Agricultural Systems 201 (2022) 103461

Contents lists available at ScienceDirect

Agricultural Systems
journal homepage: www.elsevier.com/locate/agsy

Quantifying inter-species nitrogen competition in the tomato-cornintercropping system with different spatial arrangements
Ning Chen a, Xianyue Li a, c, d, *, Jiří Šimůnek b, Haibin Shi a, d, Yuehong Zhang a, Qi Hu aaCollege of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China
Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USAc
Collaborative Innovation Center for Integrated Management of Water Resources and Water Environment in the Inner Mongolia Reaches of the Yellow River, Hohhot
010018, ChinadResearch and Development of Efficient Water-saving Technology and Equipment and Research Engineering Center of Soil and Water Environment Effect in Arid Area of
Inner Mongolia Autonomous Region, Hohhot 010018, Chinab

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• The modified HYDRUS-2D model can
precisely quantify inter-species nitrogencompetition.
• The most intense competition between
tomatoes and corn occurred 41–120
days after sowing.
• IC4–2 is the optimal system recom­
mended for sustainable agriculture
development.
• LERN of the tomato-corn intercropping
system decreased with an increase in theplanting ratio of tomatoes.

A R T I C L E I N F O

A B S T R A C T

Keywords:
Inter-species competition
Soil nitrogen dynamic
Intercropping system
Spatial arrangements
HYDRUS (2D/3D)

CONTEXT: Intercropping systems have been widely used worldwide due to their high economic benefits andland-use efficiency. While it is well known that inter-species nutrition competition in intercropping systems leads
to high production efficiency, the effects of different spatial arrangements of intercropping species on thesemechanisms remain unclear.
OBJECTIVE: The objectives of this study were to reveal differences in the soil nitrogen dynamics in the tomatocorn intercropping system with different spatial arrangements of crops and to determine suitable spatial
arrangements.
METHODS: A two-year experiment was carried out during the 2018 and 2019 seasons to determine inter-species
nitrogen competition in the tomato and corn systems with different spatial arrangements. The treatments
included sole corn (SC), sole tomatoes (ST), two rows of tomatoes intercropping two rows of corn (IC2–2), andfour rows of tomatoes intercropping two rows of corn (IC4–2) systems. Additionally, the modified HYDRUS (2D/
3D) model was used to quantify soil nitrogen concentrations, solute fluxes, soil nitrogen distribution, and soil
nitrogen balance in the crop root zone under the SC, ST, IC2–2, and IC4–2 systems.

* Corresponding author at: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China.
E-mail address: lixianyue80@126.com (X. Li).
https://doi.org/10.1016/j.agsy.2022.103461
Received 11 April 2022; Received in revised form 5 July 2022; Accepted 8 July 2022
Available online 19 July 2022
0308-521X/© 2022 Elsevier Ltd. All rights reserved.

N. Chen et al.

Agricultural Systems 201 (2022) 103461

RESULTS AND CONCLUSIONS: The modified HYDRUS (2D/3D) model could precisely capture the soil nitrogendynamics with nRMSE ranging from 2.8% to 12.4%. In general, soil NH4-N concentrations in the root zone oftomatoes of different systems decreased as follows: IC2–2 > IC4–2 > ST, while a reverse trend was observed in theroot zone of corn. Compared with soil NH4-N, differences in soil NO3-N among different systems were moreevident due to its higher mobility. IC2–2 had the highest nitrogen flux, cumulative N flux, N uptake (CNU), andthe land equivalent ratio for nitrogen (LERN) among different systems. Among different intercropping systems,
the corn's and tomato's highest crop yields occurred in IC2–2 and IC4–2, respectively, with an average of 13,406.0
and 98,732.3 kg ha− 1 in both years. Additionally, corn and tomato's highest nitrogen use efficiency (NUE)
occurred in IC2–2 and ST, respectively. However, the land-equivalent ratio for nitrogen use efficiency (LERNUE) in
IC4–2 was 5.4% higher than in IC2–2. Therefore, IC4–2 is the optimal intercropping system recommended forsustainable agriculture development.
SIGNIFICANCE: The findings of this study improve the understanding of the mechanisms of inter-species nitrogencompetition between commercial and grain crops. The study also suggests to the farmers and government
suitable spatial arrangements of an intercropping system that can be adopted to promote the development ofsustainable agriculture.

1. Introduction

esculentum Miller) intercropping system, although the atmosphere'smolecular nitrogen was not transformed to soil ammonium nitrogen to
promote crop nitrogen uptake, nitrogen uptake of the dominant species
increased by restraining nitrogen uptake of the subordinate species(Long et al., 2021; Zhang et al., 2016). For example, intercropped wheat
increased nitrogen uptake by 54.5–375% compared with sole wheat,
while nitrogen uptake of intercropped corn decreased by 38.3–42.2%
compared with sole corn (Liu et al., 2015). However, research on ni­
trogen competition between commercial and grain crops is limited.
It is essential to optimize spatial plant arrangements to take advan­
tage of competitive relationships in the intercropping system and ach­
ieve maximum economic benefits. Currently, the traditional
intercropping system usually involves one crop row intercropping
another crop row (Gitari et al., 2020), one crop row intercropping two
rows of another crop (Unay et al., 2021), or two crop rows intercropping
two rows of another crop (Galanopoulou et al., 2019). Different spatial
arrangements of various cultivation crops will result in different soil
nitrogen distributions, crop nitrogen uptakes, and nitrogen use effi­
ciencies. Chen et al. (2020a) reported that soil nitrate was distributed
mainly in the root zone of tomatoes in the system with two rows of to­matoes intercropping two rows of corn (IC2–2). Choudhary and Choud­hury (2016) illustrated that the CNU in the system with one row of cornintercropping five rows of soybeans (IC1–5) was higher than that in theIC1–2 system or in the sole crop system.
Although the nitrogen movement, nitrogen redistribution, and ni­
trogen competition in the root zone of the corn-tomato intercropping
system were studied by Chen et al. (2020a), they only focused on ni­
trogen competition in one type of the intercropping system (i.e., IC2–2).
However, soil nitrogen dynamics and competition may differ in different
intercropping systems. Various spatial arrangements of tomatoes andcorn may alter the nitrogen movement and distribution in the soil profile
and affect the competitive nitrogen relationship between intercropping
species. Understanding the mechanisms of nitrogen competition indifferent tomato-corn intercropping systems and determining the suit­able spatial arrangements are essential for decreasing non-point pollu­
tion and promoting the development of sustainable agriculture.
The numerical model HYDRUS (2D/3D) (Šimůnek et al., 2016) canaccurately capture the dynamics of soil water, nitrogen, and salts incomplex subsurface systems due to its flexible boundary conditions (e.g.,
Filipovic et al., 2014; Grecco et al., 2019; Rai et al., 2019). However, thestandard HYDRUS (2D/3D) model only considers one vegetation andone set of root water uptake parameters, and it cannot distinguish be­
tween root water and nitrogen uptakes by different crops in an inter­cropping system (Li et al., 2015). Thus, a modified version of HYDRUS(2D/3D) was developed to describe inter-species competition for waterand nutrient resources in an intercropping system by considering two
sets of root water uptake parameters for two vegetation (Šimůnek et al.,2016). This modified model is used in this study to quantify differencesin nitrogen movement, nitrogen distributions, and nitrogen competitive

Intercropping has been widely applied worldwide, e.g., in China (Xuet al., 2022), Iran (Firouzi et al., 2017), Brazil (De Conti et al., 2019),
and Germany (Nelson et al., 2021). It can effectively optimize plantation
structure (Morugán-Coronado et al., 2020), and improve land use effi­
ciency (Latati et al., 2019). In China, especially in the Northwest andSouth of China, intercropping systems are often used to improve agri­
cultural production (Li, 2016). Intercropping systems improve water use
efficiency, nitrogen use efficiency, and field productivity compared withsole crop systems (Coll et al., 2012; Daryanto et al., 2020; Drakopoulos
et al., 2021; Jensen et al., 2020; Salehi et al., 2018). In recent years,
intercropping systems using commercial crops, e.g., the tomato-cornintercropping system, have become more popular because ofincreasing economic benefits and controlling soil heavy metal pollution(Salgado et al., 2021; Upadhyay et al., 2010; van Asten et al., 2011). It
was reported that the net benefit of producing in the tomato-cornintercropping system could increase by 3467 and 5341 $ ha− 1compared with the sole corn and sole tomato systems, respectively(Upadhyay et al., 2010). Moreover, the tomato-corn intercropping sys­
tem has been shown to effectively decrease the Cd content in therhizosphere soil by 4.7% and 2.1% compared with the sole tomato andsole corn systems, respectively (Wan et al., 2020). Therefore, it is ofgreat significance to study the tomato-corn intercropping system to in­crease farmers' income and protect the agricultural ecosystem.
As an essential nutrient substance, nitrogen plays a crucial role incrop growth (Gaudinier et al., 2018). A reasonable nitrogen application
strategy can effectively improve crop growth and agricultural produc­tion (Groenveld et al., 2021). However, the nitrogen application strategy
is hard to formulate for inter-species of the intercropping system becauseof the complicated inter-species competitive relationships. For example,
intensive inter-species competition for nitrogen occurs when soil nitro­
gen concentrations in the crop root zone cannot meet crop growth needs(for low nitrogen application conditions). Depending on its stronger root
uptake ability, the dominant species uptakes soil nitrogen from the rootzone of the subordinate species to meet its crop growth. However,subordinate species' growth will be limited due to decreasing soil ni­
trogen concentrations in the root zone (Dua et al., 2016; Nassab et al.,2011; Zhang and Li, 2003). Although the growth and development ofboth intercropping species can be promoted in high nitrogen application
conditions, this may cause serious non-point source pollution (Manevski
et al., 2014). Particularly soil nitrogen from the root zone of the sub­ordinate species can be easily leached due to its greater concentration
gradient (Chen et al., 2020a). Therefore, a reasonable nitrogen appli­
cation strategy needs to be developed by determining the inter-species
nitrogen competition relationship in the intercropping system.
Differences in inter-species nitrogen competition exist in different
intercropping systems (Ghosh et al., 2009; Galanopoulou et al., 2019;
Maitra et al., 2021). In the Gramineaex-Solanaceae (Lycopersicon2

N. Chen et al.

Agricultural Systems 201 (2022) 103461

Fig. 1. The modeling domain (unshaded region), boundary conditions, and the planting pattern for the ST (a), SC (b), IC2–2 (c), and IC4–2 (d) systems. Points Pc, Pb,
and Pt are located in the middle between corn rows, between corn and tomato rows, and between tomato rows, respectively.

relationship in the tomato-corn intercropping system with different
spatial crop arrangements.
The main objectives of this study are i) to calibrate and validate
modified HYDRUS (2D/3D) using observed soil NH4-N and NO3-N
concentrations, ii) to reveal differences in soil NH4-N and NO3-N dis­tributions in the soil profile, iii) to quantify nitrogen fluxes between theroot zones of tomatoes and corn, iv) to analyze the nitrogen balance andnitrogen use efficiency in the root zones of tomatoes and corn fordifferent spatial crop arrangements, and v) to compare the competitive
ratio for nitrogen and the land equivalent ratio for nitrogen in different
systems by scenario simulation.

