Science of the Total Environment 718 (2020) 137314

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Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv

Evaluating soil nitrate dynamics in an intercropping dripped ecosystemusing HYDRUS-2D
Ning Chen a, Xianyue Li a,⁎, Jirí Šimůnek b, Haibin Shi a, Qi Hu a, Yuehong Zhang aabCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USA

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T• N balances in the root zones ofintercropped corn and tomatoes were
evaluated.
• Soil NO 3-N concentrations in 0–40 cm
were higher in tomato region than corn.
• NO3-N exchange between the bare-corn
regions was lower than the baretomato.
• HYDRUS-2D modiﬁed was calibrated
and validated using experimental data.

a r t i c l e

i n f oArticle history:
Received 27 November 2019
Received in revised form 13 February 2020
Accepted 13 February 2020
Available online 13 February 2020
Editor: Ouyang Wei
Keywords:
Intercropping
Soil nitrate dynamics
HYDRUS-2D
Solute exchange
N-fertilizer application amount⁎ Corresponding author.
E-mail address: lixianyue80@126.com (X. Li).

https://doi.org/10.1016/j.scitotenv.2020.137314
0048-9697/© 2020 Elsevier B.V. All rights reserved.

a b s t r a c t
The competition mechanisms between crop species for water and nutrients, especially nitrate (NO3-N), inintercropping ecosystems are still poorly understood. Therefore, an experiment involving high (300 kg ha−1for corn and 250 kg ha−1 for tomato), medium (210 kg ha−1 for corn and 175 kg ha−1 for tomato), and low(150 kg ha−1 for corn and 125 kg ha−1 for tomato) N-fertilizer applications (HF, MF, LF, respectively) was conducted in the corn and tomato intercropping ecosystem during 2014 (a calibration period for modeling) and2015 (a validation period for modeling). The modiﬁed HYDRUS-2D code was used to analyze soil NO3-N concentrations (SNC) in the middle between corn rows (Pc), between corn and tomato rows (Pb), and between tomato
rows (Pt), NO3-N exchange in the horizontal direction between different regions, NO3-N leaching from the corn,
the bare, and the tomato region, and N uptake by crops. Simulated SNCs were in good agreement with measurements, with RMSE, NSE, and MRE of 0.01–0.06 mg cm−3, 0.75–0.98, and 8.7–19.1%, respectively, during the validation period (2015). Average SNCs in the 0–40 cm soil layer were different between Pc, Pt, and Pb. Intensive NO3N exchange in the horizontal direction occurred during the second stage (Day After Sowing [DAS] 37-113 in
2014; DAS 29-120 in 2015). NO3-N exchange between the corn and bare regions was lower than between thetomato and bare regions due to smaller concentration gradients. However, in the vertical direction, NO3-N
leaching from the corn region in both years was 4.1 and 8.8 times larger, respectively, than from the tomato

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N. Chen et al. / Science of the Total Environment 718 (2020) 137314

region under HF since NO3-N mainly moved from the tomato region to the corn region. Our results reveal thecompetition between corn and tomato for N and provide a rationale for formulating and optimizing different fertilizer regimes for different crops in the intercropping ecosystem.
© 2020 Elsevier B.V. All rights reserved.

1. Introduction
Water and fertilizer uptake capacities of multiple crops in anintercropping ecosystem can be very different because of complex agronomic practices and different crop properties. If these differences were
optimally managed, the intercropping ecosystem could not only improve the land equivalent ratio (LER) (Martin et al., 2018; Iqbal et al.,2019), water-fertilizer utilization efﬁciency (Singh et al., 2017; Chenet al., 2019; Du et al., 2019), and light utilization efﬁciency (LUE), it
could also increase crop yields (Hauggaard et al., 2016; Raza et al.,2019; Streit et al., 2019). The intercropping planting pattern can not
only increase farmers' income but also has many additional advantages.
Therefore, intercropping ecosystems have been extensively used worldwide, including in Germany (Streit et al., 2019), Brazil (Batista et al.,2019), India (Das et al., 2019), Pakistan (Iqbal et al., 2019), and China(Zhou et al., 2019).
Usually, soil water and nitrogen distributions in intercropping
ecosystems are different than in single-crop ﬁelds because of different resource acquisition strategies, growth, and root distributions ofvarious crop species (Scalise et al., 2017; Chen et al., 2019; Zhao et al.,2019). For example, the results of an experiment in a corn/soybean
intercropping ecosystem under full irrigation indicated that thecorn roots not only penetrated deeper than those of soybean but
also extended into the soybean part of the ﬁeld to absorb more resources (Gao et al., 2010). Similarly, there were signiﬁcant differences in the crop root systems in the tomato/corn intercropping
ecosystem during the growing season, while the crop root systemsof the two species overlapped and competed for soil water and nitrogen (Li et al., 2017).
Root distributions of different crops could cause variations in soil
water distributions in the horizontal direction. For example, although irrigation volumes (with the same irrigation frequency) for tomatoes werelower than for corn, the soil water content (SWC) in the shallow rootzone (0–40 cm) of tomatoes was higher than in the corn region, especiallyin the 0–20 cm soil layer (Li et al., 2015). Similarly, SWCs in the alfalfa region were lower than in the corn region in the alfalfa/corn intercropping
ﬁeld under identical irrigation regimes, which caused a low water stressof alfalfa, and the water stress increased as corn grew (Sun et al., 2018).
The movement of nitrogen is closely related to soil water ﬂow, andthe difference in SWCs in an intercropping ecosystem could also cause
variations in nitrogen distributions, further increasing an intensive nitrogen competition between the crop species. For example, in thecorn/pea intercropping and sole crop ecosystems, intercropped pea accumulated more N than sole pea during the crop maturity period, while
the opposite occurred for corn (Zhao et al., 2019). Hu et al. (2016) further found that the N competition capacity of pea was 1.35 times larger
than of corn. These examples indicate that the N competition capacity of
pea is stronger than of corn in the corn/pea intercropping ecosystem.
Yang et al. (2016) investigated the long-term effects of an intercropping
agroforestry ecosystem (jujube trees with winter wheat/summer corn)on the soil fertility balance. Their results showed that the intercropping
ecosystem signiﬁcantly decreased soil nutrient contents and crop yields,
but increased nutrients on the edge of the tree canopy.
In general, optimal nitrogen applications are essential for improving
the nitrogen use efﬁciency (NUE) in the ﬁeld (Wang et al., 2018; Razaet al., 2019a, 2019b; Noulas et al., 2018). For example, Hartmann et al.
(2015) found that a higher recovery efﬁciency (the components ofNUE) could be achieved when the N supply was reduced by

