Predicting Cation‐Exchange Capacity from Soil Physical and Chemical Properties

Abstract

Cation‐exchange capacity (CEC) is an important soil property in describing nutrient availability for plant growth. Measurements of CEC, however, are often not available or have been measured using different analytical methods. The need, therefore, exists to develop alternative procedures to predict CEC from accessory soil properties. In this study, regression analysis was used to examine the relationships between CEC and clay (CLAY), organic carbon (OC), and other soil properties. Multiple regressions indicated that CLAY, OC, and soil pH accounted for up to 51% of the variation in CEC for all soil (n = 37921). For soil orders, CLAY and OC accounted for up to 67% of the variation in CEC for Alfisols, Inceptisols, Mollisols, and Vertisols, and up to 78% of the variation in CEC for Entisols and Spodosols. The OC alone accounted for up to 73% of the variation in CEC for Spodosols. Poor predictions of CEC resulted from CLAY for Aridisols and Vertisols, indicating that factors other than CLAY interfered with accurate predictions of CEC.

Prediction of CEC Using Fractal Parameters by Artificial Neural Networks

“…The authors are well aware that the accuracy (based on the RMSE of training) and reliability (based on the RMSE of validation) of the developed PTFs are not very high but they are comparable with the RMSE and coefficient of determination (R 2 ) values reported in the literature. For example, Manrique et al (1991) used a lot of soil physical and chemical properties such as clay content, organic carbon, sum of exchangeable bases, 1 M KC1 extractable Al, clay plus silt content, pH in soil/water ratio 1:1, dithionite-citrate extractable Al, and Al saturation to predict CEC and reported R 2 values of 0.38-0.93. Using more input variables may increase the accuracy and reliability but their measurement would be more costly and time consuming.…”

Modeling of Soil Cation Exchange Capacity Based on Fuzzy Table Look-up Scheme and Artificial Neural Network Approach

“…These functions fill the gap between the available soil data and the properties which are more useful or required for a particular model or quality assessment. Various PTFs have been developed to estimate CEC from basic physical and chemical soil properties (Breeuwsma et al, 1986;Manrique et al, 1991;Bell & Van Keulen, 1995;McBratney et al, 2002). In most of these models, CEC is assumed to be a linear function of soil organic matter and clay content (Breeuwsma et al, 1986;McBratney et al, 2002).…”

“…Cation exchange capacity (CEC) is among the most important soil properties that is required in soil databases (Manrique et al, 1991), and is used as an input in soil and environmental models (Keller et al, 2001). CEC is the amount of negative charge in soil that is available to bind positively charged ions (cations).…”

Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction

“…The significance of the CEC of the soil particles stems from the 'ease' of exchange of cations with one another to the extent that they are readily available for plants. The CEC is among the most important soil properties required in soil databases (Manrique et al 1991) and serves as an input to soil and environmental models (Keller et al 2001). Different study topics on the CEC include: fluoride sorption and desorption on soils (Gago et al 2014), monitoring general variability of soil attributes to different land use types in calcareous soils (Rezapour 2014), influence of pasture degradation on soil quality indicators that included physical, chemical, biological and micromorphological attributes (Ayoubi et al 2014), isolation of different soil organic carbon fractions and coal C in a reclaimed minesoil chronosequence (Chaudhuri et al 2013), determination of adsorption efficiency based on cation exchange capacity (Gatima et al 2006), study on adsorption of aqueous phenol solution in soil (Subramanyam and Das 2009), impact of sewage and mining activities on distribution of heavy metals in the water-soil-vegetation system (Semhi et al 2013), potential impact of fluorine-rich fertilizers on the aquifer system (Marimon et al 2013), prediction of subsurface heterogeneity of contaminated soil management (Moon et al 2013), identification of heavy metal sources in agricultural soil (Huang et al 2013), effects of cations and anions on iron and manganese sorption and desorption capacity in calcareous soils (Moharami and Jalali 2013), effect of the addition of granitic powder to an acidic soil , effects of agricultural practice and land use on the distribution and origin of some potentially toxic metals in the soils (Moghaddas et al 2013).…”