Agricultural Water Management 178 (2016) 291–303

Contents lists available at ScienceDirect

Agricultural Water Management
journal homepage: www.elsevier.com/locate/agwat

A ﬁeld-modeling study for assessing temporal variations ofsoil-water-crop interactions under water-saving irrigation strategies
Fatemeh Karandish a,∗ , Jiří Šimůnek bab

Water Engineering Department, University of Zabol, Zabol, Iran
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USAa r t i c l e

i n f oArticle history:
Received 3 May 2016
Received in revised form
27 September 2016
Accepted 9 October 2016
Keywords:
HYDRUS-2D
Maize grain yield
Partial root-zone drying
Soil water balance components
Water repartitiona b s t r a c t
Simulation models are useful tools that may help to improve our understanding of soil-water-plantinteractions under innovative water-saving irrigation strategies. In this study, the HYDRUS-2D model
was applied to evaluate the inﬂuence of deﬁcit irrigation (DI) and partial root-zone drying (PRD) on maize
water extractions during two cropping cycles of 2010 and 2011. The model was calibrated and validated
using measured soil water content data (expressed as equivalent water depths). Reliable estimates of soil
water content were provided by HYDRUS-2D, with root mean square error and mean bias error valuesof 2.3–5.11 and 1.63–4.93 mm, respectively. Root water uptake and maize grain yields were reduced by
13.2–28.8% and 13.6–52.8%, respectively, under different water-saving irrigation treatments compared tofull irrigation. However, different root and water repartitions in the PRD treatment with a 25% reductionin the irrigation depth (PRD75 ) improved soil water utilization and consequently, crop growth. Increasedroot water uptake (2.2–4.4 times higher than in other treatments) from the 60–100 cm soil depth in
the PRD75 treatment maintained a favorable daily evapotranspiration rate, resulting in no signiﬁcantreduction in maize grain yield compared to full irrigation. Consequently, a 15.7–85% increase in water
use efﬁciency for maize cultivation under PRD75 ensured 25% water savings without threatening foodsecurity in the study area. It can be concluded that HYDRUS-2D can be successfully used to optimize
water management under local water-stress conditions.
© 2016 Elsevier B.V. All rights reserved.

1. Introduction
The reduction in fresh water resources due to climate change
and rapid growth of the world’s population often leads to severe
competition between industrial, municipal, and environmental
users of water (Alberto et al., 2014). Agriculture is the biggest fresh
water user all over the world and commands over 70% of the world’s
freshwater withdrawals (OECD, 2010). Indeed, to feed the growing worldwide population and banish hunger from the world, foodand animal feed production will need to be signiﬁcantly increased(Darzi-Naftchali and Shahnazari, 2014), requiring the allocationof even more fresh water to agriculture. Consequently, irrigatedagriculture has been widely developed over the past few decades.
Nevertheless, since sustainable agriculture needs to be achieved∗ Corresponding author at: Water Engineering Department, Water and Soil Faculty, University of Zabol, Zabol, Iran.
E-mail addresses: F.Karandish@uoz.ac.ir, Karandish h@yahoo.com(F. Karandish).
http://dx.doi.org/10.1016/j.agwat.2016.10.009
0378-3774/© 2016 Elsevier B.V. All rights reserved.

with limited fresh water resources, exposing plants to water stresswill become unavoidable.
Under such circumstances, deﬁcit irrigation (DI) has been developed as a water-saving irrigation strategy where crops receive less
irrigation water during their growing season. However, althoughadapting DI may be a rational decision under an existing water crisis (Karandish et al., 2015), it may lead to a signiﬁcant yield loss
(Payero et al., 2006) that may threaten food security. Therefore, itis essential to ﬁnd an efﬁcient water saving method to sustain local
social and economic developments. During recent decades, an efﬁcient irrigation method named partial root-zone drying (PRD) has
been developed (Dry and Loveys, 1998). In this method, one half ofthe root zone is irrigated while the other half is allowed to dry out.
The irrigated and dry sides are periodically switched. It has beenreported that this water-saving irrigation method can reduce irrigation amounts without reducing crop yield and hence can increasewater and nutrient use efﬁciencies (Kang and Zhang, 2004; Shaoet al., 2008; Karandish and Shahnazari, In press).
Nevertheless, some researchers have demonstrated that reducing the amount of irrigation water under PRD should be done withspecial caution because the resulting water stress may affect crop292

F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

yields if it is higher than the plants’ tolerance. Liu et al. (2006)
reported that although a 25% reduction in irrigation water during a potato growth season signiﬁcantly increased the irrigationwater use efﬁciency (IWUE), a further 45% reduction led to a significant decrease in yield and IWUE. Similar results were reported by
Karandish and Shahnazari (In press) for maize. These results could
be associated with a linear relationship between root water uptake
and yield under water stress conditions (Traore et al., 2000). Many
researchers reported that the effects of water stress on yield dependon how signiﬁcantly the stress affects the crop evapotranspiration(Payero et al., 2006; Klocke et al., 2004; Stone, 2003). Therefore,
a better understanding of daily soil-water-plant interactions mayhelp to improve the water use efﬁciency under water-saving irrigation strategies, since such knowledge may lead to ﬁnding optimallevels of the water stress, preventing signiﬁcant yield losses andproducing optimal irrigation scheduling.
These goals could be achieved using laborious and timeconsuming, and therefore expensive, ﬁeld investigations. As aresult, direct measurements of soil water dynamics and soil-waterplant interactions under PRD have rarely been carried out. Previous
research has focused mainly on PRD effects on yield and crop
physiological responses (Kang and Zhang, 2004; Shao et al., 2008;
Karandish and Shahnazari, In press). Modeling could be an alternative tool to identify optimal conditions for applying PRD, especiallywhen the project is economically or technically impossible to be
carried out in the ﬁeld (Li and Liu, 2011).
Among the different available models, HYDRUS-2D (Šimůnek
et al., 2008, 2016) has been extensively and successfully used tosimulate daily soil water dynamics under many different conditions (e.g., Cote et al., 2003; Gärdenäs et al., 2005; Ajdary et al.,2007; Doltra and Munoz, 2010). However, a review of the literature
indicates that no research has been done on evaluating HYDRUS-2D
for PRD conditions, especially for PRD involving surface drip irrigation (SDI). Meanwhile, SDI is becoming a widely accepted irrigation
method that minimizes leaching and improves water and fertilizer
use efﬁciencies due to its advantage of applying water in precise
amounts and at exact locations throughout the ﬁeld (Bar-Yosef andSheikholslami, 1976).
PRD-SDI is expected to be an efﬁcient irrigation strategy formany crops because it has both the advantages of PRD and SDI.
However, there is a lack of knowledge about soil-water-plant interactions under PRD-SDI and therefore a need to identify the optimal
management for this irrigation strategy. To address this issue,
a two-year ﬁeld investigation was carried out in a maize ﬁeld
equipped with an SDI system under full irrigation and both DI-SDI
and PRD-SDI strategies to evaluate the following objectives: (i) tocalibrate and validate HYDRUS-2D for simulating various soil waterbalance components; (ii) to describe daily variations of soil waterbalance components; and (iii) to compare soil-water-plant interactions and evaluate the most water efﬁcient irrigation strategy
within the study area.

