Point Pedotransfer Functions Research Articles

The added value of CoreVESS score and penetration resistance in predicting the soil–water retention curve

Soil hydraulic properties, such as the soil–water retention curve (SWRC), play a crucial role in simulating water transport within the vadose zone. They can be directly measured in the laboratory or field, or indirectly predicted by using Pedotransfer Functions (PTFs). It has been advocated that accounting for soil structure could marginally improve the prediction accuracy of PTFs in its wet range. The aim of this study was to test whether the use of an easy-to-determine soil structure-related variable could effectively increase the prediction accuracy of water retention curve PTFs. Additionally, we explored whether including soil strength, another soil property that is easy and quickly to determine in the field in many replicates, could improve PTF performance. Our investigation involved extensive sampling of 252 soil horizons across 42 cropped fields, each exhibiting within-field variations in soil structural degradation. Soil structure was represented by a soil structural quality score (Sq) taken from a semi-quantitative visual soil assessment (CoreVESS), which was included in the PTFs as predictor variable or as discriminator (Sq) for data grouping in addition to or instead of ‘classical’ predictors such as soil organic carbon content, clay and sand content, bulk density and soil layer. Penetration resistance (PR) was taken as a variable for soil strength. Various regression methods from classical regression to machine learning were evaluated on Train and Test subdatasets. To evaluate the effect of data grouping, the dataset was split in two based on structure and on texture. Both point and parametric PTFs were developed, with the latter predicting the parameters of the Van Genuchten (VG) water retention equation. Overall, the k-nearest neighbors PTFs (KNN-PTFs) achieved the best performance showing the highest R2 and lowest RMSE values. Moreover, the point PTFs showed much better and more stable predictive ability than parametric PTFs. The inclusion of PR as an additional predictor variable slightly improved the prediction performance in the wet range of the SWRC. Interestingly, Sq proved beneficial when substituted for bulk density but not when used in addition to it. Grouping data based on texture and structure improved the prediction accuracy of the PTFs, particularly evident in textural grouping and MLR, but not in KNN.

Point Pedotransfer Functions (PPTF) for Estimating Soil Water Retention Curve by Multiple Linear Regression Methods

The aim of this study was to derive and find the best Point Pedotransfer Functions (PPTF) for estimating SWRC for Iraqi soils using the multiple regression methods. Soil samples were collected from 30 different sites of Iraqi soil at two depths: 0-0.3 m and 0.3-0.6 m, in order to obtain the required database for the derivation of PTF, which includes the soil particle size distribution, carbonate minerals, bulk density, particles density, organic matter, saturated hydraulic conductivity, capillary height and the boundaries of available water. Measured characteristics were considered as explanatory variables (EV). Volumetric water content was measured at suctions 0, -5, -10, -33, -500, -100, -500, -1000, and -1500 kPa and considered as response variables (RV). PPTF was derived using SAS statistical software based on multiple linear regression. Three methods for fitting the PPTF to EV were Maximum R2 Improvement (MR), Backward Elimination (BW) and Forward selection (FW). The prediction accuracy was evaluated through Mean Bias Error (MBE), Mean Absolute Error (MAE), root mean squared error (RMSE) and Coefficient Of Determination (R2). The results showed that all EVs were included in the PPFT derivation by MR method, while in the BW and FW methods the significant level of entery (SLENTRY) of EV was 0.1. It was also found that variables w1, capillary height, available water, bulk density, and porosity were included in the derivation of most of the models using the BW and FW method. Besides no statistically significant differences were found among the values of MBE, MAE, RMSE, and R2 of the PPTF although the number of selected EV are different.

Pedotransfer functions for predicting tropical soil water retention: A case study in upper Citarum watershed, Indonesia

