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DIVISION S-6 - SOIL & WATER MANAGEMENT & CONSERVATION

Quantitative Mapping of Soil Drainage Classes Using Topographical Data and Soil Electrical Conductivity

Dep. of Crop Sciences, 1102 S. Goodwin Ave., Univ. of Illinois, Urbana, IL 61801

* Corresponding author (dbullock{at}uiuc.edu)

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
In this study we applied discriminant analysis and geostatistics to create soil drainage maps using topographical and soil electrical conductivity (EC) data as auxiliary information. Drainage classes were determined on 107 soil cores collected from a 20-ha field in Illinois. Approximately 1500 elevation points and 6500 EC points were collected from the field. Slope, curvature, and flow accumulation were derived. Discriminant analysis and logistic discrimination were applied to study the effect of topography and EC on drainage. Spatial variability of the soil drainage data and its relationship with variability of topography and EC were analyzed using variograms and cross-variograms. Indicator kriging and soft indicator cokriging were used. Soil EC, terrain slope, and distance to a drainageway were selected by a stepwise discriminant procedure as significant predictors of the soil drainage class (P = 0.15). When these variables were used as additional information in predicting soil drainage class using either discriminant analysis or cokriging procedures, they slightly improved overall prediction accuracy, compared with the soil survey map (scale 1:15840) and indicator kriging. Discriminant analysis and cokriging correctly estimated drainage classes for more than 90% of the sites, compared with 85 and 63% correct estimates obtained from indicator kriging and soil survey data, respectively. Indicator kriging and cokriging are exact estimators and thus are more suitable for mapping than is discriminant analysis. We recommend the use of stepwise discriminant procedure to select among the available secondary variables and to create drainage maps using cokriging with the selected variables.

Abbreviations: EC, electrical conductivity • GPS, global positioning system • MWD, moderately well-drained soil • PD, poorly drained soil • SWPD, somewhat poorly drained soil

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
SOIL DRAINAGE is an important soil property affecting plant growth, water flow, and solute transport in soils. Accurate and inexpensive prediction and mapping of soil drainage classes is beneficial for both agricultural and environmental management. Various sources of secondary information, such as topography or easily measured soil properties, can be used to facilitate drainage mapping and prediction, however, efficient quantitative methods are needed to utilize this information. Quantitative approach to predicting soil drainage classes using discriminant analysis has been proposed by Bell et al. (1992) and has been found to be an effective method for soil drainage mapping (Bell et al., 1994). The main idea of the discriminant analysis is that, based on the measured data for a certain categorical variable of interest (i.e., drainage) and for several auxiliary variables, a quantitative decision rule is formulated. Every new observation where auxiliary variable data are available but the variable of interest has not been measured can be placed into the appropriate class of the variable of interest based on the previously determined decision rule. The necessity of finding the general decision rule pertains to the fact that each one of the studied auxiliary variables by itself may not adequately describe the variable of interest. However, the decision rule is calculated based on the measured data from all the auxiliary variables, hence it can have better predictive powers than any single variable.

One of the disadvantages of discriminant analysis approach to mapping soil properties is that the information on the data locations is not used in development of the predictive model. However, this information can be utilized by geostatistical methods of data analysis to determine spatial structure in data distribution across the studied area. Combining the predictive capabilities of discriminant analysis with those of geostatistics may provide more accurate tools for soil drainage mapping and prediction.

Bell et al. (1992)(1994) incorporated a variety of topographical and parent material data to map drainage classes of a relatively large study area (about 14000 ha) in south-central Pennsylvania with diverse lithology and topography. They found that soil parent material was one of the most important factors in predicting soil drainage classes. The factors that were found to be significant in discriminating between the drainage classes within the same parent material included slope gradient, elevation above local stream, and distances to local streams and drainageways. Soil drainage mapping and prediction for relatively small areas (e.g., agricultural fields or small watersheds) are also of great importance, particularly for farming and site-specific field management. However, the factors affecting soil drainage classes within farm fields might be different from the factors acting at regional scales. Parent materials within a single field are not usually as diverse as in a relatively large area, but at the same time they might be highly spatially variable. Unfortunately, very often it is not possible to define parent material of a particular site with a considerable degree of accuracy without taking soil cores—a task that is time-consuming and expensive. Therefore, it would be beneficial to determine other easily measured factors for quantitative prediction of soil drainage on small scales and to estimate potential accuracy of such prediction.