2. Materials and methods
2.1. Site description
The experiment was conducted at the Jiuzhuang experimental sta­tion (40◦ 41′ N, 107◦ 18′ E; 1041 m above sea level) in the Hetao Irrigation
Area, Inner Mongolia, China. This site is located in an arid agroecological zone with little annual precipitation (144 mm), high evapo­transpiration (593.7 mm), and moderate air temperature (21.2 ◦ C). Soil
texture was classified as a silty sand loam with 7% of sand, 89% of silt,
and 4% of clay (United States Department of Agriculture, 2010). The soil
is moderately alkaline with an average pH of 7.6, organic carbon of 12.1
g kg− 1, available nitrogen of 1.0 g kg− 1, available phosphorous of 0.8 g
3

N. Chen et al.

Agricultural Systems 201 (2022) 103461

were applied as topdressing to tomatoes during the crop growing season,of which 45, 60, and 45 kg ha− 1 were applied in different crop stages.
The specific fertilization dates for tomatoes were identical to corn. One
drip line was installed between two crop rows, with the emitter spacingof 30 cm, the emitter discharge of 2.4 L h− 1, and the working pressure of
50–100 kPa. The irrigation uniformity and efficiency of the drip irri­
gation system were 98% and 95%, respectively. Two valve and water
meter (with a precision of 0.001 m3) groups were installed on drip lines
to control and monitor water flow on the tomato and corn sides,
respectively. The local recommended irrigation depths for a silty sandloam (30 mm for corn and 22.5 mm for tomatoes) were applied every
7–15 days (Chen et al., 2022). Moreover, the plastic film (with 0.01 mm
thickness and 80 cm width) was used in each plot, with the fraction ofsoil covered by plastic film mulching of 0.8 (Fig. 1).
2.3. Sample collection and analysis
The soil water content (SWC) was determined by three TIME probes(IMKO GmbH Inc.; IPH, TRIME-PICO, Germany) installed in the middleof the corn rows (Pc), middle of the tomato rows (Pt), and in the barearea (Pb). SWCs in 0–20, 20–40, 40–60, 60–80, and 80–100 cm soil
depths were observed once every 3–5 days. The probes were calibratedusing gravimetric measurements (according to Topp et al., 1980),
resulting in the following calibration equation for TIME probes:()
θg = 0.998θT + 0.02 R2 = 0.96
(1)Fig. 2. Schematic of the four experimental treatments (ST, SC, IC2–2, and IC4–2)
with three replicates (12 plots).

kg− 1, and available potassium of 17.0 g kg− 1 in the 0–30 m horizons.
The soil's bulk density and field capacity in the surface soil layer (0–15cm) were 1.28 g cm− 3 and 0.30 cm3cm− 3, respectively.

where θg is the soil water content obtained by the gravimetric mea­
surement (cm3 cm− 3), and θT is the soil water content obtained by TIME
probes (cm3 cm− 3).
Soil samples for soil nitrogen (NH4-N and NO3-N) were collectedusing a soil auger 100 cm long once every 10–15 days. Collection lo­
cations for soil nitrogen were identical to those for SWC. Soil NH4-N andNO3-N concentrations were measured using the spectrophotometricmethod (GB/T 32737–2016). A dry soil sample of 40 g was added to 200
mL KCl solutions (2 mol L− 1) and shaken for one hour in a water bathwith a constant temperature of 20 ± 2 ◦ C (Bremner and Keeney, 1965).
The collected solution was filtered and mixed with phenol and sodium
dichloro isocyanurate. After five hours, the soil NH4-N concentration
was determined by ultraviolet spectrophotometry at 630 nm (Beijing
General Instrument Co. LTD., TU-1901, General Instrument, CHN). Also,
1 g of dry soil was placed into the reduction column, and 10 mL of the
NH4Cl solution was added. 0.2 mL of color development reagent wasadded to the colorimetric tube. The soil NO3-N concentration wasdetermined by ultraviolet spectrophotometry at 543 nm.
Nitrogen leaching was determined by multiplying the nitrogenconcentration of the leaching solution by the corresponding water flux.
Water fluxes and soil solutions were measured using self-made field
lysimeters and a PVC tube opened at the bottom and installed at a depthof 100 cm below the soil surface before corn seeding (Li et al., 2014).
Crop nitrogen uptake was determined using the semi-micro Kjeldahl
method (Bremner and Keeney, 1965). Crop samples (stem, leaves, root,and grain) were kept for 30 min at 105 ◦ C temperature and thenconserved at 75 ◦ C temperature in the oven to reach a constant weight.
After crushing and sieving, a 0.2 g sample was weighted with a weighing
paper and digested with 5 mL of concentrated H2SO4. The prepared
solution was measured by a flow analyzer (Brown ruby Inc., AA3, SEAL,
Germany) to determine the nitrogen content of each organ. Crop ni­
trogen uptake was calculated using the following equation:

2.2. On-farm trials and trial management
Corn seeds were sown on May 1, 2018, and May 4, 2019, while to­matoes were transplanted from the greenhouse on May 20, 2018, andMay 18, 2019. Local cultivars of corn (Zea mays L, cv. Junkai 918) andtomatoes (Lycopersicon esculentum Miller, cv. Dunhe 47) were used in theexperiment. Experimental treatments consisted of four planting struc­tures: sole corn (SC), sole tomatoes (ST), two rows of tomatoes inter­cropping two rows of corn (IC2–2), and four rows of tomatoesintercropping two rows of corn (IC4–2) (Fig. 1). The experimental design
was a completely randomized design with three replications (Fig. 2).
Row spacing (50 cm) and crop spacing (30 cm) were identical indifferent intercropping systems. Typical local planting densities for corn
and tomato were adopted in this study. Moreover, to obtain higher NUE
and economic benefits for the tomato-corn intercropping system, thisstudy further increased the planting densities of tomatoes based on theIC2–2 system. Planting densities of corn in the sole crop, IC2–2, and IC4–2
systems were 60,030, 33,367, and 22,233 seeds ha− 1, respectively,
while planting densities of tomatoes were 60,030, 33,367, and 44,489
seeds ha− 1, respectively. The plot area was 6 m wide and 20 m long,while a 3 m buffer zone separated the plots. To precisely capture theinter-species competition for nitrogen during different crop stages, theentire crop growing season was divided into three growth stages: thefirst stage (DAS 0–40, i.e., slow NO3-N exchange), the second stage (DAS
41–120, i.e., intensive NO3-N exchange), and the third stage (DAS
121–140, i.e., stable NO3-N exchange). Moreover, three typical days,
including one day (DAS 103), seven days (DAS 109), and fourteen days
(DAS 115) after the nitrogen fertilizer application, were chosen in thesecond stage to explore the differences in soil nitrogen distributions indifferent intercropping systems.
At sowing, 120 kg ha− 1 of diammonium phosphate and potassium
sulphate were uniformly applied to all treatments. Additionally, 210 kg
ha− 1 of urea were applied as topdressing to corn during the crop
growing season. 63, 84, and 63 kg ha− 1 were applied on DAS 47, 81, and
102 in 2018, respectively, while the same application amounts wereapplied on DAS 44, 75, and 109 in 2019. Moreover, 150 kg ha− 1 of urea

n
∑

NU =

mi c'i(2)

i=1where NU is the crop N uptake (mg), mi is the dry matter mass of eachorgan (g), and c’ is the nitrogen content of each organ (mg g− 1).
The leaf areas and plant heights were determined by a leaf area meter4

N. Chen et al.

Agricultural Systems 201 (2022) 103461(LI-COR Inc. Li- 3000C, LI, USA) and a tape with a precision of 0.1 cm,
respectively. Ten plants of each species from each plot were sampled at
physiological maturity to measure corn and tomato yield. Root samples
for corn and tomatoes under good growth conditions were measured
using root samples collected from a soil transect during each crop
growth stage. These samples were collected every 5 cm down to a depthof 50 cm, where higher roots density occurred, and then every 10 cm
below the 50-cm depth until no roots were found (Chen et al., 2020a).
Root samples were scanned using a root system scan (Reagent Instru.;
Perfection 4870photo, Epson, Japan), and the root distribution param­
eters were determined using the WinRHIZO software.

CRc =NU IC-c× 100%NU IC-t + NU IC-c

where CRt and CRc are the competitive ratios for nitrogen for tomatoes
and corn in the intercropping system, respectively.
7) Economic benefits
Investment and revenue were considered as the economic benefits.
Investments included the cost of fertilizer, seed, irrigation, machine,
plastic film, pesticides, and labor:
I = IF + IS + II + IM + IPF + ID + IP + IL

2.4. Calculations analysis

NU
× 100%NI(3)

R = Y × Pc

where NU is the crop nitrogen uptake (kg ha− 1), and NI is the nitrogenfertilizer application amount (kg ha− 1).

CY
× 100%
NU(4)

where CY is crop yield (kg ha− 1).
3) Nitrogen use efficiency (NUE):
CY
NUE =× 100% = UE × PENI

(5)NU IC-t NU IC-c+NU SC-t NU SC-c(7)where NUEIC-t is the nitrogen use efficiency of tomatoes in the inter­cropping system, NUESC-t is the nitrogen use efficiency of tomatoes in thesole crop system, NUEIC-c is the nitrogen use efficiency of corn in theintercropping system, and NUESC-c is the nitrogen use efficiency of cornin the sole crop system.

NU IC-t
× 100%NU IC-t + NU IC-c

(13)

NR = TR − TI

(14)

S(h) = Sc (h) + St (h) = αc (h)⋅bc (x, z)⋅Tpc ⋅Lc + αt (h)⋅bt (x, z)⋅Tpt ⋅Lt(15)

where Sc and St are the root water uptake terms (cm3 cm− 3 day− 1) for
corn and tomatoes, respectively, Tpc and Tpt are the potential transpi­
ration rates (cm day− 1) for corn and tomatoes, respectively, Lc and Lt are
the surface lengths associated with transpiration (cm) of corn and to­matoes, respectively, and bc(x,z) and bt(x,z) are root water uptake dis­tribution functions (cm− 2) for corn and tomatoes, respectively. Tp was
obtained as a fraction of potential evapotranspiration (ETp) using Beer's
law. The root density functions for corn and tomatoes (bc(x,z) and bt(x,z), respectively) were calculated based on the actual root distribution.

