Agricultural Water Management 227 (2020) 105845

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

Management of soil salinity associated with irrigation of protected crops
V. Phogat

a,b,c,⁎, Dirk Mallants , J.W. Coxda,b, J. Šimůnek , D.P. Oliver , J. Awad
e

c

T

f

a

South Australian Research and Development Institute, GPO Box 397, Adelaide SA 5001, Australia
The University of Adelaide, PMB1, Glen Osmond SA 5064, Australia
CCS Haryana Agricultural University, Hisar 125 004, Indiad
CSIRO Land and Water, PMB No. 2, Glen Osmond, SA 5064, AustraliaeDepartment of Environmental Sciences, University of California, Riverside, CA 92521, USAf
School of Natural and Built Environments, University of South Australia, SA 5095, Australiabc

A R T I C LE I N FO

A B S T R A C T

Keywords:
TomatoCapsicum
Cucumber
Eggplant
UNSATCHEM
Irrigation
ESP
Salinity

Long-term evaluation of soil chemical changes in the soil is required to optimize irrigation of protected crops andto control associated environmental issues. In this study, the multi-component solute transport module UNSATCHEM of HYDRUS-1D was used to assess the eﬀects of long-term (2018–2050) irrigation on salt build-up in thesoil under unheated greenhouse conditions. Blended water (recycled water, artiﬁcially recharged groundwaterand harvested rainwater) was used to irrigate tomato, cucumber, capsicum, and eggplant. Irrigation management included four leaching fractions (LF), i.e., accounting for 0, 15, 20, and 30% of applied excess water. The
eﬀects of four amounts of annual gypsum application, i.e. 0, 1.7, 2.6, and 3.4 t/ha, were also simulated.
Model-simulated annual root water uptake by cucumber, tomato, capsicum, and eggplant was 303, 476, 642,
and 649 mm, respectively, for an irrigation schedule based on outdoor temperature thresholds. Annual drainage
was 4.1–6.1% of irrigation for these crops. Average salinity in the soil solution (ECsw) at the end of the simulation (year 2050) reached 6.5 dS/m for cucumber, 7.6 dS/m for tomato, 8.7 dS/m for capsicum, and 9.3 dS/m
for eggplant. Soil salinity was building up at a nearly constant rate over 33 years. Exchangeable sodium percentage (ESP) at the end of the simulation was 30.8% for tomato, 27.1% for capsicum, 33.2% for eggplant, and31.4% for cucumber. These results indicate that both salinity and sodicity will exceed critical threshold values
and thus must be managed to maintain sustainability of irrigated horticulture. Modeling showed that higher
irrigation (up to 15–20%) coupled with an annual addition of gypsum of 1.7 t/ha kept both salinity and sodicity(SAR, ESP) below critical thresholds while pH was reduced from 8.7 to 7.8, thus creating a favorable soil environment for long-term sustainable vegetable production under greenhouse conditions. The new gypsum application module implemented in the UNSATCHEM model has broad applicability in evaluating soil amendment
scenarios to address sodicity hazards linked to long-term irrigation.

1. Introduction
Protected agriculture is steadily growing around the world (FAO,
2017), with greenhouse vegetable production being one of the mostimportant and proﬁtable agricultural systems. Greenhouses provide an
opportunity to partially modify climatic parameters in order to develop
eﬀective growing systems under otherwise adverse conditions due toclimate variability and climate change. In Australia, the protected
cropping industry has an estimated value of $1.8 billion at the farmgate, which accounts for approximately 20% of the total gross value ofthe production of vegetables and cut ﬂowers (PCA, 2019). Crops commonly grown in greenhouses in Australia include tomato, capsicum,
eggplant, and cucumber (AUSVEG, 2019a).

