Delineation of Site-Specific Management Zones Based on Temporal and Spatial Variability of Soil Electrical Conductivity1

Pedosphere 17(2). 156-164, 2007 ISSN 1002-0160/CN 32-1315/P @ 2007 Soil Science Society of China Published by Elsevier Limited and Science Press

PEDOSPHERE www elsevier comAocate/pedosphere

Delineation of Site-Specific Management Zones Based on Temporal and Spatial Variability of Soil Electrical Conductivity*' LI Yan'.'. SHI Zhou1.*2 and LI Feng3 'Institute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangthou 31 0029 (Chzna). E-mad: [email protected] Institute of Land Science and Property Management, College of Southeast Land Management, Zhejiang University, Hangzhou 310029 (Chzna) Department of Envzronmental Engineerzng, Zhejiang University, Hangzhou 310029 (Chzna) (Received June 21. 2006; revised November 18, 2006)

ABSTRACT X coastal saline field of 10.5 ha was selected as the study site and 122 bulk electrical conductivity (ECb) measurements were performed thrice i n sztu in the topsoil (0-20 cm) across the field using a hand held device to assess the spatial variability and temporal stability of the distribution of soil electrical conductivity (EC), t o identify the management zones using cluster analysis based on the spatiotemporal variability of soil EC, and to evaluate the probable potential for sitespecific management in coastal regions with conventional statistics and geostatistical techniques. The results indicated high coefficients of variation for topsoil salinity over all the three samplings. The spatial structure of the salinity variability remained relatively stable with time. Kriged contour maps, drawn on the basis of spatial variance structure of the data, showed the spatial trend of the salinity distribution and revealed areas of consistently high or consistently low salinity, while a temporal stability map indicated stable and unstable regions. On the basis of the spatiotemporal characteristics, cluster analysis divided the site into three potential management zones, each with different characteristics that could have an impact on the way the field was managed. On the basis of the clearly defined management zones it was concluded that coastal saline land could be riiariaged in a site-specific way.

Key U.ords:

coastal saline field, management zone. soil electrical conductivity, spatial variability, temporal variability

C i t a t i o n : Li. Y..Shi. Z. and Li. F. 2007. Delineation of site-specific management zones based on temporal and spatial variability of soil electrical conductivity. Pedosphere. 17(2): 156-164.

ISTRODUCTION Undcr a series of reclamation projects over the past 30 years, many coastal tideland areas of China have been successively enclosed and reclaimed for agricultural land uses (Li et al., 2004; Liu et al., 2005; Zhao e t al.. 2004). However. owing to the differences in soil parent materials, reclamation measures and farming practices. soil physical and chemical properties, and especially soil salinity, have been highly variable (Shi et al.. 2G03). Bccause of the effects of salt stress on seedling emergence, the variation in soil salinity has often givcn rise to variability in crops. Since they exhibit different spatial and temporal 1 J v h . i u r s . h t l i temporal and sp:.\!.ii.d~ ' o ~ I Iiiii:ii1:. ~ ~ I of salinit,y variability need t o kw considcrcd t o n r h i ~ v c ~ the ultimate goal of sustainable cropping systems. The definition and use of classified management. zones, utilizing the spatial management tools of precision agriculture. have been proposed as a cost-effective approach t o improve crop management (Iihosla et al.. 2002: Franzen et al., 2002). There are nunierous methods for defining such management "Project supported by the National Natural Science Foundation of China (Nos. 40001008 and 40571066), the German Federal 11inistry of Education and Research (BMBF) (No. A239742), and the Postdoctoral Science Foundation o China ( S o .20060101048). "Corresponding author. E-mail: shizhouQzju.edu.cn.

