analysis of the spatial variation of soil salinity and its

Research ArticleAnalysis of the Spatial Variation of Soil Salinity and ItsCausal Factors in Chinarsquos Minqin Oasis

Tana Qian1 Atsushi Tsunekawa1 Tsugiyuki Masunaga2 and TaoWang3

1Arid Land Research Center Tottori University 1390 Hamasaka Tottori City 680-0001 Japan2Life and Environmental Science Shimane University 1060 Nishikawatsu Matsue City 690-8504 Japan3Key Laboratory of Desert and Desertification Northwest Institute of Eco-Environment and Resources Chinese Academy of Sciences320 West Donggang Road Lanzhou 730000 China

Correspondence should be addressed to Tana Qian qiantanagmailcom

Received 30 November 2016 Accepted 26 January 2017 Published 15 March 2017

Academic Editor Hajime Seya

Copyright copy 2017 Tana Qian et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Land salinization and water resource deterioration negatively affect irrigated agriculture in arid and semiarid areas by limiting thearea of arable land and reducing crop yieldsThe spatial variation of soil salinity is affected bymany factors and their interactions arecomplex In this study we utilized grey relational analysis to evaluate the factors that affect soil salinity in Chinarsquos Minqin Oasis andthe interactions among them and then ranked the significance of their impacts on soil salinity for different land use and cover typesThe data used in this study include data obtained from soil chemical analyses based on field sampling in 2015 and hydrological dataobtained from local government agencies We found that the main factors that affect soil salinity in the regionrsquos sparse grassland aregroundwater salinity and vegetation cover the least important factor was the distance to the nearest irrigation canal For croplandthe most important factors were the distance to irrigation canals and hydrological factors By accounting for these factors it shouldbe possible to manage the regionrsquos limited natural water and soil resources more efficiently while allowing remediation of existingsalinized land and helping to maintain sustainable agriculture in this arid land

1 Introduction

In arid northwestern China a severe water scarcity andgrowing soil salinization are closely related problems that arestrongly affecting agricultural development and sustainabil-ity These problems are especially severe in oasis ecosystemswhere the water scarcity and soil salinization have causedan unrepairable loss of productive land within the past fewdecades and have threatened the sustainability of agricultureand ecosystem stability [1ndash3]

The Shiyang River Basin which lies east of the HexiCorridor in Gansu Province has been one of Chinarsquos mostimportant traditional agricultural regions since 121 BC TheShiyang River is the regionrsquos primary water resource Itflows from glacial headwaters in the Qilian Mountains toits lower reaches in a region called the Minqin Oasiswhich is surrounded by both gobi (gravel) deserts and sandydeserts [4] Since the late 1950s Chinarsquos government has

promoted increased agricultural practices to support theregionrsquos rapid population growth This has led to a largeexpansion of cultivation accompanied by deforestation andreclamation of the regionrsquos grasslands The requirement forirrigation of crops in this arid region has expanded rapidlyTogether these changes have contributed to the degradationof oasis ecosystem services such as freshwater provision anddesertification control [5 6]

Due to a lack of long-term water resources planning inthe Shiyang River Basin the distribution of water resourcesbetween the riverrsquos middle and lower reaches is heavilyunbalanced with excessive withdrawals of water upstreamof the oasis [7] In addition the water supply to the MinqinOasis is controlled by management of the giant upstreamHonyashan Reservoir and the construction of an extensivenetwork of irrigation canals According to data from theGansu Shiyang River Basin Administrative Bureau the sur-face runoff flowing into the Minqin Oasis from the upper

HindawiMathematical Problems in EngineeringVolume 2017 Article ID 9745264 9 pageshttpsdoiorg10115520179745264

2 Mathematical Problems in Engineering

and middle reaches of Shiyang River has decreased rapidlyfrom 4931 times 108m3 per year in the 1960s to 1218 times 108m3per year in the 2000s To meet the high water demandof the Minqin Oasis groundwater became the primarywater resource to support large-scale irrigation which ledto excessive extraction of the groundwater This in turnhas had serious environmental consequences such as strongmineralization of the groundwater increasing depth to thegroundwater table and rapid salinization of soils [8] Thedepth to groundwater in the oasis has increased at an averagerate of 047m per year since 1981 [9]

Furthermore the increasing depth to the groundwatertable has caused an increase in the concentrations of dissolvedminerals in the groundwater [8] The reuse of saline waterfor crop irrigation combined with poor drainage systemshas exacerbated the problem and degraded the hydrologicalecosystem The average total dissolved solids (TDS) contentof groundwater in the Minqin Oasis reached 6 gL withmaximum of over 10 gL [9] According to Chinarsquos nationalstandards for drinking-water quality the maximum concen-tration level is 1000mgL

In addition due to the persistent water scarcity andmineralization many branches of the Shiyang River havegradually dried out and communities of natural vegetationhave become seriously degraded or have disappeared [10]The natural bush communities and man-made shrub plan-tations fix the regionrsquos abundant deposits of sand and providewindbreaks that reduce the erosive power of thewind the lossof this vegetation due to a lack of sufficient water increasesthe risk of desertification In addition the wetland vegetationthat once grew in large areas of the oasis has declined as thewetlands have dried out These problems have been exacer-bated by an improved system of irrigation canals which hasalso contributed to increases in the depth to the groundwatertable both by increasing water withdrawals and by reducinggroundwater recharge unlike the previously unlined canalsthe concrete-lined canals lose relatively little water into thesurrounding soil [9] The effects of the processes leading todegradation of vegetation in the oasis have ultimately resultedin increased sandy desertification [11]

Damage to the environment has been extensive andrepresents a serious threat to the sustainability of social andeconomic development in the Minqin Oasis In agriculturesaline irrigation water not only restricts the growth of allcrops other than those with high salt-tolerance and drought-tolerance but also aggravates the formation of secondarysalinization which results from salt accumulation in the soilunder the strong evaporation produced by the dry atmo-sphere Irrigation in Minqin Oasis farmland is controlled bycanals lined with cement cobble and concrete to improveirrigation efficiency However in contrast with the originalunlined canals this leads to reduced groundwater rechargeand contributes to the deepening groundwater table [4]Flood and furrow irrigation are widely used in the MinqinOasis and together they exacerbate the process of secondarysalinization Unfortunately there is insufficient freshwateravailable to leach the accumulated salt below plant rootingdepth throughout the oasis

As a result of these factors the land area that is expe-riencing severe secondary salinization has increased in theoasis The average salt content of the soil is up to 167 gkg[11] More than 59 of the irrigated agricultural land hasbeen abandoned because of the combination of water scarcityand the adverse effects of secondary salinization [12] From2005 to 2011 the local government organized emigration oftwo subvillages in Huqu District with a total population of1868 (1000 people for 2005 250 people for 2006 534 peoplefor 2007 and 84 people for 2011) which has suffered froma growing water scarcity sand invasion and soil salinization[9] Since the 1960s 252 times 104 km2 of farmland in the oasishas been abandoned [5] However the low income of thefarmer households who were forced to emigrate and thesustainability of water usage in their resettlement area remainimportant social economic and environmental issues [13]

Because of its severity land salinization has receivedmuch attention from researchers Although the salinization isaffected bymany factors [1] the interaction among the factorsresponsible for different types and intensities of salinizationof different land use and cover types is still inadequatelyunderstood To provide this information we began a study oftheMinqinOasis Ourmain objectives were to (a) analyze thespatial variability of soil salinity (b) analyze the factors thataffect soil salinity and (c) rank the importance of these factorsbased on land use and cover types To accomplish these goalswe applied the technique of grey relational analysis to the fieldsurvey data supported by geographical information system(GIS) tools We also took advantage of government statisticalrecords and remote sensing data in our statistical analysis andrelational factor analysis

