Agricultural Water Management 193 (2017) 174–190

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

Two-dimensional modeling of nitrogen and water dynamics forvarious N-managed water-saving irrigation strategies using HYDRUS
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 15 February 2017
Received in revised form 20 July 2017
Accepted 23 July 2017
Available online 24 August 2017
Keywords:
Maize grain yield
Numerical simulation
NO3 −-N transport
N uptake
Partial root-zone dryinga b s t r a c t
Nitrate losses are the dominant cause of the non-point source pollution under agricultural ﬁelds. In thisstudy, the HYDRUS-2D model was ﬁrst calibrated and validated using data collected during a two-year
ﬁeld investigation in a drip-irrigated maize ﬁeld and then applied to evaluate the inﬂuence of 176 different
N-managed water-saving irrigation scenarios on water and N dynamics and maize grain yield. Various
scenarios were deﬁned by combining 11 irrigation levels (IL = 0–100% with a 10% interval), 8 N fertilization rates (NR = 0–400 kg ha−1 with a 50 kg ha−1 interval) and two water-saving irrigation strategies:
deﬁcit irrigation (DI) and partial root-zone drying (PRD). Reliable estimates of soil NO3 − -N concentrations (RMSE = 0.39–10.9 mg l−1 and MBE = −8.9–8.4 mg l−1 ), crop N uptake (RMSE = 3.9–8.9 kg ha−1 andMBE = −5.3–6.25 kg ha−1 ), and soil water contents (RMSE = 2.3–5.11 mm and MBE = 1.63–4.93 mm) were
provided by HYDRUS-2D. Based on the simulated results, the fertigation strategy with NR = 200 kg ha−1 isan optimum strategy. For the higher fertigation rates (NR ≥ 250 kg ha−1 ), the NO3 − -N leaching out of thesurface layers (0–20 cm) increased by 0.1–183% while N uptake was enhanced by only 0.3–15%. On theother hand, reducing NR below this level would have resulted in severe economic losses. A 30% reduction
in IL at NR = 200 kg ha−1 shows an enormous potential in lowering N leaching below different soil layers(12–99%) while reducing crop N uptake by only 5.4%. In addition, higher crop yield by 0.2–20.2% canbe expected under PRD since crop N uptake is enhanced by more water available in the surface layers.
While on the one hand, PRD ensures environmentally safer fertilizer applications, on the other hand, theeconomic objectives are met more easily under PRD than under DI. Additionally, it could be concluded
that the HYDRUS-2D model, instead of labor- and time-consuming and expensive ﬁeld investigations,
could be reliably used for determining the optimal scenarios under both the DI and PRD strategies.
© 2017 Published by Elsevier B.V.

1. Introduction
Nitrogen (N) is well known to be an essential crop nutrient, which highly affects crop growth and yield (Jia et a1., 2014;
Wienhold et al., 1995). As a result, farmers are usually encouraged to apply N-fertilizers especially to high-yield crops, such asmaize, which require large amounts of N for achieving their optimal yield. Unfortunately, the lack of comprehensive management
guidelines causes most farmers to apply N fertilizers based on theirown experience and without considering subsequences for theenvironment (Wei et al., 2009). Excessive unmanaged applicationsof N-fertilizers above optimal amounts and without considering∗ Corresponding author.
E-mail addresses: F.Karandish@uoz.ac.ir, Karandish h@yahoo.com(F. Karandish).
http://dx.doi.org/10.1016/j.agwat.2017.07.023
0378-3774/© 2017 Published by Elsevier B.V.

the crops N requirements lead to the pollution of water resources
as a byproduct of N leaching out of the root zone, which is well
documented for irrigated agricultural lands in many parts of theworld (Zhu et al., 2005; Thompson et al., 2007; Dudley et al., 2008;
Burow et al., 2010; Dahan et al., 2014; Karandish et al., 2017). A low
recovery of N fertilizers by crops may be more hazardous in humid
regions where the N residual in the soil could be freely leachedto the groundwater due to off-season precipitations. In irrigatedagriculture, water draining below the root-zone due to excessive irrigation is the dominant factor controlling NO3 − -N leaching(Tamini and Mermoud, 2002).
Organic N, ammonia (NH4 ) and nitrate (NO3 ) are the common
forms of N in soils. Among these forms, NO3 is the largest contributor to groundwater pollution (Li et al., 2007; Hu et al., 2009;
Wang et al., 2010) due to its high mobility and leachability. A close
correlation was reported between the amount of applied waterand NO3 leached below the root zone as well as between the N

F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

fertilization rate and the leaching fraction under sprinkler irrigation (Gheysari et al., 2009). Therefore, using an efﬁcient irrigation
scheduling as well as an optimal management of N-fertilizer applications may reduce NO3 losses from irrigated lands. In this regard,
various water-saving irrigation strategies may be highly beneﬁcial.
Previous research focused mainly on ﬁnding an optimal combination of the N-fertilization rate and the amount of applied water
under a regular deﬁcit irrigation (DI) strategy (e.g., Haverkort et al.,
2003; Daudén et al., 2004; Alva et al., 2006; Barton et al., 2006;
Hutton et al., 2008; Wei et al., 2009; Gheysari et al., 2009; Jia eta., 2014). However, while using DI when less irrigation water isapplied than potential crop evapotranspiration may indeed reduce
deep percolation and NO3 losses, it may also reduce crop yield andcrop N uptake (Hu et al., 2009; Sepaskhah and Ahmadi, 2010; Wanget al., 2012). Under such circumstances, it can be expected that
farmers will be reluctant to adopt DI since their economic interests
will be threatened due to signiﬁcant yield losses.
During the past few decades, a water-saving irrigation strategy called partial root-zone drying (PRD), in which one-half of theroot zone is irrigated while the other half is allowed to dry out,
has been developed (Dry and Loveys, 1998). The irrigated and drysides are periodically switched (Karandish and Šimůnek, 2016a). A
numerous studies have been carried out on the possible effects ofthis irrigation strategy on quantitative and qualitative propertiesof different crops, most of which agreed that there is a signiﬁcant
increase in the water use efﬁciency under PRD (Kang and Zhang,2004; Kirda et al., 2005; Li et al., 2007; Wang et al., 2009; Huet al., 2009; Wang et al., 2012; Karandish and Šimůnek, 2016a). The
results of these studies demonstrated that PRD can greatly induce
the initiation and growth of secondary roots, which improve bothwater and nutrient uptake. Kirda et al. (2005) reported that PRD
resulted in a better N-fertilizer recovery with less mineral N residual left in the soil after maize harvest. Despite focusing on the effectsof PRD on yield and crop physiological response, and to a lesser
extent on the crop N recovery, these studies have never attempted
to ﬁnd out the optimal combination of N fertilization rates andapplied water levels under PRD with respect to both economic andenvironmental factors.
These goals can be achieved using laborious and timeconsuming, and therefore expensive, ﬁeld investigations. On the
one hand, it is still challenging to directly quantify water and N
losses at the ﬁeld scale because of uncertainties in measured deep
percolation and NO3 concentrations in the leachate, even underwell-managed conditions (Phogat et al., 2013). On the other hand,
the inability of carrying out such research in the ﬁeld at a low cost
represents a restriction on the direct measurement of water andnutrient dynamics under different management scenarios. Modeling could be an alternative approach for identifying optimal
conditions for PRD, especially when it may be economically or technically impossible to carry out the project in the ﬁeld (Li and Liu,2011).
Among different available models, HYDRUS-2D (Šimůnek et al.,2008, 2016) has been extensively and successfully used to simulate water and nutrient transport in soils even for complicatedproblems. Having both the ﬂexibility to accommodate different
types of boundary conditions for water ﬂow and solute transport
calculations and the capability to simultaneously consider root
uptake of water and nutrients compared to other similar models (Li et al., 2015), the HYDRUS model can simulate soil waterand solute dynamics in the ﬁelds under different water and solute
management practices. The HYDRUS use is also greatly facilitatedby its sophisticated, graphical, user-friendly interface. There are
numerous studies in which the possible inﬂuence of soil physical properties (e.g., soil hydraulic properties, soil layering) as wellas the effects of different ﬁeld managements (e.g., the rate ofdripper discharge, irrigation frequency, and quality, and timing

175of nutrient applications) on water and nutrient dynamics havebeen investigated using HYDRUS-2D (Cote et al., 2003; Gärdenäs
et al., 2005; Assouline et al., 2006; Hanson et al., 2006; Ajdary
et al., 2007; Patel and Rajput, 2008; Šimůnek and Hopmans, 2009;
Phogat et al., 2013; Li and Liu, 2011; Ramos et al., 2012, Karandish
and Šimůnek, 2016a,b). All these studies concluded that HYDRUS2D is an effective tool to carry out such investigations and thatthe simulation results are reliable. Therefore, using experimental
results from a two-year ﬁeld investigation, the following objectives
will be addressed in this manuscript: (i) to calibrate and validate
HYDRUS-2D for simulating soil water and N dynamics under different water-saving irrigation strategies and (ii) to ﬁnd the optimal
combination of the N-fertilizer rate and the applied irrigation waterlevel for the DI and PRD conditions with respect to both economicand environmental factors.