⁎

Nevertheless, most greenhouses (up to 80%) in Australia are low
cost structures with soil-grown crops (Parks et al., 2009; AUSVEG,
2019b; Nguyen, 2019), though the number of high technology structures on soilless growing media has been increasing in recent years tomeet speciﬁc market requirements (AUSVEG, 2019b; Nguyen, 2019).
High-tech greenhouses could be ﬁve to ten times more expensive thanlow-cost soil-based greenhouses (Nguyen, 2019). Therefore, in the
Northern Adelaide Plains, most growers do not have the capital required to construct high-tech greenhouses and instead choose to use
low-cost systems where risk levels are lower.
Water availability and quality are the most important factors that
determine the expansion of protected agriculture. It is well understoodthat greenhouse crops cannot be grown proﬁtably with highly saline

Corresponding author at: South Australian Research and Development Institute, GPO Box 397, Adelaide SA 5001, Australia.
E-mail address: vinod.phogat@sa.gov.au (V. Phogat).

https://doi.org/10.1016/j.agwat.2019.105845
Received 24 July 2019; Received in revised form 1 October 2019; Accepted 2 October 20190378-3774/ Crown Copyright © 2019 Published by Elsevier B.V. All rights reserved.

Agricultural Water Management 227 (2020) 105845

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Due to the complexity involved in measuring climate variables andcrop data, such as the leaf area index, other simple methods for accurately estimating greenhouse ET0 that use, for example, the temperature-based Hargreaves equation (Fernández et al., 2010) have been
explored. Empirical approaches widely used under open ﬁeld conditions that only require temperature and greenhouse radiation data
(Allen et al., 1998; Itenﬁsu et al., 2003; Gavilán et al., 2006) could be
easily applied for determining greenhouse ET0 (Boulard and Wang,
2000). Nikolaou et al. (2017) found a good correlation between leaf
temperature and transpiration for a soilless greenhouse cucumber crop,with the prediction equation being validated for diﬀerent greenhouseclimatic conditions. However, such irrigation practices have often
oversimpliﬁed the complex nature of the interacting climate, soil, andcrop variables involved in the estimation of crop water requirements.
For example, irrigation scheduling for greenhouse crops based on outdoor temperature is practiced by growers in the NAP who consider only
60% of outdoor ETC (Awad et al., 2019). While this simpliﬁed approachrequires less data, there is a need to test its eﬀectiveness for long-term
irrigation while also safeguarding chemical quality of soil. Observationsof soil health over one or several seasons won’t reveal the potential
adverse impacts of irrigation-induced salinity and sodicity, whichusually arise only after many years of inappropriate irrigation scheduling. Hence, there is the need for long-term simulations that incorporate the soil processes that can lead to accumulation of various
salts in the soil proﬁle (e.g., Mallants et al., 2017).
Processed based numerical models are excellent tools to enhance
our understanding of the long-term impact of irrigation on salinity (e.g.,
Phogat et al., 2018) and sodicity risks in the receiving environment.
Among these models, the multi-component major ion chemistry module
UNSATCHEM of the HYDRUS-1D software package (Šimůnek et al.,2016) is an excellent tool intended to predict major ion chemistry, as
well as water and solute ﬂuxes in soils during transient conditions.
While the water ﬂow and solute transport components of the standard
HYDRUS-1D model have been used widely (e.g., Phogat et al., 2018),
applications of the major ion chemistry module have been limited because of its complexity and a lack of appropriate experimental data
(e.g., Gonçalves et al., 2006; Ramos et al., 2011). However, several
short-term studies mainly focused on assessing the impact of using high
SAR water (Suarez et al., 2006), sodic soil reclamation (Simunek andSuarez, 1997; Kaledhonkar et al., 2006; Wang et al., 2014), water
quality requirements (Mallants et al., 2017), and solute transport(Ramos et al., 2011) under irrigation with saline/degraded water(Skaggs et al., 2014). Therefore, simulations of the movement of dissolved solutes and precipitation/dissolution reactions of salts in soils
have been a useful complement to experimental data (Suarez, 2001).
Also, computer modelling may help assess the viability of diﬀerent
water uses such as blending and test the eﬃcacy of soil amelioration
methods. Applications with the major ion chemistry module UNSATCHEM of HYDRUS-1D for assessing long-term risks of irrigation withrecycled water and associated blends and reclamation options forgreenhouse crops are limited.
In this study, we evaluate the impact of the long-term (2018–2050)
use of irrigation water blended from waters of diﬀerent qualities (recycled water, managed aquifer recharge (MAR) water, and harvested
rainwater) on soil chemical changes for soil-grown crops under greenhouse conditions. We explore various management options such as increased leaching fractions and annual gypsum applications, to avoid
excessive build-up of soil salinity and sodicity. We additionally alsoseek to identify how to make the best use of blended water for sustainable irrigation in the NAP region. Finally, we also implement a newcapability into the UNSATCHEM module, allowing an annual application of a solid form of gypsum in order to reduce the exchangeablesodium percentage (ESP) levels in the soils. The outcome of these longterm simulations, accounting for future climate predictions, would help
in devising better irrigation guidelines for growing vegetable crops
under protected environments while also accommodating higher water

