Prediction of Dexter’s Soil Quality Index Using Soil Physical, Chemical, and Geophysical Properties

Prediction of Dexter’s Soil Quality Index Using Soil Physical, Chemical, and Geophysical Properties | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prediction of Dexter’s Soil Quality Index Using Soil Physical, Chemical, and Geophysical Properties Samira Mesri, Shoja Ghorbani-Dashtaki, Hossein Shirani, Abolghasem Kamkar-Rouhani, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2984354/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Dexter’s soil physical quality index (the S index), which is defined as the slope of the soil-water characteristic curve at the inflection point, can properly describe the soil physical quality. This study was conducted to derive a model to predict the S index using soil physical, chemical, and geophysical properties. For this, 72 soil samples were collected, and some of the physical, geophysical, and chemical properties of the soil samples were measured. The soil water content was measured using sand-box and pressure plate apparatus. RETC software was also used to determine the parameters of the van Genuchten equation. These parameters were employed to find the slop e of the moisture curve at the inflection point as an indicator of soil physical quality. The decision tree analysis was applied to derive the proper model for predicting the S index using different combinations of chemical, physical and geophysical soil properties in 4 scenarios and the most important predictor(s) were determined in each scenario. The effective factors on the S index in the first and third scenarios included the moisture and the mean weight diameter of aggregates; while in the second and fourth scenarios in which some of chemical properties were replaced by geophysical and structural properties, the dielectric constant and mean weight diameter of aggregate were the most influential factors on the S index. The coefficient of determination of 0.86 was obtained as a result of correlation between the measured and predicted data in the first and third scenarios, while in the second and fourth scenarios, it was obtained as 0.87. Considering the values of %RMSE, the modeling in all four scenarios can be regarded as successful. However, the % RMSE value in the second and fourth scenarios was less than that in the first and third scenarios. Decision tree Inflection point of moisture curve Modeling Van Genuchten equation Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Soil quality is an essential indicator for sustainable land management (Armenis et al., 2013), and it depends on numerous physical, chemical, and biological properties of the soil. To determine the soil quality, it is often required to select indicators that are extremely sensitive to various management operations (Bionamana et al., 2018). To assess the soil quality, a set of sensitive soil properties that reflect the capacity of a soil to function must identified as the indicators of the soil quality” (Cardus et al., 2013). The studies conducted by Carlen et al. (1992) imply that soil plowing as a useful indicator of soil physical quality is described by porosity, aggregation, and other structural measurements. Soil quality is usually significant in terms of chemical, physical, and biological aspects. Moreover, assessment of the extent of land degradation or remediation and determination of the type of management activities for sustainable land use depend on the soil quality. As stated earlier, soil quality is not directly measurable and needs to be examined using soil quality indicators. In general, if the soil quality indicators are in the optimal range, the crop yield will increase, and the soil and environmental degradation will decrease (Reynolds et al., 2009 ). Determination of the soil physical and chemical properties specifying the degree of the soil quality makes it possible to express the physical quality of the soil. For instance, the total silt and clay in rangeland soils affect the soil quality parameters based on studies conducted by Neuilmer et al. (Neelmeier, 2006). Sparling and Schipper ( 2002 ) have accepted seven soil properties as the minimum data to assess the soil quality. These properties such as acidity, carbon, total nitrogen, mineralizable nitrogen, and absorbable phosphorus can be determined by the Olsen method, bulk density, and macro porosity. Dexter (2004) introduced the slope of the soil-water characteristic curve at the inflection point as one of the indicators of soil quality. The soil-water characteristic curve, which indicates the volumetric or mass moisture of the soil against suction of water by the soil (matrix potential), includes three areas, namely the air inlet area, capillary area, and absorption area. It is impossible to provide a definite shape or equation for the water characteristic curve, since many factors affect it (Dexter, 2004a &b). However, in general, the characteristic curve is sigmoidal, and each sigmoidal curve has an inflection point at which the slope of the curve changes and is shown by S. In other words, the S index of the slope of the soil-water characteristic curve is in the case, where the curve is drawn based on the soil moisture content against the natural logarithm of water suction by soil. The soil physical quality is determined in different ways. If the soil has one or more than one of the following conditions, it will exhibit poor soil physical quality (Dexter, 2004a ): low water permeability, the occurrence of surface runoff, poor ventilation, reduction of the root growth in the soil, and low ability of the soil performance If the soil has none of these conditions, it will have appropriate physical quality. Indicators of soil physical quality directly or indirectly indicate the volume of soil pores, and it is a function related to it (Reynolds et al., 2009 ). The sizes and configuration of soil pores considerably affect the characteristic curve. This implies that the slope of the moisture curve at the S inflection point can indicate various aspects of the structure and soil physical quality, such as permeability, hardness, and compaction. Accordingly, the slope of the soil-water characteristic curve at the inflection point is called the soil physical quality index (Dexter, 2004a ). Therefore, any factor that affects the sizes and configuration of the soil pores can change the index. The S index has been introduced as an effective tool to quantify or improve physical degradation and to assess the soil physical quality (Dexter, 2004b ). It is possible that the theoretical range of S values varies between zero to infinity. However, it is reported that this index in agricultural soils changes in the range of 0.007 to 0.14 (Reynolds et al., 2009 ). Dexter ( 2004a ) expressed that the slope of the soil-water characteristic curve at the inflection point was mainly due to the small pores and soil physical properties, which were directly affected by the S index. Most of soils have poor physical quality due to the pores they have, therefore, proper soil structure increases the Dexter index and improves the physical quality of the soil. As a result, large amounts of S are necessary for proper soil quality. The characteristic curve of structural pores is drained of water between the saturation point and the inflection point of the curve when the soil dries. However, the texture pores are mostly drained of water as the drying process continues and follows the inflection point. The soil drying process can change the size distribution of soil pores, and consequently, the shape of the soil-water characteristic curve (Biomegartel et al., 2000). Vizitio et al. (2011) determined the S index employing the components of the van Genuchten equation and stated that medium-textured soils had a higher S index than clay and heavy soils. Emami and Astarai (2012) determined the proper moisture by estimating the moisture at the inflection point of the soil-water characteristic curve using easily available soil properties. Olga et al. (2012) investigated the relationship between the slope of the characteristic curve and some soil physical properties, and estimated the S-index with regression transfer functions using easily available soil properties. Asghari et al. ( 2016 ) utilized easily available soil properties to estimate the physical quality index of forest soils and reported that 79% of the changes in the S index of the soils could be explained by the quantities of calcium carbonate and relative bulk density. The equation of the soil-water characteristic curve is required to calculate the S index. Measurement of the S index can directly be made using the slope of the curve, particularly if the number of corresponding volumetric-potential moisture points is limited. In general, it is beneficial to utilize characteristic curve estimation models like the van Genuchten model. Nevertheless, it is difficult to measure this property by applying the parameters of the moisture characteristic curve, since it needs to spend considerable time and money and to have its spatial and temporal variability. Consequently, there are indirect methods as effective and relative solutions for the problems related to soil quality. Easily available soil properties are employed in the indirect methods. Applying transfer functions is one of the indirect methods that can estimate uneasily available soil properties from easily available soil properties (Cao et al., 1992). Hence, transfer functions are increasingly applied to estimate hydraulic properties of soil (Kasbay, 1984; Liege et al., 2002). Regression models (Rawls et al., 1991 ) and data-driven methods, which have recently become a controversial technology in modeling nonlinear relationships (Miniaci et al., 2004), and also, artificial neural networks are among the most important estimators for the transfer functions. The technique of artificial neural networks, compared to traditional methods, has some advantages including the possibility of using huge amount of noisy data obtained from dynamic and nonlinear systems, particularly when the relationships between variables are not fully understood. The advantages of the above-mentioned soft computational methods include improved performance of the model, faster development of the model, less computational time, and providing a bootstrapping technique to ensure the accuracy of estimates (Openshaw, 1997 ). In addition, the decision tree classification algorithm method is one of the methods that can be used to estimate the effective parameters of the moisture curve. Decision trees are effectively used as common tools for categorization and estimation or regression. A decision tree is a structure that is applied to divide a large set of collected data into smaller sets of data chains based on a series of simple decision rules. In each successive division, the members of the resulting sets become more and more similar (Rakach & Maiman, 2005). The decision tree is also employed to examine the data for better understanding of the relationships among a large number of candidate input variables to select a target variable. A significant problem in this regard is to predict or categorize accurately (Rakach & Maiman, 2005). Marghmalek area has various land uses where there is extensive cultivation that makes the soil quality conservation crucial for sustainable farming. Properties affecting soil quality are changed by considering the management approach. Thus, due to faulty management, arable lands are deteriorating. Therefore, it is indispensable to evaluate soil quality and, in this regard, the S index is an effective factor. Furthermore, the slope of the moisture characteristic curve has not been estimated particularly using modeling by decision tree. Therefore, this study is conducted to identify the effective factors on the slope of the soil moisture characteristic curve using modeling by decision tree. It is also aimed to evaluate the accuracy of the modeling and the effect of soil properties on the inflection point of the slope of the moisture characteristic curve in different scenarios, and also, to evaluate the correlation between easily available soil properties and the Dexter index. 