Soil science
Mahsa Hasanpour Kashani; Shokrollah Asghari
Abstract
Introduction Soil available water (SAW) is defined as the difference between field capacity (FC) and permanent wilting point (PWP). FC is the amount of soil water content held by the soil after the gravitational water was drained from the soil. PWP is defined as a minimum water content of a soil which ...
Read More
Introduction Soil available water (SAW) is defined as the difference between field capacity (FC) and permanent wilting point (PWP). FC is the amount of soil water content held by the soil after the gravitational water was drained from the soil. PWP is defined as a minimum water content of a soil which is needed for the crop survival and if the water content decreases lower than PWP, a plant wilts and can no longer recover itself. The direct measurement of FC and PWP soil water contents is very costly and time consuming; Therefore, it is useful the use of different intelligent models such as neuro-fuzzy (NF), gene expression programming (GEP) and random forest (RF) to estimate FC, PWP and SAW through easily accessible and low-cost soil characteristics. The objectives of this research were: (1) to obtain NF, GEP and RF models for estimating SAW from the easily accessible soil variables in the cultivated lands of Ardabil plain (2) to compare the accuracy of the mentioned models in estimating SAW using the coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) criteria.
Materials and methods The measured data from 102 soil samples taken from 0-10 cm soil depth of the cultivated lands of Ardabil plain, northwest of Iran, were used in this study. Sand, clay, mean geometric diameter (dg) and geometric standard deviation (σg) of soil particles, bulk density (BD) and organic carbon (OC) were introduced as input variables to the applied three intelligent models for estimating soil available water (SAW). Data randomly were divided in two series as 82 data for training and 20 data for testing of models. In all models, six different input variables combinations were used; SPSS 22 software with stepwise method was applied to select the input variables. MATLAB, Gene Xpro Tools 4.0 and Weka softwares were used to derive neuro-fuzzy (NF), gene expression programming (GEP) and random forest (RF) models, respectively. One of the important steps using NF method is selecting the appropriate membership functions (MFs) and its numbers. Based on a trial and error procedure, 3 numbers of MFs and 50 to 100 optimum replications were found for the NF modeling. Also, the input MFs were chosen as “triangular”, “trapezoid”, “generalized bell” and “pi” and the output MF was selected as “constant”. A set of optimal parameters were chosen before developing a best GEP model. The number of chromosomes and genes, head size and linking function were selected by the trial and error method, and they are 30, 3, 8, and +, respectively. The rates of genetic operators were chosen according to literature studies. Various tree numbers were analyzed for choosing the best random forest (RF) method. Increasing the tree numbers beyond 100 made lower variations in the average squared error values for the SAW estimation cases. The accuracy of NF, GEP and RF models in estimating SAW was evaluated by coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) statistics.
Results and discussion The studied soils had loam (n= 53), clay loam (n= 26), sandy loam (n= 15), silt loam (n= 6) and clay (n= 2) textural classes. The values of sand (24.40 to 68.00 %), clay (3.80 to 42.90 %), dg (0.02 to 0.26 mm), σg (7.48 to 19.41), BD (1.04 to 1.70 g cm-3), OC (0.31 to 1.52 %) and SAW (5.10 to 25.10 % g g-1) indicated good variations in the soils of studied region. There were found significant correlations between SAW with BD (r= - 0.59**), clay (r= 0.56**), OC (r= 0.45**) and sand (r= - 0.44**). NF, GEP and RF models were applied to estimate SAW using six different combinations of input soil variables (sand, clay, dg, σg, BD and OC). The results of the best NF, GEP and RF models indicated that the most appropriate input variables to predict SAW were OC and BD. The values of R2, RMSE, ME and NS criteria were obtained equal 0.73, 2.51 % g g-1, 0.09 % g g-1and 0.71, and 0.76, 3.10 % g g-1, - 1.41 % g g-1 and 0.56, 0.68, 3.30 % g g-1, - 1.45 % g g-1, 0.50 for the best NF, GEP and RF models in the testing data set, respectively. Numerous investigations also showed that there are significant negative correlation between SAW with BD and sand and positive correlation between SAW with OC and clay.