2 Methods

21 Study Area The Huqu region is located on the down-stream alluvial plain of the Shiyang River Basin in thenorthern part of the Minqin Oasis The terrain is relativelyflat with an elevation that ranges from 1254 to 1376m asl Itcovers an area of 1430 km2 and is located between 38∘421015840Nand 39∘101015840N and between 103∘191015840E and 103∘491015840E (Figure 1(a))The region is surrounded by the Badan Jaran Desert tothe west and north and by the Tengger Desert to the eastThe region has a temperate continental arid climate witha mean annual temperature of 78∘C (monthly means rangefrom minus86∘C in January to 218∘C in August) Total annualprecipitation averages 110mm but the potential evaporationranges between 2000 and 2600mm The mean annual windspeed is 28ms but the highest wind speed can reach 23msand winds strong enough to entrain sand occur 139 days peryear on average [9]

Themain soil types are grey-brown desert soil sandy soilsolonchak meadow soil and anthropogenic-alluvial soil [4]The main soil type in croplands is a silty clay or a sandysilt The major crops include cereals (spring wheat summermaize) and cash crops (cotton melon and fennel) Nativevegetation includes drought-resistant shrubs salt-resistantshrubs and perennial sand-loving herbaceous plants (egElaeagnus angustifolia Populus euphratica Salix purpurea

Mathematical Problems in Engineering 3

Huqu area

100 5km

Sample sitesLow 1250High 1375

39∘ N

38∘ N

39∘ N

38∘ N

102∘ E 103

∘ E 104∘ E

102∘ E 103

∘ E 104∘ E

N N

Elevation (m)

(b)(a)

km0 10 4020

Figure 1 The location of the study area (the Huqu region) within Shi Yang River Basin (b) Locations of the 94 soil samples analyzed in thepresent study

and Agriophyllum squarrosum) [14] Because most of thegrassland area in the oasis was suitable for cultivation verylittle natural grassland remains however sparse grassland hasdeveloped where cultivated land was abandoned for morethan decades

We chose the Huqu region as our study area becauseof the existence of both the original salt accumulation (pri-mary salinization) and irrigation-caused salt accumulation(secondary salinization) The area has been suffering fromsevere ecological problems for decades including high min-eralization of groundwater salinization and desertificationcharacterized by sand invasion as well as from social andeconomic pressures caused by ecological emigration andcropland abandonment

22 Soil Sampling and Laboratory Analysis The field datasetwas obtained during April 2015 when the salt concentrationin surface soils reaches its maximum in this region In Aprilthe low precipitation combines with a high evaporation rateto cause salt from deeper soil layers to migrate upwards andaccumulate near the surface of the soil [15] We obtained 94surface soil samples (Figure 1(b)) to a depth of 10 cm fromunused land (9 samples from shrubland 5 samples fromabandoned land 46 samples from sparse grassland and 13samples from saline wasteland) and cropland (used to growwheat sunflower alfalfa goji berry and fennel) The siteswere selected based on the results of previous soil salinitystudies and Google Earth images and each sample siterepresented an area of 90mtimes 90m For each sample point wecombined five topsoil samples into a single composite samplewhich we used to represent the value of the soil chemicalparameters at that siteThe location of each sample point wasrecorded with a MAP64SJZ GPS receiver (Garmin Olathe

KS USA) and we photographed the site The characteristicsof the soil surface the land use and cover types surroundingthe site and the vegetation cover were recorded in a fieldnotebook We also consulted local farmers to learn abouthistorical land use and cover types

Soil moisture was assessed during the soil sample collec-tion The samples were sealed into weighed empty specimenboxes and their total weights were measured When wereturned to the laboratory we opened the boxes and oven-dried the samples for 24 hours at 105∘C together with theboxes We calculated the soil moisture content (SMC) as apercentage of the dry soil weight

For the chemical analysis the samples were air-driedcrushed and passed through a 1mm sieve to remove largeparticles and plant residues To quantify the soil salinity wemeasured the electrical conductivity (EC

1 5) in deionized

distilled water at 1 5 gmL soil water ratioWe calculated thefrequency distribution of these values and several associatedparameters themean median standard deviation skewnesskurtosis range and maximum and minimum values Weused version 23 of the SPSS software (httpwwwibmcomanalyticsusentechnologyspss) for our statistical analysis

We also measured the soil pH To make the pH measure-ments better reflect the water content of the soil under fieldconditions we used a 1 1 gmL soil solution [16]

23 GIS and Map Preparation Maps of the distribution ofsoil EC

1 5were prepared to visualize the results at the 94

sample sites We used version 1021 of the ArcView software(httpwwwesricom) We also visualized the layout of theirrigation canal network depths to the water table and TDSdata for the water using ArcView Additional data includedan ASTER Global Digital Elevation Model (ASTER GDEM)


Table 1 Definitions of the affecting factors for the reference series (soil EC1 5

)

Affecting factor Definition Measurement119883119894(1) Elevation of the sample site Distance above mean sea level119883119894(2) Distance to the nearest irrigation canal The distance between the sample point and the nearest canal119883119894(3) Compound topographic index (CTI) Defined in Section 23119883119894(4) Vegetation cover The coverage of the soil by vegetation119883119894(5) Groundwater level Distance from the ground surface to the top of the groundwater table119883119894(6) Groundwater salinity Total dissolved salt content in the groundwater

acquired from httpsgdexcrusgsgovgdex at a 30-m res-olution To describe the topographic characteristics of thesample sites we derived the compound topographic index(CTI) which is widely used in hydrology and terrain-relatedapplications from the DEM data [17] CTI is a compoundindex that is calculated using two primary topographicattributes [18] it is also known as the steady-state wetnessindex when it is used to quantify the catenary landscapeposition [19] We used the following equation to calculate theCTI values

CTI = ln( 120572tan120573) (1)

where 120572 represents the catchment area per pixel and 120573represents the slope gradient LowCTI values represent smallcatchments steep slopes and upper catenary positions HighCTI values represent large catchments gentle slopes andlower catenary positions with a higher capacity for wateraccumulation and wetness [20]

24 Grey Relation Analysis A grey relationship is a rela-tionship among different types of data series often withdifferent units of measurement when there is considerableuncertainty and an unsatisfactory sample size [21] Greyrelational analysis (GRA) is a way to identify the key factorsthat control a system and quantify the influence that eachfactor exerts on a reference variable [22] It is often applied instudies that have an insufficient sample size and uncertaintyabout whether the data is truly representative [21 23] Thebasic idea behind GRA is to compute the strength of therelationship between variables by examining the degree ofproximity for certain geometrical figures or the degree ofcorrelation between curves [22] In this study we employedGRA to quantify the influence of several factors on the soilsalinity of the 94 samples from the Huqu area

In GRA the original data series are divided into two typesof series a reference series that will be compared with allother variables and one or more affecting series (one pervariable that potentially affects the values in the referenceseries) The grey strength of the relationship is calculatedto represent the relative proximity of two series A discretesequence of ranks can then be generated with the ranksdepending on the strengths of each factorrsquos relationship tothe reference series If the strength of the relationship for oneaffecting series is higher than that of the others the formerseries is considered to have a greater influence than the otherson the reference series [24]

The first step in GRA is to generate the reference seriesand the affecting series Since the range of values and theunits of measurement in one data series may differ fromthose in other series the original data are first normalized bydividing each value by the mean value in the series therebyproducing a range of values from 0 to 1 and are then used tocalculate the grey relational coefficient between the referenceseries and the affecting series [25] The reference series isrepresented as 119909

0(119896) = 119909

0(1) 1199090(2) 119909

0(119899) where 119896 is

the number of sample data with 119896 isin (1 2 119899) and 119899 isthe number of factors Each affecting series is represented as119909119894(119896) = 119909

119894(1) 119909119894(2) 119909

119894(119899) where 119894 represents the affecting

series In this study we used the soil EC1 5

(dSm) as thereference series Based on our review of the research literatureand the mechanisms of salinization as well as based on dataavailability we selected six affecting factors for use as theaffecting series (Table 1)

