sustainability
ArticleSustainability of a Rainfed Wheat Production System in
Relation to Water and Nitrogen Dynamics in the Soil in the
Eyre Peninsula, South Australia
Vinod Phogat 1,2,3, *, Jirka Šimůnek 4 , Paul Petrie 1,2,3,5 , Tim Pitt 1,2,3 and Vilim Filipović 6,7
1

2
3
4

5

6

7

*

Citation: Phogat, V.; Šimůnek, J.;
Petrie, P.; Pitt, T.; Filipović, V.
Sustainability of a Rainfed Wheat
Production System in Relation to
Water and Nitrogen Dynamics in the
Soil in the Eyre Peninsula, South
Australia. Sustainability 2023, 15,13370. https://doi.org/10.3390/
su151813370
Academic Editors: Wenfeng Liu,
Xiao Lin Yang and Wen Yin
Received: 11 August 2023
Revised: 30 August 2023

Crop Sciences, South Australian Research and Development Institute, GPO Box 397, Adelaide, SA 5001, Australia;
paul.petrie@sa.gov.au or paul.petrie@adelaide.edu.au or paul.petrie@flinders.edu.au (P.P.);
tim.pitt@sa.gov.au or tim.pitt@adelaide.edu.au or tim.pitt@flinders.edu.au (T.P.)
School of Agriculture, Food and Wine, The University of Adelaide, PMB No.1, Glen Osmond, SA 5064, Australia
College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia
Department of Environmental Sciences, University of California, Riverside, CA 92521, USA;jiri.simunek@ucr.edu
School of Mechanical and Manufacturing Engineering, The University of New South Wales,
Sydney, NSW 2052, Australia
Future Regions Research Centre, Federation University, Gippsland, VIC 3841, Australia;
v.filipovic@federation.edu.au or vfilipovic@agr.hr
Faculty of Agriculture, University of Zagreb, Svetošimunska Cesta 25, 10000 Zagreb, Croatia
Correspondence: vinod.phogat@sa.gov.au or vinod.phogat@adelaide.edu.au or vinod.phogat@flinders.edu.au

Abstract: Rainfed wheat production systems are usually characterized by low-fertility soils andfrequent droughts, creating an unfavorable environment for sustainable crop production. In this study,
we used a processed-based biophysical numerical model to evaluate the water balance and nitrogen(N) dynamics in soils under rainfed wheat cultivation at low (219 mm, Pygery) and medium rainfall(392 mm, Yeelanna) sites in south Australia over the two seasons. Estimated evapotranspiration
components and N partitioning data were used to calibrate and validate the model and to compute
wheat’s water and N use efficiency. There was a large disparity in the estimated water balancecomponents at the two sites. Plant water uptake accounted for 40–50% of rainfall, more at the low
rainfall site. In contrast, leaching losses of up to 25% of seasonal rainfall at the medium rainfall site
(Yeelanna) indicate a significant amount of water evading the root zone. The model-predicted N
partitioning revealed that ammonia–nitrogen (NH4 –N) contributed little to plant N nutrition, andits concentration in the soil remained below 2 ppm throughout the crop season except immediately
after the NH4 –N-based fertilizer application. Nitrate–nitrogen (NO3 –N) contributed to most N
uptake during both seasons at both locations. The N losses from the soil at the medium rainfall
site (3.5–20.5 kg ha−1 ) were mainly attributed to NH4 –N volatilization (Nv ) and NO3 –N leaching(NL ) below the crop root zone. Water productivity (8–40 kg ha−1 mm−1 ) and N use efficiency(31–41 kg kg−1 ) showed immense variability induced by climate, water availability, and N dynamics
in the soil. These results suggest that combining water balance and N modeling can help manage N
applications to optimize wheat production and minimize N losses in rainfed agriculture.

Accepted: 4 September 2023
Published: 6 September 2023

Keywords: wheat; rainfed; water balance; nitrogen uptake; water productivity; nitrogen use efficiency;
HYDRUS

Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
Water is one of the most limiting factors to increasing food and fiber production, especially in the arid and semi-arid regions of the world. In these regions, rainfall is insufficientand highly variable, often failing to satisfy the evapotranspiration demand of rainfed crop
production. Low and sporadic rainfall in rainfed cultivated regions impacts crop water
uptake and nutrient mineralization in soils of poor fertility [1,2]. Thus, crop production is

Sustainability 2023, 15, 13370. https://doi.org/10.3390/su151813370

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affected by the unpredictability of water availability at crucial crop growth stages, causing
yield and quality loss. Numerous studies have shown that grain production in semi-arid
rainfed cropping systems strongly depends on soil moisture and N supply, i.e., it is thus
co-limited [3,4]. Therefore, water-saving technologies, water retention, and effective use ofwater and nutrients are of paramount importance in fragile rainfed production systems.
Wheat production in Australia is characterized by low to medium rainfall (<450 mm)
and a very high evaporative demand relative to rainfall (>3:1), with a coefficient of variationof 25–30% [5], making it one of the driest rainfed cropping environments in the world [6].
Moreover, the soils in rainfed regions vary in texture, composition, water-holding capacity,and nutrient availability, which adds to the challenges of sustainable crop production.
These factors lead to wide region-to-region and seasonal variability in wheat production.
For example, wheat production during 2021-22 (36 Mt) was more than double that in
2019-20 [7], predominantly associated with favorable climatic conditions. However, along-term yield assessment revealed that Australia’s average annual wheat yield was only
50% (1.73 t ha−1 ) of the potential yield [8]. Therefore, identifying yield-limiting constraints
in the soil–plant–atmosphere continuum [9] can help devise ways and means to close thiswide gap in the water-limited yield and year-to-year variability in wheat production.
Achieving potential yield with less water has always been an endeavor in increasing
crop productivity and water use efficiency. One major stumbling block in this pursuit islimited and seasonally varying water availability for rainfed wheat. In this regard, theFrench and Schultz [10] model has provided a valuable benchmark for assessing the waterlimited yield potential of grain crops based on seasonal rainfall. It is widely used by many
farmers from rainfed regions in Australia and other parts of the world. For example, themodel prediction for wheat is 20 kg grain ha−1 mm−1 of water transpired above 110 mm
evaporation. This prediction has been revised numerous times to include various climatic
factors such as rainfall distribution and evaporative demand of the environment [11,12]
and co-limitation of water and nitrogen factors [13,14]. The co-limitation assessment raised
the water-limited wheat yield to 24 kg ha−1 mm−1 , suggesting that low nutrient availability
reduces water use efficiency and increases the gap between actual and water-limited yieldpotential. The major limitations of the French and Schultz [10] approach include its inability
to account for the impact of the timing of growing season rain and water losses suchas runoff or drainage and the assumption of constant seasonal evaporation [15]. Theselimitations can be addressed by more complex processed-based models commonly used
for water balance studies under cropped conditions [16,17].
Water availability in the soils tremendously impacts nutrient availability and its uptake
by the roots. The soil water content not only determines the crop N uptake but also controls
biogeochemical N transformations, such as volatilization, nitrification, and urea hydrolysis.
Therefore, water and N interactions in the soil affect crop growth and yield attributes,
including photosynthesis, foliage growth, crop yield, protein content, leaf senescence, rootto-shoot water and N translocations, and microbial enzyme activity in the soil [4,18–21].
Benjamin et al. [22] reported that N uptake and N use efficiency were reduced with limited
water availability during crop growth and corresponding limited N movement in thesoil. On the other hand, N leaching and denitrification can occur when excessive water isapplied [23,24]. Similarly, an appreciable amount of N can be lost to the atmosphere due toammonium volatilization, especially when urea is top-dressed on the soil surface during
the growing season [25–27].
Angus and Grace [1] reported that most grain cropping systems in Australia have anegative N balance, resulting from more N exported off-farm in agricultural products thanapplied as fertilizer or through biological nitrogen (N2 ) fixation. Numerous studies foundthat the recovery efficiency of N in rainfed wheat production is as low as 30–50% [1,28–30].
Furthermore, Gastal et al. [28] reported that between 50 and 75% of the applied N is either
retained in the crop residues, remains in the soil, or is lost from the system, leading toenvironmental problems. Other studies also revealed that N applications higher than thecrop demand might result in leaching losses, which could contaminate groundwater andSustainability 2023, 15, 13370

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trigger the eutrophication of freshwater and marine ecosystems [31,32]. Climate change
further aggravates the problem and uncertainty regarding the supply of resources [33]and their optimum utilization [34]. Thus, maximization of water and N use is essential
for ensuring long-term productive potential and maintaining the ecological functions of
natural resources [35]. Hence, an increase in nitrogen use efficiency (NUE) will not only
reduce the amount of applied N but also minimize N-related environmental pollution [36].
Therefore, accurate estimates of N reactive fluxes, plant uptake, and N losses (including
gaseous) are required to fully understand N dynamics in the soil under rainfed wheat
production systems.
Several process-based models (e.g., APSIM and HYDRUS) can provide estimates of effective water and N balances, use efficiencies, and losses from agricultural production systems.
These models integrate the effect of rainfall, soil, weather, and other management practices
to predict the dynamics of water and N movement in soils [37]. APSIM has been widelyused in Australia to model the fate of water and nitrogen in rainfed farming systems (e.g.,
Keating et al. [16,38]). However, most of these studies have only used the bucket-type waterbalance module, the results of which can deviate from those provided by numerical simulations(e.g., HYDRUS), which provide more precise solutions of the partial differential equations
describing non-linear water flow and convective–dispersive solute transport in soils [17].
Hence, the objectives of this investigation were to evaluate daily and seasonal soil
water balances, including wheat’s root water uptake and the dynamics of N in the soil
(mineralization, transformation, plant uptake, and gaseous losses), using HYDRUS-1D.
Water (WUE) and N use efficiency (NUE) of wheat were also estimated using the modelsimulated water and N balance components. This information can help devise better
guidelines for enhancing fertilizer use efficiency and reducing N losses in rainfed wheat
production regions.
2. Materials and Methods
2.1. Description of Study Sites
This study was part of a project to evaluate soil moisture and N information toassist farmers on the Eyre Peninsula in south Australia in making better management
decisions for profitable wheat production. Two sites, i.e., Pygery (−32.9838◦ S, 135.3642◦ E)
and Yeelanna (−34.1369◦ S, 135.7665◦ E), representing different soils, climates, and N
applications, were selected for this study to assess water and nitrogen dynamics in the soils
under wheat production. These sites were selected based on large differences in the rainfall,soil, growing conditions, and fertilizer use, which enabled the evaluation of diverse rainfed
wheat growing systems. The Pygery (Py) site is located in the west coast region of the Eyre
Peninsula and is characterized as a low rainfall zone. Annual average rainfall and reference
crop evapotranspiration (ET0 ) at this site during the last 100 years amounted to 327 and
1397 mm, respectively. The Yeelanna (Ye) site is located in the lower Eyre Peninsula region,
with annual average rainfall and ET0 of 411 and 1172 mm, respectively. More details about
this project and specific growing conditions can be found in [39].
Cereals (wheat, barley), rotated with canola or pasture legumes, are the widely growncrops in the study region. However, a wheat crop was grown at both locations during
the study period (2018 and 2019). Details about wheat variety, spacing, density, sowing,
harvesting, and fertilizer applications during the two seasons are given in Table 1. Notably,
the amount of N fertilizer applied at Yeelanna was much higher than at Pygery. At Pygery,
N was added only at the time of sowing. In 2018, 55 kg of mono ammonium phosphate(MAP) and 30 kg of a blend of urea and ammonium sulfate/ha was applied, while in 2019,
40 kg of urea and 60 kg of a blend of urea and ammonium sulfate/ha was applied. The
basal dose at Yeelanna applied at the time of sowing was 100 kg urea + 66 kg MAP/haand 75 kg urea/ha during the 2018 and 2019 seasons, respectively. Apart from the initial
application, two doses of 100 kg of N were applied during the season as a top dressing
at Yeelanna. An extra N application is typically added in medium rainfall environments
to enhance yield and, thus, profitability [40]. This reflects a wide range of farmers’ N use