DELINEATION O F SITE-SPECIFIC M A N A G E M E N T ZONES

157

zones, but most methods rely on spatial information that is stable or predictable over time and is related to crop yield (Doerge, 1999). Since the variability in soil electrical conductivity (EC) reflects the cumulative variability in multiple soil properties, it is one of the criterions for defining management zones (Sudduth et al., 1995). For some soils, EC mapping appears to integrate soil parameters related to productivity to produce a template of potential yield (Kitchen et al., 1999). Johnson et al. (2001) found that management zones on the basis of EC mapping provided a useful framework for soil sampling to reflect spatial heterogeneity and could potentially be applied to assess temporal impacts of management on soil conditions. Ferguson et al. (2003) compared management zones based on slope and surface soil texture with those on soil EC and concluded that the management zones based on easily obtained soil EC measurements were preferable and had the potential for use in the site-specific management of nitrification inhibitors. Other published research examined the relationships between EC-based management zones and crop yields in a 4-year crop rotation and evaluated the significance and potential application of these management zones for site-specific management in a semiarid cropping system (Johnson et al., 2003). Meanwhile, in China, classified management sub-zones based on spatial variability of soil nutrients have been developed to aid site-specific management of fertilizers (Bai et al., 2001; Huang et al., 2003). Very few relevant studies, however, reported the use of both spatial and temporal components of soil EC variability in the construction of management sub-zones. Thus, the objectives of this study in a coastal saline field were: 1) to characterize the spatial distribution and temporal variability of soil EC, 2) to identify the management zones using cluster analysis on the basis of the spatiotemporal variability of soil EC, and 3) to evaluate the probable potential for site-specific management in coastal regions. MATERIALS AND METHODS

Site description, sampling, and measurements The study was conducted on a cotton field of 10.5 ha in a coastal saline region, which is located in the northern region of Shangyu City, Zhejiang Province (30" 04' 00"-30" 13' 47" N, 120" 38' 32"-120" 51' 53" E) and covers an area of 26 061 ha. The region is subtropical with evergreen broadleaf vegetation, an average annual temperature of 16.5 "C, and an average annual precipitation of 1300 mm. Modern marine and fluvial deposits form the dominant soils having light loam or sandy loam soil textures with a sand content of 592 g kg-I and high concentrations of Na- and Mg-salts (> 1%). Over the past 30 years, many coastal tideland areas have been successively enclosed and reclaimed for agricultural land uses under a series of reclamation projects. The field used in the present study was reclaimed in 1996 (Fig. 1).

Fig. 1 Location of the study site in Shangyu City, Zhejiang Province, China with the spatial distribution pattern of soil sampling points (dots) and four groundwater sampling points, a, b, c, and d (triangles).

158

Y.LI

et

al.

A grid-sampling scheme (20-80 m sampling space) was imposed on thedfield with 122 composite bulk electrical conductivity (ECb) measurements for the soil profile (0-20 cm) using a portable W E T sensor. One representative sample was collected at each grid point on each of the following three dates: Jan. 2, Apr. 29, and Sep. 5 in 2004. Each ECb measurement was geo-referenced using a trimble global positioning system (GPS) (with differential correction). The GPS receiver accuracy was within 2 m of horizontal accuracy. At each sampling grid point, the WET sensor probes were inserted into the soil and five soil EC measurements were made within a 1 m diameter circle. The average for each grid point was computed as an ECb datum point. When performing ECb measurements in the open, some soil samples were collected and taken back to the laboratory for analyzing their chemical properties with conventional methods. During the harvest period in 2004, 122 cotton yield samples were also collected at each grid-point location. Five cotton plants at each grid point were harvested and the average seed cotton yield computed. Meanwhile, to help understand the variability of surface salinity, four groundwater samples across the field were collected and their mineral degrees were measured. Analysis

A correlation analysis was conducted to determine the relationship between cotton yield and soil organic matter (OM), ECb, total N (TN), available nitrogen (AN), available P (AP), and available K (AK) on Sep. 5, 2004. Then, a stepwise multiple regression analysis was performed t o obtain the optimum model for describing the effects of these soil properties on cotton yield. Following this, distributions of soil ECb measurements were tested for normality using the Kolmogorov-Smirnov statistic. To characterize the spatial distribution and temporal variability of soil EC in a coastal saline field, semivariance analyses were carried out on all the datasets using a geostatistical software package (GS+). Isotropic spherical models were also fitted to the experimental semivariograms. The fitted models were then used in an ordinary kriging procedure to estimate the value of soil ECb at unknown positions. Smoothed contour maps of soil ECb for each of the three different sampling dates were constructed for a spatial trend map. Next, five classes of soil salinity were developed from the mean ECb at each sampling point over the three sampling dates and mapped. Then, t o complete a temporal stability map, four arbitrary classes were chosen at 10% intervals from coefficients of variation ( C V ; ) determined over time at each of the sampling point using interpolated values with:

where CV; is the coefficient of variation over time a t the ith sampling point; ECbt, is the measured value of soil bulk electrical conductivity at the ith sampling point in the t t h time; and n is the number of field samplings. One promising statistical approach for identifying management zones on the basis of a number of different sources is called cluster analysis. This can be used t o identify areas that have similar soil and plant parameters, t o quantify patterns of variability, and t o reduce the empirical nature of defined management zones. Using the minimum spectral distance formula to assign a cluster for each candidate pixel the ISODATA clustering algorithm was employed to group clusters of statistically similar data. The algorithm began with either a specified number of arbitrary cluster means or means of a n existing signature set. Each time the clustering repeated, the means of these clusters shifted, and new cluster means were used for the next iteration. Clustering of the image was repeated until a maximum number of iterations had

159

DELISEATION OF SITE-SPECIFIC bIAN.4GEblENT ZONES

been performed. Through this process, maps with pixels aggregated into naturally occurring clusters, high internal homogeneity and high external or between-group heterogeneity wrre developed. To identify the management zones, first the spatial trend map and temporal stability map were transformed into raster format at a 10-m grid cell resolution and combined into a single layer. Then, using cluster analysis on the basis of the spatioternporal variability of soil EC the combined data layer WYLS used to group clusters of statistically similar data into three classes as practical management zones using an unsupervised computer classification performed by an iterative self-organizing data analysis (ISODATA) clustering algorithm. Finally, t o evaluate the probable potential for site-specific management of soil salinity in coastal regions a pseudo-F test was performed on the calculated mean ECb, G V , and measured cotton yield data in the different classes.

RESULTS AND DISCUSSIOS

Classical statistical arbalysis Table I lists the correlation coefficients between cotton yield and some selected soil chemical properties on Sep. 5, 2004. For this study site, cotton yield was significantly correlated to ECb (P < 0.01), organic matter (P < 0.01), total IX ( P < 0.05), available N (P < 0.05) and available P (P < 0.05), which indicated that these five components were the main limiting factors for cotton growth. Also ECt, was negatively correlated with cotton yield, whereas soil organic matter, total X, available N, and available P. properties indicative of yield potential, were positively correlated. Table I also revealed negative correlations between EC,, and most soil nutrient properties. T-4BLE I Correlation matrix for selected soil chemical properties and cotton yield in the study area ( n = 67) Item

Cotton yield

ECb Organic matter Total N Available N Available P Available K

Cot tori yield

1 -0.595** 0.431** O.Y35*

0.255* 0.271* 0.118

ECba)

1 -0.213 -0.117 -0.132 -0.254* 0.385*

Organic matter

1 0.524** 0.508** 0.255* 0.194

Tolal A’

1 0.641** 0.365** 0.268*

Available

N

P

1 0.273* 0.201

1 0.153

K

=’Bulk electrical coriductivitj-. *.**Significant at P = 0.05 arid P = 0.01 levels, respectively.

To obtain the optimum model for describing the effects of soil properties on the yield with the cotton yield as the dependent variable and the five main limiting factors: OM, ECb, TN, AN, and AP as independent variables. a stepwise multiple regression analysis was performed. Since no a priori information was available about the regression model, the presence of a linear combination of the variables was assumed. The most appropriate model obtained with significance levels of P = 0.05 was:

k’ = 255.67 - 2.52(EC’\,)+ 1

i 7 ( 021)

+ 1 1 S(TN) + 0 8S.?(AP)+ V,87(AY)

( r 2 z 0 40)

where 2.’ is cotton yield. ECb is soil bulk electrical conductivity. OM is the organic matter content; AP is the content of available P; and -4N is the content of available N. The r 2 of 0.40 indicated that the model using only the five soil chemical factors explained 40% of the total variance of the measured yield The regression coefficients in the equation generallv indicated the magnitude of each factor in its contribution to cotton yield performance. In Eq. 2. ZCb had a negative contribution to yield and the largest magnitude. Thus. the increaje of soil salinitv decreased the cotton yield t o a large extent, which allowed for the identification of ECh-delineated management zones on the basis of clusters of ECb