Topography is one of the easily evaluated drainage affecting factors that can greatly facilitate mapping and prediction of soil drainage (Bell et al., 1992; Bell et al., 1994; Troeh, 1964). Another easily measured factor potentially related to drainage is soil EC. It can be measured in the field using recently developed fast and nondestructive methods (Doolittle et al., 1994; Kitchen et al., 1996; Kitchen et al., 1999). Electrical conductivity depends on a number of soil physical properties, including soil salinity, and soil water and clay content (Rhoades et al., 1989). Sheets and Hendrickx (1995) found a linear relationship between the EC measurements and soil profile water content. Williams and Hoey (1987) observed positive correlations between conductivity and soil clay content. Since both soil profile water contents and clay contents are related to the soil drainage properties, it would be reasonable to expect that EC data might be helpful in mapping and predicting soil drainage classes on a field scale basis.

The objectives of this study are (i) to examine the relationships between soil drainage and various topographical factors and to determine the factors that can be most helpful in predicting soil drainage classes on a field scale using the results from a farm field in Central Illinois, (ii) to study possibility of using soil EC data with or without topographical information for predicting soil drainage classes, (iii) to compare accuracy and efficiency of multivariate statistical methods with geostatistical procedures in mapping soil drainage classes on an agricultural field scale.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Soil and Topographical Data
The studied area was a square 20-ha central portion of a 259-ha agricultural field located in central Illinois, USA. The field has been in a corn (Zea mays L.)–soybean [Glycine max (L.)] rotation for at least 20 yr. Soil sampling was conducted on a semiregular grid and sample locations are shown in Fig. 1 . A total of 107 soil cores, 100 cm deep, was collected from the studied area.

View larger version (101K): [in this window] [in a new window]   Fig. 1. Locations of the core samples used to determine soil drainage classes along with the soil survey map and elevation map of the studied area. Each sampling site is labeled according to its drainage class.

Soil survey map (Soil Survey of McLean County, Illinois, 1998) characterizes the soils of the field as the Varna series (fine, illitic, mesic Typic Argiudoll), the Drummer series (fine-silty, mixed, mesic Typic Endoaquoll), and the Chenoa series (fine, illitic, mesic Aquic Argiudoll). According to the soil classification of Illinois, soils of Varna series are described as WD and MWD, Drummer soils are described as PD, and Chenoa soils as SWPD (Fehrenbacher et al., 1984). Since soils of Varna series can belong to both WD and MWD drainage classes, for comparing accuracy in predicting drainage classes of soil survey map with that of quantitative methods we used only two drainage classes: WD/MWD (combined WD and MWD sites) and SWPD/PD. Soil series map unites of the studied area are shown in Fig. 1.

Soil EC data were collected in Fall of 1999 using a Veris 3100 sensor cart (Division of Geoprobe Systems, Salina, KS) which operates on the principle of electromagnetic induction. Georeferenced EC measurements were taken every 3 to 5 m with the distance between cart passes about 10 m, resulting in about 6900 EC measurement points for the studied area. Two sets of EC measurements were collected corresponding to depths of 0 to 30 cm (shallow EC) and 0 to 90 cm (deep EC) (Sudduth et al., 1998).

Survey grid global positioning system (GPS) (Leica 500 RTK, Leica, Heerbrugg, Switzerland) was used to measure elevation. About 1500 elevation measurements were taken on a semiregular grid with a mean distance between measurements of 10 m. The map of field elevation ranges is shown in Fig. 1. ArcView Spatial Analyst (Environmental Systems Research Institute, 1996) was used to analyze elevation data and to derive the topographical land features, namely, slope, curvature, and flow accumulation. Detailed description of the derivation procedures is presented by Kravchenko and Bullock (2000). ArcView was also used to calculate the shortest distance from each of the sampling sites to the drainageway that ran in the middle of the studied area (Fig. 1). This distance was used as one of the variables with potential effect on soil drainage class.