Sustainability 2023, 15, 13370

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practices in different rainfall regions. Essentially, the extent and timing of N applications
in dryland wheat farming systems depend on the timing and intensity of rainfall, which
provides the necessary water to dissolve the fertilizer in the soil and make it available forroot uptake.
Table 1. Wheat sowing, fertilizer details, and wheat yield at the experimental sites.
2018

2019

Mace
19 MayMace
12 May

140

160

55
30
1.45

40
60
1.6

Emu Rock
12 May
307
150

Mace
22 May
305
150

66
100

75

50 on 16 July
50 on 17 August
5.67

100 on 28 June
100 on 27 July
3.84

Pygery (Py)
Variety
Sowing date
Row spacing (mm)Plant density (plants m−2 )Fertilizers (applied at sowing)
MAP (kg ha−1 )Urea (kg ha−1 )Urea/ammonium sulfate blend
Yield (t ha−1 )Yeelanna (Ye)
Variety
Sowing date
Row spacing (mm)Plant density (plants m−2 )Fertilizers (applied at sowing)
MAP (kg ha−1 )Urea (kg ha−1 )In-season fertilizer
Urea (kg ha−1 ) and date
Yield (t ha−1 )Soil moisture probes (Sentek Sensor Technologies, Adelaide, SA, Australia) were
installed in 2017, with sensors every 10 cm down to a depth of 40 cm and every 20 cm
down to 100 cm. Soil samples were collected in triplicate from close to each sensor before
crop sowing, and a representative composite sample from each layer was analyzed. The
basic physicochemical properties of the soil were estimated following the standard procedures [41]. Data on soil texture, bulk density, pH, and organic carbon content are given in
Table 2 for both sites. Soil texture at Pygery ranged from sandy loam to sandy clay, withclay contents increasing gradually with depth, while the texture at Yeelanna represents a
typical duplex, sandy clay loam at the surface (0–10 cm) with heavy clay underneath. Bothsites have soils with pH in the alkaline range and almost similar organic carbon contents(OC), except for a higher OC level (2.03%) in the surface soil at Yeelanna. The soil’s cationexchange capacity (CEC) at different depths was almost double at Yeelanna than at Pygery
except in the surface layer (0–15 cm). The soil nitrate (NO3 –N) and ammonium N (NH4 –N)
contents were analyzed at 0–15, 15–30, 30–60, and 60–100 cm soil depths.
The particle size distribution and bulk density of different layers at the study sites
were measured to estimate the soil hydraulic parameters, which were used as inputs into
the HYDRUS-1D model [17]. Measured values of the air-dry (θr ) and saturated (θs ) watercontents were used in the simulations. Typically, the θr values (≈1500 kPa) were relatively
high, a characteristic feature of heavy sub-soil clay commonly occurring in the dryland
belt of the study region [42]. These parameters were further fine-tuned during the model
calibration using water content dynamics data in the soil. Optimized parameters for bothsites used in the numerical model are shown in Table 3.

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Table 2. Physicochemical properties of soils at the experimental sites.
Depth
(cm)

Soil
Texture

Sand
%

Silt
%

Clay
%

0–15
15–30
30–60
60–90
90–100

SL
SCL
SCL
CL
SC

64.7
58.7
47.0
42.7
45.0

13.5
12.3
21.2
21.2
19.3

19.8
28.9
31.8
36.0
35.7

0–15
15–30
30–60
60–90
90–100

SCL
C
C
C
SC

70.4
21.8
22.3
14.3
51.5

8.7
6.3
6.4
10.2
3.0

20.9
71.9
71.2
75.6
45.5

Db
(g cm−3 )Pygery (Py)
1.57
1.33
1.33
1.42
1.42
Yeelanna (Ye)
1.45
1.34
1.52
1.64
1.64

OC
(%)

pH
(H2 O)

pH(CaCl2 )CEC(Cmol (+) kg−1 )

1.17
0.75
0.55
0.34
0.34

8.5
8.7
9.3
9.5
9.5

7.8
8.0
8.3
8.5
8.5

17.0
22.5
25.0
26.2
24.7

2.03
0.70
0.42
0.32
0.32

8.1
8.5
8.6
9.3
9.3

7.7
7.9
8.0
8.3
8.3

26.1
42.0
45.5
47.2
51.5

S = sand; C = clay; L = loam; Db = bulk density; OC = organic carbon; CEC = cation exchange capacity.

Table 3. Estimated soil hydraulic parameters at Pygery and Yeelanna used in the HYDRUS-1Dmodeling simulations.
Soil
TextureSoil Depth(cm)

θr(cm3 cm−3 )θs(cm3 cm−3 )

Loam
Loam
Clay loam
Clay loam
Cay loam

0–15
15–30
30–60
60–90
90–105

0.05
0.12
0.2
0.22
0.24

0.40
0.41
0.44
0.45
0.45

Loam
Clay
Clay
Clay
Silt loam

0–15
15–30
30–60
60–90
90–105

0.07
0.15
0.19
0.21
0.24

0.45
0.46
0.44
0.49
0.49

a
(cm−1 )
Pygery (Py)
0.024
0.022
0.017
0.017
0.018
Yeelanna (Ye)
0.025
0.023
0.021
0.019
0.018

n

Ks
(cm d−1 )lDb(g cm−3 )

1.40
1.32
1.37
1.35
1.35

27.1
19.1
15.6
14.4
15.8

0.5
0.5
0.5
0.5
0.5

1.57
1.33
1.33
1.42
1.42

1.45
1.31
1.28
1.17
1.16

53.4
17.2
9.4
5.49
11.1

0.5
0.5
0.5
0.5
0.5

1.45
1.34
1.52
1.64
1.64

θ r and θ s are the residual and saturated water contents, respectively; Ks is the saturated hydraulic conductivity,
Db is the bulk density, and a, n, and l are shape parameters.

2.2. Climate Parameters
Local climate parameters were obtained from the SILO climate database [43] using
the Wudinna Aero station (station 18083) for the Pygery site and the Yeelanna station(station 18,099) for the Yeelanna site. At Pygery, both 2018 and 2019 were dry years, withtotal rainfall during the wheat growing season (May to December) amounting to 208 and
190 mm, respectively (Figure 1). Most of the rain occurred during the winter period (Mayto August) when the crop water demand was low. Corresponding values of ET0 at the Py
and Ye sites were 829 and 886 mm, respectively (Figure 1).
At Yeelanna, average values of rainfall and ET0 during the study period (2018–2019)
and the wheat cropping season (May–December) were 365 and 693 mm, respectively(Figure 1). The ET0 values were usually low during the winter season and then increased
during the wheat’s post-anthesis period, thus enhancing crop water demand between
anthesis and harvest. Thus, low rainfall and high climate water demand at Pygery impose
relatively adverse conditions for wheat cultivation compared to the Yeelanna site. Gradually

Sustainability 2023, 15, x FOR PEER REVIEW
Sustainability 2023, 15, 13370

6 of 24
6 of 22

August) whenthewaterETdemand
was low. Corresponding values of ET0 at the Py
increasing
trendsincropthe daily
0 values suggest that the wheat growing season overlaps
and
Ye
sites
were
829
and
886
mm,
respectively(Figurethe winter season, slowly transitioning to summer
under1).
the Mediterranean climate.
10ET0b) Py_2019a) Py_2018

8

ET0 (mm)

32

Rain

24

6

16

4

8

2

0
40

0
10

32

d)Ye_2019c)Ye_2018

8

24

6

16

4

8

2

0

0

ET0 (mm)

Rainfall (mm)Rainfall (mm)40

Figure1.1.Daily
Dailyvalues
valuesofof rainfall
rainfalland
andreference
referencecrop
cropevapotranspiration
evapotranspiration(ET(ET0 )0)atatthe
thePygery
Pygery(a,b)(a,b)and
and
FigureYeelanna
(c,d)
sites
during
the
2018
(a,c)
and
2019
(b,d)
wheat
growingseasons(May–December).
Yeelanna (c,d) sites during the 2018 (a,c) and 2019 (b,d) wheat growing seasons (May–December).

At Yeelanna,
average values of rainfall
and ETfor
0 during
study
period (2018–2019)
Daily
crop evapotranspiration(ETC ) values
wheatthewere
estimatedfrom dailyreferencecrop cropping
evapotranspiration(ET0 ) and cropwere
coefficients(Kc )mm,for different
growthand the wheat
season (May–December)
365 and 693
respectively(Figstages
daily ET
weredividedevaporation(Esthen) andincreasedtranspirationure 1).[44].
The TheET0 values
wereusuallylowduringinto
the thewinterseason anddurC values(T
components
based on the period,
leaf areathus
index(LAI) as crop
follows[45]:demand between antheing
wheat’s post-anthesis
enhancingwaterp ) thesis and harvest. Thus, low rainfall and high climate water demand at Pygery impose relagr × LAI
Es =ETc . e−Kcompared(1)
tively adverse conditions for wheat
cultivation
to the Yeelanna site. Gradually
increasing trends in the daily ET0 values suggest that the wheat growing season overlaps
Tp to= summerETc − Es under the Mediterranean climate.
the winter season, slowly transitioning
evapotranspiration(ETC) values
for wheatestimatedfromwhereDaily
Kgr iscrop
the light
extinction coefficientfor wheat,and itswerevalue
was set to
0.46daily[46] reffor
erencecrop
evapotranspiration
(ET
0
)
and
crop
coeﬃcients
(K
c
)
for
diﬀerent
growthstagesrainfed conditions. Estimated LAI values for wheat at both locations are shown in Figure 2.
[44]. Thedaily
C values
werethedividedinto2019
the evaporation(Es) andintranspiration
(Tp)
Daily
wheat
ETcET
valuesduring2018 andseasons are shown
Supplementarycomponents
based
on
the
leaf
area
index
(LAI)
as
follows[45]:
Material (Figure S1a–d). Annual ETc values for wheat at Pygery and Yeelanna during 2018and 2019 were 375.1 and 389.6 and 287.5 and 296.5
andSustainability 2023, 15, x FOR PEER REVIEW
7 of24- K mm,x LAI respectively. These values(1)
Es into= ETe grdaily rainfall were then used as inputs
themodel to estimate the actual
c .HYDRUS-1D
values of Es and Tp (Es act and Tp act ) for wheat at both locations.