The grey relational coefficient for two series 120577119894(119896) can be

calculated as follows

120577119894 (119896) = Δmin + 120577ΔmaxΔ0119894 (119896) + 120577Δmax

(2)

where Δ0119894(119896) = 119909

0(119896) minus 119909

119894(119896) is the absolute difference

between the reference factor 1199090(119896) and the corresponding

affecting factor 119909119894(119896) 120577 represents a grey parameter with a

value between 0 and 1 and that is often assigned a value thatequaled 05 a value that is commonly used [26] andΔmin andΔmax represent the smallest and biggest values respectivelyamong all of the Δ

0119894(119896) values

The grey relational grade 120574119894 for the relationship between

each affecting series 119909119894(119896) and the reference series 119909

0(119896)

can be calculated by averaging the grey relational coefficientcorresponding to each affecting factor

120574119894= 1119899 sum120577119894 (119896) (3)

The ranking of the affecting factors is then conductedbased on the computed grey relational grades As mentionedearlier a higher value of the grey relational grade indicates astronger relationship between the two series and suggests thatthe corresponding affecting factor is closer to the referenceseries than other affecting factors [27]

3 Results and Discussion

31 Statistical Analysis Figure 2 shows the frequency distri-bution and descriptive statistics for EC

1 5of the cropland and


Number 21Mean 135SD 245Median 5Minimum 011Maximum 966Skewness 056Kurtosis minus161

20

15

5

10

0

Freq

uenc

y

0 2 4 6 8 10EC1 5

Number 73Mean 696SD 619Median 469Minimum 012Maximum 231SkewnessKurtosis

20 25

25

20

15

15

5

5

10

10

00

Freq

uenc

y

092002

EC1 5

(a) (b)

Figure 2 Histograms of EC1 5

values for samples from (a) cropland (119899 = 21) and (b) all other land types (119899 = 73) and their associateddescriptive statistics

other land types 21 soil samples were collected from croplandduring the spring which explains why many samples have alow EC

1 5value 73 soil samples were collected from other

land types (shrubland abandoned land sparse grassland andsaline wasteland) Figure 2 also shows wide variation in theEC1 5

data with values ranging from 011 to 231 dSm (twoorders of magnitude)

To understand the spatial distribution of the salinitylevels we mapped the distribution of soil EC

1 5using

ArcView based on equal interval method (Figure 3) As thedistributionmap shows most of the samples with high EC

1 5

were distributed at the margins of the oasis but some weredistributed in the center in contrast the samples with lowerEC1 5

were distributed throughout the oasis

32 Differences in Soil Salinity among the Different Land Useand Cover Types To further explore the characteristics ofthe distribution of the soil EC

1 5values in the Huqu area

in relation to the land management in the study area weclassified the 94 samples into the five main land use andcover types cropland shrubland abandoned land sparsegrassland and saline wasteland Table 2 summarizes themeasured values of the various properties of the soil samples

The coefficient of variation (CV) is the ratio of thestandard deviation to the mean and is often used as a generalindex of variability among the samples [28] A CV valuelower than 01 (10) indicates low variability whereas aCV value higher than 10 (100) indicates high variabilityintermediate values have moderate variability [29] Table 2shows that theCVvalues of EC

1 5for cropland and shrubland

were highly variable with CV values greater than 10 whichsuggests that EC

1 5and its distribution pattern are strongly

01ndash4748ndash9394ndash139

140ndash185

186ndash231

100 5km

Cropland

N

EC1 5

Figure 3 Map of the distribution of EC1 5

values for the 94 soilsamples

influenced by human driving factors such as irrigation andfarming activities as well as by the irrigation canal systemDespite regular irrigation some of the cropland soil EC

1 5

values reached 966 dSm which indicates the existence ofa secondary salinization problem The shrubland is mainlyartificial and has resulted from a local policy to replacecropland with shrubs to combat desertification The low


Table2Descriptiv

estatisticsfor

thes

oilp

aram

etersby

land

usea

ndcoverc

lass(LUCC

)a

LUCC

(sam

ples

ize)

Parameters

Range

Mean

SDCV

LUCC

descrip

tion

Crop

land

(21)

EC15(dSm)

011ndash966

135

239

177

Thiscla

ssinclu

deslandcoveredwith

crop

s(egfenn

elcotto

nalfalfaw

heatm

aize

sunfl

owerand

gojiberry)n

ewlycultivated

land

and

fallo

wland

pH11

782ndash876

832

024

003

SMC(

)086ndash1381

715

277

039

GW

salin

ity(gL)

211ndash2739

1459

609

042

GW

tabled

epth

(m)

532ndash3233

1861

799

043

Shrubland

(9)

EC15(dSm)

012ndash1489

323

445

138

Land

with

woo

dyvegetatio

nwith

atotal

shrubcoverthatexceeds

10of

thes

urface

Someo

fthislandresultedfro

malocal

policyof

replacingcrop

land

with

shrubs

tocombatd

esertifi

catio

n

pH11

805ndash899

838

031

004

SMC(

)14

7ndash603

319

161

050

GW

salin

ity(gL)

968ndash2791

1553

638

041

GW

tabled

epth

(m)

717ndash2995

1847

834

045

Abando

nedland

(5)

EC15(dSm)

021ndash141

099

041

042

Land

hasb

eenabando

nedfor5

to20

years

with

vegetatio

ncoverlt

5

pH11

796ndash

883

835

03

004

SMC(

)15

4ndash455

266

107

040

GW

salin

ity(gL)

1006ndash

1844

1322

345

026

GW

tabled

epth

(m)

1089ndash

2878

2005

666

033

Sparse

grassla

nd(46)

EC15(dSm)

015ndash214

4618

501

081

Land

hasb

eenabando

nedform

orethan20

yearsDom

inated

bygrassesa

ndsalt-tolerant

grasseswith

vegetatio

ncover

of5

to20

pH11

789ndash

875

834

022

003

SMC(

)093ndash1018

408

214

052

GW

salin

ity(gL)

651ndash2327

1412

419

030

GW

tabled

epth

(m)

718ndash3605

2270

633

028

Salin

ewasteland

(13)

EC15(dSm)

828ndash2310

146

512

035

Land

with

asaltcrustatthes

oilsurface

Dom

inated

bynaturalsalt-tolerantg

rasses

with

vegetatio

ncovergt

20N

aturally

salt-affectedareasa

redistr

ibuted

atthe

margins

oftheo

asis

pH11

788ndash858

836

022

003

SMC(

)391ndash1496

887

309

035

GW

salin

ity(gL)

755ndash1997

1120

339

030

GW

tabled

epth

(m)

676ndash294

21580

829

052

a SMCrepresentsthes

oilm

oistu

recontentasa

percentage

ofthed

rysoilweightGW

representstheg

roun

dwater


Table 3 Grey relational grade (120574) and the resulting ranking for each affecting factor

Reference factor Affecting factor Definition 120574 Ranking Relationshipa

1198830EC1 5

119883119894(2) Distance to irrigation canals 0814 1 Positive119883119894(6) Groundwater salinity 0810 2 Negative119883119894(3) CTI 0804 3 Positive119883119894(1) Elevation of the sample site 0801 4 Negative119883119894(5) Groundwater level 0798 5 Negative119883119894(4) Vegetation cover 0771 6 Negative

aPositive relationships mean that a high value of the affecting factor increased EC1 5 negative relationships mean that a high value decreased EC1 5

Table 4 Grey relational grade (120574) for each affecting factor and the resulting ranking for the two main land use and cover types


Cropland

1198830EC1 5

119883119894(2) Distance to irrigation canals 0847 1 Positive119883119894(5) Groundwater level 0787 2 Negative119883119894(3) CTI 0783 3 Positive119883119894(1) Elevation of the sample site 0780 4 Negative119883119894(6) Groundwater salinity 0777 5 Positive119883119894(4) Vegetation cover 0760 6 Negative

