Evaluation of Estimation Methods for Water Field Capacity in Soils of Khuzestan Province

This study aimed to assess the performance of the conventional methods for estimating soil moisture at field capacity (θfc) and introduce the appropriate PTF under laboratory and field conditions in Khuzestan province soils. The buried probes of the time domain reflectometry device (TDR) inserted at various depths in order to monitor soil moisture conditions in both the physical model and experimental field under a point source drip irrigation with discharge rates of 4 lph. Then, physical soil properties and soil water contents at their specific matric potentials were measured to calculate the hydraulic parameters of Van Genuchten-Mualem model with the RETC program. The results of this research to evaluate the performance of several well-known Point-PTFs showed that the quasi-empirical models based on physical principles can be a good alternative to traditional methods for estimating θfc on the condition that they have been tested in the field. So that, the PTF of Twarakavi et al. (2009) with indices of NRMSE (3.1%) and SE (0.51%) could carefully predict θfc more accurate than the Rosetta (2001) artificial neural network approach which presented the values of NRMSE (5.2%) and SE (0.71%) or the Dexter (2004) equation with the values of NRMSE (9.7%) and SE (1.75%). However, there were not observed any differences in the indicator of model efficiency (ME) for each three PTFs. Based on the assessment results of these PTFs, the negative effects of soil compaction and the amount of sand on the θfc were clearly showed using one-way ANOVA (p