160

Y . LI e t al.

and suggested that soil ECb-delineated management zones could be a reliable indicator of cotton yield potential and a useful basis t o evaluate the probable potential for site-specific management in the saline region. The descriptive statistics for ECb in the topsoil (0-20 cm) at the three sampling dates are summarized in Table 11. TABLE I1 Descriptive statistics of soil bulk electrical conductivity in the topsoil (0-20 cm) at different sampling dates Sampling date

Distribution type

Mean

Jan. 2, 2004 Apr. 25, 2004 Sep. 9, 2004

Normal Normal Normal

135.12ba) 144.46b 137.41b

Standard deviation mS m-l 101.70 80.14 88.81

CVb)

Range

%

mS m-l

75 55 65

16.8-380.8 26.0-328.0 15.0-368.0

Skewness

Kurtosis

0.80 0.42 0.37

-0.61 -0.81 -0.81

")Means followed by the same letter are not significantly different a t the 5% probability level. b)Coefficient of variation.

The mean values of soil salinity between the three sampling dates did not differ significantly ( P > 0.05). In common with the other reports, coefficients of variations for EC measurements were fairly high (Cetin and Kirda, 2003; Warrick and Nielsen, 1980). This could be attributed to uneven crop growth and non-uniform management practices, resulting in marked changes in topsoil salinity over small distances.

Semivariance and spatial trend analysis Distributions of soil ECb measurements using the Kolmogorov-Smirnov statistic for all the three datasets were found to have normal distributions, thereby providing a basis for further structural analysis. An analysis of spatial dependence of ECb illustrated isotropic behavior. All semivariograms had good continuity in space and all could be modeled quite well with spherical models (Table 111). TABLE I11 Models and parameters of semivariograms for soil bulk electrical conductivity in the topsoil (0-20 cm) a t three sampling dates Sampling date

Fitted model

Nugget variance (Go)

Sill variance (Go C)

+

Nugget/Sill Ratio [Co/(Co ~

Jan. 2, 2004 Apr. 25, 2004 Sep. 9, 2004

Spherical Spherical Spherical

820 580 1400

10 960 6 570

8 108

+ C)]

