Evaluation of point pedotransfer functions in estimating field capacity and permanent wilting point water content

Document Type : Research/Original/Regular Article

Author

Assistant Professor, Department of Water Engineering, Faculty of Water and Soil, University of Zabol, Zabol, Iran

Abstract

Introduction
The amount of available water in the soil for crop use, called available water (AW), is described as the capacity of the soil to retain water and make it available for root uptake. Irrigation management, agricultural projects, and soil water balance are some of the well-known practical applications related to AW. It is generally accepted that matric potentials at -33 kPa and -1500 kPa represent FC and PWP, respectively. An attractive alternative to direct and often laborious measurements of these soil hydraulic properties is their estimation using soil pedotransfer functions (PTFs). The efficiency of PTFs is not the same in different locations and conditions, so the performance of each PTFs should be evaluated according to the soil characteristics of each region. The Sistan Plain is one of the strategic border areas of eastern Iran and the edge of the desert. The main objective of this study is to evaluate the performance of 23 different PTFs models in predicting soil water retention at matric potentials of -33 kPa and-1500 kPa in the Sistan Plain. In order to determine the best PTF that is appropriate for the conditions of the study area, 100 soil samples from the Sistan region were used.
 
Materials and Methods
In order to conduct this study, 100 soil samples were collected from a part of the Sistan Plain for laboratory analysis and transported to the laboratory. Particle size distribution (sand, silt, and clay) was measured by hydrometric method, organic carbon by Walkey-Black method, and bulk density by cylinder method. Twenty-three PTFs were evaluated to predict water content (gravimetric and volumetric), including twelve functions for matric potential of -33 kPa and eleven functions for matric potential of -1500 kPa. PTFs can also be categorized based on the type of prediction, namely point and continuous PTFs, and in this study, point PTFs were evaluated. The selection of PTFs is limited to PTFs that use soil properties that are present in the data set of the present study. The sum of squared error method was used to calibrate the models under study. In this study, NRMSE, ME, r, and RES criteria were used to fully evaluate the models.
 
Results and Discussion
The results showed that the changes in the amounts of sand, silt, and clay ranged from 14 to 56, 27 to 52, and 14 to 57 percent, respectively, which resulted in the creation of textural classes of loam, clay loam, sandy clay loam, clay, silty loam, and sandy loam. The function developed by Aina and Periaswamy (1985) with NRMSE, ME, r and RES values of 0.61, 0.15, 1.48 and -0.148, respectively, had the least closeness to the measured values of θ33. The results also show that among the PTFs examined for estimating θ33, the PTF developed by dos Reis et al. (2024) with NRMSE, ME, r and RES values of 0.10, 0.01, 1.04 and -0.012, respectively, and in the next rank and with a slight difference, the PTFs developed by Oliveira et al. (2002) and Minasny and Hartemink (2011) with NRMSE, ME and r values of 0.10, 0.02 and 0.05 (same for both functions) had the best agreement with the measured values of θ33. This is while the function developed by Aina and Periaswamy (1985) with NRMSE, ME, r and RES values of 0.61, 0.15, 1.48 and -0.148 respectively had the least closeness to the measured values of θ33. The summary of the evaluation of eleven PTFs for estimating θ1500 shows that the Dijkerman (1988) and Aina and Periaswamy (1985) functions provided the best performance among the functions studied with ME of 0.00 and -0.01, NRMSE values of 0.15 and 0.16, and r of 1.02 and 0.95, respectively. The function developed by Oliveira et al. (2002) showed good performance for estimating θ33, but it was the least close to the measured values of θ1500 with NRMSE, ME, r and RES values of 0.84, 0.14, 1.83 and -0.138, respectively. Then with the aim of improvement, the high-performance economic functions were selected and recalibrated. In estimating θ33, the performance of both the dos Reis et al. (2024) and Oliveira et al. (2002) functions improved (albeit only slightly) by reducing NRMSE (0.08) and ME (0.00) to reach r of 1.00. The Dijkerman (1988) and dos Reis et al. (2024) functions also improved the estimation of θ1500 with NRMSE, ME, and r of 0.14, 0.00, and 1.00, respectively.
 