2. Material and Methods 2.1. Study Area This study was conducted on different soil types classified by their texture distribution, topography and different land uses. The land use of the study area consisted of (pasture, garden, and agricultural (not plowed and plowed)). The soil textures of the surface horizons were loam, sandy clay loam and silty loam, silty clay, silty clay loam, and clay loam. 72 soil samples were randomly taken from the top 20 cm in the Marghmalek basin and Shahrekord City in Chaharmahal and Bakhtiari Province, Iran. Marghmalek is located 55 km northwest of Shahrekord City (the capital of Chaharmahal and Bakhtiari Province) and it belongs to the Zayandehrood sub-basins with an area of 97 square kilometers (excluding mountains). Marghamalek is located in the geographical latitude of 30˝ 22' 32˚ and longitude of 30˝ 22' 50˚ to 30˝ 34' 50˚. The height of the highest point in this basin is 2936 meters, and the lowest point in the basin is 2400 meters. The average temperature is 30.7˚C, and the average annual rainfall is 400 mm, which occurs mostly in the winter and spring seasons. Shahrekord City has moderate summers and cool winters, and the climate is temperate and semi-humid. The average temperature in Shahrekord is 11.5 ˚C. 2.2. Sampling Method A topographic map with a scale of 1:25000 was prepared and sampling points were randomly determined in the study area so that the points were scattered throughout the area. Random sampling was simple although it can also be called quasi-regular (Fig. 1 ). A GPS device was applied to determine the location or the geographical coordinates of the points in the area. Then, 72 soil samples were taken from the depth of 0 to 20 cm using a shovel, and next, the samples were transferred to the laboratory. Furthermore, intact samples were taken from the soil surface having dimensions of 5*5 meters using a cylinder to determine the bulk density and soil moisture curve. The taken samples were prepared for soil tests after drying and passing them through a sieve having apertures with the size of 2-mm. 2.3. Measurements of Some Soil Physical and Chemic Properties Determination of soil texture using hydrometric method (Bayox, 1962), soil acidity in saturated mud using pH meter, electrical conductivity using electrical conductivity meter in the saturated extract (Pige et al., 1987), and organic carbon with oxidation by using Potassium dichromate (Walkley & Blake, 1934) was made. Moreover, the bulk density of the samples was determined using the cylinder method with certain dimensions (Clott, 1986), and the lime of the samples was also determined by neutralization of neutralizing agents with hydrochloric acid and excess acid titration with soda. In addition, the mean weight diameter of the aggregate was determined applying the dry and wet sieving method, and the soil saturation moisture was also determined by preparing saturated mud in the laboratory, and then, drying in an oven set on the temperature of 105˚ for a time period 24 hours. The Wenner electrode array (Wenner, 1916) was used to determine the electrical resistivity. Four A, B, M and N electrodes in this array are set on the ground along a straight line, and an electrode spacing of a = AM = MN = NB is assigned in this electrode array (Fig. 2 ). In this study, the electrodes were set at a distance of a = 78 cm to measure the electrical resistivity of the soil from the ground surface to a depth of approximately estimated to be 20 cm. In practice, the electrical resistivity determined from the electrical device is apparent. The apparent resistivity is obtained using Eq. (1) in which fromthe electrical resistance is multiplied by the array geometric factor (K). In this equation, ρ is the apparent resistivity (ohm × meters), \(\frac{\varDelta \text{V}}{\text{I}}\) is theelectrical resistance (ohm) achieved from the electrical device, and K = a*2*3.14 is the array coefficient or geometric factor of the electrode array, and a is the electrode spacing or the distance between the inner electrodes. The soil dielectric constant is also obtained via Eq. (2). E soil = (C/V) 2 (2) where E is the soil dielectric constant, C is the speed of light in a vacuum, and V is the speed of subsurface radar waves that is estimated by a GPR (ground penetrating radar) device. 2.4. Measurement of Moisture Curve Moisture characteristic curve of intact soil samples in suctions 0, 1, 3, 5 and 10 kPa was measured by sandbox, and in suctions 30, 50, 150,100, 1000 and 1500 kPa, it was measured by pressure plate machine. The weight moisture content of the soil samples was determined in the mentioned suctions, and the volumetric moisture content of the samples was calculated from multiplying the apparent specific weight and the weight moisture content. Dexter index: The slope of the inflection point of the soil moisture characteristic curve (S) was evaluated as an indicator of the soil physical quality. Soil moisture characteristic curve data were used to achieve the index (S). To this end, the parameters of the soil moisture characteristic curve, suggested by van Genuchten ( 1980 ). were obtained using RETC software. In this software, after selecting the relevant equation, the values of bulk density and percentage of soil texture components for each sample were entered into the RETC software for initial estimates. The model parameters, including θs and α, n, and m were specified, and the value of S for the soil sample was determined assuming m = 1–1/n and applying the least sum of the squared error method for each sample. The following equations, presented by Dexter et al. ( 2008 ), were used for computation of the value S. m = 1–1/n (3) where θs and θr are, respectively, the volumetric amounts of saturation moisture and residual soil, h is the soil suction (cm), α is approximately equivalent to the opposite of potential at the point of air entry (cm − 1 ), and n and m are the dimensionless coefficients of the equation. According to the van Genuchten equation, soil moisture is achieved as a function of suction: For modeling using decision tree, the input properties of the software in four scenarios were as follows: Scenario 1: pH, EC, percentage of sand, organic matter, calcium carbonate, MWDdry and MWDwet, bulk density, and θs. Scenario 2: Scenario 1 - (pH, EC, θs, MWDdry, and MWDwet) + percentage of gravel, electrical resistance, dielectric constant, and mechanical resistance Scenario 3: Scenario 1 - Percentage of sand and clay + geometric mean diameter, and geometric standard deviation Scenario 4: Scenario 2 - Percentage of sand and clay + geometric mean diameter, and geometric standard deviation Dexter index was the property of the study objective. Cross-validation and Resub stitution estimators were applied in modeling using the decision tree. In this method, the measured data sets are divided into k groups. A group is excluded, and other groups or data are applied to design and adjust the model. The excluded group is then applied to the model as test data, and its error is recorded. In the next step, the second group is excluded as a test, and modeling is performed with other groups, and this process continues until all groups are applied to the model once as test data. Finally, the mean error of the test groups is considered as the modeling error. 2.5. Indicators of Model Evaluation The indicators of determination coefficient (R 2 ), root mean square error (RMSE), and root mean square error in percent (RMSE%), as defined in the following equation, were applied to measure the accuracy and validity of the model. %RMSE = (RMSE/m) * 100 where N is the number of samples, m is the average of the actual data, Pi is the measured values, and Oi is the estimated values. 2.6. Sensitivity Analysis Sensitivity analysis by applying the Stat Soft method was used to evaluate the significance of input variables in modeling. In this method, the model is created with all input variables, and the value of the error-index is calculated and considered after achieving the best performance or the lowest error. A certain input variable is then removed, and the model is recreated with other input properties. After reaching the most appropriate structure and performance in the model, the value of the error-index, in this case, is also determined. The value of the output sensitivity to the input variable is calculated via the ratio of the error-index in the second case (removal of an input feature) to the first case (presence of all inputs). 2.7. Software Used Minitab software and the Kolomogorov-Smirnov test were employed to calculate statistical indicators and the normal distribution of data. Modeling was performed by the MATLAB 2015 software, and graphs were drawn by the Excel software. In this research work, the method of decision tree has been used. The decision tree method is one of machine learning methodes. It can provide good results in classificationof little data. There are many types of decision tree algorithms but all of them follow a similar process, which consists of repeatedly dividing data into smaller groups in such a way that according to the target variable, the new generation of nodes is purer than its predecessors (Matsuyama, 1987 ). The decision tree model produces rules, thus, it is superior to the neural network model. Moreover, in the decision tree, there is no need for the data to be numerical (Matsuyama, 1987 ). In order to model the Dexter index, MATLAB software was used. In each scenario, the physical, chemical and geophysical characteristics of the soils were measured, and then, the measured data were given as the input, and the Dexter index, measred in laboratory, was given as the output in the MATLAB program to model the Dexter index. To compare the scenarios and estimate the Dexter index in each scenario, the error value and correlation coefficient were dtermined in each scenario based on the defined guidelines. 3. Results and Discussions Soil samples were prepared using six textures (silty clay loam, silty loam, clay loam, loam, silty clay, and sandy clay loam) to estimate the soil moisture characteristic curve. Acidity, electrical conductivity, saturation moisture, lime, organic matter, density, clay percentage, sand percentage, mean weight diameter of dry aggregate, mean weight diameter of wet aggregate, mean and geometric standard deviation diameter, gravel percentage, mechanical resistance, geophysical properties, and dielectric constant were the studied properties, which were entered into the software as the input data to be modeled in different scenarios (Table 1 ). The statistical description of the measured properties indicated that the geometric mean diameter, sand, and electrical conductivity percentage had the highest coefficient of variation among the input quantities. The measured quantities were from six different soil textures, in which the dominant texture was silty clay loam soil. The scattering distribution between the input quantities also indicated that all the input quantities except the geometric mean diameter (Dg) had normal scattering. Table 1 Parameters Used in Modelling as Input Goal Variables Parameters Significance Level Coefficient of Variation Standard Deviation Mean Maximum Minimum pH p > 0.5 2.16 0.16 7.55 7.9 7 Electrical Conductivity (dS/m) p > 0.9 42.72 0.30 0.7 1.69 0.27 Saturation Moisture (kg/kg) p > 0.44 15.53 0.063 0.40 0.51 0.25 Calcium Carbonate (%) p > 0.06 37.19 10.61 28.52 76.5 14.5 Organic Matter (%) p > 0.4 35.93 0.54 1.5 2.41 0.1 Bulk Density p.0.55 10.84 0.13 1.19 1.59 0.96 Clay Percentage p.0.37 19.40 5.87 30.25 44 18 Sand Percentage P > 0.12 46.29 8.25 17.81 47 4 Mean Weight Diameter of Dry Aggregate (mm) p > 0.99 22.85 0.227 0.99 1.51 0.56 Mean Weight Diameter of Wet Aggregate (mm) p > 0.78 38.97 0.150 0.38 0.8 0.12 Geometric Mean Diameter (Dg) (mm) p > 0.05 48.7 0.01 0.02 0.064 0.01 Geometric Standard Deviation p > 0.19 26 2.66 10.21 18.95 5.53 Gravel p > 0.30 72.29 13.21 18.28 43.7 0.8 Electrical Resistivity (Ω.m) p > 0.37 38.91 35.28 90.69 170.9 29.39 Dielectric constant p > 0.004 43.12 2.62 6.07 14.8 3 Mechanical Resistance (Kpa) p > 0.000 165.48 519.18 313.7 3881.28 17.58 Dexter Index (S) P > 0.4 46.2 0.18 0.038 0.11 0.012 Table 1 lists the minimum and maximum data range, mean, standard deviation, coefficient of variations, and level of significance of the studied physical and chemical properties. The results indicated that the mean value of S in the studied soils was 0.038, and its range was in the range of 0.01–0.11. Results obtained from studying the physical soil quality were appropriate according to the value of the S index, and more areas had a good physical and structure quality index according to the Dexter classification (2004). Dexter et al. ( 2008 ) suggested the following classes for the soil physical quality index based on data collected from soils of seven countries with clay values ranging from 4 to 73%: S > 0.05 (Very good), 0.035 < S ≤ 0.05 (Good), 0.02 < S < 0.035 (Poor), and S < 0.02 (Very poor). The S-index has been linked to many important soil properties and physical conditions regarding plant growth (Dexter, 2004; Dexter, 2004; Dexter et al., 2008 ). 3.1. Modeling Using the Decision Tree Table 2 presents the model evaluation indicators. The results indicated that the determination coefficient (R 2 ) for the Dexter index in the first and third scenarios was increased by 86%, and it was increased in the second and fourth scenarios by removing some chemical properties and replacing the structural properties of the soil such as geophysical properties and mechanical resistance. Dexter and Cis (2007) and Olga et al. ( 2011 ) explained that the soil structure and its related properties like describing the distribution of soil pore size were the ways of expressing physical soil behaviors. The slope of the moisture curve is a suitable indicator to measure the soil structure, thus, the S index appears to be a good feature to express the soil structure and soil physical quality. Most soil properties and behaviors, such as permeability, ventilation, bulk density, and particle size distribution, are controlled by the soil structure. The properties of structure affect the soil physical quality. In other words, the S index significantly depends on soil physical properties such as density, particle size distribution, and organic matter. Hence, it indicates the dependence of this index on the soil structure (Emami & Astarai, 2012; Klango, 2010). A comparison between RMSE and %RMSE error evaluation indicators and the determination coefficient (R 2 ) in the scenarios in Table 2 demonstrated that the error rate and the determination coefficient of the scenarios were near to each other, however, the second and fourth scenarios were more successful than the first and second scenarios. Thus, the determination coefficient, which is in the range between 0 and 1, is the most significant criterion that can explain the relationship between two variables. If the determination coefficient index R 2 is close to 1, it will be better meaning that more correlation exists between the two variables. Hengel and Hangestik (2006) state that if RMSE% is between 0 and 40, the estimation will be done well and modeling will be strong; if it is between 40 and 70, it will be moderate, and if it is above 70, the