Y. Ostovari; S.A.A. Mousavi; H. Mozaffari
Abstract
Introduction: Soil erosion is one of the most important and serious threats to food security and as a consequence of human life. In order to perform soil protection activities against soil erosion, knowledge about the amount of soil loss tolerable is very important. In fact, the soil loss tolerable is ...
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Introduction: Soil erosion is one of the most important and serious threats to food security and as a consequence of human life. In order to perform soil protection activities against soil erosion, knowledge about the amount of soil loss tolerable is very important. In fact, the soil loss tolerable is the potential for soil erosion, loss of productivity and lost production, and the final criterion for controlling soil erosion and degradation of land. Soil thickness methods, particularly Skidmore equation, based on their ability to estimate the tolerable amount of soil loss have been widely used. In the mathematical function developed by Skidmore based on soil thickness, the soil loss tolerable is calculated based on the soil's current depth, the lowest and maximum soil depth for sustained growth of crops, and the upper limit of tolerable erosion in accordance with the environment. Since the determination of soil loss tolerance by soil thickness method and the Skidmore equation requires time, cost and energy, the researchers have tried to estimate the soil tolerance is supported by regression methods using pedotransfer functions and easily available soil properties. Therefore, the present study was carried out with the aims of determining the tolerable tolerance of soil loss by thickness method and the development of regression pedotransfer functions for estimating this property in the upstream of the dam.
Materials and Methods: The study is place on Kamfiruz Watershed with an area of 422 km2, an average annual precipitation of 443 mm and an average annual temperature of 14 °C. It is closed to the Dorudzan Dam sub-basins and is considered as one of the five parts of Marvdasht plain in Fars province. For this work, 60 soil profiles were excavated by excavating machine. In addition to measuring the depth of soil, some physico-chemical soil properties were measured from the surface layer (0-30 cm) including; soil texture, organic matter, salinity, percentage calcium carbonate, mean weight diameter in the laboratory and filed. In order to develop regression models for estimating the tolerable soil loss, information from 60 soil profiles was divided into two data-sets. One set of the data with 42 samples (70% of whole samples) was used for developing the models and another set of the data with 18 soil samples (30% of whole samples) was used for validation. Multiple linear regression was used to develop the linear models. The same soil properties used in the multiple regression method were considered as inputs in the tree regression method to estimate the tolerable amount of loss.
Results and Discussion: The results showed that the minimum and maximum Z1 parameters (the lowest soil depth for stable growth of crops in the study area) were considered as 0.25 and 0.51 m based on the current depth of soil. Organic matter of the soils with the highest standardized coefficient (Beta = 0.44) and the highest correlation (-0.77) with soil loss tolerance was the most important soil properties for estimating the soil loss tolerance. In the regression model, only the coefficients of four characteristics of permeability, soil aggregate stability, pH and organic matter appeared among the soil grazing characteristics and entered into the model. Based on the evaluation statistic, tree regression method with the highest determination coefficient in both calibration data sets (R2 = 0.96) and validation (R2 = 0.78) and the lowest error value in the validation data (RMSE= 0.29 ton ha-1 year-1) and validation (RMSE = 0.125 ton ha-1 year-1) were more efficient than the multiple regression method in estimating the tolerable soil loss.
Conclusion: Soil loss tolerance was estimated using regression methods (multiple linear regression and regression tree) in Doroudzan Watershed, Fars province. The soil loss tolerable determined using Skidmore method, was 1.04 tons per hectare per year ranging from 0.29 to 2.25 ton ha-1 year-1. The soils of this area are slightly deep and their depth varies from 0.4 m in the marginal areas in the upstream parts of the catchment area of the dam and the slope of mountain up to 2 meters in the center of the plain with agricultural lands uses. In general, the tree regression method had a better performance than linear regression method for estimating the soil loss tolerance based on the statistical indices.
Yaser Ostovari; shoja ghorbani; Hosseinali Bahrami; Mahdi Naderi; mozhgan abasi
Abstract
Introduction: Soil erodibility (K factor) is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. This factor can be directly determined as the mean rate of soil loss from standard ...
