landscape and land‐use effects on the spatial variation of soil chemical properties

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Landscape and Land‐UseEffects on the Spatial Variationof Soil Chemical PropertiesZongming Wang a , Bai Zhang a , Kaishan Song a ,Dianwei Liu a , Chunying Ren a , Sumei Zhang a ,Liangjun Hu b , Haijun Yang b & Zhiming Liu ba Northeast Institute of Geography and AgriculturalEcology, Chinese Academy of Sciences, Changchun,Jilin Province, Chinab Northeast Normal University, Changchun, JilinProvince, ChinaPublished online: 04 Sep 2009.

To cite this article: Zongming Wang , Bai Zhang , Kaishan Song , Dianwei Liu ,Chunying Ren , Sumei Zhang , Liangjun Hu , Haijun Yang & Zhiming Liu (2009)Landscape and Land‐Use Effects on the Spatial Variation of Soil Chemical Properties,Communications in Soil Science and Plant Analysis, 40:15-16, 2389-2412, DOI:10.1080/00103620903111301

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Landscape and Land-Use Effects on the SpatialVariation of Soil Chemical Properties

Zongming Wang,1 Bai Zhang,1 Kaishan Song,1 Dianwei Liu,1 Chunying

Ren,1 Sumei Zhang,1 Liangjun Hu,2 Haijun Yang,2 and Zhiming Liu2

1Northeast Institute of Geography and Agricultural Ecology, Chinese Academy

of Sciences, Changchun, Jilin Province, China2Northeast Normal University, Changchun, Jilin Province, China

Abstract: The current study addressed the spatial variation of soil organic matter

(SOM), total nitrogen (TN), extractable phosphorus (EP), and extractable

potassium (EK) in agricultural soils of a representative region, northeast China.

Soil cation exchange capacity (CEC) and the effects of landscape attributes and

land use were also investigated. The techniques used included conventional

statistics, geostatistics, and geographic information systems (GIS). Our study

demonstrated that EP had the greatest coefficient of variation (CV), and CEC

had the least CV. The experimental semivariograms of the five soil chemical

properties included in this study were all fitted with exponential models. The five

soil variables all showed moderate spatial dependence. The SOM, EK, and CEC

decreased with increasing altitude. Significant negative relationships were found

between the slope gradient and EP, EK, and CEC. Relatively steeper slopes might

result in greater soil erosion, which leads to a decline in soil nutrients. Soil types

had significant impacts on all soil chemical properties, which reflect the effect of

the parent soil material. In general, the mean values of soil variables for vegetable

land were statistically greater than those for upland and paddy fields. After being

divided into two parts along the Yinma River, soil samples of the western part

have statistically greater SOM, EP, EK, and CEC values than those collected

from the eastern part.

Received 23 October 2007, Accepted 11 October 2008

Address correspondence to Zongming Wang, Northeast Institute of

Geography and Agricultural Ecology, Chinese Academy of Sciences,

Changchun 130012, China. E-mail: [email protected]

Communications in Soil Science and Plant Analysis, 40: 2389–2412, 2009

Copyright # Taylor & Francis Group, LLC

ISSN 0010-3624 print/1532-2416 online

DOI: 10.1080/00103620903111301

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Keywords: Geostatistics and GIS, landscape attributes, land use, soil chemical

properties, northeast China

INTRODUCTION

Almost all soil properties exhibited variability as a result of the dynamic

interactions among the natural environmental factors (i.e., climate,parent material, vegetation, and topography; Jenny 1941). Significant

differences in soil chemical properties in a small area on uniform geology

are known to be related to landscape position (Jenny 1941; Ruhe 1956;

Rezaei and Gilkes 2005). Soil chemical properties and in turn plant

growth are significantly controlled by the variation in landscape

attributes including slope gradient, slope aspect, and elevation, which

influence the distribution of energy, plant nutrients, and vegetation by

affecting organic activity, runoff and run-on processes, natural drainageconditions, and exposure of soil to wind and precipitation (Buol, Hole,

and McCraken 1989; Rezaei and Gilkes 2005).

To study the spatial distribution patterns of soil properties,

conventional statistics and geostatistics have been applied widely (Van

Meirvenne et al. 1996; Saldana, Stein, and Zinck 1998; McGrath and

Zhang 2003; Liu et al. 2006). Based on the theory of a ‘‘regionalized

variable’’ (Matheron 1963; Webster and Oliver 2001), geostatistics

provides advanced tools to quantify the spatial features of soilparameters and to carry out spatial interpolation. The research benefits

of the geographic information systems (GIS) approach have been

illustrated by many ecological and agricultural studies. Geographic

information systems can be utilized to spatially analyze the characteristics

of the objectives in question. In addition, GIS is useful in producing

interpolated maps for visualization.