100 kg ha−1 from the standard farmers' practice without reducing thecrop yield of summer-corn or winter-wheat, while reducing the residual
soil mineral N. Nitrate distributions and NUE in intercropping ecosystems were reported in several previous studies (e.g., Ding et al., 2014;
Sun et al., 2018; Zhao et al., 2019). However, these studies provided little insight in the water-nitrogen competition between different crop
species and were usually carried out under surface irrigation that did
not account for various needs of two species in an intercropping ecosystem, causing excessive nitrogen leaching and decreasing NUE, especiallyfor nitrate (Liu et al., 2019). Moreover, these studies did not properly
quantify nitrogen ﬂuxes, and due to high time and labor costs, they
did not provide a sufﬁciently long time series of data to evaluate N
and water mass balances. Numerical simulations with a calibrated
model can overcome these problems and can better reveal soil waterand nitrate dynamics.
The HYDRUS-2D model (Šimůnek et al., 2016) has been widely usedin the literature to simulate the movement of soil water (Simone et al.,2019, Fan et al., 2018, Filipović et al., 2014; Grecco et al., 2019, Rai et al.,2019, Karandish and Šimůnek, 2018), nitrogen species (He et al., 2018;
Grecco et al., 2019; Elasbah et al., 2019), soil salts (Xu et al., 2019), andheat (Rai et al., 2019; Ren et al., 2018). This model can capture the dynamic nature of these factors and be used to optimize their spatial andtemporal distributions (Cichota et al., 2018). Additionally, HYDRUS-2D
is ﬂexible to accommodate different types of boundary conditions andit is thus widely used to simulate complex conditions. For instance,
Wang et al. (2011) estimated subsurface lateral ﬂow and associated
NO3-N losses by modeling the water and nitrogen budgets using
HYDRUS-2D in the citrus tree/peanut intercropping ecosystem. On theother hand, Li et al. (2015a, 2015b) used HYDRUS-2D to investigate
soil water dynamics in the root zone of the tomato/corn intercropping
ecosystem.
Although Li et al. (2015a, 2015b) set independent boundary conditions for each crop, only one set of root water uptake parameters (forthe stress response function) could be used in version 2.0 of HYDRUS2D for two crops, without distinguishing between different water andnitrogen uptake capacities of different crops in an intercropping ecosystem. Therefore, the modiﬁed version of HYDRUS-2D, allowing input of
two sets of parameters for two vegetations, was used in this study.
This modiﬁed version can describe the competition between different
crops in an intercropping ecosystem for water and nutrient resources.
While earlier studies mainly focused on water or NO3-N dynamics,
there were no studies evaluating exchange processes of soil NO3-N in
the horizontal direction between different crop regions (i.e., the amountof NO3-N moved from one region to another region). The exchange process of soil NO3-N in the horizontal direction is an important process,which can reveal the competition for resources by different crops andprovide a rationale for formulating and optimizing different fertilizer regimes. Therefore, it is still necessary to quantitatively assess the NO3-N
competition between different crops in an intercropping ecosystem to optimize fertilization.
The main objectives of this study thus are (i) to collect soil water andNO3-N concentration data and (ii) to calibrate and validate the modiﬁed
version of HYDRUS-2D that considers two different crops to evaluate
these data and simulate soil water and NO3-N dynamics. Additional objectives are (iii) to assess the temporal and spatial soil NO3-N distributions, (iv) to analyze the exchange of soil NO3-N in the horizontal
direction, and (v) to asses differences in N balances in root zones ofcorn and tomatoes in an intercropping ecosystem.

N. Chen et al. / Science of the Total Environment 718 (2020) 137314

3

Table 1Agricultural activities during the growing seasons of 2014 and 2015.
Number Date

Agricultural activities

Remarks

1

Corn sowing and the basefertilizer application
Tomato transplanting

Before sowing, 300–450 kg ha−1 of Diammonium phosphate ((NH4)2HPO4, N ≥ 18%), and 150 kg ha−1of Potassium sulfate (k2SO4) were applied in the ﬁeld as basal fertilizer.
Laboratory-reared before transplanting

Irrigation

Irrigations of corn and tomato are 30 and 22.5 mm, respectively.

Irrigation and the
N-fertilizer application
Irrigation

Irrigations of corn and tomato are 30 and 22.5 mm, respectively; 30% of the total N-fertilizer

Irrigation and the
N-fertilizer application
Irrigation

Irrigations of corn and tomato are 30 and 22.5 mm, respectively; 40% of the total N-fertilizer

Irrigation and the
N-fertilizer application
Irrigation of the corn ﬁled

Irrigations of corn and tomato are 30 and 22.5 mm, respectively; 30% of the total N-fertilizer

2
3
4
5
6
7
8
9
10
11

April 21 in 2014 and April 25
in 2015
May 10 in 2014 and May 6 in2015
May 18 and 27 in 2014 andMay 19 in 2015
June 4 in 2014 and June 11 in2015
June 18 in 2014 and June 26
in 2015
July 8 in 2014 and July 4 in2015
July 23 and 30 in 2014 andJuly 19 and 27 in 2015
August 10 in 2014 andAugust 15 in 2015
August 25 in 2014 andAugust 20 in 2015
September 10 in 2014 and
September 6 in 2015
September 26 in 2014 and
September 23 in 2015

Irrigations of corn and tomato are 30 and 22.5 mm, respectively.

Irrigations of corn and tomato are 30 and 22.5 mm, respectively.

Irrigation is 30 mm

Tomato harvesting
Corn harvesting

Note: Events 3 and 7 have more than one day.

2. Material and methods
2.1. Study site, climate and soil description
The study was conducted at the Baleng Saving Water experimentalsite (40°20′15”N，107°02′05″E) in the Hetao irrigation district, Inner
Mongolia, China, over two cropping seasons (from 2014 to 2015). Thisarea, i.e., the Baleng Saving Water experimental site, is characterized bya typical arid climate with mean annual sunlight of 3180 h, mean annual
rainfall of 201 mm (very arid conditions), mean annual potential evaporation of 2259 mm (based on 30 years), and mean annual air temperature of
7.7 °C. The soil in the experimental area was classiﬁed as silt loam with thefollowing characteristics in the 0–100 cm soil layer: the bulk density of
1.52 g cm−3, the average ﬁeld capacity (θfc) of 0.34 cm3 cm−3 (thevolumetric water content). Additionally, the soil in the tillage layer(0–20 cm) was homogeneous, with total nitrogen of 3.00 g kg−1, an organic matter of 56.11 g kg−1, available nitrogen of 1.42 mg kg−1, potassium of 76.90 mg kg−1, and phosphorus of 7.07 mg kg−1.
2.2. Experimental design and treatments
The corn seeds (Z. mays L. cv. Zhongdi 77) were sown at the end of
April, and the tomatoes (Solanum lycopersicum. cv. Dunhe 48) weretransplanted into the intercropping ﬁeld after cultivation in a greenhouse at the beginning of May in the 2014 and 2015 seasons. The harvest dates were September 26 and 23 for corn in 2014 and 2015,
respectively, and September 10 and 6 for tomato, respectively(Table 1). The planting pattern was “one ﬁlm mulch, one drip line,

Fig. 1. The modeling domain, boundary conditions (a), and planting pattern (b) for the corn/tomato intercropping ecosystem. Numbers I, II, and III refer to the corn, bare, and tomato
regions, respectively. Lines 1 and 2 are between regions I and II, and II and III, respectively. Lines 3, 4, and 5 are located at a depth of 100 cm below the corn, bare, and tomato regions,
respectively. Points Pc, Pb, and Pt are located in the middle between corn rows, between corn and tomato rows, and between tomato rows, respectively.

