Irrig Sci (2014) 32:15–27
DOI 10.1007/s00271-013-0409-x

ORIGINAL PAPER

Evaluation of mulched drip irrigation for cotton in arid
Northwest China
Zaimin Wang • Menggui Jin • Jirka Šimůnek •Martinus Th. van Genuchten

Received: 13 January 2013 / Accepted: 14 June 2013 / Published online: 6 July 2013
Ó Springer-Verlag Berlin Heidelberg 2013

Abstract Field experiments were conducted in arid
Southern Xinjiang, Northwest China, for 3 years to evaluate sustainable irrigation regimes for cotton. The experiments involved mulched drip irrigation during the growing
season and flood irrigation afterward. The drip irrigation
experiments included control experiments, experimentswith deficit irrigation during one crop growth stage, andalternative irrigation schemes in which freshwater wasused during one growth stage and relatively saline water inthe others. The average cotton yield over 3 years varied
between 3,575 and 5,095 kg/ha, and the irrigation waterproductivity between 0.91 and 1.16 kg/m3. Crop sensitivities to water stress during the different growth stages
ranged from early flowering-belling (most sensitive) [ seedling [ budding [ late flowering-belling (least

Communicated by A. Ben-Gal.
Z. Wang  M. Jin (&)
State Key Laboratory of Biogeology and Environmental
Geology and School of Environmental Studies, China Universityof Geosciences, Wuhan 430074, Chinae-mail: mgjin@cug.edu.cn
Z. Wange-mail: wangchuachua1986@163.com
J. Šimůnek
Department of Environmental Sciences, University of California,
Riverside, CA, USA
M. Th. van Genuchten
Department of Mechanical Engineering, LTTC/COPPE,
Federal University of Rio de Janeiro, UFRJ, Rio de Janeiro,
RJ 21945-970, Brazil
M. Th. van Genuchten
Department of Earth Sciences, Utrecht University,
P.O. Box 80021, 3508 TA Utrecht, The Netherlands

sensitive), while sensitivities to salt stress ranged from lateflowering-belling [ budding [ seedling [ early flowering-belling. Although mulched drip irrigation during thegrowing season caused an increase in salinity in the root
zone, flood irrigation after harvesting leached the accumulated salts to below background levels. Numericalsimulations, based on the 3-year experiments and extended
by another 20 years, suggest that mulched drip irrigation
using alternatively fresh and brackish water during thegrowing season and flood irrigation with freshwater after
harvesting is a sustainable irrigation practice that should
not lead to soil salinization.

Introduction
Southern Xinjiang in Northwest China is an arid region
with a very high evaporative demand and a general shortage of freshwater resources. While the mean annual precipitation is only 58 mm, the potential evapotranspiration
rate equals 2,540 mm (Wang et al. 2010). Mulched drip
irrigation, which saves water and increases water use efficiency as compared to furrow irrigation (Ibraginmov et al.
2007), is widely used in the area to irrigate cotton, a major
cash crop in Xinjiang (Hou et al. 2009). Cotton yields
generally decrease with a reduction in irrigation, irrespective of whether conventional irrigation technologies or drip
irrigation is used (Singh et al. 2010; Ünlü et al. 2011), andare also limited by poor quality of irrigation water andrelated problems of soil salinization (Leidi and Saiz 1997;
Vulkan-Levy et al. 1998; Qadir and Shams 1997; Henggeler 2004; Chen et al. 2010).
Field experiments are a critical component of any study
delineating irrigation management strategies that are
most effective in enhancing cotton productivity while

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safeguarding local soil and water resources. At the sametime, process-based numerical models are needed for optimal analysis of the data and extrapolation to decadal or
longer time periods to more precisely understand the longterm effects of alternative practices. Computer simulation
models for these reasons have become important tools foranalyzing site-specific irrigation, soil salinization, or crop
production problems (Skaggs et al. 2006). Many software
packages simulating relevant water flow, solute transport,
and root water uptake processes could potentially be used forthis purpose, such as SWAP (van Dam et al. 1997; Kroes andvan Dam 2003) and the HYDRUS codes (Šimůnek et al.
2008), which have been applied to a number of cotton irrigation studies (e.g., Singh 2004; Forkutsa et al. 2009). The
HYDRUS (2D/3D) code (Šimůnek et al. 2006, 2012) inparticular has been found useful in numerical analyses andoptimizations of surface and subsurface drip irrigation
schemes (e.g., Skaggs et al. 2004; Fernandez-Galvez andSimmonds 2006; Hanson et al. 2008; Roberts et al. 2009;
Kandelous et al. 2011; Rodriguez-Sinobas et al. 2012).
To our knowledge, no numerical studies have been
carried out thus far to formulate guidelines for a systemthat involves a combination of mulched drip irrigation withboth saline and freshwater during the growing season andflood irrigation with freshwater after harvesting, while
simultaneously adjusting irrigation quantities as a functionof the growth stage of cotton. Such integrated numericalstudies are relatively difficult because of the multidimensional nature of the flow regime and the complexity of
imposing time-variable boundary conditions for combined
mulched drip and flood irrigation systems. These areexactly the type of problems being addressed in this study
using HYDRUS (2D/3D).
In order to evaluate sustainable irrigation regimes ofmulched drip irrigation with saline water and calibrate
HYDRUS for the long-term simulations, field experiments
were conducted in Southern Xinjiang for typical local climate, and soil and cotton management conditions. Specificobjectives of the research were to (1) compare cotton yields
for different irrigation regimes, (2) characterize the spatial
and temporal variability of the soil salinity of the root zone
for different treatments, (3) calibrate and test the HYDRUS
simulations using the experimental data, and (4) use thecalibrated model to evaluate the long-term effects ofalternative irrigation schemes.