Tropical regions such as Java, Indonesia, still lack publication of soil water retention (SWR) information, particularly at upper Citarum watershed. The SWR is one of the critical elements in water storage and movement in the soil and very important to solve ecological and environmental problems. However, getting the access requires a lot of laboratory meas-urement that is time-consuming and expensive. Therefore, utilizing pedotransfer functions (PTFs) to estimate the water in the soil is needed. This study aims to define soil properties related to the SWR and to evaluate the performance of existing PTFs in predicting SWR. The study was carried out at agroforestry land system soil at upper Citarum watershed, Indonesia. Ten point and two continuous existing PTFs developed for tropical regions were applied in this study. Pearson's correlation (r), mean error (ME), root mean square error (RMSE), and modelling efficiency (EF) were used for evaluation. Cation exchange capacity (CEC), organic carbon (OC), bulk density (BD), and clay were considered as potential soil properties for soil water retention prediction. The performance of PTFs by MINASNY, HARTEMINK [2011] at matric potential of –10 kPaand BOTULA [2013] at matric potential of –33 kPa and –1500 kPa were recommended for point PTFs, while PTFs by HODNETT, TOMASELLA [2002] was for continuous PTFs in predicting SWR. The accuracy of the point PTFs is almost better than the continuous PTFs in predicting SWR in agroforestry land system soil at upper Citarum watershed, Indonesia.

Assessing the best performing pedotransfer functions for predicting the soil‐water characteristic curve according to soil texture classes and matric potentials

AbstractThe soil‐water characteristic curve (SWCC) is a key input to many hydrological and water‐quality models that simulate water movement and solute transport in the vadose zone. Over the past four decades a considerable number of pedotransfer functions (PTFs) have been developed to predict SWCC. The goal of this study was to conduct a comprehensive evaluation of 30 PTFs, 11 discrete functions and 19 continuous functions. The SWCC predicted by different PTFs was statistically compared to SWCC measured for 2046 United States soils. The performance of each function was evaluated for soils of different textural classes and at four matric potentials: −4, −10, −33 and − 1,500 kPa. The results showed that for point PTFs, the PTF developed by Adhikary et al. (2008), was the best function to predict SWCC in very fine soils, whereas the PTF developed by Fooladmand (2011), showed the best performance in fine, medium fine, medium and coarse soils. For continuous PTFs, the PTF developed by Saxton et al. (1986), showed the best performance in very fine soil; the PTF developed by Cosby et al. (1984), showed the best performance in fine soil; the PTF developed by Rawls and Brakensiek (1985) showed the best performance in medium fine soil; and the PTF of Zacharias and Wessolek (2007), showed the best performance in medium and coarse soils. With respect to matric potentials, the PTFs of Gupta and Larson (1979), Dashtaki et al. (2010), and Hua et al. (2011), were the best performing point PTFs to predict SWCC at −4, −10, −33 and − 1,500 KPa, respectively. The continuous PTFs developed by Mayr and Jarvis (1999), Rosetta SSC (Schaap et al. 2001), Cosby et al. (1984), and Al Majou et al. (2007), showed the best performance to predict SWCC at −4, −10, −33 and − 1,500 kPa, respectively. The results of this study may be useful to hydrologic modelling as it identifies the most accurate PTF for each soil textural class and across the matric potential ranges.Highlights Performances of 30 pedotransfer functions of SWCCs were evaluated using soil database from the USA. The pedotransfer functions were classified into 11 point functions and 19 continuous functions. The pedotransfer functions were evaluated at five soil texture classes and four matric potentials Best‐performing pedotransfer functions were identified for each texture class and matric potential.

Sensitivity analysis of point and parametric pedotransfer functions for estimating water retention of soils in Algeria

Abstract. Improving the accuracy of pedotransfer functions (PTFs) requires studying how prediction uncertainty can be apportioned to different sources of uncertainty in inputs. In this study, the question addressed was as follows: which variable input is the main or best complementary predictor of water retention, and at which water potential? Two approaches were adopted to generate PTFs: multiple linear regressions (MLRs) for point PTFs and multiple nonlinear regressions (MNLRs) for parametric PTFs. Reliability tests showed that point PTFs provided better estimates than parametric PTFs (root mean square error, RMSE: 0.0414 and 0.0444 cm3 cm−3, and 0.0613 and 0.0605 cm3 cm−3 at −33 and −1500 kPa, respectively). The local parametric PTFs provided better estimates than Rosetta PTFs at −33 kPa. No significant difference in accuracy, however, was found between the parametric PTFs and Rosetta H2 at −1500 kPa with RMSE values of 0.0605 cm3 cm−3 and 0.0636 cm3 cm−3, respectively. The results of global sensitivity analyses (GSAs) showed that the mathematical formalism of PTFs and their input variables reacted differently in terms of point pressure and texture. The point and parametric PTFs were sensitive mainly to the sand fraction in the fine- and medium-textural classes. The use of clay percentage (C %) and bulk density (BD) as inputs in the medium-textural class improved the estimation of PTFs at −33 kPa.

