Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China

Geoderma 150 (2009) 141–149

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Geoderma j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / g e o d e r m a

Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau, China Yunqiang Wang a,b,⁎, Xingchang Zhang a,b,c,⁎, Chuanqin Huang a,b a State Key Laboratory of Soil Erosion and Dry-land Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences, Northwest A&F University, Yangling, Shaanxi 712100, PR China b Graduate School of Chinese Academy of Sciences, Beijing 100039, PR China c College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, PR China

a r t i c l e

i n f o

Article history: Received 5 January 2008 Received in revised form 22 January 2009 Accepted 23 January 2009 Available online 23 February 2009 Keywords: Land use Spatial variability Geostatistics Multivariate statistics The Loess Plateau

a b s t r a c t The spatial variability of soil total nitrogen (STN) and soil total phosphorus (STP) levels, which may be greatly affected by land use, plays an important role in both agriculture and the environment, especially with regard to soil fertility, soil quality, and water-body eutrophication. Little research has been done that addresses the spatial patterns of STN and STP under different land use types at a watershed scale. We collected 689 soil surface (0–20 cm) samples, using a grid sampling design, from the Liudaogou watershed (6.89 km2) on the Loess Plateau of North China. Using classical statistical and geostatistical methods, we characterized and compared the spatial heterogeneities of STN and STP under different land use types (farmland, grassland, and shrubland).Concentrations of STN and STP were normally distributed with the exception of STP in grassland, and decreased in the order: farmland N grassland N shrubland. Stepwise multiple regression analysis indicated a strong relationship between STN and soil organic carbon (which was mainly controlled by plant growth and microbial activity), while STP was associated with the content of finer soil particles (which absorb P more readily and whose distribution is related to slope aspect and altitude). Both STN and STP showed moderate variability under different land use types. Nugget ratios for STN showed a moderate spatial dependence and decreased in the order: farmland N grassland N shrubland, whereas STP increased in that order and showed strong, moderate, and weak spatial dependence, respectively. The type of optimal theoretical isotropy models differed for STN and STP as well as for the land use type. We concluded that spatial patterns of STN and STP would change significantly with land use changes currently being implemented to achieve sustainable agriculture development and environmental restoration. Taking land use type into account when considering the spatial variation of STN and STP would increase the accuracy in modeling and prediction of soil nutrient status and nutrient movement at the watershed scale. © 2009 Elsevier B.V. All rights reserved.

1. Introduction As dynamic components of the terrestrial ecosystem, with both internal changes in the vertical and horizontal directions and external exchanges with the atmosphere and the biosphere, soil nitrogen and phosphorus are distributed heterogeneously in soils (Zhang and McGrath, 2004), and this spatial heterogeneity is a function of scale (Wang et al., 2002; Walter et al., 2003; Zhang et al., 2007). In agricultural ecosystems, soil total nitrogen (STN) and soil total phosphorus (STP) are the major determinants and indicators of soil fertility and quality, which are closely related to soil productivity.

⁎ Corresponding authors. State Key Laboratory of Soil Erosion and Dry-land Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences, Northwest A&F University, Yangling, Shaanxi 712100, PR China. Tel.: +86 29 87011190; fax: +86 29 87016082. E-mail addresses: [email protected] (Y. Wang), [email protected] (X. Zhang). 0016-7061/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2009.01.021

Thus, information on the spatial distribution of STN and STP is needed for the purpose of evaluating potential crop yields (Søvik and Aagaard, 2003; Al-Kaisi et al., 2005). In environmental science, nitrogen and phosphorus are the main non-point source pollutants of surface water and ground water. Knowledge of soil variability is necessary for practical applications, as well as for model development (Søvik and Aagaard, 2003). The reduction of STN and STP levels can result in a decrease in soil nutrient supply, fertility, porosity, penetrability, and, consequently, in soil productivity (Huang et al., 2007). Furthermore, the removal of excess nitrogen and phosphorus from the soil by leaching or by rainfall scouring and soil erosion may lead to environmental problems, such as agricultural non-point source pollution and water quality degradation in both freshwater and marine ecosystems (Vervier et al., 1999; McDowell and Trudgill, 2000; Page et al., 2005; Gassman et al., 2006). Therefore, understanding and utilizing the heterogeneity of soil properties may help to improve land use efficiency, to increase crop productivity, and to benefit agricultural environment protection.

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Land use change caused by human disturbances and climate changes has historically been a common occurrence, and plays an important role in regulating STN and STP levels in the terrestrial ecosystem. For example, in arid and semi-arid regions of the world, the shift from cropland to grassland and then to shrubland has increased (Chen et al., 2006). Clearly, in order to achieve sustainable agricultural development and to alleviate the threats posed by land use changes that could exacerbate environmental problems and endanger human welfare, it is necessary to study the spatial variability of STN and STP under different land use types (Liu et al., 2006; Su et al., 2006). In recent years, considerable interest has been generated in the assessment of spatial variation characteristics of soil properties at different scales (Priess et al., 2001; Wang et al., 2002; Haynes et al., 2003; Page et al., 2005; Liu et al., 2006; Gallardo and Paramá, 2007; Huang et al., 2007; Van der Park et al., 2007; Zhang et al., 2007). The majority of the studies have revealed that the spatial variability of soil properties across the landscape of an ecosystem may be controlled by the variability in land use (Tan and Lal, 2005), topography (Wang et al., 2002; Su et al., 2006), vegetation types (Xu and Xu, 2003), cultivation (Wiliams et al., 2005), and parent material (Liu et al., 2006). Where the effect of land use type on spatial variation patterns is concerned, efforts have been made to study the spatial variation characteristics of STN and STP under a single land use type only, e.g., grassland (Gallardo and Paramá, 2007; Shorten and Pleasants, 2007), cropland (Liu et al., 2006), and shrubland (Monokrousos et al., 2004). However, little attention has been paid to comparing the spatial structure of STN and STP under different land use types in the same landscape ecosystem, which limits our capability to predict ecosystem responses to environmental changes occurring when land use is altered. To better understand relationships between soil properties and related factors and to quantify the spatial variability of STN and STP, traditional statistical and geostatistical methods have been applied widely (Couto et al., 1997; Bennett and Adams, 1999; Amador et al., 2000; Wang et al., 2002; Kılıç et al., 2004; Western et al., 2004; Schöning et al., 2006). An effective way to study the detailed spatial patterns of STN and STP under different land use types in the terrestrial ecosystem may be to combine the two statistical approaches. The Loess Plateau of North China is famous for its deep loess, unique landscapes and intense soil erosion. The ecosystem is especially susceptible to land use change, intensive human disturbance, and climate change (Li et al., 2005). Environmental restoration and protection programs have frequently been used to restrict farming and to increase vegetative cover on steeper slopes by creating grassland or shrubland. Although some studies on the spatial variability of soil properties have been conducted in this region (Chen et al., 2006; Li et al., 2005), those of STN and STP under different land use types have received very little attention. Therefore, the objectives of this study were: (1) to analyze the effect of related environmental factors (e.g., topographical features, soil properties, and vegetation) on the spatial variability of STN and STP; and (2) to identify and compare the spatial patterns of STN and STP under different land use types in a representative watershed on the Loess Plateau, North China.