Irrig Sci (2014) 32:15–27

March 2008 and September 2010. The area has an extreme
arid climate with annual precipitation and potential
evapotranspiration rates of approximately 58 and
2,540 mm, respectively. The study area is located 901 m
above mean sea level and has a mean annual temperatureof 11.5 °C, with very warm summers and relatively cool
winters. There is an abundant supply of sunlight in the area,
on average more than 8 h per day. The mean annual windspeed is approximately 0.77 m/s. Surface water used for
irrigation comes from the Kongque River, whose water
quality is affected by nearby Bosten Lake and has electricalconductivity (EC) values generally between 0.9 and1.6 dS/m (He et al. 2010). Groundwater, having EC values
between about 3–5 dS/m (see Table 1), is used often also
for irrigation of cotton, which is known to have a relatively
high tolerance for salinity (Maas 1990; Vulkan-Levy et al.
1998; Henggeler 2004; Steppuhn et al. 2005; Chen et al.
2010). The sodium adsorption ratio (SAR) of irrigationwater ranged from 3.77 to 9.97. Loamy sand was the predominant soil texture at the field site. Electronic digitalwater level gauges were installed in the fields to monitor
the water table. Drainage canals had their bottoms 2 m
below the soil surface.
Irrigation treatments
The mulched irrigation experiments were conducted inthree replicates on randomly selected field plots, eachhaving a size of 10 m 9 22.5 m. Quantities of applied
irrigation water and their distribution during the growing
season followed traditional irrigation patterns of cotton in
the area (Table 2). Tables 3 and 4 summarize the deficit,
control, and alternative irrigation experiments that werecarried out, and how water was applied during the different growth stages of cotton. Five cotton growth stages
are usually recognized: seeding (from germinating tobudding, lasting about 20 days), budding (about
15 days), early flowering-belling (about 20 days since
flowering), late flowering-belling (extends until boll
opening, about 40 days), and boll opening (until harvest,
about 30 days). Specifically, the following treatments
were studied:
1.

Materials and methods
2.
Experimental site
The experiments were carried out in cotton fields located in
the Tarim Basin of Xinjiang, Northwest China, between

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Control irrigation (Table 4): The total quantity ofirrigation water throughout the entire growth season
was 4,200 m3/ha, and either freshwater (denoted by
the letter F) or more saline water (S) was used for
irrigation.
Deficit irrigation (Table 3): 70 % of the irrigation
demand was used during one crop growth stage(compared to control irrigation) and 100 % in theothers. Deficit irrigation scenarios are denoted by theletter D.

Irrig Sci (2014) 32:15–27

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Table 1 Variations in EC of the irrigation water during 3 years
Irrigation water2008

EC (dS/m)

2009

2010

SalineFresh

SalineFresh

SalineFresh

4.04–4.86

1.21–1.64

3.35–3.78

0.94–1.17

3.68–3.77

1.14–1.27

Table 2 Quantities of irrigation water applied at different growth stages
StagesSeeding

Budding

Early flowering-belling

Late flowering-belling

Boll opening

Total

Water quantity (%)

4.5

13.6

42.8

34.5

4.6

100

Water quantity (m3/ha)

187

570

1,800

1,448

195

4,200

Late floweringbelling
11–16

Boll opening

Table 3 Deficit irrigation programs

Deficit irrigation (%)

aStagesSeeding

Budding
3–5

Early floweringbelling
6–10

Irrigation #

1–2

D1

a

70

100

100

100

100

D2

100b

70

100

100

100

D3

100

100

70

100

100

D4

100

100

100

70

100

Late floweringbelling
11–16

Boll opening
17–18

17–18

70 % irrigation water quantity in Table 2, b 100 % irrigation water quantity in Table 2

Table 4 Alternative irrigation programs

Control
Alternative irrigationaFreshwater irrigation,

3.

