Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point

Abstract

Thirteen pedotransfer functions (PTFs), namely Rosetta PTF, Brakensiek, Rawls, British Soil Survey Topsoil, British Soil Survey Subsoil, Mayr-Jarvis, Campbell, EPIC, Manrique, Baumer, Rawls–Brakensiek, Vereecken, and Hutson were evaluated for accuracy in predicting the soil moisture contents at field capacity (FC) and wilting point (WP), of fine-textured soils of the Zagros mountain region of Iran. PTFs were developed using the laboratory measurements made on soil moisture at FC and WP, particle-size distribution, bulk density, and organic matter content. PTFs were evaluated on the basis of mean-squared deviation (MSD) between the observed and predicted values. Results agreed with the concept that the PTFs developed on soils of similar properties to the ones under study generally perform better than the others. In the case of the Zagros mountain soils, the “British Soil Survey” and “Brakensiek” PTFs were found to be the best methods. Since the soils under study had a wide range of organic matter contents (0.2–5.5%), the better performance of these PTFs may also be explained by the fact that they happen to be the only ones that require organic matter content as input. Rosetta, a software package that involves an artificial neural network approach, was of intermediate value in estimating soil moistures of the soils in question. This was attributed to the fact that the texture and the bulk density of the Zagros soils were not in the range of those used to develop Rosetta.