Range

Coefficient of determination

~~~~

%

m

7.5 8.8 17.3

182.5

0.988

135.8 151.2

0.989

0.975

The semivariance values of each of the ECI, datasets displayed similar tendencies. For the three different samplings the range and nugget/sill ratios have changed appreciably, which implied that the spatial structure of the variabihty had remained relatively stable over time. The presence of nugget variance in each dataset was probably due to the short-range variability and unaccountable measurement errors. The ratios of nugget variance to sill variance, which could be regarded as a criterion t o classify the spatial dependence of soil properties (Chien et al., 1997), were all less than 25% suggesting a highly developed spatial structure. This was usually attributed to intrinsic factors, such as soil parent material. The range of spatial dependence was 135.8-182.5 m and was considered as the distance beyond which observations were not spatially dependent. Thus, the grid spacing (20--80 m in this study) was adequate for characterizing the spatial variability of soil salinity. Ag-rawal et al. (1995) found that surface soil salinity measurements had spatial dependence over a range of distances from 46 to 119 m, depending on the grid size used in sampling. Cases where no spatial structure was found for soil salinity have also been reported with reasons for this being the limited number of samples or the size of the sampling interval

161

DELISE.ATIO1 OF SITE-SPECIFIC \IASAGEhlENT ZOKES

(JlcBratnej- and TTkbster, 1986). Tliesc findings and this work confirmed that sampling interval and inherent variability in salinity influenced both the variance structure and the range of spatial dependence. Kriging interpolation was applied to the ECb data. This enabled the plot to be divided into several classes to determine the horriogenous zones. The smoothed contour maps obtained for the three sampling dates are presented iri Fig. 2.

ADr. 2004

EC, (mS m ')

0' 4 0 04 0 - 8 0 80-120 120-160 I 160-200 200-240 240 -280 ~280 Smoothed contour maps produced by ordinary kriging for bulk electrical conductivity (ECb) at three different sampling dates.

Fig. 2

The three smoothed contour maps displayed quit,e similar patterns, with high salinity in the eastern section and Iow salinity in the western and northern parts of the study area. This illustrated that the salinity distribution for the three samplings had a similar trend and a spatial trend map could represent the consistently high and low salinity areas of the field. Salinity in the groundwater induced the high salinitj- level in the east. Since there were some fish porids to the east of the field, groundwater filtered into the eastern edge of study area transporting salt,s, which were deposited and then accumulated in the topsoil when the water subsequently evaporated. The measured mineral degrees in the four water sampling sites a, b. c, and d (Fig. 1) were 3.0, 6.9, 4.5 and 2.7 g L-', respectively, and their watertables were 1.25. 0.72. 1.1. and 1.46 rn,correspondingly. Thus, the groundwater in the eastern part had higher mineral degrees and a shallower watertable, whereas t,he groundwater in the north and central parts had lower mineral degrees and deeper watertables. This result coincided wit,h thc distribution of salinity in the topsoil and implied that the salinity from the groundwater was the main influence on the distribution and variability of salinity in the topsoil. In addition, according to the study b!, Shi et al. (2003), the saline soils were characterized by high sand content. which n-as also typical of the present study site. Because of the coarse soil texture with high sand content and permeability. salt leaching with rainfall and upward transport with evaporation were frequent. This resulted in rapid salt leaching and accumulation in the topsoil in this coastal field. It has been reported that for the same coastal region. salts from the groundwater table that were below 3 in in depth could be t,ransport.ed upward in dry months and cause accumulation of salts in the topsoil (Ding et ul.. 2001). However, the low salinity in thc western arid northern parts of the study area was because of the influence of soil rriaiiagement practices. A spatial trend map from the mean EC,, for each sampling point over the three sampling dates displaying fil-c classes of soil salinity produced by ordinary kriging is shown in Fig. 3. According to the salt-tolerance-t hreshold levels of the different crops thcsc classes allowed farmers to decide which crops wiild 1w ~ U J W I Ihiiccc~ssf'ully ill u l i i c ~ hpart,s of t,hc sitc, and t,o dcterrnine the effectiveness of' salinity management options. For t,he studied area, because of its high salinity tolerance threshold, cot,ton was the main crop. If the other crops with low salinity tolerance thresholds were to be planted, special management procedures must be carried out to reduce the salinity level in order to obtain a high economic return. According to Rhoades and Miyamoto (1990), salinity toleraiice thresholds for corn and cottori plantations are 170 and 770 mS m-', respectively. Therefore, cotton could be planted safely in this region. whereas corn could not until the salinity level was reduced, especially in the eastern part of the stud>-site.