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N. Chen et al. / Science of the Total Environment 718 (2020) 137314

two crop lines,” and two rows of tomatoes were sown between cornrows (Fig. 1). The seed rate was 11,000 viable seeds ha−1 for corn, andthe real number of transplanted tomato plants was 408 for each plot.
Before sowing, 300 kg ha−1 of Diammonium phosphate ((NH4)−1of Potassium sulfate (K2SO4) were ap2HPO4, N ≥ 18%), and 150 kg ha
plied by broadcast fertilization in the ﬁeld as basal fertilizer. The experimental design was a completely random design comprising of three
replicates of three topdressing treatments, i.e., 9 ﬁeld plots. Every plot
was 9.2 m wide and 15 m long. The high N-fertilizer application of
300 kg ha−1 for corn (HFc) and 250 kg ha−1 for tomato (HFt) was
based on local recommendations. Additionally, different levels of Nfertilizer, i.e., 70% and 50% of recommended N-fertilizer, were appliedin the corn and tomato ﬁelds. There were thus three levels of applied
topdressing N-fertilizer: HFc and HFt (the local recommendation), MFc
and MFt (70% of the local recommendation), and LFc and LFt (50% of
the local recommendation). The Carbamide solution (CO(NH2)2,
N ≥ 32%) as topdressing was applied in the ﬁeld in the elongation
stage (30% of the total N-fertilizer), the tasseling stage (40% of thetotal N-fertilizer), and the ﬁlling stage (30% of the total N-fertilizer),each time accompanied with drip irrigation with an emitter dischargeof 2.4 L h−1 and a distance of 30 cm. The water meter (with an accuracyof 0.001 L) was set up to monitor water ﬂow. The groundwater table atthe experimental site was monitored using the auto water level logger(Onset Computer Inc.; U20L-01, Hobo, USA) and was 83–248 cm deepin 2014 and 2015. Large variations in the depth of the groundwatertable were caused by a nearby irrigation canal. A list of performed agricultural activities is summarized in Table 1.
2.3. Measurements and methods
Meteorological data, i.e., daily precipitation, solar radiation, air temperatures, air humidity, and wind speed, were collected in the experimental ﬁeld using the automatic meteorological station (Onset
Computer Inc.; U30, Hobo, USA) at 30 s intervals, and the 10-min statistics (average) were computed. Reference crop evapotranspiration (ET0)was then calculated using the Penman-Monteith approach (Allen et al.,1998). Daily evapotranspiration (ETc) rates for different crop stages
were calculated by multiplying ET0 by Kc (the crop coefﬁcient) thatwere taken from the FAO paper (No.56). A list of speciﬁc dates of the different crop stages for corn and tomato is summarized in Table 2.
The initial soil water contents (SWCs) were measured using gravimetric measurements before sowing. TDR probes (IMKO GmbH Inc.;
IPH, TRIME-PICO, Germany) were installed in the middle between
corn rows (Pc), between corn and tomato rows (Pb), and between tomato rows (Pt) (Fig. 1) for measuring SWCs during the crop growing
season. SWCs were taken once every 7 days at soil depths of 0–20,
20–40, and 40–100 cm, and the data were veriﬁed by gravimetric measurements at periodic intervals (Skaggs et al., 2004).
Soil samplings for nitrate (NO3-N) concentrations were taken at thesame locations as SWCs every two weeks. Soil samples were shaken
with 2 mol L−1 KCl (1:5 soil:solution ratio) for 1 h, and the obtained extract was analyzed using a spectrophotometer (Beijing General Instrument Co. LTD., TU-1901, General Instrument, CHN). Total N uptake
was determined on the same dates using ten plants randomly harvested
from each plot to determine total N uptake by the crop stem, leaves, andgrains. These samples were ﬁrst kept for 30 min at 105 °C temperature

Table 2The dates of different crop stages for corn and tomato used to compute ETp during 2014
and 2015.
Crop

Year

Early season

Mid-season

Late season

Corn

2014
2015
2014
2015

April 21–May 27
April 25–May 23
May 10–June 1
May 6–June 5

May 28–August 12
May 24–August 23
June 2–August 15
June 6–August 18

August 13–September 26
August 24–September 23
August 16–September 10
August 19–September 6

Tomatoand then conserved at 75 °C temperature in the oven to reach a constantweight. After crushing and sifting, a 0.2 g sample was weighted by aweighing paper, boiled with 5 mL of concentrated H2SO4, and then measured by a ﬂow analyzer (Brown ruby Inc., AA3, SEAL, Germany). The ultraviolet spectrophotometry method (GB/T 32737-2016) and the semimicro Kjeldahl method (Bremner and Keeney, 1965) were applied todetermine NO3-N concentrations in soil (SNC) and total N in crops(Nc) using the following formulas:
SNC ¼

ρ  V  D  γsm

ð1Þwhere SNC is the NO3-N concentration (mg cm−3), ρ is the mass concentration of NO3-N in the chromogenic solution (mg mL−1), V is thevolume of the chromogenic solution (mL), D is the dilution ratio (-), mis the soil mass (g), and γs is the dry bulk density (g cm−3).
Nc ¼

C  V  V T  14
 6:25  1000
Vs

ð2Þ

In Eq. (2), Nc is the total N concentration in the crop stem, leaves, andgrains (mg kg−1), C is the mass concentration of HCl in the titrant(mg mL−1), V is the volume of the titrant (mL), VT is the total volumeof the solution (mL), and Vs is the volume of the measured solution(mL).
NU ¼ mc  Nc

ð3Þ

In Eq. (3), NU is N uptake (mg), and mc is the dry mass of the crop(kg).
The leaf area meter (Li-3000, LI-COR, USA) and a tape (with the accuracy of 0.1 cm) were used to measure the leaf area and crop height,
respectively, for corn and tomatoes with three replicates once every
7–15 days and the leaf area index (LAI) was calculated using the FAO
method (Allen et al., 1998).
Root samples for corn and tomatoes under good growth conditions
were collected from a soil transect using a method of Li et al. (2017) during the elongation stage (June 12 and 8 in 2014 and 2015, respectively),
the tasseling stage (June 25 and 18 in 2014 and 2015, respectively), theﬁlling stage (July 11 and 18 in 2014 and 2015, respectively), and thematuration stage (August 20 and 23 in 2014 and 2015, respectively).
These samples were collected every 5 cm down to a depth of 50 cm
where higher roots density occurred, and then every 10 cm below the
50-cm depth until no roots were found. The root samples were ﬁrst
washed and solarized, and then scanned using the Epson Perfection
V700 PHOTO. The roots parameters (the root length, root surface area,
and root volume) were determined using the WinRHIZO software.
2.4. Modeling description
2.4.1. Modeling introduction
The HYDRUS-2D model modiﬁed to consider simultaneously twovegetations was used to simulate the transient two-dimensional movement of water and solutes in the soil proﬁle. This program numericallysolves the Richards equation for variably-saturated water ﬂow andadvection-dispersion equations for solute transport. The model additionally allows the speciﬁcation of different root water and solute uptake parameters for different crops, which affects the spatial
distribution of water and nitrate between irrigation cycles. The solute
equation considers the advective-dispersive transport in the liquid
phase, as well as diffusion in the gaseous phase. The theoretical part ofthe model is described in detail in the technical manual (Šimůnek
et al., 2016).
2.4.2. Soil hydraulic properties
The soil hydraulic parameters for the van Genuchten-Mualem modelfor different soil layers (0–30, 30–100, and 100–250 cm) were

