Evaluating soil quality indices in an agricultural region of Jiangsu Province, China

Geoderma 149 (2009) 325–334

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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

Evaluating soil quality indices in an agricultural region of Jiangsu Province, China Yanbing Qi a, Jeremy L. Darilek a, Biao Huang a,⁎, Yongcun Zhao a, Weixia Sun a, Zhiquan Gu b a

Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, 71 E. Beijing Road, Nanjing, Jiangsu Province 210008, People's Republic of China b Zhangjiagang County Extension Agency, Zhangjiagang, Jiangsu Province 215600, People's Republic of China

a r t i c l e

i n f o

Article history: Received 24 June 2008 Received in revised form 16 December 2008 Accepted 17 December 2008 Available online 29 January 2009 Keywords: Intensive agricultural system Soil quality evaluation Indicator methods Integrated quality index models Minimum data set

a b s t r a c t Agricultural soil quality evaluation is essential for economic success and environmental stability in rapidly developing regions. At present, a wide variety of methods are used to evaluate soil quality using vastly different indicators. A universally accepted method of soil quality evaluation would assist agriculture managers, scientists, and policy makers to better understand the soil quality conditions of various agricultural systems. This study analyzes the soil quality of Zhangjiagang County, a rapidly developing region of China (n = 431), using the Integrated Quality Index (IQI) and Nemero Quality Index (NQI) in combination with three indicator selection methods: Total Data Set (TDS), Minimum Data Set (MDS), and Delphi Data Set (DDS). A total of 22 soil parameters were used with the TDS method. These six combinations of soil quality evaluation methods were then analyzed to determine which is best suited for soil quality evaluation in the county. All evaluation methods revealed that the county has fair to favorable soil quality with Anthrosols (Inceptisols) generally having higher quality than Cambosols (Entisols). Regression and correlation analysis all showed that the IQI preformed better than the NQI, in three indicator selection methods and IQI match analysis was 9% higher than NQI. Though the TDS method is the most accurate, it was concluded that using the IQI index and the MDS method can adequately represent the TDS method (r2 = 0.65) and thus save time and money. © 2008 Elsevier B.V. All rights reserved.

1. Introduction Soil quality of agriculture land in rapidly developing regions is economically important but environmentally unstable (Wander et al., 2002). A better working knowledge of a soil's quality is important to improve sustainable land use management (McGrath and Zhang, 2003), provide early warning signs of adverse trends, identify problem areas (Bindraban et al., 2000), and provide a valuable base against which subsequent and future measurements can be evaluated. This knowledge can only come from reliable, accurate soil quality evaluation. Comprehensive evaluation of agricultural soil quality, which refers to the condition and capacity of farmland including its soil, weather, and biological properties, for purposes of production, conservation, and environmental management (Pieri et al., 1995; Stamatiadis et al., 1999), is essential to making wise decisions that will improve crop production and environmental sustainability. Soil quality evaluation is still a developing, but promising field of agriculture science. With improved technical tools, information, and methodology for evaluating soil quality comes the ability to integrate significant, site-specific remediation strategies into agriculture operations (Ditzler and Tugel, 2002). Though agricultural soil quality evaluation has progressed in

⁎ Corresponding author. Tel.: +86 25 86881296; fax: +86 25 86881000. E-mail address: [email protected] (B. Huang). 0016-7061/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.geoderma.2008.12.015

recent years due, in large part, to the emphasis on global environmental change, improving soil quality evaluation is imperative for the development of sustainable agriculture and may also be used to judge the sustainability of soil management and land use systems (Smith et al., 1994; Wang and Gong, 1998). Specifically, suitable evaluation methods and appropriate indicators of soil quality are among the most important considerations (Ditzler and Tugel, 2002) due to their significant influence on soil quality results. Many soil quality evaluation methods have been developed since the USDA Soil Conservation Service released its land capability classification system in 1961 (Klingebiel and Montgomery, 1961), such as a soil quality card design and test kit (Ditzler and Tugel, 2002), soil quality index methods (Doran et al., 1994; Doran and Jones, 1996), multiple variable indicator kriging methods (Nazzareno and Michele, 2004), and the dynamic variation of soil quality models (Larson and Pierce, 1994). Among these, soil quality indices are perhaps the most commonly used methods today (Andrews et al., 2002), because they are easy to use and quantitatively flexibility. Soil quality indices are especially relevant to soil management practices because they use site-specific indicators of soil conditions that integrate anthropogenic effects over time and over multiple types of effects (Wang and Gong, 1998; Arshad and Martin, 2002). Unfortunately, one of the most limiting aspects of soil quality evaluation today is the lack of a universally acceptable method for developing soil quality indices. There exists a tautological development of new indices, which appears to be endemic, self-propagating,

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and little justified (Wang and Gong, 1998; Sun et al., 2003; Zhang et al., 2004), and researchers should place greater emphasis on evaluating the suitability of existing indices prior to developing new ones. A number of recent papers have evaluated soil quality, but usually a selfdefined indicator method and equation is introduced for which the indices were developed. To our knowledge, no comparison has been made among indices based on different indicator methods and models. A universally accepted index should include clear methods for indicator selection, scoring, and weighting, as well as a universal model that would aid in comparison of soils of different regions and in scientific communication. The development of a universal soil quality index should follow a logical path: (1) establish a representative indicator method, (2) assign weights for selected indicators, and (3) validate the index using a model. Indices formulated on ecological principles and properly validated will better communicate the complexity of quality integrity. Soil quality indicators have been defined as soil processes and properties that are sensitive to changes in soil functions (Doran and Jones, 1996; Aparicio and Costa, 2007). It is important to build a simple, sensitive, and workable indicator method for soil quality evaluation (Dumanski and Pieri, 2000). Soil quality indicators should be a combination of chemical, physical, and biological properties (Herrick et al., 2002; Aparicio and Costa, 2007). Several authors have proposed sets of soil quality indicators (Larson and Pierce, 1994; Doran and Parkin, 1994; Karlen et al., 1998), and have evaluated soil quality based on the total data set (TDS) indicator method they selected. Also, representative indicators were suggested by many authors, such as the minimum data set (MDS), selected according to correlation between indicators and ease of measurement (Andrews et al., 2002; Rezaei et al., 2006; Govaerts et al., 2006) and the Delphi data set (DDS) (Zhang et al., 2004), selected according to the importance of the indicators to soil quality based on the opinion of experts (Herrick et al., 2002). A common feature of these indicator methods is that they are