Y . LI et al.

162

Mean EC, (mS m ’) U

80

80-140

cv,(%I Stable

0c 10%

140 - 200 200 - 260

I > 260

Fig. 3 Spatial trend map composed of the mean bulk electrical conductivity (ECb) for each sampling point over the three sampling dates displaying five classes of soil salinity produced by ordinary kriging. Fig. 4 Temporal stability map with four arbitrary classes, chosen at 10% intervals, produced for bulk electrical conductivity on the basis of the C V ; (coefficient of variation at the ith sampling point) assessment method.

Temporal stability analysis To assess the temporal stability, coefficients of variation were calculated over time at each sampling point to estimate how stable the ECh measurements were. This technique has been used by Blackmore (2000) to assess the temporal stability of crops yields and Shi et al. (2002) to assess the temporal changes in the spatial distributions of soil properties in grasslands. The four arbitrary classes, chosen at 10% intervals, on the temporal stability map (Fig. 4), clearly showed where the variation over time was high and low. Combining salinity data over diffcrent cultivation periods helped to visualize how salinity changed with time and to understand the importance of such heterogeneities with crops under various conditions. Fig. 4 revealed that the strongly saline region in the east displayed tcrnporal stability whereas the low salinity area in the west wab temporally unstable. Cluster

analysis

Results of the cluster analysis from the spatial trend and temporal stability maps transformed into raster format and divided into three classes of practical management zones are shown in Fig. 5a.

Fig. 5 Spatial distribution of the three classes of practical management zones across the field using cluster analysis (a) and the spatial distribution of cotton yield interpolated by kriging (b).

For statistical comparison, kriging was also used to interpolate the pre-processed yield data into a 10-m grid cell to examine how well these potential management zones reflected productivity levels

(Fig. 5b). Visually, the pattern of cotton yield appeared to correspond quite well with the pattern of management classes. Generally, the highest yields occurred in class 1 and the lowest yields in class 3. The results of a pseudo-F test performed on the calculated mean ECb, CV; and measured cotton yield to provide an indication of statistical distinction between the different potential management classes revealed that the average of the mean ECb, C V , and measured cotton yield within each class

163

DELINE.4TION OF SITE-SPECIFIC SlAK.4GEMEYT ZONES

were significant at P

< 0.01. P < 0.05, and P < 0.01, respectively (Table IV).

TABLE I\' The pseudo-F test for mean bulk electrical conductivity (ECt,), coeficient of variation at the i t h sampling point ( C V i ) and measured cotton yield d a t a wit.hin three defined management zonc Slanagement zonc

l l e a n ECb

cv;

Yield g plant-' 293.84

ins n - l

%

Class 1 Class 2 Class 3

61.88 141.46 244.66

26 20

197.38 107.47

F Probability

277.09 0.000

3.50 0.033

22.61 0.000

> k'

28

Number of samples 49 34 39 -

The number of ECb classes, into which a field was separated for management or sampling purposes, depended upon desired measurement sensitivity and t hc lcvcl of within-field variability. For this study, separation into three EC,, classes proved to be a good cornpromise between sensitivity and visually discernable EC], patterns. For farmers to adopt site-specific management, the development of management zones must be simple. functional, and economically feasible. Complex field assessments and data manipulation may not be justifiable in terins of time, benefit, or economics. Therefore cluster analysis, on the basis of the assumption that grouping data points into naturally occurring clusters would reduce within-zone variability. provided an opportunity for identifying EC management zones in a site and, potentially, applying site-specific management to maximize crop production across the entire area. This also represented a simplified approach for identifying threshold parameters related to yield potential.