N. Chen et al. / Science of the Total Environment 718 (2020) 137314

5

Table 3
Soil textural properties and soil hydraulic parameters for the 0–250 cm soil layer.
Soil layer
(cm)

0–30
30–100100–250

Soil particle size
distribution (%)

Residual soil water content

Saturated soil water content

Shape parameter

Shape parameter

Saturated hydraulic conductivity

θr

θs

α

n

Ks

Clay

Silt

Sand

(cm3·cm−3)(cm3·cm−3)(cm−1)(−)(cm·day−1)

4.8
3.0
8.3

39.0
56.1
59.2

56.2
40.9
32.5

0.03
0.04
0.05

0.41
0.42
0.41

0.02
0.01
0.01

1.45
1.63
1.63

55.4
52.0
37.9

estimated using the Rosetta software package (Schaap et al., 2001) in
HYDRUS-2D. Initial estimates of soil hydraulic parameters were basedon soil textural information (% of sand, silt, and clay) and the bulk density. Additionally, these values of soil hydraulic parameters (e.g., shapeparameters and the saturated hydraulic conductivity) for three soil
layers were further manually calibrated by comparing simulated andobserved values of SWCs and SNCs. Calibrated values of soil hydraulicparameters are shown in Table 3.

2.4.3. Root water uptake parameters for corn and tomato
HYDRUS-2D was modiﬁed to consider water and N uptake by rootsof two vegetations, i.e., to allow different parameters for root water uptake by corn and tomatoes. Root water extraction was computed according to the Feddes model (Feddes et al., 1978; Šimůnek andHopmans, 2009) adapted for two-dimensional conditions with two
crops:
SðhÞ ¼ ½α 1 ðhÞ  b1 ðx; zÞ þ α 2 ðhÞ  b2 ðx; zÞ  T p  St

ð4Þ

where Tp is the potential transpiration rate (cm day−1), St is the surface
length associated with transpiration (cm), b1(x,z) and b2(x,z) are root
water uptake distribution functions (cm−2) for corn and tomatoes, respectively, which can be calculated based on measured root distributions (Fig. 2), and a1(h) and a2(h) are root water uptake stress
reduction functions for corn and tomatoes, respectively, which can becalculated as follows:8>>> h1 −h>>>< h1 −h2α ðhÞ ¼
1>>>>> h−h4>:h3 −h4h2 bh≤h1
h3 ≤h≤h2

ð5Þ

h4 ≤hbh3

where h1 is the anaerobic point pressure head (cm), h2 and h3 are optimal interval pressure heads (cm), h4 is the wilting point pressure head(cm); speciﬁc parameters are listed in Table 4 (Wesseling and
Brandyk, 1985).
2.4.4. Solute parameters
Solute reaction processes include nitriﬁcation, denitriﬁcation, volatilization, immobilization, and mineralization. In this study, the denitriﬁcation process and the losses in the form of N2O-N can be neglected
because this reaction occurs mainly under saturated conditions(Ravikumar et al., 2011). The processes of immobilization and mineralization were also neglected, similarly as in many other modeling studies(Ravikumar et al., 2011; Ramos et al., 2012; Tafteh and Sepaskhah,2012), due to the low clay content in the experimental soil, which isoften correlated with these two processes. Ammonia volatilization
was neglected due to the fertilizer application with irrigation water(Ramos et al., 2012). The applied fertilizer was ammonium nitrate,
which transforms ﬁrst into NO2-N, and then further into NO3-N. Since,
the nitriﬁcation process from NO2-N to NO3-N is much faster thanFig. 2. Measured average distributions of corn and tomato roots during May–July (a) and July–September (b) in 2014 and 2015.

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N. Chen et al. / Science of the Total Environment 718 (2020) 137314

Table 4
Parameters of the stress response function for root water uptake by corn and tomato.
Croph1 (cm)h2 (cm)h3 (cm)h4 (cm)Corn
Tomato

−15
−30

−325
−800

−600
−1500

−8000
−8000

from NH4-N to NO2-N, the nitriﬁcation process can be assumed to proceed directly from NH4-N into NO3-N, which is consistent with manyother studies (Wang et al., 2010; Tafteh and Sepaskhah, 2012). The nitriﬁcation process in HYDRUS-2D is simulated as a ﬁrst-order decay reaction. The nitriﬁcation rates for reactions in the liquid and solid phases
were set to 0.12 and 5.9 d−1, respectively (Castaldelli et al., 2018). The
temperature and water content dependencies of the nitriﬁcation process were not considered, similarly as in many other modeling studies(Ravikumar et al., 2011; Ramos et al., 2012; Tafteh and Sepaskhah,2012; Li et al., 2015a, 2015b). This study also assumed that NO3-N ispresent only in the dissolved phase, while ammonium is also adsorbed
to the solid phase. The distribution coefﬁcients (Kd) were thus set to 0
and 3.5 cm3 g−1 for NO3-N and NH4-N, respectively (Hanson et al.,2006).
Soil longitudinal and transversal dispersivities (DL and DT, respectively) and the molecular diffusion coefﬁcient in free water are needed
to determine the components of the dispersion tensor. Longitudinal
dispersivities (DL) were considered to be 10, 10, and 5 cm for the
0–30, 30–100, and 100–250 cm soil layers, respectively; these values
were obtained using the trial-and-error calibration method, i.e., bycomparing simulated and observed values of soil NO3-N concentrations.
The transverse dispersivity (DT) was taken as one-tenth of DL (Hanson
et al., 2006; Ramos et al., 2012). Molecular dispersivity coefﬁcients of
NH4-N and NO3-N in free water were set to be 0.064 and0.068 cm2 h−1, respectively (Cote et al., 2003).
2.4.5. Initial and boundary conditions
The two-dimensional transport domain was deﬁned as a rectangle
with a width of 100 cm and a depth of 250 cm (Fig. 1). Observed valuesof SWCs were used as the initial condition in the model. Due to thecoarse soil texture and the low initial fertilizer content, the initial conditions for NH4-N and NO3-N concentrations were deﬁned to be zero. Besides, horizontal distributions of initial SWCs and NO3-N concentrations
were assumed to be uniform.
Four boundaries were deﬁned in HYDRUS-2D, i.e., the upper boundary, the bottom boundary, and two lateral boundaries (the left and rightboundary). The time-variable ﬂux and atmospheric boundary conditions were speciﬁed at the two emitters (in corn and tomato rows)
and the soil surface, respectively, to represent drip irrigation and to
apply precipitation, potential evaporation, and transpiration ﬂuxes, respectively. Potential evaporation (Ep) and transpiration (Tp) can be obtained as fractions of potential evapotranspiration (ETp) (Campbell
and Norman, 1989), while ETp can be obtained by multiplying ET0
with the crop coefﬁcient (Kc) as follows:
ET p1 ¼ ET 0  K c1