70% of which falls between June and September. The mean annual air temperature is 8.4 °C and the accumulated temperature above 10 °C is 3200 °C. Frost damage may occur frequently in the spring. The aridity index is 1.8 and there are on average 135 frost-free days annually (Zheng et al., 2006). Sandstorms may also often occur in the spring. The annual average wind speed is 2.2 m·s− 1. The dominant soil types are loessal mein soil (Calcaric Regosol, FAO/UNESCO, 1988), red loessal soil (Eutric Regosol, FAO/UNESCO, 1988), aeolian sand soil (Calcaric Arenosol, FAO/ UNESCO, 1988), and warp soil (Calcaric Fluvisol, FAO/UNESCO, 1988). The study area is under three main land use types that cover more than 86% of the total area, i.e., farmland (16%), grassland (44%), and shrubland (26%). The remaining area is covered by wasteland, gully channels, and manmade structures. To control soil erosion and improve the ecological environment of the Loess Plateau, the Chinese government has introduced many effective measures since the 1980s, including: (1) detailed, scientifically based, land use planning; (2) improvement of public environmental awareness; and (3) management measures related to soil and water conservation. As a result, unrestricted human activities resulting in, for example, overgrazing and poor infrastructure construction, have been controlled (Chen et al., 2007). This has also resulted in land use changes such as an increase in vegetative cover in grassland and shrubland where grazing is banned or restricted, while farmland has been restricted to lower slope gradients or terraces. 2.2. Soil sampling and analysis of STN and STP An intensive sampling strategy was employed to study in detail the spatial structure of STN and STP at a watershed scale. A grid consisting of squares, 100 m × 100 m, was superimposed on a digital topographic map of the entire study area (Fig. 1). A square dissected by the study area boundary line was regarded as an individual unit if more than half of it lay inside the study area; otherwise it was merged with a neighboring square. We used a RTK-GPS receiver to locate our sampling sites at the center of each square. At each site, five cores, 3 cm in diameter, were removed from the upper 20 cm soil layer within a circular area, approximately 30 cm in radius. These were then combined to give a soil sample that is representative of the site. A total of 689 soil samples were collected. Other site properties including land use type, slope, aspect, altitude, soil type, and vegetation species and coverage were recorded for the purpose of analyzing correlations between them and the spatial variation of STN and STP. The soil samples were air-dried, divided, and passed through either a 0.25 mm or a 1 mm mesh. The samples were then analyzed for STN using the Kjeldahl digestion procedure (Bremner and Tabatabai, 1972) and for STP using molybdenum antimony blue colorimetry (Murphy and Riley, 1962). Soil organic carbon content was measured using dichromate oxidation (Nelson and Sommers, 1982). For the samples that were passed through the 1 mm mesh, soil NO−3–N and NH+4–N were measured using a colorimetric method, analyzed by automatic flow injection (Keeney and Nelson, 1982). Available K was extracted with 1 N ammonium acetate and analyzed by atomic absorption spectrophotometry. Soil particle composition was measured by laser diffraction using a Mastersizer2000 (Malvern Instruments, Malvern, England) (Liu et al., 2005).

2. Materials and methods 2.3. Statistical and geostatistical analyses 2.1. Description of study area The study was conducted in Liudaogou watershed (110°21′–110°23′E, 38°46′–38°51′N), located 14 km west of Shenmu County, Shaanxi Province, China. The watershed has an altitude ranging between 1081 m and 1274 m, and covers an area of 6.89 km2. The study area is situated in the center of an intensively eroded region on the Loess Plateau, known as the “wind-water erosion crisscross region.” The climate is semi-arid and has an average annual precipitation of 437 mm,

Primary statistical parameters such as the mean, maximum and minimum, standard deviation, and coefficient of variation, which are generally considered as indicators of the midpoint and spread of the data, were calculated. The skewness, kurtosis, and Kolmogorov– Smirnov test value were used to determine whether the data was normally distributed (Liu et al., 2006). Pearson correlation coefficients were used to determine the strength of possible relationships between STN, or STP, and other soil properties (e.g., soil organic

Y. Wang et al. / Geoderma 150 (2009) 141–149

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

carbon (SOC), available K, NO−3–N, NH+4–N, clay and silt content, etc), the topographical features of the sites (e.g., slope, aspect, and altitude), and other attributes. Multiple linear regression analysis was also conducted to evaluate the statistical significance (at P = 0.05) of these dependent variables, and their interactions (Uyak et al., 2007; Kaiser and Rice, 1974; Ramos et al., 2007; Meersmans et al., 2008). Statistical analysis was performed using SPSS software (SPSS, 1998). Geostatistical techniques are often used to characterize the spatial patterns of spatially dependent soil properties, both isotropically and anisotropically (Western et al., 2004). Geostatistical analysis uses a semivariogram to quantify spatial patterns of a regionalized variable

and derives important input parameters used for Kriging spatial interpolation (Krige, 1951; Matheron, 1963). We derived semivariograms that described the rate of change in STN or STP as a function of the distance between sampling points, and which calculated the integrity of spatial continuity in either one specific direction or multiple directions (Schöning et al., 2006) using the following equation:

γðhÞ =

1 NðhÞ ∑ ½zðxi Þ − zðxi + h Þ2 2NðhÞ i = 1

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Table 1 Summary statistics for Soil Total Nitrogen (STN) and Soil Total Phosphorus (STP) concentrations over the Liudaogou watershed Land use

Variable

N

Min. (%)

Max. (%)

Mean (%)

S.D. (−)

C.V. (%)

Skewness (−)

Kurtosis (−)