4.

b

StagesSeeding

Budding

Irrigation #

1–2

3–5

Early floweringbelling
6–10

F

Fa

F

F

F

F

S

Sb

S

S

S

S

A1

F

S

S

S

S

A2

S

F

S

S

S

A3

S

S

F

S

S

A4

S

S

S

F

S

saline water irrigation

Alternative irrigation (Table 4): Freshwater was usedduring one growth stage and saline water in the others.
Alternative irrigation scenarios are denoted by theletter A.
Flood irrigation with freshwater was used after the
harvest in the winter or the following spring. The total
amount of irrigation water applied outside of thegrowing season was 2,250 m3/ha.

Following local practices, cotton seeds were sown in
April every year and mulched with degradable plastic
sheeting. Two irrigation lines were installed for each fourrows of cotton as shown in Fig. 1. The distance between
the irrigation lines was 70 cm, and between two cottonrows 20 cm. Drippers along each irrigation line werespaced 30 cm apart, while the cotton plants along each row
were spaced 10 cm apart. The non-mulched area of baresoil was 40 cm wide. A total of 225 kg/ha of urea, 375 kg/
ha of diammonium phosphate, 300 kg/ha of compound
fertilizer with 45 % potassium sulfate, and 225 kg/ha offarm manure were applied before sowing. Additionally
75 kg/ha of urea was applied after the budding stage, and
45 kg/ha of urea after early flowering-belling.
Monitoring
Soil water contents at various depths were measured in situ
using L520 Neutron Probe (Institute for Application of
Atomic Energy, JAAS, Nanjing, China) and Stevens Hydra

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Irrig Sci (2014) 32:15–27

Numerical modelFig. 1 Layout of cotton rows and drip irrigation lines in the field andthe simulated transport domain. Also shown are boundary conditions
used in the numerical analysis (distances are in cm)

Probe (Stevens Water Monitoring Systems Inc., Beaverton,
OR, USA) soil sensors, while the top layer was monitored
using MP160 moisture probes (ICT International Pty Ltd.,
Armidale, Australia). The leaf area index (LAI) of cotton
during the different growing season was measured with a
WDY-500A Micro-electron Leaf Area Instrument (Harbin
Optical Instrument Factory, Harbin, China). Final yields
were measured for each treatment and each replicate. The
field soil moisture capacity was measured after the basin
irrigation treatments. Both fresh and saline waters were
sampled before each irrigation. Soil solutions wereextracted from different depths in the field using a Rhizon
Soil Moisture Sampler (Eijkelkamp Agrisearch EquimentCo., Giesbeek, the Netherlands). Soils were sampled further for gravimetric water content measurements and toobtain 1–5 soil/water suspensions for EC analysis in theon-site laboratory. Chemical analyses (titrametric andatomic absorption spectrometry) were performed on thesoil solutions and suspensions in the on-site laboratory.
Measurements included EC, pH, and 8 major ions (K?,Na?, Ca2?, Mg2?, Cl-, SO42-, HCO3-, and CO32-),
except when only enough soil solution was available for
the EC measurements. The EC of the 1–5 soil/water suspension is further referred to as EC1:5.

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In order to predict the long-term effects of the various
irrigation management systems, the 2D module of the
HYDRUS (2D/3D) software (Šimůnek et al. 2008, 2012)
was selected for the simulations. We refer to the HYDRUS
technical manual (Šimůnek et al. 2012) for a detailed
description of the governing equations describing variably
saturated flow using the Richards equation, solute transport
using the advection–dispersion equation, and root water
uptake, as well as of various initial and boundary conditions that can be implemented. Since the distance between
drippers along a drip line was short compared to the otherdimensions, the drip irrigation system was modeled as aline source. The two-dimensional transport domain used
for the calculations is shown in Fig. 1. The depth of thetransport domain was 160 cm and its width 150 cm. Noflow boundary conditions were considered along the twovertical sides of the transport domain. A time-variable
pressure head specifying the position of the groundwatertable was assigned along the bottom boundary.
The soil surface boundary conditions were relatively
complicated since they had to accommodate two-dimensional surface drip irrigation, atmospheric conditions, thepresence of cotton plants, and the presence of mulchingplastic. Separate time-variable flux boundary conditions
were used for the surface drip line and the soil surface
under the mulch. The bare soil on the sides of the transportdomain was represented using an atmospheric boundary
condition. For solute transport, we used third-type Cauchy
boundary conditions along all domain boundaries.
The water stress response model of Feddes et al. (1978)
was used to account for water stress and the thresholdslope model of Maas and Hoffman (1977) for salinity
stress. Parameters (Table 5) of the water and salinity stressresponse functions were based on the literature values(Taylor and Ashcroft 1972; Feddes et al. 1978; Maas 1990;
Forkutsa et al. 2009). A multiplicative model was used toaccount for the combined effects of water and salinity
stress (Skaggs et al. 2006). Compensated root water uptake(Šimůnek and Hopmans 2006) was not considered in our
simulations since water and salinity stresses in the mulchedsystem were assumed to be distributed relatively uniformly
throughout the root zone. Cotton roots were sampled at aregular network of sampling points (Fig. 2a) and measured
using DT-SCAN (Fig. 2b). The measured spatial root distribution is shown in Fig. 3. Since HYDRUS does notallow a time-variable root zone, a constant root distribution
was assumed during the simulations. Plant growth wasaccounted for by modifying the ratio between evaporation(which dominated during the early growth stages) andtranspiration (which dominated later).

