Pedotransfer Functions for Estimating the Field Capacity and Permanent Wilting Point in the Critical Zone of the Loess Plateau, China

Home , Field capacity, Pedotransfer function

Journal of Soils and Sediments https://doi.org/10.1007/s11368-018-2036-x

SOILS, SEC 2 • GLOBAL CHANGE, ENVIRON RISK ASSESS, SUSTAINABLE LAND USE • RESEARCH ARTICLE

Pedotransfer functions for estimating the field capacity and permanent wilting point in the critical zone of the Loess Plateau, China

Jiangbo Qiao1 & Yuanjun Zhu2 & Xiaoxu Jia3 & Laiming Huang3 & Ming’an Shao2,3

Received: 26 February 2018 /Accepted: 18 May 2018 # Springer-Verlag GmbH Germany, part of Springer Nature 2018

Abstract Purpose Field capacity (FC) and permanent wilting point (PWP) are important physical properties for evaluating the available soil water storage, as well as being used as input variables for related agro-hydrological models. Direct measurements of FC and PWP are time consuming and expensive, and thus, it is necessary to develop related pedotransfer functions (PTFs). In this study, stepwise multiple linear regression (SMLR) and artificial neural network (ANN) methods were used to develop FC and PWP PTFs for the deep layer of the Loess Plateau based on the bulk density (BD),sand, silt, clay, and soil organic carbon (SOC) contents. Materials and methods Soil core drilling was used to obtain undisturbed soil cores from three typical sites on the Loess Plateau, which ranged from the top of the soil profile to the bedrock (0–200 m). The FC and PWP were measured using the centrifugation method at suctions of − 33 and − 1500 kPa, respectively. Results and discussion The results showed that FC and PWP exhibited moderate variation where the coefficients of variation were 11 and 23%, respectively. FC had significant correlations with sand, silt, clay, and SOC (P < 0.01), while there were also significant correlations between all of the variables and PWP. In addition, sand was an important input variable for predicting FC, and clay and BD for predicting PWP. The performance of the SMLR and ANN approaches was similar. Conclusions In this study, we developed new PTFs for FC and PWP as the first set of PTFs based on data obtained from deep profiles in the Loess Plateau. These PTFs are important for evaluating the soil water conditions in the deep profile in this region.

Keywords Earth’scriticalzone . Field capacity . Pedotransfer function . Permanent wilting point

1 Introduction thereby making it the key area for sustaining ecosystem func- tioning and human survival (Lin 2010). The field capacity The Earth’s critical zone (CZ) is located vertically from the top (FC) and permanent wilting point (PWP) are important input of the plant canopy to the weathered bedrock and it is related variables in a wide range of agro-hydrological models, as well to soil development, water flow, and geochemical cycles, as being important soil physical properties for evaluating the soil water conditions in the CZ. Therefore, it is important to obtain accurate estimates of FC and PWP in the CZ. However, Responsible editor: Saskia D. Keesstra obtaining direct measurements of FC and PWP is tedious, time consuming, and expensive. Pedotransfer functions * Yuanjun Zhu [email protected] (PTFs) can be used for estimating them by using the basic soil properties as inputs and producing complex variables as outputs (Bouma 1989). 1 College of Resources and Environment, Northwest A&F University, Yangling 712100, China Bouma (1989) introduced PTFs to model the basic soil properties as inputs and obtain hydraulic parameters as 2 State Key Laboratory of Soil Erosion and Dryland Agriculture on the Loess Plateau, Northwest A&F University, Yangling 712100, China outputs. Subsequently, PTFs have become increasingly important in soil science research and many PTFs with dif- 3 Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, ferent data requirements have been proposed. Reviews of Chinese Academy of Sciences, Beijing 100101, China the state of the art in this research field were provided by J Soils Sediments