all identified and described by scientists and land managers according to their own terminology (Ditzler and Tugel, 2002). Soil quality index calculation, a core issue in soil quality evaluation, is usually an indirect approach based on an integrated evaluation of quality indicators and their weights. It is a widely accepted approach because of its advantages in identifying the systematic complexity of soil productivity under natural conditions and farming practices, through the use of fuzzy mathematical methods to evaluate relationships between certain soil factors and land productivity (Burrough, 1989; Fu, 1991; Tang, 1997; Dobermann and Oberthur, 1997; McBratney and Odeh, 1997; Sun et al., 2003). Many quantitative models have been developed in soil quality index calculation, such as the integrated quality index (IQI) and Nemoro quality index (NQI). The IQI model, developed from the soil quality index of Doran and Parkin (1994), is the sum of corresponding weight values of all the selected indicators, which combines metrics into an index by an equation that uses a simple scoring system that weights all quality indicators equally. The NQI model, developed by Nemoro (Han and Wu, 1994; Qin and Zhao, 2000), is based on the average and the minimum indicator score, and indicator weights are not used in this model. The results are affected by the minimum indicator score and reflect the Law of the Minimum in crop production (van der Ploeg et al., 1999). The disparity between soil quality indicator methods and models leads to questions about whether the application of various indices would yield different results. However, opportunities for comparison among indices are rare because it is unusual to have more than one soil quality index available for any particular area. This paper compares these three indicator methods and two index models using Zhangjiagang County, a rapidly developing region of China, as a case study. The objectives of this study are to (a) evaluate soil quality of Zhangjiagang County, (b) assess the function of different indicator methods and soil quality index models, and (c) chose a suitable regional indicator method and soil quality index model.

Fig. 1. Soil map of Zhangjiagang County with sample sites.

Y. Qi et al. / Geoderma 149 (2009) 325–334

2. Materials and methods

Table 2 Standard scoring functions and parameters for quantitative soil indicators in Zhangjiagang County, 2004

2.1. Study site descriptions Zhangjiagang County, Jiangsu Province, China (Fig. 1) is located on the Yangtze River Delta (YRD) (31° 43'–32° 01' N, 120° 22'–120° 49' E) and covers a total terrestrial area of 799 km2 with arable land area of 409 km2 and a population of 0.89 million in 2004. Zhangjiagang County has a humid monsoon climate in the north subtropical zone, with four distinct seasons, plentiful precipitation, abundant sunlight, and a long frost-free period. The average annual temperature and precipitation is 15.2 °C and 1039.3 mm. Since the 1980's, at the beginning of the reform and opening of China, crop land management was transformed from collective farming to individual family farms and intensive agricultural production has flourished (Huang et al., 2006, 2007). These intensive production practices are characterized by extensive fertilizer and pesticide application, decreasing dependence on organic amendments, frequent tillage and irrigation (Andrews et al., 2002; Huang et al., 2007), and mechanized harvesting. Soil quality and environmental concerns, such as ground water quality and food safety, have also multiplied (Andrews et al., 2002). The county has predominantly flat topography, slightly elevated in the south, and is situated along the Yangtze River. Zhangjiagang County can be divided into two soil orders, Anthrosols (Inceptisols) and Cambosols (Entisols), according to Chinese Soil Taxonomy (CRGCST, 2001) (Fig. 1). Anthrosols were developed from lacustrine deposits of alluvium in the south, with a loamy clay texture, and Cambosols were developed from Yangtze River neo-alluvium in the north and has a sandy loam texture. 2.2. Sampling, processing, and analyzing A soil survey was conducted in 2004 and a total of 431 samples (287 for Cambosols and 144 for Anthrosols) were taken on agriculture land within the county based on spatial homogeneity, soil types, and land use. The samples were taken in the fall after harvest and before the next cropping season in order to avoid the effect of direct fertilization during the crop growing season. Each soil sample was a composite of sub-samples taken from 6 points within 350 m2 of agricultural land and on a soil surface disturbed by tillage (0–20 cm). Latitude and longitude of each sample site was recorded using a handheld global positioning system (GPS). Interviews were conducted with the landowners at each sample site and information was collected on their agriculture management practices such as crop rotation, fertilization, yield, irrigation, and drainage. Irrigation guarantee ratios, the number of years of Table 1 Protocol measurements for indicators selected in the study Indicator

Protocol

Reference

Organic matter Total N

Dichromate wet combustion

Nelson and Sommers (1982)

Kjeldahl

Bremner and Mulvaney (1982) Olsen and Sommers (1982) Lu (2000)

Total P HNO3–K

Digestion, spectrophotometer detection Nitrate digestion, flame photometry detection pH Soil paste CEC Sodium saturation NaHCO3–P Sodium bicarbonate extraction, colorimetric detection NH4OAC–K Ammonium acetate extraction, flame spectrometry detection DTPA–Cu, Fe, DTPA extraction, AAS detection Mn, Zn H2O–B Hot water extraction, colorimetric detection HOAc–Si Acetic acid/sodium acetate extraction, colorimetric detection

327

Indicator

FTa

L

U

Cultivated layer depth (cm) Obstacle horizon depth (cm) Irrigation guarantee ratio (%) Drainage modulus (m3/s⁎km2) CEC (clol/kg) SOM (g/kg) NaHCO3–P (g/kg) HNO3–K (g/kg) DTPA–Zn (mg/kg) H2O–B (mg/kg) HOAc–Si (g/kg) TN (g/kg) TP (g/kg) NH4OAc–K (g/kg) DTPA–Cu (mg/kg) DTPA–Fe (mg/kg) DTPA–Mn (mg/kg) Obstacle horizon thickness (cm) pH

UL

10

20

UL

0

100

UL

0

100

UL

0

4.1

UL UL UL UL UL UL UL UL UL UL UL UL UL LL

10 15 5 250 1.5 0.5 1.5 0.75 0.4 40 2 2 10 0

20 30 15 750 3 1 130 1.5 1 100 4 32 20 40

PL

4.5 (L1) 5.5 (L2)

6.5 (U1) 8.5 (U2)

SSF Equationb

f ð xÞ =

f ð xÞ =

8 < :

8 < :

0:1

x−L + 0:1 0:9× U−L 1

1

x−L 1−0:9× U−L 0:1

x≤L L≤x≤U x≥U

x≤L L≤x≤U x≥U

8 0:1 > > x−L1 > > < 0:9× + 0:1 L2 −L1 f ð xÞ = 1 > > x−U > 1 > : 0:9× + 0:1 U2 −U1

xbL1 or x≥U2 L1 ≤x≤L2 L2 ≤x≤U1 U1 ≤x≤U2

a FT means function type; UL means upper limit; LL means lower limit; PL means peak limit. b SSF means standard scoring function; in these three equations, x is the monitoring value of the indicator, f(x) is the score of indicators ranged between 0.1 and 1, and L and U are the lower and the upper threshold value, respectively.

guaranteed sufficient irrigation during the most recent period (minimum 15 years) divided by the number of years in the period (Luo, 1991), and drainage modulus, the drainage rate within a unit area (Zhou et al., 2004), were obtained from the Water Conservancy Bureau of Zhangjiagang County (Unpublished data). Soil texture of each site was obtained from the second National Soil Survey (Zhangjiagang Soil Survey Office, 1984). Topography, cultivated layer depth, obstacle horizon type, thickness, and depth were measured in the field and described for each sample site. Samples were air-dried and passed through a 2 mm sieve. Table 1 indicates the analytical protocols selected. 2.3. Soil quality evaluation method 2.3.1. Indicator scoring Because of different indicator units, a standard scoring function (SSF) (Karlen and Scott, 1994; Andrews et al., 2002) was used to score soil indicators to use with each indicator method. Four types of indicators were divided according to their function on soil quality: upper limit, lower limit, peak limit, and descriptive function. The SSF equations for the indicators are listed in Table 2. For the description function indicators, membership functions were based on expert ranking combined with the Delphi method described below. Table 3