modeling will be weak. As Table 2 shows the value of % RMSE is a desirable value in all the scenarios in this study. Table 2 Comparison of Training Data Error and Test Data Error for S Training Data Error Test Data Error Coefficient of Determination Between Measured and Estimated Data RMSE %RMSE RMSE %RMSE First Scenario 0.0058 15 0.021 37 0.86 Second Scenario 0.0054 14 0.0199 35 0.87 Third Scenario 0.0056 14.5 0.02 35.92 0.865 Fourth Scenario 0.0052 13.5 0.0193 34 0.875 The accuracy of the algorithm was compared in two stages of training and test as shown in Table 2 . From this comparison, it was realized that the error of the test stage was higher than the training stage like many other models. It should be noted that data heterogeneity disrupts the operation of machine learning algorithms, and thus, it is challenging to use them. Figure 3 shows that moisture is the most significant parameter affecting the Dexter index that is located in the highest part of the the decision tree algorithm, i.e., the root node. Electrical conductivity and weighted average diameter of aggregate are the most significant factors after moisture. The decision tree algorithm in the first scenario explains that branches have 15 child nodes and 17 leaf nodes. This branch of the studied parameter needs to be continued to obtain the desired amount of impurity (minimum impurity). Accordingly, a child with a lower degree of impurity is selected to reach this degree of impurity sooner, so that the branch of the desired parameter stops sooner and the branch of the next parameter begins. Hence, all the parameters are branched in terms of their significance and based on average so that they finally reach the leaf node with the lowest degree of impurity. Moisture content (soil saturation moisture, usable soil moisture, and minimum water amplitude) is considered an indicator of the soil structure quality for crop production (Desilova et al., 1994) and as mentioned, the S index indicates the soil structure quality (Dexter, 2004). Hence, a positive correlation is observed between these two parameters (moisture and S index) (Visto et al., 2011). The results achieved by the decision tree algorithm show that moisture is the most significant effective factor on the S index in the first scenario. After moisture, the mean weight diameter of aggregate and electrical conductivity are the most significant effective factors on the S index that are located in lower nodes (Fig. 3 ). Indeed, both the mean weight diameter of aggregate and the slope of the moisture curve at the inflection point describe the soil structure (Lee Bisance, 1996). Levy and Miller (1997) state that the mean weight diameter of aggregate is used to quantify the soil structure and affects the slope of the moisture curve at the inflection point. The research results, as indicated in Fig. 3 , imply that in addition to the moisture and electrical conductivity as the most effective parameters, the mean weight diameter of aggregate and in lower nodes, density, and calcium carbonate are next effective parameters affecting the amount of S in the first scenario. Zornoza et al. ( 2015 ) explain that texture, density, and water affect the creation of stable aggregates, and therefore, affect the soil quality. There is a direct relationship between the density increase and decrease in the S index (Kenha et al. 2011; Rahimi et al., 2000 ; Siegel et al., 2005). Emami et al. ( 2012 ) have also indicated that a significant correlation can be observed between the organic matter and the S index. Modifiers increase the S index by 0.035. In other words, soil management accumulates soil organic carbon, which is significant to improve the soil structure. According to the studies conducted by Tejada and Gonzalez ( 2006 ) and Tejada et al. ( 2006 ), increasing the electrical conductivity of saline soils decreases the soil structure stability and bulk density. They stated that increasing the exchangeable sodium ions and consequently, increasing the soil pH, which expanded and distributed clay particles, decreased the stability of aggregates. This situation degrades aggregates. The saturated electrical conductivity affects the S index. It can be owing to its role in particle flocculation and formation of the soil structure (Lear, 2014). However, successive increases in soil electrical conductivity can degrade the soil structure (Dexter, 2008 ). Table 3 illustrates the most effective and initiating factors of division for the Dexter index in different scenarios. According to Table 3 , most of the child and leaf nodes are related to the third scenario. Since the electrical resistivity of the soil is more affected by the soil moisture than any other soil-dependent factor; hence, the electrical resistivity is located in the root node as the moisture is removed in the second and fourth scenarios. Table 3 Decision Tree Modeling Results in Different Scenarios for the Dexter Index Goal Property Scenarios The Most Significant Influential Factor on the Root Node and Initiating Division Child Nodes Leaf Node Dexter Index First scenario Saturation moisture, electrical conductivity and mean weight diameter of wet aggregate 15 17 Second scenario Electrical and mechanical resistance and percentage of gravel 14 16 Third scenario Saturation moisture, electrical conductivity and mean weight diameter of wet aggregate 17 19 Fourth scenario Electrical and mechanical resistance and percentage of gravel 15 17 Figure 4 demonstrates the sensitivity of the Dexter index to the input properties or parameters in different scenarios. The Dexter index in the first and third scenarios shows the highest sensitivity to the properties of saturation moisture, acidity, aggregate stability in the wet state, and bulk density. However, in the second and fourth scenarios, the highest sensitivity of the Dexter index to the percentage of gravel, density, calcium carbonate, and dielectric constant is observed. Shirani et al. ( 2015 ) state that if the value of the sensitivity coefficient for an input property is more than 1, the variable will significantly contribute to the performance of the model and its output. If this ratio is less than 1, it means that the error in the absence ofan input property is less than the error in its presence. Therefore, this variable does not positively affect the accuracy of the model. Many researchers have used transfer functions developed by applying neural networks and artificial intelligence. These functions have been developed as an alternative way to overcome the problems of traditional methods. The results of almost all previous studies indicate that these models operate well and overcome the statistical assumptions involved in the transfer functions. Furthermore, since artificial neural networks are among non-parametric methods in which the assumption of normality of data distribution is not needed, thus, the data analysis using the neural network method will not be problematic if the data distribution is not normal (Openshaw, 1997 ). 4. Conclusions The results of this study demonstrate that the inflection point of the soil moisture characteristic curve (Dexter index) is highly influenced by the characteristics of saturation moisture, electrical conductivity, mean weight diameter of wet and dry aggregate, and acidity in the first scenario. There is no significant difference in the amount of error and the determination coefficient in the third scenario if the percentages of clay and sand are replaced by the geometric mean and standard deviation of the particle size. However, in the second and fourth scenarios, the determination coefficient has increased, and the amount of error has decreased as some chemical properties have been removed and replaced by geophysical properties and mechanical strength. Substituting the geophysical properties for some chemical properties has indicated more difference between different scenarios. However, the Dexter index is highly dependent on chemical properties like soil electrical conductivity, thus, the determination coefficient has also been high in the first and third scenarios though the determination coefficients of all four scenarios are slightly different. Various studies have been conducted on transfer functions and estimation of soil properties by applying easily available soil properties using artificial intelligence. In this regard, it has been revealed that the decision tree algorithm is more accurate than other methods. Moreover, the decision tree method is highly accurate when it is used for prediction in the case of small amount of data. Therefore, it is recommended to use this method forestimation of other hydraulic and uneasily available soil properties. It should be noted that the relationship between the hydraulic characteristics and the physical/chemical properties of soils can be influenced by the geographical source of the used data. Therefore, it isconcluded that theobtained results should not be extrapolated beyond the geomorphic region or soil type from which it was taken. However, to assure the relevance and extension of these results to locations outside of our study area, especially at the regions with different climates, further research is needed. Declarations Conflict of Interest This manuscript has not been published or presented elsewhere in part or entirety and is not considered by another journal. The author declared no conflict of interest. Funding No funding Ethics Approval Not applicable. Consent to Participate Not applicable. Author contributions Sampling, conducting the experiments, data analysis, and writing paper: Samira Mesri: Supervisor and Supervision and Providing laboratory and sampling equipment: Shoja Ghorbanidashtaki: Supervisor and Supervision and Training model with decision tree: Hosein Shirani: Advisor and helping with data processing and thoroughly editing the manuscript: Abolghasem Kamkar-Rouhani Advisor and suggesing the location: Hamidreza Motaghian. References Armenise, E., Redmile-Gordon, M.A., Atellacci, A.M., Ciccarese, A., Rubino, P.: Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment. Soil Till. Res. 130, 91-98 (2013). Asghari, S., Ruzban, V., Khodaverdiloo, H.A.: Derivation of Transient Functions for Estimation of Transient Resistance, Aggregate Stability, and Characteristic Curve Model Parameters of Vanukhten in Ardabil Hazelnut Forest Lands. Journal of Soil and Water Science (JWSS), 26(2), 129-148 (2016). Baumgartl, T., Rostek, J., Horn, R.: Internal and external stresses affecting the water retention curve. In: Horn R, van den Akker J, Arvidsson, J (Eds.), Soil Compaction: Distribution, Processes and Consequences Advances in Geo Ecology. 32, 13– 21 (2000). Blake, G.R., Hartge, K.H.: Bulk density. p. 363-375. In A. Klute (ed.) Methods of soil analysis. Part 1. 2nd ed. Agron. Monogr. No. 9. ASA and SSSA, Madison, WI (1986). Bouyoucos, G.J.: Hydrometer method improved for making particle size analysis of soils. J. Agron. 54, 464-465 (1962). 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In: Pankhurst CE, Doube BM, Gupta VVSR, Grace, PR (Eds.), Soil Biota: Management in Sustainable Farming Systems. CSIRO, Melbourne, pp. 250–256, (1994). Emami, H., Astaraei, A.R.: Effect of organic and inorganic amendments on parameters of water retention curve, bulk density and aggregate diameter of a saline-sodic soil. J. Agri. Sci. Tec. (JAST). 14: 7. 1625-1636 (2012). Emami, H., Neyshabouri, M.R., Shorafa, M.: Relationships between some soil quality indicators in different agricultural soils from Varamin, Iran. JAST. 14 (4), 951-959 (2012). Hengle, T., Husnjak, S.: Evaluation adequacy and usability of soil maps in Croatia. J. Soil Sci. Sci. Soc. Am. 70, 920-929 (2006). Kao, C.S., Hunt, J.R.: Prediction of wetting front movement during one-dimentional infiltration into soils. Water Resour Res. 9(2), 384–395 (1996). Karlen, D.L., Eash, N.S., Unger, P.W.: Soil and crop management effects on soil quality indicators. Am. J. Alter. Agri. 7(1-2), 48-55 (1992). Klute, A., Dirksen, C.: Hydraulic conductivity and diffusivity: Laboratory methods. In: A. Klute(eds). Method of soil analysis, Part1: Agronomy Soil Sci. Soc. Am. J. Madison. W.I. 687-734 (1986). Le Bissonnais, Y.: Aggregate stability and assessment of soil crustability and erodibility: I. Theory and methodology. Eur. J. Soil Sci. 47, 425-437 (1996). Leij, F., Schaap, M.G., Arya, L.M.: 3.6.3. Indirect Methods of Soil Analysis: Part 4 Physical Methods, 5, 1009-1045 (2002). Leir, Q.J.: Revisiting the S index for soil physical quality and its use in Brazil. R Bras Ci Solo. 38, 1-10 (2014). Lovey, G.J., Miller, V.P.: Aggregate stabilities of some south eastern US soils. Soil Sci. Soc. Am. J. 31, 1848–1857 (1997). Matsuyama, T.: Knowledge- Based Aerid Image Understanding system and Expert System for Image Processing. IEEE Transaction on Geoscience and Remote Sensing, 25, 305-316 (1987). Minasny, B., Hopman, J., Harter, W.T., Eching, S.O., Toli, A., Denton, M.A.: Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data. Soil Sci. Soc. Am. J. 68, 417-429 (2004). Minasny, B.: Prediction soil properties. J Ilmu Tanah dan Lingkungan, 7, 54-67 (2007). Noellemeyer, E., Quiroga, D., Estelrich, A.R.: Soil quality in three range soils of the semi-arid Pampa Argentina. J. Arid Environments. 