Discriminant Analysis
In this study discriminant analysis was used to find a decision rule for separating sites with different drainage classes based on the values of the topographical and EC variables measured at each site. The decision rule was defined based on the measured data, then, the decision rule was applied to predict drainage classes at unsampled sites where measurements of topographical and EC variables were available.

Two combinations of the variables were considered. The first combination included topographical variables, such as elevation, slope, curvature, flow accumulation, and distance to a drainageway. The purpose of this combination was to assist with the first objective of the study, i.e., to determine how accurate drainage class prediction can be if it is based on topographical data only. The second combination of the variables was related to the second objective of the study, namely, analysis of the possibility of using soil EC data with or without topographical information for predicting soil drainage classes. All the topographical and EC variables measured at each site were used in the second combination.

Since meaningful application of discriminant analysis requires data to be normally distributed, we analyzed the normality of all studied variables. Nonnormally distributed variables were transformed using either log-normal or normal score (Goovaerts, 1997) transformations. Stepwise discriminant procedure, STEPDISC (SAS Institute, Cary, NC) was applied to both combinations of variables to select the variables that had significant influence on the soil drainage class. Only variables significant at 0.15 significance level (Bell et al., 1992) were used in further discriminant analysis.

Based on the values of soil and topographical variables combined in a variable measurement vector x, for each data location discriminant analysis calculated the probability that this location belonged to each of the drainage classes, t, called posterior probability, . Then, the location was classified into the drainage class for which it had the highest posterior probability (Khattree and Naik, 2000):

[1]

The highest posterior probability is equivalent to the smallest squared distance from the studied location to the drainage class t, D²_t(x), where k is the total number of drainage classes. Subscript t in Eq. [1] refers to a certain drainage class for which the posterior probability is calculated, subscript j indicates that information on all drainage classes is used to calculate posterior probability for each particular drainage class t. Depending on the variance-covariance matrix of the studied combination of variables, the squared distance is calculated as either a linear discriminant function:

[2]

if all the drainage classes have the same variance-covariance matrixes, or as a quadratic discriminant function if the variance-covariance matrixes are not equal:

[3]

where µ_t is the mean measurement vector of drainage class t, is the common variance-covariance matrix in Eq. [2], S_t is the variance-covariance matrix of drainage class t in Eq. [3], and _t is the prior probability of occurrence of drainage class t in the studied area. In this study, we calculated prior probabilities based on the measured proportions of drainage classes. Based on the number of locations belonging to a certain drainage class, prior probabilities were equal to 0.499, 0.355, and 0.196 for WD, MWD, and SWPD/PD classes, respectively. Discriminant analysis was conducted using DISCRIM procedure (SAS Institute, Cary, NC).

Cross-validation was applied to evaluate accuracy of drainage data prediction and classification by discriminant analysis. For cross-validation each value from the data set was eliminated in turn and, then, estimated using information from the rest of the data (Khattree and Naik, 2000). Posterior probabilities of the three drainage classes obtained for each data point were compared, and the site was assigned a drainage class with the highest posterior probability. Percentage of the correct drainage class estimates was used to compare different combinations of variables with their effectiveness in predicting drainage classes.

Effect of the EC and topographical variables in discrimination between the drainage classes was also studied using logistic discrimination (Khattree and Naik, 2000). Unlike discriminant analysis, logistic discrimination is not sensitive to the departures from normality, hence, no data transformation is necessary. We conducted logistic discrimination for each drainage class separately, using constructed indicator variable i() and, then, compared the results from all drainage classes. For each of the three drainage classes, drainage class at each data location was coded using the indicator approach as (Goovaerts, 1997; Khattree and Naik, 2000):

[4]

resulting in three new indicator variables. For each drainage class, a new indicator variable was represented by sets of 1's corresponding to the locations belonging to this drainage class and sets of 0's corresponding to all other locations belonging to the other drainage classes.

