Prediction of Some Soil Hydraulic Properties Using Pedotransfer Functions

Document Type : Original Article


Assistant Professor, Department of Soil and Water Engineering, Faculty of Agriculture, Shahrood Universityof Technology, Ir


Field capacity (FC) and permanent wilting point (PWP) are efficacious in determining net irrigation water depth. However, direct measurement of these properties is tedious, time consuming and costly especially on large scale. Soil pedotransfer functions (PTFs) as the indirect methods can replace by the direct methods. In this study, performance of the six available pedotransfer functions on FC and PWP moisture content predicting was evaluated on 112 soil samples that were collected from the north and northeast regions of Iran. The Root Mean Square Error (RMSE) values of menioned available PTFs were changed between 0.05 to 0.17 and 0.03 to 0.13 in moisture prediction on FC and PWP points, respectively. Therefore new PTFs were developed by Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) techniques based on soil properties (90 samples) and the results were validated on different soils (22 samples). The results showed that both MLR technique with assigning the RMSE values approximately 0.035, 0.01, 0.027 and 0.024 to predict soil moisture content on FC and PWP, total available water and specific yield and ANN technique with assigning the values approximately 0.013, 0.007, 0.015 and 0.013 to the same properties, evaluated in appropriate performance. The results also showed that using variables such as geometric mean and geometric standard deviation particle diameter, fractal dimension and air-entry suction, for the first one on input variables of PTFs, improved the accuracy of the results significantly, although accepting of this theory requires more studies.