water (Stanghellini et al., 2007). To manage both water quantity andquality, many irrigators have to make use of several water sources,
which are either conventional (surface and groundwater) or non-conventional (recycled wastewater, desalinated water, brackish water,
treated produced water from oil and gas production, etc.). Various
qualities of diﬀerent water sources add a higher level of complexity toirrigation water management, and their use poses a new challenge tomanagers and irrigators (Reca et al., 2018). Therefore, in many arid andsemiarid regions, salinization of soil and water resources is increasing
in protected horticulture (Pardossi et al., 2004; Machado andSerralheiro, 2017). Since most crops, including vegetables grown insoil-based greenhouses, are highly sensitive to salinity, the knowledgeof salinity build-up in soil is important for optimal irrigation management (Stanghellini et al., 2007).
Similarly, in the Northern Adelaide Plains (NAP) region of South
Australia, yields of soil-grown tomato and cucumber crops have beensteadily declining for several years due to high salinity (high sodium
and chloride) levels in the soil (Ferguson et al., 2012). The reason forthe decline was found to be the poor quality of primary water sources,
which was either saline bore or recycled water with an EC of 2–4 dS/m.
Due to limited storage capacity, only a minimal amount of rainwater
was captured from greenhouse roofs that could be blended with irrigation water to dilute its salinity. Likewise, Shi et al. (2009) observedhigh accumulation of soil nutrients, which led to higher soil salinity inlong-term greenhouse cultivations.
Some nutrients that accumulate in the root zone may leach downward with the application of irrigation water, causing soil andgroundwater contamination (Colombani et al., 2015; Kim et al., 2008).
Most greenhouse farmers follow irrigation and fertigation practices
based on their experiences (Thompson et al., 2007), without monitoring
or controlling the soil water, nutrient, and salt status. Therefore, accurate estimations of crop water requirements are needed to avoid either salt build up in the soil proﬁle or excess water applications thatwould negatively impact nutrient availability for plants and leaching of
agrochemicals to groundwater (Blanco and Folegatti, 2004).
Eﬀective irrigation scheduling in closed growing structures depends
on accurate estimates of crop evapotranspiration (ETC). However, direct measurement methods of crop water requirements (e.g., weighing
lysimeters, stem gauges) are either expensive or may not be practical to
implement in commercial greenhouse settings (Villarreal-Guerrero
et al., 2012). Alternatively, simulation models oﬀer cost-eﬀective andnon-destructive approaches to estimate crop water use. Among suchmodels, the FAO-56 methodology (Allen et al., 1998) has been usedworldwide to determine crop water requirements under open ﬁeld
conditions, and this methodology can also be applied to greenhousecrops (Bonachela et al., 2006; Qiu et al., 2015). To use the FAO-56
methodology for greenhouse crops, measurements of greenhouse reference evapotranspiration (ET0) and crop coeﬃcients (KC) are required(Fernández et al., 2010). Möller and Assouline (2007) observed thatdaily ET0 in a cropped greenhouse, calculated following the FAO-56
methodology, averaged only about 62% of the estimated outdoor value.
The main reason for the reduction in ET0, compared to outdoors, was asigniﬁcant reduction in solar radiation inside the greenhouse (56% of
open ﬁeld conditions) and lower wind speeds, which decrease the exchange of water vapor between the crop canopy and greenhouse atmosphere. Farias et al. (1994) observed that ET0 inside greenhouses
was always 45–77% lower than outside. Braga and Klar (2000) observed that ET0 values inside greenhouses oriented east/west andnorth/south were 85 and 80%, respectively, of outdoor ET0. However,Harmanto et al. (2005) found that ETC, estimated using measured climate data in a greenhouse, was about 75–80% of ETC observed in an
open environment. Several software packages, such as PrHo V2.0
(Fernandez et al., 2009) and VegSyst (Gallardo et al., 2011, 2016), havebeen developed that are based on the FAO-56 method with a single KC
approach and that simulate ETC, crop growth, and crop N uptake ofgreenhouse-grown vegetable crops.
2Agricultural Water Management 227 (2020) 105845