4

LAI

3
2
1
0

(a) Pygery

4

T p = ETc − E s

where Kgr is the light2018
extinction coeﬃcientfor wheat, and its value was set to 0.46 [46] for(b) Yeelanna3
rainfed conditions. Estimated LAI values for wheat at both locations are shown in Figure
2019 during the 2018 and 2019 seasons are shown in supplementary
2. Daily wheat ETc values
2material (Figure S1a–d). Annual ETc values for wheat at Pygery and Yeelanna during 2018and 2019 were 375.1 and 389.6 1and 287.5 and 296.5 mm, respectively. These values anddaily rainfall were then used as inputs into the HYDRUS-1D model to estimate the actual
0values of Es and Tp (Es act and Tp act) for wheat at both locations.

Figure 2.
2. EstimatedEstimated leaf
leaf area
area index
Figureindex (LAI)(LAI) ofof wheat
wheatatatthe
the(a)(a)Pygery
Pygeryandand(b)(b)Yeelanna
Yeelannasites
sitesduring
duringthe
20182018
andand20192019
seasons.
theseasons.

2.3. Brief Description of HYDRUS-1D
The HYDRUS-1D software can simulate one-dimensional variably saturated water flow,heat movement, and transport of solutes involved in sequential first-order decay reactions[17]. The governing one-dimensional water flow equation is described as follows:Sustainability 2023, 15, 13370

7 of 222.3. Brief Description of HYDRUS-1D
The HYDRUS-1D software can simulate one-dimensional variably saturated water
flow, heat movement, and transport of solutes involved in sequential first-order decay
reactions [17]. The governing one-dimensional water flow equation is described as follows:


∂
∂h
∂θ
=
K (h)
− K (h) − R(h, z, t)
(2)
∂t
∂z
∂z
where θ is the soil water content (L3 L−3 ), t is the time (T), h is the soil water pressure head(L), z is the vertical coordinate (L), K(h) is the unsaturated hydraulic conductivity function(LT−1 ), and R(h,z,t) is the sink term accounting for an actual volume of water uptake byplant roots from a unit volume of soil per unit time (L3 L−3 T−1 ). Water extraction R(h,z,t)from the soil was computed using the Feddes model [47]. Different values of the stressresponse function for wheat were taken from the HYDRUS-1D data repository. In thismethod, the potential transpiration rate, Tp , is distributed over the root zone using thenormalized root density distribution between 0 and 1 and multiplied by the dimensionlesswater stress response function. Hence, this model assigns plant root water uptake rates
according to local soil water pressure heads at any point in the root zone. Therefore,
potential transpiration (Tp ) is reduced below its potential value when the soil can no longer
supply the amount of water required by the plant under the prevailing climatic conditions.
The partial differential equations governing one-dimensional dynamics of N involved
in sequential first-order decay chain reactions during transient water flow in a variably
saturated rigid porous medium [17] are given as:


dqC1
d
dθC1
w dC1
=
θD1
−
− µ0 w,1 θC1
(3)
dt
dz
dz
dz




dρS2
dqC2
dθC2
dav g2
d
dC2
d
g dg
+
+
=
θD2w
+
av D2 2 −− µ0 w,2 θC2 + γs,2 ρ + µ0 w,1 θC1 − r a,2
dt
dt
dt
dz
dz
dz
dz
dz


dqC3
dθC3
d
w dC3
=
θD3
−
− µ0 w,3 θC3 + µ0 w,2 θC2 − r a,3
dt
dz
dz
dz

(4)

(5)

where C is the solute concentration in the liquid phase (mg L−1 ), S is the solute concentrationin the solid phase (mg g−1 ), g is the solute concentration in the gas phase (mg L−1 ), ρ is thedry bulk density (g cm−3 ), q is the volumetric flux density (cm day−1 ), µ’w is the first-order
rate constant for the solute in the liquid phase (day−1 ), providing connections between
individual chain species, γs is a zero-order rate constant in the solid phase (day−1 ), ra is
the root nutrient uptake (mg L−1 day−1 ), Dw is the dispersion coefficient (cm2 day−1 ) forthe liquid phase, and Dg is the diffusion coefficient (cm2 day−1 ) for the gas phase. The
subscripts 1, 2, and 3 represent (NH2 )2 CO (urea), NH4 + –N (ammonium N), and NO3 − –N
(nitrate N), respectively. Adsorption/desorption of NH4 + is an instantaneous reaction
between the soil solution and the exchange sites of the soil matrix [48].
2.4. Nitrogen Balance Parameters
Input parameters for the nitrogen transport in HYDRUS-1D are required to characterize the three main sets of processes: solute transport, solute reactions/transformations, androot solute uptake. Baldock et al. [49] defined the following N balance components in thesoil, which are crucial to understanding and estimating the annual soil N dynamics.
N balance = (NF + NMin + Ndfa + Ndep ) − (NR + NL + NV + NDen + NE )(6)

where NF is N added to the soil in the form of chemical fertilizers, NMin is N added to thesoil in the form of organic amendments (e.g., manure, composts, etc.), Ndfa is N derived
from atmospheric N2 by symbiotic and non-symbiotic fixation, Ndep is the N deposition

Sustainability 2023, 15, 13370

8 of 22from the atmosphere, NR is N removed in harvested products, NL is N leached from theroot zone, NV is N volatilized as ammonia from fertilizers and soils, NDen is N lost as N2
and N2 O by denitrification, and NE is N lost by erosion.
In the N modeling study, all components of the N balance except for Ndfa , Ndep ,NDen , and NE were considered. Neglected components were either present only in minuteamounts (Ndfa , Ndep ) in the wheat fields or represented negligible processes in dryland
conditions (NDen , NE ) [50,51]. Organic N mineralization (NMin ) was estimated based on
the assumption of 3% annual mineralization estimated for the study region [49].
The following values of solute transport parameters were used in the simulation; themolecular diffusion coefficients in free water (Dw ) for NH4 –N and NO3 –N were 1.52 and1.64 cm2 day−1 , respectively, the molecular diffusion coefficient in the air (Dg ) for NH3 was
optimized as 18057.6 cm2 day−1 , similar to other studies [52], the longitudinal dispersivity
was considered equal to one-tenth of the profile depth [53], and Henry’s law constant (KH ,at 25 ◦ C) for NH4 –N was 2.95 × 10−4 [54]. The distribution coefficients (Kd ) for NH4 thatvaried from 1.0 to 1.8 cm3 mg−1 in different soil layers were adapted from Li et al. [52].
These values fall within the range reported for different mixed and layered soils [55]. The
urea hydrolysis rate (Kh ) of 0.74 day−1 in the topsoil layer (0–10 cm) adapted in the current
study is consistent with the reported values in numerous studies under different soils andclimate conditions [52,56,57].
The nitrification rates were calibrated to vary from 0.02 to 0.25 day−1 , with higher
surface soil values, then decreasing gradually with depths. The volatilization rate of
0.24 kg ha−1 day−1 reported under rainfed wheat cultivation in south Australia [27] wasused in the current study. While some of these processes are temperature- and watercontent-dependent, neglecting these dependencies is common [37,52] due to the lack ofsuch information and measured data. Unlimited passive uptake of NO3 –N was allowed in
the root solute uptake model [58] by specifying the maximum allowed uptake concentration
exceeding NO3 –N concentrations in the root zone. In addition to passive uptake, active
uptake of NH4 –N was also integrated into the simulations. The Michaelis–Menten constantfor active uptake of NH4 was assumed to equal the default value of 0.5 mg L−1 .
2.5. Initial and Boundary Conditions
The initial water contents at various depths were set using the soil water contents
measured by the capacitance probe. Measured ammonium and nitrate contents in thesoil, specified in terms of N concentrations (NH4 –N and NO3 –N), were set as the initialconditions for N simulations. The initial concentration representing the basal fertilizer
application was calculated using the initial soil water content, assuming fertilizer was
mixed within the surface 10 cm layer. An atmospheric boundary condition with surface
runoff was specified at the soil surface for water flow. The free drainage boundary condition
was imposed at the bottom of the domain (105 cm). Root water uptake was calculated using
the potential transpiration rate, specified rooting depth and density, and the Feddes’ stressresponse function [47]. The upper boundary condition for solute transport was set as a‘volatile’ boundary condition [59] with a stagnant boundary layer of 2.5 cm. This boundary
condition assumes a stagnant boundary layer (air) at the top of the soil profile and thatupward solute movement through this layer is by solute diffusion in air, facilitating thesimulation of volatilization losses of N. A third-type boundary condition was used at thelower boundary. The concentration fluxes of N for all fertilizer applications were calculated
in N content terms. The top-dressed fertilizer application during the wheat season isrepresented in the model by converting the amount of applied urea into the boundary
concentration using the known value of the water content in the topsoil layer (from theprevious water flow simulation) at the time of fertilizer application.
HYDRUS-1D simulations were commenced on 1 May 2018 and continued until 31 December 2019 for both locations. Theoretical details and more information on the HYDRUS1D software and related references can be found at https://www.pc-progress.com/en/
Default.aspx?HYDRUS-3D (accessed on 30 August 2023).

