Zahra Habibi; Mehdi Rahmati; Ali Asghar Alilou; Esmaeil Karimi
Abstract
Introduction: The use of soil amendments more specifically bio-polymers is increasing nowadays. Arabic Gum is also one of the hydrogels that are capable for soil modification. It seems that the main usage of amendments in soils is to improve the structure of intended soils. Saline-sodic soils are among ...
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Introduction: The use of soil amendments more specifically bio-polymers is increasing nowadays. Arabic Gum is also one of the hydrogels that are capable for soil modification. It seems that the main usage of amendments in soils is to improve the structure of intended soils. Saline-sodic soils are among the poorly structured soils. The use of soil amendments in these soils may be of the most concern. The different conditions of saline-sodic soils in terms of microbial activity and sodium concentration imply that there should be differences in effects of different soil amendments in saline-alkaline and non-saline-alkaline soils. There is no report (up to our knowledge) about the application of Arabic gum in saline soils. However, it seems that the effects of Arabic gum in saline-sodic soils may differ from what in non-saline-alkaline soils due to the interactions between Arabic gum, salinity, and sodium. Therefore, the current research was aimed to investigate the effects of Arabic gum as an analogue of exopolysaccharides on several soil characteristics of saline-sodic and non-saline-sodic soils collected from Lake Urmia catchment, northwest of Iran.
Materials and Methods: The current research was carried out using loam soil samples collected from Qareh Chopogh village located on the southeastern border of Lake Urmia, Bonab plain, Northwest of Iran. In order to evaluate the effects of Arabic gum on properties of salin-sodic and non-saline-sodic soils, a factorial experiment based on completely randomized design (CRD) with two factors (salinity - sodicity levels and Arabic gum) and three replications was carried out. Salinity - sodicity levels, as first factor, included EC = 1 dSm-1 and SAR = 1.3 (non-saline-sodic soil), EC = 6 dSm-1 and SAR = 16 (saline - sodic soil), and EC = 30 dSm-1 and SAR = 58 (severely saline-sodic soil). When soils were sampled from each salinity-sodicity classe and transformed to laboratory, pots were prepared and treated with different levels of Arabic gum including 0, 5, and 10 g kg-1 and incubated for one month with varying soil water content between around 0.5FC and FC. After incubation time, disturbed and undisturbed soil samples were collected from pots and were prepared for further analysis. Undisturbed soil samples were used to determine bulk density of pots (Db), volumetric (θv) and gravimetric (θm) saturated soil water contents, and saturated hydraulic conductivity (Ks). Disturbed soil samples were also used to determine wet-aggregate stability (WAS), mean weight diameter (MWD), and mass fractal dimension (Dm) of soil aggregates, soil pH, soil organic carbon (OC), soil cation exchange capacity (CEC), and soil respiration. Finally, results were subjected to analysis of variance in SAS software based on applied design.
Results and Discussion: The interaction of Arabic gum and soil salinity-sodicity was significant for organic carbon, microbial activity and soil structural characteristics (MWD, WAS, and mass fractal dimension). Arabic Gum improved biological soil properties even in saline-sodic soils. The higher microbial activity (16 to 443 mg CO2 kg-1 soil day-1 in higher amount of Arabic gum vs. 3 to 109 mg CO2 kg-1 soil day-1 in blank soil) and organic carbon content (0.31 to 0.36 % in higher amount of Arabic gum vs. 0.14 to 0.22 % in blank soil) were obtained in higher amount of Arabic gum in saline-sodic and non-saline soils. While, the stability (0.88 to 60 vs. 0.9 to 13 %), mean weight diameter (0.06 to 2.53 vs. 0.009 to 0.46 mm), and mass fractal dimensions (0.935 to 2.09 vs. 0.75 to 2.45) of soil aggregates were affected by Arabic gum in non-saline-sodic soils rather than saline-sodic soils. The main effect of soil salinity-sodicity was significant for soil cation exchange capacity, soil pH, gravimetric and volumetric soil water contents, and pots bulk density. The higher amounts of CEC (21 vs. 9 Cmole+.kg-1), pH (8.0 vs. 7.4), volumetric (53 vs. 41 %) and gravimetric (43 vs. 30 %) water contents, and the lower pots bulk density (1.23 vs. 1.37 g.cm-3) were recorded in severely saline-sodic soil compared to non-saline-sodic soil. The main effect of Arabic gum was significant for soil saturated hydraulic conductivity and soil pH where the higher rate of saturated hydraulic conductivity (0.06 cm.min-1 in higher amount of Arabic gum vs. 0.04 cm.min-1 in blank soil) and the lower pH (7.9 in higher amount of Arabic gum vs. 8.2 in blank soil) were recorded in 10 g.kg-1 Arabic gum.