V. Phogat, et al.

cations and Gapon constants for both soils used in the modeling simulations are shown in Table 3. Soil hydraulic (Table 1) and chemical
properties (Table 3) measured in the 20–30 cm soil layer were assumed
to extend to 200 cm (the domain depth) as the soils had almost uniform
texture below 20 cm.

needs owing to a future, hotter climate.

2. Materials and methods
2.1. Study site and soil parameters2.2. UNSATCHEM and its gypsum module

The Northern Adelaide Plain (NAP) region is one of the important
greenhouse crop growing areas in Australia. Due to its ﬂat topography,
existing agricultural and horticultural infrastructure, and access to an
urban market, the NAP is well suited for irrigated annual horticulture.
In the last decade, 220 commercial establishments have emerged in the
NAP, showing a 38% growth in greenhouse cropped area (PIRSA Spatial
Information Services, 2017). However, fresh water is a key input, whichinﬂuences crop yield and soil degradation. The region experiences aMediterranean climate, characterized by hot, dry summers and cool tocold winters. Long-term (1900–2016) average rainfall in the region is
475 mm (Department of Environment, Water and Natural Resources,
2016) and annual evapotranspiration is 1308 mm, resulting in the needfor irrigation for crop production.
Soil samples for this study were collected in triplicates from two
greenhouses in the region (Awad et al., 2019) at two depths (0–20 and
20–30 cm). These samples were analyzed for their physico-chemical
properties following standard procedures (Mallants et al., 2019). The
soil texture in the ﬁrst greenhouse (S1) is categorized as sandy clay
loam at both depths with sand, silt, and clay contents varying from 47.9
to 62.2, 12.9–23.4, and 23.4–31.5%, respectively. Similarly, the soil
texture of the second greenhouse (S2) was categorized as sandy loam at
both depths. The organic carbon (OC) content varied from 0.77 to
1.06% in S1, while it was nearly double (1.38–1.97%) in the second soil
(S2). Average soil water contents at saturation (0.01 kPa) and at 4, 8,
33, 60, 100, and 1500 kPa, and the saturated hydraulic conductivity(Table 1) of the two soils were ﬁtted using the van Genuchten-Mualem
analytical model (van Genuchten, 1980) to obtain soil hydraulic parameters for the UNSATCHEM module (Table 1).
Soil chemical parameters required by the UNSATCHEM module
include the Gapon exchange constants, the initial concentrations of soil
exchangeable cations (K+, Na+, Ca2+, Mg2+), and the initial solution
composition (K+, Na+, Ca2+, Mg2+, Alkalinity, SO42‐, Cl‐) in the soil
proﬁle. The latter was deﬁned using the solution composition of harvested rainwater collected in the greenhouses in the NAP region(Table 2, Awad et al., 2019). Exchangeable cations were determinedusing NH4Cl solution at either pH 7.0 or pH 8.5, depending on the soil
pH (Rayment and Lyons, 2011). The cation exchange capacity (CEC)
was assumed to be equal to the sum of adsorbed concentrations of thefour cations (Na+, Ca2+, Mg2+, and K+) and it was assumed that the
CEC (107.4–161.5 meq/kg soil) is constant and pH-independent. Notably, the ESP varied from 9.52 to 16.61%, which was much higher
than the threshold value (6%) for Australian conditions. Gapon constants were taken from Mallants et al. (2019) who estimated these values for similar soils in the NAP region. Values of soil exchangeable