Sustainability 2023, 15, 13370

9 of 222.6. Model Evaluation
The modeling performance for water balance was evaluated by comparing capacitance probe-measured (O) soil moisture values at various depths with those predicted by
HYDRUS-1D (P) for the 2018 and 2019 crop seasons. The statistical error estimates, meanerror (ME), mean absolute error (MAE), and root mean square error (RMSE), between themeasured and simulated spatiotemporal water contents, were estimated as:
ME =

1 N
(Oi − Pi )N ∑ i =1(7)

1 N
|Oi − Pi |N ∑ i =1

(8)

MAE =

r
RMSE =1 N(Oi − Pi )2
N ∑ i =1

(9)

3. Results
3.1. Soil Water Dynamics in the Soil
The capacitance probe measured daily soil water contents in 0–30 cm and 30–100 cm
depths at the Pygery and Yeelanna sites, which were compared with corresponding
HYDRUS-1D simulated values in Figure 3. The simulations showed small changes in
the wetting of the upper horizon (<30 cm) at the Py site due to the instant depletion ofsoil moisture in response to soil evaporation and plant transpiration. However, at themedium rainfall site (Ye), the water content rapidly increased in the 0–30 cm horizon. In thesecond horizon (30–100 cm), both data sets showed only small changes in moisture content
over the two wheat seasons. A close correspondence between the observed and modeledmoisture distribution patterns exists in both horizons, despite the probe malfunction in2018 during a short period from mid-February to mid-March 2019 (Figure 3). The moistureprobe measured slightly lower values during the post-harvest wheat season, especially at
deeper depths (>30 cm). The HYDRUS-1D results distinctly show two soil horizons with aboundary at a depth of 30 cm. It should be noted that measured and simulated moisture
contents in the soil below 30 cm are consistently high during both seasons, especially at the
Yeelanna site (between 0.25 and 0.35 cm3 cm−3 ). Similar values of soil water contents wereobserved in other regional studies [60]. This is a typical characteristic of sub-soil heavy
clay in the study region, with the clay fraction at the Yeelanna site as high as 75% (Table 1).
These soils can hold a large amount of water, varying between 0.2 and 0.25 cm3 cm−3 at
the wilting point (1500 kPa), which is unavailable to plants. In addition, these soils may
have sub-soil constraints such as compaction, which further restricts the water movement
and uptake by growing crop plants [60]. Hence, the water contents in the sub-soils (>30 cm
depth) remained consistently static during the entire cropping season.
Statistical errors (ME, MAE, and RMSE) assessing the comparison between HYDRUS-1D
simulated and measured soil water contents for the soil surface (0–30 cm) and deeper (30–100cm) layers showed a varied response (Table 4). The RMSE, MAE, and ME values for the surface
depth (0–30 cm) during 2018 and 2019 at Pygery remained between −0.02 and 0.03 cm3 cm−3 ,indicating a close agreement. The corresponding values for the 30–100 cm profile were
between −0.06 and 0.07 cm3 cm−3 , slightly higher than for the surface layer. At Yeelanna,
the error estimates during 2018 ranged from −0.02 to 0.06 and from 0.03 to 0.04 cm3 cm−3 inthe 0–30 cm and 30–100 cm soil depths, respectively. During the validation period (2019), theerror values varied from −0.04 to 0.07 cm3 cm−3 . Wang et al. [61] reported RMSE and mean
relative error (MRE) values of 0.07 cm3 cm−3 and 21.6%, respectively, as accurate estimationof water content dynamics in the soil by SWAP model under wheat irrigated with variedlevels of deficit irrigations. Error estimates reported in other studies [37,62] also corroborate
well with the values estimated in the current study, which showed a good agreement between
measured and simulated water content dynamics in the soil.

θ (cm3 cm-3)

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2023,
15,15,x FOR
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2023,
13370PEER REVIEW

0

0

0.2

0.2

0.4

(a) Py_0–30 cm

0.60θ (cm3 cm-3)

10 10
of of
2422

0.6

0.4

(c) Ye_0–30 cm
0.6
0
0.2

0.2
0.4

Measured
Simulated(b) Py_30–100 cm

0.4(d) Ye_30–100 cm

0.6
Figure3. 3.
Comparison
ofof
measuredsoil
water
contents
in in
thethe
0–30
cmcm
(top)
and
0–100
cmcm
(bottom)
FigureComparison
measuredsoil
water
contents
0–30
(top)
and
0–100
(bottom)(profile averaged) with the corresponding simulated values during the 2018 and 2019 wheat grow(profile averaged) with the corresponding simulated values during the 2018 and 2019 wheat growing
ing seasons at the Pygery (a,b) and Yeelanna (c,d) sites on the Eyre Peninsula.
seasons at the Pygery (a,b) and Yeelanna (c,d) sites on the Eyre Peninsula.

Statistical errors (ME, MAE, and RMSE) assessing the comparison between HYDRUS-1D
simulatedand measured
soil water
for thesoil absolutesurface (0–30cm) and
Table 4. Estimatedvaluesof the root meansquarecontents
error (RMSE),
mean
error (MAE),
anddeeper(30–100cm)
layersmeasured
showed aandvariedresponse (Table
The RMSE,
MAE,
and
MEcm
mean error(ME)
betweenmodel-predictedwater4).
content
in the soil
for the0–30
valuesfor thedepth(0–30cm)andduring2018sites
andduring2019 atPygeryremained between
and 30–100
cmsurfacesoil layers
at thePygeryYeelanna2018
and 2019.
3−3−0.02 and 0.03 cm cm , indicating a close agreement. The corresponding values for the
Soil Depth
MAE than for the
MEsur30–100 Site
cm profile were
between −0.06
and 0.07 cm3RMSE
cm−3, slightly higherYear
3 cm−3 )3 cm−3 )3 cm−3 )
(cm)
(cm
(cm
(cm
face layer. At Yeelanna, the error estimates during 2018 ranged from −0.02 to 0.06 and from0.03 toPygery
0.04 cm3 cm−3 inthe 0–30 cm andsoil depths, respectively.
During2018
0–30 30–100 cm 0.02
0.01
−0.01the3 cm−3. Wang et al.
validation period (2019), the error values
varied
from
−0.04
to
0.07
cm
30–100
0.04
0.03
0.02
[61] reported RMSE andvalues of 0.070.03
cm3 cm−3 and−0.02
21.6%,
2019mean relative
0–30error (MRE)0.03
respectively, as accurate estimation ofwater content 0.07
dynamics in thesoil by SWAP
model
30–1000.06−0.06
under wheat irrigated with varied levels of deficit irrigations. Error estimates reported in
Yeelanna
2018
0–30
0.06
0.02
0.04
other studies [37,62] also corroborate well with the values estimated in the current study,
30–100
0.04
0.03
0.03
which showed a good agreement between measured and simulated water content dynam2019
0–30
0.07
0.07
−0.04
ics in the soil.
30–100

0.05

0.04

−0.02

Table 4. Estimated values of the root mean square error (RMSE), mean absolute error (MAE), andmean error(ME)measuredand model-predicted
content inin thefor the 0–30cm
Therearebetween
numerouspossiblereasons for thewater
deviations
thesoilbehaviorof waterand 30–100 cm soil layers at the Pygery and Yeelanna sites during 2018 and 2019.

content dynamics in the soil. Apart from model assumptions, capacitance probes can
induce significant errors in the waterSoil
content
depends
MAE modelingME
Depthmeasurements.
RMSE Numerical
Site
Year
3 cm−3)parameters;on threecrucial factors:(a) the accuracy(cm3 cm−3)(b) the(cm3precisioncm−3)(cm) of model(cminputof observed
valuescompared
with
the
model
output;and(c)sensitivity
inthePygery
2018
0.02
0.01
−0.01initial
0–30
conditions. Ramos et al. [62] showedthatdeviations
betweenmeasuredand
30–1000.04
0.03
0.02modelpredicted water content
dynamics
in
the
soils
might
be
relatedtofieldmeasurements,
2019
0–30
0.03
0.03
−0.02
model inputs, and model structural30–100
errors. The moistureprobe’s
and the0.07
0.06calibration−0.06
inherent
complexities 2018of the soil [63] are0–30
additional crucial
affect the
Yeelanna0.06 factors that
0.02immensely0.04
extent of the divergence between observed
and modeled
data. These0.03
factors may contribute
30–1000.04
0.03
to a similar extent of deviations
in watercontents as obtainedby modelingpredictions.
It
2019
0–30
0.07
0.07
−0.04
is also likely that higher observed values
content at 0.04
lower depths(>50 cm)
30–100of the water
0.05
−0.02
at Yeelanna may have contributed to larger values of the error statistics. Overall, a good
agreement
betweenthe possible
measuredand simulated
data showsthatthe model
can predict
There arenumerous
reasons
for the deviationsin thebehaviorof watercontheeffectsofdifferent
weatherandsoilproperties
atdifferent
sites
andrespondtovarioustent dynamics in the soil. Apart from model assumptions, capacitance probes can induce
field variations.
significant
errors in the water content measurements. Numerical modeling depends on
three crucial factors: (a) the accuracy of model input parameters; (b) the precision of

Sustainability 2023, 15, 13370

11 of 223.2. Soil Water Balance
Seasonal water balance components (Tp act = actual plant water uptake, Es = soil
evaporation, Dr = drainage, ∆S = soil storage/depletion) for the wheat crop at Pygery (Py)and Yeelanna (Ye) predicted by HYDRUS-1D during 2018 and 2019 are shown in Table 5.
At Pygery, evaporation losses during the crop season (May to October) varied from 44 to
57%, with an average of 50% of the rainfall received. The remaining water (43–58%) was
attributed to plant uptake, while drainage losses were negligible. At Yeelanna, Es losses
varied from 29 to 42%, with an average value of 33%, significantly lower than at Pygery.
Plant water uptake varied from 41 to 49%, with an average value of 40%. The drainage
component (Dr ) represents 12–26% (41–90 mm) of the rainfall received during the cropping
season, which is the main difference between the two sites.
Table 5. Seasonal water balance components (mm) simulated by HYDRUS-1D during the 2018 and2019 cropping seasons at the Pygery (Py) and Yeelanna (Ye) sites.
Site

Year

Es

Tp actDr∆S

Rainfall (Season)

Rainfall (Annual)

Py

2018
2019

76.6
98.6

95.2
85.0

0.03
0

6.4
−2.8

175.6
183.6

235.3
201.8

Ye

2018
2019

96.3
104.3

140.5
140.6

40.8
90.4

54.9
9.2

333
348.3

407.6
375.5

Tp act = actual plant water uptake, Es = evaporation, Dr = drainage, ∆S = soil storage/depletion.

0

3

103

10

(a) Py_2018

202(c) Ye_2018

20

1

301

0
4

400
04

40

3
10

10

3
2

(b) Py_2019

Dr
RainfallEs actTp act

2
20

1

1
30

0

0
40

30

0

(d) Ye_20192030

Rainfall (mm)2

Rainfall (mm)04

Rainfall (mm)4Rainfall (mm)Es act,Tp act & Dr (mm d-1) Es act,Tp act & Dr (mm d-1)

Daily Tp act and Es components of the water balance simulated by HYDRUS-1D during
2018 and 2019 at Pygery and Yeelanna are shown in Figure 4. At Pygery, daily plantwater uptake varied from 0 to 2.6 mm during 2018, while the maximum value was 2.2 mm
during the 2019 crop season. As expected, daily evaporation losses (Es) were higher before
sowing and after the crop harvest. However, the magnitude remained low during 2018
(0–1.5mm), reflecting the moisture availability in the surface soil layer. During 2019,12
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of 24
in daily evaporation (up to 2 mm) were similar to plant water uptake, especially duringcrop maturity and harvest (September and October).

40

Figure 4.
4. Daily
Daily rainfall
rainfall andand predictedpredictedvalues
valuesof
ofactualactualevaporation
evaporation(E(Es sact
act),), actual
actual plant
plant water
water uptake
uptakeFigure(Tp act), and drainage (Dr) under wheat crop during the 2018 (top) and 2019 (bottom) growing seasons(Tp act ), and drainage (Dr ) under wheat crop during the 2018 (top) and 2019 (bottom) growing seasonsat the Pygery (a,b) and Yeelanna (c,d) sites on the Eyre Peninsula.
at the Pygery (a,b) and Yeelanna (c,d) sites on the Eyre Peninsula.

