characterizing variations in soil particlesize distribution along a ...

land degradation & development Land Degrad. Develop. (2011) Published online in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/ldr.1112

CHARACTERIZING VARIATIONS IN SOIL PARTICLE‐SIZE DISTRIBUTION ALONG A GRASS–DESERT SHRUB TRANSITION IN THE ORDOS PLATEAU OF INNER MONGOLIA, CHINA Z. JIN1*, Y. S. DONG2, Y. C. QI2, W. G. LIU1 AND Z. S. AN1 1

State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710075, PR China 2 Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, PR China Received 19 March 2010; Revised 6 January 2011; Accepted 23 February 2011

ABSTRACT The application of fractal geometry to describe soil degradation and dynamics is becoming a useful tool for better understanding of the performance of soil systems. In this study, four different land cover types, which represent a sequence of grass – desert shrub transition and a gradient of desertification, were selected, and soils at depths of 0 –10, 10 –20 and 20 – 40 cm were sampled in the Ordos Plateau of Inner Mongolia, PR China. The fractal theory was used to analyse the soil particle‐size distribution (PSD) and its variations. The results showed that (i) vegetation conversion and desertification significantly changed the soil PSD. During the desertification process, soil coarse fractions that ranged from 250 to 100 µm significantly increased, whereas fine fractions lower than 50 µm significantly decreased ( p < 0·01); (ii) fractal model of the accumulative volume particle‐size distribution is appropriate, and fractal dimensions (Dm) of soil PSD significantly decreased along the sequence of grass – desert shrub transition; (iii) Dm is more sensitive to the desertification process, and therefore, we suggest Dm other than soil texture and soil organic carbon as a reliable parameter to reflect the soil environment change induced by desertification. Copyright © 2011 John Wiley & Sons, Ltd. key words:

fractal dimension; soil PSD; vegetation conversion; desertification; Ordos Plateau; PR China; Mongolia; soil particle‐size

INTRODUCTION Soil particle‐size distribution (PSD) is one of the most important physical attributes in soil systems (Hillel, 1980). PSD affects the movement and retention of water, solutes, heat, and air, and thus greatly affects soil properties (Su et al., 2004). Many studies suggest that changes in PSD can provide useful indications that soils are subjected to management, erosion and desertification (Gimtnez et al., 1997; Millan et al., 2003; Su et al., 2004; Montero, 2005; Wang et al., 2006, Wang et al., 2008; Gui et al., 2010). Accordingly, characterizing variations in soil PSD is an important issue in soil science research. Several different methods were developed to represent soil PSD (Buchan et al., 1993; Kozak et al., 1996; Skaggs et al., 2001). Textural analysis was the commonly used method in the past to characterize soil PSD, although the method could not provide complete information because the size definitions of three main particle fractions (sand, silt and clay) are rather arbitrary (Wang et al., 2008). The latest developments in the study of PSD have focused on the use * Correspondence to: Z. Jin, State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710075, PR China. E-mail: [email protected]

Copyright © 2011 John Wiley & Sons, Ltd.

of fractal geometry (Montero and Martin, 2003; Montero, 2005; Wang et al., 2008; Gui et al., 2010). The common feature of fractal measures is to find a way to characterize PSD with parameters (i.e. fractal dimensions, Dm) that retain most information (Gui et al., 2010). Studies showed that the fractal method could well characterize soil particle‐size distribution, pore‐size distribution and aggregate‐size distribution (Lipiec et al., 1998; Salako et al., 1999; Millan and Orellana, 2001; Filgueira et al., 2006; Wang et al., 2008). Therefore, the fractal method is now considered as a useful tool in quantifying soil structure. Fractal parameters become also important in understanding and quantifying soil degradation and dynamics. Pachepsky et al. (1995) found that simulated soil degradation caused an increase in fractal dimensions in one or more intervals of fractal behaviour. Millan et al. (2003) found that fractal dimensions of PSD significantly correlated with clay content following a linear trend. Su et al. (2004) showed that fractal dimensions of PSD were useful parameters in monitoring soil degradation and desertification process. Wang et al. (2006, 2008) showed that land use had considerable influences on fractal dimensions of PSD and various other soil properties. Gui et al. (2010) indicated that long‐term and effective tillage management of the farmlands was beneficial to keeping and improving the states of the soil PSD.