ð6Þ

ET p2 ¼ ET 0  K c2

ð7Þ

Table 5
RMSE, NSE, and MRE for measured and simulated SWCs and SNCs under high (HF), medium (MF), and low (LF) N-fertilizer applications in regions between corn rows (Pc), between corn
and tomato rows (Pb), and between tomato rows (Pt) during calibration (2014) and validation (2015).
Year

Treatments

Depth (cm)Crop regions

Bare area
Pt

Pc

2014

SWCs

HF

MF

LF

HF
SNCs
MF

LF

2015

SWCs

HF

MF

LF

HF
SNCs
MF

LF

0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100
0–20
20–40
40–100

Pb

RMSE

NSE

MRE

RMSE

NSE

MRE

RMSE

NSE

MRE

0.03
0.02
0.03
0.04
0.03
0.03
0.04
0.03
0.03
0.03
0.01
0.02
0.02
0.02
0.01
0.03
0.04
0.02
0.03
0.02
0.03
0.04
0.05
0.04
0.04
0.02
0.03
0.03
0.03
0.02
0.03
0.02
0.01
0.03
0.03
0.01

0.85
0.92
0.90
0.83
0.84
0.90
0.87
0.88
0.93
0.83
0.99
0.89
0.85
0.86
0.95
0.92
0.87
0.97
0.82
0.90
0.90
0.86
0.85
0.91
0.86
0.90
0.88
0.82
0.90
0.86
0.83
0.92
0.90
0.82
0.85
0.90

11.0%
7.6%
8.1%
12.1%
10.0%
9.6%
11.2%
10.5%
9.8%
11.2%
9.9%
8.6%
12.9%
15.8%
11.5%
10.1%
13.1%
10.5%
11.7%
9.2%
8.4%
11.5%
12.4%
10.1%
12.8%
9.7%
10.8%
12.0%
10.6%
8.7%
13.5%
10.7%
12.2%
14.2%
11.5%
10.2%

0.03
0.02
0.02
0.04
0.03
0.03
0.04
0.02
0.03
0.04
0.02
0.02
0.03
0.04
0.04
0.05
0.04
0.03
0.03
0.02
0.03
0.03
0.02
0.03
0.04
0.03
0.03
0.04
0.03
0.02
0.04
0.02
0.02
0.04
0.03
0.02

0.95
0.96
0.99
0.85
0.93
0.98
0.85
0.92
0.95
0.82
0.97
0.87
0.88
0.83
0.87
0.82
0.84
0.89
0.89
0.96
0.92
0.89
0.94
0.92
0.90
0.91
0.91
0.95
0.95
0.96
0.84
0.87
0.91
0.81
0.85
0.83

10.0%
7.3%
7.2%
12.0%
9.1%
7.5%
11.5%
10.3%
8.7%
10.4%
9.9%
8.5%
11.7%
15.4%
13.1%
12.7%
10.5%
9.1%
10.6%
9.7%
10.5%
10.1%
7.6%
9.6%
9.9%
8.1%
9.6%
12.0%
9.7%
8.9%
15.7%
12.7%
11.5%
14.5%
11.7%
13.4%

0.02
0.02
0.03
0.02
0.04
0.03
0.04
0.03
0.03
0.05
0.03
0.02
0.03
0.04
0.01
0.05
0.04
0.02
0.04
0.02
0.03
0.02
0.07
0.03
0.05
0.03
0.03
0.04
0.04
0.02
0.06
0.05
0.02
0.06
0.05
0.03

0.93
0.95
0.96
0.95
0.92
0.88
0.81
0.90
0.91
0.85
0.93
0.88
0.81
0.82
0.84
0.80
0.84
0.91
0.83
0.86
0.95
0.86
0.82
0.87
0.79
0.84
0.89
0.84
0.96
0.98
0.75
0.83
0.88
0.83
0.86
0.87

10.2%
8.6%
8.8%
9.8%
10.5%
10.8%
13.5%
10.6%
9.8%
12.7%
10.6%
9.3%
19.5%
18.9%
16.9%
16.7%
14.8%
12.2%
12.5%
9.1%
7.9%
12.3%
12.7%
8.7%
14.3%
11.7%
8.8%
13.7%
10.7%
9.1%
18.1%
15.1%
13.2%
15.4%
13.7%
11.3%

N. Chen et al. / Science of the Total Environment 718 (2020) 137314

T p1 ¼ f1− expð−k  LAIÞg  ET p1

ð8Þ

T p2 ¼ f1− expð−k  LAIÞg  ET p2

ð9Þ

Ep1 ¼ ET p1 −T p1

ð10Þ

Ep2 ¼ ET p2 −T p2

ð11Þ

where ETp1 and ETp2 are the potential evapotranspiration rates in thecorn and tomato regions (cm day−1), respectively, Tp1 and Tp2 are thepotential transpiration rates in the corn and tomato regions (cm day−1),respectively, Ep1 and Ep2 are the potential evaporation rates in the bycorn and tomato regions (cm day−1), respectively, and Kc1 and Kc2 are
the crop coefﬁcients for corn and tomato, respectively, that weretaken from the FAO paper (Allen et al., 1998). Kc1 is equal to 0.30 during
the early season, 1.20 during the mid-season, and 0.35 during the lateseason; Kc2 is 0.60 during the early season, 1.15 during the midseason, and 0.70 during the late season (Allen et al., 1998). The extinction coefﬁcient k is equal to 0.39 for corn (Li et al., 2018) and 0.65 for tomato (Cruz et al., 2014).
The emitter ﬂuxes in the corn and tomato regions were represented
in HYDRUS-2D with two 10 cm long time-variable boundary conditions
and were calculated as follows:
q ¼ Q=L0ð12Þ

where q is the boundary ﬂux (cm day−1), Q is the actual emitter ﬂux(cm2 day−1), and L′ is the width of the variable ﬂux boundary condition(cm).
The solute ﬂux across a particular boundary or between two regions,qc (mg cm−2 day−1), can be calculated using the following equation:
qc ¼ q  SNC

ð13Þ

7

where q is the water ﬂux (cm day−1), and SNC is the NO3-N concentration (mg cm−3). The solute exchange (the amount of NO3-N moving between different regions), Qs (mg cm−1), can be calculated using thefollowing equation:
Q S ¼ qc  L  t

ð14Þ

where L is the length of the boundary or mesh-line (cm), i.e., the line
separating the two regions, and t is the time (day).
A time-variable head boundary condition was applied along the bottom boundary, which considers the effect of the shallow groundwatertable on ﬂow and soil nitrate dynamics. Additionally, the third-type
Cauchy boundary condition was used for solute transport along all
boundaries with speciﬁed water ﬂuxes (at the top boundary), while thesecond-type Neumann boundary condition was used for solute transport
along outﬂow boundaries (at the bottom boundary). Two lateral boundaries of the ﬂow domain were assigned a no-ﬂow boundary condition.
2.5. Statistical analysis
Signiﬁcant differences in soil water contents and soil NO3-N concentrations were analyzed using the ANOVA method using the SPSS software (SPSS Inc., 20.0., USA). For evaluating the model performance insimulating soil water dynamics and the NO3-N fate and transport
under different N-fertilizer applications, the root mean square error(RMSE), the Nash-Sutcliffe (NSE), and the mean relative error (MRE)were computed for observed and simulated values. The speciﬁc formulas are as follow:sﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
1 Xn
ðS −Oi Þ2
RMSE ¼n i¼1 i

ð15ÞFig. 3. Simulated soil water contents (SWCs) in regions between corn rows (Pc), between corn and tomato rows (Pb), and between tomato rows (Pt) in the 0–20 (top), 20–40 (middle), and
40–100 (bottom) cm soil layers in 2014 (left) and 2015 (right) for the high N-fertilizer application (HF).