6) The competitive ratio for nitrogen (CR) (Zhang et al., 2016):
CRt =TR = Rt + Rc

2.5.1. Modeling introduction
A modified HYDRUS (2D/3D) model (Šimůnek et al., 2016) simu­
lating the transient two-dimensional movement of water and solutes inthe soil profile was adopted to consider two vegetations and two root
water and nutrient uptakes. This model describes water flow andnutrient transport in variably saturated porous media using the Richards
and convection-dispersion equations, respectively. The solute transport
equation considers the advective-dispersive transport in the liquid phaseand diffusion in the gaseous phase. The theoretical part of the model is
described in detail in its technical manual (Šimůnek et al., 2016) andAppendix.
Actual root water uptakes of corn and tomatoes in the modified
HYDRUS (2D/3D) model are determined by the potential transpiration
rate (Tp), the root density function, and the piecewise linear water stressresponse function proposed by Feddes et al. (1978):5) The land equivalent ratio for nitrogen use efficiency (LERNUE):
NUEIC-t NUEIC-c+
NUESC-t NUESC-c(12)2.5. HYDRUS (2D/3D) model(6)

where NUIC-t is the nitrogen uptake of tomatoes in an intercropping
system, NUSC-t is the nitrogen uptake of tomatoes in a sole crop system,
NUIC-c is the nitrogen uptake of corn in the intercropping system, and
NUSC-c is the nitrogen uptake of corn in a sole crop system. LERN is thesummation of the partial LERN of single crops in the intercropping sys­tem. Partial LERN shows the relative competitive abilities of single crops
in the intercropping system. Note that LERN > 1 indicates an advantageof the intercropping system, and LERN < 1 indicates a disadvantage ofthe intercropping system.

LERNUE =TI = It + Ic

where It and Ic are the investments in the tomato and corn intercropping
systems ($ ha− 1), respectively, and Rt and Rc are the revenues of thetomato and corn intercropping systems ($ ha− 1), respectively.

4) The land equivalent ratio for nitrogen (LERN) (Salehi et al., 2018;
Szumigalski and Van Acker, 2006; Xu et al., 2020):
LERN =(11)

where R is the revenue ($ ha− 1), Pc is the local price ($ t− 1). The local
price of tomatoes and corn were assumed to be 74.6 and 450 $ t− 1,
respectively.
The total investment (TI), total revenue (TR), and net revenue (NR)for various intercropping systems were obtained using the following
equations:

2) Physiological efficiency (PE):
PE =

(10)

where I is the investment sum ($ ha− 1), IF, IS, II, IM, IPF, ID, IP, and IL are
the costs of fertilizer, seed, irrigation, machine, plastic film, pesticides,
and labor ($), respectively.
Revenue was determined based on crop yield (Y) and the local priceper unit of yield (Pc):

1) Uptake efficiency (UE) (Moll et al., 1982; Ahmad et al., 2008; Hajari
et al., 2016; Gao et al., 2018; Fernandez et al., 2022):
UE =

(9)

(8)

5

N. Chen et al.

Agricultural Systems 201 (2022) 103461

Table 1
Soil hydraulic parameters (the residual water content, θr, the saturated water content, θs, the shape parameters, α, n, and l, and the saturated hydraulic conductivity, Ks)
and solute transport parameters (the longitudinal dispersivity, DL, the transverse dispersivity, DT, the distribution coefficient, Kd, and the molecular diffusion coef­
ficient, λ) of the 0–20 cm, 20–40 cm, and 40–100 cm soil depths. Note that subscripts 1 and 2 represent NH4-N and NO3-N, respectively.
Depth (cm)

0–20
20–40
40–100

Soil hydraulic parameters

Solute transport parameters− 1

θr(cm3 cm− 3)

θs(cm3 cm− 3)

α (cm )n(− )Ks
(cm day− 1)l (− )DL (cm)

DT
(cm)

Kd1
(cm3 g− 1)

Kd2(cm3 g− 1)

λ1(cm2 h− 1)

λ2(cm2 h− 1)

0.053
0.050
0.044

0.436
0.388
0.383

0.006
0.07
0.09

1.63
1.61
1.55

65.1
43.4
22.5

0.5
0.5
0.5

10

1
0.5
0.5

3.5

0

0.064

0.068

The root water uptake stress reduction functions for corn and tomatoes(ac(h) and at(h), respectively) can be calculated as follows:⎧
h1 − h⎪⎪h2 < h ≤ h1⎪⎪⎪
⎨ h1 − h2α(h) =(16)1
h3 ≤ h ≤ h2
⎪
⎪
⎪
⎪
h
−
h
4
⎪
⎩
h4 ≤ h < h3
h3 − h4

and cc2 and ct2 are the NO3-N concentrations taken up by corn and to­matoes roots (mg cm− 3), respectively.
2.5.2. Initial and boundary conditions
In the tomato-corn intercropping system, inter-species nitrogencompetition was evaluated in a rectangular domain shown in Fig. 1. The
transport domain was discretized using 1920 two-dimensional trian­
gular finite elements to ensure a fine spatial discretization of the area
close to the soil surface. Measured SWCs were used as the initial con­ditions in the model. Additionally, the initial conditions for NH4-N andNO3-N were set to zero due to the soil profile's initially low soil nitrogenconcentration. At the soil surface, the time-variable flux and atmo­
spheric boundary conditions (BC) were specified to represent emittersand the soil surface between emitters, respectively. The time-variable BC
represented drip irrigation. Since plastic film mulching can effectively
cut off the water vapor transport between the soil surface and the at­mosphere, the mulched soil surface was assigned a “No Flow” BC. The
atmospheric BC includes precipitation, potential evaporation, and po­
tential transpiration fluxes. Potential evaporation and transpiration
were obtained as fractions of potential evapotranspiration (ETp), andETp was obtained by multiplying reference evapotranspiration (ET0) by
the crop coefficient (Kc). Kc for corn and tomatoes were taken from the
FAO paper (Allen et al., 1998). The extinction coefficients for corn andtomatoes equal 0.39 (Li et al., 2018) and 0.65 (Cruz et al., 2014),
respectively.
The emitter water and solute fluxes for a particular boundary were
calculated as follows (Chen et al., 2020a):/ ′/ ′qc = Qc Lc = Ic × Lc Lc(19)

where h1 is the anaerobic point pressure head (cm), h2 and h3 are thepressure heads between which root water uptake is optimal (cm), and h4
is the wilting point pressure head (cm). The water stress parameters forcorn and tomato provided by Wesseling and Brandyk (1985) wereadopted in this study, i.e., h1, h2, h3, and h4 were set at − 15, − 325,
− 600, and − 8000 cm for corn, respectively; and − 30, − 800, − 1500,
and − 8000 cm for tomatoes, respectively.
The reactive transport of nitrogen species is considered to be
described using a first-order sequential reaction chain, in which NH4-N
first transforms into NO2-N and then further into NO3-N. However,since the transformation from NO2-N to NO3-N is significantly fasterthan the transformation from NH4-N to NO2-N, the intermediate species(NO2-N) can be neglected in the model (Please see Appendix). Addi­
tionally, the denitrification, volatilization, immobilization, and miner­
alization processes were neglected in the model, similarly to many otherstudies (e.g., Ramos et al., 2012; Tafteh and Sepaskhah, 2012). Chenet al. (2020b) explained the reasons for this simplification in detail. This
study further assumed that NO3-N is present only in the dissolved phase,and NH4-N is linearly distributed between the dissolved and adsorbed
phases. Thus, the distribution coefficient (Kd) was set to zero and 3.5
cm3 g− 1 for NO3-N (Kd2) and NH4-N (Kd1), respectively (Hanson et al.,2006). Additionally, solute transport parameters, including the longi­
tudinal dispersivity (DL), the transverse dispersivity (DT), and the mo­lecular diffusion coefficients, depend mainly on soil texture and soluteproperties (Table 1). The calibrated longitudinal dispersivity (DL) was
considered 10 cm for 0–20 cm soil layer, 5 cm for 20–40 cm soil layer,and 5 cm for 40–100 cm soil layer. The transverse dispersivity (DT) was
taken as one-tenth of DL (Cote et al., 2003; Hanson et al., 2006; Ramos
et al., 2012). Also, the molecular diffusion coefficients (λ) for NH4-N (λ1)
and NO3-N (λ2) in free water were set to 0.064 and 0.068 cm2 h− 1,
respectively (Cote et al., 2003; Nakamura et al., 2004). Soil hydraulicparameters (including the shape parameters, α and n, and the saturated
hydraulic conductivity, Ks) and solute transport parameters (including
the longitudinal dispersivity, DL, and the transverse dispersivity, DT)
were manually adjusted by comparing simulated and observed SWC andsoil nitrogen concentration values until a good accuracy was achieved.
Nutrient uptake by roots is associated with root water uptake S,
which is computed using the following equations:Ss1 = Sc (h)cc1 + St (h)ct1(17)

Ss2 = Sc (h)cc2 + St (h)ct2

(18)

5
5

/ ′
/ ′
qt = Qt Lt = It × Lt Lt(20)

qsc = qc × cc0(21)

qst = qt × ct0(22)

where qc and qt are the boundary fluxes (cm day− 1) for corn and to­matoes, respectively, Qc and Qt are the actual emitter fluxes (cm2 day− 1)for corn and tomatoes, respectively, Ic and It are the irrigation depths(mm) for corn and tomatoes, respectively, Lc and Lt are the widths of the
canopy (cm) for corn and tomatoes, respectively, Lcˊ and Ltˊ are thewidths of the variable-flux BC (cm) for corn and tomatoes, respectively,
qsc and qst are the solute fluxes across the boundary (mg cm− 2 day− 1) for
corn and tomatoes, respectively, and cc0 and ct0 are the nitrogen con­centrations in irrigation water (mg cm− 3) for corn and tomatoes,
respectively. In this study, I, L, and Lˊ for corn were 3, 40, and 5 cm,
respectively, and 2.25, 40, and 5 cm for tomatoes.
A time-variable pressure head BC was applied along the bottom
boundary to represent the effects of the shallow groundwater table onwater flow and soil nitrogen dynamics. Additionally, the third-type
Cauchy BC was used for solute transport along all boundaries with a
specified water flux BC (at the top boundary), while the second-type
Neumann BC was used for solute transport along outflow boundaries(at the bottom boundary). Two lateral boundaries of the flow domain
were assigned a no-flow BC.

where subscripts 1 and 2 refer to corn and tomatoes, respectively, Ss isroot nutrient uptake (mg cm− 3 d− 1), cc1 and ct1 are the NH4-N concen­
trations taken up by corn and tomatoes roots (mg cm− 3), respectively,
6

N. Chen et al.

Agricultural Systems 201 (2022) 103461

Fig. 3. Observed and simulated soil NH4-N concentrations in Pc (a, b, c, g, h, i) and Pt (d, e, f, j, k, l) in the 0–20 (top), 20–40 (middle), and 40–100 cm (bottom) soil
layers in the ST, SC, IC2–2, and IC4–2 systems during the 2018 (a-f) and 2019 (g-l) seasons.