Wösten et al. (2001), Shein and Arkhangel’skaya (2006), 2000). The region is surrounded by mountains, where the and Vereecken et al. (2010). loessial landforms include Yuan (a large flat surface with little Many previous studies have developed PTFs for estimating erosion), ridges, hills, and gullies. FC and PWP in different regions and soil types (Aina and Periaswamy 1985; Saxton and Rawls 2006; Balland et al. 2008). Thus, Berg et al. (1997) developed PTFs for FC and 2.2 Soil sampling PWP for Ferralsols and related soils. Cemek et al. (2004) developed regression models for estimating FC and PWP in Three typical sampling sites (Yangling, Changwu, and An’sai) the Erzincan plain in the Eastern Anatolia region of Turkey. (Fig. 1b) were selected from south to north on the Loess Givi et al. (2004) predicted the soil moisture contents to de- Plateau, and soil samples were collected from the soil surface termine FC and PWP in the Zagros mountain region of Iran. to the bedrock using drilling equipment (assembled by Xi’an Jap (2008) proposed a reliable method for estimating the soil Qinyan Drilling Co. Ltd). At each sampling site, metal cylin- moisture to predict FC and PWP based on the soil texture and ders (diameter: 5 cm, length: 5 cm) were used to collect un- bulk density (BD) using data from the Data and Information disturbed soil samples from the middle of the soil column at 1- System of the International Geosphere Biosphere Programme m intervals (0.5, 1.5, 2.5, 3.5 m,…) in order to obtain mea- soil data set. Nguyen et al. (2015) evaluated PTFs for estimat- surements of FC, PWP, and BD. Similarly, disturbed soil sam- ing FC and PWP for soils in the tropical Mekong Delta in ples were collected to determine the soil particle composition Vietnam. In addition, Ghanbarian et al. (2015) developed sam- and soil organic matter contents. It should be noted that the ple dimension-dependent PTFs for predicting soil water reten- undisturbed soil samples were not replicated due to the possi- tion curves. However, it should be noted that the PTFs devel- ble cost incurred and challenges obtaining the samples. In oped previously for estimating FC and PWP were mainly addition, some soil cores were damaged during drilling. designed for the upper soil layers (< 1 m) and few studies have Therefore, the numbers of undisturbed soil samples were 30, developed PTFs for the deep layers in the CZ. 100, and 76 for Yangling, Changwu, and An’sai, respectively, The Loess Plateau in China (~ 620,000 km2) is covered and the corresponding soil drilling depths were 104.5, 204.5, largely by loess-paleosol layers ranging from 50 to 200 m in and 161.6 m. thickness, and two-thirds of this area comprises arid and semi- arid regions. Thus, reliable FC and PWP data are necessary in order to study the hydraulic processes that occur in the CZ. 2.3 Laboratory analysis However, the great depth of the soil on the Loess Plateau makes it difficult to obtain direct measurements of FC and The FC and PWP were measured using the centrifugation PWP in deep soil profiles. Therefore, new PTFs are required method (Hitachi CR21G centrifuge; 20 °C) at the suctions of for estimating FC and PWP in deep profiles on the Loess − 33 (1700 rpm; 42 min) and − 1500 kPa (12,000 rpml; Plateau in China. 95 min), respectively (Lu et al. 2004), and BD was determined Thus, the objectives of the present study were (1) to devel- based on the volume-mass relationship for each oven-dried op new PTFs for estimating the FC and PWP in the CZ on the core sample (105 °C, 48 h) (Wang et al. 2008). The disturbed Loess Plateau, China, and (2) to compare the performance of soil samples were air-dried and passed through a 2.0- and the developed PTFs with the existing PTFs. 0.25-mm mesh, respectively. Soil samples less than 2 mm were used to measure the soil particle composition by laser diffraction (Mastersizer 2000, Malvern Instruments, Malvern, 2 Materials and methods UK) (Liu et al. 2005). Soil samples less than 0.25 mm were used to determine the soil organic carbon (SOC) contents by 2.1 Study area description dichromate oxidation (Nelson and Sommers 1982).

The study was carried out on the Loess Plateau of China (33° 43′–41° 16′ N, 100° 54′–114° 33′ E), which is located in the 2.4 PTF development continental monsoon climate region and is about 6.5% of the area of China (Fig. 1a). The annual evaporation on the Loess In this study, the 206 soil samples were divided randomly into Plateau is 1400–2000 mm, and the annual temperature ranges two groups: group A (137) for deriving the PTFs (including from 3.6 °C in the northwest to 14.3 °C in the southeast (Shi calibration data sets for FC and PWP) and group B (69) for and Shao 2000). The annual solar radiation ranges from 5.0 × validating the PTFs. And new PTFs were developed by using 109 to 6.7 × 109 Jm−2. The annual precipitation ranges step-wise multiple linear regression (SMLR) and artificial from150 mm in the northwest to 800 mm in the southeast, neural network (ANN) approaches, respectively. where 55–78% falls from June to September (Shi and Shao J Soils Sediments