8

N. Chen et al. / Science of the Total Environment 718 (2020) 137314

XnðSi −Oi Þ2

NSE ¼ 1− i¼1 P

n


2
Oi −O

ð16Þ

simulated and observed SNC values during both calibration and validation periods indicate that HYDRUS-2D can well capture the NO3-N dynamics even though the processes of volatilization, immobilization,and mineralization have been neglected.

ð17Þ

3.2. Soil NO3-N distribution in the soil proﬁle in an intercropping ecosystem

i¼1

MRE ¼

1 Xn j Si −Oi j 100%
n i¼1
Si

where Si and S are individual and mean simulated soil NO3-N concentration values (mg cm−3), respectively, Oi and O are individual and meanobserved soil NO3-N concentration values (mg cm−3), respectively,
and n is the number of observed values.
3. Results
3.1. Soil NO3-N concentration calibration and veriﬁcation
Observed values of soil water contents (SWCs) and soil NO3-N concentrations (SNCs) under different N-fertilizer application levels at different crop locations during 2014 and 2015 were used for model
calibration and validation, respectively. Table 5 shows that RMSE, NSE,
and MRE for measured and simulated SWCs under different Nfertilizer application levels (HF, MF, LF) during the calibration period(2014) are in the range of 0.02–0.04 cm3 cm−3, 0.81–0.99, and7.2–13.5%, respectively, and for SNCs in the range of
0.01–0.05 mg cm−3, 0.80–0.99, and 8.5–18.9%, respectively. During
the validation period (2015), they are in similar ranges of 0.02–0.07
cm3cm−3, 0.79–0.96, and 7.6–14.3%, respectively, for SWCs, and
0.01–0.06 mg cm−3, 0.75–0.98 and 8.7–19.1%, respectively, for SNCs.
Additionally, visual inspection (Figs. 3 and 4) was used to assess the correspondence between simulated and observed SWCs and SNCs under
the high N-fertilizer application (HF). Small statistical errors between

In order to evaluate the effects of different N-fertilizer applicationlevels on soil NO3-N distributions in the soil proﬁle (0–100 cm) of thecorn/tomato intercropping ecosystem, DAS 80 and 85 in 2014 andDAS 84 and 89 in 2015 were selected (i.e., one day before and ﬁve
days after the N-fertilizer application).
One day before the N-fertilizer application (DAS 80 in 2014 and DAS
84 in 2015), SNCs in the middle between corn rows (Pc) and tomato
rows (Pt) were higher than in the middle between corn and tomato
rows (Pb) because ﬁlm mulching reduced water vapor exchange (soilevaporation) between the surface soil and the atmosphere and promoted the mineralization rate. The average SNCs under the high Nfertilizer application (HF) in the shallow soil layer (0–40 cm) were32.1 and 47.5% higher in Pc and Pt, respectively than in Pb during 2014
and 2015, 22.8 and 34.2% higher, respectively under the medium Nfertilizer application (MF), and 16.4 and 24.2% higher, respectively
under the low N-fertilizer application (LF). Additionally, the roots ofcorn and tomato were mainly distributed in the 0–40 cm soil layer.
Since N uptake of corn was considerably higher than that of tomato,
the SNC in Pc was lower than in Pt in the shallow soil layer. The average
SNCs for HF, MF, and LF were on average in both years (2014 and 2015)
reduced by 30.9, 24.8, and 19.8%, respectively, in Pc compared with Pt.
SNCs increased in all treatments after the N-fertilizer application(Fig. 5b, d, f, h, j, and l). There were obvious increases in SNCs in the Pc
and Pt regions due to direct fertilizer applications. Moreover, the SNC
in Pb increased not only due to the high N-fertilizer application but

Fig. 4. Simulated soil NO3-N concentrations (SNCs) in regions between corn rows (Pc), between corn and tomato rows (Pb), and between tomato rows (Pt) in the 0–20 (top), 20–40(middle), and 40–100 (bottom) cm soil layers in 2014 (left) and 2015 (right) for the high N-fertilizer application (HF).

N. Chen et al. / Science of the Total Environment 718 (2020) 137314

also due to the ﬂow of NO3-N with irrigation water from the crop regions (Pc and Pt) to the bare region (Pb). However, there were little variations in the SNC in Pc and Pt in the 40–100 cm soil layer because of alower irrigation water footprint.
3.3. Soil NO3-N dynamic in the horizontal direction
The modeling domain was divided into three regions for studying
NO3-N exchange in the intercropping ecosystem, i.e., the corn region(I), the bare region (II), and the tomato region (III) (Fig. 1). Additionally,
the entire crop growing season was divided into three growth stages,
similarly to the crop stages for computing ETp (Table 2): the ﬁrst stage(DAS 0-36 in 2014 and DAS 0-28 in 2015, i.e., slow NO3-N exchange),
the second stage (DAS 37-113 in 2014 and DAS 29-120 in 2015,
i.e., intensive NO3-N exchange), and the third stage (DAS 114-158 in2014 and DAS 121-151 in 2015, i.e., stable NO3-N exchange).
In general, the roots of the two crops may overlap during the late crop
growing season, resulting in one crop taking up resources from anothercrop's root zone. However, since this process was relatively small, thesoil NO3-N concentration was not much affected by the overlap of thetwo root systems. In order to differentiate between the effects of different
resource absorption capacities of the two crops on soil NO3-N movementin the intercropping ecosystem, it was assumed in this study that the croproots grew only in its own root zone, and the two root systems do not
overlap. Therefore, the N competition mechanisms of different crops canbe found using the analysis of NO3-N exchange between different regions.