Conclusion
The results showed that among the PTFs used to estimate θ33, the functions developed by dos Reis et al. (2024) and Oliveira et al. (2002) show the best agreement with the measured values. The functions developed by Dijkerman (1988) and Aina and Periaswamy (1985) also provide the highest performance in estimating θ1500. The results of this study also show that some of the new functions presented in this field can provide good performance compared to the basic functions in predicting soil moisture content. The performance of PTFs for the study area is not affected by the number of their input components, and PTFs that require fewer inputs will not necessarily have lower performance, and this is because PTFs depend on the location where they are developed and also on the soil structure. In order to improve the results of the functions of dos Reis et al. (2024) and Oliveira et al. (2002) were recalibrated to estimate θ33 and the functions of Dijkerman (1988) and dos Reis et al. (2024) were recalibrated to estimate θ1500. The selection of the aforementioned PTFs was based on high performance and minimum required input components, or in other words, the economy of the function. Recalibration further improved the performance of the aforementioned functions. θ33, θ1500 and AW are key components in a wide range of studies such as crop modeling, hydrological modeling, water resources management, soil nutrient cycle modeling and soil pollution modeling, therefore the results presented in this study can be used in predicting soil moisture content and AW for the study area, using the soil transfer function technique as an input parameter in various modeling.

Keywords

Main Subjects

References

منابع
چاری، محمدمهدی. (1399). پیش‌بینی چگالی ظاهری با استفاده از توابع انتقالی برای خاک‌های دشت سیستان. مدیریت خاک و تولید پایدار، 10(4)، 154- 137. doi: 10.22069/ejsms.2021.18180.1964
حسینی، سیده ویدا.، داوری، مسعود.، خالق‌پناه، ناصر. (1399). توسعه و مقایسه توابع انتقالی خاک و توابع انتقالی طیفی برای برآورد نگهداشت آب در برخی از خاک‌های استان کردستان. مدیریت خاک و تولید پایدار، 10(3)، 71- 51. doi: 10.22069/ejsms.2021.17865.1940
حق‌وردی، امیر.، قهرمان، بیژن.، خشنود‌یزدی، علی‌اصغر.، جلینی، محمد.، عربی، زهرا. (1391). اعتبارسنجی و مقایسه چند تابع انتقالی نقطه‌ای و پارامتریک برای پیش‌بینی میزان رطوبت خاک در پتانسیل‌های ماتریک مختلف. پژوهش‌های حفاظت آب و خاک، 19(2)، 22- 1. doi: 20.1001.1.23222069.1391.19.2.1.4
رضوی‌قلعه‌جوق، سکینه.، رسول‌زاده، علی.، نیشابوری، محمدرضا. (1393). ارزیابی تواﺑﻊ اﻧﺘﻘﺎلی ﻣﺨﺘﻠﻒ ﺑﺮای ﺑﺮآورد ﻣﻨﺤنی رﻃﻮﺑتی ﺧﺎک در ﺷﻬﺮﺳﺘﺎن ﻧﻘﺪه. پژوهش آب در کشاورزی، 28(3)، 624- 613. doi: 10.22092/JWRA.2014.100011
غلامی‌شرفخانه، مهدی.، ضیائی، علی‌نقی.، ناقدی‌فر، سیدمحمدرضا.، اکبری، امیر. (1404) بررسی تأثیر شوری خاک و کیفیت آب بر عملکرد بنه‌های دختری زعفران با استفاده از مدل گیاهی و اندازه‌گیری میدانی. مدل‌سازی و مدیریت آب و خاک، 5(1)، 334- 317. doi: 10.22098/mmws.2023.14109.1390
کهخامقدم، پریسا.، ضیائی، علی‌نقی.، داوری، کامران.، کانونی، امین.، صادقی، صدیقه. (1403). برنامه‌ریزی و تحویل بهینة آب در شبکه‌های آبیاری با ترکیب مدل AquaCrop و الگوریتم ژنتیک. مدل‌سازی و مدیریت آب و خاک، 4(4)، 268- 255. doi: 10.22098/mmws.2023.14039.1382
کهخامقدم، پریسا.، دلبری، معصومه. (1404). امکان‌سنجی پتانسیل انرژی باد در زابل با استفاده از توزیع ویبول. آمایش جغرافیایی فضا، 15(1)، 178- 159. doi: 10.30488/gps.2025.478128.3779
نیسی، کریم.، اگدرنژاد، اصلان.، عباسی، فریبرز. (1402). ارزیابی مدل AquaCrop برای شبیه‌سازی عملکرد ذرت و بهره‌‎وری آب تحت مدیریت مختلف کاربرد کود نیتروژن در کرج. مدل‌سازی و مدیریت آب و خاک، 3(1)، 41- 26. doi: 10.22098/mmws.2022.10969.1093
 