Fig. 1 Location of the Loess Plateau region in China (a)and the sampling sites (b)

2.4.1 Step-wise multiple linear regression where Y is the dependent variable (FC and PWP), α0 is the intercept, α1,…, α5 are regression coefficients, and X1–X5 are Multiple linear regression (MLR) is a traditional method for the independent variables (BD, sand, silt, clay, and SOC). In developing PTFs, which is widely applicable in soil science addition, a stepwise regression method was employed in the for developing the PTFs of soil parameters (Liao et al. 2011; MLR. Li et al. 2007; Santra and Das 2008;Wangetal.2012). The equation for regression analysis is as follows: 2.4.2Artificialneuralnetwork

Y ¼ α0 þ α1X 1 þ α2X 2… þ α5X 5; ð1Þ Recently, ANN models were regarded as an approach for modeling relationships between soil attributes and the more easily measurable properties, and have been used successfully J Soils Sediments

Table 1 Descriptive statistics obtained for FC and PWP as well as related soil physical properties for all data sets, development data sets, and validation data sets

Parameters No. Min Max Mean SD CV

All data BD (g cm−3) 206 1.27 1.81 1.61 0.09 0.06 Sand (%) 206 0.89 40.26 9.77 7.61 0.78 Silt (%) 206 46.31 73.54 66.32 3.83 0.06 Clay (%) 206 8.94 35.82 23.91 5.55 0.23 SOC (g kg−1) 206 0.66 5.47 1.61 0.66 0.41 FC (cm3 cm−3) 206 0.22 0.45 0.37 0.04 0.11 PWP (cm3 cm−3) 206 0.1 0.33 0.21 0.05 0.23 Development data BD (g cm−3) 137 1.27 1.81 1.61 0.09 0.06 Sand (%) 137 1.44 35.84 9.74 7.65 0.79 Silt (%) 137 46.31 72.73 66.22 3.97 0.06 Clay (%) 137 10.3 35.82 24.04 5.51 0.23 SOC (g kg−1) 137 0.66 5.47 1.61 0.69 0.43 FC (cm3 cm−3) 137 0.22 0.43 0.37 0.04 0.1 PWP (cm3 cm−3) 137 0.1 0.31 0.21 0.05 0.22 Validation data BD (g cm−3) 691.281.771.610.10.06 Sand (%) 69 0.89 40.26 9.84 7.59 0.77 Silt (%) 69 50.8 73.54 66.52 3.55 0.05 Clay (%) 69 8.94 35.54 23.64 5.66 0.24 SOC (g kg−1) 690.764.2 1.60.610.38 FC (cm3 cm−3) 690.230.450.370.040.11 PWP (cm3 cm−3) 69 0.11 0.33 0.21 0.05 0.24

No. number of soil samples, Min minimum, Max maximum, SD standard deviation, CV coefficient of variation, BD bulk density, SOC soil organic carbon, FC field capacity, PWP permanent wilting point

for PTF development of soil parameters (Motaghian and the coefficient of determination (R2), the root mean squared Mohammadi 2011; Yao et al. 2015). In this study, a feed- error (RMSE), and the mean error (ME), as follows: forward ANN model of comprising many simple computing 2 ∑N y −yb elements was used to develop new PTFs, which were connect- i−1 i l R2 ¼ – ð Þ ed by weights to form a network. The model had three layers, 1 2 2 ∑N y −y i.e., input layer, output layer, and one (or more) Bhidden^ i−1 i l vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi layer. The numbers of hidden layers and artificial neurons in u each hidden layer were determined in a trial-and-error process u N 2 t∑i¼ yi−ybi (Xiong et al. 2011). The initial values of the weights in each ¼ 1 ð Þ RMSE N 3 layer were assigned randomly and then determined by an error-correction learning rule. A pure linear function was se- N ∑i¼ yi−ybi lected for the output layer, whereas a sigmoid activation func- ¼ 1 ; ð Þ ME N 4 tion was assigned to the hidden layer. The back-propagation processes used the least mean squares algorithm to update the where yi is the measured value, yî is the predicted value, y is weights in each layer, which could make the actual response the mean of the measured value, and N is the numbers in group of the network close to the desired response in a statistical A. sense. In addition, in total, 300 replicates of each subset were generated to limit the error due to bias for any particular calibration-validation data set pair. 2.5 Statistical analysis In addition, the performance of the new PTFs of two approaches was determined based on three indexes comprising The data were analyzed with different software packages. Descriptive statistical analyses (such as maximum, minimum, average, and coefficient of variation) and Pearson’s J Soils Sediments