Lu (2000) Lu (2000) Olsen et al. (1954) Lu (2000) Lindsay and Norvell (1978) Lu (2000) Lu (2000)

Table 3 Membership functions on expert ranking of the descriptive indicators Topography

Score

Obstacle horizon

Score

Texture

Score

Flat Polder Lower than flat Higher than flat Hill

0.98 0.83 0.82 0.73 0.45

No Iron pan Ca nodule pan Claypan Albic hroizon

1 0.76 0.73 0.63 0.6

Heavy loam Medium loam Light loam

0.89 0.72 0.51

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Table 4 Results of principal component analysis (PCA) of soil quality indicators from an intensive agricultural system in Zhangjiagang County, 2004 PCsa

PC1

PC2

PC3

PC4

PC5

PC6

PC7

Eigenvalue Percent Cumulative percent

4.79 21.772 21.772

2.463 11.193 32.965

2.289 10.402 43.368

1.585 7.204 50.572

1.295 5.885 56.456

1.099 4.994 61.45

1.033 4.696 66.146

0.175 0.18 0.608 0.646

0.051 −0.209 0.485 0.469

0.628 −0.55 −0.23 0.302 0.334 −0.202 0.092 0.668 −0.275 −0.015 −0.463 0.057 −0.034 0.036 0.013 −0.446 0.064 −0.059 −0.004 −0.008

Eigenvectors Cultivated layer depth Topography Obstacle horizon type Obstacle horizon thickness Obstacle horizon depth Irrigation guarantee ratio Drainage modulus Texture pH CEC SOM NaHCO3–P HNO3–K DTPA–Zn H2O–B HOAc–Si TN TP NH4OAc–K DTPA–Cu DTPA–Fe DTPA–Mn a b c

0.285

0.569 −0.571

0.279 −0.009

0

−0.047

− 0.054

−0.238 −0.131

0.49

0.373

0.194

−0.056

0.089 0.747b 0.772 0.766 0.784c − 0.188 − 0.179 0.162 0.239 − 0.259 0.641 − 0.689 0.464 0.081 0.217 0.344

0.065 0.108 0.255 −0.37 −0.334 0.076 −0.126 0.258 −0.076 0.409 0.509 0.363 0.597 0.405 0.247 0.251 −0.177 −0.002 0.526 0.244 −0.042 0.45 0.488 0.116 0.365 0.549 −0.071 0.25 −0.08 0.208 0.97 0.241

0.226 −0.052 −0.138 −0.068 0.134 0.194 −0.088 −0.456 −0.144 −0.052 0.153 0.206 −0.154 0.578 0.63 0.073

−0.017 0.001 0.005 0.204 0.058 −0.035 0.187 −0.055 0.061 0.389 0.165 0.095 −0.004 0.129 −0.389 −0.412

0.531 −0.037 0.068 −0.163 0.006 0.358 −0.101 −0.055 0.521 −0.242 0.01 0.036 0.083 −0.083 −0.152 −0.219

−0.517 0.047 0.029 −0.101 −0.015 0.283 −0.113 0.193 −0.16 −0.251 0.07 0.077 −0.044 0.226 0.236 −0.135

PC, principal component. Underlined factor loadings are considered highly weighted. Factor loadings in bold correspond to the indicators included in the MDS.

shows the membership functions for the main soil horizon compositions found in the study area. 2.3.2. Indicator selection Representative indicators are a key concern of soil quality evaluation; they should cover a wide range of characteristics but each should affect soil quality directly (Wang and Gong, 1998). In the TDS indicator method, 22 soil physical and chemical properties and farming management practices were included (Tables 2 and 3). It is common knowledge that farm management practices, such as fertilization, irrigation, pesticide, and crop residual incorporation, affect soil quality (Huang et al., 2006, 2007), especially in intensive production systems. Fertilization, pesticide, and crop residual incorporation can be represented by soil fertility (N, P, K), micro nutrients (Cu, Zn, B, Mn), and soil carbon (C), respectively. Irrigation guarantee ratios and drainage modulus were added to the indicator data set because of their importance in rice–wheat production systems. The

obstacle horizons affect growth of crop roots significantly in this rice– wheat production system, therefore, the type, depth, and thickness of obstacle horizons were also chosen as soil quality indicators. In addition, texture was added because it plays an important role in determining the amount of organic matter that is stabilized in the soil. To select a representative MDS, the principle component analysis (PCA) method was used because of its MDS selection ability (Doran and Parkin, 1994) (Table 4). The PCA method was employed as a data reduction tool to select the most appropriate indicators of site potential for the study area from the list of indicators. Based on the MDS selection procedure described by Andrews et al. (2002) and Govaerts et al. (2006), only the PCs with eigenvalues ≥1 were considered for the MDS. Within each PC, indicators receiving weighted loading values within 10% of the highest weighted loading were selected for the MDS. When more than one variable was retained within a PC, correlation among the indicators was examined to determine if any variable was redundant. Multiple regression was performed to verify how well the MDS represented the intensive production system goal, following the methods suggested by Andrews et al. (2002). The Delphi method was used to select quality indicators in the DDS indicator method from a data set. The method allowed experts to independently select and rank quality indicators and then modify their selection after reviewing the selections of other experts. In this case, the experts were six local agricultural extension agents chosen based on their familiarity with soil quality issues in Zhangjiagang. Three rounds of selections and ranks were deemed to be optimal for this study. Thorough explanations of the Delphi method are given by Herrick et al. (2002) and Zhang et al. (2004). 2.3.3. Weight assignment In this paper, weights for TDS and MDS indicator methods were assigned by communality of each indicator (Table 5), calculated by mathematical statistics of standardized factor analysis (FA) (Sun et al., 2003; Shukla et al., 2006). The analytical hierarchy process (AHP) method was used to assign indicator weights for DDS indicator method, after the spatial data was analyzed (Table 6). The application of the AHP method, developed by Saaty (1977) for environmental assessment, has been used extensively (Lai et al., 2002; Komac, 2006) and was used to define the factors that govern soil quality more transparently and to derive their weights. The consistency test for single and general hierarchy storing was conducted by calculating the average random consistency index (RI) (Zhang et al., 2004). The results indicated that all RIs for single and general hierarchy storing were lower than 0.1, which means all the matrixes in the hierarchy A, B, and C were logically constructed (Fig. 2). 2.3.4. Calculation of soil quality index After indicators were scored and weighted, soil quality indices were calculated using the Integrated Quality Index equation (Eq. (1)) (IQI) (Doran and Parkin, 1994) and the Nemoro Quality Index equation

Table 5 Estimated communality and weight value of each soil quality indicator in TDS and MDS indicator methods Indicator Cultivated layer depth Topography Obstacle horizon type Obstacle horizon thickness Obstacle horizon depth Irrigation guarantee ratio Drainage modulus Texture pH CEC SOM a

TDS

MDS

Indicator

COMa

Weight

COM

Weight

0.687 0.647 0.825 0.844 0.787 0.496 0.624 0.767 0.739 0.78 0.781

0.047 0.044 0.057 0.058 0.054 0.034 0.043 0.053 0.051 0.054 0.054

0.557 0.444

0.158 0.126

0.295

0.083

0.506

0.143

0.626

0.177

COM means communality of each indicator.