Irrig Sci (2014) 32:15–27

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Table 5 Estimated root water uptake parameters in the water stress response model of Feddes et al. (1978)
P0 (cm)P0pt (cm)P2H (cm)

P2L (cm)

P3 (cm)r2H (cm/day)

r2L (cm/day)

-10

-25

-200

-600

-14,000

0.5

0.1

Fig. 2 a Layout of cotton root
sampling and locations of drip
irrigation lines in the field(distances are in cm), b root
distribution measured using DTSCAN

Length (cm)0010Bare soil

20

Table 6 Soil hydraulic parameters used in the simulations

30

40

50

CottonDripper

Cotton

Tarp

60

70

hs (–)

a (1/
cm)

n (–)

Ks (cm/
day)

l (–)

Initialparameters

0.054

0.400

0.036

1.56

35.3

0.5

Final
parameters

0.054

0.400

0.022

2.06

35.3

0.5

Loamy sand

10

Depth (cm)hr (–)

Soil

20

30

40

50

60
0

2

4

6

8

10

12

14

16

Fig. 3 Relative root distribution (mm/cm2)

The van Genuchten–Mualem model (van Genuchten1980) was used to describe the soil hydraulic properties.
Soil hydraulic parameters (Table 6) were estimated initially from a limited number of laboratory- and fieldmeasured retention data points using the RETC program ofvan Genuchten et al. (1991). These and the solute transportparameters were subsequently adjusted manually such thatthe model predictions more closely matched observed soil
water content and EC values of the profile below thedripper (Tables 6, 7).

The dynamics of EC in space and time was simulated asthat of a non-reactive solute. As shown by Ramos et al.
(2011), this approach can be used when the soil solution isundersaturated with respect to calcite and gypsum. In thatcase, almost identical results are obtained as simulations
accounting for the transport of, and reactions between,
major ions, such as can be done with the UnsatChem
modules of HYDRUS (Šimůnek et al. 2008).

Results and discussion
Crop yields
Cotton yields and irrigation water productivities, IWPs
(i.e., yields divided by the amount of applied irrigationwater), are presented in Table 8. Results indicate that the
average cotton yield during the 3 years varied from 3,575
to 5,095 kg/ha and the IWP from 0.91 to 1.16 kg/m3.

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Table 7 Soil-specific parameters for solute transport
Bulk density(g/cm3)Longitudinal
dispersivity (cm)Transverse
dispersivity (cm)

Initialparameters

1.70

32

25.6

Final
parameters

1.70

60

48

Cotton yields were on average highest in 2008, followed by2010 and 2009, with the IWP following a similar trend.
Both the average cotton yield (3,575 kg/ha) and the IWP(0.91 kg/m3) were lowest for scenario D3 when deficit
irrigation was used during early flowering-belling. The
highest average IWP of 1.16 kg/m3 was obtained for scenario D4 when deficit irrigation was used during lateflowering-belling. Both the cotton yield and the IWP for
scenario A3 were the second highest in 2009 and 2010, andthe fourth highest (tied with scenario F) in 2008.
The average yield of 5,095 kg/ha was highest and the
IWP of 1.13 kg/m3 the second highest for scenario A1
when freshwater was used during the seedling growthstage. Both the average yield and IWP were higher for A1
than for control treatment F (freshwater during the entire
growing season), for which the average yield was 4,555 kg/
ha and the average IWP 1.01 kg/m3. The average yield and
IWP for scenario S (saline irrigation water) were 4,845 kg/
ha and 1.08 kg/m3, which were 6.4 and 6.9 % higher,
respectively, as compared to control scenario. Also, boththe average cotton yield and IWP for A3 and D4 werehigher than for F. These results indicate slightly increased
cotton growth when some salinity in the irrigation water ispresent. The yield-promoting effect of some soil salinity