Conclusion The results of three investigated intelligent models showed that OC and BD were the most important and readily available soil variables to predict soil available water (SAW) in the studied area. According to the lowest values of RMSE and the highest values of NS, the accuracy of NF models to estimate SAW was more than GEP and RF models. RF approach gave the worst estimates for SAW in this research.
Soil science
Sh. Asghari; M. Hasanpour Kashani; H. Shahab Arkhazloo
Abstract
IntroductionThe penetration resistance (PR) of the soil shows the mechanical resistance of the soil against the penetration of a conical or flat probe; it is important in terms of seed germination, root growth and tillage operations. In general, if the PR value of a soil exceeds 2.5 MPa, the growth and ...
Read More
IntroductionThe penetration resistance (PR) of the soil shows the mechanical resistance of the soil against the penetration of a conical or flat probe; it is important in terms of seed germination, root growth and tillage operations. In general, if the PR value of a soil exceeds 2.5 MPa, the growth and expansion of roots in the soil will be significantly limited. The direct measurement of PR is also a laborious and costly task due to instrumental errors. Therefore, it is useful the use of different models such as multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) to estimate PR through easily accessible and low-cost soil characteristics. The objectives of this research were: (1) to obtain MLR, ANN and GEP models for estimating PR from the easily accessible soil variables in forest, range and cultivated lands of Fandoghloo region of Ardabil province, (2) to compare the accuracy of the aforementioned models in estimating soil PR using the coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) criteria. Materials and MethodsDisturbed and undisturbed samples (n = 80) were nearly systematically taken from 0-10 cm soil depth with nearly 50 m distance in forest (n = 20), range (n = 23) and cultivated (n = 37) lands of Fandoghloo region of Ardabil province, Iran (lat. 38° 24' 10" to 38° 24' 25" N, long. 48° 32' 45" to 48° 33' 5" E) in summer 2023. The contents of sand, silt, clay, CaCO3, pH, EC, bulk (BD) and particle density (PD), organic carbon (OC), gravimetric field water content (FWC), mean weight diameter (MWD) and geometric mean diameter (GMD) were measured in the laboratory. Relative bulk density (BDrel) was calculated using BD and clay data. Mean geometric diameter (dg) and geometric standard deviation (σg) of soil particles were computed by sand, silt and clay percentages. The penetration resistance (PR) of the soil was measured in situ using cone penetrometer (analog model) at 5 replicates. Data randomly were divided in two series as 60 data for training and 20 data for testing of models. The SPSS 22 software with stepwise method, MATLAB and Gene Xpro Tools 4.0 software were used to derive multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) models, respectively. A feed forward three-layer (2, 5 and 6 neurons in hidden layer) perceptron network and the tangent sigmoid transfer function were used for the ANN modeling. A set of optimal parameters were chosen before developing a best GEP model. The number of chromosomes and genes, head size and linking function were selected by the trial and error method, as they are 30, 3, 8, and +, respectively. The rates of genetic operators were chosen according to literature studies. The accuracy of MLR, ANN and GEP models in estimating PR were evaluated by coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) statistics. Results and Discussion The studied soils had clay loam (n = 11), sandy clay loam (n = 6), sandy loam (n = 12), loam (n = 13), silty clay loam (n = 14), silty clay (n = 1) and silt loam (n = 23) textural classes. The values of sand (13.14 to 64.79 %), silt (21.11 to 74.96 %), clay (2.95 to 42.18 %), OC (1.01 to 7.17 %), FWC (11.58 to 50.47 mass percent), BD (0.84 to 1.43 g cm-3) and PR (1.03 to 5.83 MPa) showed good variations in the soils of the studied region. There were found significant correlations between PR with FWC (r = - 0.45**), silt (r = - 0.36**) and σg (r = 0.36**). Due to the multicollinearity of silt with σg (r = -0.84**), the σg was not used as an input variable to estimate PR. Generally, 3 MLR, ANN and GEP models were constructed to estimate PR from measured readily available soil variables. The results of MLR, ANN and GEP models showed that the most suitable variables to estimate PR were FWC, silt and BDrel. The values of R2, RMSE, ME and NS criteria were obtained equal 0.44, 1.19 MPa, 0.19 MPa and 0.36, and 0.92, 0.41 MPa, -0.05 MPa and 0.92, 0.79, 0.91 MPa, 0.13 MPa, 0.63 for the best MLR, ANN and GEP models, respectively. The former researchers also reported that there is a negative and significant correlation between PR with FWC. Conclusion The results indicated that field water content (FWC), silt and relative bulk density (BDrel) were the most important and readily available soil variables to estimate penetration resistance (PR) in the studied area. According to the lowest values of RMSE and the highest values of NS, the accuracy of ANN models to predict soil PR was higher than MLR and GEP models in this research.