2. Materials and methods
2.1. Experimental setup
During 2009–2010, a two-year ﬁeld investigation was carriedout in an 825 m2 (15 × 55 m) maize ﬁeld at the Sari Agricultural
Sciences and Natural Resources University, Sari, Iran. According
to daily weather data, 2010 was a warmer year, with 0.1–7.8 ◦ Chigher daily mean air temperatures (T) than in 2011. Vapor pressure deﬁcits and net radiation were also higher in 2010 compared
to 2011. Although there was precipitation of 8 and 40 mm during the growing seasons of 2010 and 2011, respectively, no rainfall
occurred 55 and 45 days after sowing (DAS), respectively. Soil texture at 0–20 cm and 20–100 cm soil depths was sandy clay loam
and clay loam, respectively. The surface layer (0–20 cm) contained
1.22% of organic matter and had an initial EC of 1.64 dS m-1. The
measured soil pH varied in the range of 6.8–7.5 and 6.7–7.3 in 2010
and 2011. Our daily observations showed that the groundwater
depth was 122 cm at the beginning of the cropping cycle of 2010
and it continued falling to a depth of 211 cm at harvest. In 2011,
the groundwater depth ﬂuctuated in the range of 115–221 cm.
Soil properties are summarized in Table 1. For each soil layer (i.e.,every 20 cm soil depths), we collected a total of 45 soil samples,
3 soil samples per each replicate of a treatment. Hence, we had
225 soil samples for the 5 soil layers. While we had soil properties for all 5 soil layers and for all treatment, we report in Table 1
only the average values. For each treatment, less than 5% deviations
were observed among measured soil properties of each soil layer.
Hence, such results conﬁrmed the uniformity of the soil in the ﬁeld
both vertically and horizontally. Soil hydraulic properties were also
determined for different soil layers. More detailed description ofsoil properties can be found in Karandish and Šimůnek (2016a,b).
The ﬁeld experiment consisted of ﬁve irrigation treatments that
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, whichwere carried out in a complete block design. Initial soil samples
were collected every 20 cm down to a depth of 80 cm for all treatments to measure retention curves and initial NO3 and the total N
content. Thereafter, a surface drip irrigation system was installed,
in which drip lines were placed on the soil surface 75 cm apart,with emitters 20 cm apart, and an emitter discharge rate of 2 L hr−1
(Fig. 1a).
Five 100 cm long TDR tubes (Trime FM; IMKO; Germany) were
installed for each treatment to continuously monitor the soil watercontent (SWC) (i.e., 25 TDR tubes were installed in the study area;
5 probes * 5 treatments) (Fig. 1). The accuracy of SWCs measured
using TDRs was compared with SWCs simultaneous measured
using the gravimetric method. With an index of agreement of 95%, a

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F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

Table 1
Soil properties at the experimental site.
Depth (cm)Soil texture

Sand (%)

Silt (%)

Clay (%)

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

49
40
30
37
36

22
25
36
30
28

27
35
34
33
34

30
32
32
32
32

15
14
14
14
14

1.40
1.38
1.35
1.37
1.37

Fig 1. Schematic of the ﬁeld experimental site. Horizontal locations of laterals, drippers, and plants in the experimental ﬁeld (a) and locations of TDR tubes in the maize rootzone (b).

F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

good agreement was found between SWCs data measured by these
two methods, conﬁrming the acceptability of the measured SWCs
data by TDRs. TDR tubes were then used at least two times a day
to measure SWCs at depths of every 5 cm during both growing seasons. Moreover, the movement of the wetting front was observed
for at least 10 irrigation events for each treatment during eachgrowing season (Karandish and Šimůnek, 2016a).
On May 26 in both 2010 and 2011, maize single-cross hybrid
704 was sown 5 cm deep, with 75 cm × 20 cm crop row and crop
spacing, between and parallel to the drip lines. With an irrigationinterval of two days, 53 and 55 irrigation events were applied using
the surface drip irrigation system in the 2010 and 2011 growing
seasons, respectively. For each irrigation event, the NO3 − -N concentration in irrigation water was measured.
Irrigation in the FI treatment was scheduled to reﬁll the porespace in the rooting zone to the ﬁeld capacity. The irrigation
requirement (in mm) for the FI treatment was calculated using Eq.
(1) for each irrigation event:[In ]FI =

m 
i=1

 

FCi − (BIi )FI Di(1)

where [In ]FI is the net irrigation depth (mm) of the nth irrigation
event for the FI treatment,  FCi is the volumetric SWC at ﬁeld capacity (FC, %) of the ith soil layer, (BIi )FI is the average volumetric SWCof the ith soil layer (%) before irrigation in the FI treatment, Di is
the soil layer thickness (50 mm), i is the soil layer, and m refers
to the number of soil layers down to a speciﬁc soil depth (m = 16),
for which [In ]FI is calculated. ( BIi )FI was measured using TDR tubes
before each irrigation event.
The same amount of irrigation water was applied in all treatments (i.e., FI, DI, and PRD treatments) during the ﬁrst 55 days aftersowing (DAS) in 2010 and during 45 DAS in 2011. During the stressperiod (i.e., during 55–107 DAS in 2010 and during 45–110 DAS in2011), the water-saving treatments were scheduled to receive 55%(PRD55 and DI55 ) and 75% (PRD75 and DI75 ) of the FI treatment’s
irrigation amount at each irrigation event. While in the FI and DI
treatments, both drip lines were operated simultaneously, in the
PRD treatments during the PRD period, to ensure partial root-zone
drying, just one of the drip lines was operated while the other was
not during each irrigation event. Only half of the root zone was thus
irrigated during the PRD period, while irrigation shifted between
the two sides of the plants each week.
Prior to applying irrigation treatments (i.e., on 55 DAS in 2010
and 45 DAS in 2011), and once a week during the stress period (i.e.,on 60, 66, 72, 78, 84, 90, 96, 102, and 106 DAS in 2010 and 52, 58,
64, 70, 76, 82, 88, 94, 100, 106, and 110 DAS in 2011), soil samples
were collected for each treatment vertically every 20 cm to a depthof 80 cm and at ﬁve equal horizontal distances between two drip
lines. At each sampling date, three soil samples for each 20-cm soil
depth of each replicate of one treatment were collected at each of
the ﬁve horizontal distances, leading to a total of 225 soil samples
for one treatment. Soil samples were analyzed for total nitrogen(TN) (the semi-micro Kjeldhal method (Bremner and Mulvaney,1982)) and the NO3 − -N concentration (with a spectrophotometer(DR/2500, Hack Co.) using a cadmium reduction method (APHA,
1992)). At the same dates, plants were also harvested (less often in2010) to determine their total nitrogen uptake (the sum of measured N for stem, leaves, and yield) and a leaf area index. At eachsampling time, three plants per plot were harvested. The ovendried (at 70 ◦ C) weight of each part of a plant (root, stem, leaves,and yield) was determined. The semi-micro Kjeldahl method wasapplied to determine N uptake. A laboratory leaf area meter (Deltat Devices Ltd.) was used to measure the entire leaf area, which wasconverted to LAI by dividing it with the corresponding soil surface covered by the plants. Temporal variations of the horizontal
and vertical root distributions were also determined at 55, 62, 69,

177

76, and 83 DAP in 2010 and at 40, 56, 63, 70, 77, and 90 DAP in2011 for all treatments. While the maximum rooting depths weredifferent in different treatments, they did not extend below the
80 cm soil depth (Karandish and Šimůnek, 2016a). At ﬁnal harvest,
the above-discussed parameters, as well as crop yield, were determined for each treatment. Plants were harvested on September 9,2010 (107 DAS) and on September 12, 2011 (110 DAS). A list of
performed agricultural activities is summarized in Table 2. Moredetailed description of all measurements during the ﬁeld investigation is presented in Karandish and Šimůnek (2016a,b).
2.2. HYDRUS-2D simulation
Soil water and nitrogen dynamics were simulated using
HYDRUS-2D (Šimůnek et al., 2008, 2016), which is a powerful software for simulating the transient, two-dimensional movement ofwater and nutrients in soils for a wide range of boundary conditions
and soil heterogeneities. The Richards equation for water ﬂow isused in HYDRUS-2D:

∂
∂
=
∂t
∂x



∂h
Kx
∂x



∂
+
∂z



∂h
Kz
∂z



−

∂Kz
− WU (h, x, z)∂z(2)

where ␪ is the volumetric SWC (L3 L−3 ), K is the unsaturated
hydraulic conductivity 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), and WU(h, x, z) denotes root water
uptake (T−1 ). The hydraulic conductivity K was assumed to be thesame in different directions (i.e., Kx = Kz ). The following equation isapplied for quantifying WU:
WU (h, x, z) =  (h) RDF (x, z) WTpot(3)

where (h) is the soil water stress response function (dimensionless) of Feddes et al. (1978), RDF is the normalized root water uptake
distribution [L−2 ], Tpot is the potential transpiration rate [LT−1 ], andW is the width of the soil surface [L] associated with the transpiration process. Details about how water uptake is quantiﬁed in thisresearch can be found in Karandish and Šimůnek (2016a,b).
The advective-dispersive transport in the liquid phase and the
diffusive transport in the gaseous phase is considered in the solutetransport equation that is used in HYDRUS-2D. Only the solutetransport in the liquid phase is considered in this study, which issuitable for a conservative solute such as NO3 − -N:

∂c
=
∂t



−

∂
∂x



Dxx

∂qx c ∂qz c
+
∂x
∂z

∂c
∂c
+ Dxz
∂x
∂z



− Sc



+

∂
∂z



Dzz

∂c
∂c
+ Dzx
∂z
∂x



(4)

where c is the NO3 − -N concentration of the solute in the liquid
phase (ML−3 ), qx and qz are the components of the volumetricﬂux density (LT−1 ), Dxx , Dzz , and Dxz are the components of thedispersion tensor (L2 T−1 ) (Bear, 1972), Sc is a sink term, which
generally includes the local NO3 − -N uptake (through a passive process), mineralization, microbial immobilization, and denitriﬁcation(ML−3 T−1 ). The ﬁrst term on the right side of (4) is the solute ﬂux
due to dispersion, the second term is the solute ﬂux due to convection with ﬂowing water and the third term is nutrient uptake byroots, which is a function of space and time and is computed fromwater uptake values as follows:
Sc = Cs .WU (x, z, h)(5)where Cs is the NO3 − -N concentration taken up by roots (ML−3 ),which is a function of the soil NO3 − -N concentration and the
maximum NO3 − -N concentration of root nitrogen uptake (Cs,max )(Ajdary et al., 2007; Tafteh and Sepaskhah, 2012).

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F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

Table 2Agricultural activities during the growing seasons of 2010 and 2011.
Date

Agricultural activities and fertilization

Remarks

May 26 in 2010 and 2011

Planting and fertilization

June 12, 2010 and June 5, 2011 (18 and 11 DASs,
respectively)
July 14, 2010 and July 4, 2011 (50 and 40 DAS, respectively)

Fertilization

150 kg ha−1 triple superphosphate was banded in croprows prior to sowing date
65 kg ha−1 urea and 50 kg ha−1 potassium sulfate wasapplied via irrigation (fertigation).
135 kg ha−1 urea and 100 kg ha−1 potassium sulfate wasapplied via irrigation (fertigation).

July 19, 2010 and July 9, 2011 (55 and 45 DAS, respectively)
September 9, 2010 and September 12, 2011 (107 and 110
DAS, respectively)

Fertilization
Onset of PRD treatments
HarvestMineralization gains and denitriﬁcation losses were neglected inthis study, similarly as done in many other studies (e.g., Ajdary et al.,2007; Wang et al., 2010; Tafteh and Sepaskhah, 2012). Mineralization (N supply) and immobilization (N removal from the mineral Npool) (M&I) are highly dependent on the soil organic matter (OM)
and the amount of clay in a soil. Soils with less than 3% of OM in theupper horizon, as in our study, are classiﬁed as mineral soils (Huang
et al., 2009), in which the M&I processes are often limited due to thelack of OMs (Wijanarko, 2015; Deenik, 2006; Herrmann, 2003; Liet al., 2003). Moreover, mineralization tends to be greater in coarsetextured soils with the low clay content and smaller as the soil clay
content increases. Finally, textured soils with the high clay content,
as in our current research, are often abundant in micropores, inwhich organic matter can ﬁnd a physical protection from microbial
decomposition and, consequently, from M&I (Deenik, 2006).
Denitriﬁcation losses were neglected due to the following reasons. First, the process of denitriﬁcation occurs predominantly inthe saturated soil horizons (Laher and Avnimelech, 1980). Maintaining soil water contents in the optimal range for plant growth,i.e., under unsaturated aerobic conditions, stimulates microbialactivity and, consequently, enhances the nitriﬁcation rate (Valeet al., 2007). Similarly as Ajdary et al. (2007), who applied HYDRUS2D for simulating the soil water and N dynamics in the onion ﬁeld
under surface drip irrigation (in similar conditions as in the current
research), we assumed that denitriﬁcation loss around the emitters may be ignored since an aerobic conditions are not reachedhere. Second, although denitriﬁcation is reported as a notable feature of alkaline soils with high pH values (Harris, 1978; Lundquist
et al., 1999; Deenik, 2006; Vale et al., 2007; Tafteh and Sepaskhah,2012), our temporal measurements showed low soil pH during both
cropping cycles. The measured pH varied in the range of 6.8–7.5
and 6.7–7.3 in 2010 and 2011, respectively. Third, soil temperatureplays a key role in controlling the denitriﬁcation rate. While denitriﬁcation occurs at high soil temperatures, nitriﬁcation is reported totake place mainly in the temperature range of 30–40 ◦ C. Our previous research demonstrated that both DI and PRD strategies control
soil temperatures at lower values and within the considered range(Karandish and Shahnazari, 2016).
NO3 − -N concentrations tend to be signiﬁcantly higher thanNH4 + -N concentrations under favorable conditions for the nitriﬁcation process (Sepaskhah and Youseﬁ, 2007). Marschner (1995)
reported that average annual NH4 + -N concentrations are often
10–1000 times lower than NO3 − -N concentrations in well-aerated
agricultural soils. Our weekly measurements (simultaneously measured soil NH4 + -N and NO3 − -N concentrations) demonstrated that
NO3 − -N concentrations were 38–352 times higher than NH4 + N concentrations. Since NO3 − -N concentrations are signiﬁcantly
higher than NH4 + -N concentrations, only the transport of NO3 − -N
is considered in our study and the model assumes that the input of
fertilizers in the form of urea and NH4 + -N is instantaneously nitriﬁed into NO3 − -N. A similar assumption has been used by otherresearchers (e.g., Ajdary et al., 2007; Tafteh and Sepaskhah, 2012),
considering the fact that nitriﬁcation is fast compared to other pro-