65(1):142-155 (2006). Nogueira Cardoso, E.J.B., Figueiredo Vasconcellos, R.L., Bini, D., Miyauchi, M.Y.H., Alcantara dos Santos, C., Lopes Alves, P.R., Monteiro de Paula, A., Shigueyoshi Nakatani, A., Moraes Pereira, J.d., Marco Antonio Nogueira, M.: Soil health: looking for suitable indicators. What should be considered to assess the effects of use and management on soil health. J. Soil sci. agri. 70(4), 274-289 (2013). July/August. https://doi.org/10.1590/S0103-90162013000400009 Olga, V., Irina, C., Ioana, P., Simota, C.: Soil physical qualit as quantified by S index and hydrophysical indeces of some soil from arges, hydrographic basin. Res. J. Agric. Sci. 43, (3): 249-256 (2011). Openshaw, S., Openshaw, C.: Artificial Intelligence in Geography. John Wiley & Sons Ltd, Chichester Pp 329 (1997). Page, M.C., Sparks, D.L., Noll, M.R., Hendricks, G.J.: Kinetics and mechanisms of potassium release from sandy Middle Atlantic Coastal Plain soils. Soil Sci. Soc. Am. J. 51: 1460-1465 (1987). Rahimi, H., Pazira, E., Tajik, F.: Effect of soil organic matter, electrical conductivity and sodium adsorption ratio on tensile strength of aggregates. Soil Till. Res. 54, 145-153 (2000). Rawls, W.J., Gish, T.J., Brakensiek, D.L.: Estimating soil water retention from soil physical properties and characteristics. Adv. Soil Sci. 9: 213–234 (1991). RETC (Retention Curve) RETC model. USADARS U.S. Salinity Laboratory Riverside, A, USA. https://www.pc-progress.com (2008). Reynolds, W.D., Drury, C.F., Tan, C.S., Fox, C.A., Yang, X.M.: Use of indicators and pore volume function characterestics to quantify soil physical quality. Geoderma. 152,252-263 (2009). Rokach, L., Maimon, O.: Data Mining and Knowledge Discovery Handbook. Pp. 165-192 (2005). DOI:10.1007/0-387-25465-X_9. Romo, M.P., Garcia, S.R.: Neurofuzzy mapping of CPT values into soil dynamic properties. J. Soil Dyn. Earthq. Eng. 23, 473–482 (2003). Shirani, H., Habibi, M., Besalatpour, A.A., Esfandiarpour, I.: Determining the features influencing physical quality of calcareous soils in a semiarid region of Iran using a hybrid PSO-DT algorithm. Geoderma. 1(11), 259-260 (2015). https://doi.org/10.1016/j.geoderma.2015.05.002. Siegel-Issem, C.M., Burger, J.A., Powers, R.F., Ponder, F., Patterson, S.C.: Seedling root growth as a function of soil density and water content. Soil Sci. Soc. Am. J. 69,215-226 (2005). Sparling, G.P., Schipper, L.A.: Soil quality at a national scale in New Zealand. J. Environ.Qual. 31(6), 1848-1857 (2002). Tan, N.P., Steinbach, M., Kumar, V.: Introduction to Data Mining Paperback. Pp.169 (2005). Tejada, M., Garcia, C., Gonzalez, J.L., Hernandez, M.T.: Use of organic amendment as a strategy for saline soil remediation Influence on the physical, chemical and biological properties of soil. Soil Bio. Biochem. 38, 1413-1421 (2006). Tejada, M.A., Gonzalez, J.L.: Crushed cotton gin compost on soil biological properties and rice yield. Europe. J. Agron. 25, 22-29 (2006). Van Genuchten, M.T.h.: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898 (1980). Vizitiu, O., Calciu, I., Panoiu, I., Simota, C.: Soil Physical Quality as Quantified by S Index and Hydrophysical Indices of Some Soils from Arges Hydrographic Basin. Research J. Agric. Sci. 43(3), 249-256 (2011). Vizitiu, O., Calciu, I., Simota, C., Pănoiu, I., Alexandrina, M.: A Pedo-Transfer function for predictiong the physical quality of agricultural soils. ASSS. Lucrări Ştiinţifice Seria 55(2), 351-354. 4p (2012). Walkley, A., and Black, I.A.: An Examination of the Degtjareff Method for Determining Soil Organic Matter and a Proposed Modification of the Chromic Acid Titration Method. J. Soil. Sci. 37, 29-38 (1934). Zornoza, R., Acosta, J.A., Bastida, F., Dominguez, S.G., Toledo, D.M., Faz, A.: Identification of Sensitive Indicators to Assess the Interrelationship Between Soil Quality, Management Practices and Human Health. J. soil. 1(1), 173-185 (2015). doi.org/10.5194/soil-1-173-2015. Additional Declarations No competing interests reported. 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Shahrekord","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hamidreza","middleName":"","lastName":"Motaghian","suffix":""}],"badges":[],"createdAt":"2023-05-26 07:44:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2984354/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2984354/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":37693840,"identity":"b06dabb9-1624-47fc-80f9-c8d929aa76e5","added_by":"auto","created_at":"2023-05-30 18:28:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":752659,"visible":true,"origin":"","legend":"\u003cp\u003eMap of sampling points\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2984354/v1/46279ffbb9a635c0376eb986.png"},{"id":37693837,"identity":"2acb7b30-39f7-4654-9332-975c367ada50","added_by":"auto","created_at":"2023-05-30 18:28:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9534,"visible":true,"origin":"","legend":"\u003cp\u003eThe Wenner array\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2984354/v1/8b71b43ef304f56309312c94.png"},{"id":37694347,"identity":"5a1f6358-5470-4e3b-b22f-423b796cf260","added_by":"auto","created_at":"2023-05-30 18:36:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35182,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of the decision tree for the Dexter index (S) in the first scenario\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2984354/v1/d4993b4556fd2228bbc40fb4.png"},{"id":37693838,"identity":"016b7b84-84c7-4660-95ab-9132a6e57c09","added_by":"auto","created_at":"2023-05-30 18:28:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":23565,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity of the S index to input properties in different scenarios: pH (acidity), EC (electrical conductivity), CaCo3 (calcium carbonate), OM (organic matterial), clay, sand, dry MWD (weighted mean or average diameter), wet MWD, wet and dry aggregate stability, BD (bulk density), gravel, R (electrical resistivity), and E (dielectric constant).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-2984354/v1/e6836076122b28cad8f0459b.png"},{"id":88334377,"identity":"a0c123da-e73c-4424-afab-4ca1accaa446","added_by":"auto","created_at":"2025-08-05 11:39:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1685211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2984354/v1/d7d3c182-b00e-4541-b2f6-65917f139ef5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of Dexter’s Soil Quality Index Using Soil Physical, Chemical, and Geophysical Properties","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoil quality is an essential indicator for sustainable land management (Armenis et al., 2013), and it depends on numerous physical, chemical, and biological properties of the soil. To determine the soil quality, it is often required to select indicators that are extremely sensitive to various management operations (Bionamana et al., 2018). To assess the soil quality, a set of sensitive soil properties that reflect the capacity of a soil to function must identified as the indicators of the soil quality\u0026rdquo; (Cardus et al., 2013). The studies conducted by Carlen et al. (1992) imply that soil plowing as a useful indicator of soil physical quality is described by porosity, aggregation, and other structural measurements. Soil quality is usually significant in terms of chemical, physical, and biological aspects. Moreover, assessment of the extent of land degradation or remediation and determination of the type of management activities for sustainable land use depend on the soil quality. As stated earlier, soil quality is not directly measurable and needs to be examined using soil quality indicators. In general, if the soil quality indicators are in the optimal range, the crop yield will increase, and the soil and environmental degradation will decrease (Reynolds et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Determination of the soil physical and chemical properties specifying the degree of the soil quality makes it possible to express the physical quality of the soil. For instance, the total silt and clay in rangeland soils affect the soil quality parameters based on studies conducted by Neuilmer et al. (Neelmeier, 2006). Sparling and Schipper (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) have accepted seven soil properties as the minimum data to assess the soil quality. These properties such as acidity, carbon, total nitrogen, mineralizable nitrogen, and absorbable phosphorus can be determined by the Olsen method, bulk density, and macro porosity. Dexter (2004) introduced the slope of the soil-water characteristic curve at the inflection point as one of the indicators of soil quality. The soil-water characteristic curve, which indicates the volumetric or mass moisture of the soil against suction of water by the soil (matrix potential), includes three areas, namely the air inlet area, capillary area, and absorption area. It is impossible to provide a definite shape or equation for the water characteristic curve, since many factors affect it (Dexter, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e\u0026amp;b). However, in general, the characteristic curve is sigmoidal, and each sigmoidal curve has an inflection point at which the slope of the curve changes and is shown by S. In other words, the S index of the slope of the soil-water characteristic curve is in the case, where the curve is drawn based on the soil moisture content against the natural logarithm of water suction by soil. The soil physical quality is determined in different ways. If the soil has one or more than one of the following conditions, it will exhibit poor soil physical quality (Dexter, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e): low water permeability, the occurrence of surface runoff, poor ventilation, reduction of the root growth in the soil, and low ability of the soil performance\u003c/p\u003e \u003cp\u003eIf the soil has none of these conditions, it will have appropriate physical quality. Indicators of soil physical quality directly or indirectly indicate the volume of soil pores, and it is a function related to it (Reynolds et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The sizes and configuration of soil pores considerably affect the characteristic curve. This implies that the slope of the moisture curve at the S inflection point can indicate various aspects of the structure and soil physical quality, such as permeability, hardness, and compaction. Accordingly, the slope of the soil-water characteristic curve at the inflection point is called the soil physical quality index (Dexter, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e). Therefore, any factor that affects the sizes and configuration of the soil pores can change the index. The S index has been introduced as an effective tool to quantify or improve physical degradation and to assess the soil physical quality (Dexter, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004b\u003c/span\u003e). It is possible that the theoretical range of S values varies between zero to infinity. However, it is reported that this index in agricultural soils changes in the range of 0.007 to 0.14 (Reynolds et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Dexter (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004a\u003c/span\u003e) expressed that the slope of the soil-water characteristic curve at the inflection point was mainly due to the small pores and soil physical properties, which were directly affected by the S index. Most of soils have poor physical quality due to the pores they have, therefore, proper soil structure increases the Dexter index and improves the physical quality of the soil. As a result, large amounts of S are necessary for proper soil quality. The characteristic curve of structural pores is drained of water between the saturation point and the inflection point of the curve when the soil dries. However, the texture pores are mostly drained of water as the drying process continues and follows the inflection point. The soil drying process can change the size distribution of soil pores, and consequently, the shape of the soil-water characteristic curve (Biomegartel et al., 2000). Vizitio et al. (2011) determined the S index employing the components of the van Genuchten equation and stated that medium-textured soils had a higher S index than clay and heavy soils. Emami and Astarai (2012) determined the proper moisture by estimating the moisture at the inflection point of the soil-water characteristic curve using easily available soil properties. Olga et al. (2012) investigated the relationship between the slope of the characteristic curve and some soil physical properties, and estimated the S-index with regression transfer functions using easily available soil properties. Asghari et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) utilized easily available soil properties to estimate the physical quality index of forest soils and reported that 79% of the changes in the S index of the soils could be explained by the quantities of calcium carbonate and relative bulk density. The equation of the soil-water characteristic curve is required to calculate the S index. Measurement of the S index can directly be made using the slope of the curve, particularly if the number of corresponding volumetric-potential moisture points is limited. In general, it is beneficial to utilize characteristic curve estimation models like the van Genuchten model. Nevertheless, it is difficult to measure this property by applying the parameters of the moisture characteristic curve, since it needs to spend considerable time and money and to have its spatial and temporal variability. Consequently, there are indirect methods as effective and relative solutions for the problems related to soil quality. Easily available soil properties are employed in the indirect methods. Applying transfer functions is one of the indirect methods that can estimate uneasily available soil properties from easily available soil properties (Cao et al., 1992). Hence, transfer functions are increasingly applied to estimate hydraulic properties of soil (Kasbay, 1984; Liege et al., 2002).