Near-saturated soil hydraulic properties are needed to study and to model water and contaminant traort processes in the vadose zones. Soil management and land use via their effects on soil properties such as texture, bulk density, structure and organic carbon could indirectly change soil hydraulic properties. However, measurements of soil hydraulic properties in the field and lab are time-consuming and costly. Moreover, the results might not be reliable due to high spatial and temporal variabilities of soil physical and hydraulic properties. Attempts have been made to predict these properties indirectly using basic and easily-available soil data by pedotransfer functions (PTFs). In recent years, use of artificial neural networks (A) has becomes common for deriving PTFs. The objective of this study was to measured and predicted near-saturated hydraulic properties in pasture and arable lands of Farsan and Koohrang cities in Chaharmahal-va-Bakhtiari province. The major land uses in the area were pasture, dryland farming, irrigated farming and fallow. Unsaturated water infiltration was measured at consecutive inlet suctions of 15, 10, 5 and 2 cm using a tension infiltrometer at 100 locations. Then, soil water retention was measured in the lab at suctions of 0, 5, 10, 15, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, 330, 500, 2000 and 5000 cm on undisturbed soil sample which was taken from the soil under the disc of infiltrometer. Saturated hydraulic conductivity was also measured on the same sample using constant-head method. Easily-available soil properties of texture, organic matter and carbonate contents, sodium adsorption ratio, dry bulk density at field water condition (air-dry) and dry bulk density at suction of 2 cm were measured. The infiltration data was modeled using Wooding (1968) analytical method and the best-fit values of Gardner (1958) parameters were calculated. The van Genuchten (1980) parameters were predicted using the cumulative infiltration data by DISC software and using the lab water retention data by RETC software. The field and lab methods for measuring and predicting soil hydraulic properties were compared using the paired t -test. Regression equations for predicting soil hydraulic properties and parameters were derived using stepwise scheme. For the neural network analysis, feed forward back-propagation network with Marquardt-Levenberg training function and TANSIG transfer function was used for predicting soil hydraulic parameters. Sensitivity analysis in the neural network analysis was done by StatSoft method. Land use effect on soil properties was investigated using GLM method and LSD mean comparison. Results showed that lab-measured saturated hydraulic conductivity and water content were significantly greater than those predicted using the field data. Predicted shape ( n ) and scale ( ? ) partameters of van Genuchten (1980) model using the field data were significantly greater than those predicted using the lab data, too. Unsaturated hydraulic conductivity values predicted by Wooding's analytical analysis were significantly greater than those predicted by DISC numerical analysis. The PTFs of the literature were not able to predict soil hydraulic parameters accurately. Neural networks predicted soil hydraulic parameters better than linear and non-linear multiple regressions. Sensitivity analysis of neural network identified relative compaction and dry bulk density at suction of 2 cm as most important properties affecting soil hydraulic parameters in the region. Land use significantly affected unsaturated hydraulic conductivity, steady-state flux and sorptivity; the differences between land uses became greater with decreasing suction (towards saturation). The results showed that hydraulic conductivity values in the studied suction range were lower in pasture when compared with other land uses. For all of the land uses, the sorpivity was high at suction of 15 cm and decreased with suction decrease from 15 to 10 cm, and then increased with further decrease of suction toward saturation. Overall soil hydraulic properties are mainly influenced by soil structure and management practices rather than by intrinsic soil properties like texture in the region. Derived regression equations were not enough accurate to predict soil hydraulic parameters. It is suggested to consider soil structural attributes for modeling and predicting soil hydraulic parameters in the region. Keywords Tension infiltrometer, Unsaturated hydraulic conductivity, Pedotransfer functions, Artificial neural networks, Land use, Macroscopic capillary length, Sorptivity