Sparse grassland

1198830EC1 5

119883119894(6) Groundwater salinity 0755 1 Positive119883119894(4) Vegetation cover 0742 2 Positive119883119894(1) Elevation of the sample site 0714 3 Negative119883119894(5) Groundwater level 0711 4 Positive119883119894(3) CTI 0708 5 Negative119883119894(2) Distance to irrigation canals 0662 6 Negative


soil moisture content of the shrubland provides evidence ofa water scarcity problem This is consistent with previousresearch which showed that shrubs have died in large areasbecause the groundwater level fell below the level theirroots can reach The maximum EC

1 5value of shrubland

reached 1489 dSm which indicated large amounts of saltaccumulation in the topsoil as a result of a high evaporationrate combined with low rainfall and mineralization of thegroundwater

The CV values of EC1 5

for abandoned land sparse grass-land and saline wasteland were moderate (between 01 and10)These land use and cover types were less strongly affectedby human activities Abandoned landwas defined as land thathad been abandoned for 5 to 20 years and sparse grasslandhas been abandoned for more than 20 years and has begun todevelop a grassland ecosystemThemajority of the soil EC

1 5

values for abandoned land were lower than those for sparsegrassland which indicated that soil salinity increased withincreasing duration of abandonment Compared with sparsegrassland the salinity level and moisture content of soils inthe abandoned land were lower

The CV values for SMC for all land use and cover typesshowed moderate variability (01 to 10)The pH

1 1of the soil

of the study areas ranged from 782 to 899 (slightly alkaline)and its low CV values (lt01) indicated slight variation The

mean pH1 1

for all land use and cover types was greaterthan 8 which indicated most of the soil samples are weaklyalkalineThe high groundwater salinity (gt11 gL) content anddeep groundwater table depth (gt15m) indicated poor waterquality and poor access to water and their low CV valuesindicate moderate spatial variability

33 Impact Factors Many factors including soil factorshydrological factors management factors and other factorsaffected the soil salinization process and its spatial distri-bution in the Huqu area Interactions among these factorsare complex [30] Based on the calculated value of the greyrelational grade (120574) we determined the order of each affectingfactor for all 94 samples combined As shown in Table 3these values range from 0 to 1 and we obtained the followingranking of the factors based on the grey relational gradesdistance to the nearest irrigation canalgt groundwater salinitygt CTI gt elevation of the sample site gt groundwater level gtvegetation cover

Analyzing the grey relationships between the affectingfactors and soil EC

1 5for specific landuse and cover typeswill

help us to better understand the interaction between thesefactors and the salinization processes To do this we selectedthe two main land use and cover types and applied GRA totheir affecting factors (Table 4)


Table 4 shows the calculated grey relational grades for theaffecting factors for cropland and sparse grassland For thecropland soil samples the distance to the nearest irrigationcanals was the most important factor for soil EC

1 5and

had a positive relationship with soil EC1 5

The greater thedistance between the sample sites and the canal the higherthe soil EC

1 5content This is because the use of fresh river

water as an irrigation source coupled with the drainagesystem greatly decreased salt accumulation in the topsoil Incontrast the groundwater salinity had a negative relationshipwith soil EC

1 5 which wasmainly because farmers combined

fresh river water with groundwater to irrigate the croplandsThe higher groundwater salinity is the less amount of it canbe used for irrigation and the more river water must beconsumed for irrigation leading to accelerated salt leachingand reducing the salt accumulation in the soil Some sampleswere taken from the margins of the oasis where the ground-water salinity was strongly affected by highly saline desertgroundwater In addition the groundwater table depth wasat least 53m (Table 2) in those areas with high groundwatersalinity Because this large distance interrupts the capillaryrise of water from the groundwater table to the soil surfacesalt movement from the groundwater into the topsoil wasreduced [31] The vegetation cover had the weakest impacton the soil EC

1 5of cropland mainly because little vegetation

had developed at the beginning of the growing season whenwe obtained our soil samples

For sparse grassland distance to the nearest canal was theweakest affecting factor This was mainly because the sparsegrassland was close to a natural condition because of lowintervention by human activities (ie after abandonment formore than 20 years) The groundwater salinity had a strongpositive relationship with soil EC

1 5for sparse grassland

which indicated that local-scale hydrological factors affectedthe distribution and variation of surface soil salinity Thesparse grassland had been abandoned for more than 20years and most of the sparse grassland had suffered fromsecondary salinization Some of the land has been abandonedfor more than 40 years severe adverse effects of intensiveagricultural practices during the 1950s and 1960s madethe land unsuitable for cultivation Excessive groundwaterextraction for irrigation caused a rapid deepening of thegroundwater table and mineralization of the groundwaterThe use of groundwater with a high salt content coupledwith strong evaporation accelerated salt accumulation in thetopsoil This caused rapid degradation of productive landand eventually abandonment of this land The soil EC

1 5

values in this land could reach as high as the values in salinewasteland The vegetation cover was the second-strongestaffecting factor due to dense growth of halophytes in salt-affected areas [32]

4 Conclusions

Salt-affected soils were distributed throughout the Huquarea Most of the cropland had relatively low soil salinitybut several samples had high EC

1 5 which indicates a high

potential for secondary salinizationThe saline wasteland had

some of the highest salinity values and was concentratedmainly at the margin of the oasis where there was noirrigation Secondary salinized land was mainly located inareas of sparse grassland inside the oasis The CV valuesof soil EC

1 5for cropland and shrubland were greater than

10 which indicates that soil EC1 5

and its distribution werestrongly influenced by human activities

Land use practices also strongly influenced the distribu-tion of salt-affected soils The abandoned cropland had lowsalinity but the long-abandoned cropland (which becamesparse grassland) tended to have high salinity sometimesreaching values as high as those in saline wasteland Inaddition transforming cropland to shrubland could resultin further land degradation in Huqu area due to the waterscarcity and poor water quality this change led to higherEC1 5

and lower SMC Improved management should focuson these lands to prevent further degradation

The distance to the nearest irrigation canal was the mostimportant factor for determining the salinity of croplandThe access to fresh river water and good drainage combinedwith a deep groundwater table prevented salt accumulationin the topsoil However the use of highly saline water andimproper drainage and soil management will increase therisk of secondary salinization in irrigated soils Thereforeproper irrigationmethods an improved drainage system andeffective soil management will be necessary to prevent non-salinized cropland from undergoing secondary salinizationand to remediate existing salinized cropland

The small sample size in this study may have made thedata less reliable However the use of GRA should havemitigated this problem Nonetheless obtaining more andbetter data on water and soil properties would improve theability to supportmanagement of the water and soil resourcesof the Minqin Oasis particularly if combined with remotesensing and GIS techniques to improve our ability to detectthe distribution of salt-affected soils over larger areas

Competing Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper

Acknowledgments

The authors would like to thank International Platformfor Dryland Research and Education Tottori Universityfor financial support Professor Xue Xian Professor WangNinglian Dr Huang Cuihua Dr Liao Jie and Dr LuoJun from Northwest Institute of Eco-Environment andResources CAS for providing them with the equipment andthe assistance in the field the Minqin Bureaus of WaterResourcesManagement for providing hydrological data Pro-fessor Kitamura Yoshinobu and Professor Fujimaki HaruyukifromTottori University for the useful advices and equipmentGeoff Hart for helpful advice on early version of this paperThe Landsat dataset is provided by Environmental and Eco-logical Science Data Center forWest China National NaturalScience Foundation of China (httpwestdcwestgisaccn)


References

[1] P Rengasamy ldquoWorld salinization with emphasis on AustraliardquoJournal of Experimental Botany vol 57 no 5 pp 1017ndash10232006