Fig. 4. Observed and simulated soil NO3-N concentrations in Pc (a, b, c, g, h, i) and Pt (d, e, f, j, k, l) in the 0–20 (top), 20–40 (middle), and 40–100 cm (bottom) soil
layers in the ST, SC, IC2–2, IC4–2 systems during the 2018 (a-f) and 2019 (g-l) seasons.

2.5.3. Scenario analysis
To further assess the mechanisms of the nitrogen competition be­
tween tomatoes and corn in different intercropping systems, we carriedout seven scenario simulations, i.e., SC, IC2–2, IC4–2, IC6–2, IC8–2, IC10–2,
and ST. Two important indicators (CR and LERN) were used to evaluate
the nitrogen competition between tomatoes and corn in different
intercropping systems.

2.5.4. Statistical analysis
The statistical difference among different experimental treatments
was determined by the analysis of variance (ANOVA) with a significancelevel of 0.05 using Statistical Product and Service Solutions (SPSS)
version 22.0. Moreover, to evaluate the model performance in simu­
lating the NH4-N and NO3-N fate and transport, the percent bias
(PBIAS), the index of agreement (IA), and the normalized root meansquare error (Nrmse) were computed in addition to a graphical7

N. Chen et al.

Agricultural Systems 201 (2022) 103461

Fig. 5. Simulated two-dimensional distributions of NH4-N concentrations one day (DAS 103) (top), seven days (DAS 109) (middle), and fourteen days (DAS 115)(bottom) after the nitrogen fertilizer application in the ST (a, e, i), SC (b, f, j), IC2–2 (c, g, k), and IC4–2 (d, h, l) systems.

comparison of observed and simulated values.

average PBIAS, IA, and Nrmse for soil NH4-N concentrations in different
systems were 8.2%, 0.86, and 8.6%, respectively, and the corresponding
statistics for soil NO3-N concentrations were 7.5%, 0.84, and 7.8%. In
general, estimation accuracy was good for the ST, SC, IC2–2, and IC4–2
systems, with PBIAS ranging from − 13.3% to 13.5% and Nrmse ranging
from 2.8% to 12.4%. Additionally, IA for soil NH4-N and NO3-N con­centrations in different systems was above 0.82, indicating that thesimulated values agree well with the measured value (Table 1). There­
fore, HYDRUS (2D/3D) can be used to evaluate nitrogen competition
between corn and tomatoes in different intercropping systems.
Apparent spatial-temporal differences between soil nitrogen con­centrations were found between different systems. The largest differ­ences in soil NH4-N concentrations among different systems occurred in
the surface soil layer (0–40 cm), especially in 0–20 cm (Fig. 3). In
general, soil NH4-N concentrations at Pt in different systems decrease as
follows: IC2–2 > IC4–2 > ST. Average soil NH4-N concentrations at Pt in
IC2–2 were 19.6% and 10.4% higher than in ST and IC4–2, respectively.
Also, the highest soil NH4-N concentration at Pc in different systems
occurred in the SC treatment, increasing by 2.9% and 12.1% comparedwith IC2–2 and IC4–2. However, the highest soil NO3-N concentrations at
Pt and Pc occurred in the ST and IC4–2 treatments, respectively (Fig. 4).
In the 0–20 cm soil layer, the average soil NO3-N concentration at Pt in
ST increased by 28.5% and 15.7% compared with IC2–2 and IC4–2,
respectively. The average soil NO3-N concentration at Pc in IC4–2
increased by 33.8% and 23.1% compared to SC and IC2–2, respectively.
In the 20–40 cm soil layer, the average soil NO3-N concentration at Pt in
ST was 20.5% and 10.9% higher than in IC2–2 and IC4–2, respectively,
while the soil NO3-N concentration at Pc in IC4–2 was 29.6% and 22.1%
higher than in SC and IC2–2, respectively. In the 40–100 cm soil layer,
the average soil NO3-N concentrations at Pt in ST, IC2–2, and IC4–2 were0.11, 0.11, and 0.10 mg cm− 3, respectively, and the soil NO3-N con­centrations at Pc in SC, IC2–2, and IC4–2 were 0.10, 0.11, and 0.11 mgn∑(Oi − Si )PBIAS = i=1 ∑× 100%
n
Oi

(23)

i=1
n
∑

(Si − Oi )2

i− 1

IA = 1 − ∑n(|Si − O| + |Oi − O| )2

(24)

i− 1

√̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
n∑1(Si − Oi )2nnRMSE =i=1

Max(Oi ) − Min(Oi )× 100%(25)

where S is the simulated value, O is the observed value, Ō is the meanobserved value, and n is the number of observed values.
3. Results
3.1. Evaluation of HYDRUS (2D/3D)
The soil NH4-N and NO3-N concentrations measured in the root zone
in 2018 were used to calibrate the HYDRUS (2D/3D) model parameters,
while the corresponding data in 2019 were used to verify the simulation
accuracy. The result showed that HYDRUS (2D/3D) could precisely
capture the dynamics of soil NH4-N and soil NO3-N (Table 1). During thecalibration period, the average PBIAS, IA, and Nrmse for soil NH4-N
concentrations in different systems were 2.1%, 0.88, and 7.2%,
respectively. The corresponding statistics for soil NO3-N concentrations
were − 2.2%, 0.89, and 7.2%. During the verification period, the8

N. Chen et al.

Agricultural Systems 201 (2022) 103461

Fig. 6. Simulated two-dimensional distributions of NO3-N concentrations one day (DAS 103) (top), seven days (DAS 109) (middle), and fourteen days (DAS 115)(bottom) after the nitrogen fertilizer application in the ST (a, e, i), SC (b, f, j), IC2–2 (c, g, k), and IC4–2 (d, h, l) systems.

cm− 3, respectively.

zone of corn in IC2–2 and IC4–2 decreased by 5.4% and 3.3% comparedwith SC, respectively. The MCZ under different systems increased on
DAS 109. The MCZ in the root zone of tomatoes in IC2–2 and IC4–2
decreased by 18.2% and 4.1% compared with ST, respectively. The MCZ
in the root zone of corn in IC2–2 and IC4–2 was 40.2% and 33.0% higher
than in SC, respectively. On DAS 115, when nitrification of NH4-N to
NO3-N was practically over, the differences in soil NO3-N distributions
among ST, SC, IC2–2, and IC4–2 were the highest (Fig. 6). The HCZ in theroot zone of tomatoes in IC2–2 and IC4–2 was 28.5% and 16.1% lower
than in ST, respectively. The MCZ and LCZ in the root zone of tomatoes
in IC2–2 increased by 12.2% and 14.3% compared with ST, respectively,
while it was 5.8% and 2.8% higher in IC4–2. Additionally, the HCZ in theroot zone of corn only occurred in IC2–2 and IC4–2. The MCZ and LCZ inthe root zone of corn in IC2–2 decreased by 26.2% and 9.4% comparedwith SC, respectively, while it was 57.0% and 5.5% lower in IC4–2.

3.2. Soil nitrogen distribution in the soil profile under different
intercropping systems
Since soil nitrogen distributions were similar in 2018 and 2019, only
the soil nitrogen distribution in 2018 is shown (Figs. 5, 6). Additionally,soil nitrogen concentrations in the soil profile were divided into threezones, i.e., a low concentration zone (0–0.25 mg cm− 3 for NH4-N;
0–0.15 mg cm− 3 for NO3-N, LCZ), a moderate concentration zone
(0.26–0.50 mg cm− 3 for NH4-N; 0.16–0.30 mg cm− 3 for NO3-N, MCZ),
and a high concentration zone (>0.50 mg cm− 3 for NH4-N; >0.30 mg
cm− 3 for NO3-N, HCZ).
On DAS 103, soil NH4-N was mainly distributed in the 0–40 cm soil
layer in different systems. Meanwhile, there were apparent differencesin soil NH4-N distributions between intercropping and sole-crop sys­tems, especially for IC2–2 (Fig. 5). The HCZ in the root zone of tomatoes
in IC2–2 was 31.2% and 8.5% lower than in ST and IC4–2, respectively.
The HCZ in the root zone of corn in IC2–2 increased by 47.4% and 10.4%
compared with SC and IC4–2, respectively. Differences in soil NH4-N
distributions among different systems on DAS 109 were lower than on
DAS 103. On DAS 115, there was nearly no soil NH4-N in the soil profile,which indicated that soil NH4-N fully transformed into soil NO3-N bynitrification.
Soil NO3-N, which was present mainly in the 40–60 cm soil layer(Fig. 6), was distributed more widely (due to the lack of retardation)
than soil NH4-N. On DAS 103, no HCZ occurred under any systems,
except for IC2–2. The MCZ in the root zone of tomatoes in IC2–2 and IC4–2
decreased by 38.8% and 23.9% compared with ST, respectively. How­ever, the MCZ in the root zone of corn in IC2–2 and IC4–2 increased by
26.8% and 33.5% compared with SC, respectively. The LCZ in the root

3.3. Exchange of soil nitrogen between the root zones of tomatoes andcorn in different intercropping systems
A hypothetical vertical line 50 cm away from the dripper (Fig. 1) was
defined to capture the nature of nitrogen competition between inter­cropping species in different intercropping systems. The left and rightregions on two sides of the hypothetical vertical line were denoted as“Region I" and “Region II,” respectively. The nitrogen flux between
Regions I (i.e., the root zone of corn in the intercropping system) and II(i.e., the root zone of tomatoes in the intercropping system) across thisvertical line was calculated.
The highest inter-species competition for nitrogen between tomatoes
and corn occurred during the second stage (DAS 41–120) (Fig. 7).
Moreover, the largest nitrogen flux occurred in the IC2–2 system. The
average nitrogen flux in IC2–2 during the second stage in 2018 increased9N. Chen et al.

Agricultural Systems 201 (2022) 103461

Fig. 7. Simulated actual (top) and cumulative (bottom) soil nitrogen fluxes in 2018 (left) and 2019 (right) in the horizontal direction between regions I and II (at avertical line 50 cm away from the dripper) in the ST, SC, IC2–2, and IC4–2 systems. Note that regions I and II represent the root zone of corn and tomatoes in theintercropping system, respectively.