Conclusion: Based on the results, we conclude that although the effectiveness of the Arabic gum is decreased in saline-sodic soils, it significantly affects different soil characteristics. However, it seems that we need to apply higher amount of Arabic gum (higher than 10 g.kg-1) to gain the considerable effects of Arabic gum in saline – sodic soils. Since gradual drying of Urmia Lake, located in northwest of Iran, is leaving behind wide areas of saline and saline-sodic soils which is threatening habitant’s health, modification of these salt-affected areas using Arabic gum can be a useful strategy. Although, improving vegetation density seems to be main key for this aim, application of soil amendments (more specifically Arabic gum) may support the establishment of vegetation in area. Our objective observation also points to this fact that Arabic gum (specifically in higher amount of 10 g.kg-1) resulted in a crust like layer in soil surface specially in dry state that can prevent the removal of salt particles by the wind. However, the effectivity of Arabic gum in preventing the removal of salt particle by the wind (which is a common issue in area) needs to be evaluated through wind tunnel experiments.
alidad karami; R. Zara; vahid alah jahandideh mahjan abadi
Abstract
Introduction: Fractal geometry concepts have been widely applied as a useful tool to describe complex natural phenomena, in particular,for a better understanding of soil physical systems. However, limited information is available on the fractal characteristics of soil properties or soil aggregation. ...
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Introduction: Fractal geometry concepts have been widely applied as a useful tool to describe complex natural phenomena, in particular,for a better understanding of soil physical systems. However, limited information is available on the fractal characteristics of soil properties or soil aggregation. A soil aggregate is made of closely packed sand, silt, clayand organic particles building upsoil structure. Soil aggregation is a soil quality index integrating the chemical, physical, andbiological processes involved in the genesis of soil structure. Soil structure and its stability are important issuesfor many agronomic and environmental processes. Thus, quantitative description of soil structure is very important. Soil forming factors in different soils (various orders) and forms affect the soil structureformation. Characterizing aggregate size distribution for different soil orders using fractal theory is necessary for evaluating the impact of soil forming factors on soil structure and quantifying the relationship between fractal dimension and other important soil properties. Therefore, the aims of this research were quantifying the structure of different soil orders using fractal geometry, mean weight diameter of aggregates (MWD)and geometric mean diameter of aggregates (GMD). In addition, MWD and GMD indices and fractal parameters of soil aggregate size distribution were compared toevaluate soil structure and determinethe relationship between fractal parameters with MWD, GMDand other soil properties.
Materials and Methods: Fractal models which simulate soil structure are also used to better understand soil behaviors. Aggregate size distribution is determined by sieving a fixed amount of soil mass under mechanical stress and is commonly synthesized by the MWD, GMDand fractal dimensions such as the fragmentation fractal dimensions. Therefore, aggregate size distribution and its stability variation were evaluated using some fractal models and MWD and GMD (empirically indices).In the current study, the original data were obtained from analysis of diagnostic horizons of seven important soil orderslocated in Fars Province in the Southern Iran. Soil samples were collected from diagnostic horizons of seven soil orders includingEntisols, Vertisols, Aridisols, Mollisols, Alfisols, Histosols and Inceptisols. The measured physico-chemical properties of soil were aggregate size distribution, soil particle size percentage (sand, silt, and clay), saturation percentage (SP), organic carbon (OC), pH, calcium carbonate equivalent (TNV), gypsum content, soil electrical conductivity (EC) and soil bulk density (BD). The MWD and GMD indices, the fractal dimensions and fractal parameters of aggregates were then calculated. Relationships between soil properties with MWD, GMD and the fractal dimension were also determined.
Results and Discussion: The results showed that there was a significant correlation between fractal dimension of Riue and Sposito and Taylor and Wheatcraft models and soil aggregate stability indices (MWD and GMD indices of aggregates) with the other soil characteristics. This correlation between fractal parameters with organic matter, bulk density, clay and sand percentage was stronger than other soil properties. There was a significant and negative correlation (p< 0.01) between fractal dimension of Riue and Sposito and Taylor and Wheatcraft models with mean weight diameter of aggregates and geometric mean diameter of aggregates. Inverse correlation between fractal dimension and aggregate stability indices illustrateed thatlower fractal dimensionswere calculated for the soils with more stable aggregates which have the highest mean weight diameter of aggregates and geometric mean diameter of aggregates. Subsequently, the fractal dimension of aggregates could reflect the aggregate stability factors. The values of coefficient of determination (R2) and mean error (ME), root mean square error (RMSE), residual some of squares (RSS), mean square of non-fitted (Sr2) and Akaike) AIC (statistical criteria indicated that Taylor and Wheatcraft model had the better performance. Although largerfractal dimensions were estimated by Riue and Sposito modelwhich can be explained by the great model sensitivity, this model overall performed well.