cess and it takes only a few days (Havlin et al., 2006) to nitrify urea
and NH4 + -N into NO3 − -N.
In fact, the nitriﬁcation process is supposed to be stimulated
under low pH, low temperature, inorganic, and unsaturated conditions in ﬁnely textured soils with the high clay content, all of whichcause higher NO3 − -N availability for root uptake. Nitrate has beenreported to be the most common N-source used by agricultural
crops while ammonium plays a major role in crop N uptake forforest species and paddy rice, especially for low-pH saturated soils
with high temperature (Tischner, 2000). Many other researchers
indicated that agricultural crops often increase nitrate availability
by stimulating nitriﬁcation process since they prefer to uptake N in
the form of NO3 − -N rather than NH4 + -N (Lata et al., 2000; Hawkes
et al., 2005). Additionally, the higher mobility of NO3 − -N leads to amore rapid diffusion to roots and thus to an easier access for plants
to it rather than to NH4 + -N (Boudsocq et al., 2012). Our results
also demonstrated that N uptake could be accurately estimated by
HYDRUS-2D (RMSE=3.96-8.96 kg ha−1 ) when the maximum NO3 − N concentration of root nitrogen uptake (i.e., Cs,max in Eq. (5)) is wellcalibrated and validated.
Simulating complex processes of the rhizosphere dynamics
of water and nutrient uptake requires differentiating between
passive and active N uptake (Šimůnek and Hopmans, 2009), N mineralization, and denitriﬁcation (Bar-Yosef, 1999). To avoid suchcomplications, we assumed that N uptake by plant roots is strictly
passive, similarly as done in many other studies (e.g., Hanson et al.,2006; Ajdary et al., 2007; Tafteh and Sepaskhah, 2012). This simpliﬁcation leads to the assumption that other mechanisms thanthose implicitly considered either do not occur or can be neglected(Hanson et al., 2006). N uptake can then be calculated by simply
multiplying the soil N concentration and root water uptake (Eq.
(5)).
The two-dimensional transport domain was deﬁned using a
rectangle 75 cm wide (between two neighboring emitters on eitherside of one plant) and 80 cm deep (i.e., the maximum observed
rooting depth). Since the emitter spacing (20 cm) along drip lines
was relatively small, drip lines were considered to be inﬁnite line
sources. A non-uniform ﬁnite element mesh (FEM) with ﬁnite element sizes gradually increasing with distance from the emitters’line was generated by HYDRUS-2D. In order to accurately model
large spatial gradients in soil water pressure heads caused by inﬁltrating water, a high nodal density is required in the immediate
vicinity of the emitters (Kandelous et al., 2011). Since no signiﬁcant differences were observed between soil hydraulic properties
of soil layers below the 20 cm depth, only two soil horizons withdifferent soil hydraulic properties for the 0–20 cm and 20–80 cm
soil depths were deﬁned in the HYDRUS model.
Initial conditions were deﬁned using the observed water andNO3 − -N contents in different soil layers within the ﬂow domain.
The time-variable and atmospheric boundary conditions were
speciﬁed at both emitters and at the soil surface, respectively, torepresent drip irrigation and to apply precipitation, evaporation,and transpiration ﬂuxes, respectively. A free drainage boundary

F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

condition was applied along the bottom boundary, allowing fordownward drainage. Solute was applied with irrigation water anda third-type Cauchy boundary condition was used to describe theconcentration ﬂux at the top boundary. In the case of drip fertigation, the solute ﬂux is the product of water inﬁltration and thedissolved nitrate concentration. Cumulative nitrate leaching below
the root zone (i.e., the lower boundary of the ﬂow domain) is controlled by the nitrate concentration and the corresponding waterﬂux at the bottom boundary. All other remaining boundaries wereassigned a no-ﬂow boundary condition.
The HYDRUS-2D model was then calibrated and validated forsoil hydraulic and solute transport properties using the experimental data. The inverse solution option of the HYDRUS-2D model wasused to optimize the solute transport parameters, i.e., the longitudinal (DL , L) and transverse (DT , L) dispersivities, as well as thesoil hydraulic parameters, i.e., the saturated hydraulic conductivity (Ks ), the saturated soil water content (␪s ), and the residual soil
water content (␪r ). Soil water contents and soil NO3 − -N concentrations measured in 2010 were used to calibrate the model. The
adjusted values of optimized parameters were then used to validate the model using the data collected in 2011. The initial value ofthe longitudinal dispersivity (DL ) was set to be one tenth of the soil
depth and the initial value of the transverse dispersivity (DT ) was
set to be one tenth of DL (Ramos et al., 2012). Moreover, Cs,max was
obtained by comparing the simulated and observed values of crop
N uptake. The molecular diffusion coefﬁcient was always set equal
to zero since molecular diffusion in soils can be usually neglected(Radcliffe and Šimůnek, 2010). Detailed information about the calibration and validation process for the soil hydraulic properties canbe found in Karandish and Šimůnek (2016a).
2.3. Scenario analysis
Once HYDRUS-2D was calibrated and validated, it was applied
to simulate the water and N dynamics under different management
scenarios. In this regard, the two water-saving irrigation strategies
DI and PRD were selected. In each scenario, the amount of applied
irrigation water (IL) was considered to change from 0 to 100% of
FI, with a 10% interval. For each considered irrigation water level,
we also considered different N-fertilization rates (NR) between 0
and 400 kg ha−1 , with a 50 kg ha−1 interval. A total of 176 scenarios
was deﬁned by combining 11 ILs, 8 NRs, and two water-saving irrigation strategies. Each scenario is denoted ISIL-NR , where IS refers
to the irrigation strategy (DI or PRD), IL denotes the irrigation level(from 0% to 100%), and NR denotes the fertilization rate (from 0
to 400 kg ha−1 ). For each scenario (i.e., for each combination of anirrigation level and a fertigation rate), a relative change in crop
evapotranspiration (rc=ETca /ETcp ), seasonal crop N uptake (NU),
and water and NO3 − -N leached below the 20, 40, 60, and 80 cm
depths were simulated using HYDRUS-2D. The simulated results
were then used to assess the integrated inﬂuence of different irrigation schemes and N managements on the N dynamics.
3. Results and discussions
3.1. Efﬁciency of HYDRUS-2D
The results reported in Karandish and Šimůnek (2016a,b) indicated that HYDRUS-2D was able to capture temporal and spatial
trends in soil water contents (amounts of water in a soil layer) with
the Model Efﬁciency (EF) ranging from 0.893 to 0.998 and with the
Root Mean Square Error (RMSE) values ranging from 2.3 to 5.11 mm.
Small differences between observed and HYDRUS-2D simulated
soil water contents were likely due to the fact that the model
reported point values (Phogat et al., 2014), whereas measured val-179

ues were averaged over a certain soil volume, in which, due toirrigation, the gradient of soil water contents may not be linear
and can be quite high, especially around the emitters (Mguidiche
et al., 2015). Karandish and Šimůnek (2016a) demonstrated thatHYDRUS-2D can be used to develop water-saving irrigation strategies by proposing both optimal irrigation scheduling and an optimal
reduction of irrigation water without dramatically decreasing crop
yield. The high accuracy of HYDRUS-2D is mainly due to the use of adeterministic approach for simulating soil water movement basedon 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; Crevoisier et al., 2008; Siyal
and Skaggs, 2009; Mubarak, 2009; Tafteh and Sepaskhah, 2012) aswell as under pressurized irrigation systems (Assouline et al., 2006;
Ramos et al., 2012; Phoghat et al., 2013).
Using the soil hydraulic parameters optimized by Karandish andŠimůnek (2016a,b), HYDRUS-2D was further calibrated for solutetransport based on data collected during the 2010 growing season(i.e., measured NO3 − -N concentrations in different soil layers aswell as crop N uptake). The calibrated values of DL and DT were
5.0 cm and 0.42 cm for the 0–20 cm soil depth, respectively, and
4.8 cm and 0.51 cm for the 20–80 cm soil depth, respectively. A wide
range of dispersivities, due to their dependence on soil propertiesand dimensional scales, has been reported in the literature. Dispersivities DL and DT have been set to 0.1 cm and 0.01 cm by Cote et al.
(2003), to 5 cm and 0.5 cm by Gardenas et al. (2005) and Hanson
et al. (2006), to 0.3 cm and 0.03 cm by Ajdary et al. (2007), to 5 cm
and 0.5 cm by Phogat et al. (2013), and to 20 cm and 2 cm by Wanget al. (2014), respectively. Additionally, the longitudinal dispersivity DL has been set to 12.2-25.8 cm by Ramos et al. (2012), and to
2 cm by Tafteh and Sepaskhah. (2012).
The agreement between observed and simulated soil watercontent and N data during the calibration period was quantitatively assessed using the RMSE and the Mean Bias Error (MBE)
statistics (Table 3), in addition to the visual inspection (Fig. 2).
With respect to NO3 − -N concentrations, the RMSE values during the calibration process ranged from 4.2–10.9 mg l−1 for the
0–20 cm soil depth, from 3.6–8.0 mg l−1 for the 20–40 cm soil
depth, from 1.2–9.7 mg l−1 for the 40–60 cm soil depth, and from
0.38–0.55 mg l−1 for the 60–80 cm soil depth. Regardless of thetreatment, lower deviations between measured and modeled
NO3 − -N were observed for the 60–80 cm soil layer, which could
probably be attributed to lower changes in the NO3 − -N concentrations at greater depths during the calibration process.
Higher variations in NO3 − -N concentrations in the surface layers under drip irrigation were detected by Alva et al. (2006) and
Hutton et al. (2008). This was conﬁrmed in this study in whichhigher deviations in NO3 − -N were observed in the surface layers (0–40 cm soil depth), where NO3 − -N variations were higher
due to irrigation, nitrate leaching, and/or N uptake. Phogat et al.
(2014) also reported that the mean absolute error (MAE) at the
25 cm soil depth was higher (MAE=21.3 mg l−1 ) than at deeper soil
depths (MAE=8.8–10.3 mg l−1 ) when simulating the NO3 − -N distribution over a 150 cm soil proﬁle with HYDRUS-2D under ﬁeld
cropped condition. A similar match between simulated and measured NO3 − -N distributions was reported in other studies as well
(Tournebize et al., 2012; Ramos et al., 2012; Ajdary et al., 2007).
Deviations between observed and simulated data may be due tothe fact that the model assumed a homogeneous soil environment
while the ﬁeld may be far more heterogeneous and anisotropic.
In addition, only the two-dimensional movement of NO3 − -N was
considered by the model while more complex nitrate processes
were not taken into account (Phogat et al., 2014). Numerous factors affect the agreement between observed and simulated soil
water contents and solute concentrations in the soil under a pres-180