Soil science
Sh. Asghari; K. Heidari; M. Hasanpour Kashani; H. Shahab Arkhazloo
Abstract
Introduction
The study of soil mean weight diameter (MWD) of wet aggregates that is important for sustainable soil management, has recently received much attention. As the prediction of MWD is challenging, laborious, and time-consuming, there is a crucial need to develop a predictive estimation ...
Read More
Introduction
The study of soil mean weight diameter (MWD) of wet aggregates that is important for sustainable soil management, has recently received much attention. As the prediction of MWD is challenging, laborious, and time-consuming, there is a crucial need to develop a predictive estimation method to generate helpful information required for the soil health assessment to save time and cost involved in soil analysis. Therefore, it is useful to use different models such as multiple linear regression (MLR) and intelligent models including artificial neural network (ANN) and gene expression programming (GEP) to estimate MWD of wet aggregates through easily accessible and low-cost soil properties. The objectives of this study were (1) to creating MLR, ANN and GEP models for predicting MWD from the easily measurable soil variables in forest, range and cultivated lands of the Fandoghloo region of Ardabil province, (2) to compare the precision of the mentioned models in the prediction of MWD of wet aggregates using the coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) criteria.
Materials and Methods
Disturbed and undisturbed soil samples (n= 80) were nearly systematically taken from 0-10 cm depth with nearly 50 m distance in forest (n= 20), range (n= 23) and cultivated (n= 37) lands of the Fandoghloo region of Ardabil province, Iran (lat. 38° 24' 10" to 38° 24' 25" N, long. 48° 32' 45" to 48° 33' 5" E) in summer 2023. The contents of sand, silt, clay, CaCO3, pH, EC, bulk (BD) and particle (PD) density, organic carbon (OC), geometric mean diameter (GMD) of dry aggregates were determined in the laboratory using standard methods. Total porosity (n) was calculated using BD and PD data (n= 1-BD/PD). The mean geometric diameter (dg) and geometric standard deviation (σg) of soil particles were computed by sand, silt and clay percentages. The mean weight diameter (MWD) of wet aggregates was measured in the aggregates smaller than 4.75 mm by wet sieving equipment using sieves with 2, 1, 0.5, 0.25 and 0.106 mm pore diameter. All data were randomly divided into two series as 60 data for training and 20 data for testing of models. The SPSS 22 software with the stepwise method, MATLAB and Gene Xpro Tools 4.0 software were used to derive multiple linear regression (MLR), artificial neural network (ANN) and gene expression programming (GEP) models, respectively. A feed forward three-layer (9, 8, 6 and 6 neurons in the hidden layer) perceptron network and the tangent sigmoid transfer function were used for the ANN modeling. A set of optimal parameters were chosen before developing the best GEP model. The number of chromosomes and genes, head size and linking function were selected by the trial and error method, and they are 30, 3, 8, and +, respectively. The rates of genetic operators were chosen according to literature studies. The precision of MLR, ANN and GEP models in predicting MWD of wet aggregates were evaluated by the coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Nash-Sutcliffe coefficient (NS) statistics.
