Agricultural Water Management 208 (2018) 67–82

Contents lists available at ScienceDirect

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

An application of the water footprint assessment to optimize production ofcrops irrigated with saline water: A scenario assessment with HYDRUS

T⁎

Fatemeh Karandisha, , Jiří Šimůnekb
ab

Water Engineering Department, University of Zabol, Zabol, Iran
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USA

A R T I C LE I N FO

A B S T R A C T

Keywords:
Economic water productivity
Grey water footprint
Maize
Numerical simulations
Salt stress
Water deﬁcit
HYDRUS

Agriculture, due to a growing scarcity of fresh water resources, often uses low-quality waters for irrigation, suchas saline waters. However, unmanaged applications of such waters may have negative environmental andeconomic consequences. Based on the concept of the water footprint (WF), a measure of the consumptive anddegradative water use, the ﬁeld-calibrated and validated HYDRUS (2D/3D) model was applied to ﬁnd optimal
management scenarios (from 1980 diﬀerent evaluated scenarios). These scenarios were deﬁned as a combinationof diﬀerent salinity rates (SR), irrigation levels (IL, the ratio of an actual irrigation water deth and a full irrigation water depth), nitrogen fertilization rates (NR), and two water-saving irrigation strategies, deﬁcit irrigation (DI) and partial root-zone drying (PRD). The consumptive WF was deﬁned as the crop water consumption
divided by the crop yield. The grey WF was calculated for the N fertilizer and deﬁned as the volume of freshwaterrequired to dilute nitrogen (N) in recharge so as to meet ambient water quality standards. Simulated components
of water and solute dynamics were used to calculate criteria indices, which were divided into two groups: (a)environmental indices, including the degradative grey water footprint (GWF) and the apparent N recovery rateeﬃciency (ARE), and (b) economic indices, including economic water (EWP) and land (ELP) productivities.
While signiﬁcant improvements of 3.9–59.2%, 0.1–165.8%, and 0.01–166.5% in ARE, EWP, and ELP, respectively, were obtained when NR varied within the range of 0–200 kg ha−1, changes in these indices were relatively minor when NR was higher than 200 kg ha−1. At a given NR, GWF tends to increase considerably by up to
180% when DI-crops are subject to low-intermediate salt (SR < 7 dS m−1) and water (IL > 70%) stresses. This
is at the expense of up to a 55% reduction in ELP and up to a 120% increase in EWP. With N uptake 0.2–17.3%
higher, PRD seems to be a more viable agro-hydrological option than DI in reducing a pollutant load into
regional aquifers as well as in sustaining farm economics. The entire analysis reveals that the PRD strategy with
N-fertilization rates of 100-200 kg ha−1, a moderate salinity stress (SR < 5 dS m−1), and irrigation levels of
60–90% represents the best management scenario. It can be concluded that, while there is a substantial need forrescheduling irrigation and fertilization managements when crops are irrigated with saline waters, HYDRUS
modeling may be a reliable alternative to extensive ﬁeld investigations when determining the optimal agricultural management practices.

1. Introduction
It has been well documented that groundwater pollution induced by
the agricultural sector poses a serious and widespread environmental
threat to Iran (e.g., Karandish et al., 2016), California (e.g., Harter and
Lund, 2012), and many other countries. In Iran, after the 1979 Islamic
revolution, the government implemented several new agricultural policies aimed at achieving national food security through increased domestic productivity and self-suﬃciency of staple crops (Karandish andHoekstra, 2017). The construction of irrigation and drainage networks⁎

Corresponding author.
E-mail addresses: Karandish_h@yahoo.com, F.Karandish@uoz.ac.ir (F. Karandish).

https://doi.org/10.1016/j.agwat.2018.06.010
Received 1 March 2018; Received in revised form 4 June 2018; Accepted 7 June 20180378-3774/ © 2018 Elsevier B.V. All rights reserved.

and enhanced applications of organic/inorganic fertilizers were among
these policies. While Iran's policy on agricultural self-suﬃciency satisﬁed growing demands for food and raw materials, ill-thought-out
agricultural practices resulted in adverse environmental consequences.
The alteration of the balance of soil N compounds through excessive
uncontrolled applications of N-fertilizers above optimal amounts andwithout considering crops N requirements led to signiﬁcant groundwater pollution (Zhu et al., 2005; Thompson et al., 2007; Dudley et al.,2008; Burow et al., 2010; Dahan et al., 2014; Karandish et al., 2016).
Nitrate vulnerability may be of particular interest in water-scarce

Agricultural Water Management 208 (2018) 67–82

F. Karandish, J. Šimůnek

simultaneously consider root uptake of water and nutrients, and its
sophisticated graphical user interface (Li et al., 2015; Karandish andŠimůnek, 2017).
The literature review reveals that there are many research gaps inthis ﬁeld of research. (i) Almost no research has been done on the use of
the HYDRUS model to analyze soil-water-crop relationships under PRD.
The concept behind PRD is diﬀerent than behind DI and thus soil-watercrop relationships may be aﬀected by these diﬀerences. While the
HYDRUS model has been previously used to simulate soil water andnutrient dynamics under PRD (Karandish and Šimůnek, 2016a, 2016b,
2017), this has not yet been done while taking into account simultaneously both water and salinity stresses and their eﬀects on soil-watercrop relationships. (ii) Similarly, little research has been carried out toﬁnd optimal management strategies with respect to both economic andenvironmental factors. While some limited work has been done withrespect to ﬁnding an optimal combination of water-salinity-fertility
levels for DI (Azizianand Sepaskhah, 2014a, 2014b), no similar work
has been done for PRD. No research has considered simultaneously both
environmental and economic indices when ﬁnding optimal management strategies for the DI or PRD strategies. (iii) Finally, no researchhas been carried out at the ﬁeld scale to assess the total and grey water
footprints under diﬀerent combinations of water, fertility, and salinity
stress (for both DI and PRD). While developing benchmark levels of thewater footprint (WF) is still in its infancy, none of the previous studies
have addressed the salinity stress in their analysis when benchmarking
WF. In addition, the economic beneﬁts associated with a more eﬃcientwater consumption due to benchmarking have not been quantiﬁed
before.
Hence, the current study aims to advance the ﬁeld of WF benchmarking by (i) developing ﬁeld-speciﬁc benchmark levels for both total
and grey WFs for various water-salinity-fertility scenarios and (ii)comparing the economic water productivities of the current crop production with productivities if WFs were reduced to benchmark levels.
Therefore, the water footprint concept is applied in this study to provide the best 10% and 25% combinations of N-application rates andirrigation levels for diﬀerent salinity rates under two water-saving irrigation strategies of DI and PRD with respect to both environmental
and economic indices. The HYDRUS (2D/3D) model (its 2D level) is
ﬁrst calibrated and validated using a two-year ﬁeld dataset and thenused to analyze the soil-water-crop interactions for a large number ofscenarios involving diﬀerent combinations of N fertilizer rates, irrigation water levels, and salinity levels of the irrigation water under the DI
and PRD conditions. The simulated results are then used (i) to calculate
both consumptive and degradative grey WF related to crop production
for various scenarios, (ii) to economically analyze the probable consequences of the deﬁned scenarios, and, ﬁnally, (iii) to ﬁnd the optimal
management strategies with respect to both economic and environmental factors.