Northeast China is one of the main agricultural regions in China. Its

cultivated land and total crop yield now account for 19 and 30% of thenation’s total, respectively. Concerning the relationship between agri-

cultural management practices and soil properties in the northeast, many

field experiments have been conducted to investigate the effects of

cropping, tillage, and fertilization on soil chemical properties (Wang

1996; Liu and He 2002; Liu et al. 2003, 2005; Meng, Zhang, and Sui 2003;

Wang and Liu 2003; Yang et al. 2004; Xing et al. 2005; Han et al. 2006;

Wang et al. 2007).

However, in this region, there were few studies on the spatialvariability of soil properties under the influences of natural and

anthropogenic factors (Liu et al. 2006). A better understanding of the

spatial variability of soil properties is important for refining agricultural

management practices and for improving sustainable land use (McGrath

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and Zhang 2003). It provides a valuable base against which subsequent

and future measurements can be evaluated. Many soil chemical proper-

ties are of a dynamic nature. The dynamic nature of soil chemical

properties were hypothesized to be directly affected by landscape

attributes in addition to the indirect influences of landscape attributes

via providing different microclimates that support the growth of plant

species with different characteristics. Management factors may exacer-

bate or deteriorate the effects of environmental factors (Rezaei and

Gilkes 2005). This study aims to explore how soil chemical properties

[soil organic matter (SOM), total nitrogen (TN), extractable phosphorus

(EP), extractable potassium (EK), and soil cation exchange capacity

(CEC)] are controlled by natural environmental factors and anthropo-

genic land use in the agricultural soils of northeast China by taking a

representative county as a case study.

MATERIALS AND METHODS

Study Area

Jiutai County (125u 429–126u 499 E, 43u 859–44u 619 N) is located in the

middle part of Jilin Province, northeast China (see Figure 1). The county

has an altitude between 147 and 580 m with a total area of 3376 km2. The

study area is characterized by a temperate, semihumid continental

monsoon climate. Seasons alternate between dry and windy springs,

humid and warm summers with intensive rainfall, windy and dry

autumns, and long, cold, dry winters. The mean annual temperature is

about 4.8 uC, and the average annual precipitation is 582 mm, with 82%

occurring between May and September. The average amount of sunshine

each year is 2571 h, and the average wind speed is about 3.3 ms21. The

frost-free period is about 130–140 d in duration. In this county, the

Second Songhua River, the Mushi River, the Wukai River, and the

Yinma River flow through the area and then into the Songhua River. The

main soils are dark brown forest soil (Haplic Luvisol, FAO), meadow soil

(Eutric Vertisol, FAO), aeolian soil (Arenosol, FAO), black soil (Luvic

Phaeozem, FAO), and paddy soil (Hydragric Anthrosol, FAO).

As a representative agricultural county of northeast China, more

than 70% of the total area of Jiutai County is used as cultivated land. The

current fertilization and management style have prevailed for more than

20 years. In this area, single crops were replanted annually with

continuous spring maize, Zea mays L., as the prevailing cropping system.

Other cropping systems include continuously cropped rice, Oryza sativa

L., and continuously cropped vegetables. Overall, the average yield for

the county is about 9000, 8600, and 36000 kg ha21 for maize, rice, and

Spatial Variation of Soil Chemical Properties 2391

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vegetables, respectively. In the study area, hoeing by hand is a common

field practice.

Soil Sampling and Analysis

In this study, the soil data were collected in a regional soil fertility

investigation. A maximum of two sites were selected at random from

each grid (area of 10 km2). Samples of 0 to 20-cm depth from 311 sites

were taken in late October 2004, after crops were harvested. The 0 to 20-

cm depth of soil is the plowed layer for agricultural production in this

region, and soil chemical properties in this layer are important for crop

growth. Of the 311 points investigated, 260 locations are for upland

under maize, 37 locations are for paddy fields, and 14 locations are for

vegetables. The five replicate samples were homogenized by hand mixing

and were sieved after being air dried. The soil chemical properties

considered in this study are SOM, TN, EP, EK, and CEC. Soil organic

matter was determined using the potassium dichromate (K2Cr2O7)

titration method (Fu et al. 2001). Total N was determined using the

semimicro-Kjeldahl method (Bremner and Mulvaney 1982). The Olsen

method was used to determine EP using a molybdate reaction for

colorimetric detection (Olsen and Sommers 1982). To determine EK, the

neural 1 N ammonium acetate extraction method was used (Knudsen,

Peterson, and Pratt 1982). Cation exchange capacity was determined for

soil samples by the replacement of exchangeable cations with ammonium

acetate (Editorial Committee 1996).