Fig. 8. The simulated competitive ratios for nitrogen for tomatoes (CRt) and corn (CRc) in the ST, SC, IC2–2, IC4–2, IC6–2, IC8–2, and IC10–2 systems in 2018 (left) and2019 (right).

Fig. 9. The simulated land equivalent ratios for nitrogen (LERN) between tomatoes and corn in the IC2–2, IC4–2, IC6–2,IC8–2, and IC10–2 systems in 2018 (left) and2019 (right).

10

N. Chen et al.

Agricultural Systems 201 (2022) 103461

Table 2
Statistical results for simulated soil NH4-N and NO3-N concentrations in the middle of two corn rows (Pc) and two tomato rows (Pt) in the systems with sole tomato (ST),sole corn (SC), two rows of tomatoes intercropping two rows of corn (IC2–2), and four rows of tomatoes intercropping two rows of corn (IC4–2) during calibration (2018)and validation (2019). PBIAS, IA, and nRMSE are the percent bias, the index of agreement, and the normalized root mean square error, respectively.
Year

Soil
layers
(cm)
Pc
NH4-N
Pt

2018
Pc
NO3-N
Pt
Pc
NH4-N
Pt
2019
Pc
NO3-N
Pt

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

ST
PBIAS
(%)

IA
(− )

SC
nRMSE
(%)

7.7
− 7.0
8.5

0.93
0.93
0.90

5.4
5.7
6.2

− 9.9
− 8.1
7.2

0.86
0.90
0.92

7.7
6.1
5.4

9.6
11.9
9.3

0.84
0.98
0.96

7.7
10.6
11.4

12.4
9.4
8.6

0.82
0.94
0.86

8.2
6.3
7.4

IC2–2

IC4–2

PBIAS
(%)

IA
(− )

nRMSE
(%)

PBIAS
(%)

IA
(− )

nRMSE
(%)

PBIAS
(%)

IA
(− )

nRMSE
(%)

7.4
8.1
− 9.3

0.92
0.90
0.87

7.1
6.3
8.7

− 10.1
9.4
− 8.8

0.86
0.86
0.89

8.5
6.9
6.5

− 8.4
9.5
10.6

0.90
0.86
0.84

7.8
8.7
9.2

12.6
11.1
9.5

0.84
0.81
0.82

8.6
8.8
4.1

9.6
8.7
10.9
10.8
− 9.0
8.3
− 7.1
− 10.8
− 5.2
− 9.9
− 8.2
7.8
10.8
10.0
9.5
11.9
11.6
− 8.8
11.0
12.0
6.3
10.7
9.5
9.2

0.88
0.84
0.80
0.86
0.86
0.87
0.93
0.80
0.97
0.84
0.88
0.91
0.84
0.83
0.86
0.85
0.88
0.91
0.84
0.84
0.97
0.81
0.88
0.89

5.1
7.7
9.1
9.8
7.4
6.1
5.6
7.3
2.8
8.0
10.4
6.6
8.0
7.9
5.3
9.6
7.7
10.2
9.8
8.7
3.7
8.9
8.4
8.3

− 7.6
− 8.4
− 9.7
7.5
− 8.0
8.6
6.4
− 7.8
5.6
10.0
− 9.4
10.1
12.1
9.2
8.8
11.0
9.1
9.2
9.4
13.5
− 9.9
9.0
4.5
− 13.3

0.94
0.87
0.82
0.94
0.85
0.86
0.93
0.96
0.96
0.87
0.93
0.78
0.77
0.89
0.91
0.82
0.83
0.83
0.84
0.72
0.79
0.79
0.99
0.75

7.4
8.9
7.9
6.2
6.8
7.9
5.7
8.2
5.5
9.0
9.0
11.1
11.5
6.8
8.1
7.6
6.0
10.0
6.4
8.9
7.1
7.6
6.0
12.4

by 76.7%, 47.4%, and 5.8% compared with ST, SC, and IC4–2, respec­tively, while in 2019, it increased by 69.4%, 50.0%, and 5.7%, respec­tively. The highest cumulative nitrogen flux among different systems
occurred in IC2–2. The average cumulative nitrogen flux in IC2–2 in 2018
was 64.3%, 71.4%, and 207.1% higher than in ST, SC, and IC4–2,
respectively, and the corresponding values in 2019 were 90.1%, 87.2%,
and 89.6% higher. IC2–2 had the highest actual and cumulative nitrogen
fluxes among all systems.

Among different intercropping systems, the highest tomato yieldoccurred in IC4–2, with an average of 98.8 t ha− 1 in both years, 18.9%
higher than in IC2–2 (P < 0.05). The highest NUE of corn and tomatoes
occurred in IC2–2 and ST, respectively (Table 3). NUE of corn in IC2–2 was
14.1% and 5.6% higher than in SC and IC4–2, respectively. NUE of to­mato in ST was 23.5% and 5.8% higher than in IC2–2 and IC4–2,
respectively. However, the highest LERNUE occurred in IC4–2, with anaverage of 2.0 in both years, and it increased by 5.4% compared with
IC2–2 (P > 0.05). Therefore, IC4–2 was better than IC2–2 in terms ofLERNUE for the intercropping system.

3.4. Soil nitrogen balance and nitrogen use efficiency

3.5. Soil nitrogen competition between corn and tomatoes in different
intercropping systems

Inter-species nitrogen competition resulted in apparent differences inthe soil nitrogen balance in the root zone of corn and tomatoes ofdifferent intercropping systems. In general, IC4–2 had higher nitrogenleaching than IC2–2 and ST from the root zones of corn and tomatoes,
respectively (Table 3). Nitrogen leaching from the corn root zone in
IC4–2 increased by 39.4% compared with IC2–2. Furthermore, nitrogenleaching from the root zone of tomatoes in IC4–2 increased by 72.9%
compared with ST. However, the highest total nitrogen uptake (i.e., thesum of nitrogen uptake by corn and tomatoes) occurred in the IC2–2
system, with an average of 241.1 kg ha− 1 in both years. Simulated corn
nitrogen uptake in IC2–2 was 16.3% and 9.1% higher than in SC and
IC4–2, respectively. Tomato nitrogen uptake in IC2–2 was 9.1% and 2.8%
lower than in ST and IC4–2, respectively. Meanwhile, the simulation
accuracy of crop nitrogen uptake is high, with the mean relative error of
only 6.3% and 7.4% for corn and tomatoes, respectively (Table 3). The
nitrogen storage in IC4–2 was apparently higher than in other treatments,
except for ST. The average nitrogen storage in the root zone of corn inIC4–2 increased by 16.4% and 8.8% compared with SC and IC2–2,
respectively. The average nitrogen storage in the root zone of tomatoes
in IC4–2 was 1.6% higher than in IC2–2, while it was 2.6% lower than in
ST. However, corn biomass was highest in IC2–2, with an average of 29.2
t ha− 1 in both years, 11.1% and 5.3% higher than in SC and IC4–2,
respectively. Among different intercropping systems, the highest tomato
yield occurred in IC4–2, with an average of 130.7 t ha− 1 in both years.
Also, corn yield was highest in IC2–2, with an average of 13.4 t ha− 1 inboth years, 14.1% and 5.9% higher than in SC and IC4–2, respectively.

The result of these scenario simulations showed that the competitive
ratio for nitrogen by corn (CRc) decreased by 5.3% and 4.4% in 2018 and2019, respectively, with an increase in the planting ratio of tomatoes,
while the competitive ratio for nitrogen by tomatoes (CRt) increased by
7.4% and 7.1%, respectively (Fig. 8). Additionally, the highest LERN
occurred in the IC2–2 system, with an average of 2.0 in both years. Withan increase in the planting ratio of tomatoes, LERN of the tomato-cornintercropping system decreased by 12.4% and 6.1%, respectively(Fig. 9).
3.6. Economic benefit analysis of different intercropping systems
Large differences in the economic benefits of different cropping
systems were found due to different crop yields (Table 4). In general,
investment (TI) increased in response to an increase in the planting ratioof tomatoes. The highest TI (among different intercropping systems)
occurred in the IC4–2 system, with a two-year average of 4839.3 $ ha− 1,
which was 63.5% and 16.7% higher than for the SC and IC2–2 systems,
respectively. However, TI of the IC4–2 system decreased by 18.5%
compared with the ST system. Additionally, the IC4–2 system had thehighest total revenue (TR) among different systems. TR of the IC4–2
system was 37.9%, 60.1%, and 7.6% higher in 2018 and 42.9%, 60.7%,
and 8.6% higher in 2019 than for the ST, SC, and IC2–2 systems,11

N. Chen et al.

respectively. Overall, the highest net revenue (NR) occurred in the IC4–2
system, with an average of 8221.0 $ ha− 1, which was 77.1%, 58.5%, and2.9% higher than for the ST, SC, and IC2–2 systems, respectively(Table 4). Therefore, IC4–2 can be recommended as the optimal system toincrease farmer's income.

801.3a–597.3c748.0b596.0c–471.3d568.7c–61.0bc70.5a
64.3ab
–
48.6d
57.1c
56.2c
1043.4a–998.9ab1095.7a966.5b–861.1c1005.9a4.1. Soil nitrogen distribution in the soil profile of the tomato-cornintercropping system