regions where crops need to be produced with minimal fresh water. In
these regions, there is room for savings of fresh water if crops receive
less irrigation water during their growing season (Karandish andŠimůnek, 2016a). In this regard, many researchers have investigatedthe economic and environmental consequences of applying deﬁcit irrigation (DI) (Payero et al., 2006; Klocke et al., 2004; Stone, 2003) orpartial root-zone drying (PRD) (Dry and Loveys, 1998; Kang and Zhang,2004; Kirda et al., 2004; Shao et al., 2008; Tang et al., 2005; Karandishand Šimůnek, 2016a, 2016b). They mostly concluded that, while signiﬁcant economic losses could be expected under DI, PRD may produce
water savings without a signiﬁcant decrease in yields. While crops are
exposed to the water stress under both DI and PRD, the way the stress isapplied under PRD may produce better results. A regular alternation ofirrigated and non-irrigated sides of the crop under PRD results in thesecretion of root-generated Abscisic Acid (ABA) (Schachtman andGoodger, 2008), which is transported to shoot regulating stomata of theleaves. Such regulation reduces crop water losses through stomata,while maintaining crop CO2 adsorption at the favorable level (Kang andZhang, 2004), which consequently results in a higher crop water use
eﬃciency compared to DI, or even FI (Karandish and Šimůnek, 2016a).
Although irrigation with saline waters may be another alternative tousing fresh water in the agricultural sector (Pereira et al., 1995), thismay lead to serious economic and environmental losses if not managed
properly (Yurtseven and Sönmez, 1992; Mer et al., 2000; Zhu et al.,2005; Thompson et al., 2007; Corwin et al., 2007; Dudley et al., 2008;
Roberts et al., 2009; Burow et al., 2010; Dahan et al., 2014). Water orsalinity stresses may reduce crop N uptake (Karandish and Šimůnek,2017; Ramos et al., 2012) and lead to higher nutrient loads into aquifers as a byproduct of N leaching out of the root zone (Zhu et al., 2005;
Thompson et al., 2007; Dudley et al., 2008; Burow et al., 2010; Dahanet al., 2014; Karandish et al., 2016). If fresh water is applied, N leaching
may be lower under PRD than under DI due to a higher crop N recovery(Kang and Zhang, 2004; Kirda et al., 2005; Li et al., 2007; Wang et al.,2009; Hu et al., 2009; Wang et al., 2012; Karandish and Šimůnek,2017), which could translate into lower groundwater contamination.
Even so, no earlier studies have ever attempted to ﬁnd the optimal
combination of N fertilization rates and applied water levels under PRD
irrigated with saline water.
The degradative grey water footprint (GWF) may be an appropriate
measure to assess new adjustments to the N fertilization rate under
combined water and salinity stress conditions. Considered an indicatorof the pollution assimilative capacity (Chukalla et al., 2017), the GWF isdeﬁned as the volume of freshwater required to dilute a load of pollutants so as to meet ambient water quality standards (Hoekstra et al.,2011). The GWF of the crop production represents the volume of water
needed to suﬃciently lower nitrogen concentrations that reach thewater systems due to leaching or runoﬀ, given an ambient NO3−N
concentration of 10 mg l-1, which corresponds to 44 mg l-1 of NO3- (Selfand Waskom, 2013, Shyns and Hoekstra, 2013). Increased N application rates beyond the optimal rate may signiﬁcantly increase the GWF.
On the other hand, a low N application rate can potentially hamper
plant growth and result in a low crop yield (Raun et al., 2002) eventhough water pollution per hectare may still be small (Chukalla et al.,2017). Instead of carrying out laborious, time-consuming, and thus
expensive ﬁeld investigations with a limited number of treatments, an
optimal N application rate can be estimated using a modeling approach(Karandish and Šimůnek, 2016a, 2016b, 2017). This may be especiallyadvantageous in situations where it may be economically or technically
impossible to carry out the project in the ﬁeld (Li and Liu, 2011).
Among a large number of analytical and numerical models simulating
soil water and solute dynamics (e.g., Johnsson et al., 1987; Hutson andWagenet, 1991; Ma et al., 2001; Šimůnek et al., 2008, 2016; Doltra andMunoz, 2010), the HYDRUS (2D/3D) model (Šimůnek et al., 2008,2016) is among the most powerful models for evaluating sound agricultural practices due to its ﬂexibility in accommodating diﬀerent typesof boundary conditions for water ﬂow and solute transport, its ability to

2. Materials and methods
2.1. Data collection
During the 2010 and 2011 cropping cycles, a two-year ﬁeld investigation was carried out in the 825 m2 (15 × 55 m) maize ﬁeld at theSari Agricultural Sciences and Natural Resources University in Sari,
Iran. Daily weather data were collected at the weather station near theexperimental ﬁeld. Soil textures at 0–20 cm and 20–100 cm soil depths
were sandy clay loam and clay loam, respectively. The randomized
complete block design with ﬁve irrigation treatments (full irrigation[FI], two partial root-zone drying [PRD] treatments [PRD75 and
PRD55], and two deﬁcit irrigation [DI] treatments [DI75 and DI55]; thePRD and DI treatments were scheduled to receive 55% (PRD55 and DI55)
or 75% (PRD75 and DI75) of the calculated irrigation volume of the FI
treatment during each irrigation event) in three replicates was used in
the ﬁeld trial. The dimension of each treatment (i.e., the total land
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Agricultural Water Management 208 (2018) 67–82

F. Karandish, J. Šimůnek

Fig. 1. Schematic of the ﬁeld experimental site: the horizontal (a) and vertical (b) plans.
Table 1
Detailed description of ﬁeld activities during the cropping cycles of 2010 and 2011.
No.

Activity

Description

1

Sowing2

N fertilization

3
4

P fertilization
K fertilization

5

Irrigation treatments (ITs)