has been reported in several studies (e.g., Qadir and Shams
1997; Ashraf and Ahmad 2000; Ashraf 2002; Henggeler2004). For instance, Vulkan-Levy et al. (1998) found thatwith more water being applied, cotton yields increased withan increase in salinity. Hou et al. (2009) similarly showedthat seed cotton yield, dry matter, and N uptake significantly increased as the soil salinity level increased from 2.5
to 6.3 dS/m.
Some of the increases in cotton yield may have been due
also to higher concentrations of trace elements in saline
water than in freshwater. Table 9 provides concentrationsof trace elements in the irrigation water. Trace element
concentrations (Cu, Fe, Mn, and Zn) in the saline water
were much higher than in freshwater. Table 8 shows thatthe cotton yield and IWP decreased from 2008 to 2010.
This was likely caused by a reduction in the concentrationsof soil nutrients (Table 10) after 3 years of cultivation.
These and other results are very much consistent with thefact that cotton is known to be the most sensitive to waterand salinity stress during the seedling and early vegetative
growth stages, and that cotton generally is more affected bywater stress than salinity stress (e.g., Läuchli and Gratan
2007).
The yield and IWP variances for 3 years are also presented in Table 8. Both were lowest for scenario A3 whenfreshwater was used during early flowering-belling. The
highest yield and IWP variances were obtained for scenario
A2 when freshwater was used during the budding stage. The
yield and IWP variances were both second lowest for scenario A1 when freshwater was used during seedling. The
yield and IWP fluctuated only slightly for scenario A3, but
strongly for scenario A2. The variability in cotton yield forthe various scenarios increased as follows: A3 \ A1\ D1\

Table 8 Cotton yields and irrigation water productivities (IWP) for different treatments
Treatments

2008
Yield
(kg/ha)

2009

2010

Average

Variance

IWP(kg/m3)

Yield(kg/ha)IWP(kg/m3)

Yield(kg/ha)IWP(kg/m3)

Yield(kg/ha)IWP(kg/m3)

Yield
(–)

IWP
(–)

Controlled irrigation
F

5,040

1.12

4,725

1.05

3,900

0.87

4,555

1.01

833

0.182

S

5,385

1.20

5,145

1.14

4,005

0.89

4,845

1.08

1,043

0.233

D1

4,545

1.02

3,900

0.88

3,765

0.85

4,070

0.92

590

0.128

D2

4,980

1.15

3,975

0.92

3,659

0.85

4,205

0.97

976

0.222

D3

4,500

1.15

3,195

0.81

3,031

0.77

3,575

0.91

1,138

0.295

D4
5,400
Alternative irrigation

1.34

4,140

1.03

4,482

1.11

4,674

1.16

922

0.228

A1

5,190

1.15

4,665

1.04

5,430

1.21

5,095

1.13

553

0.122

A2

5,865

1.30

3,780

0.84

3,900

0.87

4,515

1.00

1,656

0.364

A3

5,040

1.12

4,725

1.05

4,815

1.07

4,860

1.08

230

0.051

A4

4,845

1.08

3,585

0.80

4,185

0.93

4,205

0.94

891

0.198

Deficit irrigation

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Table 9 Concentrations of trace elements in irrigation waters
Trace elements

Cu

Fe

Mn

Zn

Saline water (mg/L)

0.019–0.046

0.126–2.948

0.187–0.378

0.134–0.254

Freshwater (mg/L)

0.003–0.004

0.026–0.118

0.001–0.024

0.075–0.106

Table 10 Contents of soil nutrients before and after the experiments
Soil nutrientsN (mg/kg)

P (mg/kg)

K (mg/kg)

Before experiments

30.4

64.2

120.0

After 3 years

25.8

31.9

78.7

F \ A4 \ D4 \ D2 \ S \ D3 \ A2 (see Tables 3, 4 fordefinitions). For IWP, the variability increased as
A3 \ A1 \ D1 \ F \ A4 \ D2 \ D4 \ S \ D3 \ A2.
The response of cotton to water and salt stress during thedifferent growth stages was evaluated using a crop sensitivity coefficient (ki) defined as:li ¼ jYi  YC j=YC

ð1Þ

where Yi is the average yield for a particular treatment and
YC is the average yield for the control treatment. Irrigation
with saline water (S) was selected as the control scenario.
Calculated sensitivities are given in Table 11. Crop sensitivities to water stress during the different growth stages
were found to decrease as follows: early flowering-belling [ seedling [ budding [ late flowering-belling. For
salt stress, crop sensitivities decreased as follows: lateflowering-belling [ budding [ seedling [ early flowering-belling.
Spatial and temporal variability of soil salinity
Soil salinity levels may be evaluated using the electric
conductivity of the saturation extract, ECe, which can be
estimated from measured values of EC1:5. Slavich andPetterson (1993) reported the following relationship
ECe = f EC1:5, where f = 2.46 ? 3.03/hsp, and hsp is thegravimetric water content of the saturated paste (kg/kg).
For our experimental fields, the average hsp value was
0.20 kg/kg, which suggests that the value of f should be
about 17.61 if the relationship of Slavich (1993) is used.
For this reason, we related ECe and EC1:5 in this study
using ECe = 17.61 EC1:5. Our own measurements of ECeand EC1:5 showed a very similar correlation (results notfurther reported here).
Figure 4 shows the temporal variability of ECe in thesoil root zone for the different treatments. The ECe
increased during the growing seasons for all treatments.
However, ECe for scenario S increased substantially, but
increased only slightly for control scenario F. ECe values
continued to increase even when irrigation was terminated