Table 2 Pearson’s correlation coefficients between FC, PWP, Parameters BD Sand Silt Clay SOC FC PWP and related soil properties BD 1 Sand − 0.02 1 Silt 0.06 − 0.72** 1 Clay − 0.01 − 0.87** .28** 1 SOC − 0.28** − 0.43** .27** 0.41** 1 FC 0.04 − 0.66** 0.43** 0.61** 0.27** 1 PWP 0.25** − 0.50** 0.17* 0.57** 0.27** 0.730** 1

BD bulk density, SOC soil organic carbon, FC field capacity, PWP permanent wilting point *Correlation significant at P < 0.05 (two-tailed) **Correlation significant at P < 0.01 (two-tailed) correlation coefficient analysis were performed with SPSS 3.2 Correlation analysis (version 16.0). The coefficient of determination (R2), RMSE, and mean residual (MR) were calculated in Microsoft Excel Before developing the new PTFs for estimating FC and PWP, (2013). SMLR and ANN approaches were conducted using it was necessary to investigate the relationships between FC, MATLAB (version R2009a). PWP, and the related soil physical properties. Table 2 shows the correlation coefficients among the variables in the development data sets. FC and PWP both had significant and positive correlations with silt, clay, and soil organic carbon 3 Results and discussion (SOC), but a significant negative correlation with sand. In addition, PWP was significantly correlated with BD, but there 3.1 Statistical characteristics of the FC and PWP, was no correlation between BD and FC. These results are and basic soil properties consistent with those obtained by Wang et al. (2012), who also reported that FC had significant and positive correlations Table 1 shows the descriptive statistics obtained for FC and with silt, clay, and SOC, but a significant negative correlation PWP as well as related soil physical properties for all the with sand. Therefore, the variables that had significant corre- sampling sites. FC and PWP ranged from 0.22 to 0.45 and lations with FC and PWP were used to develop the new PTFs from 0.10 to 0.33 cm3 cm−3, respectively, with coefficient of for estimating FC and PWP. variation (CV) values of 11 and 23%, where the values exhibited moderate variation (Nielsen and Bouma 1985). The 3.3 New PTFs for estimating FC and PWP ranges for the BD, SOC, and sand, silt, and clay contents were − − 1.27 to 1.81 g cm 3, 0.66 to 5.47 g kg 1, 0.89 to 40.26%, 3.3.1 Stepwise multiple linear regression 46.31 to 73.54%, and 8.94 to 35.82%, respectively, with CV values of 6, 41, 78, 6, and 23%. Clearly, the ranges were wide SMLR is a traditional method for developing PTFs and it was for FC and PWP as well as the related soil physical properties, used to develop new PTFs for estimating FC and PWP. thereby indicating that the data sets were representative. In Table 3 shows the results obtained after analyzing the addition, the development data sets and validation data sets SMLR models. According to the calculated adjusted R2 had similar ranges (Table 1). Table 4 Performances of pedotransfer functions for estimating FC and Table 3 Results obtained for the SMLR models used for developing PWP using the artificial neural network (ANN) and stepwise multiple pedotransfer functions linear regression (SMLR) approaches R2 Parameter Variable Coefficient SE P EV Adjusted R2 Variables Input variables Methods RMSE ME − FC Constant 0.398 0.004 0.000 0.432 FC Sand SMLR 0.326 0.032 0.001 Sand − 0.003 0.000 0.000 0.432 ANN 0.301 0.043 0.002 PWP Constant − 0.112 0.055 0.044 0.385 PWP Clay + BD SMLR 0.321 0.041 − 0.002 Clay 0.005 0.001 0.000 0.324 ANN 0.331 0.051 − 0.004 BD 0.129 0.033 0.000 0.061 R2 coefficient of determination, RMSE root mean squared error, ME SE standard error, EV explained variation (%), FC field capacity, PWP mean error, FC field capacity, PWP permanent wilting point, BD bulk permanent wilting point, BD bulk density density J Soils Sediments