Z. JIN ET AL.

In the Ordos Plateau of Inner Mongolia, desertification is the most serious environmental problem (Zhang, 1994). One form of desertification in this area is the conversion of homogeneous grasslands into shrub‐dominated ecosystems (Jin et al., 2009). The changes in plant type and coverage from grasslands to desert shrub lands were found to greatly affect the soil organic carbon (SOC) and nutrients (Cheng et al., 2004, 2007). However, there is very limited information about the vegetation conversion effects on soil PSD. In this paper, we analyse the PSD and the fractal dimensions of soils along the grass–desert shrub transition sequence and identify relationships between some selected soil properties and the fractal dimensions of PSD. The objective was to assess the vegetation conversion and desertification effects on soil PSD and explore the potential fractal parameters to quantify the soil environment change.

MATERIALS AND METHODS Study Area This study was conducted at the Ordos Sandy Grassland Research Station, Chinese Academy of Sciences, located in the Mu Us Sand Land of the Ordos Plateau of Inner Mongolia, China (37°27′~39°51′ N, 107°20′~111°30′ E). The region is considered to have an ecotone between grassland and shrub land wherein desertification is identified as a serious problem. The area has a typically semiarid climate with marked seasonal and diurnal temperature variations and low precipitation. The annual mean precipitation is 345 mm with an annual mean evaporation of 2535 mm. From April to October, mean precipitation is observed at 322 mm, which accounts for about 93 per cent of the annual precipitation. The annual mean temperature is 6·7 °C, with monthly mean temperatures below 5 °C from November to March and between 7·4 °C and 21·9 °C from April to October (Zheng et al., 2005). In this area, grasslands are severely desertified owing to drought and overgrazing. The topography of this area is now characterized by sand dunes and desert shrub vegetation, with only a small area of grasslands distributed on lowland and upland (Zhang, 1994). Stipa bungeana, Artemisia ordosica and Cynanchum komarovii are the three dominant species representing a gradient of desertification in this area. S. bungeana is a perennial meso‐xerophytic grass, with this community being a typical steppe vegetation in the region. As a development of desertification, the grasslands are desertified and the steppe has been displaced by shrubs. A. ordosica is the most widespread xerophyte shrub in this area and can be used to represent the typical shrub vegetation. C. komarovii is a member of the milkweed family and a strong xerophyte whose presence indicates serious desertification (Cheng et al., 2007). Copyright © 2011 John Wiley & Sons, Ltd.

Field Investigation and Soil Sampling In the vicinity of the Ordos Sandy Grassland Research Station, four sites were selected as sampling units. The four sites represent perennial grass (dominated by S. bungeana), desert shrub (dominated by A. ordosica), desert milkweed (dominated by C. komarovii) and mobile dune, which indicate a sequence of vegetation transition and a gradient of desertification in this area. In the site of perennial grass, eight sampling plots were set and each plot measured 1 m × 1 m. The sampling plots were randomly distributed, and the distance between the plots was about 10 m. In each plot, the soils at depths of 0 –10, 10–20 and 20 –40 cm were separately sampled using soil auger. In the sites of desert shrub and desert milkweed, eight sampling plots were set in each site, and each plot measured 2 m × 2 m. The sampling plots were randomly distributed, and the distance between the plots was about 15 m. In each plot, the soils at depths of 0 –10, 10 –20 and 20– 40 cm were separately sampled using soil auger. In the site of mobile dune, eight sampling plots were set along the hill slope. In each plot, soils at depths of 0 –10, 10–20 and 20 – 40 cm were separately sampled as indicated in the aforementioned method. The sampling activity was conducted in October of 2009. Soil Chemical Analysis All the soil samples were air‐dried, and rocks and gravel (>2 mm) were carefully removed. The air‐dried soil samples were passed through a 2‐mm sieve and prepared for chemical and laser diffraction analysis. For the determination of SOC and total nitrogen (TN), a part of the soils was ground. The ground soil samples were digested in K2Cr2O7 –H2SO4 solution by using an oil‐bath heating and then carbon concentration was determined by titration (Ministry of Forestry, 2000). Soil TN was measured using a micro‐Kjeldahl’s digestion followed by distillation and titration (Ministry of Forestry, 2000). Laser Diffraction Analysis The soil samples were pretreated with H2O2 (30 per cent, w/w) to remove the organic matter. The aggregates were then dispersed by adding sodium hexametaphosphate (NaHMP) and by sonicating the samples for 30 s, after which they were analysed by a laser diffraction technique by using a Malvern MS 2000 (Malvern Instruments, Malvern, England). The laser particle analyzer, which can be used to measure PSD values within a range of 0·02–2000 µm, gives the continuous volume percentage of particle‐size during analysis. However, the PSD values in the present study were all found to be in the range of 0·35 –1000 µm, and the soil particle‐sizes in this range were graded into 71 fractions by using the attached software provided with the laser particle analyzer. LAND DEGRADATION & DEVELOPMENT (2011)