Table 11 Calculated crop sensitivity coefficients (Eq. 1) of cotton
growth stages to irrigation water and salinity stressSeeding

Budding

Early floweringbelling

Late floweringbelling

Water
stress

0.160

0.132

0.262

0.035

Salinity
stress

0.052

0.068

0.003

0.132

after the growing season. This was caused by the highevaporation rate and the presence of saline groundwaterwith total dissolved solids (TDS) near 3 g/L. The concentrations of major cations and SAR of groundwater samples
collected at different times are presented in Table 12. The
various measurements indicate that a drainage system isneeded. ECe decreased dramatically after flood irrigationand then increased slightly before the next cultivation.
Figure 5 shows the water table depth versus time during
the entire experiment (2.5 years). The water table during
the growing season was higher than between two growing
seasons. Irrigation caused a rise in the water table, even in
the presence of drainage canals.
As discussed in section ‘‘Crop yields,’’ the early flowering-belling and seeding growth stages were the mostsensitive to water stress, whereas the late flowering-belling
growth stage was the most susceptible to salinity stress.
The budding and seedling stages were sensitive to salinity
as well. According to the variance, the two most stableyields and IWPs were obtained for scenarios A3 and A1
when freshwater was used during the early floweringbelling and seedling growth stages. Therefore, our experimental results indicate that an alternative irrigation scenario would produce optimal yield. In this irrigation
scenario, freshwater would be used during the seedling andearly flowering-belling stages, and more saline water during the other growth stages. Additionally, flood irrigation
should be applied after harvesting or before sowing. This
scenario will be analyzed further in section ‘‘Long-term
HYDRUS simulations’’ using long-term simulations.
Irrigation with saline water during the growing season
caused an increase in salinity of the root zone. Since lesswater was used for individual irrigation events as comparedto post-season flood irrigation, and salts hence were notleached below the root zone and into groundwater, the soil
salinity in the root zone exceeded the threshold salinity
value of 7.7 dS/m for cotton (Maas and Hoffman 1977) for

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Irrig Sci (2014) 32:15–27

Fig. 5 Observed water table depth during the 3-year experimentFig. 4 Temporal variability of ECe in the root zone for different
treatments

all treatments, except for treatment F during the later
growth stages. If salinity was the only factor affecting
yield, the maximum yield would be achieved for scenario
F. However, cotton yields were likely affected not only by
the quantity of irrigation water and its salinity, but also by
nutrients dissolved in the irrigation water and present in thesoil. Nutrient concentrations in the soil decreased, resulting
in lower cotton yields, even though fertilization was thesame every year. While salts in irrigation water can reduce
the growth of cotton, trace elements to certain levels may
enhance growth. We did not, as part of this study, analyze
which trace elements and at what concentrations may haveaffected or limited cotton growth. As pointed out by Yermiyahu et al. (2008), the synergistic or antagonistic effects
between salinity and trace elements are still not wellunderstood.
HYDRUS model calibration
Figure 6 shows observed and calculated water content
distributions at various depths below the drip line for irrigation scenario S, while similar plots for salinity are given
in Fig. 7. The HYDRUS simulation results were obtainedusing the adjusted soil hydraulic parameters listed inTable 6. Figure 6 shows a generally good agreement
between observed and calculated water contents, except
perhaps at the 10- and 20-cm depths where simulated
values showed much more variation versus time inresponse to irrigation than the observed values. This was
mostly due to the relative long intervals between the fieldwater content measurements. The simulated values actuallymatched observed values quite well between days 50 and
80 when more measurements were taken. Only a relatively
few observations did not match the simulated curves wellduring the entire growing season (notably the measurements deeper in the profile at approximately days 38 and
40).
Observed and simulated EC values of soil water showedvery similar variations during the 2008 growing season.
The EC decreased slightly over time in the top 40-cm soil
layer due to the leaching effect of irrigation by the dripper.
This was also the main reason that EC values increasedslightly below a depth of 80 cm. Very similar results interms of observed versus calculated water contents (Fig. 6)
and salinities (Fig. 7) were obtained for the other growing
seasons of scenario S, as well as for the other irrigation
scenarios, and are not further shown here. We conclude
that, overall, simulated values of the soil water content andthe EC matched the observed values well.
Long-term HYDRUS simulations
An important objective of this study was to obtain a clear
understanding of the long-term effects of the various irrigation scenarios on possible salt accumulation and sustainable cotton production. The HYDRUS software
package was used for this purpose to extend the simulation
period by 20 years. To do so, we simply repeated theavailable 10 years of meteorological data twice to provide
the necessary boundary conditions for the 20-year simulations. We used for the extended simulations the same
spatial root distribution for cotton and the same soil
hydraulic and solute transport parameters as used for thesimulations shown in Figs. 6 and 7. Measured soil watercontents and ECs before sowing in 2011 were used as
initial conditions. We considered three irrigation scenarios:(a) mulched drip irrigation with freshwater (FI),

Table 12 Ion concentrations in groundwater samples (units: g/L, except SAR)
Sampling date

Ca2?