150
–
150
150
150
–
150
150

–
47.7
16.9
32.9
–
41.6
15.4
20.4

3.7
–
14.5
12.7
1.8
–
12.5
7.6

–
137.5
164.6
145.7
–
123.2
146.8
137.3

103.7
–
94.6
96.1
84
–
76.1
79.6

–
24.8
28.5
31.4
–
45.2
47.8
52.3

42.6
–
40.9
41.2
64.2
–
61.4
62.8

–
125.4c172.5a153.7b
–112.8d153.2b130.6c115.2a–89.7c102.4b
92.5c
–
82.1d
84.8d

–
28.4c
32.0a
29.2b
–
23.6e
26.4d
26.1d

152.2a–119.5b147.6a117.6b–94.3c113.7b

–
12.8c
14.8a
13.5b
–
10.2e
12.0d
11.8d

120.2a–89.6b112.2a
89.4b
–
70.7c
85.3b

–
0.6b
0.8a
0.7ab
–
0.5c
0.7ab
0.6b

0.8a
–
0.6b
0.7ab
0.6b
–
0.5c
0.6b

–
102.1a
85.8b
87.8b
–
90.4b
78.3c
90.4b

Differences in soil NH4-N distributions in the crop root zone are
primarily affected by the difference in the nitrogen fertilizer application
amounts and root nitrogen uptake (Dittrich et al., 2021). For example,
the soil NH4-N concentrations in the root zone of corn in the IC2–2 andIC4–2 systems increased by 12.4% and 3.2% compared with the root zoneof tomatoes, respectively (Fig. 3). The causes of this phenomenon are
manifold. On the one hand, the application of nitrogen fertilizer for corn
was higher than for tomatoes, which resulted in high soil NH4-N con­centrations occurring in the root zone of corn after fertilization. On theother hand, the ability of corn roots to uptake soil NH4-N was low,causing soil NH4-N to accumulate in the corn root zone (Giles et al.,2017; Livesley et al., 2002). The variation of the planting ratio of interspecies in different intercropping systems would change the soil profile's
nitrogen distribution. For example, the highest soil NH4-N concentrationin Pc occurred in the SC system among different systems (Fig. 3). The
cause of this phenomenon is that the SC system has the highest planting
ratio of corn. Moreover, the ability of corn roots to uptake soil NH4-N
was low. Therefore, more soil NH4-N was accumulated in the corn rootzone in the SC system compared with the IC2–2 and IC4–2 systems.
However, the highest soil NH4-N concentrations in Pt occurred in theIC2–2 system among different systems. Since the ability of tomato roots
to uptake soil NH4-N was higher than that of corn roots, soil NH4-N
concentrations in the soil profile increased in response to a decrease in
the planting ratio of tomatoes. Thus, soil NH4-N concentrations in Pt in
the IC2–2 system were higher than in the ST and IC4–2 systems.
Additionally, soil NH4-N in the soil profile of the tomato-cornintercropping system is mainly distributed in the top 0–40 cm soil
layer. The main reason is that soil particles easily adsorb soil NH4-N after
being hydrolyzed from urea (Kothawala and Moore, 2009; Sasaki et al.,
2002; Teutscherova et al., 2018). Furthermore, the fast nitrification rateof soil NH4-N limited its redistribution in the soil profile. Al-Wabel(2019) indicated that soil NH4-N concentrations significantly decreased
15 days after fertilization, while soil NO3-N concentrations increased
considerably. This study similarly found that soil NH4-N was fully
nitrified into soil NO3-N 12 days after fertilization.
Heterogeneous distribution of soil NO3-N was found in the tomatocorn intercropping system. In general, soil NO3-N concentrations in
the root zone of tomatoes were higher than in the root zone of corn
(Chen et al., 2020a). The reason may be attributed to the better ability ofcorn roots to uptake nitrate compared with tomatoes (Reed and Hage­man, 1980). Although a high amount of NH4-N accumulated in the rootzone of corn promoted nitrification, soil NO3-N concentrations in theroot zone of corn significantly decreased due to intensive root uptake ofcorn. For example, this study found that soil NO3-N concentrations in theroot zone of corn in the IC2–2 and IC4–2 systems decreased by 31.5% and22.9% compared with the root zone of tomatoes, respectively (Fig. 4).
Nevertheless, soil NO3-N concentrations in the root zone of corn in theIC4–2 system were higher than in the SC and IC2–2 systems. This can be
attributed to the difference in the planting ratio of the dominant species(i.e., corn) in different intercropping systems. A high planting ratio ofcorn would significantly decrease soil NO3-N concentrations in the rootzone due to its high ability to uptake nitrate. In contrast, a high planting
ratio of tomatoes would increase soil NO3-N concentrations in the rootzone due to their weak ability to uptake nitrate, e.g., soil NO3-N con­centrations in the root zone of tomatoes in the ST system were higher

–
210
210
210
–
210
210
210
2019

Corn
TomatoCorn
TomatoCorn
TomatoCorn
TomatoCorn
TomatoCorn
Tomato Corn Tomato
Corn
Corn Tomato Corn Tomato

4. Discussion

ST
SC
IC2–2
IC4–2
ST
SC
IC2–2
IC4–2
2018

Tomato

–
–
1.9b
2.0ab
–
–
2.0ab
2.1a

LERNUE
NUE
PE
UE
Y\vskip5\hfill\hbox\rot90{(t ha− 1)
Biomass\vskip5\hfill\hbox\rot90{(t ha− 1)
CNU\vskip5\hfill\hbox\rot90{(kg ha− 1)
StoredUptakenLeachedAppliedSimulated nitrogen balance (kg ha− 1)Treatment
Year

Table 3
Simulated soil nitrogen balance, observed crop nitrogen uptake (CNU), biomass, crop yield (Y), uptake efficiency (UE), physiological efficiency (PE), nitrogen use efficiency (NUE), and the land equivalent ratio for nitrogenuse efficiency (LERNUE) for corn and tomatoes in the systems with sole tomato (ST), sole corn (SC), two rows of omatoes intercropping two rows of corn (IC2–2), and four rows of tomatoes intercropping two rows of corn
(IC4–2) during the 2018 and 2019 seasons.

Agricultural Systems 201 (2022) 103461

12

N. Chen et al.

Agricultural Systems 201 (2022) 103461

Table 4Crop yields (Y), investments (I), revenues (R), total investments (TI), total revenues (TR), and net revenues (NR) of corn and tomatoes in the systems with sole tomatoes(ST), sole corn (SC), two rows of tomatoes intercropping two rows of corn (IC2–2), and four rows of tomatoes intercropping two rows of corn (IC4–2) during the 2018 and2019 seasons. Different letters in the same column indicate a significant difference (P < 0.05) among treatments.
Year

Treatment
ST

2018

SC
IC2–2
IC4–2
ST

2019

SC
IC2–2
IC4–2

Crop

Y (t ha− 1)

I ($ ha− 1)

R ($ ha− 1)

TI ($ ha− 1)

TR ($ ha− 1)

NR ($ ha− 1)

Corn
TomatoCorn
TomatoCorn
TomatoCorn
TomatoCorn
TomatoCorn
TomatoCorn
TomatoCorn
Tomato–120.2a
12.8f
–
14.8d
89.6b
13.5e
112.2a–89.4b10.2 h
–12.0 g70.7c
11.8 g85.3b–5952.41786.4–
767.0
3266.4575.0
4267.3–5919.71743.9–763.93263.3571.94264.2–8968.1a5760.0f–
6660.0c6685.1c6075.0e8371.2b–6670.1c4590.0 h–
5400.0 g
5274.9 g
5310.0 g
6364.2d5952.48968.1e3015.8d

1786.45760.0 g

3973.6c4033.4

13,345.1b

9311.6a4842.4

14,446.2a

9603.9a5919.76670.1f

750.4e1743.94590.0 h

2846.1d4027.2

10,674.9d

6647.7b4836.2

11,674.2c

6838.1bthan in the IC2–2 and IC4–2 systems.
The mobility of soil NO3-N promoted the redistribution of soil NO3-Nin the soil profile (Arauzo et al., 2022; Rath et al., 2021; Zilio et al.,2020). Soil NO3-N was, in this study, accumulated mainly in the 0–80 cm
soil layer of different intercropping systems (Fig. 6). Ranjbar et al.
(2019) similarly indicated that soil NO3-N was distributed in the 0–60
cm soil layer after fertilization. However, a deeper accumulation zone
for soil NO3-N was found in the previous literature (Izsaki and Ivanyi,2002). These differences may be attributed to different irrigation stra­tegies. For example, surface irrigation with a high flow may haveresulted in soil NO3-N leaching below the crop root zone in some of thesestudies. Drip irrigation with a low flow rate, which is beneficial for soil
NO3-N to storage in the crop root zone, was adapted in this study.

system mainly depends on corn, and an excessive increase in theplanting ratio of tomatoes will reduce the total nitrogen uptake of theintercropping system. However, LERNUE increased in response to anincrease in the planting ratio of tomatoes. For example, LERNUE in theIC4–2 system increased by 5.4% compared with the IC2–2 system(Table 2). The main reason is that the NUE of tomatoes was higher thanthat of corn. Thus, NUE in the intercropping system can be effectively
improved by reasonably increasing the planting ratio of tomatoes. The
IC4–2 system can therefore be recommended to local farmers to increase
LERNUE of the intercropping system.
5. Conclusions
HYDRUS (2D/3D), modified to consider two vegetations, can pre­cisely capture the dynamics of soil NH4-N and NO3-N in the tomato-cornintercropping system. The differences in soil NH4-N among different
systems occurred in the surface soil layer (0–40 cm), especially in the
0–20 cm soil layer. Soil NH4-N concentrations at Pt and Pc of different
systems decreased as follows: IC2–2 > IC4–2 > ST and SC > IC2–2 > IC4–2,
respectively. Compared with soil NH4-N, differences in soil NO3-N indifferent systems were more evident due to the mobility of NO3-N. Soil
NO3-N was mainly distributed in the 40–60 cm soil layer. The highest
inter-species competition for nitrogen between tomatoes and corn
occurred during the second stage (DAS 41–120). IC2–2 had the highest
nitrogen flux, cumulative nitrogen flux, and nitrogen uptake among
different systems. However, the highest LERNUE occurred in the IC4–2
system, with an average of 2.0 in both years. Therefore, IC4–2 can berecommended to local farmlands to increase the NUE of intercropping
systems. Moreover, with an increase in the planting ratio of tomatoes (asubordinate species), CRt for tomatoes and LERN for the intercropping
system showed increasing and decreasing trends, respectively.

4.2. Nitrogen use efficiency for different intercropping systems
Variations in the planting structure caused apparent differences incrop nitrogen uptake among different systems. In general, the differencein crop nitrogen uptake among different systems increased in responseto an increase in the planting ratio of dominant species (Choudhary
et al., 2014; Moghbeli et al., 2019; Neugschwandtner and Kaul, 2015).
For instance, Salehi et al. (2018) showed that the highest crop nitrogen
uptake occurred in the IC2–2 system involving fenugreek and buckwheat,and nitrogen uptake decreased with an increasing share of buckwheat.
This study similarly found that corn nitrogen uptake in the IC2–2 system
was 16.3% higher than in SC, and tomato nitrogen uptake was 9.1%
lower than in ST. The reason for this is the higher ability of corn touptake nitrogen compared with tomatoes (Chen et al., 2020a), i.e., thecompetition mechanism increased the physiological nitrogen content of
corn.
Although total nitrogen uptake in the intercropping system can be
improved by increasing the planting ratio of dominant species, the un­reasonable planting structure will break the competitive nitrogen bal­ance for inter-species. For example, the cumulative solute flux in IC2–2
was 77.2% and 79.3% higher than in ST and SC, respectively (Fig. 7).
The main reason is that the IC2–2 system has the larger horizontal solute
gradient between the root zones of tomatoes and corn, especially during
the rapid crop growth stage (i.e., the second stage). However, the cu­
mulative solute flux in the IC4–2 system remained near zero, whichindicated that the nitrogen inter-specific competition between tomatoes
and corn reached a relative balance.
Additionally, the competitive ratio for nitrogen of inter-species
reached a relative balance with an increase in the planting ratio of to­matoes, while LERN of the intercropping system markedly decreased(Figs. 8 and 9). The reason is that nitrogen uptake of the intercropping

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.
No conflict of interest exits in the submission of this manuscript, andmanuscript is approved by all authors for publication. I would like todeclare on behalf of my co-authors that the work described was original
research that has not been published previously, and not underconsideration for publication elsewhere, in whole or in part. All theauthors listed have approved the manuscript that is enclosed.