NaHCO3–P HNO3–K DTPA–Zn H2O–B HOAc–Si TN TP NH4OAc–K DTPA–Cu DTPA–Fe DTPA–Mn

TDS

MDS

COM

Weight

0.673 0.619 0.401 0.41 0.679 0.666 0.785 0.707 0.546 0.661 0.428

0.046 0.043 0.028 0.028 0.047 0.046 0.054 0.049 0.038 0.045 0.029

COM

Weight

0.493

0.139

0.613

0.173

Y. Qi et al. / Geoderma 149 (2009) 325–334 Table 6 Contribution weight of soil factors to soil productivity calculated by the AHP Hierarchy A Hierarchy C

NaHCO3–P HNO3–K DTPA–Zn H2O–B HOAc–Si Obstacle horizon type Obstacle horizon thickness Obstacle horizon depth Texture pH CEC SOM Cultivated layer depth Topography Irrigation guarantee ratio Drainage modulus Total

Hierarchy B B1

B2

B3

B4

B5

0.0604

0.1701

0.2632

0.2126

0.2936

0.1426 0.4809 0.119 0.1005 0.157 0.1238 0.4398 0.4365 0.0856 0.1875 0.2216 0.5053 0.2941 0.7059

1

1

1

1

0.241 0.759 1

Combined weight ∑ Bi × Ci 0.0086 0.0291 0.0072 0.0061 0.0095 0.0211 0.0748 0.0743 0.0225 0.0494 0.0583 0.133 0.0625 0.1501 0.0707 0.2228 1

(Eq. (2)) (NQI) (Han and Wu, 1994; Qin and Zhao, 2000), using every possible combination of index and indicator method. n

IQ I = ∑ Wi Ni

ð1Þ

i=1

2.3.5. Criteria of soil quality grades Soil quality indices were divided into four grades according to their classification criteria (Table 7). The criterions for IQIs using the TDS indicator method (IQITDS), were based on the classification of region type and fertility of cultivated land in China (ISMAPRC, 1996). Other index criterions were adjusted based on IQITDS in terms of actual index value. All criteria intervals were calculated to be equidistant (0.1). Grade I is considered most suitable for plant growth, grade II is suitable for plant growth but with some limitations, grade III has more severe limitations than grade II, and grade IV soil has the most severe limitations for plant growth. Soil quality grade spatial distribution was mapped with the aid of ArcGIS software using the ordinary kriging method. 2.3.6. Comparison of indices Match/mismatch within indices was computed by direct comparison and Kappa analysis using soil quality grades. Direct comparison of quality grades in each site was conducted by comparing the number of index and indicator combinations that computed the same soil quality grade for each sample site. For Kappa analysis, sites were classified using four soil grades as described above, and a Kappa value was computed to show next levels of agreement: (1) null b0.05; (2) very low: 0.05–0.2; (3) low: 0.2–0.4; (4) moderate: 0.4–0.55; (5) good: 0.55–0.7; (6) very Good: 0.7–0.85; (7) almost perfect: 0.85–0.99; and (8) perfect: 1 (Monserud and Leemans, 1992; Borja et al., 2008). To further describe the relationship within the indices, linear relationship (correlation) within indices and regression within indicator methods were conducted while using SigmaPlot software to plot them.

where Wi is the assigned weight, Ni is the indicator score, and n is the number of indicators.

3. Results

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi p2ave + p2min n−1 × NQI = 2 n

3.1. Soil quality based on the TDS indicator method

ð2Þ

where Pave is the average scores of the selected indicators in each site, Pmin is the minimum scores of the selected indicators in each site, and n is the number of indicators.

329

In all the indicators, the obstacle horizon (type, thickness, and depth) had higher weights, than nutrient conservation properties (SOM, CEC, pH, texture and TP), and micronutrients (DTPA–Zn, H2O–B, DTPA–Cu and DTPA–Mn) had the lowest weights (Table 5).

Fig. 2. Hierarchical structure for the indicator weight assignments.

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Table 7 Criteria for soil quality grade divisions in different indicator methods and models Soil quality index model IQI

NQI

Indicator method TDS MDS DDS TDS MDS DDS

Soil quality grade I

II

III

IV

IQITDS ≥ 0.76 IQIMDS ≥ 0.78 IQIDDS ≥ 0.91 NQITDS ≥ 0.55 NQIMDS ≥ 0.80 NQIDDS ≥ 0.65

0.76 N IQITDS ≥ 0.66 0.78 N IQIMDS ≥ 0.68 0.91 N IQIDDS ≥ 0.81 0.55 N NQITDS ≥ 0.45 0.80 N NQIMDS ≥ 0.70 0.65 N NQIDDS ≥ 0.55

0.66 N IQITDS ≥ 0.56 0.68 N IQIMDS ≥ 0.58 0.81 N IQIDDS ≥ 0.71 0.45 N NQITDS ≥ 0.35 0.70 N NQIMDS ≥ 0.60 0.55 N NQIDDS ≥ 0.45

0.56 N IQITDS 0.58 N IQIMDS 0.71 N IQIDDS 0.35 N NQITDS 0.60 N NQIMDS 0.45 N NQIDDS

According to the IQI model based on the TDS indicator method, soil quality in Zhangjiagang in 2004 was high and grade II and III were the dominant areas (Fig. 3, Table 8). There was little grade I soil, and areas with grade II, III, and IV quality accounted for 37.2% (15215 ha), 57.5% (23540 ha), and 4.5% (1826 ha) of the total soil area, respectively. Of the two soil types, Anthrosols in the south always had higher soil quality with about 70% grade II soil and about 30% grade III than Cambosols in the north with 72% grade III soil and 23% grade II soil. This disparity may, in part, be related to soil texture derived from different parent materials (Ball et al., 2000; Mubarak et al., 2005). From the NQI model based on the TDS indicator method, soil areas with grade I, II, III and IV accounted for 1.4%, 34.6%, 60.5%, and 4.0% of the total soil area, respectively. The results also showed higher soil quality in Anthrosols than Cambosols. For the Anthrosols in the south, grade II soil account for 68.2% of the total, where as grade III soil account for 75.8% of the total Cambosols area (Table 8). 3.2. Soil quality based on the MDS indicator method Seven principle components had eigenvalues ≥1 in the MDS indicator selection method (Table 4). Highly weighted variables for the first principle component (PC1) included soil texture, pH, CEC, and SOM. Correlation coefficients between these four variables were well correlated. SOM was the most highly correlated and thus chosen for the MDS as the most representative of that group. For PC2, PC3, and PC4, obstacle horizon depth and HNO3–K, obstacle horizon depth and NH4OAc–K, and DTPA–Cu and DTPA–Fe were highly weighted and well correlated, respectively. HNO3–K, obstacle horizon depth and