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Introduction: Soil erodibility (K factor) is generally considered as soil sensitivity to erosion and is highly affected by different climatic, physical, hydrological, chemical, mineralogical and biological properties. This factor can be directly determined as the mean rate of soil loss from standard plots divided by erosivity factor. Since measuring the erodibility factor in the field especially watershed scale is time-consuming and costly, this factor is commonly estimated by pedotransfer functions (PTFs) using readily available soil properties. Wischmeier and Smith (1978) developed an equation using multiple linear regressions (MLR) to estimate erodibility factor of the USA using some readily available soil properties. This equation has been used to estimate K based on soil properties in many studies. As using PTFs in large sales is limited due to cost and time of collecting samples, recently soil spectroscopy technique has been widely used to predict certain soil properties using Point SpectroTransfer Functions (PSTFs). PSTFs use the correlation between soil spectra in Vis-NIR (350-2500 nm) and certain soil properties. The objective of this study was to develop PSTFs and PTFs for soil erodibility factor prediction in the Simakan watershed Fars, Iran.
Materials and Methods: The Semikan watershed, which mainly has calcareous soil with more than 40% lime (total carbonates), is located in the central of Fars province, between 30°06'-30°18'N and 53°05'-53°18'E (WGS′ 1984, zone 39°N) with an area of about 350 km2. For this study, 40 standard plots, which are 22.1×1.83 m with a uniform ploughed slope of 9% in the upslope/downslope direction, were installed in the slopes of 8-10% and the deposit of each plot was collected after rainfall. From each plot three samples were sampled and some physicochemical properties including soil texture, organic matter, water aggregate stability, soil permeability, pH, EC were analyzed Spectra of the air-dried and sieved soil samples were recorded in the Vis-NIR-SWIR (350 to 2500 nm) range at 1.4- to 2-nm sampling intervals in a standard and controlled dark laboratory environment using a portable spectroradiometer apparatus (FieldSpec 3, Analytical Spectral Device, ASD Inc.). Some bands which had the highest correlation with K factor were chosen as input parameter for developing PSTFs. A stepwise multiple linear regression method was used for developing PTFs and SPTFs. R2, RMSE and ME were used for comparing PTFs and SPTFs.
Results and Discussion: The K values varied from 0.005 to 0.023 t h MJ−1 mm−1 with an average standard deviation of 0.014 and of 0.003 t h MJ−1 mm−1, respectively. The K estimated by Wischmeier and Smith (1978) equation varied from 0.015 to 0.045 t h MJ−1 mm−1 with an average of 0.030 t h MJ−1 mm−1. There was a significant difference (p
Y. Ostovari; K. Asgari; H. R. Motaghian
Abstract
Introduction: Estimation of cation exchange capacity (CEC) with reliable soil properties can save time and cost. Pedotransfer function (PTF) is a common method in estimating certain soil properties (e.g. CEC) that has been wieldy used for many years. One of the common techniques that have been used ...
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Introduction: Estimation of cation exchange capacity (CEC) with reliable soil properties can save time and cost. Pedotransfer function (PTF) is a common method in estimating certain soil properties (e.g. CEC) that has been wieldy used for many years. One of the common techniques that have been used to develop PTFs is multiple linear regressions. In this method, all easily obtained soil properties are linearly related to certain soil properties. In addition to multiple linear regressions method, more complex techniques such as artificial neural networks and regression tree have been used to develop PTFs. The regression tree method is a well-known method for analyzing the environmental science which determines optimal separation point of independent variables.The purposes of this study were to evaluate and compare tree and multiple linear regressions in estimating cation exchange capacity with reliable soil properties.
Materials and Methods: For this work, 106 soil samples of Unsaturated Soil hydraulic database (UNSODA), which contain a wide range of soil texture classes, were used. The examples were divided into 2 sets including 81 and 25 soil samples for developing and validating multiple linear regression and tree regression, respectively. For estimating CEC with tree and multiple regressions, soil texture properties, organic matter, pH and bulk density were used. To develop multiple linear regressions and create the tree structure, at first, correlation between cation exchange capacity with other soil properties were evaluated; then, soil properties that had significant correlation were chosen to introduce software. As well, the suggested linear function and tree structure were compared with 2 famous pedotranser functions including Bell and Van-kolen and Breeuwsma et al., which have been used for estimating CEC.For investigating the performance of multiple linear regression and tree regression to estimate CEC 1:1 lines, determination coefficient (R2), mean error (ME), root mean square error) RMSE), and geometric mean error (GMER) were used. Statistica 8.0 software that was developed by ESRI was used to develop multiple linear regressions and generate tree structure.
