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E.E.R. 2024, 14(2): 106-125 |
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Asghari S, Shahab Arkhazloo H, Hasanpour Kashani M. Geostatistical Analysis of Soil Penetration Resistance and Shear Strength in Fandoghloo Region of Ardabil. E.E.R. 2024; 14 (2) :106-125
URL: http://magazine.hormozgan.ac.ir/article-1-843-en.html
URL: http://magazine.hormozgan.ac.ir/article-1-843-en.html
Department of Soil Sciences and Engineering, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran , shokrollah.asghari@gmail.com
Abstract: (1031 Views)
1- Introduction
Soil penetration resistance (PR) and soil shear strength (SS) are used to evaluate soil erodibility. Some soil properties such sand, silt, clay, bulk and particle density, total porosity, organic carbon and CaCO3 and also some land characteristics such as percentage and direction of slope, altitude, type and density of vegetation can affect SS and PR. For example, PR values exceed 2.5 MPa, while root elongation is significantly restricted. Most of soil properties have temporal and spatial variabilities. Therefore, it is necessary to use geostatistical methods to simultaneously use quantitative information and geographic location of variables. The forest, range and cultivated soils of Fandoghloo region of Ardabil are located in sloping lands and are subject to erosion. Therefore, it is necessary to know the state of spatial variability of soil SS and PR as two important indicators affected by compaction and also effective on soil erosion in the mentioned area. The main objectives of this research were: 1) Investigating the spatial variabilities and drawing maps of soil SS and PR in forest, range and cultivated lands of Fandoghloo region of Ardabil, 2) Investigating the correlations between soil SS and PR with other soil characteristics in the study area, 3) Determining semivariogram parameters such as semivariogram models, spatial dependence classes and effective range for soil variables, 4) Comparison of the accuracy of geostatistical methods (ordinary kriging (OK) and inverse distance weighting (IDW)) in the interpolation of SS and PR.
2- Methodology
This study was conducted in the forest, range and cultivated lands of Fandoghloo region of Ardabil located at the 25 km of Ardabil city, northwest of Iran (48° 32ʹ 45ʺ to 48° 33ʹ 5ʺ E and 38° 24ʹ 10ʺ to 38° 24ʹ 25ʺ N) at summer 2023. Totally, 80 geo-referenced samples were taken from 0-10 cm soil depth with 50×50 m intervals (15 ha) in cultivated (n=37), range (n=23) and forest (n=20) land uses. Sand, silt, clay, organic carbon (OC) and particle density (PD) were measured in the disturbed soil samples. Bulk density (BD) and field water content (FWC) were measured in the undisturbed soil samples taken by steal cylinders with 5 cm diameter and height. Total porosity was calculated using BD and PD. Soil penetration resistance (PR) was directly measured in the field at three replicates using a cone penetrometer. Soil shear strength (SS) was obtained using shear vane in saturation condition in the field at three replicates. The best fitted semivariograms model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum determination coefficient (R2) for soil variables. Ordinary Kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze spatial variability of soil SS and PR. Spatial distribution maps of soil variables were provided by Arc GIS software. Normality test of data by Kolmogorov–Smirnov test and Pearson correlations were done using SPSS software. Figures were prepared using Excel software. The accuracy of OK and IDW methods in estimating soil SS and PR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria. The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.
3- Results & Discussion
According to the results of coefficient of variation (CV) from the study area, the most variable (CV=58.3 %) soil indicator was PR in range land use, whereas the least variable (CV= 3.95 %) was PD in cultivated land use. The Pearson correlation coefficient (r value) indicated that there are significant correlations between OC with sand (r=0.59) and FWC (r=0.78) and between PR with SS (r=0.31). Also, significant correlations were found between PR with FWC (r=-0.45) and silt (r=-0.36) and between SS with OC (r=0.38), sand (r=0.48) and silt (r=-0.34). The spatial dependency classes of soil variables were determined according to the ratio of nugget variance to sill expressed in percentages: If the ratio was >25% and <75%, the variable was considered moderately spatially dependent; if the ratio was >75%, variable was considered weakly spatially dependent; and if the ratio was <25%, the variable was considered strongly spatially dependent. The strong spatial dependences with the effective ranges of 752 m was found for PR. The medium spatial dependences with the effective ranges of 787 m was obtained for SS. The silt and FWC variables had the least (636 m) and the highest (2282 m) effective range, respectively. The range of influence indicates the limit distance at which a sample point has influence over another points, that is, the maximum distance for correlation between two sampling point. The models of fitted semivariograms were Gaussian for PR and spherical for SS. The high values of CCC and low values of RMSE values indicated the more precision and high accuracy of OK compared with IDW interpolation method in estimating PR in the studied area. According to the RMSE and CCC values, IDW was better than OK to predict SS. Generally, the spatial maps showed that the highest values of soil PR were observed in range land use and the lowest values of soil SS were observed in cultivated land use of the study area.
4- Conclusions
Results showed that PR negatively related to the silt and FWC. Also, SS negatively related to the silt and positively related to the sand and OC. The strong spatial dependency was found for PR and medium spatial dependency was determined for SS in the studied area. The silt revealed the smallest effective range (636 m) among the studied variables. As a suggestion, for subsequent study, soil sampling distance could be taken as 636 m instead of 50 m in order to save time and minimize cost.