a b c d 5·38 ± 0·82 4·09 ± 0·87 2·35 ± 0·44 0·63 ± 0·42 a a b bc 21·00 ± 2·70 20·96 ± 6·93 7·04 ± 1·62 3·41 ± 0·75 a a a b 9·98 ± 2·91 7·89 ± 3·99 8·37 ± 5·01 0·30 ± 0·09 a a a a Notes: *Means with different letters within a variable indicate significant differences ( p < 0·05).

15·93 ± 4·48 12·26 ± 3·57 13·87 ± 4·50 15·01 ± 3·64 28·19 ± 3·97 a 31·53 ± 8·24 a 41·76 ± 6·42 b 61·68 ± 10·28 c a a a a 2·35 ± 1·16 3·04 ± 2·71 1·86 ± 2·31 1·10 ± 2·24

17·17 ± 4·67 a 20·23 ± 6·36 ab 24·75 ± 6·49 bc 17·87 ± 10·38 abc

5·03 ± 0·61 4·31 ± 1·18 2·51 ± 0·49 1·01 ± 0·49 a a b b 16·49 ± 1·61 19·63 ± 8·55 7·57 ± 1·40 4·19 ± 1·01 a b b c 15·91 ± 2·44 8·03 ± 3·14 9·41 ± 2·11 0·37 ± 0·13 a b b b 19·30 ± 2·30 14·06 ± 2·24 15·25 ± 2·17 14·78 ± 2·97 a b c d 25·49 ± 2·16 35·15 ± 8·24 41·43 ± 4·68 62·50 ± 7·31 a ab bc a a ab ab bc 2·06 ± 1·22 1·06 ± 0·82 1·26 ± 1·51 0·35 ± 0·73

15·72 ± 3·51 17·76 ± 4·67 22·57 ± 3·00 16·80 ± 7·43

5·08 ± 0·38 4·05 ± 0·90 2·54 ± 0·38 0·94 ± 0·52 a b b b

21·94 ± 1·95 10·06 ± 3·26 9·55 ± 3·59 0·46 ± 0·46

a b b c

17·12 ± 1·30 18·22 ± 4·20 7·35 ± 1·23 4·02 ± 1·03

a a b c

< 2 µm 20 –2 µm 50 –20 µm 100 –50 µm

20·56 ± 0·89 14·66 ± 2·47 15·93 ± 3·44 16·65 ± 3·10 a b c d 21·53 ± 2·98 34·39 ± 6·10 40·93 ± 4·85 61·74 ± 9·64 a bc c ab 12·13 ± 1·34 17·47 ± 4·50 22·12 ± 5·04 15·42 ± 7·52 a* a a a 1·64 ± 1·29 1·15 ± 1·66 1·58 ± 2·03 0·77 ± 1·60

Using Equation (1), the fractal dimensions of soil PSD were calculated for each data set (Table II). Along the sequence of grass–desert shrub transition, the Dm values significantly decreased ( p < 0·0001). The determination coefficients, R2, of the linear regressions for Equation (1) were high and ranged between 0·90 and 0·96. All the regression analyses passed a two‐tailed test when evaluated at p < 0·01. Correlation analysis of the Dm values and the volume contents of sand, silt and clay showed that the Dm values were significantly negatively correlated with the contents of sand (r = − 0·959, p < 0·01) and significantly positively correlated with the contents of silt and clay (r = 0·958, p < 0·01, and r = 0·952, p < 0·01). Moreover, the Dm values presented a

0–10 cm Perennial grass Desert shrub Desert milkweed Mobile dune 10–20 cm Perennial grass Desert shrub Desert milkweed Mobile dune 20–40 cm Perennial grass Desert shrub Desert milkweed Mobile dune