Value of the indicator variable is equivalent to a probability, , of finding a certain drainage class at a certain location. For a location with known drainage such probability equals to either 1 (site belongs to a certain drainage class) or to 0 (site belongs to any of the other drainage classes). The response variable in the logistic determination is expressed in terms of probability, , as a logit function, logit():

[5]

The response variable is assumed to be a linear function of a number of explanatory variables (x₁...x_n):

[6]

where the input of each explanatory variable x_i to the logit() is determined by the unknown parameter ß_i. In this study, EC and topography variables are used as the explanatory variables and the relationship expressed in Eq. [6] produces a model for discriminating between the drainage classes. Similarly to the discriminant analysis, the posterior probabilities of the three drainage classes obtained using logistic discrimination were compared and the location was assigned a drainage class with the highest posterior probability value. Stepwise variable selection was used to determine the variables with the highest discriminative effect (P = 0.15) on drainage classes using logistic discrimination procedure, LOGISTIC (SAS Institute, Cary, NC).

Geostatistical Analysis
For geostatistical analysis, soil drainage class, t, was treated as a categorical variable. At each data location this variable assumed only one of the three mutually exclusive possible states, corresponding to either WD, MWD, or SWPD/PD drainage classes. Indicator transformation was applied to each drainage class data (Eq. [4]) resulting in three indicator variables.

Spatial variability of each indicator variable was described by a sample variogram:

[7]

where and + h were sampling locations separated by a distance h, i() and i( + h) were values of the indicator variable i at the corresponding locations. The sample variograms were fitted with variogram models and accuracy of the fitting was examined using cross-validation approach (Goovaerts, 1997). The model that produced the cross-validation results of the highest accuracy was retained for further analysis (Kravchenko and Bullock, 1999). Variable values at unsampled locations were obtained using kriging procedure based on the variogram model parameters. For an unsampled location, the kriging estimate was equivalent to the probability of finding certain drainage class at this location. Such probability assumes a value between 0 and 1 depending on the variable values from the surrounding sampled locations and on the spatial structure of variable distribution reflected in the variogram model parameters. For each drainage class kriging produces a map of probabilities of finding this drainage class at each particular location. The cross-validation results for kriging and cokriging procedures were further used to compare their accuracy with accuracy of discriminant analysis.

Two geostatistical procedures compared in the study included ordinary indicator kriging and soft indicator cokriging (Goovaerts, 1997). Indicator kriging uses the drainage data only, hence, the estimates of the indicator variables depend only on spatial structure of the available drainage data. Cokriging allows us to combine primary drainage data with any available secondary data that are related to drainage, hence, drainage estimates depend not only on distribution of the primary data, but also on spatial variability of the secondary data and their relationship with the primary variable. In case of a strong correlation between primary and secondary variables and when abundantly sampled secondary data complement sparse primary data sampling, cokriging can produce much more accurate and reliable estimation results comparing with ordinary kriging technique. One of the disadvantages of cokriging is that it becomes extremely cumbersome and time-consuming with large number of secondary variables. In this study, we conducted cokriging with one primary variable (indicator transformed drainage class) and two secondary variables. The variables that were found to have a significant effect on drainage class during stepwise discrimination procedure were further used in cokriging estimation. Variograms and crossvariograms for cokriging were modeled using linear model of coregionalization (Deutsch and Journel, 1998). Geostatistical analysis including variogram calculation, cross-validation, kriging, and cokriging was performed using the geostatistical software package GSLIB (Deutsch and Journel, 1998).

	RESULTS AND DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 
Spatial aspects of the relationships between drainage classes and topographical and EC variables were examined using variograms and cross-variograms. Sample variograms of the three studied drainage classes along with their respective variogram models are shown in Fig. 2 . Examples of sample cross-variograms between drainage classes and slope are shown in Fig. 3 . Different spatial relationships existed between slope and WD/MWD classes, and slope and SWPD/PD class (Fig. 3). Cross-variogram values for WD and MWD classes increased with distance indicating positive relationships between these drainage classes and slope. For SWPS/PD class, cross-variogram value decreased with distance, indicating negative correlation between this class and the slope. Indeed, the higher the slope, the lower was the probability of occurrence of SWPD/PD soils, while probability of occurrence of WD and MWD soils increased with increasing slope values. Similar results were observed for cross-variograms of drainage classes vs. distance to drainageway. For WD and MWD classes, cross-variograms increased with distance and for SWPD/PD class, they decreased with distance reflecting respective positive and negative relationships between the probabilities of finding WD/MWD and SWPD/PD soils and the distance to the drainageway. Cross-variograms of drainage class vs. deep EC values decreased with distance for WD and MWD drainage classes and increased with distance for SWPD/PD soils, indicating that there were negative relationships between WD and MWD drainage classes and soil EC values and positive relationship between the SWPD/PD class and soil EC.