The locations of the cropland sampling sites are recorded in Figure 1.

Geographical Datasets of the Study Area

Topographic maps of the study area (1:100,000 scale, created in 1987)

were scanned and digitized, and a raster digital elevation model (DEM)

was built. Elevation and slope gradient were extracted from DEM.

Landsat-7 images (ETM+ of August 2002, 15-m resolution) were used to

derive land-use information for the study area. The Landsat images were

enhanced using linear contrast stretching and histogram equalization to

help identify ground control points in the rectification to the common

ALBERS coordinate system based on 1:100,000 topographic maps. For

each ETM scene, at least 20 evenly distributed sites served as ground

control points (GCPs). The reference data were collected from a field

survey and existing land-use map that had been field checked. The soil

map of the study area (1:200,000) was scanned and digitized. With the

help of the spatial analyst module of the software, ArcGIS, values of soil

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chemical properties were linked to topographic variables (elevation and

slope gradient), soil type, and land-use type.

Statistical and Geostatistical Methods

Means, standard deviations, variances, coefficients of variation, and

maximum and minimum values were generated for each of the variables

studied. Data were tested for the normal frequency distribution by

Figure 1. Soil sampling locations in Jiutai County, northeast China (n 5 311).


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examining the coefficients of skewness and kurtosis (Paz Gonzalez,

Vieira, and Castro 2000). The Pearson correlation coefficients were

estimated for all possible paired combinations of the variables (SOM,TN, EP, EK, CEC, longitude, latitude, elevation, and slope gradient) to

generate a correlation coefficient matrix. The effects of soil type and

land-use type on soil properties were assessed using the analysis of

varience (ANOVA) method. These statistics were performed using Excel

2000 and SPSS 8.0. In studying the spatial differences for soil properties,

t-tests were adopted.

Geostatistics (Matheron 1963) uses the semivariogram to quantify

the spatial variation of a regionalized variable and provides the input

parameters for the spatial interpolation method of Kriging (Krige 1951).The semivariogram is half the expected squared difference between paired

data values z(x) and z(x + h) to the lag distance h, by which locations are

separated (Webster and Oliver 2001):

r hð Þ~ 1

2E z xð Þ{z xzhð Þ½ �2 ð1Þ

For discrete sampling sites, such as sampled in our study, the

function is usually written in the form

r hð Þ~ 1

2N hð ÞXN hð Þ

i~1

z xi{z xizhð ÞÞð½ �2 ð2Þ

where z(xi) is the value of the variable Z at location xi, h is the lag, and

N(h) is the number of pairs of sample points separated by h. For irregular

sampling, it is rare for the distance between the sample pairs to be exactly

equal to h. That is, h is often represented by a distance band.

The experimental variogram is calculated for several lag distances.

This is then generally fitted with a theoretical model, such as spherical,exponential, or Gaussian models. The optimal theoretical model was

determined using the determining coefficient (R2) and the least residual

sums of squares (RSS). The models provide information about the spatial

structure as well as the input parameters for the Kriging interpolation.

Kriging is considered an optimal method of spatial prediction. It is a

theoretical weighted moving average:

z x0ð Þ~Xn

i~1

liz xið Þ ð3Þ

where z x0ð Þ is the value to be estimated at a location x0, z(xi) is the known

value at the sampling site xi, and n is the number of sites within the search

neighborhood used for the estimation. The number n is based on the size

of the moving window and is defined by the user. Kriging is different

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from other methods (such as inverse distance weighting), because the

weight, li, is no longer arbitrary. The weights depend on the parameters

of the variogram model and the sampling configuration and are decided

under conditions that are unbiased and that minimize estimation

variance. A detailed description of geostatistics is covered well within

the publication record (McGrath and Zhang 2003; Zhang and McGrath,

2004; Liu et al. 2006).

In our study, the geostatistical analyses were carried out with GS+

(version 3.1a demo; Gamma Design Software LLC, Plainville, Mich.),

and maps were produced with GIS software ArcGIS (version 9.0; ESRI,

Redlands, Calif.).

RESULTS AND DISCUSSION

Descriptive Statistics

For soil chemical properties, the histograms of raw data are positively

skewed, but the log-transformed data were near normal. Table 1 indicates

the quantitative parameters of the probability distribution for the five

variables. In addition, P–P plots showed that for the raw data of each soil

variable, there is a significant deviation from the straight line. However,

the log-transformed data are close to the straight line. These results imply

that the soil chemical properties in croplands of the study area follow

lognormal distribution.