F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

Table 3Model performance criteria at different soil depths for the calibration (2010) and validation (2011) datasets. RMSE and MBE are the root mean squared and mean biased
errors, respectively, and SW is the amount of water in a soil layer.
Year

ParameterNO3 − -N (mg l−1 )2010
SW (mm)N uptake (kg ha−1 )NO3 − -N (mg l-1)
2011
SW (mm)N uptake (kg ha−1 )Soil Depth (cm)

FI

PRD75

PRD55

DI75

DI55

RMSE

MBE

EF

RMSE

MBE

EF

RMSE

MBE

EF

RMSE

MBE

EF

RMSE

MBE

EF

0–20
20–40
40–60
60–80
0–20
20–40
40–60
60–80
–

6.17
7.95
3.79
0.38
0.8
0.85
0.78
0.28
8.96

2.31
4.91
−0.23
0.05
1.1
1.2
0.85
0.48
−5.32

0.86
0.85
0.96
0.98
0.93
0.90
0.94
0.99
0.96

4.20
4.56
5.70
0.55
0.73
0.73
0.64
3.69
7.52

1.40
3.01
−3.21
0.16
0.93
0.91
0.87
10
−4.85

0.95
0.94
0.95
0.98
0.94
0.97
0.98
0.87
0.96

4.60
3.72
1.22
0.55
0.83
0.79
0.81
5.84
8.21

1.19
0.56
1.11
0.16
0.83
0.85
0.69
5.5
−4.85

0.94
0.96
0.97
0.98
0.92
0.95
0.93
0.83
0.93

10.89
5.98
9.73
0.55
0.43
0.44
0.51
1.9
8.15

−8.97
3.99
8.40
0.16
0.53
0.58
0.7
3.0
−4.25

0.81
0.96
0.95
0.94
0.99
0.99
0.98
0.89
0.94

6.24
3.59
1.67
0.55
0.71
0.70
0.60
2.50
6.13

3.11
0.84
1.50
0.16
0.81
0.92
0.70
3.50
−2.43

0.89
0.96
0.97
0.98
0.97
0.97
0.95
0.89
0.96

0–20
20–40
40–60
60–80
0–20
20–40
40–60
60–80
–

4.82
5.66
6.61
2.51
1.18
0.38
0.48
0.41
3.96

1.90
−0.26
−2.57
2.25
0.89
1.2
0.8
0.75
1.86

0.95
0.93
0.88
0.99
0.88
0.99
0.96
0.96
0.99

5.83
7.73
2.62
2.86
0.82
0.78
0.8
0.87
7.58

2.32
5.13
0.61
2.63
1.4
0.95
1.2
0.97
2.27

0.93
0.85
0.98
0.97
0.90
0.93
0.90
0.89
0.97

3.33
4.72
1.44
2.85
0.35
0.72
0.6
0.51
7.97

−1.96
−1.04
1.05
2.62
0.45
0.82
0.7
0.65
6.25

0.96
0.94
0.99
0.98
0.99
0.94
0.95
0.95
0.91

4.55
3.69
1.07
2.85
1.18
0.38
0.48
0.41
6.82

−3.09
−0.30
−0.43
2.62
2.5
0.58
0.68
0.52
5.13

0.94
0.95
0.99
0.98
0.88
0.99
0.97
0.98
0.94

4.84
4.53
1.90
2.85
2.1
0.48
0.43
0.63
6.89

−3.81
−0.99
1.50
2.62
4.2
0.55
0.63
0.85
2.65

0.91
0.90
0.99
0.98
0.84
0.97
0.97
0.95
0.97

Fig. 2. Observed and simulated NO3 − -N 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)).

F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

surized drip irrigation system (Ramos et al., 2012). These factors
may explain simulation errors related to NO3 − -N concentrationsin the current study as well. Nevertheless, low values of RMSEs
and MBEs for all treatments (RMSE=0.38–10.89 mg l−1 and MBE=8.97–8.40 mg l−1 ) indicate that the model represents well NO3 − -Nconcentrations in different soil layers during the calibration process. Moreover, the RMSE values ranged from 6.13 (for the DI55
treatment) to 8.96 ka ha−1 (for the FI treatment) among various
irrigation treatments when considering crop N uptake, indicating agood agreement between simulated and measured N uptake during
the 2010 growing season. Model performance criteria for different locations with different horizontal distances from the dripper
also indicate the high accuracy of HYDRUS-2D when modeling soil
nitrogen dynamics on a two-dimensional scale.
HYDRUS-2D was then validated using data collected during the2011 growing season. Fig. 2 compares the temporal variations of
the simulated and observed NO3 − -N concentrations in different soil
layers for various irrigation treatments during the cropping cyclesof 2010 (the calibration dataset) and 2011 (the validation dataset)to illustrate the capability of HYDRUS-2D of capturing the temporal
trends of NO3 − -N. Generally, the simulated NO3 − -N concentrations
agreed well with the observed values, with the RMSE ranging from
1.07–7.73 mg l−1 and the MBE ranging from −3.81–5.13 mg l−1 . A
close match between simulated and observed NO3 − -N concentrations, as well as their seasonal trends, was also obtained in otherstudies for various soils and crops under pressurized irrigation conditions (Cote et al., 2003; Gärdenäs et al., 2005; Hanson et al., 2006;
Ajdary et al., 2007; Šimůnek and Hopmans, 2009; Phogat et al.,2013; Li and Liu, 2011; Ramos et al., 2012). Moreover, a comparison between simulated and measured values of N uptake shown in
Fig. 3 indicates that HYDRUS-2D can be successfully used to predict
temporal variations of N uptake for both deﬁcit irrigation and partial root-zone drying strategies, as well as for full irrigation during
both calibration (2010) and validation (2011) periods. The results of
the quantitative assessment reported in Tables 3 and 4 agrees wellwith the visual inspection of the results displayed in Figs. 2 and 3
for both soil NO3 − -N concentrations and N uptake, respectively. A
close match of both water contents and solute concentrations indicates that HYDRUS-2D can be successfully used for predicting theresults of hypothetical scenarios considered in this current study
and discussed below.
3.2. Scenario analysis
The results of the HYDRUS simulations for the deﬁned scenarios are discussed in the following sections with respect toboth environmental and economic factors, including the effects oncrop evapotranspiration, deep percolation, crop N uptake, NO3 − -Nleaching, soil N residual, and crop yield.
3.2.1. Water dynamics
Fig. 4 displays the ratio of actual (ETa ) and potential (ETp ) crop
evapotranspiration, irrigation (In), as well as water moved below
the 20 (DP-20) and 40 cm (DP-40) soil depths for various irrigation scenarios. For example, for 70% ≤ IL ≤ 100%, the ETa /ETp ratio
under PRD was 10–13% higher than under DI, reﬂecting a considerable increase in the soil water extraction capacity by roots
under PRD. A better use of soil water for IL ≥ 70% under PRD is also
obvious when temporal variations of DP below the surface layer(0–40 cm) is considered. While the irrigation volume at one dripper line was increased by 40, 60, 80, and 100% in the PRD70-NR ,PRD80-NR, PRD90-NR, and PRD100-NR scenarios, respectively, the otherdripper line was alternatively not used at all. Therefore, higher DP
can be expected under these scenarios since half of the root zone
receives more irrigation water than the corresponding side under
the FI treatment. However, simulated water ﬂuxes out of the 20 cm