D.T.a

Farmland

STN STP STN STP

110 110 311 311

0.010 0.170 0.000 0.110

1.030 0.680 0.870 0.730

0.378 0.431 0.295 0.384

0.172 0.094 0.173 0.109

46 22 59 28

STN STP STN

184 184 689

0.000 0.040 0.000

0.650 0.650 1.030

0.219 0.352 0.293

0.141 0.107 0.175

64 30 60

STP

689

0.040

1.180

0.385

0.114

30

0.771 0.046 0.619 −0.370 −0.640 0.743 −0.390 0.710 −0.164 0.149 −0.547

1.511 0.066 0.231 0.401 1.699 − 0.013 0.032 0.473 − 0.186 3.268 0.866

N N N NN Nb N N NN Nc NN Nb

Grassland

Shrubland Mixed land use

Notes: N, number of samples; S.D., standard deviation; C.V., coefficient of variation. Mixed land use refers to all land use types combined. a Distribution type (D.T.), significant level of normality test: P = 0.05. b Cosine transformation with corresponding skewness and kurtosis values. c Square root transformation with corresponding skewness and kurtosis values.

where for each site, i, z(xi) and z(xi + h) are values of z at locations xi and xi + h, respectively; h is the lag and N(h) is the number of pairs of sample points separated by h. Four variogram models (spherical, exponential, linear, and Gaussian) were used to describe the semivariograms and the best-fitted models, as indicated by the smallest residual sum of squares (RSS) and the largest coefficient of determination (r2) between model predicted variances and the measured values of STN and STP, were selected for each land use type (Wang et al., 2002). Information provided by the best-fitted model was used to analysis spatial structure and to provide the input parameters for Kriging interpolation. The anisotropy ratio, i.e., the ratio between the slopes of the directions for maximum and minimum variations, was used to identify anisotropy. We considered that there was no significant anisotropy if the anisotropy ratio was less than 2.5, although there is actually no unequivocal index value for identification of anisotropy (Trangmar et al., 1985; Wang et al., 2002). Moran's I analysis was used to derive an index that quantified the spatial autocorrelation existing between sites for STN and STP. Computation of this index is achieved by division of the spatial covariation in the data by the total variation. The variable is considered to have negative or positive spatial autocorrelation if Moran's I is less than or greater than 0, respectively, while the variable is not spatially correlated, i.e., it is spatially random, if the value is equal to 0. Positive spatial autocorrelation means that similar values (either high or low) of the variables are spatially clustered. Negative

spatial autocorrelation means that neighboring values are dissimilar (Moran, 1950; Bruland and Richardson, 2005). GS+ software (version 7.05) was used to perform geostatistical analyses. 3. Results and discussion 3.1. Traditional statistics Basic statistical properties of STN and STP distributions under different land use types are shown in Table 1. Mean STN and STP values ranged from 0.219% to 0.378% and from 0.352% to 0.431%, respectively. Both mean STN and STP contents decreased under the different land uses in the same order: farmland N grassland N shrubland. Land use type had a significant (P b 0.0001) impact on STN and STP concentrations at the watershed scale. These findings are supported by earlier results from a similar landscape on the Loess Plateau (Li et al., 2005). STN and STP levels in farmland benefit from the use of NPK fertilizers, while grassland restoration is frequently encouraged by planting N fixing alfalfa. The coefficient of variation (C.V.) is an index of the overall variation or heterogeneity of a given variable. The variability of STN and STP was between 22% and 64% under the various land uses (Table 1) and the data is thus defined as moderately variable (Nielsen and Bouma, 1985). There was no significant difference in the variability of STN and STP under different land use types. Notably, STP was less variable than STN, by a factor of about 50%. Moderate degrees of STN and STP

Table 2 Pearson correlations between Soil Total Nitrogen (STN), or Soil Total Phosphorus (STP), and SOCa, NO−3–N, NH+4–N, available K, clay, silt, sand, slope, aspect, altitude, latitude and longitude of the Liudaogou watershed Farmland STN STN STP SOC NO−3–N NH+4–N Available K Clay Silt Sand Slope Aspect Altitude Latitude Longitude

Grassland STP

1.000 0.431⁎⁎ 0.549⁎⁎

0.431⁎⁎ 1.000 0.271⁎⁎

0.150 −0.010 0.233⁎ 0.170 0.352⁎⁎ −0.316⁎⁎

0.140 0.050 0.380⁎⁎ −0.030 0.301⁎⁎ −0.216⁎

−0.060 −0.040 −0.110 0.050 0.191⁎

−0.070 −0.040 −0.206⁎ 0.100 0.020

Mixed land useb

Shrubland

STN

STP

STN

1.000 0.460⁎⁎ 0.799⁎⁎ 0.300⁎⁎ 0.406⁎⁎ 0.280⁎⁎ 0.414⁎⁎ 0.563⁎⁎ −0.557⁎⁎

0.460⁎⁎ 1.000 0.401⁎⁎ 0.185⁎⁎ 0.335⁎⁎ 0.160⁎⁎ 0.407⁎⁎ 0.589⁎⁎ −0.574⁎⁎ 0.133⁎ 0.210⁎⁎ −0.120⁎ 0.249⁎⁎ 0.310⁎⁎

1.000 0.359⁎⁎ 0.743⁎⁎ 0.399⁎⁎ 0.220⁎⁎ 0.273⁎⁎ 0.375⁎⁎ 0.430⁎⁎ − 0.428⁎⁎

0.110 0.172⁎ 0.214⁎⁎ 0.405⁎⁎ 0.586⁎⁎ −0.573⁎⁎

0.010 0.120 0.169⁎ − 0.149⁎ 0.314⁎⁎

0.070 0.080 −0.08 0.050 0.120

−0.040 0.189⁎⁎ 0.090 0.080 0.462⁎⁎

Note: ⁎⁎Correlation is significant at the 0.01 level (2-tailed); ⁎Correlation is significant at the 0.05 level (2-tailed). a SOC, soil organic carbon. b Mixed land use refers to all land use types combined.

STP 0.359⁎⁎ 1.000 0.383⁎⁎

STN

STP

1.000 0.460⁎⁎ 0.721⁎⁎ 0.256⁎⁎ 0.277⁎⁎ 0.308⁎⁎ 0.401⁎⁎ 0.528⁎⁎ −0.527⁎⁎

0.460⁎⁎ 1.000 0.413⁎⁎ 0.170⁎⁎ 0.255⁎⁎ 0.190⁎⁎ 0.338⁎⁎ 0.543⁎⁎ −0.527⁎⁎

−0.048 0.109⁎⁎

0.072 0.139⁎⁎ −0.145⁎⁎ 0.160⁎⁎ 0.225⁎⁎

0.011 −0.022 0.349⁎⁎

Y. Wang et al. / Geoderma 150 (2009) 141–149 Table 3 Soil Total Nitrogen (STN) and Soil Total Phosphorus (STP) levels for different topographical factors, soil types and vegetation types in the Liudaogou watershed Elements