Comparison of statistical regression and data-mining techniques in estimating soil water retention of tropical delta soils

Although a great number of studies have been devoted to develop and evaluate pedotransfer functions (PTFs), several questions still are to be addressed, particularly pertaining to tropical delta soils which received very little attention. One such question relates to the optimal structural dependency between basic soil properties and soil water retention characteristics (SWRC), which could be formulated by various regression methods. It is hypothesised that data mining techniques provide more accurate SWRC-PTFs than statistical linear regression. However, data-mining techniques are often proven as highly data-demanding techniques. The aim of this study was, therefore, to verify that hypothesis for a limited data set of tropical delta soils by comparing the predictive capabilities of point PTFs and pseudo-continuous (PC) PTFs developed by Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), Support Vector Machine for Regression (SVR), and k-Nearest Neighbours (kNN) methods. The results show that point-PTFs derived from data-mining techniques (i.e. ANN, SVR, kNN) offer accurate and reliable estimation of soil water content at several matric potentials. In case of PC-PTFs, ANN and kNN models outperformed SVR and MLR PTFs in validation phase (RMSE of ANN and kNN PTFs were around 0.05 m3 m−3, while those of SVR PTFs and MLR PTFs rose up to 0.068 and 0.066 m3 m−3). Our findings confirm the superiority of data-mining approaches in modelling the complex system of soil and water, even when a limited data set is available. The non-parametric kNN method, though being constrained in estimating SWRC in pseudo-continuous manner, has great benefits due to its flexibility, simplicity, accuracy and capacity to append new observations.

Improvement in estimation of soil water retention using fractal parameters and multiobjective group method of data handling

Soil water retention characteristic is required for modeling of water and substance movement in unsaturated soils and need to be estimated using indirect methods. Point pedotransfer functions (PTFs) for prediction of soil water content at matric suctions of 1, 5, 25, 50, and 1500 kPa were developed and validated using a data-set of 148 soil samples from Hamedan and Guilan provinces, Iran, by multiobjective group method of data handling (mGMDH). In addition to textural and structural properties, fractal parameters of the power-law fractal models for both particles and aggregates distributions were also included as predictors. Their inclusion significantly improved the PTFs’ accuracy and reliability. The aggregate size distribution fractal parameters ranked next to the particle size distribution (PSD) in terms of prediction accuracy. The mGMDH-derived PTFs were significantly more reliable than those by artificial neural networks but their accuracies were practically the same. Similarity between the fractal behavior of particle and void size distributions may contribute to the improvement of the derived PTFs using PSD fractal parameters. It means that both distributions of the pore and particle size represent the fractal behavior and can be described by fractal models.

Assessment of pedotransfer functions for estimating soil water retention curves for the amazon region

Knowledge of the soil water retention curve (SWRC) is essential for understanding and modeling hydraulic processes in the soil. However, direct determination of the SWRC is time consuming and costly. In addition, it requires a large number of samples, due to the high spatial and temporal variability of soil hydraulic properties. An alternative is the use of models, called pedotransfer functions (PTFs), which estimate the SWRC from easy-to-measure properties. The aim of this paper was to test the accuracy of 16 point or parametric PTFs reported in the literature on different soils from the south and southeast of the State of Pará, Brazil. The PTFs tested were proposed by Pidgeon (1972), Lal (1979), Aina & Periaswamy (1985), Arruda et al. (1987), Dijkerman (1988), Vereecken et al. (1989), Batjes (1996), van den Berg et al. (1997), Tomasella et al. (2000), Hodnett & Tomasella (2002), Oliveira et al. (2002), and Barros (2010). We used a database that includes soil texture (sand, silt, and clay), bulk density, soil organic carbon, soil pH, cation exchange capacity, and the SWRC. Most of the PTFs tested did not show good performance in estimating the SWRC. The parametric PTFs, however, performed better than the point PTFs in assessing the SWRC in the tested region. Among the parametric PTFs, those proposed by Tomasella et al. (2000) achieved the best accuracy in estimating the empirical parameters of the van Genuchten (1980) model, especially when tested in the top soil layer.

