-to-silty aquifer. First, the structure orientation of this aquifer was determined by applying a structure tensor calculated using gradients of the ground penetrating radar image. The introduction of this information gives a resistivity model that is more compatible with the aquifer structure.

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