At Yeelanna, the maximum daily plant water uptake (Tp act) reached about 3 mm during the 2018 and 2019 seasons (Figure 4). The magnitude of daily Tp act losses was similar
to Pygery, but there was less fluctuation in the daily dynamics, probably due to the consistent availability of moisture in the surface layer. One of the important components of

Sustainability 2023, 15, 13370

12 of 22

At Yeelanna, the maximum daily plant water uptake (Tp act ) reached about 3 mm
during the 2018 and 2019 seasons (Figure 4). The magnitude of daily Tp act losses was
similar to Pygery, but there was less fluctuation in the daily dynamics, probably due to theconsistent availability of moisture in the surface layer. One of the important componentsof the water balance at Yeelanna is drainage (Dr ), which was negligible at Pygery. Daily
drainage (Dr ) losses occurred in August–September of 2018, while these losses were higher
early in the season (mid-May to August) during 2019.
3.3. Nitrogen Simulation in the Soils
Nitrogen species commonly occurring in a dissolved state in the soil solution are
NH4 –N and NO3 –N. These species control the plant-available forms of N in the soils. The
dynamics of these species depend on the number and magnitude of N pools in the soil, theirinteractions, transformations, and exchanges among them. The comparison of measured
and simulated values of NH4 –N and NO3 –N in the soil at various depths at the time ofwheat harvest is shown in Figure 5 for the Pygery site. Some inconsistencies include thehigher simulated value of NO3 –N than measured in the surface soil (0–15 cm) during
2018, which subsequently increased in the second layer (15–30 cm) and then showed asimilar pattern as measured values. Similarly, the measured NH4 –N value was higher
than simulated, while the reverse was true for NO3 –N in the surface layer during the 2019season. However, at other depths, the simulated values of both species matched well withthe measured values. Furthermore, the profile-averaged measured NH4 –N values13ofof0.7
Sustainability 2023, 15, x FOR PEER REVIEW
24
and 1.1 ppm were comparable to the corresponding simulated values of 0.8 and 0.9 ppm,
respectively, during the 2018 and 2019 wheat seasons.

Depth (cm)

NH4–N (ppm)
1
2
3

0

4 0
0

20

20

40

40

0

60
80

(a) 21 Oct 2018

60
80

NH4–N (ppm)
1
2
3

M
S
(b) 24 Oct 2019

4 0
0

NO3–N (ppm)
5
10

15 0
0

20

20

40

40

60

60
(c) 21 Oct 2018
80

80

NO3–N (ppm)
5
10

15

M
S

(d) 24 Oct 2019

Figure(M)(M)
andandsimulated(S) values
of NH
(c,d)
in(c,d)theFigure5.5.Comparison
Comparisonofofmeasured
measuredsimulated(S) valuesof4 –N
NH(a,b)
4–N and(a,b)NO
and
NO
3–N
3 –N
in thesoil atharvest
wheat time
harvesttime (as indicated)theand
2018
(a,c)
and
2019 (b,d)
at thesoil
at wheat(as indicated)
during the during
2018 (a,c)
2019
(b,d)
seasons
at theseasons
Pygery site.
Pygery site.

Similarly, measured profile-averaged NO3 –N contents of 5.4 and 8.5 ppm matchedwell withthe corresponding
simulated valuesof 4.9and 8.4ofppm,
respectively,
duringSimilarly,
measured profile-averaged
NO3–N
contents
5.4 and
8.5 ppm matched2018This indicates simulated
that the model
has
able8.4
to ppm,simulate
the N dynamics
well andwith2019.
the corresponding
valuesofbeen4.9 andrespectively,
duringintheandsoil2019.
profileunder
wheat
the Pygery
dynamics
in the2018
Thisindicatesthatcrop
the atmodel
has beensite.
able Modelled
to simulateNthe
N dynamics
in
soil
at
the
Yeelanna
site
could
not
be
comparedintheabsenceofappropriate
measuredthe soil profile under wheat crop at the Pygery site. Modelled N dynamics in the soil at
data.
Overall,sitethecould
patterns
valuesthe soil indicate
thatdata.
thethe Yeelanna
not ofbe NH
comparedinNO
the3 –Nabsenceof inappropriate
measured
4 –N andmodeling
approaches
usedaccount
for3–NN mineralization,transformation,
uptake,Overall, thepatterns of
NHto
4–N
and NO
values in the soil
indicate that plant
the modelingandconversions
amongfordifferent
pools in thetransformation,soils are reasonably
approaches
usedoftoNaccountN mineralization,
plantsound.
uptake,However,and conintensive
soil
for pools
nitrogentransformations,losses,sound.
and soilsolutionintensive
speciesversions ofN observations
among diﬀerent
in thesoils are reasonably
However,arerequiredto
improvethesimulation
at
both
field
sites.
soil observations for nitrogen transformations, losses, and soil solution species are reThetospatiotemporal
dynamicsatof
thefield
simulated
quiredimprove the simulationbothsites. concentrations of these species are
shownin
Figure6forbothsites.
The
distributionof N species
revealed
thatarea
The spatiotemporal dynamics of the simulatedpatternconcentrationsof these
speciesfractionofNH–N
alwaysremained
in
the
soil
due
to
the
continuous
decompositionof
4 6 for both sites. The distribution pattern of N species revealed that a fracshown in Figureorganic
N to4–N
NHalwaysmodel.
High peaksof NH4 –N in
4 –N consideredtion of NH
remainedbyinthe
theHYDRUS-1D
soil due to thecontinuous
decomposition
ofthe
orsoil,especiallyinthesurfacelayers
(0–15
and
15–30
cm),
correspondedtothefertilizer
ganic N to NH4–N considered by the HYDRUS-1D model. High peaks of NH4–N in theapplications.
However,the increasingpresenceof NH
in low concentrationsin the4 –Ncorresponded
soil, especiallyin the surface
layers (0–15
and 15–30
cm),
to the fertilizer
lower
layersofthesoil
profileindicatesaslow
downward
movement
of
NH
–N
in
the
4
applications. However, the increasing presence of NH4–N in low concentrations insoil
thelower layers of the soil profile indicates a slow downward movement of NH4–N in the soil
with time. However, NH4–N concentrations in the soil remained below 2 ppm throughout
the simulation except after applying the NH4–N fertilizer.
Simulated daily NO3–N concentrations at the Pygery site gradually increased withtime, especially in the surface soil layer (0–40 cm). They fluctuated between 1.2 and 14.9

Sustainability 2023, 15, 13370

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Sustainability 2023, 15, x FOR PEER REVIEW

14 of 24with time. However, NH4 –N concentrations in the soil remained below 2 ppm throughout
the simulation except after applying the NH4 –N fertilizer.

8(a) Py_NH4–N

NH4–N (ppm)

6
4

15cm
30cm
60cm
90cm

8
6
4

2

2

0
150

0
150

NO3–N (ppm)120(b) Py_NO3–N(c) Ye_NH4–N

120

90

90

60

60

30

30

0

0

(d) Ye_NO3–N

Figure6.6.Simulated
Simulateddistribution
distributionofofNH
NH
4–N (a,c) and NO3–N (b,d) in the soil at diﬀerent depths (15,Figure4 –N (a,c) and NO3 –N (b,d) in the soil at different depths (15,
30,
60,
and
90
cm)
at
the
Pygery(a,b)
and
Yeelanna(c,d)(c,d)sites.
sites.
30, 60, and 90 cm) at the Pygery (a,b) and Yeelanna

At Yeelanna,predicted higher
NH4–Nsite
concentrationsin the soil
Simulated
dailyHYDRUS-1DNO3 –N concentrations
at the Pygery
gradually increased
withthantime,atPygery(Figure
6c).
The
NH
4
–N
concentrations
inthesurface
layer
(0–15
cm)
reachedespecially in the surface soil layer (0–40 cm). They fluctuated between 1.2 and 14.9 ppmhighinvalues4.8layerand (Figure
7 ppm after
the fertilizer
application.
continued
presenceof NHthe4–N
the0–15ofcm
6b). The
gradual peaksin the The
surfacelayer (0–15cm) reveal
in low concentrations
the3 –N
surfacezone indicatesthepeaks
continualproduction
of NH
–N by
conversionof NH4 –N toinNO
via nitrification.
These
subsequently
moved
to 4lower
mineralizingorganic
matter. Onandtheaotherhand,
the peakNO
3–N concentration
inlayers
the 0–
depthswith lower
concentrations
lag time
reflecting
plant
uptake.
At deeper soil
15 cm50
soil
depth
at Yeelanna
wasalmost8–10
times
higherthanatPygery.
During2018,(belowcm),
the NO–N
concentrations
were
more
or
less
stablearound5ppm,
indicating
3
reduceduptakeand transformationin thisThistoalsosuggestsapplicationa lack of deep
the NON
3–N
concentration
increasedactivities
to 112 ppm
in zone.
response
fertilizer
anddrainage
at Pygery,a crucialdriverSeptember,
for transporting–Nleaching
deeper into
the soil.
The watersubsequently
decreasedin early
likely NO
due3to(Figure6d). Laterin thebalancethe Pygery
site strongly supports
observation
4).
–N concentrationin the upperseason,atduring
the post-harvest
summer thisperiod,the NO3(Table
Yeelanna,
HYDRUS-1D
predicted
higherinNH–N concentrationsin the
soil
than
3–N
consoil At
layers(0–15 cm)increased witha decrease
the4soilwater content. The
NO
atcentration
Pygery (Figure
NH4at–N
concentrations
thelayer (0–15cm) reachedin the 6c).
soil Thepeaked144
ppm in 2019 indue
tosurfacemuch higher
N application
andhighof 4.8andppmuptakeafter theapplication.
continued
presenceofthenvalues
decreaseddue
to 7plant
andfertilizer
late-season
leaching. The
At theend of thesimulationNH–N inconcentrationsin thezone indicatesthesoilcontinualin 42019(31low
December2019), theNOsurface
3–N concentrationin therangedproduction
from 34 toof91
NHby mineralizingorganic
theNO
other
peak
NO3 –N concentration
4 –NHigher
ppm.
concentrations
ofmatter.
NH4–NOnand
3–Nhand,in thethesoil
at Yeelanna
were directly
inrelatedthe 0–15
cm
soil
depth
at
Yeelannawas
almost8–10
times
higherthan
Pygery.
Duringinto higher N applications. High levels of NO3-N concentrationatwere
reported
2018,
the
NO
–N
concentration
increased
to
112
ppm
in
responsetofertilizer
application
3other modelingstudies under similar N fertilization and wheat production conditions
and[64].subsequently decreased in early September, likely due to leaching (Figure 6d). Laterin the season, during the post-harvest summer period, the NO3 –N concentration in theuppersoil layers(0–15incm)
increased with a decrease in the soil water content. The NO3 –N
3.4. Nitrogen
Balance
Soils
concentration in the soil peaked at 144 ppm in 2019 due to much higher N application and3.4.1. Mineralization of Organic N in the Soilthen decreased due to plant uptake and late-season leaching. At the end of the simulationof organicin the soil (NMin) at Pygery during the croppingin 2019The(31 breakdown
December 2019),
the NOmatter
3 –N concentration in the soil ranged from 34 to 91 ppm.
seasonconcentrations(May–December)
added
37.3 kg N ha−1 during 2018 and almost a similar amountHigherof NH
4 –N and NO3 –N in the soil at Yeelanna were directly related(Figure 7). HYDRUS-1D
higherNMin (56.2 wereto 56.4reported
kg N ha−1
thetoduring
higher2019
N applications.
High levelspredictedof NO3 -N
concentrationin) for
other
soil
at
YeelannathanatPygerydue
tothehigherorganic
carboncontent
in
the
soil
at
modeling studies under similar N fertilization and wheat production conditions [64]. theformer (2.03%) relative to the latter (1.17%) site. Additionally, relatively low rainfall at
3.4.
Nitrogen
Balancein Soilsthe microbial decomposition of organic matter because the minPygery
perhaps
impacted
eralization
rate is only
about two-thirdsof that at Yeelanna. The absolute values of daily
3.4.1.
Mineralizationof Organic
N in the Soil
−1, whereas the corresponding values
NMinTheat Pygeryrangedbetween
0.15
and
0.17
haMin
breakdown of organic matter in the kg
soilN(N) at Pygery during the cropping−1−1 duringfor Yeelanna
ranged from
0.22 to0.25, with anaverageseasonal
total ofamount37.5 andseason(May–December)
added
37.3
kgkg
NN
haha
2018
and almost
a similar
56.3 kg N ha−1 at Pygery and Yeelanna, respectively. The conversion of NH4–N to NO3–N
in the soil at Pygery was relatively poor compared to Yeelanna due to unfavorable moisture and temperature conditions. Therefore, relatively higher NH4–N deposition/storage