Using categorical soil structure information to improve soil water retention estimates of tropical delta soils

Models of soil water and solute transport require input data of soil hydraulic properties (e.g. soil water retention and hydraulic conductivity curves). Lack of such data, especially in tropical delta regions, has usually been the main constraint for the application of simulation models. Direct field or laboratory measurement of soil water retention is costly, laborious and time-consuming; therefore, indirect estimation from other easily measured soil properties has received great interest. However, indirect estimates are often unreliable. In this study, we hypothesise that including basic descriptive information of soil structure such as aspect of presence or absence of pedality can improve the prediction of the soil water retention characteristic (SWRC). Stepwise multiple linear regression was used to develop point pedotransfer functions (PTFs) to estimate soil water retention at eight pressure potentials (e.g. –1, –3, –6, –10, –20, –34, –100, –1500 kPa). Soil structural information was exploited as a preliminary grouping criterion to test our hypothesis. Soil samples were taken from 160 horizons distributed along the Mekong Delta, Vietnam. The results reveal that SWRC of tropical Mekong Delta soils could be satisfactorily estimated by typical predictors of PTFs (e.g. soil texture, organic carbon content and bulk density). Moreover, incorporating soil structure in developing PTFs did improve the prediction accuracy of SWRC, especially in the wet moisture range. Plastic limit was found to be a promising predictor for SWRC-PTFs of soils having a given degree of structural development.

ESTIMATING SOIL HYDRAULIC PROPERTIES FROM LIMITED DATA TO IMPROVE IRRIGATION MANAGEMENT IN AGRICULTURAL SOILS OF SANTIAGO ISLAND, CAPE VERDE

Limited knowledge of the hydraulic properties of semi-arid Cape Verde soils makes the use of water balance models difficult. In this paper, the water retention characteristics of agricultural soils of Santiago Island and the development of point pedotransfer functions (PTFs) to improve irrigation management are investigated. Soil water retention curves were studied in 72 horizons of 31 soil profiles located in Santiago Island. Soil textures varied from coarse to medium classes. PTFs were, at the same time, developed by regression analysis to predict total porosity (ϕ) and water retention at −0.25, −1, −3.2, −10, −33, −100 and −1500 kPa. Due to the lack of available data from Cape Verde, PTFs were developed from 85 soil horizons specifically selected from a Portuguese database. The PTFs were then validated with the data determined in the 72 soil horizons studied in Santiago Island. Statistics showed relatively good performance of Cape Verde PTFs, with RMSE varying between 0.030 and 0.086 cm3 cm-3. However, Cape Verde PTFs always underestimated the measured values by −0.014 to −0.075 cm3 cm-3. When compared with three other published PTFs, the new Cape Verde PTFs provided acceptable results for use in modelling aimed at improving irrigation water management practices. Copyright © 2013 John Wiley & Sons, Ltd.

Pedotransfer functions to estimate retention and availability of water in soils of the state of Santa Catarina, Brazil

Studies on water retention and availability are scarce for subtropical or humid temperate climate regions of the southern hemisphere. The aims of this study were to evaluate the relations of the soil physical, chemical, and mineralogical properties with water retention and availability for the generation and validation of continuous point pedotransfer functions (PTFs) for soils of the State of Santa Catarina (SC) in the South of Brazil. Horizons of 44 profiles were sampled in areas under different cover crops and regions of SC, to determine: field capacity (FC, 10 kPa), permanent wilting point (PWP, 1,500 kPa), available water content (AW, by difference), saturated hydraulic conductivity, bulk density, aggregate stability, particle size distribution (seven classes), organic matter content, and particle density. Chemical and mineralogical properties were obtained from the literature. Spearman's rank correlation analysis and path analysis were used in the statistical analyses. The point PTFs for estimation of FC, PWP and AW were generated for the soil surface and subsurface through multiple regression analysis, followed by robust regression analysis, using two sets of predictive variables. Soils with finer texture and/or greater organic matter content retain more moisture, and organic matter is the property that mainly controls the water availability to plants in soil surface horizons. Path analysis was useful in understanding the relationships between soil properties for FC, PWP and AW. The predictive power of the generated PTFs to estimate FC and PWP was good for all horizons, while AW was best estimated by more complex models with better prediction for the surface horizons of soils in Santa Catarina.