Fractal Dimensions of Soil Particle‐Size Distribution

250 –100 µm

Variations in Soil Particle‐Size Distribution Based on the data determined by the laser diffraction analysis, seven groups of soil particle fractions were classified (Table I). Results showed that vegetation conversion and desertification significantly changed the soil particle‐size distributions. In the grass site, most of the soil particles were distributed in the range of 250–20 µm; whereas in the desert site, most of the soil particles were distributed in the range of 500–50 µm. Along the sequence of grass – desert shrub transition, soil coarse fractions that ranged from 250 to 100 µm significantly increased, whereas fine fractions lower than 50 µm significantly decreased (p < 0·01).

500 –250 µm

RESULTS

1000 –500 µm

One‐way analyses of variance and least significant difference calculations at an alpha level of 0·05 (a = 0·05) were used to identify the statistically significant differences in soil texture, fractal dimensions, SOC and TN among the four sampling sites. The relationships between Dm and soil properties (e.g. sand, silt, clay, SOC, TN) were analysed using linear regression analysis.

Soil particle‐size fractions

Data Analysis

Land cover types

Where: r is the particle‐size, Ri is the particle‐size of grade i in the particle‐size grading, V (r < Ri) is the volume of soil particles with a diameter less than Ri, VT is the volume of all of the soil particles and Rmax is the maximum diameter of the soil particles.

Table I. Changes in volume percentages of soil particle‐size fractions under different land cover types in the Ordos Plateau of Inner Mongolia, PR China

The fractal dimension of PSD based on the volume distribution of the soil particle‐size, Dm, was estimated from the following equation (Gui et al., 2010): V ðr < R i Þ Ri 3−D ¼ (1) Rmax VT

a b c d

Calculation of the Dm Values Based on the Volume Distribution of the Soil Particle‐Size

a a b c

CHARACTERIZING VARIATIONS IN SOIL PARTICLE‐SIZE DISTRIBUTION

LAND DEGRADATION & DEVELOPMENT (2011)

Z. JIN ET AL.

Table II. Volume contents of sand, silt and clay and fractal dimensions of soil particle‐size distribution under different land cover types in the Ordos Plateau of Inner Mongolia, PR China Land cover types 0–10 cm Perennial grass Desert shrub Desert milkweed Mobile dune 10–20 cm Perennial grass Desert shrub Desert milkweed Mobile dune 20–40 cm Perennial grass Desert shrub Desert milkweed Mobile dune

Sand (2000 –50 µm)

Silt (50 –2 µm)

Clay (< 2 µm)

39·07 ± 3·07 a 28·28 ± 6·94 b 16·90 ± 4·06 c 4·48 ± 1·17 d

5·08 ± 0·34 4·05 ± 0·88 2·54 ± 0·37 0·94 ± 0·48

a b c d

2·34 ± 0·06 2·26 ± 0·07 2·21 ± 0·07 2·00 ± 0·09

a b c d

0·90 0·93 0·96 0·96

62·56 ± 3·60 a 68·03 ± 12·57 a 80·51 ± 2·81 b 94·43 ± 1·47 c

32·40 ± 3·34 a 27·66 ± 11·53 a 16·98 ± 2·50 b 4·57 ± 1·04 c

5·03 ± 0·60 4·31 ± 1·15 2·51 ± 0·47 1·01 ± 0·46

a a b c

2·34 ± 0·06 2·28 ± 0·06 2·20 ± 0·07 2·00 ± 0·07

a b c d

0·90 0·92 0·96 0·95

63·63 ± 2·84 a 67·05 ± 11·07 a 82·25 ± 6·10 b 95·66 ± 0·99 c

30·98 ± 2·43 a 28·86 ± 10·35 a 15·41 ± 5·92 b 3·71 ± 0·61 c

5·38 ± 0·79 4·09 ± 0·84 2·35 ± 0·42 0·64 ± 0·40

a b c d

2·30 ± 0·05 2·26 ± 0·05 2·20 ± 0·04 1·96 ± 0·04

a b c d

0·92 0·92 0·96 0·96

55·85 ± 3·30 67·67 ± 7·64 80·56 ± 4·16 94·58 ± 1·64

a* b c d

Fractal dimensions (Dm)