Results and Discussion
The values of sand (13.14 to 64.79 %), silt (21.11 to 74.96 %), clay (3 to 42.18 %), OC (1.01 to 7.17 %), PD (2.00 to 2.67 g cm-3), n (0.39 to 0.87 cm3 cm-3), GMD of dry aggregates (0.8 to 1.33 mm) and MWD of wet aggregates (0.35 to 2.65 mm) showed good variations in the soils of the studied region. The studied soils had clay loam (n= 11), sandy clay loam (n= 6), sandy loam (n= 12), loam (n= 13), silty clay loam (n= 14), silty clay (n= 1) and silt loam (n= 23) textural classes. There were found significant correlations between MWD with OC (r= 0.67**), sand (r= 0.70**), GMD (r= 0.30**) and PD (r= -0.46**). Also, significant and positive correlation was found between OC and sand (r= 0.59**). Due to the multicollinearity of sand with dg (r= 0.87**), we did not use the dg as an input variable to estimate MWD of wet aggregates. Generally, four MLR, ANN and GEP models were constructed to predict MWD of wet aggregates from measured readily available soil variables. The results of MLR, ANN and GEP models indicated that the most suitable variables to estimate MWD of wet aggregates were sand, OC and GMD of dry aggregates. The values of R2, RMSE, ME and NS criteria were obtained equal 0.52, 0.48 mm, 0.13 mm and 0.48, and 0.85, 0.30 mm, 0.03 mm and 0.78, 0.79, 0.35 mm, -0.10 mm, 0.95 for the best MLR, ANN and GEP models in the testing data set, respectively. Many researchers also reported that there is a positive and significant correlation between MWD of wet aggregates and OC.
Conclusion
The results showed that sand, OC and GMD of dry aggregates were the most important and readily available soil variables to predict the mean weight diameter (MWD) of wet aggregates in the Fandoghloo region of Ardabil province. According to the lowest values of RMSE and the highest values of R2 and NS, the precision of ANN models to predict MWD of wet aggregates was more than MLR and GEP models in this study. Because ANN is more flexible and effectively captures non-linear relationships, it performed better than the other models in predicting MWD.
shokrollah asghari; Mahmood Shahabi
Abstract
Introduction: Over the last few years, due to the depletion of Lake Urmia located in the northwest of Iran, the proportion of surrounding saline agricultural lands increased at a fast pace. Digital mapping of regional soils affected by salt is essential when monitoring the dynamics of soil salts and ...
Read More
Introduction: Over the last few years, due to the depletion of Lake Urmia located in the northwest of Iran, the proportion of surrounding saline agricultural lands increased at a fast pace. Digital mapping of regional soils affected by salt is essential when monitoring the dynamics of soil salts and planning land development and reclamation schemes. The soil hydraulic and mechanical parameters are very important factors that affect water and chemical transport in soil pores. In the salt-affected soils, saturated hydraulic conductivity (Ks) is very low due to the high contents of sodium and weak aggregate stability. Penetration resistance (PR) indicates soil mechanical strength to penetration of a cone or flat penetrometer; it is important in seedling, root growth and tillage operations. Generally, PR values exceed 2.5 MPa, while root elongation is significantly restricted. The analysis of spatial variability of Ks and PR is essential to implement a site-specific soil management especially in the salt-affected lands. The objective of this study was to evaluate the influence of two different bare and agricultural land uses on the spatial variability of Ks and PR in the salt-affected soils around Lake Urmia.
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ to 45° 36ʹ 38ʺ E and 38° 6ʹ 37ʺ to 38° 7ʹ 42ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), mean weight diameter of aggregates (MWD), sodium adsorption ratio (SAR) and electrical conductivity (EC), were measured in the collected soil samples. The EC and SAR were measured in 1:2.5 (soil: distilled water) extract. Ks was measured using constant or falling head method. 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 calculated from BD and particle density (PD). PR was directly measured at the field using a cone penetrometer. The best fit semivariograms model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum coefficient of determination (R2). Ordinary Kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze the spatial variability of Ks and PR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating Ks 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.