DTPA–Fe were the most highly weighted and chosen for the MDS. Only one indicator each was highly weighted under PC5, PC6, and PC7, i.e. topography, drainage modulus, and cultivated layer depth, therefore these variables were all added to the MDS. The final MDS was thus comprised of cultivated layer depth, topography, obstacle horizon depth, drainage modulus, SOM, HNO3–K, and DTPA–Fe. From the IQI index based on the MDS indicator method, soil quality was evaluated quantitatively. As shown in Fig. 3 and Table 8, soil areas with grade I, II, III, and IV accounted for 1.5%, 35.5%, 60.6%, and 2.4% of the total soil area, respectively. With more grade II soil, Anthrosols in the south had higher quality than Cambosols (Table 8). The soil quality grades from the NQI model based on the MDS indicator method had similar results as the IQI model for Cambosols as well as Anthrosols. 3.3. Soil quality based on the DDS indicator method Using the IQI index and the DDS indicator method, grade III soil was dominant and accounted for 61.9% of the total soil area in Zhangjiagang County. Grade II accounted for 34.0% of the total soil area with a total area of 13903.7 ha, and grade I and IV account for very small areas (Table 8). Grade II soil, dominantly distributed in Anthrosols in the southern of the county accounts for 70.5% of the total Anthrosols; grade III soil, dominantly distributed in Cambosols in the northern of the county, accounts for 77.5% of the total Cambosols; grade IV soil is dominantly distributed in Cambosols; and grade I soil is dominantly distribute in Anthrosols (Fig. 3, Table 8). Using the NQI index and the DDS indicator method, soil areas with grade I, II, III, and IV accounted for 0.2%, 33.2%, 62.5%, and 4.1% of the

Fig. 3. Soil quality grade distribution based on different indicator methods and indices in Zhangjiagang County.

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Table 8 Area and percentage of soil quality grades in different indicator methods and models in Zhangjiagang County, 2004 Indicator

Grade

Method TDS

MDS

DDS

IQI

NQI

Cambosols I II III IV I II III IV I II III IV

Anthrosols

Total

Cambosols

Anthrosols

Total

Area

%

Area

%

Area

%

Area

%

Area

%

Area

%

13 6341 19927 1505 416 5603 20813 953 0 4650 21523 1612

0.05 22.82 71.72 5.41 1.5 20.17 74.91 3.43 0 16.74 77.46 5.8

322 8875 3613 322 194 8935 3979 23 75 9254 3785 17

2.45 67.58 27.52 2.45 1.48 68.04 30.31 0.17 0.57 70.47 28.83 0.13

335 15215 23540 1826 610 14538 24790 975 75 13904 25308 1628

0.82 37.19 57.53 4.46 1.49 35.53 60.59 2.38 0.18 33.98 61.85 3.98

94 5199 21051 1651 79 5077 21339 1290 54 4588 21712 1432

0.34 18.71 75.76 5.94 0.28 18.27 76.8 4.64 0.19 16.51 78.14 5.15

477 8950 3698 5 150 9221 3692 68 15 9002 3873 241

3.63 68.16 28.16 0.04 1.14 70.22 28.12 0.52 0.11 68.56 29.5 1.84

571 14149 24749 1656 229 14297 25032 1358 69 13590 25585 1673

1.4 34.58 60.49 4.05 0.56 34.94 61.18 3.32 0.17 33.21 62.53 4.09

total soil area, respectively. The results also showed higher soil quality in Anthrosols than Cambosols. For the Anthrosols in the south, grade II soil accounted for 68.6% of the total area, and for Cambosols, grade III soil accounted for 78.1% of the total area (Table 8). 3.4. Soil grade distribution comparison The evaluation results, based on the six indices, all showed that moderate quality (grade II and III) soil areas were dominant and accounted for about 90% of the total soil area in Zhangjiagang County. Areas with high quality (grade I) and low quality (grade IV) were limited in terms of the suitable soil quality grade criterions established (Table 7). From the soil quality grades distribution map (Fig. 3), a similar soil grade distribution trend could be found with every combination of indicator method and index model. Grade I and grade II were distributed in the south and grade III and IV were distributed in the north. There was no large mapping disparity found between indicator methods or indices. Visually, IQITDS is strikingly similar to NQITDS and IQIMDS is similar to NQIMDS, but IQIDDS and NQIDDS are not as similar to one another. This visual trend is confirmed by comparing match analysis of the three indicator methods. 3.5. Match analysis Match analysis showed a high level of agreement among the six indices. Direct comparison showed that agreement for the 431 sample

sites represents 35.3% of the cases for six indices, 62.7% for five out of six indices, and 100% for four out of six indices. Agreement within the IQI model accounted for 57.3% of all cases, and 48.3% for the NQI model. Match analysis showed 88.4% agreement within the TDS method, 81.2% within the MDS method, and 66.4% within the DDS method. Disagreement accounted for 10.0%, 14.2%, and 17.4% for IQITDS, IQIMDS, and IQIDDS, respectively, and 12.5%, 18.8%, and 28.3% for NQITDS, NQIMDS, and NQIDDS, respectively. Kappa statistical analysis showed an acceptable level of agreement among soil quality grades with a total Kappa value of 0.62. Kappa values for IQI and NQI models were 0.76 and 0.71, respectively, and for TDS, MDS, and DDS were 0.87, 0.82, and 0.78, respectively. 3.6. Regression and correlation Statistical analysis showed significant differences among six indices but high correlation coefficients. The IQI value calculated from the three indicator methods showed significant differences (p b 0.01), where IQIDDS (0.79) N IQIMDS (0.66) N IQITDS (0.64), but the IQI's have significant correlation between each other (Fig. 4). Similar results were derived based on the NQI values calculated from these three indicator methods which also had significant differences (p b 0.01), where NQIMDS (0.64) N NQIDDS (0.51) N NQITDS (0.43), and also had significant correlation among them (Fig. 4). From the linear relationship of the indices among different indicator methods and models (Fig. 4), the correlation coefficients were higher and more predictable when the IQI model was used than

Fig. 4. Linear relationship of indices comparing the MDS and DDS indicator methods with the TDS indicator method.