Items

Altitude (m) b1100 34 1100–1170 268 N1170 387 Slope (°) b7 291 8–15 242 N15 156 Aspect Ridge 160 South-facing slope 193 North-facing slope 258 Soil type Aeolian sand soil 163 Loessal mein soil 351 Red loessal soil 124 Warp soil 45 Vegetation Medicago sativa 89 type Glycines max 42 Caragana korshinskii Kom 148 Stipa bungeana Trin 64 Artemisia desertorum Spreng 96 a

S.D.a (−)

STP (%)

S.D. (−)

0.299 0.295 0.292 0.278 0.328 0.266 0.286 0.260 0.324 0.189 0.342 0.280 0.320 0.367

0.225 0.182 0.165 0.182 0.161 0.176 0.177 0.159 0.169 0.148 0.164 0.173 0.189 0.153

0.404 0.400 0.371 0.369 0.403 0.384 0.368 0.365 0.407 0.324 0.399 0.410 0.402 0.420

0.106 0.113 0.106 0.114 0.106 0.106 0.112 0.110 0.099 0.120 0.103 0.092 0.101 0.101

0.367 0.258 0.244 0.132

0.158 0.160 0.126 0.103

0.404 0.362 0.388 0.286

0.103 0.095 0.081 0.125

Number of STN Samples (%)

S.D., standard deviation.

variability have also been reported by others (Chen et al., 2006; Liu et al., 2006; Gallardo and Paramá, 2007; Wei et al., 2008). STN and STP contents were normally distributed, as indicated by the shape parameters (skewness and kurtosis) of the data (Table 1), for a given land use type with the exception of STP under grassland but were non-normally distributed when data for all land uses were combined (hereafter referred to as mixed land). Cosine or square root transformations of the non-normally distributed data were subsequently performed to satisfy the normality requirement of further statistical analysis. 3.1.1. Correlation analysis Pearson correlation analysis shows that STN was significantly and positively correlated with STP under all land use types (Table 2). Under farmland, STN and STP were significantly correlated with SOC, available K, and both silt and sand contents. Under grassland, shrubland, and mixed land use, STN and STP were also significantly correlated to the same variables as under farmland and, additionally, to NO−3–N, NH+4–N, and clay content. These correlations indicate that there are some intrinsic relationships among soil characteristic indices (Dercon et al., 2003; Wei et al., 2008). Moreover, some topographical factors such as aspect and altitude were also significantly correlated with STN or STP, but this result was not consistent within either the variable or the land use types (Table 2). A series of studies have shown that the variability of STN and STP may be related to such elements as land use, topography, soil type, vegetation type, cultivation, and parent material in the terrestrial ecosystem (Liu et al., 2007). Table 3 shows the comparisons of mean STN and STP values for various site attributes. Spatial variation in STN and STP may be explained in part by differences in the five main vegetation types. The mean values of STN increased significantly (P b 0.006) in the order: Artemisia desertorum Spreng b Stipa bungeana Trin b Caragana korshinskii Kom b Glycine max = Medicago sativa, and STP increased significantly (P b 0.033) in the following similar order: A. desertorum Spreng b C. korshinskii Kom b S. bungeana Trin b G. max b M. sativa. Leguminous plants such as G. max and M. sativa are able to fix nitrogen and thus increase STN in the upper horizon of the soil. Possibly the order of the three lower STP and STN levels associated with the plant species reflects a decreasing order of the plant's ability to extract N and P, i.e., the plants are affecting conditions in the soil.

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Alternatively, it may be that the order reflects the decreasing ability of the plant species to survive in conditions of poor nutrient levels. Spatial variation of STN and STP can also be attributed to the complex topography of the Liudaogou watershed (Liu et al., 2006). Both STN and STP decreased significantly along the directional gradient from north-facing to south-facing slopes (P b 0.031 and P b 0.050). No statistically significant differences were found for STN and STP along either the altitude gradient (P b 0.314) or the slope gradient (P b 0.066) (Table 3). The differences between STN or STP concentrations, when elevation, aspect, and slope change, would reflect the combined influence of biotic and abiotic factors, such as differences in rainfall, soil moisture content, exposure to sunlight, plant growth, and litter fall rates, along such topographic gradients (Wang et al., 2002). The significant changes noted along the directional gradient from north-facing to south-facing slopes can be explained, at least in part, as follows. In the study area, the vegetative growth on more shaded slopes (i.e., north-facing slopes) is generally greater than on those exposed to more sunlight (i.e., south-facing slopes). This is mainly attributable to the more favorable water and heat balances of those slopes that affect both floral and microbial populations. Plant growth may be limited by water on the south-facing slopes resulting in less favorable growth conditions than on north-facing slopes. Warm, moist conditions on the north-facing slopes would most likely also favor microbial activity over those on the south-facing slopes, which in turn would affect the rate of N mineralization, volatilization, and the soil N fixation processes associated with plants such as M. sativa and G. max. The balance between increased N and P uptake by thriving vegetation and the breakdown of organic material by the microbial population could lead to the observed heterogeneities, related to slope aspect, occurring in the distribution of nutrients in the soil, and especially in that of the STN because of the large areas of nitrogen fixing plants growing in the Liudaogou watershed. A further factor related to slope aspect is that coarser loessial material, carried by the wind coming from the south–west during the continental monsoon, are typically deposited on the south-facing slopes while the finer particles tend to be deposited on the northfacing slopes. This process leads to soils of different texture on the slopes that, consequently, may affect the soil holding capacity of P. in soil, since in coarser soils it may be less firmly held. Furthermore, since finer material is more readily transportable by wind, the amount of material deposited on north-facing slopes may be greater than that on the south and this material may be a source of P originating from outside the watershed. Both of these processes could be connected with the variation observed in STP levels. Differences in STN and STP were also associated, in part, with different soil types. The means of STN increased significantly (P b 0.017) in the order: Aeolian sand soil b Red loessal soil b Warp soil b Loessal mein soil, whereas STP significantly (P b 0.043) increased in the following order: Aeolian sand soil b Loessal mein soil b Warp soil b Red loessal soil (Table 3). This can readily be related to the basic properties of the soils such as texture where the concentrations of STN and STP were both found to be significantly correlated with clay, silt, and sand contents (Table 4). When the soil has a greater content of

Table 4 Selected properties of the soils in the Liudaogou watershed Soil Number of Clay Silt type samples (%) (%)

Sand (%)

SOC TN (%) (%)

TP (%)

NO−3N NH+4−N Available (g·kg− 1) (g·kg− 1) K (g·kg− 1)

LMS 319 RLS 156 ASS 162 WS 44

53.57 65.19 73.00 63.23

2.94 2.74 1.90 3.34

0.41 0.40 0.33 0.40

9.24 6.09 5.50 7.54

5.08 3.13 2.26 3.56

41.01 31.32 24.12 32.15

0.47 0.40 0.19 0.40

28.37 23.71 22.96 28.30

119.34 109.61 110.23 123.63

Notes: LMS, Loessal mein soil; RLS, Red loessal soil; ASS, Aeolian sand soil; WS, Warp soil; SOC, soil organic carbon; TN, Total nitrogen; TP, Total phosphorus.