2. Materials and methods
2.1. Field trial
A two-year ﬁeld investigation was carried out in a 15 × 55 m
maize ﬁeld at the Sari Agricultural Sciences and Natural Resources
University (SANRU: 36.3◦ N, 53.04◦ E; 15 m below sea level). Climatic
variables during the study period (i.e., maize growing seasons of2010 and 2011) are displayed in Fig. 1, which shows daily recorded
weather data collected at the weather station near the experimental ﬁeld. Daily mean air temperatures (T) in 2010 were between 0.1
and 7.8 ◦ C higher than in 2011. Both vapor pressure deﬁcits (VPD)
and net radiations (Rn ) followed the same seasonal trend as tem-

Fig. 1. Daily mean temperatures (a), net radiations (Rn ) (b), and vapor pressuredeﬁcits (VPD) (c). during two growing seasons of 2010 and 2011.

peratures. During more than 66% of days in 2010, Rn was between
about 0.3 and 56.8% higher than in 2011. Total precipitations of 8
and 40 mm were recorded during the entire growing seasons of2010 and 2011, respectively. There was no rainfall 55 and 45 days
after planting (DAP) in 2010 and 2011, respectively. Selected soil
physical properties for the study area are summarized in Table 1.
A complete block design with ﬁve SDI treatments [full irrigation (FI), two partial root-zone drying (PRD) treatments (PRD75 and
PRD55 ), and two deﬁcit irrigation (DI) treatments (DI75 and DI55 )]in three replicates was used in the ﬁeld trial. The surface drip irrigation system was installed before sowing. Drip lines with emitters
20 cm apart and an emitter discharge rate of 2 L h−1 were placed on
the soil surface 75 cm apart (Fig. 2a).
Thereafter, for each treatment, ﬁve 100 cm long TDR probes(Trime FM; IMKO; Germany) were installed as illustrated in Fig. 2b(i.e., 25 TDR probes were installed in the study area; 5 probes × 5
treatments). TDR probes were used at least two times a day to measure soil water contents (SWCs) at depths of every 5 cm (i.e., atmeasuring points displayed in Fig. 2b) during both growing seasons.
Overall, SWCs were measured at each measuring time in 100 points
for each treatment. Moreover, information about the movement of
the wetting front during irrigation events was collected at least 10
times in each treatment by measuring SWCs one hour before, andimmediately and 2, 6, 12, 24, 48, 72, and 96 h after the irrigation
events in the 2010 growing season. To measure retention curves,
soil samples were taken every 20 cm to a depth of 80 cm for eachtreatment in three replicates. SWCs at 11 different pressure heads
were measured in the laboratory at each sample using a pressure
plate apparatus.
The single-cross, hybrid maize 704 was then planted 5 cm deep;
parallel to and between the drip lines on May 26 (in both 2010 and2011), at 75 × 20 cm row and crop spacings (Fig. 2a). The experi-

F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

293

Table 1
Soil physical properties in the study area.
Depth (cm)Soil texture

Field capacity (%)

Wilting point (%)

Bulk density (g cm−3 )

0–20
20–40
40–60
60–80
80–100

Sandy clay loam
Clay loam
Clay loam
Clay loam
Clay loam

30
32
32
32
32

15
14
14
14
14

1.4
1.38
1.35
1.37
1.37

Fig. 2. Schematic of the ﬁeld experimental site. Horizontal locations of laterals, drippers, and plants in the experimental ﬁeld (a) and locations of TDR probes in the maize
root zone (b).

mental plots were irrigated every other day during both growing
seasons. For the FI treatment, the net irrigation depth ([In ]FI , mm)
was calculated as follows:[In ]FI =ji=1((FCi − (BIi )FI ) × Di )(1)

where FCi is the volumetric soil water content at ﬁeld capacity(cm3 cm−3 ) of the ith soil layer (ﬁeld capacity for individual soil
layers was determined prior to the ﬁeld experiment), (BIi )FI is thevolumetric soil water content before irrigation in the ith soil layer(cm3 cm−3 ) in the FI treatment, Di is the soil layer thickness (mm), i
is the soil layer counter, and j refers to the number of soil layers forwhich [In ]FI is calculated. (␪BIi )FI is the average SWC measured using
Time Domain Reﬂectometry (TDR) probes in different measuring
points in the ith soil layer (Fig. 2). The net irrigation depth thus
refers to the actual soil water depletion between two consecutive
irrigation events under the FI treatment. Applying [In ]FI will reﬁll
the pore space in the rooting zone to the ﬁeld capacity.
All treatments received the same amount of irrigation water
during the ﬁrst 55 and 45 DAP in 2010 and 2011, respectively,

which was prior to applying different irrigation treatments. Different irrigation treatments were not imposed during the early
growth stage since it is commonly believed that initiating the PRD
treatments in this stage may result in a signiﬁcant reduction incrop’s yield and crop’s quality (e.g., Dry et al., 2000; Kang and Zhang
2004; Kirda et al., 2004; Tang et al., 2005; Shao et al., 2008). Similarly as done by others (e.g., Shahnazari et al., 2007; Yazar et al.,2009; Dry et al., 2000; Kang and Zhang, 2004; Kirda et al., 2004;
Tang et al., 2005; Shao et al., 2008), applications of the PRD irrigation treatments were postponed until the midway of the corn
growth season in order to sustain the positive effects of the PRD
treatment on the water use efﬁciency. Different irrigation treatments were applied during 55–107 DAP in 2010 and 45–110 DAP
in 2011. During this stress period, the PRD and DI treatments werescheduled to receive 55% (PRD55 and DI55 ) or 75% (PRD75 and DI75 )of the calculated irrigation volume of the FI treatment (Eq. (1))
during each irrigation event. While in the DI treatments, the irrigation volume at each dripper was reduced by 25 or 45% (in DI75
and DI55 , respectively), in the PRD treatments one dripper line
was alternatively not used at all, while the irrigation volume at

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F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

the other dripper line was increased by 50 and 10% in PRD75 and
PRD55 , respectively. Therefore, the vertical movement of irrigationwater in the wetted side of the PRD treatments is expected to be
more extensive than that for the DI or FI treatments. The irrigatedand non-irrigated sides were switched weekly in the PRD treatments because the idea of using PRD as a tool to manipulate theplant water deﬁcit response has its origin in the observation thatroot-generated Abscisic Acid (ABA) can be transported to shootregulating stomata of the leaves. To sustain the effect of PRD on
stomata, it is necessary to regularly alternate the wet and dry compartments, usually every 6–14 days (Shahnazari et al., 2007; Stollet al., 2000; Yazar et al., 2009); the length of this period depends oncrop species, evaporative demands, and soil conditions. Irrigatedand non-irrigated sides of the PRD treatments were switched inthis study weekly, as recommended for maize (Yazar et al., 2009).
During both growing seasons, plants were sampled in all treatments to determine temporal variations of the leaf area index andhorizontal and vertical root distributions. LAI was determined at 55,
62, 69, 76, and 83 DAP in 2010 and at 40, 56, 63, 70, 77, and 90 DAP
in 2011 for all treatments. At each sapling time, three plants per plot
were harvested. A laboratory leaf area meter (Delta-t Devices Ltd.)was used to measure the entire leaf area, which was converted to
LAI by dividing it with the corresponding soil surface covered by theplants. At the same time, the horizontal and vertical root distributions were determined using the Auger Sampling method (Kumar
et al., 1993). Roots were sampled with a 2-in ID auger every 10 cm
down to a depth of 80 cm and horizontally every 10 cm between
two drip lines.
At the ﬁnal harvest, the whole root system of one plant pereach replicate of a treatment (i.e., three plants per treatments) was
extracted and the root dry mass (RDM) was measured by oven drying roots at 70 ◦ C for 24 h. Maize grain yields were also measured
at the end of the cropping cycles. The plants were harvested on
September 9, 2010 (107 DAP) and September 12, 2011 (110 DAP).
The soil water contents (SWCs) collected during the FI and PRD
treatments were used to assess the ability of various machinelearning techniques to predict SWCs in a study by Karandish andŠimůnek (in review).