View larger version (18K): [in this window] [in a new window]   Fig. 2. Sample variograms and variogram models of indicator transformed data for (a) well-drained sites, (b) moderately well-drained sites, and (c) somewhat poorly and poorly drained sites.

View larger version (23K): [in this window] [in a new window]   Fig. 3. Sample cross-variograms and cross-variogram models for terrain slope and indicator transformed data for (a) well-drained sites, (b) moderately well-drained sites, and (c) somewhat poorly and poorly drained sites.

Distance to drainageway seemed to be a useful parameter in discriminating between WD and MWD drainage classes in this field. Sites with MWD soils were located generally closer to the drainageway than the sites with WD soils (Table 1). Slopes and deep EC values of WD and MWD soils were similar, hence, these parameters were not effective in separating WD and MWD drainage classes using stepwise discriminant procedure.

Further insight in the factors discriminating between soil drainage classes was achieved using logistic discrimination (Table 2). Since logistic regression procedure was conducted separately for each drainage class, a set of variables was obtained for each drainage class that were significant in discriminating between this class and all the other drainage classes. When only topographical variables were included in the logistic discrimination procedure, the variables significant in discriminating between WD soils and other drainage classes were distance to drainageway and flow accumulation. Well drained soils were characterized by lower flow accumulation values than either MWD or SWPD/PD soils. For SWPD/PD soils significant discriminating variables were distance to drainageway and slope. When shallow, and deep EC measurements were added to the topographical data, then, distance to drainageway, flow accumulation and deep EC were found to be significant for separating WD soils. For SWPD/PD sites, both shallow and deep EC measurements along with the distance to drainageway were significant for separating this drainage class from WD and MWD soils. Slope was a significant variable for discriminating between MWD soils and all the other drainage classes, however, the effectiveness of the logistic discrimination was extremely low for MWD soils (Table 2).

The most substantial difference between discriminant analysis and kriging and cokriging for creating maps pertains to the fact that kriging and cokriging procedures are exact estimators, while discriminant analysis is not. Hence, each of the sampled locations is estimated correctly in the maps produced by kriging and cokriging, while the values in between the sampled locations are estimated using kriging and cokriging procedure. The maps created based on the discriminant analysis ignore the actual data in sampled locations. Hence, indicator kriging and cokriging are preferable comparing with discriminant analysis for creating maps. Drainage maps obtained from (i) discriminant analysis with deep EC and distance to drainageway, (ii) indicator kriging, and (iii) indicator cokriging with slope and distance to drainageway are shown on Fig. 4a, 4b, and 4c , respectively. The data for the maps were obtained on a 11 by 11 m grid. The grid size was selected in correspondence with sampling density of the secondary data, i.e., elevation and EC.

View larger version (96K): [in this window] [in a new window]   Fig. 4. Drainage maps obtained from (a) discriminant analysis with deep electrical conductivity and distance to drainageway, (b) indicator kriging based on the measured drainage classes, and (c) indicator cokriging based on the measured drainage classes with slope and distance to drainageway as secondary variable.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

Accuracy of discriminant analysis (62–64% correct) in predicting soil drainage class was comparable with that of cokriging (58–65% correct), whereas kriging, which does not use secondary information in data estimation, was less accurate (56% correct). However, since kriging and cokriging are exact estimators, they are more effective for mapping purposes than discriminant analysis. Since cokriging procedure becomes time-consuming and cumbersome with a large number of secondary variables, the most advantageous seems to be an approach to drainage class mapping that would combine both discriminant analysis and geostatistics. In this approach the data are, first, analyzed with discriminant analysis and the limited number of variables with significant effect on the drainage classes is selected among all the available topographical and soil variables. Then, the significant variables are used in the cokriging for most accurate mapping of soil drainage classes.

