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Assessment of Two Methods for Predicting Soil Retention Relationship from Basic Soil Properties

Mohammed Zahraa M., Salim Salloom B.

Ecological Engineering & Environmental Technology

2023

Vol. 24, iss. 6

61--69

The purpose of this study was to develop the best transfer functions for estimating the soil water retention curve (SWRC) for Iraqi soils using multiple regression methods. Soil samples were collected from 30 different sites in Iraq at two depths (0–0.3 m and 0.3–0.6 m) to create a database for the development of predictive transfer functions. The database included information on soil particle size distribution, carbonate minerals, mass density, particle density, organic matter, saturated hydraulic conductivity, capillary height, and available water limits. Explanatory variables (EV) were the measured characteristics, while response variables (RV) were the volumetric water content measured at different potentials (0, 5, 10, 33, 500, 1000, 1500 kPa). Two methods were used to develop predictive transfer functions: the logit model and beta model. Prediction accuracy was assessed using mean bias error (MBE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The results showed that the variables included in the derivation of the two models for predicting θ(Ψ) were similar, except at θ(0). The variables w1 (w1 = 2Psand° − Psilt° − Pcaly° − Pcarbonate), capillary height, available water, and porosity were found to be included in most of the logit and beta models. Additionally, there were no statistically significant differences between the MAE, RMSE, and R2 values of the two models. However, the beta model performed better in terms of MBE compared to the logit model. The models also demonstrated highly significant R2 values (0.9819–1.00) for a linear relationship between the measured and predicted water content values.

Development of pedotransfer functions for predicting soil bulk density : a case study in Indonesian small island

Yanti Evi Dwi, Mulyono Asep, Djuwansah Muhammad Rahman, Narulita Ida, Putra Risandi Dwirama, Surinati Dewi

Journal of Water and Land Development

2021

no. 51

181--187

Unlike many other countries, tropical regions such as Indonesia still lack publications on pedotransfer functions (PTFs), particularly ones dedicated to the predicting of soil bulk density. Soil bulk density affects soil density, porosity, water holding capacity, drainage, and the stock and flux of nutrients in the soil. However, obtaining access to a laboratory is difficult, time-consuming, and costly. Therefore, it is necessary to utilise PTFs to estimate soil bulk density. This study aims to define soil properties related to soil bulk density, develop new PTFs using multiple linear regression (MLR), and evaluate the performance and accuracy of PTFs (new and existing). Seven existing PTFs were applied in this study. For the purposes of evaluation, Pearson’s correlation (r), mean error (ME), root mean square error (RMSE), and modelling efficiency (EF) were used. The study was conducted in five soil types on Bintan Island, Indonesia. Soil depth and organic carbon (SOC) are soil properties potentially relevant for soil bulk density prediction. The ME, RMSE, and EF values were lower for the newly developed PTFs than for existing PTFs. In summary, we concluded that the newly developed PTFs have higher accuracy than existing PTFs derived from literature. The prediction of soil bulk density will be more accurate if PTFs are applied directly in the area that is to be studied.