∂
∂
∂h
∂
∂h
∂K(h)
K(h)
+
K(h)
+
=
− S(x, z, h)
∂t
∂x
∂x
∂z
∂z
∂z

(2)

where x is the horizontal coordinate (L), z is the vertical coordinate
taken as positive upward (L), t is the simulation time (T),  is the volumetric soil water content (L3 L−3 ), h is the soil water pressure head(L), K(h) is the unsaturated hydraulic conductivity function (LT−1 ),(3)where ˛(x, z, h) is the soil water stress response function (dimensionless), b(x1 , z1 ) is the normalized spatial root water uptake
distribution (L−2 ), W is the width of the soil surface associatedwith transpiration (L), and Tp is potential transpiration (LT−1 ). Cropevapotranspiration (ETc ) under the FI treatment was supposed
to represent the potential crop evapotranspiration (i.e., potential
evapotranspiration of maize) since crops under the FI treatment
were well-irrigated (i.e., there was no water stress in the rooting
zone in the FI treatment since irrigation events in this treatment
were always scheduled to reﬁll soil water content to ﬁeld capacity),
well fertilized and were treated with pesticides to control weeds,
aphids and fungal diseases during both growing seasons. Moreover, these crops achieved their full production when considering
the maize yield potential for given climate conditions in the study
area. ETc in the FI treatment (ETc -FI ) was calculated using the soil
water balance equation (Allen et al., 1998):([I n ]FI + P) + S RZ -FI = DP RZ -FI + ET c -FI × t(4)

where [In ]FI is the net irrigation water depth (mm) for the FI treatment, P is precipitation (mm), SRZ - FI is the change (depletion) in
the soil water storage in the rooting zone between two irrigation
events (mm), DPRZ-FI is the amount of deep percolation between
two irrigation events (mm), ETc -FI is the crop evapotranspiration(mm/d), and t is the time interval between two irrigation events(d). SRZ - FI was calculated as the difference between the soil water
storage at the beginning (S1 ) and the end of a speciﬁc time period(S2 ) (i.e., SRZ = S1 –S2 ). DPRZ -FI was assumed to be zero since irrigation events in the FI treatment were scheduled to reﬁll the porespace to ﬁeld capacity in the rooting zone (Eq. (1)).
Measured leaf area index (LAI) was then applied to divide ETc -FI
into potential evaporation (Ep ) and potential transpiration (Tp ) as
follows (Belmans et al., 1983):(5)

Tp = ETc − Ep

The HYDRUS (2D/3D) software (Šimůnek et al., 2008, 2016) simulates two- or three-dimensional variably-saturated water ﬂow
and root water uptake in soils. In this study, water movement
was simulated in a two-dimensional vertical plane. The domain
geometry was deﬁned to represent a typical maize ﬁeld in which
driplines are located between maize rows with a row spacing of
75 cm (Fig. 2b). The domain geometry was deﬁned to be 75 cm
wide and 80 cm deep, with the driplines on the upper left andright corners of the domain and a maize plant in the middle ofthe domain surface. The 80 cm soil depth was selected so that theobserved maximum rooting depth was situated above this depth.
The driplines were considered to be line sources, since the emitter
spacing along the driplines was relatively small (i.e., 20 cm in thisﬁeld investigation) (Skaggs et al., 2004).
The following mixed form of the Richards equation, describing
two-dimensional water ﬂow in soil, was numerically solved using
the ﬁnite element method (Šimůnek et al., 2011):S(x, z, h) = ˛(x, z, h)b(x1 , z1 )WTp

Ep = ETc e−Kgr ∗LAI

2.2. Simulation approachand S(x,z,h) is root water uptake (T−1 ). The equation of Feddes et al.
(1978) was used for determining the root water uptake sink termof Eq. (2):where Ep is potential evaporation [LT−1 ], Tp is potential transpiration [LT−1 ], ETc is crop evapotranspiration [LT−1 ], and Kgr is anextension coefﬁcient for global solar radiation [−]. Kgr was set to0.39 following the suggestions by Ritchie (1972) and Feddes et al.
(1978). Estimated values of Ep and Tp were used as input parametersin HYDRUS-2D.
The stress response function ˛(x, z, h) was obtained from the
HYDRUS-2D database for maize (Šimůnek et al., 2011). The b(x1 , z1 )function was deﬁned according to Vrugt et al. (2001):

b(x1 , z1 ) = 1 −

x1
x1m



1−



z1
− p /x|x∗ −x |+pz/z1m |z ∗ −z1 |1e ( x 1m ) 1 1
z1m

(6)

where z1m is the maximum rooting depth in the vertical direction, x1m is the maximum rooting length in the horizontal direction,
x1 ∗ and z1 * are parameters that describe the location of maximum
root water uptake in the horizontal and vertical directions, respectively, and px and pz are the shape parameters for the horizontal
and vertical directions, both set to 1 (Vrugt et al., 2001).
The van Genuchten-Mualem model (Mualem, 1976; van
Genuchten, 1980) was used to describe the soil hydraulic properties. The soil hydraulic parameters for two soil horizons (i.e.,
0–20 cm and 20–80 cm soil depths) were obtained by ﬁtting thismodel to the observed retention curves data of both layers using
the RETC software. Fitted soil hydraulic parameters included theF. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

saturated SWC (␪s ), the residual SWC (␪r ), and the shape parameters ␣ and n. Since no hydraulic conductivity data was available,
the pedotransfer functions embedded in the HYDRUS (2D/3D) software were used to predict the saturated soil hydraulic conductivity(Ks ) using measured soil physical properties for each treatment.
Initial conditions were deﬁned using the measured initial soil
water contents of each soil layer. The time-variable ﬂux andatmospheric boundary conditions were speciﬁed at the surface
boundary. While a time-variable ﬂux boundary condition was used
to represent both emitters, an atmospheric boundary condition
with speciﬁed values of precipitation, evaporation, and transpiration was used on the rest of the soil surface. A free drainage
boundary condition was applied at the bottom boundary, allowing
for downward drainage and leaching. All other remaining boundaries were assigned a zero water ﬂux boundary condition.
Calibration and validation are two important processes that
ensure the optimal use of a model. Therefore, HYDRUS-2D shouldﬁrst be calibrated and validated to correctly simulate water movement in soils against the experimental data involving a wide rangeof observed SWCs. The calibration process was carried out for all
treatments using collected SWCs in the 2010 growing season. SWC
data collected in 2010 one hour before, and immediately and 2, 6,
12, 24, 48, 72, and 96 h after the irrigation events were used in thecalibration process. Soil hydraulic properties for the two horizonsof the soil proﬁle (i.e., 0–20 cm and 20–80 cm soil depths) were
optimized for all treatments using the inverse option of HYDRUS2D that minimizes deviations between observed and simulated
SWCs. During the calibration process, the saturated hydraulic conductivity (Ks ), the residual SWC (␪r ), and the saturated SWC (␪s )were optimized, while parameters ␣ and n were kept equal to values obtained by RETC. HYDRUS-2D was then validated using thecalibrated soil hydraulic parameters and daily variations of SWCs
during the growing season of 2011.