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Table 5 Results of multiple stepwise regression analysis for significant STN and STP relationships with various attributes of the sampling sites (n = 689) Dependent Variables variable

Coefficients S.E.a

STN

103.557 0.054 0.011 4.67E−05 −3.19E−05 0.008 0.007 0.668 0.101

STP

a b

Constant SOCb Silt Longitude Latitude Sand Clay Adjusted R2 S.E. of the Estimate Constant Silt Altitude Clay SOC NH+4–N Slope Adjusted R2 S.E. of the estimate

0.783 0.005 −0.001 −0.009 0.009 0.001 0.001 0.436 0.083

tvalue

Significance Explained level variability (%)

20.799 4.979 0.000 0.002 26.650 0.000 0.002 4.356 0.000 0.000 7.955 0.000 0.000 −6.289 0.000 0.003 3.110 0.002 0.003 2.373 0.018

51.96 10.77 1.83 2.01 0.21 0.27

0.108 7.284 0.000 0.000 16.590 0.000 0.000 −5.630 0.000 0.001 −6.689 0.000 0.002 5.127 0.000 0.000 3.261 0.001 0.000 2.558 0.011

31.26 5.40 3.38 2.64 0.91 0.53

S.E., standard error; SOC, soil organic carbon.

finer-sized particles, the specific surface area is larger and this is associated with a higher adsorption capacity, and thus higher concentrations of STN and STP. 3.1.2. Multiple linear regression analysis Table 5 presents the results of a stepwise multiple linear regression analysis of STN or STP with 12 selected explanatory variables, in which data was log-transformed if necessary in order to satisfy the requirement for normal distribution. The analysis detected relationships between both STN and STP with SOC, texture, and some geographical factors. The regression model for STN explained 66.8% of the overall STN variability in which the greater part of the variability was attributable to SOC (52.0%) and the silt content (10.8%). This indicated the importance of SOC as a source of N that affected STN levels. The amount of SOC in soils would be directly related to the type of vegetation growing in the soil, which affects the C: N ratio and N fixation, the amount of biomass, and the breakdown of the litter and organic residues produced by the plants. These processes are also related to microbial activity. As discussed above, plant growth and microbial activity are affected by a variety of factors that include soil moisture content and temperature. These processes are influenced by the texture of the soil since gas and water exchange processes are also likely to be enhanced in coarser textured soils leading to more efficient microbial activity that affects N mineralization, volatilization, and fixation rates. Furthermore, coarser soils would be better drained and leaching of nutrients, particularly of N, would be more likely. Since the silt content is related to texture, these processes probably explain why it is a significant factor affecting STN levels. All of these processes combine to influence STN levels across the landscape (Wang et al., 2003). In contrast to STN, 43.6% of the overall STP variability was mostly explained by the silt content (31.3%) while SOC only accounted for 2.6% (Table 5). The relation of P to particle size has been mentioned above, as well as the relation of particle size to slope aspect. In addition, the soils in the watershed have higher silt and clay contents at the bottom of the slopes. Thus, because of the greater STP levels associated with finer particles, a relation between altitude and STP can be attributed to the particle size distribution of the soils on the slopes.

The levels of STP are not expected to be affected by SOC distributions to the same extent as those of STN although there could be some influence of organic matter on P, e.g., the production of humic substances may inhibit P fixation (Bedrock et al., 1997), while both P and SOC may be affected by land management practices involving fertilization Mandal et al., 2008. In the absence of erosion and without management intervention, P is relatively immobile in the landscape. 3.2. Geostatistical analysis Theoretically, the above statistical analysis assumes that all STN and STP samples were collected using a randomized sampling design. However, we used a systematic, non-randomized design. Therefore, we took spatial dependence into account by using geostatistical methods. From the normally distributed dataset, transformed where necessary, we derived semivariance values and then determined if there was directional variation based on the anisotropy ratios. Weak directional variances of STN and STP under the various land use types were detected as evidenced by small anisotropy ratios, i.e., less than 2.5 (Table 6). We identified the best geostatistical model that fitted each variogram (Table 6) from the lowest RSS values and highest r2 values (Table 7). All STNs and STPs under the various land uses were spatially related to differing degrees, and the type of best-fitted model also depended on land use (Fig. 2). The best-fitted model parameters and some spatial structural indices are shown in Table 6. Nugget values of STN and STP, representing undetectable experimental error, field variation within the minimum sampling space, and inherent variability, were low and varied from 0.8 to 2.1% and from 0.1 to 1.3%, respectively. Sill values, representing total spatial variation, for STN or STP under different land use types were also small. The small positive nugget value is indicative of a positive nugget effect, a sampling error, and/or random and inherent variability of STN or STP under different land uses (Liu et al., 2006). Ranges indicate different influence zones of environmental factors at different scales. In Table 6, STN has a larger range (4464 m) spatial autocorrelation in shrubland than in other land use types, and STP has the largest range (3141 m) in grassland among the four land use types. The smallest range values for STN and STP concentrations are 234 m and 213 m, respectively, both occurring in mixed land use. To achieve more accurate analytical results such as more detailed spatial distribution patterns, it is necessary that a greater sampling density should be used in ecosystems with mixed land uses compared with those with a single land use. To confirm this, we calculated the smallest number of sampling points, which would be required in order to obtain typical characteristics of STN and STP variability under different land use types. It shows that the smallest number of sampling points for STN and STP in mixed land use is generally greater than for all single land use types (Shao et al., 2006). Table 6 Spatial structures of Soil Total Nitrogen (STN) and Soil Total Phosphorus (STP) in the Liudaogou watershed Land use Farmland

Variable Modelc

STN STP Grassland STN STPa Shrubland STN STP Mixed land STNb usee COSa a b c d e

Linear, M Gaussian, M Spherical, M Exponential, M Exponential, M Linear, S Exponential, M Exponential, M

Nugget Nugget Sill Range Anisotropy ratiod (%) (%2) (%2) (m) ratio (−) 59.2 12.4 37.3 49.7 33.9 97.9 11.2 11.4

2.1 0.1 1.3 0.1 0.8 1.1 0.3 0.1

3.6 0.9 3.4 0.2 2.2 1.1 2.9 0.7

1947 339 1560 3141 4464 1948 234 213

COS transformation. Square root transformation. S, strong spatial dependence; M, moderate spatial dependence. Nugget ration = nugget semivariance/total semivariance(or sill) × 100%. Mixed land use refers to all land use types combined.