Determination coefficients

Notes: *Means with different letters within a variable indicate significant differences ( p < 0·05).

stronger negative correlation with the contents of the coarse fractions that ranged from 250 to 100 µm (r = − 0·987, p < 0·01). These findings demonstrate that the Dm values can well represent the characteristics of the soil PSD. Soil Organic Carbon and Total Nitrogen Related with Soil Texture and Dm Along the sequence of grass–desert shrub transition, the concentrations of SOC and TN significantly decreased (Table III), showing the same trend as the variations of silt and clay. Regression analysis showed that SOC and TN linearly decreased with the decreases of silt and clay in soils (SOC: r = 0·866, p < 0·01; TN: r = 0·830, p < 0·01). Moreover, we found that the Dm values were significantly positively correlated with the contents of SOC and TN

(SOC: r = 0·826, p < 0·01; TN: r = 0·770, p < 0·01). This indicates that the Dm values can also be used to reflect the changes of soil fertility. DISCUSSION Effects of Vegetation Conversion and Desertification on Soil Particle‐Size Distribution and Fertility In this study, the vegetation conversion is caused by desertification, and the sequence of grass–desert shrub transition actually represents a gradient of desertification. Along the desertification process, fine particles lower than 50 µm (silt and clay) significantly decreased, whereas coarse particles that ranged from 250 to 100 µm (fine sand) greatly increased, suggesting that desertification accelerated the

Table III. Changes in soil organic carbon, total nitrogen and fractal dimensions of soil particle‐size distribution under different land cover types in the Ordos Plateau of Inner Mongolia, PR China Land cover types Perennial grass Desert shrub Desert milkweed Mobile dune Between groups LSD ( p < 0·05)

Soil depths (cm)

SOC

TN

Averaged Dm

0 –10 10 –20 20 – 40 0 –10 10 –20 20 – 40 0 –10 10 –20 20 – 40 0 –10 10 –20 20 – 40

4·72 ± 0·46 4·23 ± 0·37 3·83 ± 0·32 4·35 ± 0·65 2·62 ± 0·62 2·25 ± 0·55 2·73 ± 0·68 2·21 ± 0·58 1·74 ± 0·43 1·94 ± 0·61 1·24 ± 0·43 0·85 ± 0·43 0·005

0·62 ± 0·09 0·51 ± 0·13 0·43 ± 0·12 0·44 ± 0·07 0·34 ± 0·03 0·25 ± 0·03 0·33 ± 0·04 0·22 ± 0·02 0·14 ± 0·01 0·22 ± 0·03 0·15 ± 0·00 0·14 ± 0·00 0·006

2·34 2·34 2·30 2·26 2·28 2·26 2·21 2·20 2·20 2·00 2·00 1·96 0·000

SOC, soil organic carbon; TN, total nitrogen. Copyright © 2011 John Wiley & Sons, Ltd.


CHARACTERIZING VARIATIONS IN SOIL PARTICLE‐SIZE DISTRIBUTION

losses of fine fractions and caused progressive coarsening. Moreover, we found that SOC and TN linearly decreased with the decreases of fine particles (silt and clay) in soils. It is well known that coarse sand does not favour physical protection of soil organic matter and nutrients (Sollins et al., 1996; Carter et al., 2003; Zinn et al., 2007). Lobe et al. (2001) suggested that SOC and nutrients tend to be associated with fine particles (silts and clays) rather than sands. Therefore, the losses of SOC and TN were mainly due to the depletion of the fine particles in soils. Significance of Volume‐Based Fractal Model in Characterizing Soil Particle‐Size Distribution In the past, most of the studies calculated the Dm based on the mass distribution of soil PSD. The mass distribution assumes that the density of the same soil particles is the same (Posadas et al., 2001). The assumption is obviously not ideal given the actual situation of soils (Clifton et al., 1999; Gui et al., 2010). It has been approved that the volume‐based fractal model is ideal for use in evaluating the soil PSD (Montero, 2005; Yang et al., 2008; Wang et al., 2008; Gui et al., 2010). In this study, the Dm values were calculated based on the volume distribution of the soil particle‐sizes. Results showed that the determination coefficients, R2, of the linear regressions for Equation (1) were high and ranged between 0·90 and 0·96. Moreover, the Dm values were found to well represent the characteristics of the soil PSD. Therefore, we think that the fractal model of the accumulative volume particle‐size distribution is appropriate.