295

Before irrigation relative Available soil Water) and was calculated
for all treatments as follows (Nagore et al., 2014):

16
i=1

((BIi )T − PWPi )

16

BAW =

i=1

(FCi − PWPi )

× 100

(7)

where PWPi is the SWC at the permanent wilting point of the ith soil
layer (i.e., the sum is over 16 soil layers since TRD measurements
were taken every 5 cm down to a soil depth of 80 cm).
2.4. Statistical analysis
The root mean square error (RMSE), the mean bias error (MBE),
and the model efﬁciency (EF) were calculated to provide a quantitative assessment of the correspondence between predicted andobserved data as follows:n(Oi − Pi )2
i=1

RMSE =

n

n
MBE =

i=1

(Oi − Pi )n
EF = 1 −

n

(9)

n

i=1

(Oi − Pi )2(Oi − Oi )
i=1

(8)

2

(10)

where Pi and Oi are the predicted and observed data, respectively,
and n is the number of observations. Collected data was subjectedto the analysis of variance (ANOVA) (SAS Institute Inc., 2004), andthe standard errors of means (SEM) were calculated. The DUNCAN
test was applied to compare measured plant variables for plantsthat were exposed to different irrigation treatments.
3. Results and discussion
3.1. Growth parameters2.3. Calculation of mass balance components
Once HYDRUS (2D/3D) was calibrated and validated, it wasapplied to simulate the selected components of the soil water balance for all treatments. The entire rooting zone was ﬁrst divided
into eight rectangular sections 37.5 cm wide and 20 cm deep (i.e.,eight sub-regions in HYDRUS (2D/3D) as illustrated in Fig. 3). There
were thus four sections on both right and left sides of the rooting
zone of a crop. In the PRD treatments, the left side was irrigatedduring the ﬁrst week of the stress period while the right side wasleft to dry out. The irrigated and dry sides were then switchedweekly. Both right and left sides were irrigated under the FI and
DI treatments.
HYDRUS (2D/3D) directly simulates the temporal variations of
all soil water balance components in the rooting zone as well as in
the deﬁned sub-regions, including changes in the soil water storage (S), water ﬂuxes (WF), and deep percolation (DP). Therefore,
the actual water uptake (WU) from each individual soil layer could
be easily calculated by applying soil water balance equation foreach soil layer. In addition, HYDRUS (2D/3D) also directly simulates actual soil evaporation (Ea ) and actual transpiration (Ta ),from which the simulated ETc was calculated as ETc = Ea + Ta . The
daily adjusted crop coefﬁcient (Kc,adj ) was then obtained by dividing daily ETc by daily reference evapotranspiration (ET0 ), which
was calculated using the FAO-56 Penman-Monteith equation (Allenet al., 1998) from collected weather data at the experimental site.
In addition to soil water balance components and daily ETc , therelative available soil water in the soil proﬁle (down to a depthof 80 cm) before each irrigation event was expressed as BAW (i.e.,

The leaf area index is a driving factor for crop evapotranspiration and is highly inﬂuenced by the water stress (Nagore et al.,2014; Yazar et al., 2009; Howell et al., 2004; Traore et al., 2000).
Temporal variations of the leaf area index (LAI) are illustrated inFig. 4. In the FI treatment, LAI increased after the ﬁrst observation(55 and 40 DAP in 2010 and 2011, respectively) and reached its
maximum value 76 DAP in 2010 (4.4 m2 m−2 ) and 70 DAP in 2011(3.6 m2 m−2 ). Thereafter, LAI declined with time due to crop canopy
senescence. LAI could be ﬁtted using the second-degree polynomial function except for the DI55 treatment in 2011, for which athird-degree polynomial function was ﬁtted (Fig. 4).
Similar to earlier ﬁndings (Nagore et al., 2014; Yazar et al., 2009;
Howell et al., 2004; Traore et al., 2000), LAI was affected by thewater stress, except in the PRD75 treatment. Compared with the FI
treatment, a signiﬁcant reduction in LAI of 5.5%–35% was observed
under PRD55 , DI75 , and DI55 treatments. Nevertheless, at all sampling dates, there was no signiﬁcant difference in LAI between the
FI and PRD75 treatments. Moreover, PRD75 led to a 4–25% increasein LAI compared to the DI75 treatment despite receiving the sameamount of irrigation water. This result was also conﬁrmed by Liu
et al. (2006).
The root dry mass (RDM) is an additional important factor that
affects root water uptake. In 2011, RDM measured at harvest was
14.8, 17.2, 8.3, 7.6, and 6.1 g plant−1 for the FI, PRD75 , PRD55 , DI75 ,and DI55 treatments, respectively, which is in agreement with theﬁndings of Hu et al. (2009), who reported the root dry mass at harvest to be between 9 and 11 g plant−1 . RDM for the FI treatment
was signiﬁcantly higher than for the water-saving irrigation treatments, except for the PRD75 treatment, for which RDM was about

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F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

Fig. 3. Sub-regions deﬁned in the transport domain for evaluating soil water balance components by HYDRUS (2D/3D) (a) and a detail of one sub-region (b).

Fig. 4. Time variations of LAI for different irrigation treatments (FI, PRD75 , PRD55 ,DI75 , and DI55 ) in 2010 (a) and 2011 (b). Bars indicate the range of LAI variations.