\u003c/p\u003e \u003cp\u003eRegression models (Rawls et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and data-driven methods, which have recently become a controversial technology in modeling nonlinear relationships (Miniaci et al., 2004), and also, artificial neural networks are among the most important estimators for the transfer functions. The technique of artificial neural networks, compared to traditional methods, has some advantages including the possibility of using huge amount of noisy data obtained from dynamic and nonlinear systems, particularly when the relationships between variables are not fully understood. The advantages of the above-mentioned soft computational methods include improved performance of the model, faster development of the model, less computational time, and providing a bootstrapping technique to ensure the accuracy of estimates (Openshaw, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In addition, the decision tree classification algorithm method is one of the methods that can be used to estimate the effective parameters of the moisture curve. Decision trees are effectively used as common tools for categorization and estimation or regression. A decision tree is a structure that is applied to divide a large set of collected data into smaller sets of data chains based on a series of simple decision rules. In each successive division, the members of the resulting sets become more and more similar (Rakach \u0026amp; Maiman, 2005). The decision tree is also employed to examine the data for better understanding of the relationships among a large number of candidate input variables to select a target variable. A significant problem in this regard is to predict or categorize accurately (Rakach \u0026amp; Maiman, 2005).\u003c/p\u003e \u003cp\u003eMarghmalek area has various land uses where there is extensive cultivation that makes the soil quality conservation crucial for sustainable farming. Properties affecting soil quality are changed by considering the management approach. Thus, due to faulty management, arable lands are deteriorating. Therefore, it is indispensable to evaluate soil quality and, in this regard, the S index is an effective factor. Furthermore, the slope of the moisture characteristic curve has not been estimated particularly using modeling by decision tree. Therefore, this study is conducted to identify the effective factors on the slope of the soil moisture characteristic curve using modeling by decision tree. It is also aimed to evaluate the accuracy of the modeling and the effect of soil properties on the inflection point of the slope of the moisture characteristic curve in different scenarios, and also, to evaluate the correlation between easily available soil properties and the Dexter index.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study Area\u003c/h2\u003e\n \u003cp\u003eThis study was conducted on different soil types classified by their texture distribution, topography and different land uses. The land use of the study area consisted of (pasture, garden, and agricultural (not plowed and plowed)). The soil textures of the surface horizons were loam, sandy clay loam and silty loam, silty clay, silty clay loam, and clay loam.\u003c/p\u003e\n \u003cp\u003e72 soil samples were randomly taken from the top 20 cm in the Marghmalek basin and Shahrekord City in Chaharmahal and Bakhtiari Province, Iran.\u003c/p\u003e\n \u003cp\u003eMarghmalek is located 55 km northwest of Shahrekord City (the capital of Chaharmahal and Bakhtiari Province) and it belongs to the Zayandehrood sub-basins with an area of 97 square kilometers (excluding mountains). Marghamalek is located in the geographical latitude of 30˝ 22\u0026apos; 32˚ and longitude of 30˝ 22\u0026apos; 50˚ to 30˝ 34\u0026apos; 50˚. The height of the highest point in this basin is 2936 meters, and the lowest point in the basin is 2400 meters. The average temperature is 30.7˚C, and the average annual rainfall is 400 mm, which occurs mostly in the winter and spring seasons. Shahrekord City has moderate summers and cool winters, and the climate is temperate and semi-humid. The average temperature in Shahrekord is 11.5 ˚C.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Sampling Method\u003c/h2\u003e\n \u003cp\u003eA topographic map with a scale of 1:25000 was prepared and sampling points were randomly determined in the study area so that the points were scattered throughout the area. Random sampling was simple although it can also be called quasi-regular (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eA GPS device was applied to determine the location or the geographical coordinates of the points in the area. Then, 72 soil samples were taken from the depth of 0 to 20 cm using a shovel, and next, the samples were transferred to the laboratory. Furthermore, intact samples were taken from the soil surface having dimensions of 5*5 meters using a cylinder to determine the bulk density and soil moisture curve. The taken samples were prepared for soil tests after drying and passing them through a sieve having apertures with the size of 2-mm.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Measurements of Some Soil Physical and Chemic Properties\u003c/h2\u003e\n \u003cp\u003eDetermination of soil texture using hydrometric method (Bayox, 1962), soil acidity in saturated mud using pH meter, electrical conductivity using electrical conductivity meter in the saturated extract (Pige et al., 1987), and organic carbon with oxidation by using Potassium dichromate (Walkley \u0026amp; Blake, 1934) was made. Moreover, the bulk density of the samples was determined using the cylinder method with certain dimensions (Clott, 1986), and the lime of the samples was also determined by neutralization of neutralizing agents with hydrochloric acid and excess acid titration with soda. In addition, the mean weight diameter of the aggregate was determined applying the dry and wet sieving method, and the soil saturation moisture was also determined by preparing saturated mud in the laboratory, and then, drying in an oven set on the temperature of 105˚ for a time period 24 hours.\u003c/p\u003e\n \u003cp\u003eThe Wenner electrode array (Wenner, 1916) was used to determine the electrical resistivity. Four A, B, M and N electrodes in this array are set on the ground along a straight line, and an electrode spacing of a\u0026thinsp;=\u0026thinsp;AM\u0026thinsp;=\u0026thinsp;MN\u0026thinsp;=\u0026thinsp;NB is assigned in this electrode array (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In this study, the electrodes were set at a distance of a\u0026thinsp;=\u0026thinsp;78 cm to measure the electrical resistivity of the soil from the ground surface to a depth of approximately estimated to be 20 cm.\u003c/p\u003e\n \u003cp\u003eIn practice, the electrical resistivity determined from the electrical device is apparent. The apparent resistivity is obtained using Eq.\u0026nbsp;(1) in which fromthe electrical resistance is multiplied by the array geometric factor (K).\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"268\" height=\"49\"\u003e\u003c/p\u003e\n \u003cp\u003eIn this equation, \u0026rho; is the apparent resistivity (ohm \u0026times; meters), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\varDelta \\text{V}}{\\text{I}}\\)\u003c/span\u003e\u003c/span\u003e is theelectrical resistance (ohm) achieved from the electrical device, and K\u0026thinsp;=\u0026thinsp;a*2*3.14 is the array coefficient or geometric factor of the electrode array, and a is the electrode spacing or the distance between the inner electrodes.\u003c/p\u003e\n \u003cp\u003eThe soil dielectric constant is also obtained via Eq.\u0026nbsp;(2).\u003c/p\u003e\n \u003cp\u003eE \u003csub\u003esoil\u003c/sub\u003e = (C/V)\u003csup\u003e2\u003c/sup\u003e (2)\u003c/p\u003e\n \u003cp\u003ewhere E is the soil dielectric constant, C is the speed of light in a vacuum, and V is the speed of subsurface radar waves that is estimated by a GPR (ground penetrating radar) device.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Measurement of Moisture Curve\u003c/h2\u003e\n \u003cp\u003eMoisture characteristic curve of intact soil samples in suctions 0, 1, 3, 5 and 10 kPa was measured by sandbox, and in suctions 30, 50, 150,100, 1000 and 1500 kPa, it was measured by pressure plate machine. The weight moisture content of the soil samples was determined in the mentioned suctions, and the volumetric moisture content of the samples was calculated from multiplying the apparent specific weight and the weight moisture content.\u003c/p\u003e\n \u003cp\u003eDexter index: The slope of the inflection point of the soil moisture characteristic curve (S) was evaluated as an indicator of the soil physical quality. Soil moisture characteristic curve data were used to achieve the index (S). To this end, the parameters of the soil moisture characteristic curve, suggested by van Genuchten (\u003cspan class=\"CitationRef\"\u003e1980\u003c/span\u003e). were obtained using RETC software. In this software, after selecting the relevant equation, the values of bulk density and percentage of soil texture components for each sample were entered into the RETC software for initial estimates. The model parameters, including \u0026theta;s and \u0026alpha;, n, and m were specified, and the value of S for the soil sample was determined assuming m\u0026thinsp;=\u0026thinsp;1\u0026ndash;1/n and applying the least sum of the squared error method for each sample. The following equations, presented by Dexter et al. (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e), were used for computation of the value S.\u003c/p\u003e\n \u003cp\u003em\u0026thinsp;=\u0026thinsp;1\u0026ndash;1/n \u0026nbsp;(3)\u003c/p\u003e\n \u003cp\u003e\u003cimg 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Y3W6N37hbelxS0NMWRqKl/wf87YeJqfP2SDYlp9PiVnB2d0Z8yVzn13OZHQPICi8PkHlfb5KT5eQmXoUtYZzmqrtDL//MJWZa0+QecX3rkMXPoixY/oT7X1RDwaHA43OjKt/GJFRbujKbzm9IIEjKSoOtYbzshonjBYHFlsCR45YKYkE4zVOqSmP2CdPnsz27dtr7h6v4Nlnn6Vhw4bXZW4hhBDiRnD69OkKz8XGxjJjxoxrGsdisZCYmFjp+a9cqKkJpfXQ0bTu0Yif5y9j+sylrPnuU44UhPDmqDB8LrmghMzty5i3eBvHrVeZ2S2aQcOG0CGogvygqqLa7djKyii7YiDoQLHZ/9y+tjCXjPQ6NOkznJH3NMQEFOZpcQtxwdVNdw29VhUUxRmfICeioh/lxdf6En0uYb13Pu9NncAsBdyvOs5foaI6bJRd9b2rqDbHZToQmPD28qBuswb4j/2IDwb92fn21Nf7SFvrS15mLjVf16ye/VPFFgk229nP4HpQq2s/ZCGEEEJUysSJE3nssccu2ymiOlW8MC7vIJv2ORHZNBBv7870fbAzfXs25H9j/8v0n35hx7Ax9L1k0wUHxRnHObhzO3uvVhPs5Ubbm21Q0UNyjR/R977Ge/dW5u2cpWhsWIvzz1u858BepnJ+XKNzccFYkklebhHFcF7rKQWdPhzf0BKKso5xKgWigwFUss8cJj3dgmukL141mEhVdAG0HD6elsOrOoITdo0zXnXS2XX0OLlqfSwKQB7HD2ZiM3fBz6WGm205iijN05Kna05YuB5TJT4vhwPQwuOPP15jtyeEEEKI2ik2NrbC47Gxsaxfv77S9b+XU/HCOPsGprx+hD7j3+C+VuX5Uw8PPLzcKDqTR2ExcEkQ7ETdvk8wqe8Tf/nGqsyox6Q3oDOY/ihz0JucMeu16EzOmMuTovrgUAJKEklJTCaFhhd0INAa6xEc7Erm/J/ZefIBBgQbgSSSdu1l59563PR/AVW+vbLTh7HVicRcw9UIektdwn2szFi9nK331aeXO5C/mF/XqFhuakRwvWpckHg5hTmkn0kl2bMtAX6Gs18ySlM5ui+JNF1dQsKD8K+hneuEEEIIUfsFBJyNp/LyLmzOf7mnsYqi8M477/yxWcbFwsPDKz1/hUGwI1tDYP4PLJrTBGt2KA3MkLNlMbtTnWjWKpqoms1QV40jl/TDxziRnkIOJ9iTXEgdP5XsYwkkpheSduR3th9ugE8DT5y9o4gO/5XD2Yc4nNeb+ud3SdNBix4P0O7nd1j943R8yhrQ+PSPLN8ChgF3c3fH8kVlail5Jw5wNKf8OmdfogLrYDY7sGUfZ++Jsz9UrWcoEXWcKDu9l/kTXiCz8wg8g0NoXy+KwCB3aiIcNQU3pFWLlgRNn8lHn0ejdNBTuOw7Nrv2o9vgtrSvA9hyyTx9huRCDd4WM5npWajOAYSH+eCiyeDInlMU2gGTLyFBdXB3Amx5pB45Skpp+URugUTU9cJZf1FUn5tBckYy5iYxhBsMZ6uoM1cw58UJzPJ4grEvjWFE4xp440IIIYT4RwgLCyM8PJw9e/YwYMCAKo+zatUq+vTpU+nrKgyCNfXuZ+xj23juy0m8NTcPraKg2gPoPfJ13nyuC7VyyVDpPuJX7OWXg7+Tf9zGnMUtaT1UZeOcT5lz0E7xkcl8E96IhnW70cijBS3amvhl2S5Wbyhg0IDzQ1EdSpuxTHjqJOO++Ix3HsxB4zDSPuY1nn6hO1EA2CnJ2MLMF5/hi635WA1FnGnwMB8+MZKbm6bw+7w43vrwR47ma9C2e47vJw9EXfQMUxev5/CifRhMftz+1HuMergzTa9eqFx55jCa3fECr2Q+zWPfjOahL1RUWzj3ffwf7unqjytA3j7iv5nAkwtP0bF7L5IXLOVo2Gi+/fpumiTN4LUJM/k9uRhrZB/+c/cYnrw1nNKDS3l3dCxLc13QKGWcjH6OxW/eS+d6pvMmt5F7cj8Jh6HZsMaEn6uF0Lmic/PEw1mDm7SKEEIIIW54o0ePZtWqVRVmeK/Fli1beOyxxyp9XcUL4xQtQbdN5LOexRRZy7Db7eh0JpzcvXHTV3jV9WVqSe8n36fjo3YAFKMFd2foO24jXV5QzqbXja64uwDoCe7Sk0YrlzF3/gq29riVNhcUrmrw7f86kzo+R0FpGaBgdvPGzfncawopPH2YA7rbeHJyL9r47+KTRc6YcjaxYdYh1p5qzcfzB3Pgxxl8+O0x0o3u9HrwA0ZvvIX1ES/R586+9AzxxLUmAuBzvJrQ86lZrH2wDKvVil5vxtXb889uGO4tadOpB3fs/po1NOal76LZMDUP85F5vPHaYere9gqx/RuQOP0Z5i+azKLo12iQeIrssKf4JLYLPlnx/HdVHTQlpcCfQbCasZFNPx9iPXfy+pAQTOeaSnv356HPuzJCY8J0SUuOv5/DWkKRXYvZrK+g77UQQgghatLIkSN5++23SUpKqnDDDKh4wXpcXBzh4eFVyiRfuTuEzhmLV0U7dtVCihGzxXhJyzNnDx+cL/f6gIHc+0AGWbOWMfHTID78v5b4a7VozsW5WjOunmYu31HXGUu9ntzZ+nX+N2khH2WEcetrr9LpJg/KNiSzftmz3D3MldLMfApsd2BFRW/wx9vFgJd/MPUaBOD1N3yZUEzu+JgqOKkxYjD50a5dAxzN72BgRyNdwrPQ7HqXnWd+5dD7a/nlezPWMwfRtxtF/WxXBrbuSK/413lm+CwM+i7c8/HNNC6vL1YddhyOfWz+YTGztnly3/jhdHfWXDCfk8V4aSn530y127FlrGTeh6uZV3wTr74/gCbX+Z6EEEKIG5GXlxdTpkzhrrvuYvny5ZXqCJGZmcm4ceOYN29eleauwl62/yKKE24d7mJMUH22rfiJCQ98Q/jD7/NQp0sD6UuUZJG/ZylLnW9h+FOeeCYvJS7lGPNmLcZNzcPRfjzvtrOQdWATiz7UYFJVwE6Z3YHdZsNaCtSCjLqtrAwnV1e8fJxRADc/X+z+gYQ1v5NePTrQOcoZXXICeQFtKTUeIePYIXY2fI73uxVTvHslU7Ye4SZfb6JDtKSt/B8fLtiO2uIFxvzXn8ZNXajhHhRVcIwdn3zCqzMWcjS3IV1uuqmWb3wihBBC/LvFxMSwbt06+vXrd82BcEJCAqNGjWL8+PFX3VGuIjd2EAyg9cQnpAd9Bjhj8MnCLUR7bbGpYsMlbx+LZ67kN60NS2kChd07MfzuLuQs/oS5X63nQLwRQ1kOe3M1HPi0G43HtqBl2ygW/PIuo3/4kDsfep2Y+9rQoCZLIq4kdwe7ty7k5WlbcKyZTJP3HqZfACgRtzPqpg9YdmAxHy5LgZCO3D60IYPDkyha8TsfTJjBgUgtpuJj2G4Zgcbp7K+Ra1R3+t3SCn1UNO2Cq7nAoGA/W3/8lnfW5dGtWzsOzP6Rk4pCyJ1v8PSQaEKueQM8T+p2HsQDPh5sW3iaguL8a+gdLYQQQoiaNGXKFJYuXcrcuXMZM2bMVV8/depU3n///b/UKk2C4HLawHb0DKzEBQYvbFF3EhtzkFPZOTg59aZOy8Y0iwomp/MtvKA7RrbVisViofcdOpyDAqnr4gq3jCPGPYFmpwuoF+Fd3r/3OjF4EdzlHkZ59MPoHEagkwIoaMx16XTLQDJXHcEjMA9tYGvaBrjg7hqJNaI3HzweRX5+PhaLhfrdQwj3Oftr5BTSji4hNXSvegtuRtAnLmWOqSWPxgwlf9c8Pl67i7bNwghplsqvn3zFd6sOXpLZNbk0Z+BTYxnS3AsX3PBr1Y07WgUSeHwWX+y84m4kQgghhPibVKaud8qUKX95PgmCq0oxYA7rwrCwLpeccmp7MyPbVnCdX1v63tWWvjV7d9fGHEJE+xAi2l/mXGB3br6v+0UHXfGNHsDDVXvq8NcYAwhp2o+bW65gRmAXBg2rj7nBMT57/Rg2gxkw4OzhS0DdQoovutTg5I3FpL0o45tLXkkZyvX8EiKEEEKI60aCYPGPYS1RCQwLoWv3+mdrtq1lOHsFonNYgWA63PMkHe651tHcsTg5YTK6/nMWfgohhBCi2kg5pKgdVBVFo8Xo4o775cqJremkJq1jyW9nWDt3M4ez97Nh0SYyD//GiWwbmWXXPlXB0fVsWPgFS7Zv50DiSqbPWcSqwwXYL9d9xd0FJ6O+wtYsQgghhPhnkkywqAVU3NycUUsK2L9uPrOwEBzZmqZh/niea+9mTSOvOItNyR5Y2MKxoij2HNQSoDlK5skTZLZocs0t5wqS1rNhwTrWpRpAl8pvswuwObWmU4TLH/2C847Gc+B4BmnH1rD7RCZO9d2Q6mEhhBDi30NRJcUlrrtsUtZ/S+wT37OtxIoK1H/gI14e3pOWvudeo6ICCtVQxKuqXFwMfHbsPx2aMYIP4uJZk6igKEH0vP9VHovtSMRfn10IIYQQtYAEwaIWUFFVFYfj7K+izWYDrR69VvPnxiV/M4fdis3mQNFo0Wg0KIoGRVMtIbgQQgghagEJgoUQQgghxA1HFsYJIYQQQogbjgTBQtQaRWRsn8eUkR+y5JQD2984c9qaz5kX9xFTtsuDISGEEDcGCYKFqBUKSF69lp0LS9D3bE6oi+bP1i1nlvPR698xbUvqJbvhkXucIys/5eNVx8m4eJeQSnCq24RwfTrpW79l4uJTVR9ICCGE+IeQIFiIWsCRvIr8gnTWmltw27CuNLY4sOZt5aeXn+aRp8fz/ecbySk0XRoEF6VhTd7MigP55FqrPr9LREea976P/h5pnP79Z/YWnu2YIYQQQvxbSZ9gIa47Ozm7T5Js9aTZ6EZ4AWc7ZtgoLSwi38UPNxwY8jJwXLy/nbc3AC4uLvicfypnD+vXb+OA1ZMoDwN79ieCsy9tendHt24W8VmAWzP6DOlMpFt5zwu3KKIbtEBXsIK5u4t4pb1Tzb91IYQQ4jqRIFhcf2oumYlH+G3XcYptNopVA4HR3ekSZcFQy59VOIqzSNqygE1HHZhcLGg0/jTt0ppQbxN6skjdu5uV8YcpuuRKb5r26kHTUDecyCYz1ZdjSQY63H7uvBajpQN3vdeBu3Lm8eqhLeQU28pbtOWScWAXv244SGH+SY5s288h5Rs+zQvCy+RB4x69iHZKI2nFt0zZrRLdcSi+J1eyNSmJDceK6KHdy9bdh9ix7zh12nQmyA3O7Uli9IASs47fth2H9g3/ls9QCCGEuB4kCBa1QBKHF77HE6//SqF/ABGRDWmrb0mHSAuG631rV+EoyuDgr5/y1cpSslNPcSylBy+t+IzR3iYsZJG6ayXfTl5E+kXXKUpD7g1rTVioG05kknL8NOnJoZcvQSgspczuQOHchh7ZpO9bw/eTf+RMWTGFuWmkkcv3u80YNOEMC25NZJ8e3NJ3O3vzV3EkuD9fvdGTVa8/RMycDfSa/yFfvbyXz9rPJu9AIjkNIvA/N5erOyZXL07uSwW1IdIYWQghxL+VBMGiFlAozMumXvRA+r3/GY81U9BodehqSxZYzSf1dAm4WfB2NfyxtTKAziuS/q9uoPfLJdhWjWfQMwXk5BcBnkAYTYe+wpLbx10muFXQ6nTlRfl6TE5O6I0VxJxGAzqNHoPJjBGAUBrc8gILBsWipv5O/PxJvK88zf+GNSfE9ey4WuBUSQGd+3UjvH0YWsdBQjv0xMvRlVvCTGgBo7MFvU5z4X8E7DYctjK0htr+9UMIIYT4a2pLmCFuaAp2hx1Fq8PsbMCg16O7XlvFnUd1lFCQkUraqg95PGY8H604Se4lr1LQ6AwYDC44OZnQKuCw28vPaVC0OvQGA4ZL/ujR/rEDnS/u/kYCI+0UqxfcANaCNDKSz5BvzSc3K5XTeUUU20DRlI/rYsbibASdGSfnP8fFXkJefhY5eaUU5ZdQWpxPfnYRJYXZ5BZaKS1IodCWT05xGYVljj+mdGRlY83LpGlYXckCCyGE+FeTTLCoJRRUhx3bXzG6/5AAABd0SURBVOhwUL0clOWt57sHJvBFZgalB5rT3WSi4tuzYiuz46jSBozOBITmEeKlZeteaNPk7FG1JJutnw5lwrxDbN5diublxazb/zIPjfwPdzYoj1CtNopKbWgVG2VW/izuTVnJ+oSdvD47n/YjI2l6WzY/PPcl+U7r+aJHc3oc+oh5h37n8NxQPBsEENLUFYCcrDKK8oz06BdYhfchhBBC/HNIECzEZWnQOTej3wuvUT9zA7NeTaUwN7+G5lJwbtkO1/iTFCxLIKdJNO6AYnClwZA3eLGjFo3m7EMbh0sAIefHpx5RNL3tdf6rBFDH+bzjnq3o98hHNL3LirNnIP7+Kj5zW/CgquJWzwfvhuOZ1NmBzRxAeIi5/KJEsjQaluR15tUwrSSChRBC/KtJECxEBTR6P8I7+RFemMumj/IosdVg51zn5gQ3gZ5H1vNB7C7aP3EvvesY8IrqQqeoK1xncMbVPxLXi4+b/QmO8if4vEO+Hc7/Wzt8I//8W/bmb/hm/hKsN02i3+1euJ9f+CyEEEL8C0lNMAD5pKz8kLdefI+fjoDj6heIiznKODj3OWLf+o41F7dC+EdTIa+IUpsDpUZTo3rc/RsS3qURjaP88DP9vSW5Bs9QoqIHEObjSt8GLn/jzEIIIcT1IZng7C2s/m0LSaec0Lg40GvO+2aQuYnpcavZfSIHo9GTFkNG0ruVN/+cECGLlK0rmLr0IIWFhXgEdmXAqIFEO1/9yspT0Bqdcdcd5uCCNRQ3iqRTx0DcamKqv5UCHm446Z1wdvXEvUbncsG9UQ/ubFSjk1yWc2R3BkV2//snFkIIIcrFxcWRm5vLmDFjrvral156iaFDhxIdHV3l+a4aBFsPruSXrfs5mGbFbDbjFNCY8OhWdA6tkUjqb5ZF0rwFrNzthmPQMCaM9iw/7oCCA/z28StM2+FNoSMfj5RDnMx35qTtFu5tVxfPK45bGxSSGT+DhT+s4ssdNpq75KLduJsDOPNwTHta+5mq9xuQRkfE4Fd5YfBelj3/Nj/Gd+OEyx2MaGZBX11zpOwgMV9LgVszov0qkSfNP01qykkOKI3oGOaK/lqff9hKOLlpOqs3rmBTcgGGZV9jN/SkR6s2lZtfCCGEEFf0yCOPkJWVxeTJk/84lpmZSWRkJNnZ2X8cW7JkCQMGDGDo0KF0796dKVOmEBMTU6U5rxgO5J/YRFzc/5g+/TvmzJnD9x+/zqvj3+PZ7/dRU0uE/j4qxUlz+HEjlIUO5sne54W1tgKSfpnEm3NzaD7iIxYuXsSiH5+n8bHpzJ60iDXpNVgbWk2s6etZMXs+c8605ZMFi1i0fCFv/8fIvrdf4bu1qaTbamrmxvR/dhitld/4ddpKtuVVx5iZHNu8lk/feJYp81ew4KD96pecL3UnyVtn88GadIrLKnGdw0ra7mX8sjGT4iAbuUdXE7/pAIk5tf/nL4QQQvxTTJ06laysLOLi4vDy8vrj+C+//HJBAOzh4cGAAQMAiI6OZvXq1YwbN46EhIQqzVtxEGzPZPXb9/Bd6T3c98FG4uPjiV88mQntPAlMPsOeKk1XizhOs2vmMnaZnQnv1wif807Zcs6wZdkyjjeMoXNzt7NZ3+D+9OxQD//sHcT/XnidbvraZW38lVVH7Xj2HEpvLwALQX2GMTggme2bfie1JgM5r14M7qnDM30pP8YXXH4XtGuh2ilJO0hC/EJ+nPYhE2dvYvHmYtzMlczCKlpKrGUYdUrl6noNbrR6eB7TFq5kxYoVrFixgtlv3M9t9aWUXgghhKgOmZmZvPDCC0yYMOGSc+PGjSMxMRFVVVFVlaysrAvOR0dHM378eGJjY6s0d4VPxB32TeyNL8W9vz/+YeW7R4UNovvNTmi251ewna0Da14G6ZkFlF4t8tG54uPnjauxGh8rW3NJzyqgzGHA1dsdE4VkZBShAorBA29v85+P5nO2sX6LAUOzFrSsf/4gKiVFR8jKcCOgeXPq+517p/7UaxRGvZRjJB9LxYFLLV5VWEJRdjYOcyRRTcJxAkDB2acLzW8q4/vCM+TnWcHbWEPz66kTGYn7L7s4uO8YuX2bVK2W1lFC2trJvLEylL4jRvFGaQozjuRTmWQuAIqCw6GiSMcDIYQQolb56quv6NOnD2FhYRccj4uLIzExkUceeYQHHnigwpKHmJgY7r77bjZs2EDnzp0rNXeFQbBGG0RQQ1e++uVH4m9pSVS0O05GI3V69+WOTiUVdFAo4PiiiTz/9k8kFF9l5sA7eGvKK9zV2KlSN3xFSfP58J1vmL5Ky6DYB+kReIQfn/6OeIcBv7DRvDLvEXq7GdAqoJ5KYl+2K4p/AHUviGbLcJSlcvKASmloHjmZeeRbFLRaLRmFVjISj1MaXEI+YKm+O69m6eSkFZF2zIhXQTrFxS7YbDbsOQXkl9rgVBqpNdnuC1ACgvGz7WdH4inOUMUgWGOmTr9xfNTFiLtvClviXNGqauUzy3odOq0GjVaPvtoKlIUQQgjxV33++eeMHj36kuPffvstAD///DM///wzkyZNYvny5ReUS5zTt29fZs6cWX1BMNqGDHpqBOsfm8GHI/vzabt7ePLBBxjZzoLOuaLA1YnAng/xWsTtFFytz5jRl7CQas5EBvXhgaFJZKb/xOz/TSZv2BM88dNP3L/lM74dv4Bp8/vR5p4GeOkh/+QxsryciQr2w/uiYexAUWYWe75/gnuWuWAszyDa8lNJLajHkIH6Wt5GrQwbJpJ3LmDzI1tY6lQe5dvLyEvLITPShsXddOUh/iqfQEK8NLidTOJEATSsSksNRYPe1ZdAV4BU7HZwVOXBgc2O3aHiUGzYbFB9K/WEEEIIUVVJSUkkJibSsWPHS84tX76cpKQktmzZwrhx49i2bRtjx44lLi7ukte2atWKWbNmVXr+KzQI0OPe+kHGfdKe3vPeYtqOOGLv/YHN/xnL0FH30cv38sM5+UfQ1D+i0jdyMXvJEdZOepn3lx4j44oRpwnn1g/y5su308E3gIimEfgYoghrexO3De1B6yaeOEzt2Br6C+vSCymzA3qwWUtxmMwYjfpLPgTVXobW5Exg83sZeVs0dZ1AURSyt83gh19PUWqrQjayEkrSt7Jg/EtM3ZLPFRPqGk+C+jzBW7E9iTRdWJzhKCvGUrcl4fePIKaR4Ww9TVEmW+JeIq5UwVpjC+PK6Z1wMp+hJPcgx04DV9rw4Zo4uLZPPY8js//HW5N/5cC5PY5Lc8nLzSZV2UCPKUa05wLpwFt46e3HGRRx4ReC/fv3M2rUKByOq3/ViYiIYNq0aUhkLYQQQlTO6dOnr3g+LCyMsLAwevfuzT333MOsWbOYMGHCJaUTFouFxMTESs9/lS5Z7gS1vImgBh8R9dtWls2azs6V3/OVpgn1n29B0CWvL+TE0ql89PVqDpZeZWbf3jz64mj6hl8+I6nRuVO3TV8GO2VRcMXYR4chuD7+5vJ0bXER+flehPXvSZfosx0fim16zN5GjMbzA8XyQS/JLOpwd69LYH1XgjoN4d7hbf5YNGfzSSKpNJ5DDrVGewVrzb5EdhnCrSGlV65/VZzwaBCMu/biN+GJX6AbIS18aTBkGHec10LP+/BbLEh2R19YAtRkNtiBqjpj0LviZL76q6uPAUtke3rd7kHTc4F+5kGSEvfxm9KVW1p4YNKVf16WZtSzXPqvgKenJ3fccQeqevWg28fn3G9H7X42IIQQQvxTeXl5MWXKFMLDwzl9+vQlQXBVVbwwLm0j89a60KZnY0I9o2jWK4pmTc188shbTF3/G7sfbUHQJZGgFoPFn6CISBzWy416Hg9fLMaKl5YpOm+i+jxAVJ9rfzPlV6JobFiL8ikAPAHVYcd+UebTyd0bQ3YOOZl5FOLFn12PNdgcdfAKyCLj6CEOp7XCx1cDFHPi8GFSTrri0cmrgoWB1UPvEkLL28fSssojWDC4GNFxlBOHslGjPVAAa+ZGDu1woAv1I8iphvdJKcgiPcsdq0cDwuvU7FQXMuHTYiD3tBj456HczexeMY8C7bO8eNvVK7n9/Px48sknKzlvTafWhRBCiBtXWFgY4eHhBAQEVNuYFS+Mc0tiwWfx5ARPZkQ70AIoKmCnzObg8k+KTfh3GsbjnYZV2w1WmrMJk8GE0cn5j8DW5OyKs1GHwdmCZ3ny0xRaj8CideSfSCONetQ7bwi9ayhRoe6kLJjHptP30NEXsMazJT6FM6W9uK/VpUXZ1yonYQ0FjbtRt0ZjUAW3oHrU1W5g8Ya17LvzFhoDBbt+ZMm2CHoMb4uzZw0HwWdOcqLYihoeQZAOoIyik9tYs+YIRRHd6NkyCI9KlYR7YDEbMOjNOHtUss1DZg45+cWUarPIt1pwrZFvMLJ5hhBCCFEZ5wLavLyrbyqQmZlJREREhVng8PDwSs9fcSY43YXG2sUs+iaUo1u9CDOp5Py+lCRbY3oNaE/j2rgfbukJ9q7eyv7kg6QkbGfl/lBuDs7j9I5VbD6axZ6Vs5nV9D/c0sIPV98mdGywnJ8zDnOkoB31zstqK04mmvd/hMHxE1k49X0KW7pR5+AcNhe1ofG9A+lf72wGWy1NJyl+PQnn2tb5NWNQmwgMhiIy9mxm3aFswIh7o450CFVIXjWHrz6eTEnfRwlwsdCpay+aRnrUyNbCro17073VOrZv/IYXXs3gZp/TnFqzg9LBjzG4ax3qmoHcg2zZeYQMvSd+GhvHz6RjjuxCn6beaLO28+Oa42cH82/DgFZBmIzgyDzAprX7SAHAhEuDNvRo6IPuohiw7FQSmVorHvX8y8tJCjGlLGfGW7M4em9T6je4liBYhYIjbFmTwJ6UvRzfk8zJM1qWffUFxlbB1GnYhoGNa//efUIIIYS41Lns7p49e/7YBAPOLpibO3cuI0eOxMvLi4SEBGJjY3nnnXcuO86qVavo06fSpQNXyAQH3cqY2N2c+mIZK77Nx263o9OF0ffB5xk7uil/6xPua1WSxK49xWSYi1CO/cbve3vS2zOZ+LVbOKwJwPP0albt6k+PJn64OjWnTUcjP6/Zw/r4Enr3Pq8+VnHCqd0YXnv8NC9+NZ+fPitEq/Wm3+N3MPyuiPItk0tQcn7n27cmsN5uwaimsd3//4h71kik+yF2LF9L3K9rSDoGrj1f5tv3mnIq4QsWb9tPyt538fGoj71OS4JrKAjGtSnd7n2GUuUTXpnzBVPKyvAMHsyoFwfTy99wtsdx1j5WfvEKM9KCad8ojBPxR9B1c6flq+Gk/jCTT5fuQrHbyPDthm3kSAa1dyd7yxI+eH0WuXVM5GstpNfXs+Pt3hdlV8/w+/ZksnURtG8ZWF55rCfT7kNg48YYPay4XtM6MhU1bz/x82cwc89ptFonTK7ppK38gskH29LyjnrXFgQ7bDibDRQXOriGMl8hhBBC/E1Gjx7NqlWreO655y44/vnnnxMbG4uHhwcxMTHMmDHjsu3RALZs2cJjjz1W6bkV9SqrfxwOB3a7HVVV0Wi0aHTa2rtJhOrA4VA5ty2YioJWUXGUv0NVVVEVDVqNcvbh9cnZTIz9mS0ht/HM8wNob7n4kbaKw6Fit9tRFAWtTnfeQ+9Ujq5bxKSvc+n3SDfqFu7gy+1BjLjJRsKynRxzjiZmoIlVH8xgwe4WjI9/gmaZa5n20Ei29pjBozEtaGTRo9PU9GN0B3a7isPhQKvVoTl/PtVB4rRRvLUkH0f/Efwnws7eZak0bXGC8c+dotfUEfQPsvPrS/9hY8QrPP1YZ1Jnz2BjZhQP3BtA8sFEfjremIkPtsTl3MJESsjZ8An//eAMKZ1ieOvJNtQt/yzPTnn2Z3DB21YTWPFqLBPXBjL44694vBkX3KPdZgeNBqX85+pwOHCooGg06LXX8Nt4cj3bNq9mhnYE4wfVxblGGjmUYFv5Jn0fz6LtO8/z/MCQWtxHWgghhKgdMjMziYyMZNu2bVVa8BYXF8ekSZPYunVrpa+9amGoRqNBo6m1Ye+FFA2aS8pFFSqMM4NuZezDp8idu4yvZ9ch4PZwvFwt5wVJChqNcvn3r3pQt0E7BtR/lbcejaMwvzX3f9mPelFGjAc3kTA3lrtnaCnKVHALaYUWMJidMGg1GJ0teHgYrv7hVwsNWi1otZepo1U0aBUzvYa0oaRtP7pGOOjQJJGj88ezLWM1h5/YwkyDQklKKv4NXcmy+9G9TwTJX3/FPTH51It+kHvfbYyz6ezYZQWZ5GWsZeYX+ylpdDsPPdS6PACGczWzyrkvINdK0aDVX/j5V/r3MbA9zQe3oqFioqbXAwohhBDi2p3r/HDXXXdVuBlGRTIzMxk3bhzz5s2r0tw3eEigx7nTSJ6OSmDDtM946UEjnV75mDHR17DMKS+JzIPrWOD5KG9PMmI+MJ+3th/CP2El+/PMBA2czJet4NSaTSyaU0YpUFxUDArYSgrIySmjjpvub8gEX4HqoKi4GI27Di0KoMXg7obGN4oe/ZsxcFRHmngY0R5cw8GG7QjKXs2WZDunO0ziy1v3cOrAcd6btZchD7dBq4Xd343gzU31uCnmJV7qGIi3uZYsFtPo0V2mH/QVOezYVdBoNajnHiVoNGg4b1GoRlN7n4oIIYQQ/xAxMTGsW7eOfv36XXMgnJCQwKhRoxg/fjzR0dFXff3l3OBBMKBxw9OvE32HWQjo5MAz9BrX+etUlPR9xP3ve3b56TAUHMV8/510GNyTzHc/5JNvlrHQHxy5uRwvrEf6l/2ZHRNG02Z+fPHtWO78xIfh//2QeweGX6bf8t/k+AKWxCfwxcaNNL8niEbP96ON0YugNoMZvnUCU95fzjeFZdDqft7qFECUYQN7Fv/CN9OmssG7AEtAFMG3PYhDc7Z7SL1+sTzX3pvQpmH41mQPuZpWls7xRe/y6Ld7aNx3IMU/L2NLegZNR46jr/kYW76YznprMG1Gv8wrDzSh6r1ChBBCCAEwZcoUli5dyty5cxkzZsxVXz916lTef//9Sm+VfD4JggHQYAxoRuvKtJ5zCsHU7B6+frs7efn5uLi4ULdFA4LqGRhynxXfDpmUOhy4uLiAyQWfAF+MLt40ue9dxtU/RWq+jYgoz+tbN+rRiJ6j3iJ8cBEedSMJ1ALoMflG0/3ekZQ2zScvLw+nep1p7Q9GfVfa9nXn3XoZlJWV4eodQrNOXn/swOYR3pEO1/P9VBetM25B0USVfsqKje0Y88CzdE+cwuTvPmJhrxHc/9qLeM/8hZT1Jzj2RxBcS7LeQgghxD/U+R0irmbKlCl/eT4JgqtKccIS0ZHbL7NDdHD7/gS3v/xlhrrt6Ff38uf+dpb6tOxU/7KbcjhH9LnMe/MjvHUfwlv/Dfd2PWmccK8/gD7RHhwyt6btoC60OLmdhXsOYHJvTJduTYjKSOP7T09QcL3vVQghhBBVIkGwEJdhd4DRbKRNjzbU1wFaI3Vv+w+d3MLQk4tN74rRbJUOEEIIIcQ/lATBolZQVQdavRFn9+t9J4CjkNx981m4OZd9mb+xMbIxnis2szf+AKcaQ1CghtSdy9mYFUDelnSat/UBjOgsZvRaDVfpOiiEEEKIWkCCYFEL2HF3cyU/7Sir4ubg1cQDvwbtaRPqgv56tF+wF1GSc5SDansch3aSX+xBxmlnOHUAa9Msjp3UcvpMMoXBZo5tP8hx1pFwqpSirb+RmlsXi4cHEgYLIYQQtdtVN8sQosaph9g5/TuembSEDFXF5F+f9g++y1u3BONSy7+mOTKPsOTdO5n4axl5Vi2K0pUnv36V21p64Xq9b04IIYQQFZIgWNQCKqCgqmd3trM7HCgaHXrtP6PjgsNmxeYAnU6Hoih/7GwnhBBCiNpLgmAhhBBCCHHDkQ2vhBBCCCHEDUeCYCGEEEIIccORIFgIIYQQQtxwJAgWQgghhBA3HAmChRBCCCHEDUeCYCGEEEIIccORIFgIIYQQQtxwJAgWQgghhBA3HAmChRBCCCHEDUeCYCGEEEIIccP5f0gkH9Mm+i5cAAAAAElFTkSuQmCC\" height=\"100\" width=\"705\"\u003e\u003c/p\u003e\n \u003cp\u003ewhere \u0026theta;s and \u0026theta;r are, respectively, the volumetric amounts of saturation moisture and residual soil, h is the soil suction (cm), \u0026alpha; is approximately equivalent to the opposite of potential at the point of air entry (cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and n and m are the dimensionless coefficients of the equation.\u003c/p\u003e\n \u003cp\u003eAccording to the van Genuchten equation, soil moisture is achieved as a function of suction:\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" height=\"46\" width=\"671\"\u003e\u003c/p\u003e\n \u003cp\u003eFor modeling using decision tree, the input properties of the software in four scenarios were as follows:\u003c/p\u003e\n \u003cp\u003eScenario 1: pH, EC, percentage of sand, organic matter, calcium carbonate, MWDdry and MWDwet, bulk density, and \u0026theta;s.\u003c/p\u003e\n \u003cp\u003eScenario 2: Scenario 1 - (pH, EC, \u0026theta;s, MWDdry, and MWDwet)\u0026thinsp;+\u0026thinsp;percentage of gravel, electrical resistance, dielectric constant, and mechanical resistance\u003c/p\u003e\n \u003cp\u003eScenario 3: Scenario 1 - Percentage of sand and clay\u0026thinsp;+\u0026thinsp;geometric mean diameter, and geometric standard deviation\u003c/p\u003e\n \u003cp\u003eScenario 4: Scenario 2 - Percentage of sand and clay\u0026thinsp;+\u0026thinsp;geometric mean diameter, and geometric standard deviation\u003c/p\u003e\n \u003cp\u003eDexter index was the property of the study objective.\u003c/p\u003e\n \u003cp\u003eCross-validation and Resub stitution estimators were applied in modeling using the decision tree. In this method, the measured data sets are divided into k groups. A group is excluded, and other groups or data are applied to design and adjust the model. The excluded group is then applied to the model as test data, and its error is recorded. In the next step, the second group is excluded as a test, and modeling is performed with other groups, and this process continues until all groups are applied to the model once as test data. Finally, the mean error of the test groups is considered as the modeling error.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. Indicators of Model Evaluation\u003c/h2\u003e\n \u003cp\u003eThe indicators of determination coefficient (R\u003csup\u003e2\u003c/sup\u003e), root mean square error (RMSE), and root mean square error in percent (RMSE%), as defined in the following equation, were applied to measure the accuracy and validity of the model.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"297\" height=\"166\"\u003e\u003c/p\u003e\n \u003cp\u003e%RMSE = (RMSE/m) * 100\u003c/p\u003e\n \u003cp\u003ewhere N is the number of samples, m is the average of the actual data, Pi is the measured values, and Oi is the estimated values.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. Sensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eSensitivity analysis by applying the Stat Soft method was used to evaluate the significance of input variables in modeling. In this method, the model is created with all input variables, and the value of the error-index is calculated and considered after achieving the best performance or the lowest error. A certain input variable is then removed, and the model is recreated with other input properties. After reaching the most appropriate structure and performance in the model, the value of the error-index, in this case, is also determined. The value of the output sensitivity to the input variable is calculated via the ratio of the error-index in the second case (removal of an input feature) to the first case (presence of all inputs).