1 Pedotransfer functions for prediction of near saturated hydraulic conductivity at different applied tensions in medium texture soils of a semi-arid region Ali Akbar Moosavi 1* , Ali Reza Sepaskhah 2 1 Shiraz University, Soil Science Department, Shiraz, I.R. of Iran 2 Shiraz University, Irrigation Department, Shiraz, I.R. of Iran * Corresponding Author: aamousavi@gmail.com Abstract Development of pedotransfer functions (PTFs) for prediction of soil hydraulic properties from readily available soil properties had been received increasing attention due to tedious and time consuming nature of laboratory or field measurements. Multiple- regression-equations were used to link the easily available physico-chemical properties of 138 medium texture soil samples of a semi-arid region to their corresponding measured unsaturated hydraulic conductivity (K ψ ) at six applied tensions of 0.2, 0.15, 0.1, 0.06, 0.03, and 0 m. The most influential physical soil characteristics in prediction of soil hydraulic conductivity using PTFs were the soil particle fractions, bulk density (BD), total soil porosity (F) and i nitial and near saturated volumetric soil water content (θ i and θ s, respectively). The most influential chemical attributes were cation exchange capacity to organic matter content ratio (CEC/OM), CEC to electrical conductivity ratio (CEC/EC e ), OM, and calcium carbonate equivalent (CCE). Some combination of both physic- chemical soil attributes (e.g., their square, logarithm or multiplication etc.) also played key rule in prediction of unsaturated soil hydraulic conductivity. The PTFs predictions of unsaturated soil hydraulic conductivities at all of the applied tensions were enough accurate for the most applications except for the measured K ψ at applied tension of 0.1 m (K 0.1 ) and to some extent at applied tension of 0.03 m (K 0.03 ) that were less accurate than the other predictions of K ψ . Keywords: Multiple regression equation; unsaturated hydraulic conductivity; soil physical-chemical properties; tension disc infiltrometer. Abbreviations: ANNs- artificial neural networks; BD- bulk density; CCE- calcium carbonate equilibrium; CEC- cation exchange capacity; ECe- electrical conductivity of saturated paste; EKP- exchangeable potassium percentage; ESP- exchangeable sodium percentage; K ψ - near saturated soil hydraulic conductivity; OM- organic matter; PTFs- Pedotransfer functions; SAR- sodium adsorption ratio; θ i - initial water content; θ s - saturated water content. Introduction Modeling of water flow and chemical transport in the vadose zone is recommended as an inexpensive approach to study the problems related to soil and environmental remediation (Merdun et al., 2006). Models require knowledge of soil hydraulic attributes such as soil water retention curve θ(ψ), and unsaturated hydraulic conductivity function K(ψ) (Mermoud and Xu, 2006). Various methods have been developed to estimate these soil attributes or determine them directly in the field or laboratory such as the crust method, the instantaneous profile method, various unit-gradient type techniques and sorptivity methods (Klute, 1986). Direct field or laboratory measurements of hydraulic attributes are tedious, costly, time-consuming, labor intensive and they give only local scale results (Mermoud and Xu, 2006). Due to the highly spatial and temporal variability, point measurement may not produce accurate results. Therefore, it necessitates a large number of soil samples have to be collected to accurately characterize the field or the watershed systems. Furthermore, published information for soils around the world may have data on soil particle size distribution, organic matter content and bulk density, but the data on soil hydraulic properties may be incomplete or missing. Due to these reasons, recently a great deal of research has been devoted to develop alternative indirect approaches to estimate the soil water retention or unsaturated hydraulic conductivity curves either from widely available or more easily measured basic soil properties, and/or limited data (Timlin et al., 2004). Attempts have been made to estimate these properties indirectly from readily available soil properties. Such equations are often called pedotransfer functions, PTFs (Rawls et al. 2004). Pedotransfer functions indeed aim to predict hard-to-measure soil properties that are required by the soil data user, from primary soil properties. They have become an interesting topic in the area of soil science and environmental research (Bouma, 1989). In general, PTFs transfer the data we have into the data we need (Baker and Ellison, 2008). Numerous PTFs have been developed in recent decades, such as those proposed by Saxton et al. (1986), Vereecken et al. (1990), Wosten (1997), and Zhuang et al. (2001), among the others. A summary of pedotransfer functions (PTFs) and their status is given by and McBratney et al. (2002). Reviews on the development and the use of PTFs, particularly for predicting soil hydraulic properties, have given by Wosten (1997), Wosten et al. (2001) and McBratney et al. (2002). Khodaverdiloo et al. (2011), Mosaddeghi and Mahboobi (2011) and Ghorbani Dashtaki et al. (2010) also developed or evaluated some PTFs for Plant Knowledge Journal Southern Cross Publishing Group ISSN: 2200-5390 Australia EISSN: 2200-5404 PKJ 1(1): 1-9 (2012)