Results and Discussion: The results showed for developing multiple linear regression model to estimate CEC among all inputs parameters (sand, silt, clay, organic matter, pH and bulk density) only just two parameters including organic (with r=0.70) and clay percentage (with r=0.59) had a significant coefficient, so organic and clay percentage appeared, and suggested multiple linear regression models based on this two parameters, with coefficient of 3.183 and 0.274, respectively, were developed. Also, only organic matter and clay percentage from inputs parameter in tree were shown. In tree structure most nods were divided into 2 Childs nods based on organic matter and only in the left side of tree structure in the second level clay percentage was appeared. Regression tree in two data sets (validation and development) based on R2, RMSE, ME and GMER had a high quality for CEC estimation than regression methods. Proposed linear regression model had high performance than Bell and Van-kolen and Breeuwsma et al. to estimate CEC.
Conclusions: The main aim of this study was to investigate the efficiency of multiple linear regression model and regression tree to predict cation exchange capacity (CEC) based on relationships between CEC and easily measurable soil properties. For this work, 106 soil samples of UNSODA data set were used. Results showed that just clay percentage and organic matter that had higher correlation with CEC appeared in suggested linear regression and tree structure. Based on 1:1 lines, R2 ,RMSE, ME and GMER, tree regression model had higher performance than all linear regression models (suggested function , Bell and Van-kolen and Breeuwsma et. al.) to estimate cation exchange capacity. As well, suggested function had more efficiency than Bell and Van-kolen and Breeuwsma to predict CEC.
Abstract
Estimation of soil moisture content at different soil suctions is preferred to its determination due to required cost and time. The aim of the present work was to explore the relationship between soil texture fractal dimension and soil volumetric water content. A dataset of 195 soil samples from UNSODA ...
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Estimation of soil moisture content at different soil suctions is preferred to its determination due to required cost and time. The aim of the present work was to explore the relationship between soil texture fractal dimension and soil volumetric water content. A dataset of 195 soil samples from UNSODA database was selected. A pedotransfer developed by Sepaskhah & Tafteh (2013) was used to estimate soil fractal dimension. Exponential functions better describe the fractal-water content relationship than linear functions. A set of exponential pedotransfer functions using texture fractal dimension or additionally soil bulk density is proposed for predicting water content at several suctions across soil water retention curve. These pedotransfer functions, generally, function well or better than the most recent pedotransfer functions proposed by Ghanbarian-Millan (2010).
H. Beigi Harchegani; Y. Ostovari
Abstract
Particle size distribution (PSD) is one of the most important soil physical properties. The Grey Model GM(1,1) is a new method and different from empirical and parametrical models for description and estimation of soil particle size distribution. In this study, the models of Grey GM(1,1) and Skaggs ...
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Particle size distribution (PSD) is one of the most important soil physical properties. The Grey Model GM(1,1) is a new method and different from empirical and parametrical models for description and estimation of soil particle size distribution. In this study, the models of Grey GM(1,1) and Skaggs have been used to estimate PSD in five soil textural classes including 138 soil samples taken from Shahrekord Plain. For evaluating and comparison of two models, four statistical indices (MSE, MAPE, AAE, R2) and 1:1 lines were used. The results showed that the performance of both models was relatively good in all five textures. However, Skaggs and Grey GM(1,1) had the best performance in loam and clay textures, respectively. It seems that the performance of Skaggs and Grey GM(1,1) models improved when soil textures changed to coarser and finer textures, respectively. Absolute cumulative error (AAE) of the Skaggs model in some textures tended to decrease while that of the Grey GM(1,1) tended to slightly increase with increasing uniformity and curvature indices of soil.