6
7

Stress periods
Irrigation events

8
9

Irrigation water sampling
Soil water content (SWC)
measurements

10

Soil sampling during cropping cycles

11

Crop sampling during cropping cycles

12
13

Soil and crop sampling at harvest
HarvestMaize single-cross hybrid 704 was sown 5 cm deep, with 75 cm × 20 cm crop row and crop spacings, respectively, between andparallel to the drip lines.
Urea was applied via irrigation (fertigation) with rates of 65 kg ha−1 and 135 kg ha−1 on July 14, 2010 and July 4, 2011,
respectively.
150 kg ha−1 triple superphosphate was banded in crop rows prior to sowing date.
Potassium sulfate was applied via irrigation (fertigation) with rates of 50 kg ha−1 and 100 kg ha−1 on July 14, 2010 and July 4,
2011, respectively.
ITs included a full irrigation (FI) treatment, two partial root-zone drying (PRD) treatments (PRD75 and PRD55), and two deﬁcit
irrigation (DI) treatments (DI75 and DI55)], each in three replicates, which were carried out in the randomized complete block
design.
ITs were applied during 55-107 and 45-110 days after sowing (DAS) in 2010 and 2011, respectively.
All treatments received the same amount of irrigation water during the ﬁrst 55 and 45 DAS in 2010 and 2011, respectively.
Diﬀerent ITs were applied during 55-107 DAS in 2010 and 45-110 DAS in 2011. During this stress period, the PRD and DI
treatments were scheduled 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 theirrigation volume at the other dripper line was increased by 50 and 10% in PRD75 and PRD55, respectively. The irrigated and nonirrigated sides were switched weekly in the PRD treatments.
Irrigation water quality was analyzed for all irrigation events.
TDR probes were used at least two times a day to measure SWCs in 5-cm depth increments during both growing seasons. Moreover,
information about the movement of the wetting front during irrigation events was collected at least 10 times in each treatment bymeasuring SWCs one hour before, and immediately and 2, 6, 12, 24, 48, 72, and 96 hours after irrigation events in the 2010
growing seasons.
Prior to applying ITs, and once a week during the stress period, soil samples were collected in each treatment vertically every 20 cm
to a depth of 80 cm and at ﬁve equal horizontal distances between two drip lines. Soil samples were analyzed for total nitrogen(TN) (the semi-micro Kjeldhal method (Bremner and Mulvaney, 1982)), the NO3−-N concentration (with a spectrophotometer(DR/2500, Hack Co.) using a cadmium reduction method (APHA, 1992)), and the electrical conductivity (ECsw).
At the same dates as soil sampling, three plants per plot (i.e., each plot is considered as one replicate of a treatment) were harvested
to determine total crop N uptake, total wet and dry biomass, and leaf area index (LAI). Crop N uptake was determined using a semimicro Kjeldahl apparatus. The oven-dried weight of crops at 70 °C was considered as the crop dry biomass. 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 thecorresponding soil surface covered by the plants.
All considered soil and crop properties as well as maize grain yield were also determined at harvest.
Crops were harvested on September 9, 2010 and September 12, 2011 (107 and 110 DAS, respectively).

allocated to three replicates of one treatment) was 165 m2. The surface
drip irrigation system was installed before sowing. Drip lines withemitters 20 cm apart and an emitter discharge rate of 2 L hr−1 wereplaced on the soil surface 75 cm apart (Fig. 1). Five 100 cm long TDR
probes (Trime FM; IMKO; Germany) were then installed in each treatment as illustrated in Fig. 1 for continuous measurements of soil watercontents (SWCs) (see Table 1). The accuracy of soil water contents

measured using TDRs was compared with that measured using thegravimetric method. An index of agreement for these two methods was
95%, conﬁrming the acceptability of TDR measurements.
After sowing, the experimental 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 (Karandish and Šimůnek,2016a, 2016b, 2017):

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Agricultural Water Management 208 (2018) 67–82

F. Karandish, J. Šimůnek

[In ]FI = ∑j

i=1((θFCi − (θBIi )FI ) × Di )Using the HYDRUS (2D/3D) database of suggested crop-speciﬁc parameters for the solute stress (Šimůnek et al., 2011), the salinity threshold
ECT and slope for maize were set to 1.7 dS m−1 and 12%, respectively.
Since ECT denotes the electrical conductivity of the saturation extract(ECe), this value was converted into ECsw, which is required by HYDRUS(2D/3D). Following earlier recommendations (US Salinity Laboratory,
1954; Skaggs et al., 2006), it was assumed that ECsw/ECe = 2. Detailed
information about how root water and nutrient uptake was simulated
can be found in Karandish and Šimůnek (2016a, 2016b, 2017).
The rectangular two-dimensional transport domain (75 cm wide and80 cm deep, i.e., the maximum observed rooting depth) was deﬁned
between two neighboring emitters on either side of one plant. Drip lines
were considered to be line sources since the emitter spacing (20 cm)
along the driplines was relatively small. The simulation domain was
discretized with a non-uniform ﬁnite element mesh generated by the
HYDRUS model with ﬁnite element sizes gradually increasing with
distance from the emitters. Soil hydraulic properties, measured as described in Karandish and Šimůnek (2016a), were deﬁned for two soil
horizons of the 0–20 cm and 20–80 cm soil depths.
Within the ﬂow domain, measured SWC, NO3−-N, and ECsw at the
beginning of the experiment were used as initial conditions for simulations. The time-variable and atmospheric boundary conditions were
speciﬁed at the soil surface to represent drip irrigation and to apply
precipitation, evaporation, and transpiration ﬂuxes, respectively. A freedrainage boundary condition was applied along the bottom boundary,allowing for downward drainage. Both solute species (NO3−-N and EC)
were applied with irrigation water, and therefore a third-type Cauchy
boundary condition was used to describe concentration ﬂuxes along thesurface boundary. All other remaining boundaries were assigned a noﬂow boundary condition.
The HYDRUS model was calibrated and validated for the soil hydraulic and chemical properties using the two-year experimental data.
An inverse solution option in the model was used to optimize the saturated hydraulic conductivity (Ks), the saturated soil water content(θs), and the residual soil water content (θr) as soil hydraulic properties,
as well as the longitudinal (DL, L) and transversal dispersivities (DT, L)
as soil solute transport properties. The initial value of DL was set to one
tenth of the soil depth, and the initial value of DT was set to one tenth of
DL (Ramos et al., 2012). The molecular diﬀusion coeﬃcient was always
set to zero since molecular diﬀusion can usually be neglected (Radcliﬀeand Šimůnek, 2010). In the calibration process, the capability of HYDRUS was assessed by comparing the simulated and observed data (i.e.,
SWC, NO3−N, and ECsw) during the 2010 growing season. Afterwards,
the optimized soil hydraulic and solute transport parameters obtainedby calibration were used to validate the model by comparing the simulated and observed data (SWC, NO3–N, and ECsw) during the 2011
growing season.
As discussed in our earlier publications, the eﬀects of PRD may
diﬀer from DI due to its positive inﬂuence on the abscisic acid (ABA)
secretion, which causes improved root hydraulic conductivity andsecondary root initiations and, consequently, improved root water uptake and crop water productivity (Karandish and Šimůnek, 2016a).
Hence, we attempted to address such diﬀerences between PRD and DI
when calibrating HYDRUS-2D. In fact, the model was separately calibrated for full irrigation, DI, and PRD treatments since a comprehensive
dataset was collected on temporal variations of root and shoot growthduring both cropping cycles of 2010 and 2011. The horizontal andvertical root distributions at each sampling date were determined using
the Auger Sampling method (Kumar et al., 1993). Roots were sampledwith a 2-in ID auger every 10 cm down to a depth of 80 cm and horizontally every 10 cm between two drip lines. Feddes' parameters were
adjusted for each water-saving irrigation strategy (DI or PRD), which
led to a higher agreement between observed and simulated soil waterbalance components (Karandish and Šimůnek, 2016a, 2016b, 2017).
Detailed information about the calibration and validation process andthe soil hydraulic and solute transport parameters can be found in(1)