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when the NQI was used, and IQI had higher r2 values. When linear regression is analyzed to compare MDS to DDS using both indices, the r2 value of DDS (0.86) was better than MDS (0.27). When the indices are separated, the MDS gives better results than the DDS (Fig. 4). Additionally, the correlation coefficient was the highest when the MDS indicator method and IQI model was utilized (Fig. 4). 4. Discussion 4.1. Soil quality In general, the soil quality status in Zhangjiagang County is moderate to high. The evaluation results based on the six indices showed that the soil areas with moderate quality (grade II and III) were dominant and accounted for 90% of the total soil area, and high quality (grade I) and low quality (grade IV) were limited (Table 8). Generally, an area can be divided into many different soil quality regions due to soil heterogeneity, as has been indicated by research in other areas (Zhang et al., 2004). Zhangjiagang can be divided into two distinct regions: (1) the sandy loam, medium quality region, which closely corresponds with the Cambosols soil order located in the northern part of the county along the Yangtze River. Soil quality in this area is significantly limited by low SOM and high soil pH that is considered to be too alkaline for rice cultivation; (2) the loamy clay, higher quality region corresponds with Anthrosols, located in the southern part of the county. Although this soil has higher concentrations of SOM and nutrients, soil quality in some soils of this area is classified as severely limited due to a compacted obstacle horizon. Similar to other areas in the YRD region like Rugao, Wuxi, and Gaoyou, soil quality was affected by continual intensive production which has taken place since the 1980's (Zhang et al., 2004; Huang et al., 2006, 2007). Smallholder farm communities often improve soil quality by increasing the active participation of farmers (Mowo et al., 2006; Huang et al., 2007). Incorporation of crop residues usually improves SOM and TN directly, and induces accumulation of C and N (Fischer et al., 2002; Karlen et al., 2006; Raiesi, 2006; Huang et al., 2007). Land use from dry land to paddy fields usually improves soil quality (Bhandari et al., 2002), and fertilization results in SOM and nutrient increase directly. Although continual intensive production practices have improved soil fertility significantly, there can be averse consequence for the overall environment. Heavy application of chemical fertilizers, especially N, and decreased use of organic fertilizers, has induced acidification (Shao et al., 2006) and soil carbon/nitrogen (C/N) ratio decrease in Anthrosols (Qi et al., 2008). Soil acidification may also induce variation of nutrient cycling (Kemmitt et al., 2005) and bioavailability of some heavy metals (Myaer, 1998). Fortunately, the soil pH of Anthrosols in Zhangjiagang is still above 4.5. The C/N ratio decrease, which enhances the activity of microbes and accelerates mineralization of SOM will result in high N mineralization rates (Vanlauwe et al., 1996; Lupwayi and Haque, 1998; Raiesi, 2006), slow inorganic N accumulation in the soil due to N leaching and denitrification (Vanlauwe et al., 1996; Lupwayi and Haque, 1998), and decreased C fixation capacity due to C release from the soil. C release and N leaching contribute to the greenhouse effect and threaten safety of ground and surface water.

wheat system, or were of no importance for our index comparison on a small regional scale. Inclusion of micronutrients, such as DTPA–Fe, would give farmers and scientists a more balanced view of soil nutrition, which is not solely focused on N, P, and K. Different from the TDS method, the SOM and DTPA–Fe have the highest weights and the obstacle horizon depths have the lowest weights in the MDS indicator method (Table 5). The MDS and DDS indicators were selected using the TDS data set, and the indicator number decreased from TDS (22) to DDS (16) to MDS (7). Typically, more indicators represent soil quality more comprehensively, but reduplication becomes a problem when there is significant correlation between properties, such as SOM and TN, and lab analysis becomes cumbersome with so many soil properties. On the other hand, the deletion of some soil properties means the lost of soil quality information contained by the deleted indicators. For instance, in the MDS selection procedure, only the PCs with eigenvalues ≥1 and indicators receiving weighted loading values within 10% of the highest were selected for the MDS (Table 4). If the indicator with the highest weighted loading has significant correlation with other indicators, the indicator can adequately represent another indicator in the PC, but, some soil quality information will lost. Based on correlation analysis, the MDS method is more suitable than the DDS method to adequately represent the TDS method in our study. With the MDS method, there is little duplication, but indicator numbers are adequate for soil quality evaluation. One reason for higher r2 values, better correlation analysis, and lower instances of duplication is due to the smaller number of indicators in the MDS method than the DDS. 4.3. Indices Using IQI and NQI have some distinct advantages over other indices: (1) soil researchers, managers, and farmers easily understand both types of indices, due to their intuitive nature as mentioned by Wang and Gong (1998) and Sun et al. (2003); (2) both indices incorporate information based on mathematical methods, which lead to increased confidence in the results; and (3) both indices can serve as a platform for planning other agricultural research. It should be noted that the classification criteria definition calculated from the two models are so subjective that comparison between different regions are affected by limiting factors. Though similar soil quality evaluation results were obtained, the IQI model is better for soil quality index calculation in Zhangjiagang County. By using indicator weights, the IQI model differentiates the importance of various indicators. Soil quality was determined using all the indicators, but always directed by several important indicators, and higher weights were placed on key indicators. Instead of assigning indicator weights, the NQI model treated all indicators, except the one with the lowest score, the same. The lowest scoring indicator is added to the average of the scores, effectively giving it a higher weighted value. In other words, while the IQI assigns each indicator score independently, NQI only gives preferential importance to the indicator with the lowest score. Although the NQI model showed a good level agreement in match analysis, the agreement percentage using direct comparison and Kappa analysis was lower than IQI. Also, the correlation coefficients were higher and the linear slope was nearest to 1 when the IQI model was used.

4.2. Indicator methods 4.4. Interaction between indicator methods and indices Almost all of the seven indicators used with the MDS method can be found in previously created MDS indicator methods (Doran and Parkin, 1994, Karlen and Stott, 1994; Larson and Pierce, 1994; Singer and Ewing, 2000; Ditzler and Tugel, 2002). However, our list also included thickness of obstacle horizons and drainage modulus which were not found in any other MDS methods. Others, such as stoniness (Singer and Ewing, 2000) and earthworm populations (Govaerts et al., 2006) were not included as they were not applicable for the rice–

One of the reasons match analysis showed a high level of agreement and soil distribution was so similar is likely because they all used suitable criteria to define soil grades. Definition of quality grade has no one uniform criteria. Many researchers define the criteria based on experience, crop yield, or regional soil quality situations. In this study, the criteria for the IQITDS was defined based on the classification of region type and fertility of cultivated land in China