The study focussed on long-term simulations (1970–2050) using
HYDRUS-1D with the UNSATCHEM module (Šimůnek et al., 2016) for
evaluating the impact of the use of irrigation water blended from different water sources [recycled water, managed aquifer recharge (MAR),and harvested rainwater] for irrigation of crops grown under greenhouses conditions. UNSATCHEM is a multi-component, major ion
chemistry module, which is particularly well suitable for quantitative
predictions of processes involving major ions, such as simulations of theeﬀects of salinity on plant growth and estimating the amount of waterand amendments required to reclaim soil proﬁles to desired levels of
salinity and ESP (Šimůnek et al., 2016).
Diﬀerent components of the HYDRUS-1D model, which are applicable for this study are described comprehensively in Šimůnek et al.
(2013) and Šimůnek et al. (2016). The form of the one-dimensional
Richards equation that is solved in HYDRUS-1D by the Galerkin-typelinear ﬁnite element scheme assumes that the air phase plays an insigniﬁcant role in the liquid ﬂow process and that water ﬂow due tothermal gradients can be neglected (Šimůnek et al., 2013). The single
porosity van Genuchten-Mualem model (Šimůnek et al., 2016) was usedin this study.
The major ion chemistry module in HYDRUS-1D enables the simulation of equilibrium reactions involving Ca, Mg, Na, K, SO4, Cl, NO3,
H4SiO4, HCO3, and CO2 (Šimůnek and Suarez, 1997; Šimůnek et al.,2013). The phases considered are aqueous, gaseous, adsorbed, precipitated, and complexed (Šimůnek and Suarez, 1997; Šimůnek et al.,2013). Partitioning of dissolved major ions between the solid and solution phases is described using the Gapon Eqs. (1)–(3) (White andZelazny, 1986) and Gapon selectivity coeﬃcients (Table 3). This requires the deﬁnition of the Gapon exchange constants for the exchangeof calcium and magnesium, calcium and potassium, and calcium andsodium (Gapon, 1933; Šimůnek and Suarez, 1994) given as:

Depth (cm)θr
(cm3/cm3)

θs
(cm3/
cm3)

α (cm−1)

n (-)

Ks
(cm/
day)

l (-)

Bulk
density(g/cm3)

S1
S1
S2
S2

0-20 cm
20-30 cm
0-20 cm
20-30 cm

0.18
0.20
0.15
0.15

0.46
0.42
0.49
0.44

0.0881
0.04840.03020.0245

1.17
1.17
1.27
1.26

23.5
46.1
49.2
75.3

0.50
0.50
0.50
0.50

1.4
1.5
1.4
1.5

(1)

0.5Ca + K − X ↔ K + Ca0.5 − X(2)

0.5Mg + Ca0.5 − X ↔ 0.5Ca + Mg0.5 − X(3)The Gapon selectivity or exchange coeﬃcients for reactions((1)–(3)) are deﬁned as (Šimůnek and Suarez, 1994):

Table 1
Soil hydraulic properties of two soils (S1 - sandy clay loam; S2 - sandy loam) for
two depths (0–20, 20–30 cm) used in the modelling simulations (Mallants et al.,2019).
Soil

0.5Ca + Na − X ↔ Na + Ca0.5 − X

K Ca / Na =[Ca − X ][Na][Na − X ][Ca]0.5(4)

K Ca / K =[Ca − X ][K ][K − X ][Ca]0.5(5)KMg / Ca =[Mg − X ][Ca]0.5[Ca − X ][Mg ]0.5

(6)where [Na], [K], [Mg], and [Ca] are molal activities in the soil solution(dimensionless), and [Na-X], [K-X], [Mg-X], and [Ca-X] are adsorbed
concentrations (mmolc/kg soil). For the precipitation–dissolution of
calcite and the dissolution of dolomite, either equilibrium or multicomponent kinetic expressions are used, which include both forward
and backward reactions.
A new annual gypsum application module has been incorporated in
the existing UNSATCHEM module, which simulates the real-timeamelioration of the soil suﬀering from increased sodicity (ESP) due tothe prolonged use of irrigation waters of low quality. High ESP develops
as a result of dynamic chemical exchanges between the solution and theθr is the residual water content; θs represents saturated water contents; α represents inverse of air entry value; n & l are the pore shape parameters and Ks is
the saturated hydraulic conductivity of soils.
3Agricultural Water Management 227 (2020) 105845