Soil Water Retention Characteristics of Black Soils of India and Pedotransfer Functions Using Different Approaches

Information on soil hydraulic properties is a prerequisite in irrigation management decisions and crop planning. Such information on soils of the black soil region (BSR) occupying 7.7×107 ha of India is sparse. Soil profile information for 49 representative sites (244 samples) was collected and used for analysis. Ten different functions were evaluated for their efficacy to describe soil water retention characteristics (SWRC) of the BSR soils. Campbell model fitted to measured SWRC data with relatively lower root mean square error (RMSE=0.0214 m3·m−3), higher degree of agreement d=0.9653), and lower absolute error on average (MAE=0.0165 m3·m−3). The next best description was by van Genuchten (VG) function with RMSE (0.0249 m3·m−3), d(0.9489), and MAE (0.0868 m3·m−3). Pedotransfer functions (PTF) were developed to predict field capacity (FC) and permanent wilting point (PWP) using nearest neighbor (kNN) algorithm and artificial neural networks (ANN). Four levels of input information used for point PTF development include (1) textural data (data on sand, silt, and clay fraction-SSC), (2) level 1+bulk density data (SSCBD), (3) level 2+organic matter (SSCBDOM), and (4) level 1+organic matter (SSCOM). The RMSE of predictions by kNN PTFs ranged from 0.0346 to 0.0611 m3·m−3 with an average of 0.0483 m3·m−3. The ANN PTFs performed with an average RMSE of 0.0550 m3·m−3 and a range of 0.0367 to 0.0905 m3·m−3. Relatively better estimates of FC/PWP were obtained using SSCBD-based PTF. Accuracy of FC and PWP estimates obtained by using analytical functions was relatively greater than the estimates by kNN and ANN PTFs. Campbell and VG functions were relatively more accurate. The study demonstrated the efficacy of kNN technique vis-a-vis neural regression with the additional benefit of appending the development data as and when desired. The proposed PTFs could be useful in making irrigation management decisions for BSR soils of India. Identification of the most suitable SWRC function for the study soils will help in crop modeling/water balance studies of the region.

Moisture characteristics and their point pedotransfer functions for coarse‐textured tropical soils differing in structural degradation status

AbstractThe effect of soil structure on hydraulic pedotransfer functions (PTFs) in tropical soils with similar mineralogy and texture has not been well documented. Structurally contrasting soils from representative locations in southeastern Nigeria were analyzed for moisture retention at 0, 6, 10, 33, 100, 300 and 1500 kPa among other properties. They were grouped by depth (topsoils or subsoils) and also by their structural degradation status into low‐ and high‐stability soils, corresponding to organic matter (OM):[silt + clay] of <7.5 and >7.5%, respectively. Soil depth and structural stability influenced the soil moisture characteristic curves. The data were fitted to three tropical point PTFs, but none of them proved appropriate for predicting moisture retention in the soils. We therefore derived new ones using multiple linear stepwise regressions before and after the dataset grouping and compared their performances by means of cross‐validation. Moisture retention in the soils (sand content, 73.2–93.8%) could not be calibrated from texture and OM concentration alone, until when bulk density, total porosity and microporosity were included among the regressors. Microporosity's role was particularly outstanding at all matric potentials but the 1500 kPa. The ensuing PTFs represent a good fit for the soil moisture retention data. The two grouping strategies resulted in lower SE of the estimates in some cases, but this did not enhance the performances of the concerned PTFs. At the 1500 kPa, however, the PTF incorporating all datasets performed better than separate PTFs for topsoils/subsoils but worse than the one for high‐stability soils. Information on soil structure can therefore benefit PTF derivation for kaolinite‐dominated, coarse‐textured tropical soils at all the matric potentials considered. Copyright © 2012 John Wiley & Sons, Ltd.

Pedotransfer functions based on nearest neighbour and neural networks approach to estimate available water capacity of shrink-swell soils

Point pedotransfer functions (PTF) were developed by nearest neighbour (kNN) algorithm as an alternative to widely used artificial neural networks (ANN) for prediction of field capacity, permanent wilting point, and available water capacity of the seasonally impounded clay soils in central India using the available dataset. They were compared with parametric PTFs to estimate Van Genuchten parameters. Basic soil properties (texture, bulk density and organic carbon content) were related to the properties of interest. The root mean square error (RMSE) in predictions of the three properties (FC, PWP, AWC) by kNN PTFs ranged from 0.0237 to 0.0279 m3/m3with an average of 0.0265 m3/m3. The ANN PTFs performed relatively better with average RMSE 0.0223 m3/m3 and a range of 0.0141 to 0.0295 m3/m3. Magnitude of RMSE was relatively higher in VG parametric PTFs (0.032 m3/m3) followed by kNN (0.0237 m3/m3) and ANN (0.0223 m3/m3) PTFs. kNN algorithm provided viable alternative to neural regression with additional benefit of flexibility in appending reference database.