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7. Software Used\u003c/h2\u003e\n \u003cp\u003eMinitab software and the Kolomogorov-Smirnov test were employed to calculate statistical indicators and the normal distribution of data. Modeling was performed by the MATLAB 2015 software, and graphs were drawn by the Excel software.\u003c/p\u003e\n \u003cp\u003eIn this research work, the method of decision tree has been used. The decision tree method is one of machine learning methodes. It can provide good results in classificationof little data. There are many types of decision tree algorithms but all of them follow a similar process, which consists of repeatedly dividing data into smaller groups in such a way that according to the target variable, the new generation of nodes is purer than its predecessors (Matsuyama, \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe decision tree model produces rules, thus, it is superior to the neural network model. Moreover, in the decision tree, there is no need for the data to be numerical (Matsuyama, \u003cspan class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn order to model the Dexter index, MATLAB software was used. In each scenario, the physical, chemical and geophysical characteristics of the soils were measured, and then, the measured data were given as the input, and the Dexter index, measred in laboratory, was given as the output in the MATLAB program to model the Dexter index. To compare the scenarios and estimate the Dexter index in each scenario, the error value and correlation coefficient were dtermined in each scenario based on the defined guidelines.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results and Discussions","content":"\u003cp\u003eSoil samples were prepared using six textures (silty clay loam, silty loam, clay loam, loam, silty clay, and sandy clay loam) to estimate the soil moisture characteristic curve. Acidity, electrical conductivity, saturation moisture, lime, organic matter, density, clay percentage, sand percentage, mean weight diameter of dry aggregate, mean weight diameter of wet aggregate, mean and geometric standard deviation diameter, gravel percentage, mechanical resistance, geophysical properties, and dielectric constant were the studied properties, which were entered into the software as the input data to be modeled in different scenarios (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The statistical description of the measured properties indicated that the geometric mean diameter, sand, and electrical conductivity percentage had the highest coefficient of variation among the input quantities. The measured quantities were from six different soil textures, in which the dominant texture was silty clay loam soil. The scattering distribution between the input quantities also indicated that all the input quantities except the geometric mean diameter (Dg) had normal scattering.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eParameters Used in Modelling as Input Goal Variables\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eParameters\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSignificance Level\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCoefficient of Variation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStandard Deviation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMaximum\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMinimum\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epH\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElectrical Conductivity (dS/m)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e42.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSaturation Moisture (kg/kg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.063\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.25\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCalcium Carbonate (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e28.52\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.5\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrganic Matter (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBulk Density\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep.0.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.96\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eClay Percentage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep.0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSand Percentage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean Weight Diameter of Dry Aggregate (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMean Weight Diameter of Wet Aggregate (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.150\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeometric Mean Diameter (Dg) (mm)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.064\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.01\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGeometric Standard Deviation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGravel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e13.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e18.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.8\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElectrical Resistivity (Ω.m)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.91\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e35.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e90.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e170.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29.39\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDielectric constant\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e43.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMechanical Resistance (Kpa)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep\u0026thinsp;\u0026gt;\u0026thinsp;0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e165.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e519.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e313.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3881.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.58\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDexter Index (S)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.012\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e lists the minimum and maximum data range, mean, standard deviation, coefficient of variations, and level of significance of the studied physical and chemical properties. The results indicated that the mean value of S in the studied soils was 0.038, and its range was in the range of 0.01\u0026ndash;0.11. Results obtained from studying the physical soil quality were appropriate according to the value of the S index, and more areas had a good physical and structure quality index according to the Dexter classification (2004). Dexter et al. (\u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) suggested the following classes for the soil physical quality index based on data collected from soils of seven countries with clay values ranging from 4 to 73%: S\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (Very good), 0.035\u0026thinsp;\u0026lt;\u0026thinsp;S\u0026thinsp;\u0026le;\u0026thinsp;0.05 (Good), 0.02\u0026thinsp;\u0026lt;\u0026thinsp;S\u0026thinsp;\u0026lt;\u0026thinsp;0.035 (Poor), and S\u0026thinsp;\u0026lt;\u0026thinsp;0.02 (Very poor). The S-index has been linked to many important soil properties and physical conditions regarding plant growth (Dexter, 2004; Dexter, 2004; Dexter et al., \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1. Modeling Using the Decision Tree\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the model evaluation indicators. The results indicated that the determination coefficient (R\u003csup\u003e2\u003c/sup\u003e) for the Dexter index in the first and third scenarios was increased by 86%, and it was increased in the second and fourth scenarios by removing some chemical properties and replacing the structural properties of the soil such as geophysical properties and mechanical resistance.\u003c/p\u003e\n\u003cp\u003eDexter and Cis (2007) and Olga et al. (\u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) explained that the soil structure and its related properties like describing the distribution of soil pore size were the ways of expressing physical soil behaviors. The slope of the moisture curve is a suitable indicator to measure the soil structure, thus, the S index appears to be a good feature to express the soil structure and soil physical quality. Most soil properties and behaviors, such as permeability, ventilation, bulk density, and particle size distribution, are controlled by the soil structure. The properties of structure affect the soil physical quality. In other words, the S index significantly depends on soil physical properties such as density, particle size distribution, and organic matter. Hence, it indicates the dependence of this index on the soil structure (Emami \u0026amp; Astarai, 2012; Klango, 2010).\u003c/p\u003e\n\u003cp\u003eA comparison between RMSE and %RMSE error evaluation indicators and the determination coefficient (R\u003csup\u003e2\u003c/sup\u003e) in the scenarios in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrated that the error rate and the determination coefficient of the scenarios were near to each other, however, the second and fourth scenarios were more successful than the first and second scenarios. Thus, the determination coefficient, which is in the range between 0 and 1, is the most significant criterion that can explain the relationship between two variables. If the determination coefficient index R\u003csup\u003e2\u003c/sup\u003e is close to 1, it will be better meaning that more correlation exists between the two variables. Hengel and Hangestik (2006) state that if RMSE% is between 0 and 40, the estimation will be done well and modeling will be strong; if it is between 40 and 70, it will be moderate, and if it is above 70, the modeling will be weak. As Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the value of % RMSE is a desirable value in all the scenarios in this study.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eComparison of Training Data Error and Test Data Error for S\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTraining Data Error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eTest Data Error\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCoefficient of Determination Between Measured and Estimated Data\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRMSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%RMSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRMSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e%RMSE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst Scenario\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0058\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecond Scenario\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0199\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThird Scenario\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.865\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFourth Scenario\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0052\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.875\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe accuracy of the algorithm was compared in two stages of training and test as shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. From this comparison, it was realized that the error of the test stage was higher than the training stage like many other models. It should be noted that data heterogeneity disrupts the operation of machine learning algorithms, and thus, it is challenging to use them.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that moisture is the most significant parameter affecting the Dexter index that is located in the highest part of the the decision tree algorithm, i.e., the root node. Electrical conductivity and weighted average diameter of aggregate are the most significant factors after moisture. The decision tree algorithm in the first scenario explains that branches have 15 child nodes and 17 leaf nodes. This branch of the studied parameter needs to be continued to obtain the desired amount of impurity (minimum impurity). Accordingly, a child with a lower degree of impurity is selected to reach this degree of impurity sooner, so that the branch of the desired parameter stops sooner and the branch of the next parameter begins. Hence, all the parameters are branched in terms of their significance and based on average so that they finally reach the leaf node with the lowest degree of impurity. Moisture content (soil saturation moisture, usable soil moisture, and minimum water amplitude) is considered an indicator of the soil structure quality for crop production (Desilova et al., 1994) and as mentioned, the S index indicates the soil structure quality (Dexter, 2004). Hence, a positive correlation is observed between these two parameters (moisture and S index) (Visto et al., 2011).\u003c/p\u003e\n\u003cp\u003eThe results achieved by the decision tree algorithm show that moisture is the most significant effective factor on the S index in the first scenario. After moisture, the mean weight diameter of aggregate and electrical conductivity are the most significant effective factors on the S index that are located in lower nodes (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Indeed, both the mean weight diameter of aggregate and the slope of the moisture curve at the inflection point describe the soil structure (Lee Bisance, 1996). Levy and Miller (1997) state that the mean weight diameter of aggregate is used to quantify the soil structure and affects the slope of the moisture curve at the inflection point.