V. Phogat, et al.

Table 2The median water quality of diﬀerent waters used for blending in the IQ-QC2 model (Awad et al., 2019).
Water

ECw
dS/m

Ca

Mg

Na

K

a

Alk

SO4

Cl

pH

SAR

2.70
0.68
0.17

12.36
1.83
0.02

0.09
0.09
0.08

2.42
2.90
0.40

3.77
0.50
0.13

11.57
0.99
0.37

7.2
6.95
7.4

8.10
1.60
0.65

meq/L
Recycled water
Salisbury MAR
Harvested rain
a

1.8
0.47
0.12

1.90
1.95
0.50

Alkalinity.

Table 3
Soil chemical properties used in the modelling simulations. Gapon coeﬃcients are for sand over clay soil (Mallants et al., 2019).
Soil

Texturea

Exch Ca

Exch Mg

Exch Nab

Exch K

CEC

meq/kg
S1
S1
S2
S2
a

Scl*
Scl*
Sl#
Sl#

62.05
72.72
72.07
53.32

45.47
40.86
35.71
32.39

26.83
21.97
12.68
14.83

17.61
18.85
14.85
13.88

161.52
153.61133.26107.36

ESP

Gapon Coeﬃcient (KG)

(%)

Mg/Ca

Ca/Na

Ca/K

16.61
14.30
9.52
13.81

0.02
0.02
0.02
0.02

1.88
1.88
1.88
1.88

0.014
0.014
0.014
0.014

Exchangeable, bCation exchange capacity, *sandy clay loam, #sandy loam.

salinity level (measured as TDS value) is used ﬁrst when the required
volume is less than the volume-licensed for this source. Otherwise, theother source (the source with a higher salinity level) is used. Water
qualities used in this modeling study are given in Table 3. Water quality
data represents the median water quality of treated sewage water at the
Bolivar plant, MAR water from the Salisbury storage recovery well, andthe harvested rainwater from greenhouse roofs (Awad et al., 2019).
Concentrations of all chemical constituents of blended water (K+, Na+,
Ca2+, Mg2+, Alkalinity, SO42‐, Cl‐) are estimated by IQ-QC2 asweighted averages of constituent concentrations of each water used.
Monthly averaged blended irrigation water quality data thus obtained
from IQ/QC2 for multiple years was used in UNSATCHEM (Table 4).
Other crop speciﬁc inputs, such as root water uptake parameters(Feddes et al., 1978) were obtained from the HYDRUS database.
Parameters for the salinity threshold-slope functions for tomato, capsicum, and cucumber were taken from Maas and Hoﬀman (1977) andfor eggplant from Heuer et al. (1986). The two parameters for thesefunctions, i.e. the threshold ECe (dS/m) and slope (% yield reduction
per dS/m), for tomato, capsicum, cucumber, and eggplant were 2.5 dS/
m and 9.9%, 1.5 dS/m and 14%, 2.5 dS/m and 13%, and 1.1 dS/m and
6.9%, respectively. The maximum rooting depth was assumed as 1 m
for all crops, which falls within the range of 0.7–1.2, 0.5–1.0, 0.7–1.2,
and 0.7–1.2 m for tomato, capsicum, cucumber, and eggplant, respectively, as reported by Allen et al. (1998).