Table 5 Existing pedotransfer functions (PTFs) for estimating FC and PWP, which were compared with the new PTFs developed in this study

No. PTFs No. of PTFs Soil types Geographical samples domain

1 Dijkerman (1988)166 θ− 33kpa = (0.3697 − 0.0035 * Sa) * BD Ultisols, Sierra Leone θ− 1500kpa = (0.0074 + 0.0039 * Cl) * BD Oxisols, Inceptisols 2Adhikaryetal. 1104 θ− 33kpa =0.5637− 0.0051 * Sa − 0.0027 * Si Various India (2008) θ− 1500kpa = 0.0071 + 0.0044 * Cl

3 Berg et al. (1997)91 θ− 1500kpa = 0.00334 * Cl * BD + 0.00104 * Si * BD Oxisols and Global related soils 4 Minasny and nm θ− 33kpa =0.565− 0.0749 * BD − 0.0034 * Sa Various Tropical regions 2 Hartemink θ− 1500kpa = 0.0795 + 0.0086 * SOC + 0.004 * Cl − 0.00004 * (Cl − 0.377) (ISRIC (2011) database)

5Botula(2013)196 θ− 33kpa =0.4193− 0.0035 * Sa Highly Lower Congo θ− 1500kpa =0.0841− 0.00159 * Sa + 0.0021 * Cl + 0.0779 * BD weathered soils

No. number, PTFs pedotransfer functions, Sa sand, Si silt, Cl clay, BD bulk density, SOC soil organic carbon, nm information was not mentioned by the authors

values, the regression model for FC explained 43.2% of the universally applicable because many studies (e.g., total variation, while that for PWP explained 38.5% of the Motaghian and Mohammadi 2011; Yao et al. 2015)havealso total variation, thereby indicating the similar capacities of reported that the PTFs obtained using ANNs had the best the PTFs for predicting FC and PWP. In addition, SMLR prediction accuracy compared with those produced by MLR. could identify factors that were significantly correlated with FC and PWP.Clearly, sand was an important input variable for predicting FC, whereas clay and BD were important for 3.4 Comparison with existing PTFs predicting PWP. The results are similar to those obtained by Ghanbarian-Alavijeh and Millán (2010) who also reported Based on the validation data, we compared the performance of that sand was an important input variable for predicting FC, the new PTFs for the FC and PWP developed using the SMLR and clay and BD for predicting PWP. method, which obtained similar performance to ANN, with In addition, analysis of variance showed that all of the new that of existing PTFs (Table 5). Table 6 shows the values of PTFs were highly significant (P < 0.001), where most of the the evaluation indexes for the new and existing PTFs. Clearly, standardized residuals obtained from the regression were dis- tributed randomly between − 2 and + 2, and thus, there were Table 6 Performance of the existing and new PTFs using the validation no significant correlations between the standardized residuals data sets and the standardized predicted values. Thus, the new PTFs Variables PTFs R2 RMSE ME satisfied the assumption of linearity and no statistical bias, and then they could provide robust predictions of the FC FC This study 0.326 0.032 − 0.001 and PWP. Dijkerman (1988) 0.140 0.066 − 0.172 Adhikary et al. (2008) 0.333 0.047 0.033 3.3.2Artificialneuralnetwork Berg et al. (1997) Minasny and Hartemink (2011) 0.337 0.054 − 0.044 − Based on the significantly correlated variables obtained by Botula (2013) 0.326 0.035 0.012 − SMLR analysis (Table 3), the ANN approach was used to PWP This study 0.321 0.041 0.002 develop new PTFs for estimating FC and PWP. Table 4 shows Dijkerman (1988) 0.316 0.066 0.051 the performance of the PTFs established using ANN and Adhikary et al. (2008) 0.305 0.109 0.101 SMLR based on evaluation indexes comprising R2,RMSE, Berg et al. (1997) 0.310 0.050 − 0.027 and ME. Clearly, the accuracies of SMLR and ANN were Minasny and Hartemink (2011) 0.283 0.064 0.047 similar in terms of the estimates of FC and PWP, which is Botula (2013) 0.321 0.054 − 0.035 consistent with the results obtained in other studies (Merdun 2 PTFs pedotransfer functions, R coefficient of determination, RMSE root et al. 2006;Zhaoetal.2016) where the performance of the mean squared error, ME mean error, FC field capacity, PWP permanent two methods was similar. However, this conclusion is not wilting point J Soils Sediments

