Showing 7 results for Cation Exchange Capacity
M. Maftoun, H. Haghighat Nia, N. Karimian,
Volume 4, Issue 2 (7-2000)
Abstract
As apparent Zn recovery in mineral soils (saturated and unsaturated) is nill, the precise assessment of processes responsible for Zn retention in these soils is of great importance. A laboratory study was conducted to characterize Zn adsorption in eight lowland calcareous soils. The fit of sorption data was evaluated by Freundlich and Langmuir isotherms. In this study, 2-g soil samples were equilibrated for 24 hours with 40 mL 0.0lM CaCl
2 solution containing 5 to 500 mg Zn L
-1. The amount of Zn adsorbed was calculated based on the difference between the initial and equilibrium Zn concentrations.
Zinc adsorption data were fitted to a linear form of Freundlich equation. However the Langmuir isotherm was divided into two distinct linear portions, representing two different types of adsorption sites. The Langmuir K1 was higher and adsorption maxima (b1) was lower in part I (corresponding to lower Zn concentration) than in part II (corresponding to higher Zn concentration). Thus, it seems that in parts I and II, sites are more important for their high adsorption energy and adsorption capacity, respectively. Langmuir adsorption maxima (b2) was positively correlated with clay content, CCE and P concentration and negatively correlated with CEC.
F. Nourbakhsh, A. Jalalian, H. Shariatmadari,
Volume 7, Issue 3 (10-2003)
Abstract
Cation exchange capacity (CEC) is one of the most important chemical characteristics which influences soil quality from different aspects. At the same time, CEC is an input parameter of many computer models being applied in soil science and agriculture. Methods of CEC determination are always time-consuming and laborious. Therefore, developing a model for CEC estimation from other soil properties is essential. The objective of this study was to understand the associations between CEC (as a dependent variable) and sand, silt, clay, organic matter and pH (as independent variables). In this study 464 soil samples from A, B, and C horizons of different soils were used. Results revealed that CEC is negatively correlated with sand (r=-0.389***) and is positively correlated with organic matter (r=0.772***), clay (r= 0.391***) and silt (r= 0.233***). No significant correlation was observed between CEC and pH. Stepwise regression analysis showed that both organic matter and clay enter the model and that coefficients of determination (r2) for the multiple models are higher than those of simple linear correlations. Other parameters could not increase the r2 considerably. Correlation analysis on data from A, B, and C horizons revealed that the CEC of organic matter in different horizons are not the same. Separation of Aridisols could not increase the r2 of the model and the accuracy of the estimations. Correlation studies in acid soils showed that the contribution of organic matter in CEC is much higher than that of clays.
R Mohajer, M.h Salehi, H Beigi Herchegani,
Volume 13, Issue 49 (10-2009)
Abstract
Soil fertility measures such as cation exchange capacity (CEC) may be used in upgrading soil maps and improving their quality. Direct measurement of CEC is costly and laborious. Indirect estimation of CEC via pedotransfer functions, therefore, may be appropriate and effective. Several delineations of two consociation map units consisting of two soil families, Shahrak series and Chaharmahal series, located in Shahrekord plain were identified. Soil samples were taken from two depths of 0-20 and 30-50 cm and were analyzed for several physico-chemical properties. Clay and organic matter percentages as well as moisture content at -1500 kPa correlated best with CEC. Pedotransfer functions were successfully developed using regression and artificial neural networks. In this research, it seemed that one hidden layer with one node was sufficient for all neural networks models. The best regression model consisting of organic matter and clay variables showed R2=0.81 and RMSE=7.2 while best corresponding neural network with a learning coefficient of 0.3 and an epoch of 40 had R2=0.88 and RMSE=0.34. Data partitioning according to soil series and soil depths increased the accuracy and precision of the functions. Compared to regression, artificial neural network technique gave pedotransfer functions with greater R2 and smaller RMSE.
S. Moallemi, N.davatgar,
Volume 15, Issue 55 (4-2011)
Abstract
Measuring the cation exchange capacity (CEC) as one of the most important chemical soil properties is very time consuming and costly. Pedotransfer functions (PTFs) provide an alternative to direct measurement by estimating CEC. The objective of this study was to develop PTFs for predicting CEC of Guilan province soils using artificial neural network (ANN) and multiple-linear regression method and also determine whether grouping based on soil textural class and organic carbon content improved estimating CEC by two methods. For this study, 1662 soil samples of Guilan province were used from soil chemistry laboratory database of Rice Research Institute. 1109 data were used for training (the development of PTFs) and 553 data for testing (the validation of PTFs) of the models. The results showed that organic carbon was the most important variable in the estimation of cation exchange capacity for total data and all classes in textural and organic C groups in both methods. ANN performed better than the regression method in predicting CEC in all data, and grouping of data only improved the prediction of PTFs in Sand and Sandy clay loam classes by ANN method.