Point pedotransfer functions for prediction of water retention of selected soil series in a semi-arid region of western Iran

This study was conducted to derive point pedotransfer functions (PPTFs) for soil water retention (SWR) in western Iran. Topsoil and subsoil of 63 soil series, which were representative of different regions of Hamadan province, were sampled. Soil water retention was determined by the sand box and pressure plate at matric suctions (h m) of 0, 1, 2, 5, 10, 25, 50, 100, 200, 500, 1000 and 1500 kPa. PPTFs were derived through multiple linear regressions for the topsoils and subsoils. These used particle size distribution, bulk density, organic matter, calcium carbonate and gravel contents as easily-available inputs. To increase the accuracy of the PPTFs, saturated water content was also included as an input variable in a group of PPTFs but they are not better as assessed using the Akaike Information Criterion. All of the PPTFs were statistically significant (p < 0.001) and could be used to predict the SWR. The absolute effect of bulk density on the SWR diminished as h m increased. Bulk density decreased the SWR for low h m and increased it for high h m. In the wet range, organic matter increased the SWR. Clay and silt increased SWR whereas gravel decreased it. The effect of calcium carbonate on SWR was negligible.

Soil Water Retention Characteristics of Vertisols and Pedotransfer Functions Based on Nearest Neighbor and Neural Networks Approaches to Estimate AWC

Irrigation management in vertisols is one of the major challenges to increase agricultural productivity in India and many developing countries. Unfortunately, information on hydraulic properties of these soils is very sparse. In an attempt to understand these soils for better management, 10 different functions were evaluated for their efficacy to describe soil-water retention characteristics (SWRC) of vertisols of India, and point pedotransfer functions (PTFs) were developed by using a nearest neighbor (k-NN) algorithm as an alternative to widely used artificial neural networks (ANN) for prediction of available water capacity (AWC). Soil profile information of 26 representative sites comprising 157 soil samples was used for analysis. The Campbell model fit to measured SWRC data better than any other model, with relatively lower root mean square error (RMSE) (0.0199), higher degree of agreement (0.9867), and lower absolute error on an average (0.0134). Three other functions, namely, modified Cass-Hutson, B...

Pedotransfer functions for point estimation of soil moisture characteristic curve in some Iranian soils

Soil moisture characteristic (SMC) curve is a basic soil property. Since its direct measurement is costly and time-consuming, an alternative to measurement is to estimate this property indirectly, using the more easily available soil properties. These methods are called pedotransfer functions (PTFs). Point PTFs estimate the water content of the soil at different matric potentials. For this study, twenty soil samples from the topsoils (A horizons) were selected from different locations in Fars province, South of Iran, and SMC curve of each soil was measured with the combination of hanging column for matric potentials of 0 (saturated soil moisture content), 3, 6, 9 and 12 kPa and pressure plate methods for matric potentials of 30, 100, 500, 1000 and 1500 kPa. Also, the particle size distribution curve of each soil was measured and the percentages of clay, silt and sand, and the geometric mean particle-size diameter of each soil was determined. Then, three PTFs were derived based on clay or sand fraction of the soil or geometric mean particle-size diameter, saturated soil moisture content and bulk density for point estimation of SMC curve. To evaluate the proposed PTFs another five independent soils were used. The results showed that, the PTFs for point estimation of SMC curve based on clay content were appropriate for soils with clay, silty clay and silty clay loam textures, and the PTFs based on sand content were appropriate for soils with loam and sandy loam textures. Key words: Soil moisture characteristic curve, pedotransfer functions, clay, sand, saturated soil moisture content.