References
Abdelbaki, A. M., Youssef, M. A., Naguib, E. M., Kiwan, M. E., and El-giddawy, E. I. (2009). Evaluation of pedotransfer functions for predicting saturated hydraulic conductivity for US soils. In 2009 Reno, Nevada, June 21-June 24, 2009 (p. 1). American Society of Agricultural and Biological Engineers. doi: 10.13031/2013.27309
Abdelbaki, A. M. (2021). Assessing the best performing pedotransfer functions for predicting the soil‐water characteristic curve according to soil texture classes and matric potentials. European Journal of Soil Science, 72(1), 154-173. doi: 10.1111/ejss.12959
Adhikary, P. P., Chakraborty, D., Kalra, N., Sachdev, C. B., Patra, A. K., Kumar, S., omar, R.K., Chandna, P., Raghav, D., Agrawal, K.and Sehgal, M. (2008). Pedotransfer functions for predicting the hydraulic properties of Indian soils. Soil Research, 46(5), 476-484. doi: 10.1071/SR07042
Aina, P. O., and Periaswamy, S. P. (1985). Estimating available water-holding capacity of western Nigerian soils from soil texture and bulk density, using core and sieved samples. Soil Science, 140(1), 55-58.
Arruda, F. B., Zullo Jr, J., and De Oliveira, J. B. (1987). Parãmetros de solo para o cáculo da água disponível com base na textura do solo. Revista Brasileira de Ciência do Solo, 11(1), 11-15.
Bellocchi, G., Rivington, M., Donatelli, M., and Matthews, K. (2010). Validation of biophysical models: issues and methodologies. A review. Agronomy for Sustainable Development, 30(1), 109-130. doi: 10.1051/agro/2009001
Botula, Y. D. (2013). Indirect methods to predict hydrophysical properties of soils of Lower Congo (Doctoral dissertation, Ghent University).
Botula, Y. D., Cornelis, W. M., Baert, G., and Van Ranst, E. (2012). Evaluation of pedotransfer functions for predicting water retention of soils in Lower Congo (DR Congo). Agricultural Water Management, 111, 1-10. doi: 10.1016/j.agwat.2012.04.006
Botula, Y. D., Van Ranst, E., and Cornelis, W. M. (2014). Pedotransfer functions to predict water retention for soils of the humid tropics: a review. Revista Brasileira de Ciência do Solo, 38, 679-698. doi: 10.1590/S0100-06832014000300001
Blake, G. R., and Hartge, K. H. (1986). Bulk density. Methods of Soil Analysis: Part 1 Physical and Mineralogical Methods, 5, 363-375. doi: 10.2136/sssabookser5.1.2ed.c13
Chari, M. M. (2021). Predicting bulk density using pedotransfer functions for soils in Sistan plain. Journal of Soil Management and Sustainable Production, 10(4), 137-154. doi: 10.22069/ejsms.2021.18180.1964. [In Persian]
Cornelis, W. M., Khlosi, M., Hartmann, R., Van Meirvenne, M., and De Vos, B. (2005). Comparison of unimodal analytical expressions for the soil‐water retention curve. Soil Science Society of America Journal, 69(6), 1902-1911. doi:10.2136/sssaj2004.0238
Cornelis, W. M., Ronsyn, J., Van Meirvenne, M., and Hartmann, R. (2001). Evaluation of pedotransfer functions for predicting the soil moisture retention curve. Soil Science Society of America Journal, 65(3), 638-648. doi: 10.2136/SSSAJ2001.653638X
Dijkerman, J. C. (1988). An ustult-aquult-tropept catena in Sierra Leone, West Africa, II. Land qualities and land evaluation. Geoderma, 42(1), 29-49. doi: 10.1016/0016-7061(88)90021-3
dos Reis, A. M. H., Pires, L. F., and Armindo, R. A. (2024). New empirical-point pedotransfer functions for water retention data for a wide range of soil texture and climates. International Soil and Water Conservation Research, 12(4), 855-867. doi: 10.1016/j.iswcr.2024.01.001
Feddes, R. A., de Rooij, G. H., and van Dam J. C. (2004). Unsaturated-Zone Modeling: Progress, Challenges and Applications. Kluwer Academic, 364 p. doi: 10.2136/vzj2006.0162br
Gee, G. W., and Or, D. (2002). Particle‐size analysis. Methods of Soil Analysis: Part 4 Physical Methods, 5, 255-293. doi: 10.2136/sssabookser5.4.c12
Gijsman, A. J., Jagtap, S. S., and Jones, J. W. (2002). Wading through a swamp of complete confusion: how to choose a method for estimating soil water retention parameters for crop models. European Journal of Agronomy, 18(1-2), 77-106. doi: 10.1016/S1161-0301(02)00098-9