The coefficient of variation, standard deviation, and basic statistical

parameters of percentiles and means are shown in Table 2. Among the

five soil chemical properties, extractable P and extractable K showed the

greatest CV (65.0% and 46.0%, respectively). The larger CV for soil-

extractable P and extractable K could be linked to the heterogeneity of

the land-use pattern, fertilizer or erosion, which is in agreement with

studies by Chien et al. (1997) and Sun, Zhou, and Zhao (2003). Because

soil chemical properties have the log-normal feature here, we argue that

Table 1. Shape parameters of the probability distributions (n 5 311)

Parameter Raw data Log-transformed data

Skewness Kurtosis Skewness Kurtosis

SOM 2.15 7.75 0.76 1.53

TN 2.42 8.60 0.93 2.56

EP 1.34 1.42 20.22 20.11

EK 2.93 13.42 0.88 1.65

CEC 0.49 1.62 20.62 2.99


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Table 2. Coefficient of variation (CV), standard deviation (SD), and basic statistical parameters of soil chemical properties (n 5 311)

Variables CV SD Min. 5% 25% Median 75% 95% Max. Mean GeoMean

SOM (%) 30.86 0.83 1.17 1.84 2.15 2.54 3.01 4.14 7.90 2.69 2.80

TN (%) 29.23 0.038 0.06 0.09 0.11 0.12 0.14 0.19 0.35 0.13 0.13

EP (mg kg21) 65.04 16.84 3.20 6.36 14.50 22.10 32.70 71.10 73.50 25.89 29.53

EK (mg kg21) 46.02 57.75 54.00 68.60 92.00 114.00 139.00 236.40 555.00 125.50 134.51

CEC (cmol kg21) 19.17 4.05 7.50 15.02 18.66 20.89 23.04 28.24 37.97 21.13 21.78

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the geometric mean and median are more representative for the mean

values of the data than the arithmetic mean.

Correlation between Soil Chemical Variables

Pearson (linear) correlation coefficients between the five variables and

landscape attributes were given in Table 3, together with the correspond-

ing significance levels.

Abundant literature sources indicate a positive relationship between

soil organic carbon (SOC) and the capacity of the soil to supply essential

plant nutrients including N, P, and K (Rezaei and Gilkes 2005). In this

study, the Pearson linear correlation analysis indicates highly significant

positive relationships between SOM and N (r 5 0.78), P (r 5 0.17), and K

(r 5 0.39).

For our results, a positive correlation was confirmed between SOM

and CEC, which represents the total capacity of a soil to hold

exchangeable cations, part of the soil reservoir providing nutrient

elements for plant growth. The correlation coefficient for the linearrelationship between SOM and CEC (0.409) in this study was consistent

with SOC contributing to soil fertility by serving as a reservoir of plant

cationic nutrients through providing a cation exchange surface (Kay, da

Silva, and Baldock 1997) and also through its capacity to act as a pH

buffer. A strong positive relationship between CEC and organic matter

may indicate that in the first layer organic matter provides a surface area

and negative charge to retain exchangeable cations (Rezaei and Gilkes

Table 3. Correlation coefficient matrix for soil chemical variables and

landscape attributes (longitude, latitude, elevation, and slope gradient)

Variable SOM TN EP EK CEC LON LAT ELE SLO

SOM 1 ** ** ** ** ** ns * ns

TN 0.783 1 ** ** ** ** ** ns ns

EP 0.174 0.259 1 ** ns ** ** ns *

EK 0.386 0.459 0.457 1 ** ** ns * *

CEC 0.409 0.347 0.087 0.366 1 ** ns * *

LON 20.240 20.205 20.329 20.278 20.410 1 ** * **

LAT 20.003 20.184 20.341 20.098 20.079 0.612 1 ns ns

ELE 20.136 20.105 20.002 20.163 20.253 0.133 20.090 1 *

SLO 20.078 20.028 20.127 20.135 20.210 0.219 0.106 0.680 1

Notes. For sake of brevity, the symbols are designated as follows: soil organic

matter (SOM), total nitrogen (TN), extractable phosphorus (EP), extractable

potassium (EK), cation exchange capacity (CEC), longitude (LON), latitude

(LAT), elevation (ELE), and slope (SLO).

**Denotes significance at the 0.01 level. ‘‘ns’’ means not significant. The

number of observations for all variables was 311.


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2005). Thus, one may conclude that SOM within the cropland system

provides nutrients for plant growth, resulting in a positive feedback as

more plant biomass is likely to produce more SOM. In this study, all soil

chemical variables demonstrated a significant negative correlation with

longitude, which may reflect the effect of different levels of rainfall, andTN and EP were correlated significantly to latitude. For these reasons,

future research is needed.