16.2% higher than for the FI treatment (signiﬁcant at p = 0.05), and2.1, 2.3, and 2.8 times higher than for the PRD55 , DI75 , and DI55
treatments, respectively. Alternate wetting and drying of the rootsystem has been reported to have positive effects on root growthunder PRD (Liu et al., 2006; Sepaskhah and Ahmadi, 2010; Wanget al., 2012).
3.2. Model efﬁciency
Based on the model calibration results, optimized parameters␪s , ␪r , and Ks in FI treatment were 0.47 cm3 cm−3 , 0.1 cm3 cm−3 ,and 1.3 cm day−1 , respectively, for the 0–20 cm soil depth and0.47 cm3 cm−3 , 0.071 cm3 cm−3 , and 1 cm day−1 , respectively, forthe 20–80 cm soil depth. Optimized ␪s , ␪r , and Ks for the othertreatments are summarized in Table 2. The agreement between
observed and simulated SWCs (expressed as equivalent water
depths) during the calibration period was quantitatively assessedusing the RMSE and MBE statistics (Table 3). The RMSE values
ranged from 2.3 to 5.11 mm, indicating a good agreement between
the simulated and measured SWCs. Kandelous and Šimůnek (2010)
and Wang et al. (2014) reported similar RMSE values of 5–10 mmin their studies. Lower deviations between measured and modeled
SWCs were observed for the deeper soil layer, which could probably
be attributed to lower changes in the SWCs at greater depths during
the calibration process. This is further conﬁrmed by higher deviations near the drippers, where SWC variations were higher. Similar
results were reported by Ramos et al. (2012) and Wang et al. (2014).
Differences between observed and simulated soil water contents
could be a consequence of comparing local simulated soil watercontents with measured values that are averaged over a certain
soil volume, in which, due to irrigation, the gradient of soil watercontents may not be linear and can be quite high, especially aroundthe emission point (Mguidiche et al., 2015).
Fig. 5 compares the temporal variations of the simulated andobserved SWCs (averaged over the soil proﬁle) for various irrigation treatments during the cropping cycles of 2010 (the calibration
dataset) and 2011 (the validation dataset), to illustrate the capability of HYDRUS-2D in capturing the temporal and spatial trends of
SWCs. Generally, simulated SWCs agreed well with the observed
values, with EF ranging from 0.893 to 0.998. The close match
between simulated and observed SWCs, as well as their seasonal
trends, was also obtained in other studies for various soils and crops
under pressurized irrigation conditions (Pang et al., 2000; Skaggs
et al., 2004; Kandelous and Šimůnek, 2010; Mguidiche et al., 2015).
Comparisons between simulated and measured values of SWCs
with the 1:1 line in Fig. 5 indicate that HYDRUS-2D can be successfully used to predict daily variations of various components of thesoil water balance for both deﬁcit irrigation and partial root-zone
drying strategies, as well as for full irrigation. These results indicatethat numerical models can be used to develop water-saving irrigation strategies by proposing both optimal irrigation scheduling
and an optimal reduction of irrigation water without dramaticallydecreasing crop yield. The high accuracy of HYDRUS-2D is mainly
due to the use of a deterministic approach for simulating soil water
movement based on the Richards equation (Doltra and Munoz,2010). Earlier research has also demonstrated the high potential of
HYDRUS-2D for simulating soil water contents under full and deﬁcit
irrigation conditions (Cote et al., 2003; Ajdary et al., 2007; Rahil,F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

297

Table 2The optimized parameters ␪s , ␪r , and Ks for different treatments.
3−3
r (cm cm )3−3
s (cm cm )Ks (cm day−1 )Soil depth (cm)Treatment

0.10
0.07
0.10
0.08
0.09
0.07
0.09
0.10
0.10
0.08

0.47
0.47
0.47
0.47
0.47
0.47
0.47
0.47
0.47
0.47

1.3
1.0
1.22
0.95
1.15
0.90
1.10
0.88
1.19
0.93

0–20
20–80
0–20
20–80
0–20
20–80
0–20
20–80
0–20
20–80

FI
PRD75
PRD55
DI75
DI55

Table 3Model performance criteria at different soil verticals for the calibration dataset.
Parameter

Time (h)b

PositionaA

Soil water content in
the wetting front

BFI
JAI1
2h
6h
12 h
24 h
48 h
JAI2
72 h
96 h

B

C

D

E

MBE

RMSEc

MBE

RMSE

MBE

RMSE

MBE

RMSE

MBE

RMSE

4.54
−3.05
4.93
4.87
4.80
−3.52
−3.42
3.00
3.49
3.44

4.69
4.03
5.11
5.03
4.96
3.63
3.52
4.08
3.66
3.50

4.54
−3.50
−3.53
−3.53
−3.51
−3.46
−3.37
−3.45
−3.43
−3.39

4.69
3.61
3.64
3.64
3.62
3.57
3.47
3.66
3.60
3.45

4.55
2.29
2.29
2.28
2.26
2.24
−1.82
2.34
2.27
1.84

4.70
2.36
2.36
2.35
2.33
2.31
2.44
2.41
2.34
2.42

1.63
1.93
2.35
2.35
2.34
2.31
2.25
1.98
2.34
2.23

2.49
2.52
2.43
2.43
2.41
2.38
2.32
2.57
2.41
2.30

1.95
−3.05
−2.47
4.27
−3.60
−3.52
−3.42
−3.00
−3.49
−3.44

2.42
4.03
2.55
4.95
3.72
3.63
3.52
4.08
3.66
3.50

a

A: below the ﬁrst dripper (Fig. 2), B, C, D and E are 18.75, 37.5, 56.25, and 75 cm away from the ﬁrst dripper, respectively.
BFI – before irrigation; JAI1 – immediately after the ﬁrst irrigation; 2, 6, 12, 24, 48, 72, and 96 h after the ﬁrst irrigation, and JAI2–immediately after the second irrigation.
c
RMSE is the root mean square error and MBE is the mean bias error. Units for both RMSE and MBE are “mm”.

bFig. 5. Observed and simulated SWCs during the 2010 (a through e; the calibration dataset) and 2011 (f through j; the validation dataset) growing seasons for different
irrigation treatments (FI (a,f), PRD75 (b,g), PRD55 (c,h), DI75 (d,i), and DI55 (e,j)).

298

F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

Fig. 6. Temporal variation of ET0 and simulated ETc during the 2010 (a through e) and 2011 (f through j) growing seasons for different irrigation treatments (FI (a,f), PRD75(b,g), PRD55 (c,h), DI75 (d,i), and DI55 (e,j)).