1.94 2.31 2.39 1.45 2.38 0.95 1.39 1.35

Y. Wang et al. / Geoderma 150 (2009) 141–149

147

Table 7 RSS and r2 of the fit for four model types to the scaled semivariance data Land use

Variable

Farmland

STN STP STN STPa STN STP STNb COSa

Grassland Shrubland Mixedd a b c d

RSSc (−)

r2 (−)

E

L

S

G

E

L

S

G

2.816E−04 1.398E− 05 2.499E−05 1.069E− 07 3.298E−05 7.453E− 06 4.093E−06 2.499E−07

2.704E− 04 2.562E− 05 1.178E− 05 2.386E− 07 7.759E−05 7.354E−06 5.968E−05 2.864E−06

5.254E− 04 1.270E− 05 1.119E−05 4.553E− 07 3.664E−05 7.498E−06 7.779E−06 3.933E−07

5.244E− 04 1.196E−05 1.731E−05 4.658E−07 4.392E−05 7.454E−06 7.667E−06 3.517E−07

0.563 0.586 0.972 0.846 0.885 0.000 0.948 0.930

0.580 0.229 0.868 0.636 0.730 0.013 0.242 0.193

0.187 0.638 0.987 0.337 0.872 0.000 0.906 0.895

0.187 0.649 0.981 0.309 0.851 0.000 0.906 0.902

Cosine transformation. Square root transformation. E, Exponential; L, Linear; S, Spherical; G, Gaussian. Mixed land use refers to all land use types combined.

Usually, the nugget ratio, i.e., nugget semivariance/total semivariance (or sill) × 100%, can be regarded as a criterion for classifying the spatial dependence of soil properties. A variable would be considered

to have strong, moderate, or weak spatial dependence if the ratio is equal to or lower than 25%; between 25 and 75%; and when it is greater than 75%, respectively (Cambardella et al., 1994). In this study,

Fig. 2. Semivariograms of Soil Total Nitrogen (STN) and Soil Total Phosphorus (STP) under different land use types (Mixed land use refers to all land use types combined).

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Y. Wang et al. / Geoderma 150 (2009) 141–149

Fig. 3. Moran's I for Soil Total Nitrogen (STN) and Soil Total Phosphorus (STP) under different land use types (Mixed land use refers to all land use types combined).

the nugget ratios of STN in farmland, grassland, and shrubland, and that of STP in grassland, indicates a moderate spatial dependence that may be controlled by intrinsic variations in soil characteristics (e.g., texture, mineralogy, and soil forming processes) and by extrinsic variations (e.g., soil fertilization and cultivation practices) (Cambardella and Karlen, 1999; Kılıç et al., 2004). Based on the nugget ratio (12.4%), STP showed a strong spatial dependence in farmland. In contrast, the spatial dependence of STP in shrubland was very weak (97.9%). The nugget ratios for STN and STP in mixed land use also indicate a strong spatial dependence (see Table 6). Regular sampling schemes can be susceptible to production of data that mimics spatial dependence of a kind that is not actually there, i.e., “aliasing”. Aliasing can lead to under- or over- estimation of the estimation parameters in the kriging process. In this study, however, to decrease sampling error, we used an intensive sampling strategy that should improve the accuracy of kriging and should be sufficient to avoid aliasing to a large extent. 3.3. Spatial autocorrelation analysis To understand the spatial autocorrelation characteristics of regionalized variables at different scales, it is necessary to examine the effects of separation distance on the pattern of such spatial autocorrelation (Fig. 3). As shown in Fig. 3, STN has positive, decreasing autocorrelation at distances less than 1100 m in farmland, grassland, and shrubland (Fig. 3a), and has negative autocorrelation at distances between 1100 m and about 2000 m. Similarly, STP in farmland, grassland, and shrubland showed similar autocorrelation characteristics, albeit with different ranges for negative or positive correlations, affected by the land use type (Fig. 3b). Thus, it can be concluded that the effect of land use type on the spatial autocorrelation for STN is more significant than that for STP. However, STN and STP in mixed land use should receive careful attention, as they show spatial non-correlation at greater distances. Soils, as a regionalized landscape component, are characterized by properties with high spatial variations at multiple scale levels (Garten et al., 2007). Western and Blöschl (1999) defined the scale as a scale

triplet, consisting of spacing, extent, and support, where, ‘spacing’ refers to the distance between samples; ‘extent’ refers to the overall coverage; and ‘support’ refers to the area integrated by each sample. With the rapid development of remote sensing technologies, prediction models, and geosciences, it becomes increasingly important to understand at what scale, or in how much detail, to study soil properties and processes, i.e., whether it is important to consider these factors over areas that could range between small domains (e.g., centimeters or less) and larger spatial domains (e.g., watersheds). Hence, it is vital to choose a scientific sampling scale based on the need to explore the spatial variability of particular soil properties (Page et al., 2005; Purtauf et al., 2005; Garten et al., 2007; Huang et al., 2007). Having information about the distribution and levels of STN and STP in soils is important in its implications for land management both in terms of rectifying nutrient deficiencies and for assessing potential losses of P and N from the watershed and assessing the consequent risk to water quality, etc. (Bennett and Adams, 1999; Page et al., 2005). Such losses may occur by leaching, particularly in the case of nitrogen, leading to possible ground water contamination, or by soil erosion that threatens surface waters and downstream areas. Various models have been developed that predict long-term watershed P and N losses. The main inputs for these models are generally obtained or derived from readily available, generic spatial databases that include land use maps, soil maps, etc. (Van der Park et al., 2007). Since our results indicate that the spatial structure of STN and STP alters with land use changes in the same watershed ecosystem, having the capability to quantify these changes has the potential to greatly improve the accuracy of watershed-scale P and/or N transfer models. This requires an understanding of the soil and ecosystem properties associated with such changes and, ideally, these properties should be readily quantifiable from existing databases. 4. Conclusions This study examined STN and STP concentrations in 689 samples collected from a watershed landscape using a predetermined, regularly spaced, grid sampling design. Levels of STN and STP were significantly correlated with SOC, NO−3–N, NH+4–N, available K, slope aspect, and clay, silt, and sand contents, and were associated with different vegetation types. Multiple linear regression models identified a strong relationship between STN and SOC, attributable to SOC as a source of N and to conditions affecting plant and microbial processes, whereas STP was more strongly associated with the content of finer soil particles to which P binds more firmly. Spatial autocorrelation characteristics of STN and STP demonstrated a clear scale-dependency that varied under different land use types. The three main categories of land use, farmland, grassland, and shrubland, were associated with differences in the spatial characteristics of STN and STP. Nugget ratios for STN showed a moderate spatial dependence and decreased in the sequence: farmland N grassland N shrubland, whereas those for STP increased in this sequence and showed strong, moderate, and weak spatial dependences, respectively. STN and STP concentrations both decreased in the order: farmland N grassland N shrubland. Therefore, when combined with the effect of topographic factors, vegetation types, and soil types, etc., land use change can influence STN and STP concentrations that subsequently affect their respective biogeochemical cycles as well as soil fertility and land quality. Understanding these changes can improve land management and the accuracy of watershed-scale phosphorus and/or nitrogen transfer models. Acknowledgements This research was supported by the Key Project of Chinese National Programs for Fundamental Research and Development of China (No. 2007CB106803), the National Sci-Tech Support Program of China (No.