Soil penetration resistance (PR) and soil shear strength (SS) are used to evaluate soil erodibility. Some soil properties such sand, silt, clay, bulk and particle density, total porosity, organic carbon and CaCO3 and also some land characteristics such as percentage and direction of slope, altitude, type and density of vegetation can affect SS and PR. For example, PR values exceed 2.5 MPa, while root elongation is significantly restricted. Most of soil properties have temporal and spatial variabilities. Therefore, it is necessary to use geostatistical methods to simultaneously use quantitative information and geographic location of variables. The forest, range and cultivated soils of Fandoghloo region of Ardabil are located in sloping lands and are subject to erosion. Therefore, it is necessary to know the state of spatial variability of soil SS and PR as two important indicators affected by compaction and also effective on soil erosion in the mentioned area. The main objectives of this research were: 1) Investigating the spatial variabilities and drawing maps of soil SS and PR in forest, range and cultivated lands of Fandoghloo region of Ardabil, 2) Investigating the correlations between soil SS and PR with other soil characteristics in the study area, 3) Determining semivariogram parameters such as semivariogram models, spatial dependence classes and effective range for soil variables, 4) Comparison of the accuracy of geostatistical methods (ordinary kriging (OK) and inverse distance weighting (IDW)) in the interpolation of SS and PR.
2- Methodology
This study was conducted in the forest, range and cultivated lands of Fandoghloo region of Ardabil located at the 25 km of Ardabil city, northwest of Iran (48° 32ʹ 45ʺ to 48° 33ʹ 5ʺ E and 38° 24ʹ 10ʺ to 38° 24ʹ 25ʺ N) at summer 2023. Totally, 80 geo-referenced samples were taken from 0-10 cm soil depth with 50×50 m intervals (15 ha) in cultivated (n=37), range (n=23) and forest (n=20) land uses. Sand, silt, clay, organic carbon (OC) and particle density (PD) were measured in the disturbed soil samples. Bulk density (BD) and field water content (FWC) were measured in the undisturbed soil samples taken by steal cylinders with 5 cm diameter and height. Total porosity was calculated using BD and PD. Soil penetration resistance (PR) was directly measured in the field at three replicates using a cone penetrometer. Soil shear strength (SS) was obtained using shear vane in saturation condition in the field at three replicates. The best fitted semivariograms model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum determination coefficient (R2) for soil variables. Ordinary Kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze spatial variability of soil SS and PR. Spatial distribution maps of soil variables were provided by Arc GIS software. Normality test of data by Kolmogorov–Smirnov test and Pearson correlations were done using SPSS software. Figures were prepared using Excel software. The accuracy of OK and IDW methods in estimating soil SS and PR was evaluated by mean error (ME), mean absolute error (MAE), root mean square error (RMSE) and concordance correlation coefficient (CCC) criteria. The CCC indicates the degree to which pairs of the measured and estimated parameter value fall on the 45° line through the origin.
3- Results & Discussion
According to the results of coefficient of variation (CV) from the study area, the most variable (CV=58.3 %) soil indicator was PR in range land use, whereas the least variable (CV= 3.95 %) was PD in cultivated land use. The Pearson correlation coefficient (r value) indicated that there are significant correlations between OC with sand (r=0.59) and FWC (r=0.78) and between PR with SS (r=0.31). Also, significant correlations were found between PR with FWC (r=-0.45) and silt (r=-0.36) and between SS with OC (r=0.38), sand (r=0.48) and silt (r=-0.34). The spatial dependency classes of soil variables were determined according to the ratio of nugget variance to sill expressed in percentages: If the ratio was >25% and <75%, the variable was considered moderately spatially dependent; if the ratio was >75%, variable was considered weakly spatially dependent; and if the ratio was <25%, the variable was considered strongly spatially dependent. The strong spatial dependences with the effective ranges of 752 m was found for PR. The medium spatial dependences with the effective ranges of 787 m was obtained for SS. The silt and FWC variables had the least (636 m) and the highest (2282 m) effective range, respectively. The range of influence indicates the limit distance at which a sample point has influence over another points, that is, the maximum distance for correlation between two sampling point. The models of fitted semivariograms were Gaussian for PR and spherical for SS. The high values of CCC and low values of RMSE values indicated the more precision and high accuracy of OK compared with IDW interpolation method in estimating PR in the studied area. According to the RMSE and CCC values, IDW was better than OK to predict SS. Generally, the spatial maps showed that the highest values of soil PR were observed in range land use and the lowest values of soil SS were observed in cultivated land use of the study area.
4- Conclusions
Results showed that PR negatively related to the silt and FWC. Also, SS negatively related to the silt and positively related to the sand and OC. The strong spatial dependency was found for PR and medium spatial dependency was determined for SS in the studied area. The silt revealed the smallest effective range (636 m) among the studied variables. As a suggestion, for subsequent study, soil sampling distance could be taken as 636 m instead of 50 m in order to save time and minimize cost.
Received: 2024/03/1 | Published: 2024/06/30
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