Analysis of Spatial Dependence of Soil Chemical Properties

For the five soil chemical properties, the semivariogram models and best-fit model parameters are shown in Table 4 and Figure 2. The soil chemical

properties included in this study all show a positive nugget, which can be

explained by sampling error, short-range variability, and random and

inherent variability. In general, the nugget-to-sill ratio can be used to

classify the spatial dependence of soil properties (Cambardella et al.

1994). The variable is considered to have a strong spatial dependence if

the ratio is less than 25% and has a moderate spatial dependence if the

ratio is between 25% and 75%; otherwise, the variable has a weak spatialdependence. In the study area, the nugget-to-sill ratio of soil chemical

properties showed a moderate spatial dependence (0.70, 0.68, 0.69, 0.50,

and 0.62, for SOM, TN, EP, EK, and CEC, respectively), which could be

attributed to intrinsic (soil-forming processes) and extrinsic factors (soil

fertilization and cultivation practices).

Analysis of Landscape and Land-Use Factors in Relation to Soil Chemical

Properties

Effects of Elevation and Slope Gradient

Results (Table 3) showed that, SOM, EK, and CEC were significantly

negatively correlated to elevation. This means these three soil properties

Table 4. Parameters for variogram model for soil chemical properties

Parameter Model Range

a (km)

Effective

range

(km)

Nugget

C0

Sill

C0 + C

Nugget/

sill C0 /

(C0 + C)

R2 RSS

LnSOM Exponential 7.60 22.80 0.316 1.055 0.700 0.911 0.000058

LnTN Exponential 3.80 11.40 0.324 1.015 0.681 0.788 0.000026

LnEP Exponential 3.40 10.20 0.321 1.020 0.685 0.630 0.0020

LnEK Exponential 97.00 291.00 0.772 1.545 0.500 0.866 0.00024

LnCEC Exponential 38.00 114.00 0.527 1.384 0.619 0.893 0.000066

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Figure 2. Omnidirectional experimental variograms of soil chemical properties.


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steadily decreased with increasing altitude. Correlations between eleva-

tion and soil CEC (r 5 20.253) was stronger than correlations between

elevation and SOM (r 5 20.136) and EK (r 5 20.163). However, hardly

any correlation of elevation, TN, and EP was found (Table 3).

For relationship between elevation and SOM, some studies have

found that a higher elevation has greater SOM concentrations in

grassland soils (e.g., McGrath and Zhang, 2003). In explanation, it can

be assumed that high elevations tend to result in increased precipitation

and decreased temperature; both of these environmental factors tend to

favor accumulation of humus. Our correlations show a decrease in SOM

with elevation. The reason is that in this study, all soil samples were

collected from cropland. Soils from higher elevation usually have a bigger

slope (Figure 1), which leads to relatively greater soil erosion and a

reduction in soil nutrients.

Decreased values with slope gradient were found for EP, EK, and

CEC. There was almost no correlation between slope gradient and SOM

and TN. In explanation, in agricultural regions of northeast China, soil

erosion is considered one of the important factors affecting soil nutrient

loss (Tang 2004). It can be assumed that a relatively steeper slope might

result in more soil erosion, which would lead to a decline in soil nutrients.

Together with parent material, climate, biota, and time, topography

is one of the five fundamental elements of the soil-forming-factor theory

(Jenny 1941); topography is a major factor controlling soil process at the

landscape scale (Seibert, Stendahl, and Sorensen 2007). The local slope

determines not only such processes as erosion and sediment redistribu-

tion but also local drainage capacity. However, the greatest effect of

topography on soils in, for instance, boreal regions is its influence on

water flow patterns at the landscape level. Topographical features such as

curvature, slope, and upslope area influence the hydrological conditions

of a location and generate different soil moisture conditions and flow

patterns, and these influences inevitably affect soil physical and chemical

properties (Seibert, Stendahl, and Sorensen 2007). Results obtained in

this study displayed a strong correlation between topographical features

and soil chemical properties.

Differences among Chemical Properties of Soil Samples from Different

Soils

To define the effect of soil type on soil chemical properties, comparison

of data among soil samples from different soil types was conducted.

Analysis of variance (ANOVA) indicates that there are significant

differences between variables with different soil types (p , 0.05). These

results reflect the effect of soil parent materials on the condition of soil

variables. Furthermore, the post hoc tests were conducted.