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(ISMAPRC, 1996), and other index criteria were adjusted based on the IQITDS in terms of their actual index value. Though the DDS method outperformed the MDS method, when analyzed using overall linear regression, which combines the IQI and NQI data, separating the two shows that using the MDS method has a clear advantage (Fig. 4). 5. Conclusions This study suggests that using soil quality indices to evaluate agricultural soil quality can provide similar results even when different indicator methods and models have been used in the study area. In this study, IQITDS was determined to be the most accurate qualitative soil quality evaluation method, because it took all soil parameters into consideration and gave the most consistent results. However, in order for one method to become the standard for research and to facilitate discussion and cooperation, a standard should be rapid, reliable, and economically feasible. For this reason, the MDS indicator method is the most suitable of the three methods compared in this study because it adequately represents the TDS method and is more accurate than the DDS method. We suggest using the IQI index with the MDS indicator method as a starting point towards an international standard for future research. Care should be taken in determining which indicators are included in the MDS method. In addition, research should be conducted to further refine an IQIMDS model to make it more suitable as an international standard. Acknowledgements The authors are grateful for funding from the Natural Science Foundation of China (40773075; 40601039), the Knowledge Innovation Program of Chinese Academy of Sciences (KSCX1-YW-09-02). Thanks extended to local agricultural extensionists for their help during sampling. References Andrews, S.S., Mitchell, J.P., Mancinelli, R., Karlen, K.L., Hartz, T.K., Horwath, W.R., Pettygrove, G.S., Scow, K.M., Munk, D.S., 2002. On-farm assessment of soil quality in California's central valley. Agron. J. 94, 12–23. Aparicio, V., Costa, J.L., 2007. Soil quality indicators under continuous cropping systems in the Argentinean pampas. Soil Tillage Res. 96, 155–165. Arshad, M.A., Martin, S., 2002. Identifying critical limits for soil quality indicators in agro-ecosystems. Agric. Ecosyst. Environ. 88, 153–160. Ball, B.C., Campbell, D.J., Hunter, E.A., 2000. Soil compactibility in relation to physical and organic properties at 156 sites in UK. Soil Tillage Res. 57, 83–91. Bhandari, A.L., Ladha, J.K., Pathak, H., Padre, A.T., Dawe, D., Gupta, R.K., 2002. Yield and soil nutrient changes in a long-term rice–wheat rotation in India. Soil Sci. Soc. Am. J. 66, 162–170. Bindraban, P.S., Stoorvogel, J.J., Jansen, D.M., Vlaming, J., Groot, J.J.R., 2000. Land quality indicators for sustainable land management: proposed method for yield gap and soil nutrient balance. Agric. Ecosyst. Environ. 81, 103–112. Borja, A., Dauer, D.M., Diaz, R., Llanso, R.J., Muxika, I., Rodriguez, J.G., Schaffner, L., 2008. Assessing estuarine benthic quality conditions in Chesapeake Bay: a comparison of three indices. Ecol. Ind. 8, 395–403. Bremner, J.M., Mulvaney, C.S., 1982. Nitrogen-total. In: Page, A.L., et al. (Ed.), Methods of Soil Analysis. Part 2. Chemical and Microbiological Properties. American Society of Agronomy, Madison, WI, pp. 595–624. Burrough, P.A., 1989. Fuzzy mathematical methods for soil survey and land evaluation. J. Soil Sci 40, 477–492. Cooperative Research Group on Chinese Soil Taxonomy (CRGCST), 2001. Chinese Soil Taxonomy. Science Press, Beijing. New York. Ditzler, C.A., Tugel, A.J., 2002. Soil quality field tools of USDANRCS soil quality institute. Agron. J. 94, 33–38. Dobermann, A., Oberthur, T., 1997. Fuzzy mapping of soil fertility — a case study on irrigated riceland in the Philippines. Geoderma 77, 317–339. Doran, J.W., Jones, A.J. (Eds.), 1996. Methods for Assessing Soil Quality. Soil Science Society of America Special Publication, vol. 49. Soil Science Society of America, Madison, WI. Doran, J.W., Parkin, B.T., 1994. Defining and assessing soil quality. In: Doran, J.W., Coleman, D.C., Bezdicek, D.F., Stewart, B.A. (Eds.), Defining Soil Quality for a Sustainable Environment. Soil Science Society of America, Inc., Madison, WI, USA, pp. 3–21. Special Publication. Number 35. Doran, J.W., Coleman, D.C., Bezdicek, D.F., Stewart, B.A., 1994. Defining soil quality for a sustainable environment. SSSA Special Publication, vol. 35. Soil Science Society of America, Madison, WI, USA.

333

Dumanski, J., Pieri, C., 2000. Land quality indicators: research plan. Agric. Ecosyst. Environ. 81, 93–102. Fischer, R.A., Santiveri, F., Vidal, I.R., 2002. Crop rotation, tillage and crop residue management for wheat and maize in the sub-humid tropical highlands: I. Wheat and legume performance. Field Crops Res. 79, 107–122. Fu, B., 1991. Theory and practice of land evaluation. China Science and Technology Press, Beijing. Govaerts, B., Sayre, K.D., Deckers, J., 2006. A minimum data set for soil quality assessment of wheat and maize cropping in the highlands of Mexico. Soil Tillage Res. 87, 163–174. Han, W.J., Wu, Q.T., 1994. A primary approach on the quantitative assessment of soil quality. Chinese J. Soil Sci. 25, 245–247 (In Chinese with English Abstract). Herrick, J.E., Brown, J.R., Tugel, A.J., Shave, P.L., Havstad, K.M., 2002. Application of soil quality to monitoring and management: paradigms from rangeland ecology. Agron J. 94, 3–11. Huang, B., Shi, X.Z., Yu, D.S., Öborn, I., Blombäck, K., Pagella, T.F., Wang, H.J., 2006. Environmental assessment of small-scale vegetable farming systems in peri-urban areas of the Yangtze River Delta region, China. Agric. Ecosyst. Environ. 112, 391–402. Huang, B., Sun, W.X., Zhao, Y.C., Zhu, J., Yang, R.Q., Zou, Z., Ding, F., Su, J.P., 2007. Temporal and spatial of soil organic matter and total nitrogen in an agricultural ecosystem as affected by farming practices. Geoderma 139, 336–345. Industry standard of ministry of agriculture of the people's republic of China (ISMAPRC), 1996. Classification of type regions and fertility of cultivated land in China (NY/T 309-1993). Karlen, D.L., Scott, D.E., 1994. A framework for evaluating physical and chemical indicators of soil quality. In: Doran, J.W., Coleman, D.C., Bezdicek, D.F., Stewart, B.A. (Eds.), Defining Soil Quality for a Sustainable Environment. ASA and SSSA, Madison, WI, USA, pp. 53–72. Karlen, D.L., Gardner, J.C., Rosek, M.J., 1998. A soil quality framework for evaluating the impact of CRP. J. Prod. Agric. 11, 56–60. Karlen, D.L., Hurley, E., Andrews, S., Cambardella, C., Meek, D., Duffy, M., Mallarino, A., 2006. Crop rotation effects on soil quality in the northern corn/soybean belt. Agron. J. 98, 484–495. Kemmitt, S.J., Wright, D., Jones, D.J., 2005. Soil acidification used as a management strategy to reduce nitrate losses from agricultural land. Soil Biol. Biochem. 37, 867–875. Klingebiel, A.A., Montgomery, P.H., 1961. Land capability classification. USDA Handbook, vol. 210. United States Department of Agriculture, Washington, DC. Komac, M., 2006. A landslide susceptibility model using the analytical hierarchy process method and multivariate statistics in perialpine Slovenia. Geomorphology 74, 17–28. Lai, V.S., Wong, B.K., Cheung, W., 2002. Group decision making in a multiple criteria environment: a case using the AHP in software selection. Eur. J. Oper. Res. 137, 134–144. Larson, W.E., Pierce, F.J., 1994. The dynamics of soil quality as a measure of sustainable management. Defining Soil Quality for a Sustainable Environment. Soil Science Society of America, Madison, Wisconsin, pp. 37–52. USA. Lindsay, W.L., Norvell, W.A., 1978. Development of a DTPA soil test for zinc, iron, manganese and copper. Soil Sci. Soc. Am. J. 42, 421–428. Lupwayi, N.Z., Haque, I., 1998. Mineralization of N, P, K, Ca and Mg from sesbania and leucaena leaves varying in chemical composition. Soil Biol. Biochem. 30, 337–343. Lu, R.K., 2000. Soil analytical methods of agronomic chemical. China Agricultural Science and Technology Press, Beijing. (in Chinese). Luo, G.R., 1991. Conception of the irrigation guarantee ratio of irrigated area and its calculation. J. Wuhan Univ. Hydraul. Electr. Eng. 24 (1), 102–110 (In Chinese with English abstract). McBratney, A.B., Odeh, I.O.A., 1997. Application of fuzzy sets in soil science: fuzzy logic, fuzzy measurements and fuzzy decisions. Geoderma 77, 85–113. McGrath, D., Zhang, C.S., 2003. Spatial distribution of soil organic carbon concentrations in grassland of Ireland. Appl. Geochem. 18, 1629–1639. Monserud, R., Leemans, R., 1992. Comparing global vegetation maps with the Kappa statistic. Ecol. Model. 62, 275–293. Mowo, J.G., Janssen, B.H., Oenema, O., German, L.A., Mrema, J.P., Shemdoe, R.S., 2006. Soil fertility evaluation and management by smallholder farmer communities in northern Tanzania. Agric. Ecosyst. Environ. 116, 47–59. Mubarak, A.R., Elshami, O.M.E., Azhari, A.A., 2005. Long- and short-term effects of cultivation on properties of a Vertisol under sugarcane plantation. Soil Tillage Res. 84, 1–6. Myaer, R., 1998. Soil acidification and cycling of metal elements: cause–effect relationships with regard to forestry practices and climatic changes. Agric. Ecosyst. Environ. 67, 145–152. Nazzareno, D., Michele, C., 2004. Multivariate indicator kriging approach using a GIS to classify soil degradation for Mediterranean agricultural lands. Ecol. Ind. 177–187. Nelson, D.W., Sommers, L.E., 1982. Total carbon, organic carbon, and organic matter. In: Page, A.L., R.H., D.R., Keeney (Eds.), Methods of Soil Analysis, Part 2-Chemical and Microbiological Properties. ASA-SSSA, Madison, WI, pp. 539–594. Olsen, S.R., Sommers, L.E., 1982. Phosphorus. In: Page, A.L., Miller, R.H., Keeney, D.R. (Eds.), Methods of soil analysis. 2nd ed. Agronomy Series No.9, part 2. Soil Science Society of America, Inc., Madison, WI, pp. 403–430. Olsen, S.R., Cole, C.V., Watanable, F.S., Dean, L.A., 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate. USDA Circular 939, Washington. Pieri, C., Dumanski, J., Hamblin, A., Young, A., 1995. Land Quality Indicators. World Bank Discussion Papers, 315. Qin, M.Z., Zhao, J., 2000. Strategies for sustainable use and characteristics of soil quality changes in urban-rural marginal area: a case study of Kaifeng. Acta Geogr. Sin. 55, 545–554 (In Chinese with English abstract).