Results and Discussion: According to coefficient of variation (CV) from the study area, the most variable soil indicator was Ks (CV=155.6%), whereas the least variable was PD (CV= 3.05%) both in bare land use. The Lognormal distribution was found for Ks data in the studied region. The Pearson correlation coefficients (r values) indicated that there are significant correlations between Ks and OC (r=0.36), sand (r=0.60), SAR (r=-0.35), EC (r=-0.22), BD (r=-0.52), TP (r= 0.31), silt (r=-0.60), and clay (r=-0.43). Also, significant correlations were obtained between PR and FWC (r=-0.32), BD (r=0.21), and TP (r=-0.21). 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 2443m were found for Ks. The PR and PD variables had the least (335 m) and the highest (2844 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 spherical for Ks and exponential for PR. According to RMSE and CCC criteria, there was not found significant difference between Ks estimates by OK and IDW interpolation methods. The high CCC and low RMSE values for OK compared with IDW indicated the more precision and accuracy of OK in estimating PR in the studied area. Generally, the spatial maps showed that from agricultural to bare land use by nearing to Lake Urmia, the BD and PR increased and consequently TP and Ks decreased.
Conclusion: The results showed that Ks negatively related to the SAR, EC, BD, silt and clay and positively related to the OC, sand, MWD and TP in the study area. Also, PR negatively related to the FWC and TP and positively related to the BD and silt. The spatial dependency was found strong for Ks. The PR revealed the smallest effective range (335 m) among the studied variables. As a suggestion, for subsequent study, soil sampling distance could be taken as 335 m instead of 100 m in order to save time and minimize cost.
shokrollah asghari; Mahmood Shahabi
Abstract
Introduction: Salinity and sodicity are the most important land degradation problems particularly in arid and semi-arid regions. Due to the depletion of Urmia Lake located in the northwest of Iran during recent years, the proportion of surrounding saline agricultural lands increased at a past pace. In ...
Read More
Introduction: Salinity and sodicity are the most important land degradation problems particularly in arid and semi-arid regions. Due to the depletion of Urmia Lake located in the northwest of Iran during recent years, the proportion of surrounding saline agricultural lands increased at a past pace. In the salt-affected soils, aggregate stability is weak due to the high contents of sodium. The analysis of spatial variability of mean weight diameter of aggregates (MWD) and sodium adsorption ratio (SAR) is necessary to implement a site-specific soil management especially in the salt-affected soils. The main object of this study was evaluating the effects of different land uses (bare and agriculture) on the spatial variability of MWD and SAR in the salt-affected soils around Urmia Lake.
Materials and Methods: This study was conducted in the agricultural and bare lands of Shend Abad region located at the 15 km of Shabestar city, northwest of Iran (45° 36ʹ 34ʺ E and 38° 6ʹ 37ʺ N). Totally, 100 geo-referenced samples were taken from 0-10 cm soil depth with 100×100 m intervals (80 ha) in agricultural (n=49) and bare (n=51) land uses. Sand, silt, clay, organic carbon (OC), CaCO3, pHe, MWD, SAR and electrical conductivity (EC), were measured in the collected soil samples. Thewet sieving method was used to determine MWD of wet aggregates. The sieves were: 2, 1, 0.5, 0.25 and 0.106mm. The EC and SAR were measured in 1:2.5 (soil: distilled water) extra. The SAR was calculated from concentrations of Na+ and Ca+ + Mg+. The best fit semivariogram model (Gaussian, spherical and exponential) was chosen by considering the minimum residual sum of square (RSS) and maximum determination coefficient (R2). Ordinary kriging (OK) and inverse distance weighting (IDW) interpolation methods were used to analyze spatial variability of MWD and SAR. Spatial distribution maps of soil variables were provided by Arc GIS software. The accuracy of OK and IDW methods in estimating MWD and SAR 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.
Results and Discussion: According to the results of coefficient of variation (CV) from the study area, the most variable (CV=113.05%) soil indicator was SAR (bare land use), whereas the least variable (CV= 3.52%) was pHe (agricultural land use). The Pearson correlation coefficients (r value) indicated that there are significant (P
Gholam Reza Sheykhzadeh; shokrollah asghari; Tarahom Mesri Gundoshmian
Abstract
Introduction: Penetration resistance is one of the criteria for evaluating soil compaction. It correlates with several soil properties such as vehicle trafficability, resistance to root penetration, seedling emergence, and soil compaction by farm machinery. Direct measurement of penetration resistance ...