Gholami Sharafkhane, M., Ziaei, A. N., Naghedifar, S. M., and Akbari, A. (2025). Investigation of the effect of soil salinity and water quality on saffron daughter corms using crop modeling and measured data. Water and Soil Management and Modeling, 5(1), 317- 334. doi: 10.22098/mmws.2023.14109.1390. [In Persian]
Haghverdi, A., Ghahreman, B., Khoshnood Yazdi, A. A., Joleini, M., and Arabi, Z. (2012). Evaluation and comparison between some point and parametric pedotransfer functions in predicting soil water contents in different matric potentials. Journal of Water and Soil Conservation, 19(2), 1-22. doi: 20.1001.1.23222069.1391.19.2.1.4. [In Persian]
Hosseini, S. V., Davari, M., and Khaleghpanah, N. (2021). Developing and comparing pedotransfer functions and spectral transfer functions for predicting water retention in some soils of Kurdistan province. Journal of Soil Management and Sustainable Production, 10(3), 51-71. doi:10.22069/ejsms.2021.17865.1940. [In Persian]
Jaefarzade Andabili, S., Rasoulzadeh, A., Moghadam, J. R., Pollacco, J. A. P., and Fernández‐Gálvez, J. (2025). Improved understanding of soil water content at field capacity and estimates from pedotransfer functions. Irrigation and Drainage, 74(2), 516-528. doi: 10.1002/ird.3032
Jury, W. A., and Horton, R. (2004). Soil Physics. John Wiley & Sons, Inc., Hoboken, NJ.
Kahkhamoghaddam, P., and Delbari, M. (2025). Feasibility of wind energy potential in Zabol using Weibull distribution. 15(1), 159-178. doi: 10.30488/gps.2025.478128.3779. [In Persian]
Kahkhamoghadam, P., Ziaei, A. N., Davari, K., Kanooni, A., and Sadeghi, S. (2024). Scheduling and optimal delivery of water in irrigation networks by combining the AquaCrop model and genetic algorithm. Water and Soil Management and Modeling, 4(4), 255- 268. doi: 10.22098/mmws.2023.14039.1382. [In Persian]
Lal, R. (1978). Physical properties and moisture retention characteristics of some Nigerian soils. Geoderma, 21(3), 209-223. doi: 10.1016/0016-7061(78)90028-9
Martinez, P., and Souza, I. F. (2020). Genesis of pseudo-sand structure in Oxisols from Brazil–A review. Geoderma Regional, 22, e00292. doi: 10.1016/j.geodrs.2020.e00292
McBratney, A. B., Minasny, B., Cattle, S. R., and Vervoort, R. W. (2002). From pedotransfer functions to soil inference systems. Geoderma, 109(1-2), 41-73. doi: 10.1016/S0016-7061(02)00139-8
Minasny, B., and Hartemink, A. E. (2011). Predicting soil properties in the tropics. Earth-Science Reviews, 106(1-2), 52-62. doi: 10.1016/j.earscirev.2011.01.005
Neysi, K., Egdernezhad, A., and Abbasi, F. (2023). Evaluation of AquaCrop model for corn simulation under different management of nitrogen fertilizer in Karaj. Water and Soil Management and Modeling, 3(1), 26- 41. doi: 10.22098/mmws.2022.10969.1093. [In Persian]
Oliveira, L. B., Ribeiro, M. R., Jacomine, P. K. T., Rodrigues, J. J. V., and Marques, F. A. (2002). Funções de pedotransferência para predição da umidade retida a potenciais específicos em solos do estado de Pernambuco. Revista Brasileira de Ciência do Solo, 26, 315-323. doi: 10.1590/S0100-06832002000200004
Pidgeon, J. D. (1972). The measurement and prediction of available water capacity of ferralitic soils in Uganda. Journal of Soil Science, 23(4), 431-441. doi: 10.1111/j.1365-2389.1972.tb01674.x
Qiao, J., Zhu, Y., Jia, X., Huang, L., and Shao, M. A. (2019). Pedotransfer functions for estimating the field capacity and permanent wilting point in the critical zone of the Loess Plateau, China. Journal of Soils and Sediments, 19(1), 140-147. doi: 10.1007/s11368-018-2036-x
Rab, M. A., Chandra, S., Fisher, P. D., Robinson, N. J., Kitching, M., Aumann, C. D., and Imhof, M. (2011). Modelling and prediction of soil water contents at field capacity and permanent wilting point of dryland cropping soils. Soil Research, 49(5), 389-407. doi: 10.1071/SR10160
Razavi, S., Rasoulzadeh, A., and Neyshabouri, M. R. (2014). Evaluation of Pedotransfer Functions for Estimating Soil Water Characteristic Curve in Naqadeh County. Journal of Water Research in Agriculture, 28(3), 613-624. doi: 10.22092/JWRA.2014.100011. [In Persian]