Received for publication March 1, 2001.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION CONCLUSIONS REFERENCES

 

• Bell, J.C., R.L. Cunningham, and M.W. Havens. 1992. Calibration and validation of a soil-landscape model for predicting soil drainage class. Soil Sci. Soc. Am. J. 56:1860–1866.[Abstract/Free Full Text]
• Bell, J.C., R.L. Cunningham, and M.W. Havens. 1994. Soil drainage probability mapping using a soil-landscape model. Soil Sci. Soc. Am. J. 58:464–470.[Abstract/Free Full Text]
• Deutsch, C.V., and A.G. Journel. 1998. Geostatistical software library and user's guide. Oxford University Press, New York.
• Doolittle, J.A., K.A. Sudduth, N.R. Kitchen, and S.J. Indorante. 1994. Estimating depths to claypans using electromagnetic induction methods. J. Soil Water Conserv. 49:572–575.
• Environmental Systems Research Institute (ESRI). 1996. ArcView Spatial Analyst. ESRI, Redlands, CA.
• Fehrenbacher, J.B., J.D. Alexander, I.J. Jansen, R.G. Darmody, R.A. Pope, M.A. Flock, E.E. Voss, J.W. Scott, W.F. Andrews, and L.J. Bushue. 1984. Soils of Illinois. Bull. 778. Illinois Agric. Exp. Stan., Univ. of Illinois, Urbana-Champaign, IL.
• Goovaerts, P. 1997. Geostatistics for natural resources evaluation. Oxford University Press, New York.
• Khattree, R., and D.N. Naik. 2000. Multivariate data reduction and discrimination with SAS software. SAS Institute, Cary, NC.
• Kitchen, N.R., K.A. Sudduth, and S.T. Drummond. 1996. Mapping of sand deposition from 1993 midwest floods with electromagnetic induction measurements. J. Soil Water Conserv. 51:336–340.
• Kitchen, N.R., K.A. Sudduth, and S.T. Drummond. 1999. Soil electrical conductivity as a crop productivity measure for claypan soils. J. Prod. Agric. 12:607–617.
• Kravchenko, A., and D.G. Bullock. 1999. A comparative study of interpolation methods for mapping soil properties. Agron. J. 91: 393–400.[Abstract/Free Full Text]
• Kravchenko, A.N., and D.G. Bullock. 2000. Correlation of grain yield with topography and soil properties. Agron. J. 92:75–83.[Abstract/Free Full Text]
• Rhoades, J.D., N.A. Manteghi, P.J. Shouse, and W.J. Alves. 1989. Soil electrical conductivity and soil salinity: New formulations and calibrations. Soil Sci. Soc. Am. J. 53:433–439.[Abstract/Free Full Text]
• Sheets, K.R., and J.M.H. Hendrickx. 1995. Noninvasive soil water content measurement using electromagnetic induction. Water Resours. Res. 31:2401–2409.
• Soil Survey Division Staff. 1993. Soil survey manual. USDA–SCS Agric. Handb. 18. U.S. Gov. Print. Office, Washington, DC.
• Soil Survey of McLean County, Illinois. 1998. Illinois Agric. Exp. Stn. Rep. 159. Univ. of Illinois, Ubana-Champaign.
• Sudduth, K.A., N.R. Kitchen, and S.T. Drummond. 1998. Soil conductivity sensing on claypan soils: Comparison of electromagnetic induction and direct methods. p. 979–990. In P.C. Robert et al. (ed.) Proc. 4th Int. Conf. on Precision Ag. 19–22 July 1998, St. Paul, MN. Part B. ASA, CSSA, and SSSA, Madision, WI.
• Troeh, F.R. 1964. Landform parameters correlated to soil drainage. Soil Sci. Soc. Am. J. 28:808–812.[Abstract/Free Full Text]
• Williams, B.G., and D. Hoey. 1987. The use of electromagnetic induction to detect the spatial variability of the salt and clay contents of soils. Aust. J. Soil Res. 25:21–27.

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This Article Abstract Figures Only Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (18) Citing Articles via Google Scholar Google Scholar Articles by Kravchenko, A. N. Articles by Bullock, D. G. Search for Related Content PubMed Articles by Kravchenko, A. N. Articles by Bullock, D. G. GeoRef GeoRef Citation Agricola Articles by Kravchenko, A. N. Articles by Bullock, D. G. Related Collections Water Management Watershed and Landscape Processes Spatial Distribution