[2] W Genxu and C Guodong ldquoWater resource development andits influence on the environment in arid areas of China-the caseof theHei River basinrdquo Journal of Arid Environments vol 43 no2 pp 121ndash131 1999

[3] C Tian and H Zhou ldquoThe proposal on control of soil salin-ization and agricultural sustainable development in the 21stcentury in XinjianrdquoArid LandGeography vol 23 no 2 pp 177ndash181 2000

[4] S Danfeng R Dawson and L Baoguo ldquoAgricultural causes ofdesertification risk inMinqin Chinardquo Journal of EnvironmentalManagement vol 79 no 4 pp 348ndash356 2006

[5] Y Ma S Fan L Zhou Z Dong K Zhang and J Feng ldquoThetemporal change of driving factors during the course of landdesertification in arid region ofNorthChina the case ofMinqinCountyrdquo Environmental Geology vol 51 no 6 pp 999ndash10082007

[6] R TMunang IThiaw andM Rivington ldquoEcosystemmanage-ment tomorrowrsquos approach to enhancing food security under achanging climaterdquo Sustainability vol 3 no 7 pp 937ndash954 2011

[7] G Schneier-Madanes and M F CourelWater and Sustainabil-ity in Arid Regions Bridging the Gap between Physical and SocialSciences Springer Dordrecht The Netherlands 2009

[8] D K VajpeyiM Bondes AM Buainain et alClimate ChangeSustainable Development and Human Security A ComparativeAnalysis Lexington Books 2013

[9] Local Chronicles Office of Minqin County Annals of Shi YangRiver Wuwei Guangxin Kemao Printing Co Ltd 2014

[10] Z L Huo S Y Feng S Z Kang S J Cen and Y MaldquoSimulation of effects of agricultural activities on groundwaterlevel by combining FEFLOW and GISrdquo New Zealand Journal ofAgricultural Research vol 50 no 5 pp 839ndash846 2007

[11] J Ma and HWei ldquoThe ecological and environmental problemscaused by the excessive exploitation and utilization of ground-water resources in the Minqin Basin Gansu provincerdquo AridZone Research vol 20 no 4 pp 261ndash265 2002

[12] C Guo L Wang F Han et al ldquoStudies of soil physical propertyon different abandoned lands in theMinqin Oasis downstreamof the Shiyang Riverrdquo Agricultural Science amp Technology vol 16no 5 pp 1014ndash1018 2015

[13] Y Li D Lopez-Carr andW Chen ldquoFactors affecting migrationintentions in ecological restoration areas and their implicationsfor the sustainability of ecological migration policy in aridNorthwest Chinardquo Sustainability vol 6 no 12 pp 8639ndash86602014

[14] S Kang X Su L Tong et al ldquoThe impacts of human activitieson the water-land environment of the Shiyang River basin anarid region in northwest Chinardquo Hydrological Sciences Journalvol 49 no 3 p 427 2004

[15] A A Elnaggar and J S Noller ldquoApplication of remote-sensingdata and decision-tree analysis to mapping salt-affected soilsover large areasrdquo Remote Sensing vol 2 no 1 pp 151ndash165 2010

[16] G W Thomas D L Sparks A L Page et al Soil pH and SoilAcidity Sparks D Methods of Soil Analysis Part II Soil ScienceSociety of America 1996

[17] K J Beven and M J Kirkby ldquoA physically based variable con-tributing area model of basin hydrologyrdquo Hydrological SciencesBulletin vol 24 no 1 pp 43ndash69 1979

[18] I D Moore R B Grayson and A R Ladson ldquoDigital terrainmodelling a review of hydrological geomorphological andbiological applicationsrdquo Hydrological Processes vol 5 no 1 pp3ndash30 1991

[19] P E Gessler O A Chadwick F Chamran L Althouse andKHolmes ldquoModeling soilndashlandscape and ecosystempropertiesusing terrain attributesrdquo Soil Science Society of America Journalvol 64 no 6 pp 2046ndash2056 2000

[20] T RMarthews S J Dadson B Lehner S Abele andNGedneyldquoHigh-resolution global topographic index values for use inlarge-scale hydrological modellingrdquo Hydrology and Earth Sys-tem Sciences vol 19 no 1 pp 91ndash104 2015

[21] J L Deng A Course in Grey SystemsTheory Press of HuazhongUniversity of Science and Technology Wuhan China 1990

[22] Y Lin and S F Liu ldquoA systemic analysis with data (II)rdquoInternational Journal of General Systems vol 29 no 6 pp 1001ndash1013 2000

[23] S F Liu and J Forrest ldquoThe current development status onGreySystemTheoryrdquo Journal of Grey System vol 19 no 2 pp 111ndash1232007

[24] J L Deng ldquoIntroduction to Grey SystemTheoryrdquoThe Journal ofGrey System vol 1 no 1 pp 1ndash24 1989

[25] C L Lin ldquoUse of theTaguchiMethod andGreyRelationalAnal-ysis to optimize turning operations with multiple performancecharacteristicsrdquo Materials and Manufacturing Processes vol 19no 2 pp 209ndash220 2004

[26] N Tosun ldquoDetermination of optimum parameters for multi-performance characteristics in drilling by using grey relationalanalysisrdquoThe International Journal of Advanced ManufacturingTechnology vol 28 no 5 pp 450ndash455 2006

[27] S F Liu and Y Lin Grey Information Theory and PracticalApplications Springer London UK 2006

[28] L PWilding and L R Drees ldquoSpatial variability and pedologyrdquoDevelopments in Soil Science vol 11 pp 83ndash116 1983

[29] P Adhikari M K Shukla and J G Mexal ldquoSpatial variabilityof electrical conductivity of desert soil irrigated with treatedwastewater implications for irrigation managementrdquo Appliedand Environmental Soil Science vol 2011 Article ID 504249 11pages 2011

[30] M Aslam and S A Prathapar ldquoStrategies to mitigate secondarysalinization in the Indus Basin of Pakistan a selective reviewrdquoTech Rep 97 IWMI 2006

[31] H Solomon Y Kitamura Z Li et al ldquoClassification of salin-ization processes in Luohui Irrigation Scheme Chinamdashpart ofwater management research to prevent salinization in semiaridlandrdquo Journal of Arid Land Studies vol 15 no 2 pp 89ndash1052005

[32] Z F Chang H Liu M Zhao F Han S Zhong and J TangldquoA primary study on the process of formation and successionof desert vegetation in Minqinrdquo Journal of Arid Land Resourcesand Environment vol 21 no 7 pp 116ndash124 2007

Submit your manuscripts athttpswwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of


Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of


Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of


Mathematical PhysicsAdvances in

Complex AnalysisJournal of


OptimizationJournal of


CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of


Operations ResearchAdvances in

Journal of


Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Algebra

Discrete Dynamics in Nature and Society



Decision SciencesAdvances in

Discrete MathematicsJournal of


Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of


and middle reaches of Shiyang River has decreased rapidlyfrom 4931 times 108m3 per year in the 1960s to 1218 times 108m3per year in the 2000s To meet the high water demandof the Minqin Oasis groundwater became the primarywater resource to support large-scale irrigation which ledto excessive extraction of the groundwater This in turnhas had serious environmental consequences such as strongmineralization of the groundwater increasing depth to thegroundwater table and rapid salinization of soils [8] Thedepth to groundwater in the oasis has increased at an averagerate of 047m per year since 1981 [9]

Furthermore the increasing depth to the groundwatertable has caused an increase in the concentrations of dissolvedminerals in the groundwater [8] The reuse of saline waterfor crop irrigation combined with poor drainage systemshas exacerbated the problem and degraded the hydrologicalecosystem The average total dissolved solids (TDS) contentof groundwater in the Minqin Oasis reached 6 gL withmaximum of over 10 gL [9] According to Chinarsquos nationalstandards for drinking-water quality the maximum concen-tration level is 1000mgL