Sustainability 2023, 15, 13370

14 of 22150

Amount (kg/ha)

200(a) 2018

100
50

200

(b) 2019

150

PygeryYeelanna

100
50

0

0
2018

2018

2018

NF

NMin

NV

2018

2018

2018

2018

NR_NH4 NR_NO3 NL_NH4 NL_NO3

N components

2019

2019

2019

NF

NMin

NV

2019

2019

2019

2019

NR_NH4 NR_NO3 NL_NH4 NL_NO3

N components

Figure 7.
7. Predicted
F = fertilizer nitrogen; NMin = N mineralization from
FigurePredicted components
componentsof
ofNNbalancebalance((N((N
F = fertilizer nitrogen; NMin = N mineralization
organic matter; NV = N volatilization; NR_NH4 = plant uptake of ammonium N; NR_NO3 = plant uptake
from organic matter; NV = N volatilization; NR_NH4 = plant uptake of ammonium N; NR_NO3 = plantof nitrate N; NL_NH4 = leaching of ammonium N; NL_NO3 = leaching of nitrate N) during the (a) 2018uptake of nitrate N; NL_NH4 = leaching of ammonium N; NL_NO3 = leaching of nitrate N) during theand (b) 2019 at Pygery (Py) and Yeelanna (Ye).
(a) 2018 and (b) 2019 at Pygery (Py) and Yeelanna (Ye).

NH4–N (mg L-1)

3.4.2. Nitrogen
Nitrogen Uptake
Uptake byby Wheat
Wheat
3.4.2.
–Nand
andNO
NO3–N),–N), such
such
Nitrogenavailable
available totowheatwheatincludes
includesinorganic
inorganicnitrogen
nitrogen(NH(NH4–N
Nitrogen
4
3
as
fertilizers,
andsolubleorganic
nitrogen.
The
amountofN
delivered
from
soil
to
plantsas fertilizers, and soluble organic nitrogen. The amount of N delivered from soil to plants
is location-specificlocation-specific due
due toto thethe variations
variations inin thetheenvironmental
environmental conditions,conditions, soil
soil types,
types, andandis
diﬀerent
agricultural
management
practices
implemented
at
the
two
locations.
Simuladifferent agricultural management practices implemented at the two locations. Simulations
tions showedthat
NH4–N contributed very little to the plant N nutrition and that most N
showedthat NH
4 –N contributed very little to the plant N nutrition and that most N uptake
uptakewasformin theofformNO
3–N during
both seasonscrop seasons(Figure8). Thesimulatedtotal
wasin theNO3of–N
duringboth crop(Figure8). Thesimulatedtotal
N
N uptake
at Yeelanna
more
twice
at Pygery.
Daily–N and–N upuptake
at Yeelanna
waswasmorethanthantwice
thatthatat Pygery.
Daily
NH4NH
–N 4andNO3NO–N 3uptake
−1mg
take
by wheatat Pygery0 toandto L13L−1, respectively.
bywheat
at Pygeryrangedranged
from 0from
to 0.16
mg0.16
L−1mg
andL0−1 to
13 0mg, respectively.
While
1 in
While
at
Yeelanna,
maximum
daily
NH
4
–N
and
NO
3
–N
uptakes
were
0.9
andL−
0.8
mg
L−1
at Yeelanna, maximum daily NH4 –N and NO3 –N uptakes were 0.9 and 0.8 mg
2018
−
1
−1
in 2018
L inrespectively.
2019, respectively.
Notably,maximumNand
15.5andand15.5
27.8andmg 27.8
L mg
in 2019,
Notably,
wheat’swheat’s
maximum
daily N daily
uptakeuptake occurred
fromAugust
early August
late September,coinciding
withwheat’s
the wheat’s
maxioccurredfrom early
to late to
September,coinciding
with themaximum
mum growth
period.
Seasonalcrop
N uptakeat Pygeryrangedto 62%of thetotal
growthperiod.
Seasonal
crop N
uptakeat Pygeryrangedfromfrom
55 to5562%
of the
total
N
N F(N
+ Min
NMin
S
),
whereasthecorrespondingN
uptakeat
Yeelanna
was
only
(N
+F N
+ +NN
),
whereasthecorrespondingN
uptakeat
Yeelannawas
only
40–44%.
S
However,
However,wheat
wheatN
Nuptakeuptakeduring
during2019
2019atatYeelanna
Yeelannawas
was roughlyroughly double
double thethe amountamount ofof NNmineralized
mineralized inin thethe soil
soil (Figure(Figure 7).
7). ThisThis implies
implies thatthat almost
almost half
half ofof thethe N
N uptakeuptake byby wheatwheat
waswascontributed
contributedby
bythe
theN
Nfertilizer.
fertilizer.

O3–N (mg L-1)

Amount (kg ha-1)

during 2019 (Figure 7). HYDRUS-1D predicted higher NMin (56.2 to 56.4 kg N ha−1 ) for
the soil at Yeelanna than at Pygery due to the higher organic carbon content in the soil at
the former (2.03%) relative to the latter (1.17%) site. Additionally, relatively low rainfall
at Pygery perhaps impacted the microbial decomposition of organic matter because themineralization rate is only about two-thirds of that at Yeelanna. The absolute values of daily
NMin at Pygery ranged between 0.15 and 0.17 kg N ha−1 , whereas the corresponding values
for Yeelanna ranged from 0.22 to 0.25 kg N ha−1 , with an average seasonal total of 37.5 andSustainability 2023, 15, x FOR PEER REVIEW
15 of 24
56.3 kg N ha−1 at Pygery and Yeelanna, respectively. The conversion of NH4 –N to NO3 –N
in the soil at Pygery was relatively poor compared to Yeelanna due to unfavorable moistureand temperature conditions. Therefore, relatively higher NH4 –N deposition/storage inin theoccurredat Pygerythan
Yeelanna.
There
wasa slight
increaseininthe
theNN
Minraterate
thesoilsoiloccurredat Pygery
than
at at
Yeelanna.
There
wasa slight
increaseMin
during thethecropping/wintercropping/winter season at both locations. However, these
these estimatesestimates will
will vary
vary
duringover thethe years,
years, depending
depending onon thethe substrate’s
substrate’s content
content andand quality,
quality, thethe C:N
C:N ratio,
ratio, microbial
microbial
overactivity,
andclimatevariability,
including
rainfallandtemperature
variations[1,65].
activity, and climate variability, including rainfall and temperature variations [1,65].

0.9
0.6
0.3
0
30
20
10

3.4.3. Simulated Volatilization Losses of N(c) Ye_NH
uptake(a) Py_NH4–N
uptakeVolatilization
N losses (NV ) usually occur
when4–Nurea
or ammonium fertilizers are
top-dressed or applied on the soil surface. Typically, this is a regular feature of the dryland
cropping system in Australia [27]. When there is not enough moisture in the soil tohydrolyze and move the dissolved fertilizer into the soil, NH4 –N is partially converted
into NH3 and escapes into the atmosphere. High temperatures, high wind, and low(b) Py_NO3–N uptake(d) Ye_NO3–N uptake

NH4–N (mg L-1)

Sustainability 2023, 15, 13370

diﬀerent agricultural management practices implemented at the two locations. Simulations showed that NH4–N contributed very little to the plant N nutrition and that most N
uptake was in the form of NO3–N during both crop seasons (Figure 8). The simulated tota
N uptake at Yeelanna was more than twice that at Pygery. Daily NH4–N and NO3–N uptake by wheat at Pygery ranged from 0 to 0.16 mg L−1 and 0 to 13 mg L−1, respectively
While at Yeelanna, maximum daily NH4–N and NO3–N uptakes were 0.9
15andof 220.8 mg L−1
in 2018 and 15.5 and 27.8 mg L−1 in 2019, respectively. Notably, wheat’s maximum daily N
uptake occurred from early August to late September, coinciding with the wheat’s maximum growth period. Seasonal crop N uptake at Pygery ranged from 55 to 62% of the tota
water contents in N
the(Nsurface
layer, commonly present during wheat sowing, create ideal
F + NMin+ NS), whereas the corresponding N uptake at Yeelanna was only 40–44%
conditions for NV losses.
Moreover,may movetowards
thewassoilroughly
surfacedouble
via capillary
4 –Nduring
However, wheat N NH
uptake2019 atYeelanna
the amount of N
rise with intense evaporative
contributing
gaseous
Therehalf
is an
even
mineralized fluxes,in the soil
(Figure 7). toThis
implieslosses.
that almostof theN greater
uptake by wheat
potential for NH3 waslossesin areas bywithsoils [66,67].
contributed
thealkaline
N fertilizer.
0.9(c) Ye_NH4–N uptake(a) Py_NH4–N uptake

0.6
0.3

NO3–N (mg L-1)