Conclusion: The results indicated that fractal theory can be used to characterize soil structure at different soil orders and fractal dimensions of soil aggregate seems to be more effective in this regard, except forHistosols. Fractal dimension can be estimated using some easily available soil properties. Fractal theory can be applied to characterize and quantify soil structure in different soil orders of Fars Province.
Ali Asghar Besalatpour; Hossein Shirani; Isa Esfandiarpour Borujeni
Abstract
Introduction: Soil aggregate stability is a key factor in soil resistivity to mechanical stresses, including the impacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010). Various indicators have been proposed to characterize and quantify soil aggregate stability, for ...
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Introduction: Soil aggregate stability is a key factor in soil resistivity to mechanical stresses, including the impacts of rainfall and surface runoff, and thus to water erosion (Canasveras et al., 2010). Various indicators have been proposed to characterize and quantify soil aggregate stability, for example percentage of water-stable aggregates (WSA), mean weight diameter (MWD), geometric mean diameter (GMD) of aggregates, and water-dispersible clay (WDC) content (Calero et al., 2008). Unfortunately, the experimental methods available to determine these indicators are laborious, time-consuming and difficult to standardize (Canasveras et al., 2010). Therefore, it would be advantageous if aggregate stability could be predicted indirectly from more easily available data (Besalatpour et al., 2014). The main objective of this study is to investigate the potential use of support vector machines (SVMs) method for estimating soil aggregate stability (as quantified by GMD) as compared to multiple linear regression approach.
Materials and Methods: The study area was part of the Bazoft watershed (31° 37′ to 32° 39′ N and 49° 34′ to 50° 32′ E), which is located in the Northern part of the Karun river basin in central Iran. A total of 160 soil samples were collected from the top 5 cm of soil surface. Some easily available characteristics including topographic, vegetation, and soil properties were used as inputs. Soil organic matter (SOM) content was determined by the Walkley-Black method (Nelson & Sommers, 1986). Particle size distribution in the soil samples (clay, silt, sand, fine sand, and very fine sand) were measured using the procedure described by Gee & Bauder (1986) and calcium carbonate equivalent (CCE) content was determined by the back-titration method (Nelson, 1982). The modified Kemper & Rosenau (1986) method was used to determine wet-aggregate stability (GMD). The topographic attributes of elevation, slope, and aspect were characterized using a 20-m by 20-m digital elevation model (DEM). The data set was divided into two subsets of training and testing. The training subset was randomly chosen from 70% of the total set of the data and the remaining samples (30% of the data) were used as the testing set. The correlation coefficient (r), mean square error (MSE), and error percentage (ERROR%) between the measured and the predicted GMD values were used to evaluate the performance of the models.
Results and Discussion: The description statistics showed that there was little variability in the sample distributions of the variables used in this study to develop the GMD prediction models, indicating that their values were all normally distributed. The constructed SVM model had better performance in predicting GMD compared to the traditional multiple linear regression model. The obtained MSE and r values for the developed SVM model for soil aggregate stability prediction were 0.005 and 0.86, respectively. The obtained ERROR% value for soil aggregate stability prediction using the SVM model was 10.7% while it was 15.7% for the regression model. The scatter plot figures also showed that the SVM model was more accurate in GMD estimation than the MLR model, since the predicted GMD values were closer in agreement with the measured values for most of the samples. The worse performance of the MLR model might be due to the larger amount of data that is required for developing a sustainable regression model compared to intelligent systems. Furthermore, only the linear effects of the predictors on the dependent variable can be extracted by linear models while in many cases the effects may not be linear in nature. Meanwhile, the SVM model is suitable for modelling nonlinear relationships and its major advantage is that the method can be developed without knowing the exact form of the analytical function on which the model should be built. All these indicate that the SVM approach would be a better choice for predicting soil aggregate stability.
Conclusion: The pixel-scale soil aggregate stability predicted that using the developed SVM and MLR models demonstrates the usefulness of incorporating topographic and vegetation information along with the soil properties as predictors. However, the SVM model achieved more accuracy in predicting soil aggregate stability compared to the MLR model. Therefore, it appears that support vector machines can be used for prediction of some soil physical properties such as geometric mean diameter of soil aggregates in the study area. Furthermore, despite the high predictive accuracy of the SVM method compared to the MLR technique which was confirmed by the obtained results in the current study, the advantages of the SVM method such as its intrinsic effectiveness with respect to traditional prediction methods, less effort in setting up the control parameters for architecture design, the possibility of solving the learning problem according to constrained quadratic programming methods, etc., should motivate soil scientists to work on it further in the future.