0.7 environmental issues, as well as in a wide range of agro-

0.6 hydrological models (Dijkerman 1988;Bergetal.1997; Adhikary et al. 2008; Minasny and Hartemink 2011;Botula )

-3 0.5 2013). cm 3 This study 0.4 Dijkerman Adhikary 0.3 4 Conclusions Minasny Botula Predicted FC (cm 0.2 In this study, we obtained FC and PWP data from three sites in

0.1 the Loess Plateau region, which ranged from the top of the soil layer to the bedrock, and these data were employed to develop 0 new PTFs using the SMLR and ANN techniques. The results 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 showed that FC and PWP exhibited moderate variation. 3 -3 Measured FC (cm cm ) Analysis based on Pearson’s correlation coefficients and SMLR showed that sand was an important input variable for This study predicting FC, whereas clay and BD were important for 0.35 Dijkerman predicting PWP. The performance of ANN was similar to that Adhikary 0.3 of SMLR but the many advantages of ANN (such as nonlin-

) Berg

-3 earity and modeling the relationships between large amounts Minasny cm 3 0.25 Botula of input and output data) mean that it is more suitable for widespread utilization in future studies. The new PTFs per- 0.2 formed better than existing PTFs. Thus, in this study, we developed new PTFs for estimating FC and PWP in the deep soil 0.15 profile in the Loess Plateau, which can be employed for eval-

Predicted PWP (cm 0.1 uating the soil water conditions in the deep profile as well as in related agro-hydrological models. 0.05 Acknowledgements The authors thank the editor and reviewers for their 0 valuable comments and suggestions. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 3 -3 Measured PWP (cm cm ) Funding information This study was supported by the National Natural Fig. 2 Measured and predicted values obtained for FC and PWP using Science Foundation of China for a major international cooperation pro- the new PTFs and other existing PTFs with the validation data sets. The gram between China and England (41571130081), the National Natural other existing PDFs comprised those developed by Dijkerman (1988), Science Foundation of China (41371242 and 41530854), and the Adhikary et al. (2008), Berg et al. (1997), Minasny and Hartemink National Key Research and Development Program of China (2011), and Botula (2013). The straight line in each plot is the 1:1 line (2016YFC0501706-03). the R2 and RMSE values for most of the existing PTFs were References similar to those obtained using the new PTFs developed in the present study. However, the ME values for the existing PTFs Adhikary PP, Chakraborty D, Kalra N, Sachdev CB, Patra AK (2008) ranged from − 0.072 to 0.033 and from − 0.035 to 0.101 for Pedotransfer functions for predicting the hydraulic properties of FC and PWP, respectively, whereas those in the present study Indian soils. Aust J Soil Res 46:476–484 were − 0.001 and − 0.002, thereby indicating that the bias of Aina PO, Periaswamy SP (1985) Estimating available water-holding ca- the new PTFs was low compared with that of the existing pacity of Western Nigerian soils from soil texture and bulk density, using core and sieved samples. Soil Sci 140:55–58 PTFs. Thus, the PTFs developed for specific regions may Balland V, Pollacco JAP, Arp PA (2008) Modeling soil hydraulic prop- not be suitable for others, and thus, it is important to develop erties for a wide range of soil conditions. Ecol Model 219:300–316 new PTFs for each region (Fig. 2). Berg M, Klamt E, van Reeuwijk LP, Sombroek WG (1997) Pedotransfer In summary, in this study, we developed new PTFs for functions for the estimation of moisture retention characteristics of – estimating FC and PWP in the deep soil profile on the Loess Ferralsols and related soils. Geoderma 78:161 180 Botula YD (2013) Indirect methods to predict hydrophysical properties of Plateau, China. These new PTFs save time and labor, and they soils of Lower Congo. Ghent University, Gent, p 236 can be employed to address many soil water management Bouma J (1989) Using soil survey data for quantitative land evaluation. problems related to agricultural, hydrological, and Springer US J Soils Sediments

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