where θFCi is the volumetric soil water content at ﬁeld capacity(cm3cm−3) of the ith soil layer (ﬁeld capacity for individual soil layers
was determined prior to the ﬁeld experiment), (θBIi )FI is the volumetricsoil water content before irrigation in the ith soil layer (cm3cm−3) inthe FI treatment, Di is the soil layer thickness (mm), i is the soil layercounter, and j refers to the number of soil layers for which [In ]FI is
calculated. (θBIi )FI is the average SWC measured using TDR probes in
diﬀerent measuring points in the ith soil layer (Fig. 1). All plots received
the same amount of irrigation water during the ﬁrst 55 and 45 DAS in2010 and 2011, respectively. Irrigation treatments were started on 55
and 45 DAS in 2010 and 2011, respectively. Additional information
about ﬁeld activities during the stress period (55–107 and 45–110 DAS
in 2010 and 2011, respectively) can be found in Table 1. Detailed information can also be found in Karandish and Šimůnek (2016a, 2016b,
2017).
2.2. HYDRUS (2d/3d)
The HYDRUS (2D/3D) model (its 2D level) (Šimůnek et al., 2008,2016) was used for simulating water, NO3−N, and EC dynamics in soil.
In this model, water ﬂow is described using the Richards equation (Eq.
(2)) while the convection-dispersion equation (CDE) is used for thesolute (electrical conductivity of the soil solution (ECsw) and NO3−N)
transport (Eq. 3):∂θ∂ ⎛ ∂h ⎞∂ ⎛ ∂h ⎞
∂k
=
Kx
+
Kz
−
− WU (h, h∅, x , z )∂t∂x ⎝ ∂x ⎠∂z ⎝ ∂z ⎠
∂z

(2)

∂
∂c
∂c
∂θc
∂ ⎛
∂c
∂c
+ θDxz ⎞ +θDzz= ⎧ ⎛θDxx+ θDzx ⎞ ⎫
⎨
∂x
∂z ⎠
∂t
∂z ⎝
∂z
∂x ⎠ ⎬⎩ ∂x ⎝⎭∂qz c ⎞∂q c− ⎜⎛ x +⎟ − Sc
∂z ⎠
⎝ ∂x

(3)
3 −3

where ϴ is the volumetric SWC (L L ), K is the unsaturated hydraulicconductivity function (LT-1), h is the soil water pressure head (L), x is
the lateral coordinate, z is the vertical coordinate (positive downwards), t is time (T), WU (h, h∅, x , z ) denotes root water uptake (T-1), c
is the solute concentration in the liquid phase (ML−3), qx and qz are thecomponents of the volumetric ﬂux density (LT-1), Dxx, Dzz, and Dxz are
the components of the dispersion tensor (L2T-1), and Sc is the sink term
(nutrient uptake, ML−3T-1).
The equation of Feddes et al. (1978) was used to determine the root
water uptake sink term in Eq. (2):WU (x , z , h, h∅) = α (x , z , h, h∅ ) b (x , z ) WTp(4)

where α (x , z , h, h∅) is the soil water (h) and osmotic (h∅) stress response function (dimensionless), b (x , z ) is the normalized spatial root
water uptake distribution (L−2), W is the width of the soil surface associated with transpiration (L), and Tp is potential transpiration (LT-1).
A full description of how potential crop water requirements, potential
evaporation (Ep), and potential transpiration (Tp) are calculated is described in Karandish and Šimůnek (2016a).
It is assumed that potential root water uptake is reduced due to thewater stress when crops are supplied with an insuﬃcient amount ofirrigation water and due to the osmotic stress when crops are irrigatedwith saline water. Following van Genuchten (1987), the inﬂuence ofwater and salinity stresses were considered to be multiplicative (i.e.,α (x , z , h, h∅) = α (x , z , h) α (x , z , h∅) , so that diﬀerent stress response
functions could be used for the water and salinity stresses (Ramos et al.,2012). Karandish and Šimůnek (2016a, 2016b, 2017) described theroot water uptake reduction caused by the water stress (α (x , z , h) )using the piecewise linear model proposed by Feddes et al. (1978). The
salinity threshold (ECT) and slope function (Mass, 1990) was used tosimulate the root water uptake reduction due to the salinity stress.
70Agricultural Water Management 208 (2018) 67–82

F. Karandish, J. Šimůnek

Karandish and Šimůnek (2016ab, 2017).

2.4. Statistical analysis
The root mean square error (RMSE), the mean bias error (MBE), andthe model eﬃciency (EF) were calculated to provide a quantitative
assessment of the correspondence between predicted and observed data
as follows:

2.3. Scenario assessment
After calibrating and validating the HYDRUS models, diﬀerent
management scenarios were deﬁned to analyze the combined inﬂuenceof the N-management, water-saving strategies, and irrigation water
quality strategies on the economic and environmental measures in the
study area. Various scenarios were deﬁned using combinations of theratio of the applied irrigation water depth to the full irrigation water
depth (IL) (note that the full irrigation water depth was calculated
based on Eq. (1), the N-fertilizer application rate (NR), and the salinityof irrigation water (SR). All scenarios were deﬁned for two irrigation
strategies of DI and PRD. IL was increased in 10% steps from 0 to 100%of the full irrigation water depth, NR in 50 kg ha−1 intervals from 0 to
400 kg ha-1, and SR in 1 dS m-1 increments from 0 to 8 dS m-1. HYDRUS
was then run for all scenarios to estimate water and solute balancecomponents. Using simulated crop N uptake, the yield equation of
Karandish and Šimůnek (2017) was then applied to estimate crop yield
for all scenarios:

Yield = 0.0311 × (N uptake ) + 1.0654

n

ET
Yield

(11)

∑i = 1 (Oi − Pi )2n∑i = 1 (Oi − Oi )2(12)

where Pi and Oi are the predicted and observed data, respectively, n is
the number of observations, and Oi is the average of observed data.
3. Results and discussion
3.1. The performance of the numerical model(5)