181

and 40 cm soil depths indicate that DP-20 and DP-40 for the PRD
scenarios under IL ≥ 70% were considerably lower than those forthe corresponding FI and DI scenarios. DP-20 for DI was 13–33%
of the total applied water volume during the stress period while it
was only 11–30% for PRD. In addition, DP-40 for DI was 1.5–3.3 timehigher than for PRD when IL ≥ 70%. This result demonstrated thatthe soil water content at the wetted side of the row of the PRD plants
was depleted more rapidly than at the same side of the control (FI)plants, indicating that the root system can partially compensate forthe increasingly limited water availability at the dry side of the row(Kang et al., 2002).
These results are in agreement with the ﬁndings of other studies. For example, Sepaskhah and Ahmadi (2010) reported that theroot system can partially compensate for a decreasing water availability on the non-irrigated side of PRD due to an increase in theroot hydraulic conductivity. Liu et al. (2006) reported that soil
water depletion was higher under PRD than under other deﬁcit
irrigation strategies due to a higher root hydraulic conductivity.
Karandish and Šimůnek (2016a) reported that higher ETc under
PRD75 , despite a lower mean BAW (i.e., the relative Before-irrigation
Available soil Water) during the stress period, may indicate that
there is a lower available soil water threshold, below which actual
ET in other water-saving treatments is reduced compared 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 for the older species. Nevertheless, thesimulated results indicate that the reduction in the irrigation level
under the PRD scenarios is a key factor for maintaining ETa at afavorable level. Fig. 4 shows that for IL < 70%, no considerable differences were observed between the PRD and DI strategies regarding
the ETa /ETc ratio while both DP-20 and DP-40 under the PRD scenarios were higher than under the DI scenarios. Previous studies havealso stressed the importance of the reduction in the irrigation levelfor sustaining the positive effects of PRD (Liu et al., 2006; Karandish
and Shahnazari, 2016; Karandish and Šimůnek, 2016a,b).
3.2.2. N uptake
N uptake was affected by both ILs and NRs (Fig. 5). Regardless of
the irrigation strategy (DI or PRD) and the irrigation level (0–100%),
crop N uptake increased in response to an increase in the application of N fertilizer from 0 to 200 kg ha−1 (i.e., NR ≤ 200 kg ha−1 ).
An increase in NR from 200 kg ha−1 to 250 kg ha−1 caused only aslight increase of 0.27–5.9% in crop N uptake, while there was no
considerable increase in crop N uptake when NR increased from250 kg ha−1 to 400 kg ha−1 for all ILs. In other words, crop N uptake
is limited when the soil NO3 − -N concentration increases above
Cs,max (Eq. (5)). Therefore, an additional application of N fertilizer
beyond 200 kg ha−1 may lead to a negative environmental impact
since more N will be left in the soil at ﬁnal harvest. In addition,
while increasing IL beyond 70% did not produce a corresponding
increase in crop N uptake for all NRs, increasing IL from 0 to 70%
had a positive effect on crop N uptake due to enhanced root water
uptake. Such results may be attributed to the lower N availability inthe surface layer as a result of increased N leaching under enhanced
IL (Figs. 5 and 6). These results are in agreement with the ﬁndings ofGheysari et al. (2009) who reported the negative effect of increasing
irrigation levels for different amounts of N fertilization rates.
Different water-saving irrigation strategies (DI and PRD) did
not perform similarly under various scenarios. Fig. 5 shows thatfor NR ≥ 150 kg ha−1 , N uptake under the DI scenarios did not considerably increase for IL ≥ 60%, while it continued growing for IL
up to IL=70% under the PRD scenarios. Moreover, when IL ≥ 70%
and regardless of NRs, N uptake under PRD was considerably
higher than under DI, indicating an increased N use efﬁciency
under the PRD scenarios. This difference was more obvious for
NR ≥ 150 kg ha−1 . For example, when NR=100 kg ha−1 , N uptake

182

F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

Fig. 3. Scatter plots between observed and HYDRUS-simulated N uptake (NU) during 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)).

F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

183

Table 4Model performance criteria at different locations with different horizontal distances from the dripper for the calibration (2010) and validation (2011) datasets. RMSE and
MBE are the root mean squared and mean biased errors, respectively.
Growing season

Treatment

Positiona
A

B
b

C

D

E

RMSE

MBE

RMSE

MBE

RMSE

MBE

RMSE

MBE

RMSE

MBE

2010

FI
PRD75
PRD55
DI75
DI55

4.75
3.80
3.75
7.28
3.12

1.96
2.03
1.11
5.78
1.96

4.62
3.95
3.73
6.75
2.85

1.69
2.61
1.64
5.64
1.68

3.69
3.89
3.12
5.22
2.65

1.70
1.36
0.36
−2.35
1.43

4.55
3.83
3.65
6.92
3.10

1.49
1.52
1.43
5.43
1.41

4.80
3.91
4.18
7.10
3.25

1.82
2.12
1.55
5.95
1.72

2011

FI
PRD75
PRD55
DI75
DI55

4.95
5.20
3.32
3.22
4.12

2.23
2.72
2.12
2.66
2.50

4.32
4.75
3.20
3.00
3.75

−1.57
2.47
−1.67
−1.57
−1.45

4.12
4.60
2.95
2.80
3.13

−1.31
2.44
−1.41
−1.34
−1.42

4.65
4.68
3.35
3.11
3.45

−1.38
2.32
−1.65
−1.41
−2.10

5.12
4.95
3.80
3.60
3.68

2.40
3.12
2.08
2.58
2.65

a
b

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.
RMSE is the root mean square error and MBE is the mean bias error. Units for both RMSE and MBE are “mg l−1 ”.

Fig. 4. The ratio of actual (ETa ) and potential (ETp ) crop evapotranspiration (ETa /ETp , the left axis), and irrigation and water moved below the 20 (DP-20) and 40 cm (DP-40)
soil depths (the right axis) for various irrigation scenarios.

under PRD for IL=70, 80, 90, and 100% was 3.9, 7.6, 13.6, and 13.6%
higher, respectively than the corresponding values under the DI
scenarios. For NR=200 kg ha−1 , N uptake under PRD for IL=70, 80,
90, and 100% was 17.4, 19.3, 25, and 26.1% higher, respectively,
than the corresponded values under the DI scenarios. Improved N
uptake under PRD may be better understood when considering N
uptake from the surface soil layers, especially from the 0–20 cm soil
depth. For a speciﬁc NR, the amount of N taken up by roots from the
0–20 cm soil depth under the PRD scenarios was 5.9–31.2% higher
than under the DI scenarios when IL ≥ 70%. In addition, 5.6–12.8%
more N was taken up by roots from the 0–20 cm soil depth under
the PRD scenarios when IL=60% although total N uptake from the
0–80 cm soil depth under PRD was slightly higher than under DI;

accounting for 0.5–5.3%. It can also be seen from Fig. 5 that fora speciﬁc NR, N uptake from the 0–20 cm soil layer considerably
decreased under DI when IL > 50% while this downward slope wassmaller under PRD. The trend in N uptake from the 20–40 cm depth
was the same as from the 0–80 cm soil depths.
Overall, our results demonstrated that for IL ≥ 70%, PRD performed substantially better than DI for all NRs with respect toseasonal crop N uptake. Increased N uptake under the PRD scenarios could be ascribed to an increase in available N in the soil.
It is plausible to state that more root water uptake under the PRD
scenarios for IL ≥ 70% led to a considerable reduction in DP below
the surface soil layer (a 0–40 cm soil depth), which consequently
led to a signiﬁcant reduction in NO3 − -N leaching below the sur-184

F. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

Fig. 5. Crop N uptake from the entire rooting zone and from the 0–20 cm and 20–40 cm soil depths for the DI (left) and PRD (right) scenarios. NU is N uptake (kg/ha), N rateis the N fertilization (kg/ha), and IL is irrigation level.