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For log-transformed data, SOM, TN, EP, and EK have homogenous

variances between different soil type groups at a significance level of

0.176, 0.076, 0.084, and 0.258, respectively, and thus Duncan’s test can be

applied (Table 5). For LnSOM, the five soil type groups of samples can

be divided into three subsets. The first subset contains samples from the

dark brown forest soil, the Aeolian soil, and the black soil; the second

subset contains aeolian soil, black soil, and meadow soil groups; and the

third subset consists of meadow soil and paddy soil groups. The

differences within either of the subsets are not significant at the 0.05 level,

with significance levels of 0.498, 0.063, and 0.063, respectively. However,

the difference between the subsets is statistically significant at the 0.000

Table 5. Results of post hoc tests in ANOVA with Duncan’s method (with

mean values of LnSOM, LnTN, LnEP, and LnEK in each soil type group)

Soil n SD Subset 1 Subset 2 Subset 3 Subset 4

LnSOM

Dark brown forest soil 28 0.231 0.868

Meadow soil 80 0.309 1.026 1.026

Aeolian 26 0.212 0.904 0.904

Black soil 157 0.238 0.913 0.913

Paddy soil 20 0.285 1.141

Significance level 0.498 0.063 0.063

LnTN

Dark brown forest soil 28 0.225 22.104 22.104

Meadow soil 80 0.290 22.017

Aeolian 26 0.225 22.136

Black soil 157 0.209 22.142 21.843

Paddy soil 20 0.277

Significance level 0.530 0.133 1.000

LnEP

Dark brown forest soil 28 0.654 3.034

Meadow soil 80 0.732 2.968

Aeolian 26 0.594 2.924

Black soil 157 0.591 3.180

Paddy soil 20 0.587 2.595

Significance level 1.000 0.128

LnEK

Dark brown forest soil 28 0.367 4.603 4.603

Meadow soil 80 0.390 4.765 4.765

Aeolian 26 0.297 4.558

Black soil 157 0.339 4.790

Paddy soil 20 0.370 4.959

Significance level 0.598 0.057 0.766 1.000


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level (with an F-value of 6.167). It can be found that in this area,

statistically, samples from paddy soil have the greatest SOM (3.25%) and

those from dark brown forest soil have the least SOM values (2.44%).

For LnTN, the soil type groups were divided into three subsets, too.

The differences within any of the subsets are not significant at the 0.05

level, with significance levels of 0.530, 0.133, and 1.000, respectively.

However, the difference between the subsets is statistically significant at

the 0.000 level (with an F-value of 9.284). Soil samples from paddy soil

have the greatest TN values (0.16%), and samples from aeolian soil and

black soils have the least TN values (0.12%).

For LnEP, the five soil type groups were divided into two subsets.

The differences within either of the subsets are not significant at the 0.05

level, with significance levels of 1.000 and 0.128, respectively. The

difference between the subsets is statistically significant at the 0.001 level

(with an F-value of 4.782). Soil samples from black soil have the greatest

EP values (28.41 mg kg21), and those from paddy soil have the least EP

values (15.65 mg kg21).

For LnEK, the soil type groups were divided into four subsets. The

differences within any of the subsets are not significant at the 0.05 level,

with significance levels of 0.598, 0.057, 0.766, and 1.000, respectively.


the 0.000 level (with an F-value of 5.353). Soil samples from paddy soil

have the greatest EK values (151.95 mg kg21), and samples from aeolian

soil have the least EK values (99.81 mg kg21).

For raw data or log-transformed data of CEC, the Levene test

showed that the variances between the groups of the data set are not

homogenous, and thus Duncan’s test cannot be applied. The Tamhane

method was adopted. Results showed that soil samples from paddy soil

have the greatest CEC values (24.99 cmol kg21), and samples from dark

brown forest soil have the least CEC values (18.77 cmol kg21).

Comparison of Means of Soil Chemical Properties under Different Land

Uses

The land-use map for the study is illustrated in Figure 3. In this study, the

soil samples were from three land-use types. Comparison of chemical

properties among soil samples from different land-use types was

conducted. Analysis of variance (ANOVA) indicates that there are

significant differences between variables from different land-use types (p

, 0.05). These results reflect the effect of soil management, cropping, and

fertilization on the condition of soil variables. Furthermore, the post hoc

tests were conducted.

For log-transformed data, SOM and CEC have homogenous

variances between different land-use type groups, at significance levels

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of 0.307, 0.113, and 0.82, respectively, and thus Duncan’s test can be

applied (Table 6). For LnSOM, the three land-use type groups of samples

can be divided into two subsets. The first subset contains samples from

upland, and the second subset contains paddy field and vegetable land

groups. Differences within either of the subsets are not significant at the

0.05 level, with significance levels of 1.000 and 0.054, respectively.


the 0.000 level (with an F-value of 29.916). It can be found that, samples

from upland have lesser SOM values (2.55%) than those from paddy field

and vegetable land (3.29% and 3.68%, respectively).

