Determination of a suitable model for prediction of soil cation exchange capacity

Abstract

Analysis and design of land-use management scenarios requires detailed soil data. The cation exchange capacity (CEC) of soil is a basic chemical property, as it has been approved that the spatial distribution of CEC is important for decisions concerning pollution prevention, crop and farming management. Since laboratory procedures for measuring CEC are cumbersome and time-consuming, it is essential to develop an indirect approach such as pedotransfer functions to predict this parameter from more readily available soil data. The aim of this study was to compare multiple linear regression, multiple non-linear regression, adaptive neuro-fuzzy inference system and artificial neural network including feed-forward back propagation (FFBP) model to develop PTFs for predicting paddy soils CEC in Guilan province, northern Iran. Two soil parameters including organic carbon and clay were considered as input variables for proposed models. 171 soil samples were used. The data set was divided into two subsets for calibration and testing of the models. The models prediction capability was evaluated by comparison with observed data through various descriptive statistical indicators include root mean square error, determination coefficient, mean bias error and relative improvement values. Results showed that the FFBP model had the most reliable prediction when compared with other models and that provide a new methodology with acceptable accuracy to estimate the CEC of soil that diminished the engineering effort, time and funds and can provide the scientific basis for the study of soil CEC and be helpful for the estimation of soil CEC in other places with similar conditions, too.

Soil CEC Predicting Model of Tobacco-Planting Fields in Chenzhou, South Hunan Province

“…CEC was high in tobacco-planting fields in Chenzhou, the mean value of CEC was 22.05 cmol(+) kg -1 , higher than the very high grade of CEC ( 20 cmol(+) kg -1 ). The high CEC could be attributed to the high values of fine particles, pH and OM of the samples, because many studies have proved well that CEC usually is positively correlated with clays, pH and OM, while negatively correlated with sands [5][6][7][8][9][10][11][12][13][14][46][47][48][49][50][51][52][53], and Zhang and Zhu (1993) found that the positive contribution of silts to soil CEC could not be ignored [54]. From Table 4 it could be seen that both the contents of silts and clays were high, which were ranged from 10%a56% and 10%a66% with a mean of 38% and 29%, respectively, in total constituted of 2/3 of the particle composition.…”

“…Meanwhile, CEC is also an index for soil classification or taxonomy [3][4]. Because the traditional methods determining CEC are time-consuming and laborious, more studies were conducted to setup CEC predicting model from soil properties measured more easily [5][6][7][8][9][10][11][12][13][14], and the results showed that for different regions or soils, the variables used in predicting soil CEC are different, for examples, Rahal and Alhumairi [5] predicted soil CEC in mid-Mesopotamian plain by using texture class, bulk density, total available water content, soil color, sodium adsorption ratio, electrical conductivity and Ca2+. Khaledian et al [6] proved that soil CEC was affected by different variables in different situations, clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC, CEC was significantly and negatively correlated with pH in agricultural land uses in Spain, significant positive relationship between CEC and OC in Spain, the USA, Iraq, and pasture.…”

“…Shiri et al [7] used the contents of silts, clays, sands, organic carbon and pH in modeling soil CEC in Iran. Seyedmohammadi et al [8] proved soil organic carbon and claycould be used as input variables (positively correlated) for predicting CEC of paddy soils in Guilan province, northern Iran. Liao et al [9] modeled soil CEC with organic matter, silt, clay and pH (positively correlated) as well as sand (negatively correlated) in Qingdao in China.…”

Modeling Soil Cation Exchange Capacity in Arid Region of Iran: Application of Novel Hybrid Intelligence Paradigm.

“…The recent researches have focused on improving the estimation accuracy of soil CEC by means of artificial intelligence (AI) techniques. Artificial neural network (ANN) model based PTF's have become popular to predict/estimate soil CEC of different soil types under diverse climatic zones (Amini et al, 2005;Bayat et al, 2014;Seyedmohammadi et al, 2016;Tang et al, 2009;Zolfaghari et al, 2016). Kalkhajeh et al, (2012) conducted the accurate prediction of soil CEC using different soft computing models.…”

Evaluation of adaptive neural-based fuzzy inference system approach for estimating saturated soil water content

“…Adaptive neuro-fuzzy inference system (ANFIS) can be an appropriate approach using the learning capability of an ANN for parameter optimization. ANFIS have been used for database management, signal processing, hydrologic applications (Jang 1993;Nayak et al 2004) and developing PTFs for estimation of paddy soils CEC (Seyedmohammadi et al 2016). Researchers have not assessed the potential of this approach in saturated soil water study.…”