ترابی فارسانی، ن. و ب. قهرمان. 1386. مقایسه چند تابع انتقالی متداول برای برآورد منحنی رطوبتی خاک در چند خاک در ایران. مجله آبیاری و زهکشی ایران، سال دوم، شماره 1، ص 55-45.
قربانی دشتکی ش. و م. همایی. 1381. برآورد پارامتریک توابع هیدرولیکی بخش غیر اشباع خاک با استفاده از توابع انتقالی. مجله تحقیقات مهندسی کشاورزی، سال سوم، شماره 12، ص 15-1.
Acutis, M and M. Donatelli. 2003. SOILPAR 2.00, software to estimate soil hydrological parameters and functions. European Agronomy, 18:373-377.
Borgesen C.D. and M.G. Schaap. 2005. Point and parameter pedotransfer functions for water retention predictions for Danish soils. Geoderma, 127:154-167.
Botula Y.D., W.M. Cornelis, G. Baert and E. Van Ranst. 2012. Evaluation of pedotransfer functions for predicting water retention of soils in Lower Congo (D.R. Congo). Agricultural Water Management, 111:1-10.
Brahim N., M. Bernoux and T. Gallali. 2012. Pedotransfer functions to estimate soil bulk density for Northern Africa: Tunisia case. Arid Environments, 81:77-83.
Brillante, L., B. Bois, O. Mathieu, V. Bichet, D. Michot and J. Leveque. 2014. Monitoring soil volume wetness in heterogeneous soils by electrical resistivity. A field-based pedotransfer function. Journal of Hydrology, 516: 56-66.
Givi J., O.S. Prasher and R.M. Patel. 2004. Evaluation of pedotransfer functions in predicting the soil water contents at field capacity and wilting point. Agricultural Water Management, 70:83-96.
Haghverdi A., W.M. Cornelis and B. Ghahraman. 2012. A pseudo-continuous neural network approach for developing water retention pedotransfer functions with limited data. Journal of Hydrology, 442-443:46-54.
Hernadi, H and A. Mako. 2014. Preliminary investigation to estimate soil NAPL retention using parametric pedotransfer functions. International Agrophysics, 28: 435-445.
Kaihua, L., F. Xua, J. Zheng, Q. Zhu and G. Yang. 2014. Using different multimodel ensemble approaches to simulate soil moisture in a forest site with six traditional pedotransfer functions. Environmental modelling and software, 57: 27-32.
Kamenickova, I. and L. Larisova. 2014. Using two pedotransfer functions to estimate soil moisture retention curves from one experimental site of south Moravia. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 62(3): 501-506.
Khodaverdiloo, H., M. Homaee, M.T.H. van Genuchten and S. Ghorbani Dashtaki. 2011. Deriving and validating pedotransfer functions for some calcareous soils. Journal of Hydrology, 399: 93-99.
Koekkoek E.J.W., and H. Booltink. 1999. Neural network models to predict soil water retention. European Soil Science, 50:489-495.
Liao K.H., S.H. Xu, J.C. Wu, S.H. JI and Q. Lin. 2011. Assessing soil water retention characteristics and their spatial variability using pedotransfer functions. Pedosphere, 21:413-422.
Liao, K., F. Xua, J. Zheng, Q. Zhu and G. Yang. 2014. Using different multimodel ensemble approaches to simulate soil moisture in a forest site with six traditional pedotransfer functions. 2014. Environmental Modelling and Software, 57: 27-32.
Manyame C., C.L. Morgan, J.L. Heilman, D. Fatondji, B. Gerard and W.A. Payne. 2007. Modeling hydraulic properties of sandy soils of Niger using pedotransfer functions. Geoderma, 141:407-415.
Medina H., M. Tarawally, A. Valle and M.E. Ruiz. 2002. Estimating soil water retention curve in rhodic Ferralsols from basic soil data. Geoderma, 108:277-285.
Merdun H. 2010. Alternative methods in the development of pedotransfer functions for soil hydraulic characteristics. Eurasian Soil Science, 43:62-71.
Merdun H., Ö. Cnar, R. Meral and M. Apan. 2006. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research, 90:108-116.
Minasny B., A.B. McBratney and K.L. Bristow. 1999. Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma, 93:225-253.
Mohanty, M., N.K. Sinha, D.K. Painuli, K.K. Bandyopadhyay, K.M. Hati, K. Sammi Reddy and R.S. Chaudhary. 2015. Modelling soil water contents at field capacity and permanent wilting point using artificial neural network for Indian soils. National Academy Science Letters, 38(5): 373-377.
Nanko, K., S. Ugawa, S. Hashimoto, A. Imaya, M. Kobayashi, H. Sakai, S. Ishizuka, S. Miura, N. Tanaka, M. Takahashi and S. Kaneko. 2014. A pedotransfer function for estimating bulk density of forest soil in Japan affected by volcanic ash. Geoderma, 213: 36-45.
Nemes A and W. Rawls. 2006. Evaluation of different representations of the particle size distribution to predict soil water retention. Geoderma, 132:47-58.
Nemes A., M.G. Schaap and J.H.M. WÖsten. 2003. Functional evaluation of pedotransfer functions derived from different scales of data collection. Soil Science Society of America Journal, 67:1093-1102.
Nemes A., W.J. Rawls and Y.A. Pachepsky. 2006. Use of the nonparametric Nearest Neighbor approach to estimate soil hydraulic properties. Soil Science Society of America Journal, 70:327-336.
Ostovari, Y., K. Asgari and W. Cornelis. 2015. Performance evaluation of pedotransfer functions to predict field capacity and permanent wilting point using UNSODA and HYPRES datasets. Arid Land Research and Management, 29: 383-398.
Pachepsky Y.A., D. Timlin and G. Varallyay. 1996. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Science Society of America Journal, 60:727-733.
Rab, M.A., S. Chandra, P.D. Fisher, N.J. Robinson, M. Kitching, C.D. Aumann and M. Imhof. 2011. Modelling and prediction of soil water contents at field capacity and permanent wilting point of dryland cropping soils. Soil Research, 49: 389-407.
Santra P and B.S. Das. 2008. Pedotransfer functions for soil hydraulic properties developed from a hilly watershed of Eastern India. Geoderma, 136:439-448.
Sarmadian F and R.T. Mehrjardi. 2008. Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan province, north of Iran. Global Environmental Research, 2:30-35.
Schaap M.G., F.J. Leij and M.Th. van Genuchten. 2001. Rosetta: a computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions. Journal of Hydrology, 251:163-176.
Shirazi M.A. and L. Boersma. 1984. A unifying quantitative analysis of soil textre. Soil Science Society of American Journal, 48:142-147.
Tomasella J and M.G. Hodnett. 1998. Estimating soil water retention characteristic from limited data in Brazilian Amazonia. Soil Science, 163:190-202.
Tyler S.W and S.W. Wheatcraft. 1990. Fractal processes in soil water retention. Water Resources Research, 26(5):1047-1054.
Vereecken H., M. Weynants, M. Javaux, Y. Pachepsky, M.G. Schaap and M.T.h. van Genuchten. 2010. Using pedotransfer functions to estimate the van Genuchten–Mualem soil hydraulic properties: a review. Vadose Zone Journal, 9:1-26.
Wagner B., V.R. Tarnawski, V. Hennings, U. Muller, G. Wessolek and R. Plagge. 2001. Evaluation of pedotransfer function for unsaturated soil hydraulic conductivity using anindependent data set. Geoderma, 102:275-297.
Walczak R.T., F. Moreno, C. Sławin´ski, E. Fernandez and J.L. Arrue. 2006. Modeling of soil water retention curve using soil solid phase parameters. Journal of Hydrology, 329:527-533.
Wosten J.H.M., A. Lilly, A. Nemes and C. Le Bas. 1999. Development and use of a databaseof hydraulic propertiesof european soils. Geoderma, 90:169-185.
Wosten J.H.M., Y. Pachepsky and W.A. Rawls. 2001. Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251:123-150.
Yi X., G. Li and Y. Yin. 2013. Comparison of three methods to develop pedotransfer functions for the saturated water content and field water capacity in permafrost region. Cold Regions Science and Technology, 88:10-16.
Zolfaghari, Z., M.R. Mosaddeghi and S. Ayoubi. 2015. ANN-based pedotransfer and soil spatial prediction functions for predicting atterberg consistency limits and indices from easily available properties at the watershed scale in western Iran. British Society of Soil Science, Soil Use and Management, 1-13.
Schaap, M.G. and F.J. Leij. 1998. Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil and Tillage Research, 47: 37-42.