face layers (Figs. 4 and 6). A reduction in N leaching resulted in
more available N in the 0–40 cm soil depth, which subsequently
caused more N uptake under the PRD scenarios for IL ≥ 70%. This isin agreement with ﬁndings of other researchers who believed that
more frequent wetting and drying cycles under PRD would increase
the N availability and, consequently, N uptake (Nourbakhsh and
Karimian Eghbal., 1997; Vale et al., 2007; Wang et al., 2010). Someother researchers also observed that the root systems extend todeeper layers under PRD, which induces the initiation and growthof secondary roots, which improves the ability of the plant to absorb
both water and nutrients (Fort et al., 1997; Sepaskhah and KamgarHaghighi, 1997; Liang et al., 1996; Poni et al., 1992).
Despite the lower movement of both water and NO3 − N below
the surface soil layers when IL is decreased to 70% (Fig. 6), plant
N uptake under DI was considerably lower than under PRD when
70 ≤ IL ≤ 100 (Fig. 5). Hu et al. (2009) reported that both mass ﬂow

and diffusion rates were signiﬁcantly reduced under water deﬁcit
irrigations, which decreased nitrogen availability and its transport
to the roots. Water deﬁcits under DI were additionally reported
to signiﬁcantly limit root growth (Sepaskhah and Ahmadi, 2010).
These results are in agreement with several studies that reported asigniﬁcant reduction in plant N uptake under DI (Shahnazari et al.,2008; Hu et al., 2009; Wang et al., 2012).
3.2.3. NO3 − -N leaching
Temporal variations of NO3 − -N leaching below the 20, 40, 60,
and 80 cm soil depths were simulated for various scenarios (datanot shown). The HYDRUS-simulated results showed that higher
ILs along with higher NRs resulted in the higher NO3 − -N content
in the top soil layers during the early maize growth stages while
the NO3 − -N content in the deeper soil layers increased graduallyas the season progressed. Deep percolation and NO3 − -N leachingF. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

185

Fig. 6. The total N lost due to leaching of NO3 − -N below the 20 and 40 cm soil depths under the DI (left) and PRD (right) scenarios. NL is NO3 − -N leaching (kg/ha), N rate is
the N fertilization (kg/ha), and IL is irrigation level.

were always observed immediately after an irrigation event. For all
scenarios, most NO3 − -N leaching below the rooting zone (at the
0–80 cm soil depth) occurred before 63 DAS, while NO3 − -N leaching below the 20 or 40 cm continued untill the end of the simulation
period. NO3 − -N leaching out of the root zone accounted for only asmall part of the total applied N and was correlated with deep percolation. Nevertheless, NO3 − -N leaching out of the 0–20 cm soil
depth, which is the most important soil layer regarding crop N
uptake, had the highest contribution to the leaching fraction, ranging from 0.02–35.3% of the total N applied for DI and 0.04-36.4% for
PRD. Chuan et al. (2010) also indicated that the soil depth is a key
factor determining the amount of N losses under a certain IL andNR.
Lower NO3 − -N leaching at the 80 cm soil depth may have been aconsequence of improved irrigation management by using a pressurized drip irrigation system under which the water use efﬁciency
is improved due to lower deep percolation and evaporation losses(Phoghat et al., 2013; Wang et al., 2014). Barton and Colmer (2006)
also showed that the N use efﬁciency is strongly inﬂuenced by
the irrigation efﬁciency. Similarly, lower deep percolation of bothwater and NO3 − -N was also observed by Tarkalson et al. (2006)
when an adequate irrigation scheduling technique was used. Such
results ensure environmentally safer applications of fertilizer compared to other irrigation systems (Mmolawa and Or, 2003).
N leaching from the surface layer is important because the sufﬁciently high concentrations of N in the top soil layers enable
continued uptake by roots (Phogat et al., 2013). Therefore, seasonal
NO3 − -N leaching from the 0–20 and 0–40 cm soil depths were simulated for various scenarios and the results are presented in Fig. 6.
It is obvious that as more water and N were applied, more water
moved below the 20 and 40 cm soil depths occurred, eventually
increasing NO3 − -N leaching. This is consistent with the ﬁndings of

Jia et al. (2014) who reported that NO3 − -N leaching was signiﬁcantly affected by both irrigation and N levels as well as by the typeof fertilizer and by the mutual interactions between these three factors. Other studies also concluded that the application of high levelsof irrigation and N fertilizers result in higher NO3 − -N leaching fromthe soil, greatly exceeding the crop N requirement and thus leading to the lower fertilizer use efﬁciency (Daudén and Quilez, 2004;
Gasser et al., 2002; Mosier et al., 2002; Kumar et al., 1993).
It is evident from Fig. 6 that both water movement and NO3 − -Nleaching could be reduced by adopting an appropriate irrigationand fertilization management. It is worth noting that a reduction in NR is a suitable agro-hydrological strategy. For example,
Kurtzman et al. (2013) reported that under different environmental conditions a 25% reduction in the application of the N fertilizer
resulted in a 50% lower nitrate ﬂux to the groundwater. Nevertheless, the appropriate NR should be selected with caution since
plant N uptake is highly dependent on the N availability in the soil.
Therefore, we further analyzed possible consequences of various
considered scenarios on both N leaching (Fig. 6) and plant N uptake(Fig. 5). For IL=100%, reducing NR from 400 to 250 kg ha−1 will considerably diminish NO3 − -N leaching below the 20 and 40 cm soil
depths by 12.4–56.4% and 12.3–46.1%, respectively (Fig. 6), while
only slightly reducing crop N uptake by 0.2–8.7% in (Fig. 5). However, lower NO3 − -N leaching below 20 cm for PRD were up to 13.3%
higher than for DI. Although N uptake was reduced by only 0.3–5.9%,
lowering NR from 250 kg ha−1 to 200 kg ha−1 produced savings of
19.7–24.6% and 19.6–28.5% of NO3 − -N in the 0–20 and 0–40 cm
soil layers, respectively. However, when the NR reduction is larger(i.e., when NR ≤ 150 kg ha−1 ), the negative effects on N uptake willovershadow the positive consequences with respect to N leachingfrom the surface layers. As shown in Fig. 6, while an additional
25% reduction in NR (i.e., NR=150 kg ha−1 at IL=100%) results in

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Fig. 7. Residual NO3 − -N at 0–80, 0–20 and 20–40 cm soil depths under the DI (left) and PRD (right) scenarios. NR is NO3 − -N residual in soil (kg/ha), N rate is the N fertilization(kg/ha), and IL is irrigation level.

a pronounced decrease in NO3 − -N leaching below depths of 20(24.5% for DI and 31.5% for PRD) and 40 cm (24.4% for DI and 38.6%
for PRD), the corresponding reduction in plant N uptake is muchhigher, 15–22% compared to NR=200 kg ha−1 (Fig. 5). When NR isreduced even more by 50% (NR=100 kg ha−1 at IL=100%) and 75%(NR=50 kg ha−1 at IL=100%), plant N uptake is reduced by 28.8–50%
and 37.4–70%, respectively, compared to NR=200 kg ha−1 . There arevery small NO3 − -N leaching from the surface layer for these low
NR scenarios. Such results suggest that reducing NRs is not a viableoption. A signiﬁcant reduction in plant N uptake would adversely
affect plant growth and yield, resulting in negative impacts on thesustainability of the expensive irrigation system.
It is worth noting that when NR=200–250 kg ha−1 , decreasing
IL seems to be a better proposition to sustain plant N uptake.
A 30% reduction in IL (i.e., lowering IL from 100% to 70%) at
NR=250 kg ha−1 shows an enormous potential for reducing N leaching below the 20 (45.7% and 48.7% for DI and PRD, respectively) and40 cm (99.5% and 99.7% for DI and PRD, respectively) soil depths

while plant N uptake remains at a favorable rate (i.e., about 7% lessthan when plants are fully irrigated). Similarly, plant N uptake isslightly reduced (i.e., by less than 6%) when IL is lowered to 70%
at NR=200 kg ha−1 . Nevertheless, reducing IL more will adversely
affect plant N uptake although it may produce a desirable control
on NO3 − -N leaching out of the 20 and 40 cm soil depths. For example, lowering IL to 50% at NR=200 kg ha−1 produced a pronounced
reduction in NO3 − -N leaching by 84–99.8% below the surface layers, but it also resulted in a considerable decrease in plant N uptake
by up to 32.3% compared to full irrigation (IL=100%), which is due
to a signiﬁcant reduction in root water uptake under the soil water
stress. The mismatch between NR and plant N uptake ultimately
increases when IL is lower than 50%. Such results suggest that theexcessive water and N leaching from the surface layer and maintaining N uptake at a favorable rate may be controlled by modifying
both irrigation and fertilization scheduling.
Moreover, Fig. 6 well conﬁrms that when IL ≥ 70%, NO3 − -Nleaching to deeper soil layers are lower under PRD compared toF. Karandish, J. Šimůnek / Agricultural Water Management 193 (2017) 174–190