\u003c/p\u003e\n\u003cp\u003eThe research results, as indicated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, imply that in addition to the moisture and electrical conductivity as the most effective parameters, the mean weight diameter of aggregate and in lower nodes, density, and calcium carbonate are next effective parameters affecting the amount of S in the first scenario. Zornoza et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) explain that texture, density, and water affect the creation of stable aggregates, and therefore, affect the soil quality. There is a direct relationship between the density increase and decrease in the S index (Kenha et al. 2011; Rahimi et al., \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Siegel et al., 2005). Emami et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) have also indicated that a significant correlation can be observed between the organic matter and the S index. Modifiers increase the S index by 0.035. In other words, soil management accumulates soil organic carbon, which is significant to improve the soil structure. According to the studies conducted by Tejada and Gonzalez (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e) and Tejada et al. (\u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), increasing the electrical conductivity of saline soils decreases the soil structure stability and bulk density. They stated that increasing the exchangeable sodium ions and consequently, increasing the soil pH, which expanded and distributed clay particles, decreased the stability of aggregates. This situation degrades aggregates.\u003c/p\u003e\n\u003cp\u003eThe saturated electrical conductivity affects the S index. It can be owing to its role in particle flocculation and formation of the soil structure (Lear, 2014). However, successive increases in soil electrical conductivity can degrade the soil structure (Dexter, \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the most effective and initiating factors of division for the Dexter index in different scenarios. According to Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, most of the child and leaf nodes are related to the third scenario. Since the electrical resistivity of the soil is more affected by the soil moisture than any other soil-dependent factor; hence, the electrical resistivity is located in the root node as the moisture is removed in the second and fourth scenarios.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDecision Tree Modeling Results in Different Scenarios for the Dexter Index\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eGoal Property\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eScenarios\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eThe Most Significant Influential Factor on the Root Node and Initiating Division\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eChild Nodes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eLeaf Node\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eDexter Index\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst scenario\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSaturation moisture, electrical conductivity and mean weight diameter of wet aggregate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecond scenario\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElectrical and mechanical resistance and percentage of gravel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThird scenario\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSaturation moisture, electrical conductivity and mean weight diameter of wet aggregate\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFourth scenario\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eElectrical and mechanical resistance and percentage of gravel\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e demonstrates the sensitivity of the Dexter index to the input properties or parameters in different scenarios. The Dexter index in the first and third scenarios shows the highest sensitivity to the properties of saturation moisture, acidity, aggregate stability in the wet state, and bulk density. However, in the second and fourth scenarios, the highest sensitivity of the Dexter index to the percentage of gravel, density, calcium carbonate, and dielectric constant is observed.\u003c/p\u003e\n\u003cp\u003eShirani et al. (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) state that if the value of the sensitivity coefficient for an input property is more than 1, the variable will significantly contribute to the performance of the model and its output. If this ratio is less than 1, it means that the error in the absence ofan input property is less than the error in its presence. Therefore, this variable does not positively affect the accuracy of the model.\u003c/p\u003e\n\u003cp\u003eMany researchers have used transfer functions developed by applying neural networks and artificial intelligence. These functions have been developed as an alternative way to overcome the problems of traditional methods. The results of almost all previous studies indicate that these models operate well and overcome the statistical assumptions involved in the transfer functions. Furthermore, since artificial neural networks are among non-parametric methods in which the assumption of normality of data distribution is not needed, thus, the data analysis using the neural network method will not be problematic if the data distribution is not normal (Openshaw, \u003cspan class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe results of this study demonstrate that the inflection point of the soil moisture characteristic curve (Dexter index) is highly influenced by the characteristics of saturation moisture, electrical conductivity, mean weight diameter of wet and dry aggregate, and acidity in the first scenario. There is no significant difference in the amount of error and the determination coefficient in the third scenario if the percentages of clay and sand are replaced by the geometric mean and standard deviation of the particle size. However, in the second and fourth scenarios, the determination coefficient has increased, and the amount of error has decreased as some chemical properties have been removed and replaced by geophysical properties and mechanical strength. Substituting the geophysical properties for some chemical properties has indicated more difference between different scenarios. However, the Dexter index is highly dependent on chemical properties like soil electrical conductivity, thus, the determination coefficient has also been high in the first and third scenarios though the determination coefficients of all four scenarios are slightly different. Various studies have been conducted on transfer functions and estimation of soil properties by applying easily available soil properties using artificial intelligence. In this regard, it has been revealed that the decision tree algorithm is more accurate than other methods. Moreover, the decision tree method is highly accurate when it is used for prediction in the case of small amount of data. Therefore, it is recommended to use this method forestimation of other hydraulic and uneasily available soil properties.\u003c/p\u003e \u003cp\u003eIt should be noted that the relationship between the hydraulic characteristics and the physical/chemical properties of soils can be influenced by the geographical source of the used data. Therefore, it isconcluded that theobtained results should not be extrapolated beyond the geomorphic region or soil type from which it was taken. However, to assure the relevance and extension of these results to locations outside of our study area, especially at the regions with different climates, further research is needed.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript has not been published or presented elsewhere in part or entirety and is not considered by another journal. The author declared no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling, conducting the experiments, data analysis, and writing paper:\u003c/strong\u003e Samira Mesri: \u003cstrong\u003eSupervisor and Supervision and Providing laboratory and sampling equipment:\u003c/strong\u003e Shoja Ghorbanidashtaki: \u003cstrong\u003eSupervisor and Supervision and Training model with decision tree:\u003c/strong\u003e Hosein Shirani: \u003cstrong\u003eAdvisor and helping with data processing and thoroughly editing the manuscript:\u0026nbsp;\u003c/strong\u003eAbolghasem Kamkar-Rouhani\u003cstrong\u003e\u0026nbsp;Advisor and suggesing the location:\u0026nbsp;\u003c/strong\u003eHamidreza Motaghian.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArmenise, E., Redmile-Gordon, M.A., Atellacci, A.M., Ciccarese, A., Rubino, P.: Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment. 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ASA and SSSA, Madison, WI (1986).\u003c/li\u003e\n\u003cli\u003eBouyoucos, G.J.: Hydrometer method improved for making particle size analysis of soils. J. Agron. 54, 464-465 (1962).\u003c/li\u003e\n\u003cli\u003eB\u0026uuml;nemanna, E.K., Bongiorno, G., Bai, Z.h., Creamer, R.E., Deyn, G.D., Goede, R.d., Fleskens, L., Geissen, V., Kuyper, T.h.,M\u0026auml;der, P., Pulleman, M.,Sukkel, W.,Groenigen, J.W., Brussaard, L.: Soil quality-A critical review. Soil Biol. Biochem. 120, 105-125 (2018). https://doi.org/10.1016/j.soilbio.2018.01.030.\u003c/li\u003e\n\u003cli\u003eCalonego, J.C., Rosolem, C.A.: Soybean Root Growth and Yield in rotation with cover crops under chiseling and No-Till, Europe. J. Agron. 33, 242-249 (2010).\u003c/li\u003e\n\u003cli\u003eCosby, B.J., Hornberger, G.M., Clapp, R.B., Ginn, T.R.: A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils. 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Part I: Theory, effects of soil texture, density,\u003cspan dir=\"RTL\"\u003e \u003c/span\u003eand organic matter, and effects on root growth. Geoderma. 120, 201-214 (2004a).\u003c/li\u003e\n\u003cli\u003eDexter, A.R.: Soil physical quality. Part II: Friability, tillage, tilth and hard-setting. Geoderma. 120, 215\u0026ndash;226 (2004b).\u003c/li\u003e\n\u003cli\u003eDexter, R., Czyż, E.A.: Application of S Theory in the study of soil physical degradation and its consequences. Land Degrad Dev. 18, 369-381 (2007).\u003c/li\u003e\n\u003cli\u003eElliott, E.T.: The potential use of soil biotic activity as an indicator of productivity, sustainability and pollution. In: Pankhurst CE, Doube BM, Gupta VVSR, Grace, PR (Eds.), Soil Biota: Management in Sustainable Farming Systems. 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Soil. Sci. 37, 29-38 (1934).\u003c/li\u003e\n\u003cli\u003eZornoza, R., Acosta, J.A., Bastida, F., Dominguez, S.G., Toledo, D.M., Faz, A.: Identification of Sensitive Indicators to Assess the Interrelationship Between Soil Quality, Management Practices and Human Health. J. soil. 1(1), 173-185 (2015). doi.org/10.5194/soil-1-173-2015.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Decision tree, Inflection point of moisture curve, Modeling, Van Genuchten equation","lastPublishedDoi":"10.21203/rs.3.rs-2984354/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2984354/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDexter\u0026rsquo;s soil physical quality index (the S index), which is defined as the slope of the soil-water characteristic curve at the inflection point, can properly describe the soil physical quality. This study was conducted to derive a model to predict the S index using soil physical, chemical, and geophysical properties.\u003c/p\u003e \u003cp\u003eFor this, 72 soil samples were collected, and some of the physical, geophysical, and chemical properties of the soil samples were measured. The soil water content was measured using sand-box and pressure plate apparatus. RETC software was also used to determine the parameters of the van Genuchten equation. These parameters were employed to find the slop\u003cb\u003ee\u003c/b\u003e of the moisture curve at the inflection point as an indicator of soil physical quality.\u003c/p\u003e \u003cp\u003eThe decision tree analysis was applied to derive the proper model for predicting the S index using different combinations of chemical, physical and geophysical soil properties in 4 scenarios and the most important predictor(s) were determined in each scenario.\u003c/p\u003e \u003cp\u003eThe effective factors on the S index in the first and third scenarios included the moisture and the mean weight diameter of aggregates; while in the second and fourth scenarios in which some of chemical properties were replaced by geophysical and structural properties, the dielectric constant and mean weight diameter of aggregate were the most influential factors on the S index. The coefficient of determination of 0.86 was obtained as a result of correlation between the measured and predicted data in the first and third scenarios, while in the second and fourth scenarios, it was obtained as 0.87. Considering the values of %RMSE, the modeling in all four scenarios can be regarded as successful. However, the % RMSE value in the second and fourth scenarios was less than that in the first and third scenarios.\u003c/p\u003e","manuscriptTitle":"Prediction of Dexter’s Soil Quality Index Using Soil Physical, Chemical, and Geophysical Properties","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-30 18:28:11","doi":"10.21203/rs.3.rs-2984354/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b9f3037d-fc7e-4902-bea1-3d74a0823c2c","owner":[],"postedDate":"May 30th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-05T11:38:26+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-30 18:28:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2984354","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2984354","identity":"rs-2984354","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}