process. In this study, the contents of SOC and TN exhibited significant declines along the sequence of grass–desert shrub transition (SOC: p = 0·005; TN: p = 0·006). However, the Dm values were more sensitive to the desertification process (Dm: p < 0·0001).Therefore, we suggest Dm other than soil texture and SOC as a reliable parameter in reflecting the soil environment change induced by desertification. CONCLUSION According to our study, vegetation conversion and desertification significantly changed the PSD of soils and caused progressive soil coarsening. The fractal analysis showed that the PSD‐based volume fractal model could well characterize the soil PSD and its changes. Dm is more sensitive to the desertification process, and therefore, we suggest Dm other than soil texture and SOC as a reliable parameter in reflecting the soil environment change induced by desertification.

ACKNOWLEDGEMENTS This study was financially supported by the Knowledge Innovation Program of the Chinese Academy of Sciences (grant no. KZCX2‐YW‐149) and the National Basic Research Program (973 programme, grant No. 2010CB833400). The authors thank Zhao Min for assistance during the laser diffraction analysis. Moreover, we extend our gratitude to the two anonymous reviewers for their comments and suggestions on the original manuscript.

Significance of Dm as a Parameter to Reflect the Soil Environment Change Soil texture was the commonly used parameter in the past to reflect soil environment change induced by desertification. However, more and more studies tend to use Dm as a reliable parameter to monitor desertification process because Dm is considered to be more sensitive than soil texture, fertility, vegetation cover and biomass in reflecting soil environment change (Su et al., 2004; Wang et al., 2008; Fu et al., 2009). In this study, soil texture by using the size definitions of sand, silt and clay was obtained (Table II). Results showed that sand, silt and clay exhibited non‐significant changes between the grass site and the desert shrub site at some soil layers. According to the Chinese Soil Taxonomy, the soil type of the grassland is chestnut soil (kastanozem), and the desert shrub land is aeolian sandy soil. The variations in soil texture could not detect the fundamental changes in soil structure and attributes of the two sampling sites. However, the Dm values significantly decreased following the vegetation conversion from grassland to desert shrub land ( p < 0·05). This indicates that Dm is more sensitive than soil texture in detecting the soil environment change induced by desertification. SOC is another important parameter in monitoring the desertification Copyright © 2011 John Wiley & Sons, Ltd.

REFERENCES Buchan GD, Grewal KS, Robson AB. 1993. Improved models of particle size distribution: An illustration of model comparison techniques. Soil Science Society of American Journal 57: 901–908. Carter MR, Angers DA, Gregorich EG, Bolinder MA. 2003. Characterizing organic matter retention for surface soils in Eastern Canada using density and particle size fractions. Canadian Journal of Soil Science 83: 11–23. Cheng X, An S, Chen J, Li B, Liu Y, Liu S. 2007. Spatial relationships among species, above‐ground biomass, N, and P in degraded grasslands in Ordos Plateau, northwestern China. Journal of Arid Environments 68: 652–667. Cheng X, An S, Liu S, Li G. 2004. Micro‐scale spatial heterogeneity and the loss of carbon, nitrogen and phosphorus in degraded grassland in Ordos Plateau, northwestern China. Plant and Soil 259: 29–37. Clifton J, Mcdonald P, Plater A, Oldfield F. 1999. An investigation into the efficiency of particle size separation using Stokes’ law. Earth Surface Processes and Landforms 24: 725–730. Filgueira RR, Fournier LL, Cerisola CI, Gelati P, Garcia MG. 2006. Particle‐size distribution in soils: A critical study of the fractal model validation. Geoderma 134: 327–334. Fu H, Pei SF, Wan CG, Sosebee RE. 2009. Fractal dimension of soil particle size distribution along an altitudinal gradient in the Alxa rangeland, western Inner Mongolia. Arid Land Research & Management 23: 137–151. Gimtnez D, Perfect E, Rawls WJ, Pachepsky Ya. 1997. Fractal models for predicting soil hydraulic properties: A review. Engineering Geology 48: 161–183. LAND DEGRADATION & DEVELOPMENT (2011)