For LnCEC, results were obtained similar to those for LnSOM:

samples from upland have lesser CEC values (20.49 cmol kg21) than

Figure 3. Land-use map of Jiutai County, northeast China.


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those from paddy field and vegetable land (24.81 cmol kg21 and

23.44 cmol kg21, respectively).

For raw data or log-transformed data of TN, EP and EK, the Levene

tests showed that the variances between the groups of the data set are not

homogenous, and thus Duncan’s test cannot be applied. Then the

Tamhane method was adopted. Results showed that soil samples from

upland have the least TN values (0.12%), and those from vegetable land

have the greatest TN values (0.15%). Soil samples from paddy field have

the least EP values (17.55 mg kg21), and those from vegetable land have

the greatest EP values (72.07 mg kg21). Soil samples from upland and

paddy field have lesser EK values (115.85 mg kg21 and 140.92 mg kg21,

respectively) than those from vegetable land (264.07 mg kg21).

In summary, the mean values of soil variables for vegetable land were

statistically greater than those for upland and paddy field. In explana-

tion, in the study area, farmers often apply more chemical fertilizer for

vegetable land to get more vegetable output. According to household

investigations, in this county, application of manure is very common for

different land-use types. The amount applied is about 3000 kg ha21, and

the average N content of the manure is 0.7%. The average N inputs are

218, 216, and 253 kg ha21 for upland growing maize, paddy fields, and

vegetable lands, respectively. The average P fertilizer inputs are 87, 81,

and 150 kg ha21 for uplands, paddy fields, and vegetable lands,

respectively. The average K inputs are 29, 37, and 40 kg ha21 for

uplands, paddy fields, and vegetable lands, respectively. A comparison of

the chemical fertilizer inputs for the three land-use types is shown in

Figure 4.

Table 6. Results of post hoc tests in ANOVA with Duncan’s method (with

mean values of LnSOM and LnCEC in each land use type group)

Parameter n SD Subset 1 Subset 2 Subset 3

LnSOM

Upland 260 0.243 0.905

Paddy field 37 0.269 1.156

Vegetable land 14 0.225 1.280


LnCEC

Upland 260 0.187 3.003

Paddy field 37 0.204 3.191

Vegetable land 14 0.0762 3.152


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Spatial Distribution of Soil Chemical Properties

The parameters of the exponential model were used for Kriging to

produce spatial distribution maps for SOM, TN, EP, EK, and CEC in the

agricultural soils of the study area. The final results of the spatial

interpolation are recorded in Figure 5. It was found from the spatial

distribution maps of soil chemical properties that for SOM, EP, EK, and

CEC, data values in the western part are greater than those in the eastern

part. However, for TN, there is no obvious visual trend.

To further explore the spatial differences for soil chemical properties,

the study area was divided into two parts: the western part and the

eastern part, with a line along the Yinma River (see Figure 1). The 311

soil samples were then separated into two subsets. The sample sizes were

112 and 199, respectively. The mean values of SOM, TN, EP, EK, and

CEC in the two areas are shown in Table 7. Results indicated that for

SOM, EP, EK, and CEC, the average values in the western part were

noticeably greater than those of the eastern part, with relative differences

of 14.7, 32.5, 25.4, and 25.4%, respectively. For EP, the average value of

0.132 in the western part was somewhat more than that of the eastern

part, 0.126. The relative difference was 4.8%.

Figure 4. Chemical fertilizer inputs for different land use types in Jiutai County,

northeast China.


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Figure 5. Spatial distribution map of SOM (a), TN (b), EP (c), EK (d), and

CEC (e) in Jiutai County, northeast China.

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Table 7. Mean values of soil chemical properties in the western part and the eastern part

Variable Region n Minimum Maximum Average Median Geometric

mean

Std. deviation

SOM (%) Western 112 1.91 6.71 2.93 2.79 2.85 0.75

Eastern 199 1.17 7.90 2.56 2.30 2.45 0.84

TN (%) Western 112 0.07 0.31 0.13 0.13 0.13 0.035

Eastern 199 0.06 0.35 0.13 0.12 0.12 0.040

EP (mg kg21) Western 112 5.00 73.50 30.71 26.70 25.44 18.61

Eastern 199 3.20 73.50 23.18 20.40 19.11 15.13

EK (mg kg21) Western 112 56.00 555.00 144.20 127.00 135.46 64.14

Eastern 199 54.00 375.00 114.98 101.00 107.06 51.05

CEC(cmol kg21) Western 112 17.24 37.97 23.26 22.73 23.05 3.27

Eastern 199 7.50 34.56 19.93 19.90 19.54 3.95

Sp

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The t-tests were conducted to further compare the differences

between soil variables in the two parts of the study area, and the results

are listed in Table 8.