Sustainability 2023, 15, x FOR PEER REVIEW0

16 of 2430
20(d) Ye_NO3–N uptake(b) Py_NO3–N uptake

3.4.3. Simulated Volatilization Losses of N
10

Volatilization N losses (NV) usually occur when urea or ammonium fertilizers are0
top-dressedor applied on the soil surface. Typically, this is a regular feature of the dryland
cropping system in Australia [27]. When there is not enough moisture in the soil to hydrolyze and move the dissolved fertilizer into the soil, NH4–N is partially converted into NH3and escapes into theatmosphere.
High temperatures, high wind, and low water contents
8. Daily NH4–N (a,c) and NO3–N (b,d) uptake by wheat at Pygery (a,b) and Yeelanna (c,d)
Figure8. Dailylayer,
NH4Figure–N(a,c)
andpresent
NO3 –Nduring(b,d) uptake sowing,by wheat at Pygery (a,b) and Yeelanna (c,d)
in the surface
commonly
during 2018 and 2019 simulatedwheatby HYDRUS-1D.create ideal conditions forduring
2018
and
2019
simulatedby
HYDRUS-1D.
NV losses. Moreover, NH4–N may move towards the soil surface via capillary rise withintense evaporative fluxes, contributing to gaseous losses. There is an even greater potenHYDRUS-1Din the soils
current
study suggest that volatilization losses of N
tial forNH3 losses insimulations
areas with alkaline[66,67].
(Nv ) HYDRUS-1D
at Pygery aresimulations
smaller thanat Yeelanna
becausefertilizer
applications
at thein thecurrent study
suggestthat volatilization
lossesof Nlatter site1 ) are higher
1 ) for(121–151
kg Nareha−than at theformersite (11–15
kg N hathesitesame crop(Nv) at Pygery
smallerthan at Yeelanna
becausefertilizer
applications
at−thelatter−1) are higher than at the former site (11–15 kg N ha−1) for the same crop
(121–1519).
kg TheN hamaximum(Figuredaily NV losses at Pygery were only 0.02 and 0.03 kg N ha−1
(Figure 2018
9). Themaximum
daily NV losses
at Pygerywere only 0.02 amounts
and 0.03 kg
N ha−1duringand2019, respectively(Figure9a). Corresponding
at Yeelanna
were−1
during
2018
and
2019,
respectively(Figure9a).
Corresponding
amountsatYeelanna
were
0.26 and 0.15 kg N ha , respectively. At the Yeelanna site, daily NV losses
increased
0.26 and in
0.15
kg N ha−1to, respectively.
At the Yeelannasite, daily
NV losses
increased
initially
response
fertilizer applicationand thenrapidly
droppedas theiniapplied N
tially in response to fertilizer application and then rapidly dropped as the applied N was
was translocated deeper into the soil. This was due to high NH4 –N concentrations fromtranslocated deeper into the soil. This was due to high NH4–N concentrations from thethe applied fertilizer in the surface soil layer with low water contents providing favorable
applied fertilizer in the surface soil layer with low water contents providing favorable
conditions
forNNv vlosses.
losses.
Seasonal
at Pygery
1.10.8andkg−1 Nha−1 during
V lossesconditions for
Seasonal
NV N
lossesat Pygerywere were
1.1 andkg 0.8N haduringtheand 2019
2019cropping
croppingseasonsseasons(Figurerespectively.
At Yeelanna,
the corresponding
the 2018
2018 and(Figure9), 9),
respectively.
At Yeelanna,
the correspond−
1
losseswere
7.37.3and, respectively.
These
represent
4.6 toing losses
wereand6.9
6.9kg
kg N
N ha
ha−1, respectively.
Theselosseslosses
represent
4.6 to 7.3%
of 7.3% oftheapplied.This
Thisamountamountfalls
withinthe range
NV losses(1.8–23%)
measuredby
the N applied.
falls
withinthe rangeof NVoflosses(1.8–23%)
measuredbyTurneret al.
al.[27][27]atatdiﬀerent
differentsouthern
Australian
locationsinvolvingfertilizers
applied toTurner et
southern
Australian
locationsinvolvingfertilizers
applied torainfed crops,
wheat.
rainfedcrops,including
includingwheat.

NV loss (kg ha-1)

0.3(a) volatilization

0.2

PygeryYeelanna

0.1

NL loss (kg ha-1)

0
0.4
0.3

(b) leaching

0.2
0.1
0

Figure 9.
9. Daily(N(Nv) (a)
andleaching
(NL(N
) (b)
losses at the Pygery (Py, red
FigureDaily nitrogen
nitrogenvolatilization
volatilization
andleachingv ) (a)L ) (b) losses at the Pygery (Py, red line)line) and Yeelanna (Ye, blue line) sites simulated by HYDRUS-1D during the 2018 and 2019 wheat
and Yeelanna (Ye, blue line) sites simulated by HYDRUS-1D during the 2018 and 2019 wheat seasons.
seasons.

3.4.4. Leaching Losses of Nitrogen
Leaching losses refer to the N losses induced by a drainage flux from the root zone
to deeper soil layers and groundwater. These losses usually occur in the form of NO3–N,

Sustainability 2023, 15, 13370

16 of 223.4.4. Leaching Losses of Nitrogen
Sustainability 2023, 15, x FOR PEER REVIEW

17 of 24
Leaching losses refer to the N losses induced by a drainage flux from the rootzone
to deeper soil layers and groundwater. These losses usually occur in the form of NO3 –N,
a mobile component of N. At Pygery, leaching losses of NO3 –N (NL ) were negligible as
there
was
notIna2018,sufficientdrainageflux
to trigger
N losses.
However,at Yeelanna,(Figure9b).
drainagelossesoccurred
duringmid-season(mid-August
to seasonallate Sep−1 during 2018 and 2019, respectively
NO–N
leaching
amounted
to
3.5
and
20.5
kg
ha
tember),even
thoughdrainage
fluxeswere
lowduring
that
time.
The
maximumdaily
3−1(Figure
9b).
In
2018,
drainagelossesoccurred
duringmid-season(mid-August
tolate
NO3–N losses were 0.1 kg ha (Figure 9b). On the other hand, N losses increased many
September),eventhoughdrainage
fluxeswere
lowduring
that
time.
The
maximumdailyfolds during 2019. Most NO3–N losses occurred early in the season when the Tp require−1 (Figure 9b). On the other hand, N losses increased manyNOlosseswere
0.1 kghaevents
3 –N
ment
was
small.
Heavy
rain
during this period can leach an enormous quantity offolds
duringMostzone.
NO3 –Nlosses occurred
earlyseasonwhen theTp requirement
NO3–N
from2019.
the root
Therefore,
the daily
rateinoftheNO
3–N leaching
increased
to 0.35
wassmall.
rain
duringthis period
can leachOn
an aenormous
quantity3 –N
kg N
ha−1, Heavy
leading
to events
N losses
via oﬀ-site
movement.
percentage
basis,of
NNO
L losses
−1
from
the
root
zone.
Therefore,
the
daily
rate
of
NO
–N
leaching
increased
to
0.35
kg
N
ha
3
accounted for 3–13.5% of N applied by fertilizers and 1.5–7.6% of the total plant-available,
leading
N losses via off-site movement. On a percentage basis, NL losses accounted for
N in thetosoil.
3–13.5% of N applied by fertilizers and 1.5–7.6% of the total plant-available N in the soil.

Water and N use efficiency

3.5. Water Productivity and N Use Eﬃciency
3.5. Water Productivity and N Use Efficiency
Model-simulated seasonal water and nitrogen use and yield estimates for the experModel-simulated seasonal water and nitrogen use and yield estimates for the experiimental sites at Pygery and Yeelanna were used to estimate the water productivity in terms
mental sites at Pygery and Yeelanna were used to estimate the water productivity in termsof transpiration (Wp_Tp) and evapotranspiration (Wp_ET), and nitrogen use eﬃciencyof transpiration (Wp_Tp) and evapotranspiration (Wp_ET), and nitrogen use efficiency(NUE) of dryland wheat (Figure 10). Both water productivities (Wp_Tp and Wp_ET) and(NUE) of dryland wheat (Figure 10). Both water productivities (Wp_Tp and Wp_ET) and
NUE were higher at Yeelanna (high rainfall zone) as compared to Pygery (low rainfall
NUE were higher at Yeelanna (high rainfall zone) as compared to Pygery (low rainfall zone).
zone). This explains a three times higher wheat yield at Yeelanna than the corresponding
This explains a−1three times higher wheat yield at Yeelanna than the corresponding yield
yield (1.52 t ha ) obtained at Pygery. The Wp_Tp and Wp_ET eﬃciencies at Pygery varied(1.52 t ha−1 ) obtained at Pygery. The Wp_Tp and Wp_ET efficiencies at Pygery varied from−1 mm−1, respectively, while corresponding values at Yeelanna
from
15 to
188andto 9hakg−1ha−1 , respectively, while corresponding values at Yeelanna
15
to 18
and
to 98 kgmm
−1
−1. Diﬀerent Wp values obtained at both sites
ranged
from
27
to
40
and
16
to
24
1 mm
−1 . Different Wp values obtained at both
ranged from 27 to 40 and 16 to 24kg
kgha
ha−mm
signifytheimportanceofrainfallquantities,
soil’sproperties,
and farmers’sites signify the importance of rainfall quantities,water
soil’sretention
water retention
properties,andmanagement
practices.
farmers’ management practices.

50
40

PygeryYeelanna

2018

2019

2018

2019

2018

2019

Wp_Tp

Wp_Tp

Wp_ET

Wp_ET

NUE

NUE

30
20
10
0

−−1
1 mm
Figure 10.
10. EstimatedEstimatedwater
waterproductivity
productivity(kg(kghaha
mm−−11))for
Figurefor transpirationtranspiration (Wp_Tp)(Wp_Tp) andand evapotranevapotran−1) of wheat at Pygery (Py) and Yeelanna
spiration(Wp_ET),andnutrientuse
eﬃciency
(NUE)
(kg
kg
−
1
spiration (Wp_ET), and nutrient use efficiency (NUE) (kg kg ) of wheat at Pygery (Py) and Yeelanna(Ye) during the 2018 and 2019 seasons.
(Ye) during the 2018 and 2019 seasons.

Similarly, NUE
NUE variedvaried fromfrom 31
31 to
to 34
34 andand34
34toto41
41kg
kggraingrainyield
yieldkg
kg−−11 N
N uptakeuptake atat
Similarly,
PygeryandYeelanna,
respectively(Figure8).
Normally,theNuse
eﬃciencyisrelatively
Pygery and Yeelanna, respectively (Figure 8). Normally, the N use efficiency is relatively
low, irrespective
irrespective ofof crop
about
40%
of of
thetheNlow,crop type.
type. For
Forexample,
example,Hu
Huetetal.al.[68][68]foundfoundthat
that
about
40%
fertilizer
is recovered
in inthetheabovegroundparts
of dryland
wheat.
TheTherestrest
waswaseitherreN
fertilizer
is recovered
abovegroundparts
of dryland
wheat.
eithertained ininthe
retainedthesoil,soil,denitrified,denitrified,ororlost
lostbybyNNleaching.
leaching.
4. Discussion
4.1. Soil Water Balance and Wheat Water Uptake
Under rainfed conditions, the soil moisture regime is dictated by the timing,amounts, and intensity of rain, which are crucial for sustainable crop production.