Z. JIN ET AL. Gui DW, Lei JQ, Zeng FJ, Mu GJ, Zhu JT, Wang H, Zhang Q. 2010. Characterizing variations in soil particle size distribution in oasis farmlands: A case study of the Cele Oasis. Mathematical and Computer Modelling 51: 1306 –1311. Hillel D. 1980. Fundamentals of Soil Physics. Academic Press: New York. Jin Z, Qi YC, Dong YS, Domroes M. 2009. Seasonal patterns of soil respiration in three types of communities along grass – desert shrub transition in Inner Mongolia, China. Advances in Atmospheric Sciences 26: 503–512. Kozak E, Pachepsky YaA, Sokolowski S, Sokolowska Z, Stepniewski W. 1996. A modified number‐based method for estimating fragmentation fractal dimensions of soils. Soil Science Society of American Journal 60: 1291–1297. Lipiec J, Hatano R, Slowinska‐Jurkiewicz A. 1998. The fractal dimension of pore distribution patterns in variously‐compacted soil. Soil and Tillage Research 47: 61– 66. Lobe I, Amenlung W, Du Preez CC. 2001. Losses of carbon and nitrogen with prolonged arable cropping from sandy soils of the South African Highveld. European Journal of Soil Science 52: 93–101. Millan H, Gonzalez‐Posasa M, Aguilar M, Domínguez J, Céspedes L. 2003. On the fractal scaling of soil data. Particle‐size distributions. Geoderma 122: 43–49. Millan H, Orellana R. 2001. Mass fractal dimensions of soil aggregates from different depths of a compacted Vertisol. Geoderma 101: 65 –76. Ministry of Forestry. 2000. Forest Soil Analysis Methods (in Chinese). Chinese Criteria Press: Beijing. Montero E, Martin MA. 2003. Hölder spectrum of dry grain volume‐size distributions in soil. Geoderma 112: 197–204. Montero E. 2005. Rényi dimensions analysis of soil particle‐size distributions. Ecological Modelling 182: 305 –315. Pachepsky YaA, Polubesova TA, Hajnos M, Sokolowska Z, Jozefaciuk G. 1995. Fractal parameters of pore surface area as influenced by simulated soil degradation. Soil Science Society of American Journal 59: 68–75.


Posadas AND, Gimenez D, Bittelli M, Vaz CMP, Flury M. 2001. Multifractal characterization of soil particle‐size distributions. Soil Science Society of American Journal 65: 1361–1367. Salako FK, Babalola O, Hauser S, Kang BT. 1999. Soil macroaggregate stability under different fallow management systems and cropping intensities in southwestern Nigeria. Geoderma 91: 103–123. Skaggs TH, Arya LM, Shouse PJ, Mohanty BP. 2001. Estimating particle‐ size distribution from limited soil texture data. Soil Science Society of American Journal 65: 1038 –1044. Sollins P, Homann P, Caldwell BA. 1996. Stabilization and destabilization of soil organic matter: Mechanisms and controls. Geoderma 74: 65 –105. Su YZ, Zhao HL, Zhao WZ, Zhang TH. 2004. Fractal features of soil particle size distribution and the implication for indicating desertification. Geoderma 122: 43 – 49. Wang D, Fu BJ, Zhao WW, Hu HF, Wang YF. 2008. Multifractal characteristics of soil particle size distribution under different land‐use types on the Loess Plateau, China. Catena 72: 29 –36. Wang XD, Li MH, Liu SZ, Liu GC. 2006. Fractal characteristics of soils under different land‐use patterns in the arid and semiarid regions of the Tibetan Plateau, China. Geoderma 134: 56 – 61. Yang JL, Li DC, Zhang GL, Zhao YG, Tang XG. 2008. Comparison of mass and volume fractal dimensions of soil particle size distributions, Acta Pedologica Sinica 45: 413 – 419 (in Chinese). Zhang XS. 1994. The optimization pattern and principle of grassland construction and ecological background of Maowusu sandland. Acta Phytoecologica Sinica 18: 1–16 (in Chinese). Zheng Y, Xie Z, Jiang L, Wu Y, Shimizu H. 2005. Model simulation and comparison of the ecological characteristics of three degraded grassland types in China. Belgian Journal of Botany 138: 109–118. Zinn YL, LaI R, Bigham JM, Resck DVS. 2007. Edaphic controls on soil organic carbon retention in the Brazilian Cerrado: Texture and mineralogy. Soil Science Society of American Journal 71: 1204 –1214.