Similarly as in other studies (e.g., Cote et al., 2003; Gärdenäs et al.,2005; Assouline et al., 2006; Hanson et al., 2006; Ajdary et al., 2007;
Crevoisier et al., 2008; Siyal and Skaggs, 2009; Mubarak, 2009; Li andLiu, 2011; Ramos et al., 2012; Tafteh and Sepaskhah, 2012; Phogat
et al., 2013), HYDRUS successfully simulated soil water and NO3−-N
dynamics in the study area (Karandish and Šimůnek, 2016a, 2016b,
2017). HYDRUS was further applied to simulate EC dynamics (Fig. 2)
using the soil hydraulic and solute transport parameters optimized by
Karandish and Šimůnek (2016a, 2016b, 2017) The agreement between
observed and simulated soil ECsw data during both growing seasons
was, in addition to the visual inspection (Fig. 2), quantitatively assessedusing the Root Mean Squared Error (RMSE) and the Mean Bias Error(MBE) statistics (Table 2). With respect to ECsw concentrations, theRMSE ranged from 0.5 to 1.06 dS m-1 for the 0–20 cm soil depth, from
0.43 to 0.90 dS m-1 for the 20–40 cm soil depth, from 0.17 to 0.58 dS m1
for the 40–60 cm soil depth, and from 0.14 to 0.53 dS m-1 for the
60–80 cm soil depth. While the MBE (0.09-0.92 dS m-1) between observed and simulated ECsw in diﬀerent soil layers was relatively small, it
was larger in the surface layer (MBE = 0.17-0.92), which can probably
be attributed to higher changes in the ECsw concentrations during thesimulation process (Karandish and Šimůnek, 2016a,b).
Fig. 2 compares temporal variations of simulated and observed ECsw
concentrations in diﬀerent soil layers for the FI treatment during the2010 cropping cycle so as to illustrate the capability of HYDRUS of
capturing the temporal trends of EC. Simulated EC concentrations
agreed well with observed values, with the EF ranging from 0.88-0.99
and 0.91-0.97 in 2010 and 2011, respectively. A close match between
simulated and observed ECsw concentrations, as well as their seasonal
trends, was also obtained in other studies for various soils and crops
under pressurized irrigation conditions (e.g., Ramos et al., 2011 and2012; Phogat et al., 2014; Zeng et al., 2014; Mguidiche et al., 2015).
Hence, the results of the quantitative assessment reported in Table 2
and the visual inspection of the results displayed in Fig. 2 indicate thatHYDRUS can be used to evaluate the hypothetical scenarios consideredin this study and discussed below.

(7)

where β is the leaching-runoﬀ fraction, S (kg ha−1 y−1) is the amountof N applied but not taken up by plants, Cmax and Cnat are the maximum
acceptable and natural N concentrations (kg m-3), respectively, and Y isthe crop yield (t ha−1y−1). Based on values suggested in Franke et al.
(2013), we set β = 0.44 for N. A maximum acceptable N concentrationof 50 mg L−1 (or 11.3 mg L−1) is adopted based on the EU Nitrates
Directive (Monteny, 2001). The natural concentration of N was considered to be zero.
The economic analysis was carried out based on the economic water(EWP, $ m−3) and land (ELP, $ ha-1) productivity indices, calculated asfollows (Aldaya et al., 2010):

EWP =

n
n

(6)

βS
(Cmax − Cnat ) Y(10)∑i = 1 (Oi − Pi )EF=1 −

where ET is the actual crop water consumption (m3 ha−1) during theentire cropping cycle and Yield is crop yield (t ha−1). The consumptive
WF is diﬀerent from the degradative grey WF, or the so-called grey WF(GWF), which represents the volume of water required to suﬃciently
dilute pollutants entering freshwater bodies. The GWF, which may beconsidered as an indicator of the pollution assimilation capacity(Chukalla et al., 2017), was calculated for the N fertilizer following the
Global Water Footprint Standard (Hoekstra et al., 2011) as the volume
of water per a ton of crop as follows:

GWF =

n
n

MBE=

where both crop Yield and N uptake (i.e., the seasonal amount of crop N
uptake) are presented in kg ha−1.
The water footprint (WF, m3t−1), introduced by Hoekstra (Hoekstra
et al., 2011), is considered to be a multi-dimensional measure of theamount of freshwater that is used directly by the producer or indirectlyby the consumer. The consumptive WF (m3t−1) of the crop production
includes both green and blue components, which refer to rainfall andirrigation water, respectively. The consumptive WF is calculated asfollows:

consumptive WF =∑i = 1 (Oi − Pi )2

RMSE=

3.2. Analysis of evaluated scenariosP
consumptive WF(8)

The results of HYDRUS simulations for evaluated scenarios are
discussed in the following sections with respect to both environmental
and economic factors, including the eﬀects on the crop water consumption, crop N uptake, crop yield, the consumptive WF, the degradative grey WF, and the economic land and water productivity.

(9)

ELP = P *Yield−1

where P is the producer price ($ ton ), which was set to 323.4 $ ton-1
according to the FAO statistics for Iran (FAO, 2015).
71Agricultural Water Management 208 (2018) 67–82

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Fig. 2. Observed and simulated ECsw at diﬀerent depths (top to bottom: 0–20 cm, 20–40 cm, 40–60 cm, and 60–80 cm) during the 2010 growing season for the FI
irrigation treatment at diﬀerent horizontal locations. DAS is the number of days after sowing. E1, E2, E3, E4, and E5 refer to the lateral positions of EC measurements(i.e., the ﬁrst dripper (Fig. 1), and 18.75, 37.5, 56.25, and 75 cm away from the ﬁrst dripper, respectively).

Table 2Model performance criteria (dS m−1) at diﬀerent soil depths for the 2010 and 2011 datasets. RMSE and MBE are the root mean squared and mean biased errors,
respectively.
Year

depth (cm)Treatment
FI

2010

2011

0-20
20-40
40-60
60-80
0-20
20-40
40-60
60-80

PRD75

PRD55

DI75

DI55

RMSE

MBE

RMSE

MBE

RMSE

MBE

RMSE

MBE

RMSE

MBE

0.74
0.63
0.47
0.36
0.58
0.49
0.37
0.28

0.28
0.23
0.18
0.13
0.23
0.19
0.15
0.11

1.01
0.85
0.55
0.34
0.54
0.43
0.28
0.17

0.92
0.74
0.48
0.30
0.37
0.30
0.19
0.12

0.74
0.56
0.17
0.14
0.58
0.43
0.17
0.14

0.37
0.28
0.15
0.12
0.46
0.34
0.15
0.12

0.85
0.66
0.43
0.39
1.06
0.90
0.58
0.53

0.25
0.21
0.14
0.13
0.64
0.54
0.35
0.32

0.50
0.44
0.39
0.27
0.70
0.61
0.53
0.37

0.17
0.15
0.13
0.09
0.28
0.24
0.21
0.15

72

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Fig. 3. The ratio of actual (Ta) and potential (Tp) transpiration for various combinations of irrigation levels (IL: from 100% [the ﬁrst column] to 0% [the last column]
in 10% increments) and irrigation salinities (SR: SR1 through SR10) under PRD (a) and DI (b) irrigation strategies.