Read More
Introduction: Penetration resistance is one of the criteria for evaluating soil compaction. It correlates with several soil properties such as vehicle trafficability, resistance to root penetration, seedling emergence, and soil compaction by farm machinery. Direct measurement of penetration resistance is time consuming and difficult because of high temporal and spatial variability. Therefore, many different regressions and artificial neural network pedotransfer functions have been proposed to estimate penetration resistance from readily available soil variables such as particle size distribution, bulk density (Db) and gravimetric water content (θm). The lands of Ardabil Province are one of the main production regions of potato in Iran, thus, obtaining the soil penetration resistance in these regions help with the management of potato production. The objective of this research was to derive pedotransfer functions by using regression and artificial neural network to predict penetration resistance from some soil variations in the agricultural soils of Ardabil plain and to compare the performance of artificial neural network with regression models.
Materials and methods: Disturbed and undisturbed soil samples (n= 105) were systematically taken from 0-10 cm soil depth with nearly 3000 m distance in the agricultural lands of the Ardabil plain ((lat 38°15' to 38°40' N, long 48°16' to 48°61' E). The contents of sand, silt and clay (hydrometer method), CaCO3 (titration method), bulk density (cylinder method), particle density (Dp) (pychnometer method), organic carbon (wet oxidation method), total porosity(calculating from Db and Dp), saturated (θs) and field soil water (θf) using the gravimetric method were measured in the laboratory. Mean geometric diameter (dg) and standard deviation (σg) of soil particles were computed using the percentages of sand, silt and clay. Penetration resistance was measured in situ using cone penetrometer (analog model) at 10 replicates. The data were divided into two series as 78 data for training and 27 data for testing. The SPSS 18 with stepwise method and MATLAB software were used to derive the regression and artificial neural network, respectively. A feed forward three-layer (8, 11 and 15 neurons in the hidden layer) perceptron network and the tangent sigmoid transfer function were used for the artificial neural network modeling. In estimating penetration resistance, The accuracy of artificial neural network and regression pedotransfer functions were evaluated by coefficient of determination (R2), root mean square error (RMSE), mean error (ME) and Akaike information criterion (AIC) statistics.
Results and discussion: The textural classes of study soils were loamy sand (n= 8), sandy loam (n= 70), loam (n= 6) and silt loam (n= 21). The values of sand (26.26 to 87.43 %), clay (3.99 to 17.34 %), organic carbon (0.3 to 2.41 %), field moisture (4.56 to 33.18 mass percent), Db (1.02 to 1.63 g cm-3) and penetration resistance (1.1 to 6.6 MPa) showed a large variations of study soils. There were found significant correlations between penetration resistance and sand (r= - 0.505**), silt (r= 0.447**), clay (r= 0.330**), organic carbon (r= - 0.465**), Db (r= 0.655**), θf (r= -0.63**), CaCO3 (r= 0.290**), total porosity (r= - 0.589**) and Dp (r= 0.266*). Generally, 15 regression and artificial neural network pedotransfer functions were constructed to predict penetration resistance from measured readily available soil variables. The results of regression and artificial neural network pedotransfer functions showed that the most suitable variables to estimate penetration resistance were θf, Db and particles size distribution. The input variables were n and θf for the best regression pedotransfer function and also Db, silt, θf and σg for the best artificial neural network pedotransfer function. The values of R2, RMSE, ME and AIC were obtained equal to 0.55, 0.89 MPa, 0.05 MPa and -14.67 and 0.91, 0.37 MPa, - 0.0026 MPa and -146.64 for the best regression and artificial neural network pedotransfer functions, respectively. The former researchers also reported that there is a positive correlation between penetration resistance with Db and a negative correlation between penetration resistance with θf and organic carbon.