Rosseti, R. D. A. C., Amorim, R. S. S., Raimo, L. A. D. L. D., Torres, G. N., Silva, L. D. C. M. D., and Alves, I. M. (2022). Pedotransfer functions for predicting soil-water retention under Brazilian Cerrado. Pesquisa Agropecuária Brasileira, 57, e02474. doi: 10.1590/S1678-3921.pab2022.v57.02474
Rustanto, A., Booij, M. J., Wösten, H., and Hoekstra, A. Y. (2017). Application and recalibration of soil water retention pedotransfer functions in a tropical upstream catchment: case study in Bengawan Solo, Indonesia. Journal of Hydrology and Hydromechanics, 65(3), 307-320. doi: 10.1515/johh-2017-0020
‏Samaras, D. A., Reif, A., and Theodoropoulos, K. (2014). Evaluation of radiation-based reference evapotranspiration models under different Mediterranean climates in central Greece. Water Resources Management, 28, 207-225. doi: 10.1007/s11269-013-0480-3
Santra, P., Kumar, M., Kumawat, R. N., Painuli, D. K., Hati, K. M., Heuvelink, G. B. M., and Batjes, N. H. (2018). Pedotransfer functions to estimate soil water content at field capacity and permanent wilting point in hot Arid Western India. Journal of Earth System Science, 127(3), 35. doi: 10.1007/s12040-018-0937-0
Saxton, K. E., and Willey, P. H. (2006). The SPAW model for agricultural field and pond hydrologic simulation. Watershed Models, 28(Sep), 400-435. doi: 10.1201/9781420037432.ch17
Silva, B. M., Silva, É. A. D., Oliveira, G. C. D., Ferreira, M. M., and Serafim, M. E. (2014). Plant-available soil water capacity: estimation methods and implications. Revista Brasileira de Ciência do Solo, 38, 464-475. doi: 10.1590/S0100-06832014000200011
Souza, J. M., Bonomo, R., Pires, F. R., and Bonomo, D. Z. (2014). Funções de pedotransferência para retenção de água e condutividade hidráulica em solo submetido a subsolagem. Revista Brasileira de Ciências Agrárias, 9(4), 606-613. doi: 10.5039/agraria.v9i4a3732
Steduto, P., Hsiao, T. C., Raes, D., and Fereres, E. (2009). AquaCrop—The FAO crop model to simulate yield response to water: I. Concepts and underlying principles. Agronomy Journal, 101(3), 426-437. doi: 10.2134/agronj2008.0139s
Tian, Z., Chen, J., Cai, C., Gao, W., Ren, T., Heitman, J. L., and Horton, R. (2021). New pedotransfer functions for soil water retention curves that better account for bulk density effects. Soil and Tillage Research, 205, 104812. doi: 10.1016/j.still.2020.104812
Tomasella, J., and Hodnett, M. (2004). Pedotransfer functions for tropical soils. Developments in Soil Science, 30, 415-429. doi: 10.1016/S0166-2481(04)30021-8
Ungaro, F., Calzolari, C., and Busoni, E. (2005). Development of pedotransfer functions using a group method of data handling for the soil of the Pianura Padano–Veneta region of North Italy: water retention properties. Geoderma, 124(3-4), 293-317. doi: 10.1016/j.geoderma.2004.05.007
van den Berg, M., Klamt, E., Van Reeuwijk, L. P., and Sombroek, W. G. (1997). Pedotransfer functions for the estimation of moisture retention characteristics of Ferralsols and related soils. Geoderma, 78(3-4), 161-180. doi: 10.1016/S0016-7061(97)00045-1
Vinhal-Freitas, I. C., Corrêa, G. F., Wendling, B., Bobulska, L., and Ferreira, A. S. (2017). Soil textural class plays a major role in evaluating the effects of land use on soil quality indicators. Ecological Iindicators, 74, 182-190. doi: 10.1016/j.ecolind.2016.11.020
Walkley, A., and Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil Science, 37(1), 29-38. doi: 10.1097/00010694-193401000-00003
Willmott, C. J. (1984). On the evaluation of model performance in physical geography. Spatial Statistics and Models, 443-460. doi: 10.1007/978-94-017-3048-8_23
Wösten, J. H. M., Pachepsky, Y. A., and Rawls, W. J. (2001). Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. Journal of Hydrology, 251(3-4), 123-150. doi: 10.1016/S0022-1694(01)00464-4
Water and Soil Management and Modelling
Volume 6, Issue 1
March 2026
Pages 192-207

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  • Receive Date: 23 August 2025
  • Revise Date: 30 September 2025
  • Accept Date: 30 September 2025

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