9

During the ﬁrst stage, there was no intensive NO3-N exchange due torelatively dry soil and small concentration gradients. As crops grew, NO3N exchange between different root zones increased. During the second
stage, NO3-N moved in both year in the horizontal direction from thecrop regions (I and III) to the bare region (II) (Fig. 6), and simulated
NO3-N exchange was 38.4, 26.9, and 19.2 mg cm−1 under HF, MF, and
LF, respectively. Simulated outﬂow of NO3-N from the bare region (II)into the crop regions (I and III) was 4.6, 3.2, and 2.3 mg cm−1 under HF,
MF, and LF, respectively, and annual net inﬂows (inﬂow of NO3-N intominus outﬂow of NO3-N out of the region) were 33.9, 23.7, and16.9 mg cm−1, respectively. On the other hand, different N-fertilizer applications and different uptake capacities of corn and tomato caused different NO3-N inﬂow into the bare region (II) from the corn and tomato
regions (I and III, respectively). Average simulated NO3-N exchanges between the corn region (I) and the bare region (II) were 12.5, 8.8, and6.3 mg cm−1 under HF, MF, and LF in both years, respectively, while average simulated NO3-N ﬂuxes were 0.10, 0.07, and 0.05 mg cm−2 day−1, respectively. Average simulated NO3-N exchange of 25.9, 18.1, and13.0 mg cm−1 under HF, MF, and LF, respectively, were transferred fromthe tomato (III) to the bare region (II) in both years, while the averagesoil NO3-N ﬂux was 0.21, 0.14, and 0.10 mg cm−2 day−1. Additionally,
the soil NO3-N also moved from the bare region (II) to the crop regions(I and III) during some periods of the N-fertilizer application. For example,
average NO3-N exchange between the bare (II) to the corn region (I) were
3.6, 2.5 and 1.8 mg cm−1 under HF, MF and LF in both year, respectively,
while 1.0, 0.7 and 0.5 mg cm−1 of NO3-N were transmitted into theFig. 5. Simulated soil NO3-N concentrations (SNC) in the region between corn rows (Pc), between corn and tomato rows (Pb), and between tomato rows (Pt) in the soil proﬁle (0–100 cm)
one day before (DAS 80 and 84 in 2014 and 2015, respectively) and ﬁve days after (DAS 85 and 89 in 2014 and 2015, respectively) the N-fertilization application for the high (HF), medium(MF), and low (LF) N-fertilizer application. (a, b, e, f, i, and j for 2014; c, d, g, h, k, and l for 2015).

10

N. Chen et al. / Science of the Total Environment 718 (2020) 137314

Fig. 6. Simulated soil NO3-N ﬂuxes in the horizontal direction between the corn (I) and bare (II) regions (left, a and c) and between the tomato (III) and bare (II) regions (right, b andd) during 2014 (top, a and b) and 2015 (bottom, c and d). SF refers to the daily solute ﬂux while SF refers to the mean values of the solute ﬂux during different crop stages. Subscripts
HF, MF, and LF refer to the high, medium, and low N-fertilizer applications, respectively.

tomato region (III). During the late crop growing season (i.e., during thethird stage), large NO3-N exchange occurred only in the corn region(I) because tomatoes had stopped growing. An average NO3-N ﬂux fromthe bare (II) to the corn region (I) was 2.9, 2.0, and 1.5 mg cm−1 underHF, MF, and LF in both years, respectively.
3.4. N balance in the root zone of the corn/tomato intercropping ecosystem
Differences in the spatiotemporal ecological functioning of corn andtomato resulted in great differences in the N balance between different
root zones of the intercropping ecosystem. The physiological activity ofcorn was clearly stronger than that of tomato under different Nfertilizer application levels (Fig. 6). N uptake by corn in 2014 and 2015 increased by 38.3% and 42.2%, respectively, compared to tomatoes (Table 6)
under the high N-fertilizer application (HF). Additionally, there were signiﬁcant differences in NO3-N leaching in the corn and tomato regions.
NO3-N leaching in the corn region in both years was 4.05 and 8.79

times larger than in the tomato region, respectively. First, more Nfertilizer was applied in the corn region, which correspondingly increased
NO3-N leaching in that region. Second, there was intensive NO3-N exchange in the horizontal direction between different crop regions, while
NO3-N mainly moved from the tomato region (the higher NO3-N concentration) to the corn region (the lower NO3-N concentration). As a result,
NO3-N leaching in the tomato region was low. Meanwhile, the soil NO3N storage (i.e., Soilend) in the root zones of corn and tomato varied widely
because of the imbalance in the supply and demand of NO3-N. The soil
NO3-N storage in the root zone of corn was 51.9% and 52.5% lower thanin the tomato region in 2014 and 2015, respectively.
Both N uptake in and NO3-N leaching from the root zone decreased
when an application of the N-fertilizer was reduced by 30% (the medium N-fertilizer application, i.e., MF). N uptake of corn and tomato inboth years under MF were 27.7% and 25.6% lower than under HF,
while NO3-N leaching decreased by 37.9% and 54.1%, respectively.
When the N-fertilizer application was reduced by 50%, N uptake ofTable 6
Simulated components of the N balance and the N use efﬁciency (NUE) for corn and tomato under the high (HF), medium (MF), and low (LF) N-fertilizer applications in years 2014 and
2015.
Year

2014

2015

Treatment

HF
MF
LF
HF
MF
LF

Corn

TomatoApplied (kg ha−1)

Uptaken(kg ha−1)

Leached(kg ha−1)

Soilend(kg ha−1)

NUE

Applied(kg ha−1)

Uptaken(kg ha−1)

Leached(kg ha−1)

Soilend(kg ha−1)

