Development and functional evaluation of pedotransfer functions for soil hydraulic properties for the Zambezi River Basin

Abstract

AbstractWater retention and saturated hydraulic conductivity are soil properties that are key determinants in crop growth and hydrological modelling. They are commonly estimated from basic soil characteristics such as bulk density, organic carbon content and texture by means of pedotransfer functions (PTFs). In order to assess and compare the inherent performance and the functional applicability in the Zambezi River Basin (ZRB) of the widely used Saxton & Rawls PTFs and a set of newly developed PTFs, we compiled measurements of water retention at pF0.0, 1.0, 2.0, 2.8, 3.4 and 4.2 and of saturated hydraulic conductivity (Ksat) on 631 soil samples throughout the ZRB. A total of 329 of the samples were related to 55 soil profiles available in the Africa Soil Profile database, whereas our own field campaign carried out in a 2,426‐km2 subbasin of the ZRB provided the remaining 302 samples related to 119 soil profiles. Apart from evaluating the Saxton & Rawls PTFs, we developed multiple linear regression (MLR) PTFs, and PTFs derived by three machine learning (ML) models: artificial neural network (ANN), random forest (RF) and support vector machine (SVM). All PTFs were first evaluated based on a comparison of the estimated and measured property values by means of R2, mean absolute error (MAE) and root mean squared error (RMSE). For the ensemble of MLR‐PTF and ML‐PTFs, the R2 of the six water content variables and the Ksat ranged from 0.55 to 0.85, whereas for the Saxton & Rawls PTFs the range was between 0.10 and 0.50. Secondly, all PTFs were subjected to a functional evaluation using the Food and Agriculture Organization (FAO) AquaCrop crop growth model. Dry season irrigation requirements for maize as computed by AquaCrop with measured versus estimated soil hydraulic properties revealed that ANN‐PTFs provide AquaCrop outputs that come closest to AquaCrop outputs generated with measured soil hydraulic properties. This study shows the importance of performing functional evaluation of pedotransfer functions before their widespread application.Highlights Developed machine learning and multiple linear regression pedotransfer functions (PTFs). The Saxton & Rawls PTFs are not recommended for use in the Zambezi River Basin. PTFs were functionally evaluated through use of estimated soil hydraulic properties in AquaCrop. More accurate PTFs have better functional performance, although differences are small.