334

Y. Qi et al. / Geoderma 149 (2009) 325–334

Qi, Y.B., Huang, B., Gu, Z.Q., Zhao, Y.C., Sun, W.X., Wang, Z.G., Yang, Y.F., 2008. Spatial and temporal variation of C/N ratios of agricultural soils in typical area of Yangtze Delta Region and its environmental significance. Bull. Miner. Petrol. Geochem. 27, 50–56 (In Chinese with English abstract). Raiesi, F., 2006. Carbon and N mineralization as affected by soil cultivation and crop residue in a calcareous wetland ecosystem in Central Iran. Agric. Ecosyst. Environ. 112, 13–20. Rezaei, S.A., Gilkes, R.J., Andrews, S.S., 2006. A minimum data set for assessing soil quality in rangelands. Geoderma 136, 229–234. Saaty, T.L., 1977. A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15, 234–281. Shao, X.X., Huang, B., Gu, Z.Q., Qian, W.F., Jin, Y., Bi, K., Yan, L.X., 2006. Spatial-temporal variation of pH values of soil in a rapid economic developing area in the Yangtze Delta Region and their causing factors. Bull. Miner. Petrol. Geochem. 25, 143–149 (In Chinese with English abstract). Shukla, M.K., Lal, R., Ebinger, M., 2006. Determining soil quality indicators by factor analysis. Soil Tillage Res. 87, 194–204. Singer, M.J., Ewing, S., 2000. Soil quality. In: Sumner, M.E. (Ed.), Handbook of Soil Science. CRC Press, Boca Raton, FL, USA, pp. G–271–G-298. Smith, J.L., Halvaorson, J.J., Papendick, R.I., 1994. Using multiple-variable indicator kriging for evaluating soil quality. Soil Sci. Soc. Am. J. 57, 743–749. Stamatiadis, S., Doran, J.W., Kettler, T., 1999. Field and laboratory evaluation of soil quality changes resulting from injection of liquid sewage sludge. Appl. Soil Ecol. 12, 263–272.

Sun, B., Zhou, S.L., Zhao, Q.G., 2003. Evaluation of spatial and temporal changes of soil quality based on geostatistical analysis in the hill region of subtropical China. Geoderma 115, 85–99. Tang, X., 1997. Fuzzy comprehensive evaluation of the productivity of purple soil in Sichuan Province, China. Adv. Soil Res. 28, 107–109 (In Chinese with English abstract). van der Ploeg, R.R., Böhm, W., Kirkham, M.B., 1999. On the origin of the theory of mineral nutrition of plants and the law of the minimum. Soil Sci. Soc. Am. J. 63–1055–1062. Vanlauwe, B., Nwoke, O.C., Sanginga, N., Merckx, R., 1996. Impact of residue quality on the C and N mineralization of leaf and root residues of three agroforestry species. Plant Soil 183, 221–231. Wander, M.M., Walter, G.L., Nissen, T.M., Billero, G.A., Andrews, S.S., Cavanaugh-Grant, D.A., 2002. Soil quality: science and process. Agron. J. 94, 23–32. Wang, X.J., Gong, Z.T., 1998. Assessment and analysis of soil quality changes after eleven years of reclamation in subtropical China. Geoderma 81, 339–355. Zhang, B., Zhang, Y., Chen, D., White, R.E., Li, Y., 2004. A quantitative evaluation system of soil productivity for intensive agriculture in China. Geoderma 123, 319–331. Zhangjiagang soil survey office, 1984. Zhangjiagang soil species resource. China, Suzhou. Zhou, J.K., Zhu, C.L., Luo, G.P., 2004. Calculation for the designed flood drainage modulus in small low-lying region of Taihu lake basin. China Water Wastewater 20 (12), 64–66 (In Chinese with English abstract).