NUE

300.00210.00150.00300.00210.00150.00222.42156.83105.14219.57
155.75105.55

26.43
18.59
18.26
21.17
11.40
7.06

51.15
34.58
26.60
59.27
42.85
37.38

0.74
0.75
0.70
0.73
0.74
0.70

250.00175.00125.00250.00175.00125.00137.15104.0863.69
121.67
88.65
57.60

6.53
3.37
1.39
3.45
1.39
0.56

106.3267.55
59.91
124.88
84.96
66.84

0.55
0.59
0.51
0.49
0.51
0.46

N. Chen et al. / Science of the Total Environment 718 (2020) 137314

corn and tomato in both years were 52.3% and 53.1% lower than under
HF, while NO3-N leaching decreased correspondingly 48.8% and 81.2%,
respectively. Additionally, the N use efﬁciency (NUE) increased
nonlinearly with an increase in the N-fertilizer application due to thenonlinearity between the leaf area and N-fertilizer application. For example, the highest NUE can be found for the MF treatment, when itwas 3.5% and 9.9% higher than for HF and LF, respectively. Therefore,
the MF treatment (210 kg ha−1 for corn and 175 kg ha−1 for tomato)is suggested to be the optimal fertilization strategy in the corn/tomato
intercropping ecosystem.
3.5. Analysis of soil NO3-N concentrations
Since the NO3-N movement was consistent for all N-fertilizer application levels, only the NO3-N behavior in the plot with the high Nfertilizer application (HF) was visually analyzed (Fig. 4). Overall, horizontal differences in SNCs under different crop locations decreased
with an increase in the soil depth (Fig. 4). However, SNCs varied widelyin the middle between corn rows (Pc), corn and tomato rows (Pb), andtomato rows (Pt) due to differences in applied N-fertilizer amounts, irrigation volumes, and N uptake in the intercropping ecosystem. An obvious difference in SNCs in different horizontal locations (i.e., Pc, Pt, andPb) can be found in the 0–40 cm soil layer; the difference is highly significant (P b .01), especially in the 0–20 cm soil layer. The average SNCs in
the 0–20 cm soil layer in both years (2014 and 2015) were 30.9%, and63.4% higher in Pc and Pt, respectively, compared to Pb during the coexistence period (May–September), while the average SNC in Pt was 29.7%
higher than in Pc. As a result, there were large horizontal solute concentration gradients in the shallow soil layer (0–40 cm) in the corn/tomato
intercropping ecosystem. On the other hand, there were no signiﬁcantdifferences (P N .05) in SNCs under different treatments and crop regions in the deep soil layer (40–100 cm) due to intensive N uptake bycrops in the shallow soil layer.
4. Discussion
4.1. Soil NO3-N distribution in an intercropping ecosystem
The distribution of soil NO3-N in different crop regions of anintercropping ecosystem varied widely due to differences in N uptake
capabilities of different crops. Generally, soil NO3-N concentrations(SNCs) in the region of a crop with higher N uptake were lower thanin the region of a crop with lower N uptake. These differences increased
in response to an increase in the N-fertilizer application rate (Ojiem
et al., 2014; Akanksha et al., 2019). For example, Tsialtas et al. (2018)used the 15N natural abundance method to assess the N competition
between the pea and oat and found that the SNC in the oat region was
lower than in the pea region of the oat/pea intercropping ecosystem.
Similarly, there was a higher SNC in the corn region of the corn/soybean
intercropping ecosystem (Chen et al., 2019). Additionally, Ma et al.
(2018) indicated that both the root length and distribution of wheat signiﬁcantly increased, while the corn root growth was suppressed in thewheat/corn intercropping ecosystem. It seems that the crop with stronger root activity absorbs resources from the region of the other crop,which may cause the rooting depth of the crop with stronger root activity to decrease while increasing the rooting depth of the crop withweaker root activity. Overall, the main competition for resources inintercropping ecosystems was concentrated in the 0–40 cm soil layer(Yang et al., 2017; Tian et al., 2018; Chen et al., 2019). Similar results
were obtained in this study, with signiﬁcant differences in SNC foundin the 0–40 cm soil layer. The SNC in the corn region decreased on average by 29.7% compared to the tomato region in both years. On the other
hand, due to different N-fertilizer applications for different crops in theintercropping ecosystem, larger solute concentration gradients wereformed in the horizontal direction, which caused NO3-N movementfrom the region with higher solute concentrations to the region with11

lower solute concentrations (Hillel et al., 1981; Šimůnek and Suarez,
1993). For example, the percentage of N transferred from the pea region
with higher solute concentrations to the oat region with lower soluteconcentrations was between 14.8 and 26.8% (Tsialtas et al., 2018). Similarly, 9.6% of N was transferred from Dalbergia odorifera to Eucalyptus
urophylla under the 3 g pot−1 urea treatment (Yao et al., 2019). This
study similarly revealed that during the stage with an intensive soluteexchange, NO3-N moved in the horizontal direction from the crop region (a high concentration region) to the bare area (a low concentration
region) (Fig. 6).
4.2. Soil NO3-N competition in an intercropping ecosystem
It is important to assess the spatial and temporal N competition between the two crop species to improve ﬁeld productivity of anintercropping ecosystem (Duan et al., 2019). The difference in N absorption by intercrop species was mainly affected by the root activity, while
the crop roots with a stronger activity usually had a relatively strong N
uptake capability (Marschner, 2013). For example, Li et al. (2019) studied the N uptake by calla lily in the calla lily/rubber tree intercropping
ecosystem. They found that N in the leaves, tubers, and roots ofintercropped calla lily was reduced by 11.0%, 20.6%, and 22.6%, respectively. Xie and Kristensen (2016) revealed, based on the analysis of
the Relative Competition Index (RCI), that interspecies competition facilitated the growth of leek but hampered that of dyer's woad. Nissen
et al. (1999) indicated that although N uptake by cabbage was stronger
than that by eucalyptus, the trees can still beneﬁt from theintercropping ecosystem. Similar results were obtained in this study,
in which the physiological activity of corn was stronger than that of tomato in the corn/tomato intercropping ecosystem (Fig. 6). Additionally,
the nonuniformity of the soil NO3-N distribution has resulted in differences in N uptake in different root zones of the intercropping ecosystem
as well (Fu et al., 2019; Li et al., 2019). For instance, N uptake by corn in2014 and 2015 increased by 38.3 and 42.2%, respectively, compared tothat by tomato, while the SNC in the corn region decreased on averageby 29.7% compared with the tomato region in both years (Fig. 4 andTable 6). Similar results were reported by Chen et al. (2019) and Fan
et al. (2018), who found that N uptake by corn was higher than by soybean, and the residual NO3-N in the corn region was lower than in thesoybean region.
5. Conclusion
The updated version of HYDRUS-2D capable of considering parameters for two vegetations was used for an intercropping ecosystem. Numerically simulated results showed that soil water contents (SWCs)
and soil NO3-N concentrations (SNCs) were in good agreement withmeasurements for scenarios with high (300 kg ha−1 for corn and
250 kg ha−1 for tomato), medium (210 kg ha−1 for corn and
175 kg ha−1 for tomato), and low (150 kg ha−1 for corn and
125 kg ha−1 for tomato) N-fertilizer applications (HF, MF, and LF, respectively) during the validation (2015) period. Signiﬁcant differencesin SNCs in the corn, tomato, and bare regions could be found in the
0–40 cm soil layer due to differences in NO3-N applications to different
regions and limited NO3-N exchange between root zones of different
crops. The intensive exchange of NO3-N in the horizontal direction
was found during the second stage (DAS 37-113 in 2014 and DAS 29120 in 2015). Because of the low soil water potential and soil NO3-N
concentrations gradient between the corn region (I) and the bare area
(II), the exchange of NO3-N between these regions was lower than between the tomato region (III) and the bare area (II). Additionally, NO3N leaching in the vertical direction in the corn region was higher thanin the tomato region, while N uptake by corn was higher compared tothat by tomato. Overall, the results of this study can thus be very useful
in designing optimal N-fertilizer applications for intercropped ﬁelds.

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N. Chen et al. / Science of the Total Environment 718 (2020) 137314

CRediT authorship contribution statement
Ning Chen: Formal analysis, Investigation, Writing - original draft,
Visualization. Xianyue Li: Conceptualization, Methodology, Validation,
Formal analysis, Resources, Data curation, Writing - review & editing,Visualization, Supervision, Project administration. Jirí Šimůnek: Methodology, Software, Data curation, Writing - review & editing. Haibin
Shi: Data curation, Writing - review & editing. Qi Hu: Investigation.
Yuehong Zhang: Investigation.

Declaration of competing interest
The authors declare that they have no known competing ﬁnancial
interests or personal relationships that could have appeared to inﬂuence the work reported in this paper.
Acknowledgments
This research was jointly supported by the National Natural Science
Foundation of China (51669020, 51969024, and 51539005), and the
Major science and technology projects of Inner Mongolia(zdzx2018059).
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