Mg2?

K?

Na?

CO32-

HCO3-

Cl-

SO42-

TDS

SAR

03 July 2009

0.276

0.097

0.058

0.226

0.001

0.414

0.442

0.672

2.186

4.201

15 July 2009

0.326

0.050

0.057

0.396

0.001

0.414

0.489

0.893

2.626

7.612

20 March 2010

0.278

0.145

0.029

0.416

0.001

0.324

0.442

1.301

2.936

7.097

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Irrig Sci (2014) 32:15–27

23

Fig. 6 Observed and simulated soil volumetric water contents at different depths under the drip line for scenario S

Fig. 7 Observed and simulated soil water EC values at different depths under the drip line for scenario S(b) mulched drip irrigation with saline water (SI), and(c) mulched drip irrigation with the alternative use of freshand saline water (AI). For the AI scenario, freshwater wasused for irrigation during seedling and the early floweringbelling stages, and saline water during the other growthstages, as described in section ‘‘Spatial and temporal

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variability of soil salinity.’’ Post-season flood irrigation
with freshwater was considered for all three scenarios.
The simulation results are shown in Fig. 8. For all three
scenarios (FI, SI, and AI), variations in the EC of soil water
were very similar between the different years, except that
EC values decreased slightly year after year. While EC of
the top 30-cm soil layer varied markedly, little variations
were observed below a depth of 40 cm. For scenario SI(drip irrigation with saline water), the EC of soil water firstincreased slightly during the growing season, but then more
substantially after harvesting until flood irrigation. Floodirrigation subsequently caused the EC to decrease, whichthen remained more or less constant until the next growing
season. These fluctuations reflect mainly variations in thesoil water content during the year.
The EC of soil water during the growing seasons did notchange much because of the relatively high soil watercontent due to irrigation. When irrigation was terminatedafter the growing season, the decreasing water content due

Fig. 8 Simulated soil water EC
values during 20 years for theFI (top), SI (middle), and AI(bottom) irrigation regimes, in
combination with post-season
leaching using 2,250 m3/ha offreshwater

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Irrig Sci (2014) 32:15–27

to evaporation and some continuing transpiration caused
the EC to increase. Subsequent flood irrigation caused mostsalts to leach below the root zone, thus allowing the EC todecrease again to background levels. EC variations for the
FI and AI scenarios were similar as for SI, except that theEC fluctuations were more pronounced for FI and AI. The
EC values for these two scenarios were also always lower
than for scenario SI.
The HYDRUS simulation results showed that none of
the three assumed irrigation scenarios (i.e., FI, SI, and AI)
would result in soil salinization during the next 20 years.
This indicates that the use of the mulched drip irrigation
during the growing season, followed by flood irrigation
with freshwater in the post-harvest season, is a feasible andsustainable irrigation strategy, provided that leaching is not
limited by other factors such as a high water table. The
literature suggests that the salinity tolerance threshold ofcotton is about 7.7 dS/m (Maas and Hoffman 1977).
However, analyses of field experiments with the alternativeIrrig Sci (2014) 32:15–27

25

Fig. 9 Simulated soil water EC
values during 20 years for
scenario FI without post-season
leaching

Fig. 10 Simulated soil waterEC values during 20 years for
the SI and AI irrigation
scenarios assuming different
post-season leaching options:a SI ? annual leaching(1,575 m3/ha), b AI ? biennial
leaching (2,250 m3/ha), andc AI ? biennial leaching(1,575 m3/ha)

use of fresh and saline waters indicate that different cotton
growth stages have different sensitivities to salinity. Several studies (e.g., Beltran 1999; Murtaza et al. 2006; Zheng
et al. 2009) have shown that cotton growth may be affectedalready by ECe values close to 3 dS/m. Therefore, alternative irrigation schemes such as scenario AI in our study
may well be sustainable for irrigating cotton in arid areas

experiencing a general shortage of freshwater resources,
but with modestly saline water available.
Because of the shortage of freshwater supplies in the
study area and based on the simulation results presented
above (which indicated that it may be possible to reduce
post-season leaching), we considered additional scenarios
in which post-season leaching was either eliminated