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3.2.1. WF components
As introduced by Hoekstra (2002), WF (consumptive and degradative) is highly dependent on several components, which include
crop yield, crop water consumption, and solute residual in soil. As
mentioned by Chukalla et al. (2017), developing benchmark levels forvarious ﬁeld practice scenarios requires a deep knowledge on their effects on WF components. Hence, here we ﬁrst discussed the probableeﬀects of various scenarios on the consumptive and degradative WF
components and then, discussed their direct eﬀects on consumptive anddegradative WF.
3.2.1.1. Blue and green water consumption. Fig. 3 displays the ratio of
actual (Ta) to potential (Tp) crop transpiration for various combinationsof ILs and SRs. Regardless of the irrigation strategy (DI or PRD), the Ta/
Tp ratio tends to progressively decrease in response to the salinity
increase while only a minor variation in Ta/Tp was found when a highsalinity stress was imposed (i.e., 7 ≤ SR (dS m−1) ≤10). Moreover, ahigher Ta/Tp ratio for 40%≤IL≤70% and SR ≥ 7 dS m−1 indicates that
imposing a mild water stress may improve root water uptake whencrops are under a high salinity stress. Such results may be attributed to
the reduced salt loaded into the rooting zone under deﬁcit irrigation
with highly saline water (Yazdani et al., 2004). The low Ta/Tp variationin response to a salinity increase under a high water stress also
demonstrates the higher inﬂuence of the matric potential on the
reduction in root water uptake compared to the osmotic stress, which
was also conﬁrmed by Kaya et al. (2015).
Reductions in root water uptake due to water and salinity stresses
were a function of the adopted irrigation strategy. For 70%≤IL≤100%,
the Ta/Tp ratio under PRD was up to 15.5% higher than under DI, whilefor IL ≤ 60%, water and salinity stresses caused a 3.7–8.8% lower reduction in the Ta/Tp ratio under DI than under PRD. Earlier researchdemonstrated that the root system could partially compensate for theincreasingly limited availability of water at the dry side of the row
when crops are under PRD with a low to mild water stress (Kang et al.,
2002; Liu et al., 2006; Sepaskhah and Ahmadi, 2010). Nevertheless, therate of water supply was reported to be a prominent factor for sustaining the positive eﬀects of PRD (Liu et al., 2006; Karandish andShahnazari, 2016; Karandish and Šimůnek, 2016a, 2016b, 2017).
Karandish and Šimůnek (2016a) reported favorable crop evapotranspiration under PRD with a 25% reduction in the irrigation demand(PRD75) compared to DI75, while DI led to higher root water uptake
when the irrigation demand was reduced by 45% (DI55) compared to
PRD55.
3.2.1.2. N uptake and yield. Crop N uptake (NU) was aﬀected by ILs,
NRs, and SRs (Fig. 4). While higher NU was achieved in the scenarioswith lower SRs and higher NRs and ILs, the pattern of the increase was
diﬀerent among various scenarios. For all ILs and SRs, NU increased
considerably (by 1–235%) when NR increased from 0 to 200 kg ha−1
and only slightly (0.3–10%) when it increased from 200 to 250 kg ha−1.
A considerable reduction in the NU upward slope for NR ≥ 250 kg ha-1
may lead to considerable environmental deterioration since theimbalance between N supply and N demand leaves more N in the soil
after harvest, which may be subsequently leached out of the rooting
zone as a result of oﬀ season precipitation (Karandish et al., 2016).
Fig. 4 also shows that the range of NU variations is limited when crops
are irrigated with brackish water with high salinity, resulting in a highwater deﬁcit stress.
PRD seems to be a more viable agro-hydrological option for reducing a pollutant load into groundwater since it led to higher NU and,
consequently, lower N left in the soil after the harvest. Regardless ofNRs, NU under PRD was 2.7–17.3%, 0.7–3.4%, and 0.2–1.3% higher
than under DI for low (IL = 20%), moderate (IL = 50%), and high(IL = 80%) water stress conditions, respectively. This diﬀerence wasmore obvious for low to moderate salinity stresses while NU wasslightly higher under PRD than under DI under the high osmotic stressFig. 4. Crop N uptake (NU; kg/ha) for various combinations of N fertilizations(NR, kg/ha), salinity rates (SR, dS/cm), irrigation levels (IL; %; from 0% [top]to 100% [bottom]), and irrigation (PRD - left, DI - right) strategies.

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Fig. 5. The relation between relative maize grain yield (Ya/Yp) and salinity stress rates (SR) under various fertilization rates (NR, kg ha−1, from 100 kg ha−1 [top] to
400 kg ha−1 [bottom]) and irrigation levels (IL) for the PRD (left) and DI (right) irrigation strategies.

(SR ≥ 7 dS m−1), accounting for 3–6.6%, 0.7–1.1%, and 0.2-0.3% for
low, moderate, and high water stress conditions, respectively. A substantially better performance of PRD may be attributed to the higher
availability of N in the rooting zone, which may be caused by the frequent wetting and drying cycles (Nourbakhsh and Karimian Eghbal,
1997; Vale et al., 2007; Wang et al., 2010) or by the improved root
growth and the extension of the root zone to deeper soil layers (Fortet al., 1997; Sepaskhah and Kamgar-Haghighi, 1997; Liang et al., 1996;
Poni et al., 1992).
Fig. 5 relates the relative yield (Ya/Yp) to the salinity rate undervarious NRs and ILs. Although Ya/Yp decreased in response to any increase in the salinity stress, the pronounced Ya/Yp reduction (0.6–14%
for PRD and 0.6–55% for DI) occurred when SR increased from 5 to
8 dS m−1. A low to mild water stress under 5 ≤ SR ≤ 8 dS m−1 mayenhance the downward slope of Ya/Yp to a greater extent. Nevertheless,the amount of irrigation water could be more proﬁtably reduced when
the high salinity stress is imposed. When 8 < SR ≤ 10 dS m−1, Ya/Yp
ranges from 0.38 to 0.58 under PRD and from 0.28 to 0.52 under DI,
while Ya/Yp varied between 0.41–1 under PRD and between 0.29–1
under DI when 0 < SR ≤ 8 dS m−1.
The negative inﬂuence of irrigation with saline waters on maizegrain yield may be moderated under PRD since it led to 0.6–188%
higher yield compared to DI for various scenarios. Such results may beparticularly interesting when SR ≤ 4 dS m−1, while signiﬁcant improvements may not be observed when PRD crops are irrigated withhighly saline water. In addition, maize grain yields are considerably
higher (by 0.6–30.4%) under PRD than under DI for all NRs and SRs
when IL ≥ 70%, while no considerable diﬀerence was observed in PRDor DI yields for IL ≤ 60%. Previous research also demonstrated higher
yields under PRD compared to DI for non-saline conditions (Dry andLoveys, 1998); Kang and Zhang, 2004; Kirda et al., 2004; Tang et al.,2005; Shao et al., 2008; Wang et al., 2012; Karandish, 2016; Karandishand Šimůnek, 2016a), mainly due to better N nutrition and subsequent
higher duration of vegetation growth (Haverkort et al., 2003) and agreater photosynthesis rate (Varvel et al., 1997; Gianquinto et al.,2003). The results of our previous research also demonstrated a signiﬁcant inﬂuence of crop N nutrition on maize grain yield under PRD(Karandish and Šimůnek, 2017).
3.2.2. Consumptive and degradative grey WF
Fig. 6 shows the consumptive WFs of the crop production for different scenarios. When crops are fully irrigated (IL = 100%) with fresh75

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Fig. 6. The consumptive WF (m3t−1) related to the crop production for various PRD (top) and DI (down) irrigation scenarios. IL (%; 80, 50, 20%) is the irrigationlevel and NR (kg ha−1; 00, 200, 300 kg ha−1) is the N-application rate.