In addition due to the persistent water scarcity andmineralization many branches of the Shiyang River havegradually dried out and communities of natural vegetationhave become seriously degraded or have disappeared [10]The natural bush communities and man-made shrub plan-tations fix the regionrsquos abundant deposits of sand and providewindbreaks that reduce the erosive power of thewind the lossof this vegetation due to a lack of sufficient water increasesthe risk of desertification In addition the wetland vegetationthat once grew in large areas of the oasis has declined as thewetlands have dried out These problems have been exacer-bated by an improved system of irrigation canals which hasalso contributed to increases in the depth to the groundwatertable both by increasing water withdrawals and by reducinggroundwater recharge unlike the previously unlined canalsthe concrete-lined canals lose relatively little water into thesurrounding soil [9] The effects of the processes leading todegradation of vegetation in the oasis have ultimately resultedin increased sandy desertification [11]

Damage to the environment has been extensive andrepresents a serious threat to the sustainability of social andeconomic development in the Minqin Oasis In agriculturesaline irrigation water not only restricts the growth of allcrops other than those with high salt-tolerance and drought-tolerance but also aggravates the formation of secondarysalinization which results from salt accumulation in the soilunder the strong evaporation produced by the dry atmo-sphere Irrigation in Minqin Oasis farmland is controlled bycanals lined with cement cobble and concrete to improveirrigation efficiency However in contrast with the originalunlined canals this leads to reduced groundwater rechargeand contributes to the deepening groundwater table [4]Flood and furrow irrigation are widely used in the MinqinOasis and together they exacerbate the process of secondarysalinization Unfortunately there is insufficient freshwateravailable to leach the accumulated salt below plant rootingdepth throughout the oasis

As a result of these factors the land area that is expe-riencing severe secondary salinization has increased in theoasis The average salt content of the soil is up to 167 gkg[11] More than 59 of the irrigated agricultural land hasbeen abandoned because of the combination of water scarcityand the adverse effects of secondary salinization [12] From2005 to 2011 the local government organized emigration oftwo subvillages in Huqu District with a total population of1868 (1000 people for 2005 250 people for 2006 534 peoplefor 2007 and 84 people for 2011) which has suffered froma growing water scarcity sand invasion and soil salinization[9] Since the 1960s 252 times 104 km2 of farmland in the oasishas been abandoned [5] However the low income of thefarmer households who were forced to emigrate and thesustainability of water usage in their resettlement area remainimportant social economic and environmental issues [13]

Because of its severity land salinization has receivedmuch attention from researchers Although the salinization isaffected bymany factors [1] the interaction among the factorsresponsible for different types and intensities of salinizationof different land use and cover types is still inadequatelyunderstood To provide this information we began a study oftheMinqinOasis Ourmain objectives were to (a) analyze thespatial variability of soil salinity (b) analyze the factors thataffect soil salinity and (c) rank the importance of these factorsbased on land use and cover types To accomplish these goalswe applied the technique of grey relational analysis to the fieldsurvey data supported by geographical information system(GIS) tools We also took advantage of government statisticalrecords and remote sensing data in our statistical analysis andrelational factor analysis

2 Methods

21 Study Area The Huqu region is located on the down-stream alluvial plain of the Shiyang River Basin in thenorthern part of the Minqin Oasis The terrain is relativelyflat with an elevation that ranges from 1254 to 1376m asl Itcovers an area of 1430 km2 and is located between 38∘421015840Nand 39∘101015840N and between 103∘191015840E and 103∘491015840E (Figure 1(a))The region is surrounded by the Badan Jaran Desert tothe west and north and by the Tengger Desert to the eastThe region has a temperate continental arid climate witha mean annual temperature of 78∘C (monthly means rangefrom minus86∘C in January to 218∘C in August) Total annualprecipitation averages 110mm but the potential evaporationranges between 2000 and 2600mm The mean annual windspeed is 28ms but the highest wind speed can reach 23msand winds strong enough to entrain sand occur 139 days peryear on average [9]

Themain soil types are grey-brown desert soil sandy soilsolonchak meadow soil and anthropogenic-alluvial soil [4]The main soil type in croplands is a silty clay or a sandysilt The major crops include cereals (spring wheat summermaize) and cash crops (cotton melon and fennel) Nativevegetation includes drought-resistant shrubs salt-resistantshrubs and perennial sand-loving herbaceous plants (egElaeagnus angustifolia Populus euphratica Salix purpurea


Huqu area

100 5km


39∘ N

38∘ N

39∘ N

38∘ N

102∘ E 103

∘ E 104∘ E

102∘ E 103

∘ E 104∘ E

N N

Elevation (m)

(b)(a)

km0 10 4020








1 5) in deionized








)



CTI = ln( 120572tan120573) (1)





0(119896) = 119909

0(1) 1199090(2) 119909

0(119899) where 119896 is


119894(1) 119909119894(2) 119909






120577119894 (119896) = Δmin + 120577ΔmaxΔ0119894 (119896) + 120577Δmax

(2)

where Δ0119894(119896) = 119909

0(119896) minus 119909





0119894(119896) values



0(119896)


120574119894= 1119899 sum120577119894 (119896) (3)







20

15

5

10

0

Freq

uenc

y

0 2 4 6 8 10EC1 5


20 25

25

20

15

15

5

5

10

10

00

Freq

uenc

y

092002

EC1 5

(a) (b)








1 5using


1 5











140ndash185

186ndash231

100 5km

Cropland

N

EC1 5




1 5



Table2Descriptiv

estatisticsfor

thes

oilp

aram

etersby

land

usea

ndcoverc

lass(LUCC

)a

LUCC

(sam

ples

ize)

Parameters

Range

Mean

SDCV

LUCC

descrip

tion

Crop

land

(21)

EC15(dSm)

011ndash966

135

239

177

Thiscla

ssinclu

deslandcoveredwith

crop

s(egfenn

elcotto

nalfalfaw

heatm

aize

sunfl

owerand

gojiberry)n

ewlycultivated

land

and

fallo

wland

pH11

782ndash876

832

024

003

SMC(

)086ndash1381

715

277

039

GW

salin

ity(gL)

211ndash2739

1459

609

042

GW

tabled

epth

(m)

532ndash3233

1861

799

043

Shrubland

(9)

EC15(dSm)

012ndash1489

323

445

138

Land

with

woo

dyvegetatio

nwith

atotal


10of

thes

urface

Someo


malocal

policyof

replacingcrop

land

with

shrubs

tocombatd

esertifi

catio

n

pH11

805ndash899

838

031

004

SMC(

)14

7ndash603

319

161

050

GW

salin

ity(gL)

968ndash2791

1553

638

041

GW

tabled

epth

(m)

717ndash2995

1847

834

045

Abando

nedland

(5)

EC15(dSm)

021ndash141

099

041

042

Land

hasb

eenabando

nedfor5

to20

years

with

vegetatio

ncoverlt

5

pH11

796ndash

883

835

03

004

SMC(

)15

4ndash455

266

107

040

GW

salin

ity(gL)

1006ndash

1844

1322

345

026

GW

tabled

epth

(m)

1089ndash

2878

2005

666

033

Sparse

grassla

nd(46)

EC15(dSm)

015ndash214

4618

501

081

Land

hasb

eenabando

nedform

orethan20

yearsDom

inated

bygrassesa

ndsalt-tolerant

grasseswith

vegetatio

ncover

of5

to20

pH11

789ndash

875

834

022

003

SMC(

)093ndash1018

408

214

052

GW

salin

ity(gL)

651ndash2327

1412

419

030

GW

tabled

epth

(m)

718ndash3605

2270

633

028

Salin

ewasteland

(13)

EC15(dSm)