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altogether, or where the leaching frequency or water
quantity was adjusted.
Three scenarios (i.e., FI, SI, and AI) without post-season
leaching were considered first. All three irrigation scenarios without post-season leaching were found to lead tosalinization over the 20-year simulation period. The results
for scenario FI, presented in Fig. 9, indicate that EC
gradually increased over time, even when freshwater wasused for irrigation. Therefore, some leaching is recommended, even for the mulched drip irrigation scenario
using freshwater (FI).
We additionally considered scenarios in which either the
post-season leaching frequency was decreased (e.g., fromonce every year to once every 2 years), or the water
quantity was reduced (e.g., from 2,250 to 1,575 m3/ha).
Figure 10 shows the results for the scenario SI with annual
post-season leaching using 1,575 m3/ha water, scenario AI
with biannual post-season leaching using 2,250 m3/ha
water, and scenario AI with biannual post-season leaching
using 1,575 m3/ha water. Of the three options, both AI
scenarios (with either annual or biannual leaching) did notlead to soil salinization over time, while scenario SI performed the worst. For these reasons, we recommend scenario AI, which uses freshwater during the early growthstages of cotton and saline water during the remaining
growth stages, combined with post-season leaching every
2 years using 1,575 m3/ha of freshwater. Predictions showthat this scenario is sustainable while using less freshwater.
Flood irrigation with freshwater could be adapted tomatch cotton growth stages based on soil salinity conditions and available freshwater resources. If soil salinity isclose to or exceeds the threshold value during the growing
season, flood irrigation could be applied. However, if
freshwater is not immediately available or scarce at thattime, leaching could be scheduled either earlier or later
depending upon freshwater availability. Similarly, drip
irrigation with freshwater could be continued longer thanstandard practice to promote some leaching of the salts outof soil root zone.
Freshwater is extremely scarce in some areas of Southern
Xinjiang. For example, the total quantity of water available
during the entire year in the middle and lower reaches of theTarim River was only 3,000 m3/ha in 2010, while groundwater had a relatively high salinity of 4.6 dS/m. The engineering cost of water delivery may then be far too high tojustify maximum yields (according to our investigation).
Considering economic issues, the overall goal hence must
be optimal yields (most yield per unit water), rather thanmaximum yields, so that the income of farmers is stable andtheir living conditions may improve. As such, there is also
no absolute need to always keep soil salinities below thegenerally poorly defined salinity threshold value (van Genuchten and Gupta 1993; Steppuhn et al. 2005).

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Irrig Sci (2014) 32:15–27

Conclusions
Our 3-year study showed that somewhat higher cotton yields
and irrigation water productivities could be obtained using
alternative scenarios A1 and A3 when freshwater is supplied
only to the seeding and early flowering-belling growthstages, as compared to the control scenarios. The average
cotton yield over the 3-year period was highest for scenario
A1, while scenario D4 (deficit irrigation during late flowering-belling) produced the highest average IWP. Both thecotton yield and the IWP for scenario A3 were the second
highest in 2009 and 2010, and the fourth highest in 2008. The
yield was more stable for scenario A3 than for scenario A1
during the experiments. Different stages of cotton growth
showed different responses to water and salinity stress. The
early flowering-belling and budding stages were found to be
more sensitive to water stress than other stages, while the lateflowering-belling and seedling stages were more susceptible
to salinity. However, we emphasize some caution in these
conclusions since the variability in yield among the different
treatments was found to be relatively high. This suggests that
it would be beneficial to carry out a few selected long-term
experiments for some of the more promising scenarios.
Long-term numerical simulations were performed forthree irrigation scenarios (FI, SI, and AI). Results showedthat soil water EC variations were similar in different
years, and that EC values gradually decreased over time forall scenarios involving annual post-season leaching using
2,250 m3/ha of freshwater. This indicates that irrigation
scenarios consisting of mulched drip irrigation during thegrowing season and flood irrigation in the post-season
would not lead to soil salinization. Post-season leaching is
recommended even for mulched drip irrigation withfreshwater (scenario FI). Simulation results for the AI
scenarios indicate that sustainable irrigation of cotton canbe achieved when freshwater is used during the early
growth stage of cotton and saline water during theremaining growth stages, in combination with post-season
leaching every 2 years using 1,575 m3/ha of freshwater.
Acknowledgments This study was financed in part by the National
Natural Science Foundation of China (41172218) and the National
Key Technology R&D Program (2007BAD38B01). We gratefully
acknowledge the Bazhou Experimental Station of Irrigation, Xinjiang,
and Xinjiang Agricultural University for providing facilities for ourfield experiments and Dr. Bingguo Wang, Dr. Yujiang He, Mr.
Xianwen Li, Mr. Jiao Chen, and others for their fruitful cooperation
on experiments both in the field and laboratory. We also acknowledge
support from CAPES-Brazil.

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