Results indicated that for SOM, TN, EK, and CEC, the variances

between the two data sets are homogenous with significance levels of

0.473, 0.128, 0.607, and 0.343, respectively. Thus, the t-test values of

3.934, 1.352, 4.408, and 7.580 with ‘‘equal variances assumed’’ were used.

Significance levels of 0.000, 0.000, and 0.000 for the two-tailed test

showed that soil samples of the western part have statistically greater

SOM, EK, and CEC values than those collected from the eastern part.

For TN, the significance level of 0.178 for the two-tailed test indicated

that there is no significant difference between TN values from the western

part compared to those from the eastern part. For EP, the variances

between the two data sets are not homogenous because the significance

Table 8. Results of Levene’s test and t-test of soil chemical properties for the

western and eastern regions

Variable Variance assumption Levene’s test for

equality of

variances

t-Test for equality

of means

F Significance t Significance

(two-tailed)

SOM Equal variances

assumed

0.516 0.473 3.934 0.000**

Equal variances not

assumed

4.062 0.000

TN Equal variances

assumed

2.335 0.128 1.352 0.178

Equal variances not

assumed

1.402 0.162

EP Equal variances

assumed

9.597 0.002 3.873 0.000

Equal variances not

assumed

3.657 0.000**

EK Equal variances

assumed

0.265 0.607 4.408 0.000**

Equal variances not

assumed

4.138 0.000

CEC Equal variances

assumed

0.903 0.343 7.580 0.000**

Equal variances not

assumed

7.992 0.000

**Denotes significance at the 0.01 level.

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level was 0.002 (,0.05). For this reason, the t-test value of 3.657 with

‘‘equal variances not assumed’’ was used. A significance level of 0.000

indicates that soil samples collected from the western part have

statistically greater EP values than those from the eastern part of the

study area.

Comparison between spatial distribution map of soil chemical

properties (Figure 5) and the elevation map (Figure 1) and the land-use

map (Figure 3) of the study area can give us the information that the

spatial distribution of SOM, EP, EK, and CEC is generally consistent

with the topographical features. In the eastern part, croplands have

greater elevation and greater slope (Figure 6). However, in the western

part, cropland is characterized with flat plain. In the eastern part, water

Figure 6. Slope degree map of Jiutai County, northeast China.


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and tillage erosion may be the main reason for the decline in soil chemical

properties resulting from a relatively greater water and soil loss caused by

the relatively steeper slope.

CONCLUSIONS

A better understanding of the spatial variability of soil nutrients is

essential for refining agricultural management practices and for improv-

ing sustainable land use in northeast China. This article took a

representative county as an example to explore the spatial distribution

characteristics and related affecting factors of soil chemical properties in

croplands. Results indicated that soil chemical properties had a moderate

spatial dependence, which could be attributed to intrinsic (soil-forming

processes) and extrinsic factors (soil fertilization and cultivation

practices). Elevation and slope gradient both significantly affect soil

chemical properties, which reflect the effect of soil loss. The effects of soil

type could be explained by the differences between soil parent materials.

The spatial distribution of SOM, EP, EK, and CEC is generally

consistent with the topographical features. For TN, there is no obvious

spatial difference in different parts.

Soils in northeast China before reclamation have high nutrient values

because of their parent material, climate, and natural vegetation

characteristics. However, after about 100 years of reclamation and

tillage, the soil quality in this region has declined greatly. The application

of manure and crop residues, either alone or in combination with

chemical fertilizers, should have a positive effect, helping to maintain and

restore soil quality for the croplands of northeast China. The adoption of

proper agricultural management systems may maintain and restore soil

productivity. In addition, the adoption of soil conservation practices to

reduce soil erosion is in increasing in this region.

ACKNOWLEDGMENTS

This research was jointly supported by the National Basic Research

Program of China (No. 2009CB421103) and the Knowledge Innovation

Program of Chinese Academy of Sciences (No. KZCX-2-YW-341). We

thank Profs. Jian Huang and Huilin Zhang for help in obtaining soil

samples and in conducting soil analysis. We thank the editor of the

journal and anonymous reviewers for their useful comments and

suggestions on an earlier draft.

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landscape and land‐use effects on the spatial variation of soil chemical properties

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