water (SR = 1 dS m−1), consumptive WFs vary within the range of 6561985 m3t−1 for PRD and 698-2080 m3t−1 for DI, with higher values forlower NRs. At a given NR, the consumptive WF is reduced by
0.08–15.1% under PRD and by 0.1–25.1% under DI when the waterdeﬁcit is induced. Moreover, a reduction of 0.8–25.5% and 0.6–47.0%
is obtained under PRD and DI, respectively, when crops are exposed tothe salt stress. The consumptive WF, which is deﬁned as ET divided byyield, takes into account both crop yield and the crop water demand.
While an increase in yield or a reduction in ET may directly increase theconsumptive WF, any changes in the driving factors of yield and ET may
aﬀect the consumptive WF indirectly. Crop yield is a result ofinteractions between plant genetic traits, soil properties, ﬁeld management, and climatic conditions (Karandish et al., 2014). Amongthese, ﬁeld management is reported to be of the highest importance(Chukalla et al., 2015). This may explain diﬀerent amounts of produced
yield with a unit volume of consumed water in various scenarios.
For all combinations of ILs and SRs, the N application rates of 0200 kg ha−1 caused a considerable reduction (up to 63%) in the consumptive WF due to a signiﬁcant yield improvement (Fig. 5), while aless signiﬁcant reduction (up to 17%) in the consumptive WF was observed when NRs increased above 200 kg ha−1, likely due to the stabilized yield at large N application rates (Fig. 5). Furthermore,76

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under higher water deﬁcits and salinity stresses, which may be attributed to the fact that under such conditions, the Ta reduction is likely to
be considerably higher compared to the corresponding yield reduction.
Fig. 4 also shows that for all scenarios, applied water in the PRD scenarios yielded 0.1–163.9% more economic values compared to the DI
scenarios.
While the EWP is an economic measure from the water viewpoint,
the economic land productivity (ELP, $/ha) may be more interesting forfarmers since it represents the economic value of the farm output per
hectare of the cultivated land. ELP values for various scenarios are illustrated in Fig. 9. Maize ELPs varied in the range of 463–1802 $ ha−1
and 395–1785 $ ha−1 for the PRD and DI scenarios, respectively. While
the ELP increased considerably when NRs increased from 0 to 200 kg
ha−1 (29–238% and 4–244% under the PRD and DI scenarios, respectively), only a slight increase in the ELP of 0.1–13.1% occurred for an
additional increase in NRs above 200 kg ha−1. When NR = 250 kg
ha−1, the ELP decreased considerably by 0.6–9.7% per a unit EC increase, except for non-irrigated crops, for which the ELP remained
unchanged in response to the salinity increase. The ELP is non-linearly
related to ILs for all combinations of NRs and SRs, indicating that thewater stress induced at low rates (0 ≤ IL≤70%) may lead to a more
economically beneﬁcial land use.
To ﬁnd the optimal scenarios for NR = 200 kg ha−1 (i.e., the mostbeneﬁcial threshold) from the viewpoint of both land and water economic returns, dimensionless EWP (dl.EWP) and ELP (dl.ELP) curves
were plotted together against SRs or ILs in Fig. 10. The dimensionless
EWP and ELP were calculated by dividing the absolute EWP or ELP
values by the maximum EWP or ELP across the whole range of SRs andILs at a given NR of 200 kg ha−1. Fig. 10 shows that for PRD, diﬀerent
combinations of 60%≤IL≤80% and 1 ≤ SR (dS m−1)≤4 may sustain
both land and water economics, while optimal results for DI may beachieved only when a lower degree of the water stress 70%≤IL≤90%
is imposed on plants.

improved N uptake under PRD aggravated the positive inﬂuence of anincreased N application rate within the range of 0-200 kg ha−1 on the
consumptive WF compared to DI. When NR = 200 kg ha−1, an increased salinity stress reduced the consumptive WF at an average rate of
2.1 (for IL = 10%) to 34.2 (for IL = 100%) m3 t−1 per a unit EC increase under PRD and at an average rate of 2 (for IL = 10%) to 40 (forIL = 100%) m3 t−1 per a unit EC increase under DI. These ﬁndings
result from a considerable reduction in the crop water consumption
under the salt stress. Although the ET reduction may be converted into
the yield reduction (Payero et al., 2006; Klocke et al., 2004; Stone,2003), the consumptive WF may grow with a declining slope underincreasing osmotic and matric water stresses when the rate of the ET
reduction is far above the corresponding yield reduction.
With the absence of the salt stress, consumptive WF values may
remain at their maxima when a low water stress is imposed (IL ≥ 70%)
because of the higher crop water consumption. An intermediate to highwater stress (IL < 70%) at SR = 1 dS m−1 may result in a quick reduction in the consumptive WF as a consequence of a pronounced decline in ET under low water availability conditions. When crops areunder a low to intermediate salt stress, an increased matric water stressresults in a steep linear reduction in the consumptive WF. The consumptive WF, however, exponentially decreases with IL when crops are
exposed to the high saline stress (SR > 8 dS m−1).
Fig. 7 presents the degradative grey WF (GWF) for various evaluatedscenarios. The GWF linearly responds to an increase in the N application rate or salinity. The smallest GWF can always be found for an N
application rate of zero or 50 kg ha−1, where yield is very low (recall
that yield is linearly related to N uptake). However, an increase in yieldunder higher N application rates comes at a price of a larger GWF due tohigher amounts of N left in the soil after harvest. In the absence of salt
or water stresses, the GWF may increase at average rates of 0.23 and0.31 m3 t−1 per a unit NR increase for PRD and DI, respectively. Whencrops are fully irrigated, inducing a salt stress may enhance the negativeeﬀect of large N application rates by increasing the GWF at an averagerate of 0.1-1.8 m3 t−1 per a unit EC. This increasing rate may be muchhigher when the irrigation water deﬁcit is also integrated into the salt
stress.
While higher GWFs were found for larger water deﬁcits, an increasing trend in the GWF as a consequence of a lower soil water
availability did not follow a linear equation. At NR = 200 kg ha−1,which seems to be an optimal NR with regards to both economic andenvironmental issues, inducing a water stress of up to 30% (IL ≥ 70%)
while crops are under a low to intermediate salt stress (SR ≤ 6 dS m−1)
results in a small increase in the GWF compared to full irrigation. The
GWF also increases when ILs < 70%, but with a slope 1.8–54 times
steeper. Nevertheless, GWFs tend to keep an increasing trend against an
increase in the water stress when crops are under high salt stress(SR ≥ 6 dS m−1).