13

N. Chen et al.

Agricultural Systems 201 (2022) 103461

Acknowledgments

Fernandez, J.A., Habben, J.E., Schussler, J.R., Masek, T., Weers, B., Bing, J., Ciampitti, I.
A., 2022. Zmm28transgenic maize increases both n uptake- and n utilizationefficiencies. Commun. Biol. 5, 555.
Filipovic, V., Coquet, Y., Pot, V., Houot, S., Benoit, P., 2014. Modeling the effect of soil
structure on water flow and isoproturon dynamics in an agricultural field receiving
repeated urban waste compost application. Sci. Total Environ. 499, 546–559.
Firouzi, S., Nikkhah, A., Rosentrater, K.A., 2017. An integrated analysis of nonrenewable energy use, GHG emissions, carbon efficiency of groundnut sole croppingand groundnut-bean intercropping agro-ecosystems. Environ. Prog. Sustain. Energy
36 (6), 1832–1839.
Galanopoulou, K., Lithourgidis, A.S., Dordas, C.A., 2019. Intercropping of faba bean withbarley at various spatial arrangements affects dry matter and N yield, nitrogen
nutrition index, and interspecific competition. Not. Bot. Horti Agrobot. Cluj-Napoca
47 (4), 1116–1127.
Gao, R., Guo, G., Fang, C., Huang, S., Chen, J., Lu, R., Huang, J., Fan, X., Liu, C., 2018.
Rapid generation of barley mutant lines with high nitrogen uptake efficiency bymicrospore mutagenesis and field screening. Front. Plant Sci. 9, 450.
Gaudinier, A., Rodriguez-Medina, J., Zhang, L., Olson, A., Liseron-Monfils, C.,Bågman, A.M., Foret, J., Abbitt, S., Tang, M., Li, B.H., Runcie, D.E., Kliebenstein, D.
J., Shen, B., Frank, M.J., Ware, D., Brady, S.M., 2018. Transcriptional regulation of
nitrogen-associated metabolism and growth. Nature. 563 (7730), 259.
Ghosh, P.K., Tripathi, A.K., Bandyopadhyay, K.K., Manna, M.C., 2009. Assessment of
nutrient competition and nutrient requirement in soybean/sorghum intercropping
system. Eur. J. Agron. 31 (1), 43–50.
Giles, C.D., Brown, L.K., Adu, M.O., Mezeli, M.M., Sandral, G.A., Simpson, R.J.,
Wendler, R., Shand, C.A., Menezes-Blackburn, D., Darch, T., Stutter, M.I.,
Lumsdon, D.G., Zhang, H., Blackwell, M.S.A., Wearing, C., Cooper, P., Haygarth, P.
M., George, T.S., 2017. Response-based selection of barley cultivars and legumespecies for complementarity: root morphology and exudation in relation to nutrient
source. Plant Sci. 255, 12–28.
Gitari, H.I., Nyawade, S.O., Kamau, S., Karanja, N.N., Gachene, C.K.K., Raza, M.A.,
Maitra, S., Schulte-Geldermann, E., 2020. Revisiting intercropping indices withrespect to potato-legume intercropping systems. Field Crop Res. 258, 107957.
Grecco, K.L., de Miranda, J.H., Silveira, L.K., van Genuchten, M.T, 2019. HYDRUS-2D
simulations of water and potassium movement in drip irrigated tropical soil
container cultivated with sugarcane. Agric. Water Manage. 221, 334–347.
Groenveld, T., Argaman, A., Šimůnek, J., Lazarovitch, N., 2021. Numerical modeling to
optimize nitrogen fertigation with consideration of transient drought and nitrogenstress. Agric. Water Manag. 254, 106971.
Hajari, E., Snyman, S.J., Watt, M.P., 2016. The effect of form and level of inorganic N on
nitrogen use efficiency of sugarcane grown in pots. J. Plant Nutr. 40 (2), 248–257.
Hanson, B., Šimůnek, J., Hopmans, J., 2006. Evaluation of urea–ammonium–nitratefertigation with drip irrigation using numerical modeling. Agric. Water Manag. 86(1–2), 102–113.
Izsaki, Z., Ivanyi, I., 2002. Effect of N fertilizer on the nitrogen balance of the soil and on
NO3-N leaching in a long-term mineral fertilization experiment. Int. J. Syst. Sci. 51(1), 115–124.
Jensen, E.S., Chongtham, I.R., Dhamala, N.R., Rodriguez, C., Carlsson, G., 2020.
Diversifying European agricultural systems by intercropping. Int. J. Agr. Nat. Res. 47(3), 174–186.
Kothawala, D.N., Moore, T.R., 2009. Adsorption of dissolved nitrogen by forest mineralsoils. Can. J. For. Res. 39 (12), 2381–2390.
Latati, M., Dokukin, P., Aouiche, A., Rebouh, N.Y., Takouachet, R., Hafnaoui, E.,
Hamdani, F.Z., Bacha, F., Ounane, S.M., 2019. Species interactions improve aboveground biomass and land use efficiency in intercropped wheat and chickpea under
low soil inputs. Agronomy 9 (11), 765.
Li, L., 2016. Intercropping enhances agroecosystem services and functioning: Current
knowledge and perspectives. Chin. J. Eco-Agric. 24 (4), 403–415.
Li, X.Y., Shi, H.B., Šimůnek, J., Gong, X.W., Peng, Z.Y., 2015. Modeling soil water
dynamics in a drip-irrigated intercropping field under plastic mulch. Irrig. Sci. 33(4), 289–302.
Li, Y., Šimůnek, J., Jing, L., Zhang, Z., Ni, L., 2014. Evaluation of water movement andwater losses in a direct-seeded-rice field experiment using Hydrus-1D. Agric. Water
Manag. 142, 38–46.
Li, J., Xie, R.Z., Wang, K.R., Hou, P., Ming, B., Zhang, G.Q., Ming, B., Zhang, G.Q., Liu, G.
Z., Wu, M., Yang, Z.S., Li, S.K., 2018. Response of canopy structure, lightinterception and grain yield to plant density in maize. J. Agric. Sci. 156 (6),785–794.
Liu, Y.X., Zhang, W.P., Sun, J.H., Li, X.F., Christie, P., Li, L., 2015. High morphologicaland physiological plasticity of wheat roots is conducive to higher competitive abilityof wheat than maize in intercropping systems. Plant Soil 397 (1–2), 387–399.
Livesley, S.J., Gregory, P.J., Buresh, R.J., 2002. Competition in tree row agroforestry
systems. 2. Distribution, dynamics and uptake of soil inorganic n. Plant Soil 247 (2),
177–187.
Long, G., Li, L., Wang, D., Zhao, P., Tang, L., Zhou, Y., Yin, X., 2021. Nitrogen levels
regulate intercropping-related mitigation of potential nitrate leaching. Agric.
Ecosyst. Environ. 319, 107540.
Maitra, S., Hossain, A., Brestic, M., Skalicky, M., Ondrisik, P., Gitari, H., Brahmachari, K.,
Shankar, T., Bhadra, P., Palai, J.B., Jena, J., Bhattacharya, U., Duvvada, S.K.,
Lalichetti, S., Sairam, M., 2021. Intercropping—a low input agricultural strategy forfood and environmental security. Agronomy 11 (2), 343.
Manevski, K., Børgesen, C.D., Andersen, M.N., Kristensen, I.S., 2014. Reduced nitrogenleaching by intercropping maize with red fescue on sandy soils in North Europe: a
combined field and modeling study. Plant Soil 388 (1–2), 67–85.