828ndash2310

146

512

035

Land

with

asaltcrustatthes

oilsurface

Dom

inated


rasses

with

vegetatio

ncovergt

20N

aturally

salt-affectedareasa

redistr

ibuted

atthe

margins

oftheo

asis

pH11

788ndash858

836

022

003

SMC(

)391ndash1496

887

309

035

GW

salin

ity(gL)

755ndash1997

1120

339

030

GW

tabled

epth

(m)

676ndash294

21580

829

052

a SMCrepresentsthes

oilm

oistu

recontentasa

percentage

ofthed

rysoilweightGW

representstheg

roun

dwater




1198830EC1 5





Cropland

1198830EC1 5


Sparse grassland

1198830EC1 5








1 5



1 1of the soil


mean pH1 1








1 5and












1 5


4 Conclusions












Competing Interests


Acknowledgments



References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Huqu area

100 5km


39∘ N

38∘ N

39∘ N

38∘ N

102∘ E 103

∘ E 104∘ E

102∘ E 103

∘ E 104∘ E

N N

Elevation (m)

(b)(a)

km0 10 4020








1 5) in deionized








)



CTI = ln( 120572tan120573) (1)





0(119896) = 119909

0(1) 1199090(2) 119909

0(119899) where 119896 is


119894(1) 119909119894(2) 119909






120577119894 (119896) = Δmin + 120577ΔmaxΔ0119894 (119896) + 120577Δmax

(2)

where Δ0119894(119896) = 119909

0(119896) minus 119909





0119894(119896) values



0(119896)


120574119894= 1119899 sum120577119894 (119896) (3)







20

15

5

10

0

Freq

uenc

y

0 2 4 6 8 10EC1 5


20 25

25

20

15

15

5

5

10

10

00

Freq

uenc

y

092002

EC1 5

(a) (b)








1 5using


1 5











140ndash185

186ndash231

100 5km

Cropland

N

EC1 5




1 5



Table2Descriptiv

estatisticsfor

thes

oilp

aram

etersby

land

usea

ndcoverc

lass(LUCC

)a

LUCC

(sam

ples

ize)

Parameters

Range

Mean

SDCV

LUCC

descrip

tion

Crop

land

(21)

EC15(dSm)

011ndash966

135

239

177

Thiscla

ssinclu

deslandcoveredwith

crop

s(egfenn

elcotto

nalfalfaw

heatm

aize

sunfl

owerand

gojiberry)n

ewlycultivated

land

and

fallo

wland

pH11

782ndash876

832

024

003

SMC(

)086ndash1381

715

277

039

GW

salin

ity(gL)

211ndash2739

1459

609

042

GW

tabled

epth

(m)

532ndash3233

1861

799

043

Shrubland

(9)

EC15(dSm)

012ndash1489

323

445

138

Land

with

woo

dyvegetatio

nwith

atotal


10of

thes

urface

Someo


malocal

policyof

replacingcrop

land

with

shrubs

tocombatd

esertifi

catio

n

pH11

805ndash899

838

031

004

SMC(

)14

7ndash603

319

161

050

GW

salin

ity(gL)

968ndash2791

1553

638

041

GW

tabled

epth

(m)

717ndash2995

1847

834

045

Abando

nedland

(5)

EC15(dSm)

021ndash141

099

041

042

Land

hasb

eenabando

nedfor5

to20

years

with

vegetatio

ncoverlt

5

pH11

796ndash

883

835

03

004

SMC(

)15

4ndash455

266

107

040

GW

salin

ity(gL)

1006ndash

1844

1322

345

026

GW

tabled

epth

(m)

1089ndash

2878

2005

666

033

Sparse

grassla

nd(46)

EC15(dSm)

015ndash214

4618

501

081

Land

hasb

eenabando

nedform

orethan20

yearsDom

inated

bygrassesa

ndsalt-tolerant

grasseswith

vegetatio

ncover

of5

to20

pH11

789ndash

875

834

022

003

SMC(

)093ndash1018

408

214

052

GW

salin

ity(gL)

651ndash2327

1412

419

030

GW

tabled

epth

(m)

718ndash3605

2270

633

028

Salin

ewasteland

(13)

EC15(dSm)

828ndash2310

146

512

035

Land

with

asaltcrustatthes

oilsurface

Dom

inated


rasses

with

vegetatio

ncovergt

20N

aturally

salt-affectedareasa

redistr

ibuted

atthe

margins

oftheo

asis

pH11

788ndash858

836

022

003

SMC(

)391ndash1496

887

309

035

GW

salin

ity(gL)

755ndash1997

1120

339

030

GW

tabled

epth

(m)

676ndash294

21580

829

052

a SMCrepresentsthes

oilm

oistu

recontentasa

percentage

ofthed

rysoilweightGW

representstheg

roun

dwater




1198830EC1 5





Cropland

1198830EC1 5


Sparse grassland

1198830EC1 5








1 5



1 1of the soil


mean pH1 1








1 5and












1 5


4 Conclusions












Competing Interests


Acknowledgments



References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











)



CTI = ln( 120572tan120573) (1)





0(119896) = 119909

0(1) 1199090(2) 119909

0(119899) where 119896 is


119894(1) 119909119894(2) 119909






120577119894 (119896) = Δmin + 120577ΔmaxΔ0119894 (119896) + 120577Δmax

(2)

where Δ0119894(119896) = 119909

0(119896) minus 119909





0119894(119896) values



0(119896)


120574119894= 1119899 sum120577119894 (119896) (3)







20

15

5

10

0

Freq

uenc

y

0 2 4 6 8 10EC1 5


20 25

25

20

15

15

5

5

10

10

00

Freq

uenc

y

092002

EC1 5

(a) (b)








1 5using


1 5











140ndash185

186ndash231

100 5km

Cropland

N

EC1 5




1 5



Table2Descriptiv

estatisticsfor

thes

oilp

aram

etersby

land

usea

ndcoverc

lass(LUCC

)a

LUCC

(sam

ples

ize)

Parameters

Range

Mean

SDCV

LUCC

descrip

tion

Crop

land

(21)

EC15(dSm)

011ndash966

135

239

177

Thiscla

ssinclu

deslandcoveredwith

crop

s(egfenn

elcotto

nalfalfaw

heatm

aize

sunfl

owerand

gojiberry)n

ewlycultivated

land

and

fallo

wland

pH11

782ndash876

832

024

003

SMC(

)086ndash1381

715

277

039

GW

salin

ity(gL)

211ndash2739

1459

609

042

GW

tabled

epth

(m)

532ndash3233

1861

799

043

Shrubland

(9)

EC15(dSm)

012ndash1489

323

445

138

Land

with

woo

dyvegetatio

nwith

atotal


10of

thes

urface

Someo


malocal

policyof

replacingcrop

land

with

shrubs

tocombatd

esertifi

catio

n

pH11

805ndash899

838

031

004

SMC(

)14

7ndash603

319

161

050

GW

salin

ity(gL)

968ndash2791

1553

638

041

GW

tabled

epth

(m)

717ndash2995

1847

834

045

Abando

nedland

(5)

EC15(dSm)

021ndash141

099

041

042

Land

hasb

eenabando

nedfor5

to20

years

with

vegetatio

ncoverlt

5

pH11

796ndash

883

835

03

004

SMC(

)15

4ndash455

266

107

040

GW

salin

ity(gL)

1006ndash

1844

1322

345

026

GW

tabled

epth

(m)

1089ndash

2878

2005

666

033

Sparse

grassla

nd(46)

EC15(dSm)

015ndash214

4618

501

081

Land

hasb

eenabando

nedform

orethan20

yearsDom

inated

bygrassesa

ndsalt-tolerant

grasseswith

vegetatio

ncover

of5

to20

pH11

789ndash

875

834

022

003

SMC(

)093ndash1018

408

214

052

GW

salin

ity(gL)

651ndash2327

1412

419

030

GW

tabled

epth

(m)

718ndash3605

2270

633

028

Salin

ewasteland

(13)

EC15(dSm)

