Soil water retention curve (SWRC) is a crucial soil property required for solving many soil and water management problems. But, its direct measurement needs a lot of time, effort and money. The aim of this study was to develop pedotransfer functions (PTFs) for estimating water content through the van Genuchten (1980) model by employing tensile strength (TS) models. One hundred forty eight samples were gathered from five provinces in Iran. Bulk density, TS curve, SWRC and particle size distribution were measured. Four empirical TS models were fitted to the experimental soil mechanical data. Also, three physically based equations were used to estimate soil water content. In order to develop PTFs to estimate the parameters of van Genuchten (1980) model, artificial neural networks (ANNs) and regression (MLR) methods were used. In nine PTFs, the parameters of the empirical TS models and other soil properties were used as predictors for estimating SWRC. In developing the PTFs, ANNs were superior to MLR. Using the parameters of the TS models as predictors improved the estimation of water content between 2.8 and 6.9 %. The SWRC was estimated better by using the parameters of the developed model of TS along with texture fractions and bulk density as predictors. The result showed the high capability of three physically based equations in the estimation of water content. Lu et al. had the highest accuracy in the SWRC estimation, in comparison with other physically based equations. The results showed the significance of TS in the estimation of SWRC.