Conclusion: The results showed that silt, standard deviation of soil particles (σg), bulk density (Db), total porosity and field water content (θf) are the most suitable readily available soil variables to predict penetration resistance in the studied area. According to the RMSE and AIC criteria, the accuracy of artificial neural network in estimating soil penetration resistance was more than regression pedotransfer functions in this research.
Sh. Asghari; S. Dizajghoorbani Aghdam; Abazar Esmali
Abstract
Knowledge of the spatial distribution of soil properties is the major issues in identifying, program planning, management and utilization of soil and water resources. This study was carried out to investigate the spatial variability of some important soil physical quality indices including sand, silt, ...
Read More
Knowledge of the spatial distribution of soil properties is the major issues in identifying, program planning, management and utilization of soil and water resources. This study was carried out to investigate the spatial variability of some important soil physical quality indices including sand, silt, clay, mean weight diameter of aggregates (MWD), organic carbon (OC), saturated hydraulic conductivity (Ks), saturated water content (θs) and bulk density (Db) in the three adjacent land uses i.e. forest, agriculture and range land located at Fandoghlou region of Ardabil. Totally, 100 soil samples were systematically (100 × 100 m grade) taken from 0-15 cm depth in spring 2013. At first, the accuracy of Kriging and inverse distance weighting (IDW) geostatisticaly methods in mapping of studied parameters was evaluated then the final map was presented. The values of nugget effect to sill ratio for clay, sand and silt were 0.5, 0.47 and 0.49, respectively so these parameters have an average spatial structure. The values of above mentioned ratio for OC, Db, θs, Ks, and MWD were obtained 0.002, 0.014, 0.0007, 0.05 and 0.008, respectively, indicating strong spatial structure. According to the R2 criteria, Kriging method in estimating clay, sand and silt and IDW method in estimating MWD, OC, Ks ،θs and Db had the highest accuracy. The final map indicated that forest land had higher OC, MWD and Ks and lower Db compared with agriculture and range land. The results of this research showed that soil physical quality of the studied region in agriculture and range land uses was lower than forest lands.
Sh Asghari
Abstract
Abstract
One of the methods for improving soil physical quality of arid and semiarid regions is the application of cheap organic conditioners such as sewage sludge. This research was conducted in large pots (50 cm diameter, 25 cm height) to study the effects of Tabriz petrochemical biological sludge ...
Read More
Abstract
One of the methods for improving soil physical quality of arid and semiarid regions is the application of cheap organic conditioners such as sewage sludge. This research was conducted in large pots (50 cm diameter, 25 cm height) to study the effects of Tabriz petrochemical biological sludge at 4 rates (25, 50, 75 and 100 tons/ha) on organic carbon (OC), mean weight diameter (MWD) of aggregates, water-dispersible clay (WDC), liquid limit (LL) and plastic limit (PL) moistures and plasticity index (PI = LL – PL) during time in a semiarid soil. There was also control treatment (without sludge) and all treatments included 3 replications. Incubation of treatments was done in a greenhouse with field capacity moisture content of 0.7 – 0.8 and temperature of
22 ± 4 ˚C for 6 months. All parameters were measured at 60, 120 and 180 days. The experiment was conducted as factorial (5 used rates of sludge (factor A) and 3 incubation times (factor B)) with randomized completely blocks design. Results showed that all used sludge rates significantly (P < 0.01) increased OC and decreased WDC as compared with the control. Negative correlation (r = - 0.84*) between OC and WDC was significant (P < 0.05). Moistures of LL and PL significantly (P < 0.01) increased with sludge application only at the rates of 75 and 100 tons/ha. Significant and positive correlation (r = 0.99***, P < 0.001) was found between OC with LL and PL. The effect of sludge used rates on MWD and PI was not significant. Significant and negative correlation (r = - 0.92***, P < 0.1) was found between MWD and WDC at 3 incubation times. This research indicated that petrochemical sludge as a cheap organic conditioner improved physical quality of the semiarid soil.
Keywords: Sewage sludge, Semiarid soil, Organic carbon, Aggregate stability, Consistency limit