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2006BAD09), and the Scientific Research Innovation Team Support Program of the Northwest A & F University. We thank the editor of the journal and the reviewers for their useful comments and suggestions. Special thanks also go to Dr. Robert Horton and Mr. David Warrington for their zealous help in improving the English of the manuscript. References Al-Kaisi, M.M., Yin, X., Licht, M.A., 2005. Soil carbon and nitrogen changes as influenced by tillage and cropping systems in some Iowa soils. Agric. Ecosyst. Environ. 105, 635–647. Amador, J.A., Wang, Y., Savin, M.C., Gorres, J.H., 2000. Fine-scale spatial variability of physical and biological soil properties in Kingston, Rhode Island. Geoderma 98, 83–94. Bedrock, C.N., Cheshire, M.V., Shand, C.A., 1997. The involvement of iron and aluminum in the bonding of phosphorus to soil humic acid. Commun. Soil Sci. Plant Anal. 28, 961–971. Bennett, L.T., Adams, M.A., 1999. Indices for characterising spatial variability of soil nitrogen semi-arid grasslands of Northwestern Australia. Soil Biol. Biochem. 31, 735–746. Bremner, J.M., Tabatabai, M.A., 1972. Use of an ammonia electrode for determination of ammonia in Kjedahl. Analysis 3, 159–165. Bruland, G.L., Richardson, C.J., 2005. Spatial variability of soil properties in created, restored, and paired natural wetlands. Soil Sci. Soc. Am. J. 69, 273–284. Cambardella, C.A., Karlen, D.L., 1999. Spatial analysis of soil fertility parameters. Precis. Agr. 1, 5–14. Cambardella, C.A., Moorman, T.B., Novak, J.M., Parkin, T.B., Karlen, D.L., Turco, R.F., Konopka, A.E., 1994. Field-scale variability of soil properties in Central Iowa soils. Soil Sci. Soc. Am. J. 58, 1501–1511. Chen, F.S., Zeng, D.H., He, X.Y., 2006. Small-scale spatial variability of soil nutrients and vegetation properties in semi-arid Northern China. Pedosphere 16, 778–787. Chen, L.D., Wei, W., Fu, B.J., Lü, Y.H., 2007. Soil and water conservation on the Loess Plateau in China: review and perspective. Prog. Phys. Geogr. 31, 389–403. Couto, E.G., Stein, A., Klamt, E., 1997. Large area spatial variability of soil chemical properties in central Brazil. Agric. Ecosyst. Environ. 66, 139–152. Dercon, G., Deckers, J., Govers, G., Poesen, J., Sanchez, H., Vanegas, R., Ramirez, M., Loaiza, G., 2003. Spatial variability in soil properties on slow-forming terraces in the Andes region of Ecuador. Soil Tillage Res. 72, 31–41. FAO/UNESCO, 1988. Soil map of the world, revised legend. FAO/UNESCO, Rome. Gallardo, A., Paramá, R., 2007. Spatial variability of soil elements in two plant communities of NW Spain. Geoderma 139, 199–208. Garten, C.T., Kang, S., Brice, D.J., Schadt, C.W., Zhou, J., 2007. Variability in soil properties at different spatial scales (1 m–1 km) in a deciduous forest ecosystem. Soil Biol. Biochem. 39, 2621–2627. Gassman, P.W., Osei, E., Saleh, A., Rodecap, J., Norvell, S., Williams, J., 2006. Alternative practices for sediment and nutrient loss control on livestock farms in northeast Iowa. Agric. Ecosyst. Environ. 117, 135–144. Haynes, R.J., Dominy, C.S., Graham, M.H., 2003. Effect of agricultural land use on soil organic matter status and the composition of earthworm communities in KwaZuluNatal, South Africa. Agric. Ecosyst. Environ. 95, 453–464. Huang, B., Sun, W., Zhao, Y., Zhu, J., Yang, R., Zou, Z., Ding, F., Su, J., 2007. Temporal and spatial variability of soil organic matter and total nitrogen in an agricultural ecosystem as affected by farming practices. Geoderma 139, 336–345. Kaiser, H.F., Rice, J., 1974. Little jiffy, mark IV. Educ. Psychol. Meas. 34, 111–117. Keeney, D.R., Nelson, D.W., 1982. Nitrogen-inorganic forms, In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis, Part 2, 2nd ed. Agronomy Monograph, vol. 9. ASA and SSSA, Madison, WI, pp. 643–698. Kılıç, K., Özgöz, E., Akbaş, F., 2004. Assessment of spatial variability in penetration resistance as related to some soil physical properties of two fluvents in Turkey. Soil Tillage Res. 76, 1–11. Krige, D.G., 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. J. Chem. Metall. Mining Soc. S. Afr. 52, 119–139. Li, Y.Y., Shao, M.A., Zheng, J.Y., Zhang, X.C., 2005. Spatial–temporal changes of soil organic carbon during vegetation recovery at Ziwuling, China. Pedosphere 15, 601–610. Liu, Y., Tong, J., Li, X., 2005. Analysing the silt particles with the Malvern Mastersizer 2000. Water Conserv. Sci. Technol. Econ. 11, 329–331 (in Chinese with English abstract). Liu, D.W., Wang, Z.M., Zhang, B., Song, K.S., Li, X.Y., Li, J.P., Li, F., Duan, H.T., 2006. Spatial distribution of soil organic carbon and analysis of related factors in croplands of the black soil region, Northeast China. Agric. Ecosyst. Environ. 113, 73–81. Liu, S.L., Guo, X.D., Fu, B.J., Lian, G., Wang, J., 2007. The effect of environmental variables on soil characteristics at different scales in the transition zone of the Loess Plateau in China. Soil Use Manage. 23, 92–99. Mandal, U.K., Warrington, D.N., Bhardwaj, A.K., Bar-Tal, A., Kautsky, L., Minz, D., Levy, G.J., 2008. Evaluating impact of irrigation water quality on a calcareous clay soil using principal component analysis. Geoderma 144, 189–197. Matheron, G., 1963. Principles of geostatistics. Econ. Geol. 58, 1246–1266.