828ndash2310

146

512

035

Land

with

asaltcrustatthes

oilsurface

Dom

inated


rasses

with

vegetatio

ncovergt

20N

aturally

salt-affectedareasa

redistr

ibuted

atthe

margins

oftheo

asis

pH11

788ndash858

836

022

003

SMC(

)391ndash1496

887

309

035

GW

salin

ity(gL)

755ndash1997

1120

339

030

GW

tabled

epth

(m)

676ndash294

21580

829

052

a SMCrepresentsthes

oilm

oistu

recontentasa

percentage

ofthed

rysoilweightGW

representstheg

roun

dwater




1198830EC1 5





Cropland

1198830EC1 5


Sparse grassland

1198830EC1 5








1 5



1 1of the soil


mean pH1 1








1 5and












1 5


4 Conclusions












Competing Interests


Acknowledgments



References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











20

15

5

10

0

Freq

uenc

y

0 2 4 6 8 10EC1 5


20 25

25

20

15

15

5

5

10

10

00

Freq

uenc

y

092002

EC1 5

(a) (b)








1 5using


1 5











140ndash185

186ndash231

100 5km

Cropland

N

EC1 5




1 5



Table2Descriptiv

estatisticsfor

thes

oilp

aram

etersby

land

usea

ndcoverc

lass(LUCC

)a

LUCC

(sam

ples

ize)

Parameters

Range

Mean

SDCV

LUCC

descrip

tion

Crop

land

(21)

EC15(dSm)

011ndash966

135

239

177

Thiscla

ssinclu

deslandcoveredwith

crop

s(egfenn

elcotto

nalfalfaw

heatm

aize

sunfl

owerand

gojiberry)n

ewlycultivated

land

and

fallo

wland

pH11

782ndash876

832

024

003

SMC(

)086ndash1381

715

277

039

GW

salin

ity(gL)

211ndash2739

1459

609

042

GW

tabled

epth

(m)

532ndash3233

1861

799

043

Shrubland

(9)

EC15(dSm)

012ndash1489

323

445

138

Land

with

woo

dyvegetatio

nwith

atotal


10of

thes

urface

Someo


malocal

policyof

replacingcrop

land

with

shrubs

tocombatd

esertifi

catio

n

pH11

805ndash899

838

031

004

SMC(

)14

7ndash603

319

161

050

GW

salin

ity(gL)

968ndash2791

1553

638

041

GW

tabled

epth

(m)

717ndash2995

1847

834

045

Abando

nedland

(5)

EC15(dSm)

021ndash141

099

041

042

Land

hasb

eenabando

nedfor5

to20

years

with

vegetatio

ncoverlt

5

pH11

796ndash

883

835

03

004

SMC(

)15

4ndash455

266

107

040

GW

salin

ity(gL)

1006ndash

1844

1322

345

026

GW

tabled

epth

(m)

1089ndash

2878

2005

666

033

Sparse

grassla

nd(46)

EC15(dSm)

015ndash214

4618

501

081

Land

hasb

eenabando

nedform

orethan20

yearsDom

inated

bygrassesa

ndsalt-tolerant

grasseswith

vegetatio

ncover

of5

to20

pH11

789ndash

875

834

022

003

SMC(

)093ndash1018

408

214

052

GW

salin

ity(gL)

651ndash2327

1412

419

030

GW

tabled

epth

(m)

718ndash3605

2270

633

028

Salin

ewasteland

(13)

EC15(dSm)

828ndash2310

146

512

035

Land

with

asaltcrustatthes

oilsurface

Dom

inated


rasses

with

vegetatio

ncovergt

20N

aturally

salt-affectedareasa

redistr

ibuted

atthe

margins

oftheo

asis

pH11

788ndash858

836

022

003

SMC(

)391ndash1496

887

309

035

GW

salin

ity(gL)

755ndash1997

1120

339

030

GW

tabled

epth

(m)

676ndash294

21580

829

052

a SMCrepresentsthes

oilm

oistu

recontentasa

percentage

ofthed

rysoilweightGW

representstheg

roun

dwater




1198830EC1 5





Cropland

1198830EC1 5


Sparse grassland

1198830EC1 5








1 5



1 1of the soil


mean pH1 1








1 5and












1 5


4 Conclusions












Competing Interests


Acknowledgments



References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










Table2Descriptiv

estatisticsfor

thes

oilp

aram

etersby

land

usea

ndcoverc

lass(LUCC

)a

LUCC

(sam

ples

ize)

Parameters

Range

Mean

SDCV

LUCC

descrip

tion

Crop

land

(21)

EC15(dSm)

011ndash966

135

239

177

Thiscla

ssinclu

deslandcoveredwith

crop

s(egfenn

elcotto

nalfalfaw

heatm

aize

sunfl

owerand

gojiberry)n

ewlycultivated

land

and

fallo

wland

pH11

782ndash876

832

024

003

SMC(

)086ndash1381

715

277

039

GW

salin

ity(gL)

211ndash2739

1459

609

042

GW

tabled

epth

(m)

532ndash3233

1861

799

043

Shrubland

(9)

EC15(dSm)

012ndash1489

323

445

138

Land

with

woo

dyvegetatio

nwith

atotal


10of

thes

urface

Someo


malocal

policyof

replacingcrop

land

with

shrubs

tocombatd

esertifi

catio

n

pH11

805ndash899

838

031

004

SMC(

)14

7ndash603

319

161

050

GW

salin

ity(gL)

968ndash2791

1553

638

041

GW

tabled

epth

(m)

717ndash2995

1847

834

045

Abando

nedland

(5)

EC15(dSm)

021ndash141

099

041

042

Land

hasb

eenabando

nedfor5

to20

years

with

vegetatio

ncoverlt

5

pH11

796ndash

883

835

03

004

SMC(

)15

4ndash455

266

107

040

GW

salin

ity(gL)

1006ndash

1844

1322

345

026

GW

tabled

epth

(m)

1089ndash

2878

2005

666

033

Sparse

grassla

nd(46)

EC15(dSm)

015ndash214

4618

501

081

Land

hasb

eenabando

nedform

orethan20

yearsDom

inated

bygrassesa

ndsalt-tolerant

grasseswith

vegetatio

ncover

of5

to20

pH11

789ndash

875

834

022

003

SMC(

)093ndash1018

408

214

052

GW

salin

ity(gL)

651ndash2327

1412

419

030

GW

tabled

epth

(m)

718ndash3605

2270

633

028

Salin

ewasteland

(13)

EC15(dSm)

828ndash2310

146

512

035

Land

with

asaltcrustatthes

oilsurface

Dom

inated


rasses

with

vegetatio

ncovergt

20N

aturally

salt-affectedareasa

redistr

ibuted

atthe

margins

oftheo

asis

pH11

788ndash858

836

022

003

SMC(

)391ndash1496

887

309

035

GW

salin

ity(gL)

755ndash1997

1120

339

030

GW

tabled

epth

(m)

676ndash294

21580

829

052

a SMCrepresentsthes

oilm

oistu

recontentasa

percentage

ofthed

rysoilweightGW

representstheg

roun

dwater




1198830EC1 5





Cropland

1198830EC1 5


Sparse grassland

1198830EC1 5








1 5



1 1of the soil


mean pH1 1








1 5and












1 5


4 Conclusions












Competing Interests


Acknowledgments



References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra












1198830EC1 5





Cropland

1198830EC1 5


Sparse grassland

1198830EC1 5








1 5



1 1of the soil


mean pH1 1








1 5and












1 5


4 Conclusions












Competing Interests


Acknowledgments



References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra











1 5and












1 5


4 Conclusions












Competing Interests


Acknowledgments



References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra










References








































Volume 2014




Journal of











Journal of


Function Spaces






Algebra
















Volume 2014




Journal of











Journal of


Function Spaces






Algebra









analysis of the spatial variation of soil salinity and its

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