COKCLUSIONS Spatially hoinogcnous or temporally stable regions of soil salinity were evaluated in a coastal saline field and when combining geostatistics with mult,ivariate analysis classified management zones showed favorable agreement. These results will provide a basis of information for managing coastal saline land in a rationally site-specific and precise way and may be uscd to develop a targeted soil sampling plan to capture variability in various soil properties that are possible to influence crop yield. Knowledge of such management zones may enable a reduction in the nuniber of soil analysis needed to create application maps for certain cultivation operations. Since the cost of obtaining soil samples to characterize field variability is a key problem in precision agriculture, this is particularly advantageous. There is a need for further studies to investigate the effectiveness of site-specific management of fertilizer inputs levels in specified managcment zones. REFEREXCES Agrawal, 0. P., Rao, K. V. G. K., Chatthan, H. S. and Khandelwal, M. K. 1995. Geostatistical analysis of soil salinity improvement with subsurface drainage system. Trans. A S A E . 38(5): 1427-1 433. h i . 1.. L.. J i n , J . Y . , Fang, L. P. and Liang. M. Z. 2001. Kesearcli 011 the subarea rnanagement model of soil nut,l-ientfsby GIs. Sczentza Agrzcultuva Szmca ( i n Clhirlese). 34( 1): ,41550. Blackmorr. S. 2000. Thc intcrprctation of trcnds from miiltiplc yield ma.ps. Computers and Electronics in Agriculture. 26: 37-51, Cetin. 11. and Kirda. c'. 2003. Spatial and temporal changes of soil salinity in a cotton field irrigated with low-quality water. JOILT-TLU~ o j Hydrology. 272: 238-249. Guo, H. Y . and Houng, K. H. 1997. Geost,at,istical analysis of soil properties of mid-west Taiwan Chien. Y.J., Lee. D. Y., soils. Soil Scz. 162: 291- 297. Uong, B. R.. Fu: Q. L. and Wang: J. H. 2001. Long-term observation and study on salinity and Ding, S. F.. Li. H. -4.. nutrients of coastal saline soils (sandy soil). Chznese Journal of Sod Science (in Chinese). 32(2): 57-59. . Defining rllanagenient zones for precision farming. ( h o p Insights. 8(21): 1-5.

164

Y. LI et al.

Ferguson. R. B.. Lark, R. M. and Slater, G. P. 2003. Approaches to management zones definition for use of nitrification inhibitors. Soil Scz. Soc. A m . J . 67: 937-947. Franzen. D. W-.. Hopkins, D. H.. Sweeney. &I. D., Clmer, M. K. and Halvorson, A. D. 2002. Evaluation of soil survey scale for zone development of site-specific nitrogen management. Agron. J . 94: 381-389. Huang. S. \\-., Jin, J . l~.. Yang. L. P. and Cheng. M. F. 2003. Spatial variability and regionalized management of soil nutrients in the grain crop region in Yutian County. Acta Pedologica Sinicu (in Chinese). 40(1): 79-88. Johnson. C. K.. Doran. J . W..Duke, H. R., Wienhold, B. J., Eskridge, K. M. and Shanahan, J. F. 2001. Field-scale electrical conductivity mapping for delineating soil condition. Soil Sci. Soc. A m . J . 65: 1829-1 837. Johnson, C. K., Nortensen, D. A , . Wienhold, B. J., Shanahan, J. F. and Doran, J . W. 2003. Site-specific management zones based on soil electrical conductivity in a semiarid cropping system. Agron. J . 95: 303-315. Khosla, R., Fleming, K . , Delgado, J. A., Shaver, T. and Westfall, D. G. 2002. Use of site-specific management zones t o improve nitrogen management for precision agriculture. Journal of Soil and Water Conservation. 57(6): 513-518. Kitchen, N. R., Sudduth, K. A . and Drummond, S. T. 1999. Soil electrical conductivity as a crop productivity measure for claypan soils. J . Prod. Agric. 1 2 : 607-617. Li, Y., Shi, 2.. Wang, R. C. and Huang, h1. X. 2004. Estimates of electrical conductivity for coastal saline soil profile using cokriging under different sampling density. Acta Pedologica Sinica (in Chinese). 41(3): 434-443. Liu, G. II.,Yang, J. S. and Jiang, Y. 2005. Salinity characters of soils and groundwater in typical coastal area in Jiangsu Province. Sods (in Chinese). 37(2): 163-168. McBratney. A . B. and Webster. R. 1986. Choosing functions for semi-variograms of soil properties and fitting them to sampling estimates. J . Soil Scz. 37: 617-639. Rhoades. J. D. and Miyamoto, S. 1990. Testing soils for salinity and sodicity. In Westerman, R. L. (ed.) Soil Testing and Plant Analysis. Soil Science Society of Aniercian, Madison, Wisconsin, USA. pp. 299-366. Shi, Z.. Huang. hl. X. and Li. Y. 2003. Physico-chemical properties and laboratory hyperspectral reflectance of coastal saline soil in Shangyu City of Zhejiang Province, China. Pedosphere. 13(3): 111-120. Shi. Z.. \\ang, K.. Bailey, J . S.. Jordan, C. and Higgins, A . H. 2002. Temporal changes in the spatial distributions of some soil properties on a temperate grassland site. Soil Use and Management. 18(4): 353-362. Sudduth. K . A . , Kitchen, N. R., Hughes, D. F. and Drummond, S. T . 1995. Electromagnetic induction sensing as a n indicator of productivity on claypan soils. In Robert, P. C., Rust, R. 11. and Larson, W. E. (eds.) Proceedings of the Second International Conference on Site-Specific Management for Agricultural Systems. 2nd Edition. ASA, CSSA, and S S S I . XIadison, WI. pp. 671-681. Warrick. .A. \V. and Xielsen, D. R. 1980. Spatial variability of soil physical properties in the field. I n Hillel, D. (ed.) Xpplications of Soil Physics. Academic Press, New York. pp. 319-344. Zhao. G . N..Lin, G.. Fletcher, J. J. and Yuill. C. 2004. Cultivated land changes and their driving forces-A satellite remote sensing analysis in the Yellow River Delta, China. Pedosphere. 14(1): 93-102.