149

McDowell, R., Trudgill, S., 2000. Variation of phosphorus loss from a small catchment in south Devon, UK. Agric. Ecosyst. Environ. 79, 143–157. Meersmans, J., De Ridder, F., Canters, F., De Baets, S., Van Molle, M., 2008. A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium). Geoderma 143, 1–13. Monokrousos, N., Papatheodorou, E.M., Diamantopoulos, J.D., Stamou, G.P., 2004. Temporal and spatial variability of soil chemical and biological variables in a Mediterranean shrubland. Forest Ecol. Manag. 202, 83–91. Moran, P.A., 1950. Notes on continuous stochastic phenomena. Biometrika 37 (1–2), 17–23. Murphy, J., Riley, J.P., 1962. A modified single solution method for determination of phophate in natural waters. Anal. Chim. Acta 27, 31–36. Nelson, D.W., Sommers, L.E., 1982. Total carbon, organic carbon and organic matter, In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of Soil Analysis, Part 2, 2nd Edition. Agronomy Monograph, vol. 9. ASA and SSSA, Madison, WI, pp. 534–580. Nielsen, D.R., Bouma, J., 1985. Soil Spatial Variability. Pudoc, Wageningen. Page, T., Haygarth, P.M., Beven, K.J., Joynes, A., Butler, T., Keeler, C., Freer, J., Owens, P.N., Wood, G.A., 2005. Spatial variability of soil phosphorus in relation to the topographic index and critical source areas: sampling for assessing risk to water quality. J. Environ. Qual. 34, 2263–2277. Priess, J.A., de Koning, G.H.J., Veldkamp, A., 2001. Assessment of interactions between land use change and carbon and nutrient fluxes in Ecuador. Agric. Ecosyst. Environ. 85, 269–279. Purtauf, T., Thies, C., Ekschmitt, K., Wolters, V., Dauber, J., 2005. Scaling properties of multivariate landscape structure. Ecol. Indicators 5, 295–304. Ramos, M.C., Cots-Folch, R., Martínez-Casasnovas, J.A., 2007. Effects of land terracing on soil properties in the Priorat region in Northeastern Spain: a multivariate analysis. Geoderma 142, 251–261. Søvik, A.K., Aagaard, P., 2003. Spatial variability of a solid porous framework with regard to chemical and physical properties. Geoderma 113, 47–76. Schöning, I., Totsche, K.U., Kögel-Knabner, I., 2006. Small scale spatial variability of organic carbon stocks in litter and solum of a forested Luvisol. Geoderma 136, 631–642. Shao, M.A., Wang, Q.J., Huang, M.B., 2006. Soil physics. Higher education press, Bei Jing. Shorten, P.R., Pleasants, A.B., 2007. A stochastic model of urinary nitrogen and water flow in grassland soil in New Zealand. Agric. Ecosyst. Environ. 120, 145–152. SPSS, 1998. SigmaPlot 5.0 Programming Guide. SPSS Inc., Chicago, IL. Su, Z.Y., Xiong, Y.M., Zhu, J.Y., Ye, Y.C., Ye, M., 2006. Soil organic carbon content and distribution in a small landscape of Dongguan, South China. Pedosphere 16, 10–17. Tan, Z., Lal, R., 2005. Carbon sequestration potential estimates with changes in land use and tillage practice in Ohio, USA. Agric. Ecosyst. Environ. 111, 140–152. Trangmar, B.B., Yost, R.S., Uehara, G., 1985. Application of geostatistics to spatial studies of soil properties. Adv. Agron. 38, 45–94. Uyak, V., Ozdemir, K., Toroz, I., 2007. Multiple linear regression modeling of disinfection by-products formation in Istanbul drinking water reservoirs. Sci. Total Environ. 378, 269–280. Van der Park, M., Owens, P.N., Deeks, L.K., Rawlins, B.G., Haygarth, P.M., Beven, K.J., 2007. Controls on catchment-scale patterns of phosphorus in soil, streambed sediment, and stream water. J. Environ. Qual. 36, 694–708. Vervier, P., Pinheiro, A., Fabre, A., Pinay, G., Fustec, E., 1999. Spatial changes in the modalities of N and P inputs in a rural river network. Water Res. 33, 95–104. Walter, C., Viscarra Rossel, R.A., McBratney, A.B., 2003. Spatio-temporal simulation of the field-scale evolution of organic carbon over the landscape. Soil Sci. Soc. Am. J. 67, 1477–1486. Wang, H.Q., Hall, C.A.S., Cornell, J.D., Hall, M.H.P., 2002. Spatial dependence and the relationship of soil organic carbon and soil moisture in the Luquillo, Experimental Forest, Puerto Rico. Landscape Ecol. 17, 671–684. Wang, J., Fu, B., Qiu, Y., Chen, L., 2003. Analysis on soil nutrient characteristics for sustainable land use in Danangou catchment of the Loess Plateau, China. Catena 54, 17–29. Wei, J.B., Xiao, D.N., Zeng, H., Fu, Y.K., 2008. Spatial variability of soil properties in relation to land use and topography in a typical small watershed of the black soil region, northeastern China. Environ. Geol. 53, 1663–1672. Western, A.W., Blöschl, G., 1999. On the spatial scaling of soil moisture. J. Hydrol. 217, 203–224. Western, A.W., Zhou, S.L., Grayson, R.B., McMahon, T.A., Bloschl, G., Wilson, D.J., 2004. Spatial correlation of soil moisture in small catchments and its relationship to dominant spatial hydrological processes. J. Hydrol. 286, 113–134. Wiliams, A., Xing, B.S., Veneman, P., 2005. Effect of cultivation on soil organic matter and aggregate stability. Pedosphere 15, 255–262. Xu, Q.F., Xu, J.M., 2003. Changes in soil carbon pools induced by substitution of plantation for native forest. Pedosphere 13, 271–278. Zhang, C., McGrath, D., 2004. Geostatistical and GIS analyses on soil organic carbon concentrations in grassland of southeastern Ireland from two different periods. Geoderma 119, 261–275. Zhang, X.Y., Sui, Y.Y., Zhang, X.D., Meng, K., Herbert, S.J., 2007. Spatial variability of nutrient properties in black soil of Northeast China. Pedosphere 17 , 19–29. Zheng, J.Y., Wang, L.M., Shao, M.A., Wang, Q.J., Li, S.Q., 2006. Gully impact on soil moisture in the gully bank. Pedosphere 16, 339–344.