2007; Crevoisier et al., 2008; Siyal and Skaggs, 2009; Mubarak,2009; Tafteh and Sepaskhah, 2012).
3.3. Temporal soil-water-plant interactions
The calibrated and validated HYDRUS-2D model was employed
to assess temporal effects of the water stress on soil-water-plantinteractions on a daily basis. Daily crop evapotranspiration (ETc )was simulated for all treatments, and then the daily adjusted crop
coefﬁcient (Kc,adj ) was calculated by dividing daily ETc by dailyreference evapotranspiration (ET0 ). In addition to ETc , different
components of the soil water balance, BAW, and the relative contributions of different soil layers to total root water uptake werealso calculated on a daily basis. As discussed below, these simulated results were then used for the analysis of the effects of thewater stress on ETc .
3.3.1. ET0 and ETC
ET0 varied between 2.2 and 9.2 mm d−1 , with a mean value of
6.7 mm d−1 and a cumulative value of 543.4 mm in the 2010 growing season; it varied between 1.7 and 8.8 mm d−1 , with a mean valueof 5.5 mm d−1 and a cumulative value of 433.6 mm in the 2011
growing season (Fig. 6). Mean daily ET0 during the maize earlyseason (before 22 DAP), during the developing stage (23–44 DAP),
mid-season (45–82 DAP), and late-season (83–110 DAP) in 2010
were 6.58, 6.85, 7.28, and 5.28 mm d−1 , respectively; they were 5.5,
5.9, 6.1, and 4 mm d−1 , respectively, in 2011 (Table 4). In agreementwith a lower Rn , VPD, and T (Fig. 1), ET0 was on average reduced by
20% in 2011 compared to 2010, except during the late season whenmean daily ET0 was about 45% lower than in 2010.
In the FI treatment, the early-season values of ETc were about
3–4.5 mm d−1 and thereafter gradually increased (Fig. 6) to about
8–10 mm d−1 in the mid-season, with a few atypical values of
12 mm d−1 (Fig. 6). ETc values then gradually decreased toward
the end of the growing season to about 2 mm d−1 at harvest. In
agreement with ET0 , mean daily ETc values during the mid- andlate-season stages in 2010 were higher than in 2011 in the FI treat-ment (Table 4). The relatively high ETc values during these growthstages in 2010 are related to higher climatic parameters, especiallyhigher Rn (Alberto et al., 2014) since climatic conditions are themost dominant factors for ETc during the mid- and late-season
growth stages (Singh and Bhakar, 2002). On the one hand, a larger
intercepted Rn in 2010 produced higher LAI than in 2011. On theother hand, higher LAI produced increased ETc in 2010 since ETc waslinearly related to LAI with R2 = 0.55–0.92 among different treatments (data not shown). In addition, Fig. 6 shows that despite lower
ET0 in 2011, an increase of about 3.5% and 13.3% in ETc occurredunder the FI treatment during the initial and developing stages,
respectively, which is due to the fact that ETc is more inﬂuenced bydifferent irrigation strategies during the vegetation growth stage
than by climatic variables (Allen et al., 1998; Shahrokhnia andSepaskhah, 2013).
Except for the PRD75 treatment, the comparative analysis well
reﬂects the negative effect of the water stress on ETc (Fig. 6 andTable 4). During the stress period, a signiﬁcant reduction occurredin ETc and KC,adj under deﬁcit irrigation, which could be associated with a signiﬁcant reduction in RDM. North and Nobel (1991)
stated that root growth into the dry soil under deﬁcit irrigation
may produce various anatomical changes in the roots, such as thesuberization of the epidermis, the collapse of the cortex, and theloss of succulent secondary roots. However, a signiﬁcantly higher
reduction in ETc during the mid-season stage than during the lateseason stage indicated the importance of the plant growth stage inadapting to water stress (Table 4).
Unexpectedly, the PRD55 treatment also did not lead to better results compared to the DI55 treatment, which may imply thatwater supplied at a rate of 55% of the evaporative demand could
not maintain a high ETc level, which is in agreement with results
reported by Liu et al. (2006). In contrast, compared with the FI treatment, the PRD75 treatment led to no signiﬁcant reduction in ETc
during the water stress period and also resulted in higher ETc thanthe other water stress treatments (Fig. 6 and Table 4). The only
exception was a one-week period after the onset of the irrigation
treatments (55–62 DAP in 2010 and 45–52 DAP in 2011) during

F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

299

Table 4
ETo , ETc , and adjusted Kc for different irrigation treatments (FI, PRD75 , PRD55 , DI75 , and DI55 ) and different growth stages of maize in 2010 and 2011.
Growth stage

Initial stage (Kcin )Development stage
Mid season stage (Kcmid )Late season stage
Harvest time (Kcend )Growth stage

Initial stage
Development stage
Mid season stage
Late season stage

Average KCadj a
DAPb

FAOc
2010

FI

PRD75

PRD55

DI75

DI55

DAP

FAO
2011

FI

PRD75

PRD55

DI75

DI55

0–22
23–44
45–82
83–107

0.65

0.52
0.71
1.26
0.79
0.45

0.52
0.71
1.24
0.69
0.41

0.52
0.71
1.01
0.49
0.33

0.52
0.71
1.13
0.65
0.38

0.52
0.71
0.99
0.49
0.30

0–22
23–44
45–82
83–110
–

0.72

0.66
0.93
1.24
0.74
0.54

0.66
0.93
1.22
0.72
0.42

0.66
0.93
0.91
0.50
0.17

0.66
0.93
1.07
0.66
0.25

0.66
0.93
0.91
0.52
0.18

1.2
0.35–0.6

1.2
0.35–0.6

Average ETc (mm d−1 )
DAP

ETo
2010

FI

PRD75

PRD55

DI75

DI55

DAP

ETo
2011

FI

PRD75

PRD55

DI75

DI55

0–22
23–44
45–82
83–107

6.85
6.85
7.28
5.82

3.58
4.85
9.20
4.61

3.58
4.85
9.00
3.99

3.58
4.85
7.34
2.86

3.58
4.85
8.25
3.77

3.58
4.85
7.23
2.85

0–22
23–44
45–82
83–110

5.6
5.9
6.1
4.0

3.70
5.50
7.55
2.97

3.70
5.50
7.45
2.90

3.70
5.50
5.57
2.01

3.70
5.50
6.50
2.64

3.70
5.50
5.54
2.08

a
ETo = Reference evapotranspiration; Average ETc = average daily actual evapotranspiration during the considered growth stage; average Kcadj = average adjusted crop
coefﬁcient during the considered growth stage.
b
DAP = days after planting.
c
Recommended Kc for different maize growth stages in FAO-56 (by Allen et al., 1998).