3.2.4. Benchmarking WFs
To develop benchmark levels for the consumptive and degradative
WF within the study area, we need to ﬁnd the optimal agricultural
management practices. To ﬁnd the optimal scenarios for a given SR, we
ﬁrst classiﬁed the evaluated measures into two groups: environmental
measures including the apparent N-recovery eﬃciency (ARE) and theGWF, and economic measures including the EWP and the ELP.
Following Mosier et al. (2004), the ARE was calculated by dividing crop
N-uptake (kg ha−1) by the N application rate (kg ha−1). Four selectedindices (ARE, GWF, EWP, and ELP) were ﬁrst sorted from the worst to
the best, and then they were scored. For each index, the highest score
was considered to be the best value. Finally, the total score (TS) for eachscenario (i.e., TS is the sum of the scores of the indices) was calculated,
and then the optimal scenarios were determined based on the 10% and
25% highest TSs.
While there is an inevitable need for adjusting irrigation water andN-application rates when crops are irrigated with saline water, the results in Table 3 conﬁrm that there is not one optimal combination ofmanagement practices when both environmental and economic issues
are considered. In fact, the best management scenario highly depends
on what variable is optimized (Chukalla et al., 2017). While high Nfertilization rates applied without water and salt stresses may yieldhigher economic returns, such scenarios may cause environmental deterioration due to the low ARE and high GWF. On the other hand, theoptimal ARE and GWF may be achieved when less N is applied undermoderate water and salt stresses. Hence, we developed WF benchmark
levels through suggesting a range of optimal values for NRs and ILs at agiven SR instead of proposing a single optimal management scenario.
Table 3 shows the optimal agricultural practices by which thebenchmark levels for both consumptive and degradative grey WFs will
be achieved within the study area. Regardless of the irrigation strategy(PRD or DI), both IL and NR need to be considerably reduced when

3.2.3. Economic land and water productivity
Farmers may not adopt a new technology unless they are convincedthat they can achieve an adequate economic return (Paredes et al.,2014; Karandish, 2016). Hence, we incorporated the accounted WFs
into economic issue to calculate the economic water productivity (EWP,$ m−3) for various scenarios. The results are presented in Fig. 8. Under
full irrigation with fresh water, the maize production yielded economic
values of 0.16-0.50 $ m−3. Here, EWPs were 0.35, 0.44, 0.48, and 0.50
$ m−3 for NR = 100, 200, 300, and 400 kg ha-1, respectively, indicatingthat an increase in the N application rate above 200 kg ha-1 does not
signiﬁcantly improve the state of farm economics. Similar results were
found for other combinations of IL and SRs. When a low water stress is
imposed (70%≤IL≤100%), the EWP increases non-linearly in responseto an increase in SR for all NRs, with a steeper upward slope when
5 ≤ SR ≤ 10 dS m-1. When IL > 70%, the rising trend of the EWP
tends to follow a linear equation with R2 = 0.85-0.98. Such results
demonstrate the enormous potential for sustaining farm economics
77Agricultural Water Management 208 (2018) 67–82

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Fig. 7. The degradative grey WF (GWF; m3t−1) related to the crop production for various PRD (top) and DI (down) scenarios. IL (%; 80, 50, 20%) is the irrigationlevel and NR (kg ha−1; 00, 200, 300 kg ha−1) is the N-application rate.

4. Conclusions

crops are irrigated with moderately or highly saline waters (SR >5 dS m−1) while a moderate adjustment may be required when crops
are exposed to a milder salt stress (SR ≤ 5 dS m−1). Besides, higher Napplication rates and irrigation levels may be required when DI is applied compared to PRD, which in turn results from lower yield andwater productivity under DI (Dry and Loveys, 1998); Kang and Zhang,2004; Kirda et al., 2004; Tang et al., 2005; Shao et al., 2008; Wanget al., 2012; Karandish, 2016; Karandish and Šimůnek, 2016a). The
analysis of obtained results (the evaluation indices) for evaluated scenarios indicates that the N-fertilization rates of 100-200 kg ha−1 applied at a mild salinity stress (SR < 5 dS m−1) and the irrigation levelsof 60–90% under the PRD irrigation strategy may be the best management scenarios.

The subject of establishing sound agricultural practices in the arid
and semi-arid regions of the world presents many challenges, mainly
related to water scarcity which may require the use of low-quality
water and lead to subsequent environmental hazards. Hence, we conducted the ﬁrst explorative study based on the concept of the water
footprint to ﬁnd out the optimal combination of N application rates andwater application levels when crops are irrigated with saline water. The
results simulated using HYDRUS (2D/3D) revealed that an increase inboth yield and crop N uptake would level oﬀ at NR > 200 kg ha−1.
This is contrary to the viewpoint of many farmers who believe thathigher economic returns are achieved by higher NR. Additionally, N
78

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Fig. 8. The economic water productivity (EWP, $/m3) for various combinationsof N fertilizations (NR, kg ha−1), salinity rates (SR, dS/cm), irrigation levels (IL,%), and irrigation (PRD – left, DI - right) strategies.

Fig. 9. The economic land productivity (ELP, $/ha) for various combinations of
N fertilizations (NR, kg ha−1), salinity rates (SR, dS/cm), irrigation levels (IL,%), and irrigation (PRD – left, DI - right) strategies.

79Agricultural Water Management 208 (2018) 67–82

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Fig. 10. Dimensionless ELP (dl. ELP) and EWP (dl. EWP) for the PRD (left) and DI (right) irrigation strategies for various water deﬁcits (IL, %) (top) and salinity
stresses (SR, dS/cm) (bottom) at the N application rate (NR) of 200 kg ha−1.
Table 3The best 10% and 25% combinations of N-application rates (NR, kg ha−1) and irrigation levels (IL, %) for diﬀerent salinity rates (SR, dS m−1) under PRD and DI.
Irrigation strategy

PRD

DI

Salinity rate (SR)Low (SR ≤ 2)
Low to intermediate(2 < SR ≤ 5)
Intermediate to high(5 < SR ≤ 8)
High (SR > 8)
Low (SR ≤ 2)
Low to intermediate(2 < SR ≤ 5)
Intermediate to high(5 < SR ≤ 8)
High (SR > 8)

Optimal at the best 10%

Optimal at the best 25%

Optimal NR

Optimal IL

Optimal NR

Optimal IL

100-200
100-200

70-90
60-90

100-300
50-25050-10050-100100-15050-80

50-20050-9050-150100-200
100-15040-70
80-10070-9050-200100-200
50-20030-8060-10060-10050-10060-9050-15050-9050-10040-7050-15030-80applications above the rate of 200 kg ha-1 increased the risk of environmental deterioration as reﬂected by a linear increase in the GWF
while it did not signiﬁcantly improve the farm economics. Furthermore,
crops exposed to low-intermediate salt and water deﬁcit stresses performed better economically, which may indicate an enormous potential
for reducing crops water footprint as well as the pollutant load togroundwater. Our results demonstrated that the PRD scenarios produced better results than the DI scenarios, suggesting that a sustainableagriculture may be easier achieved under PRD. It can be concluded that
regional-speciﬁc benchmark levels for the total consumptive and degradative grey WFs that were developed while considering a widerange of ﬁeld management strategies can provide enough information
to farmers to achieve higher income, save scarce blue water resources,
and prevent agricultural-related environmental hazards by adjusting
the fertilization and irrigation management as a function of the irrigation water salinity. Reliable estimations of these indices based on the
results calculated by the HYDRUS (2D/3D) model (previously calibrated and validated against corresponding experimental data) indicatethat this approach may represent an alternative approach to the laborand time-consuming ﬁeld investigations.

Acknowledgement
Fatemeh Karandish would like to thank the University of Zabol forﬁnancing the project (Grant Number: UOZ_GR_9517_6).
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