H. Khodaverdiloo, N. Hosseini Arablu,
Volume 18, Issue 67 (6-2014)
Abstract
Cation exchange capacity (CEC) is one of the important indices in soil fertility. Direct measurement of CEC is time consuming and expensive, especially in aridisols containing high amounts of carbonates and gypsum. Alternatively, CEC could be indirectly predicted through pedotransfer functions (PTF). The objective of this study was to predict CEC using class and continuous PTFs.A data set (n = 977) was classified according to the soil textural class and was used to derive the PTFs. Another independent set (n = 173) was used to test the reliability of the PTFs. The root mean of square error (RMSE), mean error (ME) and index of agreement (d) were applied to evaluate the PTFs. Within every textural class, we furthermore evaluated the relative improvement (RI) of the continuous PTFs over the corresponding class PTF. The continuous PTFs were more accurate than class PTFs for finer textural classes while the former showed higher reliability in coarser textural classes. With an increase in relative particle size, prediction bias of class PTFs decreased RMSE was 8.55 and 3.88 in clay and sandy loam textural classes, respectively. Consequently, according to the results obtained in this study, for the prediction of soil CEC, continuous PTFs are suggested to be used for silty loam and finer textural classes while for loam and coarser classes application of class PTFs is preferred.
F. Parsadoust, Z. Eskandari, B. Bahreyninejad, A. Jafari Addakani,
Volume 19, Issue 71 (6-2015)
Abstract
Evaluation of chemical and biological indicators of soil in different land uses could be helpful in sustainable range management, preventing degradation of soil quality trend. This study was conducted in Friedan in Isfahan province in 2010 to compare chemical and biological indicators in three land uses (rangeland, degraded dry land and dry land), during two growing seasons (May and September) in three slopes (0-10, 10-20, 20-30 %). Nitrogen, phosphorus, potassium, organic matter, cation exchange capacity and microbial soil respiration were measured. Results showed that all measured characteristics except potassium decreased over an increase in the slope. Maximum values of phosphorus, organic matters, cation exchange capacity and soil respiration were obtained in pasture (28.4 mg/kg, 0.62%, 20.38 cmol/kg, 33.2 mgC/day, respectively)but potassium maximum rate was seen in dry land form (406.8 mg/kg).The effect of season on all measured parameters was significant except for N, while the highest amounts of phosphorus, potassium, cation exchange capacity and soil respiration (28.7 mg/kg, 377.3 mg/kg, 19.6 cmol/kg and 25.9 mgC/day, respectively) were seen in May and the highest organic matter rate (0.68%)in September. The results of this study showed that an increase in the slope, poor range management, and the end of the growing season could be major factors degrading the soil quality indices and soil productivity.
M. Servati, H. Beyrami, O. Ahmadi,
Volume 24, Issue 1 (5-2020)
Abstract
The soil engineering evaluation can be useful for construction and soil use. Aljarafe model has been used to evaluate the soil engineering properties by multiple regression techniques. In this research, Aljarafe model was used to predict the optimum moisture and plasticity index based on 184 series soils data of the Miandoab region. Based on all correlations between clay percentage and plasticity index, the optimum moisture proved to be highly significant (0.88 & 0.72). Also, Cation Exchange Capacity was significantly correlated (0.84 & 0.70) with the engineering properties. However, the correlation coefficients for the organic matter with optimum moisture and plasticity index were very low in the absolute amount. Application of the aljarafe model revealed that 50.3, 5.7, 0 and 44 % of the total extension could be classified as low, moderate and very high, respectively; on the other hand, based on the experiment data, 46, 13, 6 and 35 % could be classified as low, moderate, high and very high plasticity index classes, respectively. So, there was an overall agreement between the aljarafe model and Analytical Plasticity index maps, which was 80.4. Also, the coefficient of Determination, Root Mean Square Error (RMSE), Nash-Sutcliffe index (NES) and Geometric Mean Error Ratio (GMER) between calculated and experiment engendering properties was calculated to be 0.767, 9.3, 0.671 and 0.86 for the plasticity index and 0.739, 14.5, 0.543 and 0.73 for optimum moisture, respectively, were significant (P>5%). Finally, the aljarafe model provided a reliable estimate of engineering properties.