which ETc under the PRD75 treatment was slightly lower than under
the DI75 treatment on some days. Thereafter, ETc rapidly increased
and exceeded ETc values for the DI75 treatment by about 10–50%.
Similarly, Liu et al. (2006) reported lower ETc under the PRD treatment than under the DI treatment during the 1st day after the onsetof the irrigation treatment, which may be associated with reduced
soil evaporation under the PRD treatments due to only wetting halfof the soil surface during each irrigation event.
The possible causes of PRD75 ’s ability to maintain ETc at a favorable level may lie in two major reasons: better utilization of soil
water supply and higher LAI. Signiﬁcantly higher LAI for PRD75(Fig. 4) led to greater ETc , which is in agreement with previous studies (NeSmith and Ritchie, 1992; Traore et al., 2000; Howell et al.,
2004; Yazar et al., 2009; Nagore et al., 2014). Nevertheless, higher
LAI in the PRD75 treatment could be considered a consequence ofbetter utilization of soil water supply, which will be discussed in
Section 3.4.
3.3.2. Adjusted KC
Fig. 7 shows daily changes in the adjusted crop coefﬁcient(KC,adj ) for the “stress period” in 2010 and 2011. After the initialstage, KC,adj increased rapidly and reached its maximum value during the mid-season stage. Thereafter, KC,adj decreased toward thelate-season stage due to the senescence of maize leaves and reached
its minimum value at harvest. The calculated mean KC,adj for different maize growth stages are summarized in Table 4, which also
includes the predicted FAO-56 values for KC for different growthstages.
According to Allen et al. (1998), the adjusted crop coefﬁcientKC,adj is equal to KC × RS , where RS is the stress factor, which isequal to 1 for no-stress conditions and smaller than one for stressconditions. Since irrigation was applied without causing the water
stress under the FI treatment (RS = 1), KC,adj is equal to KC during all
growth stages for this treatment. Results showed larger deviations
in KC during different growth stages for well-watered maize underFI than predicted by the FAO values (Table 4). Several reasons may
explain the lower KC,ini for the FI treatment (0.52 and 0.66 in 2010
and 2011, respectively) than predicted by FAO-56 (0.65 and 0.72 in2010 and 2011, respectively). During the initial and development
periods, KC,ini is a function of the irrigation interval and the potential evaporation rate and is highly inﬂuenced by different irrigation
strategies (Allen et al., 1998; Shahrokhnia and Sepaskhah, 2013).
Thus, the ﬁeld management adapted in this study may not be thesame as, or even similar to, the normal FAO-56 management. The
obtained results are in agreement with studies of Gao et al. (2009)
and Mirzaei et al. (2011), who reported that the FAO-predicted KC
values may not always be close to the observed values and poorly
predict evaporation during the initial growth stage.
Higher values of KC,mid for the FI treatment than the
FAO-predicted values are also supported by the literature:
KC,mid = 1.2–1.65 in Liu and Luo (2010); KC,mid = 1.34 in Zhao andNan (2007); KC,mid = 1.38 in Liu et al. (2002); KC,mid = 1.43 in Kang
et al. (2003); and KC,mid = 1.26 in Li et al. (2003). The KC,mid value isless affected by the wetting frequency because vegetation during
this stage provides a nearly full ground cover and, the effect of surface evaporation on KC,mid is thus smaller (Singh and Bhakar, 2002).
In addition to climate conditions, the crop density and LAI also playa major role in KC,mid , which is not directly considered by the FAOproposed procedure, and that may lead to some deviation in KC,mid .
On the other hand, KC,end for the FI treatment is in agreement with
the FAO-predicted value for KC,end .
Similar to ETc , the comparative analysis revealed no signiﬁcantdifferences between FI and PRD75 in terms of KC,adj during the stressperiod (Table 4). In contrast, the water stress under the other watersaving treatments caused about 11–36% reduction in KC,mid and
12–61.5% reduction in KC,end . Despite receiving the same amount ofirrigation water, daily KC,adj for the DI75 treatment was considerably
smaller than for the PRD75 treatment during both the mid- andlate-season stages.
3.4. Soil water utilization
In order to assess the effects of different ETc patterns under
different water-saving irrigation treatments on soil water-plant
relationships, temporal variations of the relative soil water content
before irrigation (BAW) for the 0–80 cm soil depth were calculated
for all treatments and plotted in Fig. 8. The soil depth of 80 cm wasselected because the root system extended to this depth. Lower
BAWs for irrigation treatments with the water stress (i.e., PRD andDI) compared to the treatment without the water stress (FI) were
evident as early as a few days after initiating water-saving irrigation
treatments. During the stress period, the mean BAW for the PRD75 ,PRD55 , DI75 , and DI55 treatments was on average 43, 40.5, 24.5, and
35% lower, respectively, than that for the FI treatment. The difference in BAW between the FI treatment and the other water-saving
treatments gradually increased until 84 DAP for PRD75 and until 72

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F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

Fig. 7. Daily ﬂuctuations of KC,adj of maize during the “stress period” of 2010 (a) and 2011 (b) for different irrigation treatments (FI, PRD75 , PRD55 , DI75 , and DI55 ). KC,adj wascalculated by dividing simulated ETc by ET0 ; ET0 was calculated using the FAO-56 Penman-Monteith equation.
Table 5
Relative contributions of different soil layers to the total water depletion (WUi) in
2010 and 2011 for different irrigation treatments (FI, PRD75 , and PRD55 ). WU was
calculated based on the results simulated using HYDRUS (2D/3D).
Soil depth (cm)

FI

PRD75

PRD55

WUi (%) in 2010
0–20
20–40
40–60
60–80

64.5
27.2
7.3
1

51.4
28.2
17.6
2.8

61
26.5
11.8
0.7

WUi (%) in 2011
0–20
20–40
40–60
60–80

63.5
28.8
6.9
0.8

48.6
22.8
23.4
5.2

65
27.5
6.2
1.3

DAP for the other water stress treatments. Thereafter, the differences seemed to be nearly constant until the end of the growing
season. Both signiﬁcantly lower mean BAWs and a longer period of
gradual reduction of BAW under the PRD75 treatment compared tothe other treatments were evident for both years.
Since a lower BAW may be attributed to higher root water
uptake, the relative contributions of different soil layers (WUi ) to
the total water depletion (WU) from 0 to 80 cm soil depths under the
FI, PRD75 , and PRD55 treatments were calculated and summarizedin Table 5. A similar water depletion pattern was found for both the
FI and PRD55 treatments. For the FI treatment, 64, 28, 7.1, and 0.9%of the total water depletion occurred in soil depths of 0–20, 20–40,
40–60, and 60–80 cm, respectively. For the PRD55 treatment, thecorresponding total water depletion values were 63, 27, 9, and 1%.
The water depletion pattern in the PRD75 treatment was considerably different than in the other treatments, especially in the soil
depths below 40 cm. The contribution of the 40–60 and 60–80 cm
soil depths to the total water depletion in the PRD75 treatment was
2.8 and 4.4 times higher than in the FI treatment and 2.2 and 4 times
higher than in the PRD55 treatment, respectively.

A better use of soil water under PRD75 is also obvious when
the soil water balance components are considered (Table 6). Different irrigation treatments signiﬁcantly inﬂuenced the cumulative
evapotranspiration (cET) during the stress period. While cET in the
FI treatment was 400 mm during 55–107 DAP and 370 mm during 45–110 DAP in 2010 and 2011, respectively, cET was reducedby 28.8, 14.5, and 30% in the PRD55 , DI75 , and DI55 treatments,
respectively, in 2010 and by 27.6, 13.2, and 27.3%, respectively, in2011. Nevertheless, there was no considerable reduction of cET in
the PRD75 treatment compared to that in the FI treatment. PRD75
increased cET by 9.6-35.8% compared to other water-saving irrigation treatments during the stress period. Moreover, the water
stress, under water-saving treatments, caused an increased use of
the initially available soil water (S) compared to the FI treatment
in which crops were fully irrigated (Table 6). However, a significantly higher S for PRD75 than for the other treatments was
obvious in both seasons.
Higher S and lower BAW under the PRD75 treatments reﬂect
a considerable increase in the soil water extraction capacity (Fig. 8
and Table 6). Such results are conﬁrmed when deep percolation
under different treatments is considered. As stated previously,
while the irrigation volume at one dripper line was increased by 50
and 10% in the PRD75 and PRD55 treatments, respectively, the otherdripper line was alternatively not used at all. Therefore, higher DP
can be expected under the PRD75 treatment since half of the rooting zone in this treatment receives 50% more irrigation water thanthe corresponding side under the FI treatment (Table 6). However,simulated water ﬂuxes out of the rooting zone (DP) indicate that
DP ceased under the PRD75 treatment after 62 and 52 DAP in 2010
and 2011, respectively. This result demonstrated that the soil watercontent at the wetted side of the row of PRD plants is depleted
more rapidly than at the same side of control (FI) plants, indicatingthat the root system can partially compensate for the increasingly
limited water availability at the dry side of the row (Kang et al.,2003).
The better utilization of the soil water content could be ascribedto a signiﬁcant increase in the root dry mass (RDM) under the PRD75

F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

301

Fig. 8. Temporal variations of BAW of the 0–80 cm soil depth during the stress period of 2010 (left) and 2011 (right) for different irrigation treatments (FI, PRD75 , PRD55 , DI75 ,and DI55 ). Bars indicate variations of calculated BAW (using Eq. (10)) for all measured points in the 0–80 cm soil depth.