187

those under DI while plant N uptake is considerably higher. This
result is consistent with our ﬁndings for DP and plant N uptake
under PRD. In other words, PRD may ensure environmentally safer
fertilizer applications compared to DI. While higher root water
uptake and less DP below the surface layers reduces the downward movement of NO3 − -N, a considerable reduction in NO3 − -Nleaching causes more N to be available in the surface layer for plat
N uptake.
3.2.4. N residual
The HYDRUS-2D simulation results showed that the storage ofNO3 − -N in different soil layers varied greatly among different scenarios (Fig. 7). For the 0–80 cm soil depth, the lower amounts ofirrigation combined with the higher amounts of fertilizer resulted,
as expected, in the higher mass of NO3 − -N left in the root zone after
ﬁnal harvest. The higher amount of NO3 − -N remaining in the soil
indicates that the plant was not able to take up all nitrogen added
through fertilization and that nitrogen will thus build up in the soil
over time. A higher level of residual NO3 − -N in the soil at the end of
the growing season is unwelcomed since it could be easily leached
by offseason rainfall and potential pollute water body. It could also
ﬂow into stream and rivers and pollute water resources which may
be used for drinking or irrigation. These results are in agreementwith the ﬁndings of Barlow et al. (2009) and Correll et al. (2010)
who reported high NO3 − concentrations in drainage water underdrip and furrow fertigation irrigation systems.
For a speciﬁc NR, the NO3 − -N content in the deeper soil layers
was higher when higher IL was applied since more water inﬁltrated
downwards after irrigation events. Similarly, the higher amount ofNR (i.e., NR ≥ 200 kg ha−1 ) for a speciﬁc IL led to similar results. This
is in agreement with the ﬁndings of Jia et al. (2014) who suggested
that the application of a large amount of N fertilizer and irrigation
led to the enrichment of deeper soil layers by NO3 − -N and to muchgreater overall N losses due to leaching. Nevertheless, the PRD scenarios retained less N in the soil at ﬁnal harvest, suggesting that thevulnerability to offseason N losses may be reduced if the irrigation
strategy is modiﬁed. In agreement with the results of hypothetical scenarios, our ﬁeld data also revealed that of all water-saving
treatments (PRD75 , PRD55 , DI75 , and DI55 ), the PRD75 treatment had
the lowest residual nitrogen in the root zone at ﬁnal harvest (datanot shown). This is in accordance with the ﬁndings that the PRD75
plants absorbed more N compared to the DI and PRD55 plants.
3.2.5. Grain yield
From the farmers’ point of view, grain yield (Y) is a key factor for adopting a new technology since they expect to achieve anadequate economic return (Paredes et al., 2014; Karandish, 2016).
Therefore, based on the observed data, a linear equation was developed to describe the relationship between the amount of N uptake
and maize grain yield (an index of agreement=85%) (Fig. 8). Whileagronomist may focus on ﬁnding the relationship between GY andfertilization rate (NR), such relationship may be of high interest
when only fertilizer management scenarios are analyzed. Irrigationwater depth (IL) may also affect GY other that NR, as is demonstrated by other researchers. Hence, the relation between GY andNR may not be sufﬁcient when analyzing the combined inﬂuenceof both ILs and NRs. N uptake, which is demonstrated to signiﬁcantly affect GY (Lifen et al., 2016), is highly depended on both ILs
and NRs (Gheysari et al., 2009). Hence, we extracted the relationship between GY and NU rather than between GY and NR in our
research.
Increased N uptake increases grain yield, which was also
reported by others (Shahnazari et al., 2008; Karandish andShahnazari, 2016). As conﬁrmed by our results (Fig. 5) and thosereported by the other researchers (Van Dijk and Brouwer, 1998;
Montemurro et al., 2006; Gheysari et al., 2009), NU may not increase

Fig. 8. The linear relationship between maize grain yield and crop N uptake.

after a special amount of NR. As a consequent result, the relationship between GY and NR tend not to be linear when NR exceeds the
maximum rate since GY is signiﬁcantly correlated with NU (Biswas
and Ma, 2016; Gheysari et al., 2009). Nevertheless, linear relationship may be found when considering this relationship between NU
and GY (Sheoran et al., 2016) rather than between NR and GY
The yield equation was then used to calculate maize grain yieldfor different scenarios and the results are presented in Fig. 9 for the
DI and PRD scenarios. Regardless of the irrigation strategy (i.e., PRD
or DI), increased IL and NR led to higher yield, which is in agreementwith ﬁndings of Jia et al. (2014) who reported that maize grain yield
was affected by both the amount of irrigation and the N applicationlevel. The highest yield of 7.5 t ha−1 was obtained for IL=100% andNR=400 kg ha−1 under PRD, which was about 20% higher than forthe same conditions under DI (6.3 t ha−1 ). Nevertheless, there was
not a considerable increase in grain yield when IL varied between
70 and 100% or when NR varied from 200 to 400 kg ha−1 under both
PRD and DI.
When IL ≥ 70%, maize grain yield under PRD is considerably
higher (by 0.2–20.2%) than under DI for all NRs, while for IL ≤ 60% a
no considerable difference was observed between yields obtained
for the PRD or DI scenarios. These results are in agreement with our
2-years ﬁeld data (Karandish and Šimůnek, 2016a), for which thestatistical analysis revealed that the water stress led to a signiﬁcantreduction in Y for the PRD55 , DI75 , and DI55 treatments comparedto the FI treatment, while it caused no signiﬁcant reduction in Y
for the PRD75 treatment. On the other hand, increased NR above
200 kg ha−1 did not affect considerably crop yield under the PRD
or DI scenarios, although yield was higher for the PRD than DI scenarios. Such results indicate that both ILs and NRs are important
for adopting PRD since they both determine the yield efﬁciency.
In other words, PRD may lead to better results with respect toboth economic (maize grain yield) and environmental issues (Ndynamics) compared to DI when a favorable IL and NR are adopted.
A large body of evidence shows that PRD can signiﬁcantly
improve yield (Dry et al., 2000; Kang and Zhang 2004; Kirda et al.,
2004; Tang et al., 2005; Shao et al., 2008; Wang et al., 2012;
Karandish, 2016; Karandish and Šimůnek, 2016a). Higher availability of N increases the duration of the vegetation growth andpostpones leaf senesce due to more N nutrition in leaves (Haverkort
et al., 2003). In agreement with this, our ﬁeld data revealed that theleaf nitrogen in PRD75 was signiﬁcantly higher than that for othertreatments (data not shown). A better N nutrition in the leaves andmore N accumulated in plants in the PRD scenarios result in an
increase in grain yield by maintaining a greater photosynthesis ratein the PRD scenarios (Varvel et al., 1997; Gianquinto et al., 2003).

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Fig. 9. Maize grain yield for various hypothetical scenarios under the DI (left) and PRD (right) scenarios. N rate is the N fertilization (kg/ha), and IL is irrigation level.

4. Conclusions
We have conducted a combined ﬁeld and model investigations
involving a drip-irrigated maize ﬁeld to evaluate the inﬂuence ofvarious N and water managed scenarios under deﬁcit irrigation (DI)and partial root-zone drying (PRD) on water and N dynamics. The
quantitative analysis indicated the capability of the HYDRUS-2D
model to simulate soil water contents, soil NO3 − -N concentrations,and crop N uptake, and their temporal trends for all treatments.
Simulated results reﬂected well the effects of the irrigation andfertilization managements on water and N dynamics as well as
on grain yield. The simulated results indicate that when there
are restrictions on water applications, NR needs to be adjusted
depending on IL in order to alleviate the mismatch between the
N application and uptake to prevent environmental damage. Furthermore, IL also requires an adjustment depending on a particular
NR to ensure a desirable N use efﬁciency. On the other hand, both
IL and NR need to be optimized to prevent economic losses while
preventing environmental damage. Reducing the amount of irrigation water by 30% and applying NR=200 kg ha−1 seems to be theoptimum scenario for which both economic and environmental
objectives are satisﬁed. Additionally, our results demonstrated that
PRD, by increasing crop N uptake, ensures environmentally safer
fertilizer applications compared to DI. Moreover, the use of PRD
results in 0.2–20.2% higher yield compared to DI under various
scenarios. The combination of IL=70% and NR=200 kg ha−1 under
PRD is thus the most efﬁcient N-managed water-saving irrigation
strategy for the maize cultivation in the study area. Additionally, it
could be concluded that the HYDRUS-2D model, instead of laborand time-consuming and expensive ﬁeld investigations, could be
reliably used for determining the optimal scenarios under both DI
and PRD strategies.

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