Sustainability 2023, 15, 13370

17 of 22

4. Discussion
4.1. Soil Water Balance and Wheat Water Uptake
Under rainfed conditions, the soil moisture regime is dictated by the timing, amounts,
and intensity of rain, which are crucial for sustainable crop production. Therefore, accurate
estimation of soil water balance helps understand the interrelationship among differenthydrological components, including plant water availability and incipient water losses.
Water content dynamics in the soil showed that most of the soil profile moisture regime
variability occurred in the surface layer (0–30 cm), with a more static moisture regime in the
deeper depths (Figure 3). The water content retained in the 0–30 cm horizon is crucial forseed germination and subsequent growth of crops, as the bulk of the fibrous roots of cereal
crops mine this region for water and nutrient needs [69]. Wang et al. [61] reported that theroot activity of winter wheat is concentrated within the 0–40 cm soil layer and reported
small changes in water content dynamics at the deeper depths.
The model-simulated actual seasonal transpiration (Tp act ) at both locations falls within
the range estimated by French and Schultz [10] and Sadras and Angus [11] for the rainfed
wheat crop. Site-specific climate, soil properties, and crop variety highly influence theextent of water uptake by wheat. In addition to low rainfall at Pygery, soils had low water
holding capacity compared to Yeelanna, significantly influencing wheat’s water availability
in the soil. Indeed, soils with low water holding capacity and scanty rainfall during winterled to frequent terminal droughts [11]. Seasonal evaporation can tremendously impact thewater availability for crop needs in the surface soils. Several studies [10,11] assumed a fixedvalue of the seasonal evaporation loss (110 mm) when determining water availability for
rainfed wheat, which seems unreasonable. However, inter- and intra-season variabilities inthe evaporation losses are common and are highly correlated with the diurnal and seasonalchanges in the climate parameters and ground cover [61]. In the current study, seasonal Es
losses were 5–30% lower than a fixed value (110 mm) suggested by French and Schultz [10].
Numerous other studies [70–72] reported soil evaporation significantly lower (45–70 mm)
than the proposed fixed value. Direct water loss from the soil surface can be reduced byadopting appropriate water storage and mulching practices, improving water retention
in the soil [73]. Improved water regimes in surface soils can boost wheat growth andsustainable production in a water-limited environment.
Water uptake and crop yields of rainfed wheat are severely influenced by climate,soil and crop characteristics, and a large gap exists between the potential and actual crop
yield [11,15]. These variabilities ultimately impact the water use efficiency of crops in rainfed environments. The average water productivity (WP_ET) values (8.6 kg ha−1 mm−1 )estimated in the current study are comparable to water use efficiency reported in otherstudies in the rainfed region of Australia [11,72]. Similarly, the transpiration efficiency(WP_Tp) values are similar to values reported by Harries et al. [72] and Sadras and Lawson [14] for the rainfed wheat production environments. However, the average values of
WP_ET (19.8 kg ha−1 mm−1 ) and WP_Tp (33.8 kg ha−1 mm−1 ) obtained at the Yeelannasite were much higher than those reported in previous studies. Unlike the current study,
these studies usually ignored the deep drainage component. The model predicted that 25%of rainfall received at Yeelanna is lost as deep drainage, a significant factor contributing
to higher WP_ET and WP_Tp at this site. Thus, HYDRUS-1D has been able to predict
the actual water balance fluxes, including deep drainage, depending on the climate, crop,and soil conditions [74], and provide an accurate assessment of water use efficiency andwater-limited yield estimation for the rainfed wheat production system.
4.2. Nitrogen Losses and Recovery by Crop
Nitrogen supplement in the form of fertilizer is essential for profitable crop production.
The fertilizer requirement of wheat may vary each season, depending on NMin in the soil
and climate variability. Angus and Grace [1] reported that the minimum level of N fertilizer
applied to dryland wheat should be 45 kg N ha−1 for sustainable crop production. However,the long-term (15 years) application of the fixed amount of N fertilizer (45 kg ha−1 ) at

Sustainability 2023, 15, 13370

18 of 2250 farms resulted in a reduction in the wheat yield by 60% of the water-limited potential
yield [75]. This suggests that the N fertilizer application at the Pygery site was much lower
than required, significantly impacting obtaining a potential water-limited yield and N
use efficiency. On the other hand, the amount of N applied was very high at Yeelanna(121–151 kg ha−1 ), potentially leading to high N concentration in the soil and consequentlosses of applied N. Therefore, blanket applications of N adopted by the growers in thecurrent study can have varied impacts on crop growth and yield and may lead to potential
N losses from the fields. Moreover, N transformation and loss mechanisms are highly
influenced by climate variability and soil environment at different locations, impacting
plant N uptake by rainfed crops. Angus et al. [76] reported that the average abovegroundrecovery of total N was around 36% at six commercial dryland wheat sites in south-eastern
Australia. However, with improved management practices, the N recovery efficiency ingrain production can be increased to 44% [1]. Thus, the wheat’s NUE estimates in thecurrent study corroborate well with other studies.
Soil factors such as texture, pH, and organic matter content, as well as soil constraints
such as salinity and sodicity, also significantly impact the processes of N transformationin the soils [77]. Therefore, N recovery improvements require better temporal matchingof N supply to periods of high crop demand and avoiding periods when risks of losses
surge [37,65]. Monjardino et al. [78] concluded that adopting non-limiting or near-nonlimiting nitrogen fertilizer practices could help close the wheat yield gap in the Australianrainfed cropping system. The results from this study indicate that site-specific water
availability and N management play a crucial role in enhancing the efficient resource
utilization of rainfed cropping systems. Thus, we recommend conducting further researchon fertilizer-application timing and evaluating different fertilizer-application scenarios toincrease crop recovery of fertilizers applied to dryland wheat production.
Ammonium volatilization (NV ), similar to ammonia and nitrate leaching (NL ) fromthe root zone, represents an important N loss for rainfed wheat cropping systems. The
Nv losses from applied urea fertilizers typically contribute to greenhouse gas emissions
from rainfed wheat production systems. However, in the present study, this fraction islower than the emission threshold (10%) from the applied synthetic fertilizers consideredby IPCC [79]. Nonetheless, NV losses to the extent observed in the current study (4.6 to 7.3%of the N applied) still represent a major economic loss to farmers. Therefore, an accurate
assessment of NV losses could help devise better management practices for improving the
productive and sustainable practices of rainfed wheat production systems.
Leaching drives the NL losses due to rain events and the mass of N in the NO3 –N formin the soil. In sandy soils, N leaching (NL ) can be significantly higher [80], ranging between
6 and 20% of the total N flux (16–159 kg N ha−1 year−1 ). Numerous other studies [81–84]
have reported significant leaching losses of NO3 –N, varying from 4 to 59 kg ha−1 year−1
for different cropping systems in Australia. This represents a financial loss to the growers
and an increased risk of groundwater pollution. Indeed, NO3 leaching occurs infrequently
at most dryland cropping farms in Australia because the soil water-holding capacity is
generally sufficient to retain the surplus rainfall over potential evapotranspiration.
The HYDRUS-1D simulations suggested that maintaining the N balance is crucial for
the wheat production system, explaining how soil N storage changes over the years. Matching the supply of available N to the crop N demand will reduce the potential accumulationof available N and potential N losses. Although increasing N stocks is encouraged, it shouldbe acknowledged that temporary periods of mining N stocks are acceptable, provided theextent of N mining is quantified and followed by a rebuilding phase, in which N stocks
are replenished [49]. It is recommended that annual N balance calculations are performed.
However, these values should be integrated and accumulated over time to define the full
effect of applied management practices and temporal trends. Such information will allow
grain growers to implement appropriate actions to maintain their production base in the
future and continue to maximize profitable grain yield outcomes. Further optimization of
N applications can reduce the N losses by linking them with soil water content, rainfall, andSustainability 2023, 15, 13370

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meteorological data for a particular site. This requires more modeling efforts and intensive
N estimation at the field site for developing rainfall-based guidelines.
5. Conclusions
Mathematical modeling tools can play a pivotal role in understanding the water andfertilizer used by the rainfed wheat production system. This study used the numericalmodel HYDRUS-1D to simulate the water balance and nitrogen dynamics under rainfed
wheat cultivation at two locations (Pygery and Yeelanna) with varied climate and soil
conditions. The model output of water and N balance was compared with measured data
across various soil depths at both locations.
The modeled and measured water content suggested that plant water uptake by
rainfed wheat mostly occurred in the top 30 cm of soil, signifying the importance of thesurface soil layer, which stores water received by small rain events in rainfed environments.
Nevertheless, moisture retained in the surface layer is vulnerable to evaporation imposedby hot and dry weather conditions in arid and semi-arid environments. In the current
study, 50 and 30% of seasonal rainfall at low and medium rainfall sites were lost via
evaporation. Significant leaching losses (25% of seasonal rainfall) at the medium rainfall
site indicate considerable water loss in the rainfed wheat production system. Therefore,
adopting appropriate water storage and mulching practices can reduce this direct water
loss, enhancing water availability in the soil and improving the water-limited yield potentialof rainfed wheat.
Assessing the off-site movement of N (leaching losses) can help devise better strategies
for N fertilizer applications, which will reduce the environmental impacts of fertilizer use.
This study showed that ammonium volatilization (NV ) and nitrate leaching (NL ) represent
large potential N losses under the rainfed wheat system, depending on the seasonal rainfall
and climate pattern. The NV losses account for 4.6 to 7.3% of the added N fertilizer, while
NL losses ranged between 3 and 13.5% of N applied, especially at the medium rainfall
site. Low N volatilization losses suggest that the contribution of dryland wheat farming togreenhouse N gas emissions is very low. This study evaluated water and N dynamics inthe soil of the rainfed wheat production system for two years only. However, longer-term
efforts are needed to reduce N leaching losses by managing the appropriate timing anddose of N applications in response to available soil moisture levels and crop needs.
Supplementary Materials: The following supporting information can be downloaded at https://www.
mdpi.com/article/10.3390/su151813370/s1, Figure S1a–d: Daily values of estimated seasonal crop
evapotranspiration (ETc) at the Pygery and Yeelanna sites during 2018 and 2019.
Author Contributions: Conceptualization, V.P.; methodology, V.P.; software, V.P. and J.Š.; formal analysis, V.P.; investigation, V.P.; resources and project management, P.P.; data curation, V.P.; writing—originaldraft preparation, V.P.; writing—review and editing, V.P., J.Š., P.P., V.F. and T.P.; visualization, V.P.;
supervision, P.P. All authors have read and agreed to the published version of the manuscript.
Funding: The authors acknowledge the financial support provided by Grain Research Development
Corporation (GRDC) vide Grant Number GRDC1911-004BXL for this investigation on using soil
water information to make better decisions on the Eyre Peninsula.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data are available upon reasonable request to the corresponding author.
Acknowledgments: We acknowledge the collaboration with Amanda Cook and SARDI, Minnipa
Agriculture Centre and the soil moisture probe landholders.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the
study’s design, in the collection, analyses, or interpretation of data, in the writing of the manuscript,
or in the decision to publish the results.

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