treatment. More root biomass could also be associated with a signiﬁcant increase in the root density. A higher contribution froma deeper soil layer (40–80 cm) to the total water depletion in the
PRD75 treatment than in the other treatment (Table 5) also conﬁrmed a notable increase in the root density in deeper soil layers.
A higher root density may imply that the root surface area of the
PRD75 plants was also higher (Wang et al., 2012). The root surface
area has been reported to have a big effect on water and nutrient
uptake from the soil matrix (Wang et al., 2012). Thus, it is plausible to state that a signiﬁcantly higher RDM in the PRD75 treatment
caused a signiﬁcant increase in the soil water extraction capacity
and a better utilization of soil water supply.
A higher water extraction capacity due to higher RDM under
PRD75 is in agreement with the ﬁndings of other studies. Sepaskhah
and Ahmadi (2010) reported that the root system can partiallycompensate for decreasing water availability on the non-irrigatedside of PRD due to an increase in the root hydraulic conductivity.
Liu et al. (2006) reported that more soil water depletion occurredunder PRD than under other deﬁcit irrigation strategies due to ahigher root hydraulic conductivity. Thus, signiﬁcantly higher RDM
and root density at deeper soil layers improved the ability of the
PRD75 plants to absorb more water from the soil matrix (Liang et al.,1996). In addition to the positive effect of increased RDM on an
increase in the soil water extraction capacity and consequently ETc ,higher ETc under PRD75 , despite a lower mean BAW during the stressperiod, may indicate a lower available soil water threshold, below
which actual ET in the other water-saving treatments is reducedwith respect to its maximum value in the PRD75 treatment. Nagore
et al. (2014) also reported a lower water content threshold for the
ET reduction for a new maize hybrid than in the older ones.

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F. Karandish, J. Šimůnek / Agricultural Water Management 178 (2016) 291–303

Table 6
Soil water balance components for different irrigation treatments (FI, PRD75 , PRD55 , DI75 , and DI55 ) in 2010 and 2011. cET, DP, and S were simulated using HYDRUS (2D/3D)
while I and P were directly measured in the ﬁeld.
Water balance components*

I (mm)

Treatments
FI
PRD75
PRD55
DI75
DI55

2010 (during the stress period)***372
400a **
0
279
375ab
0
204
285c
0
279
342b
0
204
280c
0

0
13.3
0
0
0

FI
PRD75
PRD55
DI75
DI55

2011 (during the stress period)
361
370a
0
271
364a
0
199
268c
0
271
321b
0
199
269c
0

0
11
1
0
0

cET (mm)

P (mm)DP (mm)S (mm)I (mm)cET (mm)

28
109
81
63
76

613
520
445
520
445

2010 (during the entire growing season)
649
8
0
624
8
13.3
534
8
0
591
8
0
529
8
0

P (mm)DP (mm)S (mm)28d
109.3a
81b
63c
76b

9.6
104
70
50
70

523
433
361
433
361

2011 (during the entire growing season)
572
40
0
566
40
11
470
40
1
523
40
0
471
40
0

9.6
104
70
50
70

*
Soil water balance components include irrigation (I), cumulative evapotranspiration (cET), precipitation (P), deep percolation (DP) and a change in soil water storage(S); S (S = S1 –S2 ) was calculated as a difference between the soil water content at the beginning (S1 ) and the end of a speciﬁc time period (S2 ). I and P were measured,
while ETc , DP, and S were obtained by HYDRUS (2D/3D).
**
Same letters indicate no signiﬁcant differences between treatments (p < 0.05).
***
The stress period was during 55–107 DAP in 2010 and during 45–110 DAP in 2011 when different irrigation treatments were applied. Before the start of the stress period,
all treatments received the same amount of irrigation water in all irrigation events (before 55 DAP in 2010 and 45 DAP in 2011).

Table 7
Maize grain yields (Y) for different irrigation treatments (FI, PRD75 , PRD55 , DI75 , andDI55 ) in 2010 and 2011.
Treatment

FI
PRD75
DI75
PRD55
DI55
*

Y (Mg ha−1 )
2010

2011

7a *
6.9a
5.8b
3.3c
3.2c

6.6a
6.2a
5.3b
5.7ab
4.9c

Same letters indicate no signiﬁcant difference between treatments (p < 0.05).

3.5. Maize grain yield
The grain yield (Y) was signiﬁcantly affected by different irrigation treatments. The grain yield for the FI, PRD75 , PRD55 , DI75 ,and DI55 treatments was 7, 6.9, 3.3, 5.8, and 3.2 t ha−1 , respectively,
in 2010, and 6.6, 6.2, 5.7, 5.3, and 4.9 t ha−1 , respectively, in 2011
(Table 7). The statistical analysis revealed that the water stress led
to a signiﬁcant reduction in Y for the PRD55 , DI75 , and DI55 treatments compared to the FI treatment, while it caused no signiﬁcantreduction in Y for the PRD75 treatment. Higher ETc and KC,adj , due
to a better utilization of soil water supply and consequently, higher
LAI, caused PRD75 to produce a signiﬁcantly higher grain yield thanthe other water-saving treatments; there was no signiﬁcant difference between Y in the FI and PRD75 treatments. Regardless of thegrowing season, a relative grain yield (Ya /Ym where Ya is the actual
grain yield, and Ym is the maximum grain yield) was closely associated with relative seasonal ET (ETa /ETm where ETa and ETm are
the actual and maximum ET, respectively) in different treatments(R2 = 0.70 and 0.86 for 2010 and 2011, respectively). The positiveeffect of favorable ETc on maintaining the maize grain yield has
also been reported by other researchers (Stone, 2003; Klocke et al.,2004).
4. Conclusions
A global water crisis and increased population growth necessitate special attention to water consumption management in the
agricultural sector. Water-saving irrigation strategies are assured
solutions under the conditions of water scarcity, while their effects
on food security still needs to be assessed. Combined ﬁeld andmodel investigations were thus carried out in a drip-irrigated maizeﬁeld to evaluate the inﬂuence of water stress on soil-water-plantinteractions under deﬁcit irrigation (DI) and partial root-zone drying (PRD). The quantitative analysis indicated the capability of the
HYDRUS-2D model to simulate both values and temporal-trendsof soil water balance components. Simulated results and ﬁeld data
reﬂected well the signiﬁcant inﬂuence of the irrigation water management on water-yield functions. The special soil water statusunder the PRD75 treatment improved root repartitions, resulting
in a better utilization of soil water supply through enhanced water
uptake from deeper soil layers. As a result, actual crop evapotranspiration was maintained at a favorable level and no signiﬁcantyield loss was observed under PRD75 . In fact, optimal management
under PRD75 led to a considerable increase in the water use efﬁciency despite receiving 25% less irrigation water compared to full
irrigation. These concluding remarks are further conﬁrmed when
the negative results under DI with the same reduction in the irrigation water depth as that for PRD75 are considered. PRD75 is thus
the most efﬁcient water-saving irrigation strategy for maize cultivation in the study area. Additionally, it could be concluded thatthe HYDRUS-2D model, instead of labor- and time-consuming andexpensive ﬁeld investigations, could be reliably used for determining the optimal soil-water-plant interactions under both DI and
PRD strategies.

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