This research was jointly supported by the National Natural Science
Foundation of China (51969024, 52079064 and 51669020), the Local
Science and Technology Development Fund Projects Guided by theCentral Government (2020ZY0023), the Trans formation Projects of
Scientific and Technological Achievements of Inner Mongolia(2022YFHH0039), and the Inner Mongolia Autonomous Region Grad­uate Education Innovation Plan Subsidize Project. Additionally, we aresincerely appreciate the careful and precise reviews by the anonymous
reviewers, editors.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.agsy.2022.103461.
References
Ahmad, A., Khan, I., Abrol, Y.P., Iqbal, M., 2008. Genotypic variation of nitrogen use
efficiency in Indian mustard. Environ. Pollut. 154 (3), 462–466.
Allen, R., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: guidelines forcomputing crop requirements. In: FAO Irrigation and Drainage Paper No. 56. FAO,
Rome.
Al-Wabel, M.I., 2019. A short-term effect of date palm biochars on NH3 volatilization and
N transformation in calcareous sandy loam soil. Arab. J. Geosci. 12 (12).
Arauzo, M., Valladolid, M., García, G., Andries, D.M., 2022. N and P behaviour in alluvial
aquifers and in the soil solution of their catchment areas: how land use and thephysical environment contribute to diffuse pollution. Sci. Total Environ. 804,
150056.
Bremner, J., Keeney, D., 1965. Steam distillation methods for determination ofammonium, nitrate and nitrite. Anal. Chim. Acta 32, 485–495.
Chen, N., Li, X.Y., Šimůnek, J., Shi, H.B., Hu, Q., Zhang, Y.H., 2020a. Evaluating soil
nitrate dynamics in an intercropping dripped ecosystem using HYDRUS-2D. Sci.
Total Environ. 137314.
Chen, N., Li, X.Y., Šimůnek, J., Shi, H.B., Ding, Z.J., Zhang, Y.H., 2020b. The effects ofbiodegradable and plastic film mulching on nitrogen uptake, distribution, andleaching in a drip-irrigated sandy field. Agric. Ecosyst. Environ. 292, 106817.
Chen, N., Li, X.Y., Shi, H.B., Hu, Q., Zhang, Y.H., Hou, C.L., Liu, Y.H., 2022. Modelingevapotranspiration and evaporation in corn/tomato intercropping ecosystem using amodified ERIN model considering plastic film mulching. Agric. Water Manag. 260,
107286.
Choudhary, V.K., Choudhury, B.U., 2016. A staggered maize-legume intercrop
arrangement influences yield, weed smothering and nutrient balance in the eastern
Himalayan region of India. Exp. Agric. 54 (02), 181–200.
Choudhary, V.K., Dixit, A., Kumar, P.S., 2014. Productivity, competition behaviour andweed dynamics of various row proportions of maize (zea mays)-legumes
intercropping in Arunachal Pradesh. Indian J. Agric. Sci. 84 (11), 1329–1334.
Coll, L., Cerrudo, A., Rizzalli, R., Monzon, J.P., Andrade, F.H., 2012. Capture and use ofwater and radiation in summer intercrops in the south-east pampas of Argentina.
Field Crop Res. 134, 105–113.
Cote, C., Bristow, K., Charlesworth, P., Cook, F., Charlesworth, P., 2003. Analysis of soil
wetting and solute transport in subsurface trickle irrigation. Irrig. Sci. 22 (3–4),
143–156.
Cruz, I.L., Aguilar, A.R., Raquel, S.M., L’opez, R.L., 2014. Global sensitivity analysis ofcrop growth SUCROS model applied to husk tomato. Rev. Fitotec. Mex. Publ. 37 (3),279–288.
Daryanto, S., Fu, B., Zhao, W., Wang, S., Jacinthe, P.A., Wang, L., 2020. Ecosystemservice provision of grain legume and cereal intercropping in Africa. Agric. Syst. 178,
102761.
De Conti, L., Ceretta, C.A., Melo, G.W.B., Tiecher, T.L., Silva, L.O.S., Garlet, L.P.,
Mimmo, T., Cesco, S., Brunetto, G., 2019. Intercropping of young grapevines withnative grasses for phytoremediation of cu-contaminated soils. Chemosphere 216,
147–156.
Dittrich, F., Iserloh, T., Treseler, C.-H., Hüppi, R., Ogan, S., Seeger, M., Thiele-Bruhn, S.,
2021. Crop diversification in viticulture with aromatic plants: effects ofintercropping on grapevine productivity in a steep-slope vineyard in the Mosel area,
Germany. Agriculture 11 (2), 95.
Drakopoulos, D., Kägi, A., Six, J., Zorn, A., Wettstein, F.E., Bucheli, T.D., Rudolf
Forrer, H., Vogelgsang, S., 2021. The agronomic and economic viability ofinnovative cropping systems to reduce Fusarium head blight and related mycotoxins
in wheat. Agric. Syst. 192, 103198.
Dua, V.K., Kumar, S., Jatav, M.K., 2016. Effect of nitrogen application to intercrops onyield, competition, nutrient use efficiency and economics in potato (solanum
tuberosum l.) + french bean (phaseolus vulgaris l.) system in north-western hills of
India. Legum. Res. 40 (4), 698–703.
Feddes, R., Kowalik, P., Zaradny, H., 1978. Simulation of Field Water Use and Crop Yield.
John Wiley & Sons, New York.

14

N. Chen et al.

Agricultural Systems 201 (2022) 103461
Szumigalski, A.R., Van Acker, R.C., 2006. Nitrogen yield and land use efficiency inannual sole crops and intercrops. Agron. J. 98 (4), 1030.
Tafteh, A., Sepaskhah, A., 2012. Application of HYDRUS-1D model for simulating waterand nitrate leaching from continuous and alternate furrow irrigated rapeseed andmaize fields. Agric. Water Manag. 113, 19–29.
Teutscherova, N., Houška, J., Navas, M., Masaguer, A., Benito, M., Vazquez, E., 2018.
Leaching of ammonium and nitrate from Acrisol and Calcisol amended with holmoak biochar: a column study. Geoderma 323, 136–145.
Topp, G.C., Davis, J.L., Annan, A.P., 1980. Electromagnetic determination of soil watercontent: measurements in coaxial transmission lines. Water Resour. Res. 16 (3),574–582.
Unay, A., Sabanci, I., Cinar, V.M., 2021. The effect of maize (zea mays l.)/soybean (glycinemax (l.) merr.) intercropping and biofertilizer (azotobacter) on yield, leaf area index
and land equivalent ratio. J. Agric. Sci. Tarim Bili. 27 (1), 76–82.
United States Department of Agriculture, 2010. Keys to Soil Taxonomy. United States
Departmentof Agriculture, Soil Conservation Service.
Upadhyay, K.P., Sharma, M.D., Shakya, S.M., Ortiz-Ferrara, G., Tiwari, T.P., Sharma, R.
C., 2010. Performance and profitability study of baby corn and tomato
intercropping. Pak. J. Agric. Sci. 47 (3), 183–193.
van Asten, P.J.A., Wairegi, L.W.I., Mukasa, D., Uringi, N.O., 2011. Agronomic andeconomic benefits of coffee–banana intercropping in Uganda’s smallholder farming
systems. Agric. Syst. 104 (4), 326–334.
Wan, J.Y., Bao, H., Peng, W.H., An, L.Y., Jiang, Q., Yang, J.M., Zhu, C., 2020. Effects ofintercropping on cadmium uptake by maize and tomato. Chin. J. Biotechnol. 36 (3),518–528.
Wesseling, J.G., Brandyk, T., 1985. Introduction of the occurrence of high groundwaterlevels and surface water storage in computer program SWATRE, Nota 1636. Institutefor Land and Water Management Research (ICW), Wageningen, The Netherlands.
Xu, Z., Li, C., Zhang, C., Yu, Y., van der Werf, W., Zhang, F., 2020. Intercropping maize
and soybean increases efficiency of land and fertilizer nitrogen use; A meta-analysis.
Field Crop Res. 246, 107661.
Xu, R.X., Zhao, H.M., Liu, G.B., Li, Y., Li, S.J., Zhang, Y.J., Liu, N., Ma, L., 2022. Alfalfa
and silage maize intercropping provides comparable productivity and profitability
with lower environmental impacts than wheat-maize system in the North China
plain. Agric. Syst. 195, 103305.
Zhang, F.S., Li, L., 2003. Using competitive and facilitative interactions in intercropping
systems enhances crop productivity and nutrient-use efficiency. Plant Soil 248 (1–2),
305–312.
Zhang, W.P., Liu, G.C., Sun, J.H., Fornara, D., Zhang, L.Z., Zhang, F.F., Li, L., 2016.
Temporal dynamics of nutrient uptake by neighbouring plant species: evidence fromintercropping. Funct. Ecol. 31 (2), 469–479.
Zilio, M., Motta, S., Tambone, F., Scaglia, B., Boccasile, G., Squartini, A., Adani, F., 2020.
The distribution of functional N-cycle related genes and ammonia and nitrate
nitrogen in soil profiles fertilized with mineral and organic N fertilizer. PLoS One 15(6), e0228364.

Moghbeli, T., Bolandnazar, S., Panahande, J., Raei, Y., 2019. Evaluation of yield and its
components on onion and fenugreek intercropping ratios in different planting
densities. J. Clean. Prod. 213, 634–641.
Moll, R.H., Kamprath, E.J., Jackson, W.A., 1982. Analysis and interpretation of factors
which contribute to effificiency of nitrogen utilization. Agron. J. 74, 562e564.
Morugán-Coronado, A., Linares, C., Gómez-López, M.D., Faz, Á., Zornoza, R., 2020. The
impact of intercropping, tillage and fertilizer type on soil and crop yield in fruitorchards under Mediterranean conditions: a meta-analysis of field studies. Agric.
Syst. 178, 102736.
Nakamura, K., Harter, T., Hirono, Y., Horino, H., Mitsuno, T., 2004. Assessment of rootzone nitrogen leaching as affected by irrigation and nutrient management practices.
Vadose Zone J. 3, 1353–1366.
Nassab, A.D.M., Amon, T., Kaul, H.P., 2011. Competition and yield in intercrops of maize
and sunflower for biogas. Ind. Crop. Prod. 34 (1), 1203–1211.
Nelson, W.C.D., Siebrecht-Scholl, D.J., Hoffmann, M.P., Rotter, R.P., Whitbread, A.M.,
Link, W., 2021. What determines a productive winter bean-wheat genotype
combination for intercropping in central Germany? Eur. J. Agron. 128, 126294.
Neugschwandtner, R.W., Kaul, H.P., 2015. Nitrogen uptake, use and utilization efficiency
by oat–pea intercrops. Field Crop Res. 179, 113–119.
Rai, V., Pramanik, P., Das, T.K., Aggarwal, P., Bhattacharyya, R., Krishnan, P., Sehgal, V.
K., 2019. Modelling soil hydrothermal regimes in pigeon pea under conservation
agriculture using Hydrus-2D. Soil Tillage Res. 190, 92–108.
Ramos, T., Šimůnek, J., Gonçalves, M., Martins, J., Prazeres, A., Pereira, L., 2012. Twodimensional modeling of water and nitrogen fate from sweet sorghum irrigated withfresh and blended saline waters. Agric. Water Manag. 111, 87–104.
Ranjbar, A., Rahimikhoob, A., Ebrahimian, H., Varavipour, M., 2019. Simulation of
nitrogen uptake and distribution under furrows and ridges during the maize growth
period using HYDRUS-2D. Irrig. Sci. 37 (4), 495–509.
Rath, S., Zamora-Re, M., Graham, W., Dukes, M., Kaplan, D., 2021. Quantifying nitrateleaching to groundwater from a corn-peanut rotation under a variety of irrigationand nutrient management practices in the Suwannee River basin, Florida. Agric.
Water Manag. 246, 106634.
Reed, A.J., Hageman, R.H., 1980. Relationship between nitrate uptake, flux, andreduction and the accumulation of reduced nitrogen in maize (zea mays l.): ii. Effectof nutrient nitrate concentration. Plant Physiol. 66 (6), 1184–1189.
Salehi, A., Mehdi, B., Fallah, S., Kaul, H.P., Neugschwandtner, R.W., 2018. Productivity
and nutrient use efficiency with integrated fertilization of buckwheat–fenugreek
intercrops. Nutr. Cycl. Agroecosyst. 110 (3), 407–425.
Salgado, G.C., Ambrosano, E.J., Rossi, F., Otsuk, I.P., Ambrosano, G.M.B., Santana, C.A.,
Muraoka, T., Trivelin, P.C.O., 2021. Biological N fixation and N transfer in anintercropping system between legumes and organic cherry tomatoes in succession togreen corn. Agriculture 11 (8), 690.
Sasaki, Y., Ando, H., Kakuda, K., 2002. Relationship between ammonium nitrogen in soil
solution and tiller number at early growth stage of rice. Soil Sci. Plant Nutr. 48 (1),57–63.
Šimůnek, J., van Genuchten, M.T., Šejna, M., 2016. Recent developments andapplications of the HYDRUS computer software packages. Vadose Zone J. 15, 25.

15