Transferability of continuous- and class-pedotransfer functions to predict water retention properties of semiarid Syrian soils

Hydraulic properties of soils, particularly water retention, are key for appropriate management of semiarid soils. Very few pedotransfer functions (PTFs) have been developed to predict these properties for soils of Mediterranean regions, where data are particularly scarce. We investigated the transferability of PTFs to semiarid soils. The quality of the prediction was compared to that for soils originating from temperate regions for which most PTFs were developed. We used two soil data sets: one from the Paris basin (French data set, n=30) and a Syrian data set (n=30). Soil samples were collected in winter when the water content was near field capacity. Composition and water content of the samples were determined at seven water potentials. Continuous- and class-PTFs developed using different predictors were tested using the two data sets and their performance compared to those developed using artificial neural networks (ANN). The best performance and transferability of the PTFs for both data sets used soil water content at field capacity as predictor after stratification by texture. The quality of prediction was similar to that for ANN-PTFs. Continuous- and class-PTFs may be transferable to other countries with performances that vary according to their ability to account for variation in soil composition and structure. Taking into account predictors of composition (particle size distribution, texture, organic carbon content) and structure (bulk density, porosity, field capacity) did not lead to a better performance or the best transferability potential.