Calibration and evaluation of pedotransfer functions to estimate available water capacity of seasonally impounded shrink-swell soils of central India

Pedotransfer functions (PTF) to estimate available water capacity of seasonally impounded shrink-swell soils of central India are presented. Performance of the calibrated PTFs is compared with that of ‘Rosetta’ a widely used general PTF. Available information on soil properties contained nine point soil water retention data for 175 samples measured at varied potentials, textural composition, bulk density and organic carbon content. Nine widely used water retention functions proposed by different researchers were fitted to the measured data and evaluated for efficacy to describe water retention characteristics (WRC). Of the nine functions evaluated, Brooks-Corey, van Genuchten, and Campbell functions were recommended for describing WRC of these soils. We present point PTFs to estimate available water capacity (AWC) using two approaches-regression and artificial neural networks (ANN). Point estimation PTFs were calibrated for water contents at −33 and −1500 kPa and consequently AWC. Performance evaluation with root mean square error (RMSE) criteria suggested that ANN based PTFs were better than regression PTFs. Performance evaluation of ‘Rosetta’ suggested its limited applicability for the study area. Region-specific PTFs to predict AWC were recommended. Increasing the number of predictor variables improved performance of neural PTFs and ‘Rosetta’.

Derivation and validation of pedotransfer functions for estimating soil water retention curve using a variety of soil data

Soil textural components are the most common inputs in pedotransfer functions (PTFs), although other soil properties, such as bulk density, organic matter content and structural indexes, are also used. The possibility of using the geometric mean d g and the geometric standard deviation σ g of soil particle diameters instead of soil particle size distribution to derive PTFs was investigated. Soil samples (234) having different particle size distributions were collected randomly. The predictor variables were separated into two groups: (i) particle-size distribution and bulk density, (ii) bulk density, geometric mean and geometric standard deviation of particle diameters. Two point PTF types and two parametric PTF forms were developed to predict six points on the retention curve and the parameters of the van Genuchten model, using the stepwise regression method. The derived PTFs were compared with the Rosetta package. The results indicated that the second set of variables accounted for 65 and 90% of the variation in α and 0 s parameters of the van Genuchten, model respectively. The results also indicated that these variables better explained the variation of the n parameter (the shape parameter of the van Genuchten model) and the water contents at -30, -100, -300, -500 and -1500 kPa matric potentials. It was concluded that, at the drier end of the retention curve, d g and σ g predict the water contents better than the soil bulk density. Although the coefficients of determination of the derived PTFs with the second set of variables were higher than that of the first group, the first set of variables gave more reliable estimates. Results showed that the descriptors of the particle size distribution (d g and σ g ) did not improve the quality of soil water retention prediction. The reliability test indicated that the derived PTFs predict soil hydraulic properties better than the Rosetta package.

Alternative methods in the development of pedotransfer functions for soil hydraulic characteristics

Soil hydraulic properties are needed in the modeling of water flow and solute movement in the vadose zone. Pedotransfer functions (PTFs) have received the attention of many researchers for indirect determination of hydraulic properties from basic soil properties as an alternative to direct measurement. The objective of this study was to compare the performance of cascade forward network (CFN), multiple-linear regression (MLR), and seemingly unrelated regression (SUR) methods using prediction capabilities of point and parametric PTFs developed by these methods. The point PTFs estimated field capacity (FC), permanent wilting point (PWP), available water capacity (AWC), and saturated hydraulic conductivity (Ks) and the parametric PTFs estimated the van Genuchten retention parameters. A total of 180 soil samples was extracted from the UNSODA database and divided into two groups as 135 for the development and 45 for the validation of the PTFs. The model performances were evaluated with three statistical tools: the maximum error (ME), the model efficiency (EF), and the D index (D) using the observed and predicted values of a given parameter. Despite the fact that the differences among the three methods in prediction accuracies of the point and parametric PTFs were not statistically significant (p > 0.05) except θr and α (p < 0.05) based on the ANOVA test, overall MLR and SUR were somewhat better than CFN in prediction of the point PTFs, whereas CFN performed better than the other two methods in prediction of the parametric PTFs. The F.F values of FC and θr for CFN, MLR, and SUR methods were 0.705. 0.805, 0.795 and 0.356, −0.290, −0.290, respectively, which refer to the best and worst predictions. Properties (Ks, θr, α) having some difficulty in prediction were better predicted by CFN and SUR methods, where these methods predict all hydraulic properties from basic soil properties simultaneously rather than individually as in MLR. This suggests that multivariate analysis using such functional relationships between hydraulic properties and basic soil properties can be utilized in developing more accurate point and parametric PTFs with less time and effort.