solid phase after the application of irrigation water of high sodium
adsorption ratio (SAR).
2.3. Irrigation inputs and crop parameters
A survey by Awad et al. (2019) found that four crops (tomato,
capsicum, cucumber, and eggplant) are commonly grown undergreenhouse conditions in the NAP. The growing season for these crops
in the NAP starts in January and lasts until the end of September, November, May, and November for tomato, capsicum, cucumber, andeggplant, respectively. Growers schedule irrigation events based on
their own experience, the growth stage of various crops, and air temperature (Awad et al., 2019). From information provided by growers,
the following temperature threshold values and ranges were identiﬁed
for scheduling of diﬀerent irrigation frequencies: temperature < 20 °C,
temperature between 20 °C–25 °C, temperature between 25 °C–30 °C,
temperature between 30 °C–35 °C, and temperature > 35 °C with irrigation frequencies of once per week, every 4th day, 3 times per week,
every second day, and daily, respectively. Utilizing the information
provided by the growers, Awad et al. (2019) developed a simple model(IQ-QC2), which estimates daily irrigation requirements of crops andallows the mixing of diﬀerent available water qualities.
The model adopted a temporally uniform 0.6 factor to convert open
ﬁeld crop evapotranspiration (ETC) to greenhouse water requirement of
the crops. Monthly KC values for tomato, cucumber, capsicum, andeggplant used in the IQ-QC2 mixing model were taken from FAO-56(Allen et al., 1998). Average seasonal ETC of all crops evaluated by IQQC2 matched the temperature-based irrigation schedule adopted by the
local growers. Therefore, we adopted the daily irrigation application
output from IQ-QC2 for the long-term UNSATCHEM simulations foreach crop. The average annual irrigation amount over the modeling
period (1970–2050) predicted by IQ-QC2 amounted to 490 ± 12,654 ± 16, 312 ± 8, and 667 ± 16 mm for tomato, capsicum, cucumber, and eggplant, respectively.
Apart from daily crop water requirements, IQ-QC2 also generates
the associated water quality parameters based on the usage of available
waters for irrigation, such as recycled water, harvested rainwater, andartiﬁcially recharged groundwater. The IQ-QC2 tool also calculates roof
runoﬀ water capture based on the roof area (m2) and available storage
capacity (m3). This was done to reduce the required storage size andsubsequently to increase the total volume of runoﬀ that could be captured. If two sources of water are available (e.g., reclaimed water andartiﬁcially recharged groundwater), the source that has the lower

Table 4
Average monthly blended (recycled and harvested rain water) water quality
parameters used in the UNSATCHEM simulations.
Month

ECw
dS/m

Ca

Mg

Na

K

a

Alk

SO4

Cl

b

0.53
0.74
0.72
0.65
0.52
0.39
0.28
0.29
0.41
0.38
0.26
0.20

2.39
3.34
3.26
2.93
2.35
1.77
1.27
1.32
1.85
1.70
1.17
0.90

0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01

0.38
0.54
0.52
0.47
0.38
0.28
0.20
0.21
0.30
0.27
0.19
0.14

0.73
1.02
0.99
0.89
0.72
0.54
0.39
0.40
0.57
0.52
0.36
0.28

2.24
3.13
3.05
2.75
2.20
1.65
1.19
1.24
1.74
1.59
1.09
0.84

3.6
4.2
4.2
3.9
3.5
3.1
2.6
2.7
3.1
3.0
2.5
2.2

SAR

meq/L
January
February
March
April
May
June
July
AugustSeptember
OctoberNovember
December
a

0.30
0.42
0.41
0.37
0.30
0.22
0.16
0.17
0.23
0.21
0.15
0.11

0.37
0.52
0.50
0.45
0.36
0.27
0.20
0.20
0.29
0.26
0.18
0.14

Alkalinity, bSodium adsorption ratio.

4Agricultural Water Management 227 (2020) 105845

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Fig. 1. Model-simulated annual water balance components for a) tomato, b) capsicum, c) eggplant, and d) cucumber crops during 2018–2050. Root = root water
uptake; Drain = deep drainage.

water ﬂow is directed out of the modeled domain (Šimůnek et al.,2013).
Initially, the model was warmed up for 48 years from 1970 till 2018
under historical climate, with an estimated irrigation schedule for eachcrop and rainwater composition (Table 2), discussed in Section 2.3, toestablish a quasi-equilibrium between the soil solution and the soil
exchange complex. Subsequently, a monthly varied average irrigation
quality (Table 4) obtained from the IQ-QC2 model (Awad et al., 2019)
was applied during the irrigation application to each crop.

2.4. Climate parameters
Climate parameters for the ETC estimation were required for two
periods: the ﬁrst one is a 48-year “warming up” period (from January
1970 to December 2017) for each crop to attain equilibrium conditions
for the soil water and chemical balance, while the second one is theactual 33-year impact assessment period (from January 2018 to
December 2050). Data for the ﬁrst period were obtained from the
Bureau of Meteorology station at Edinburgh (34.71 °S, 138.62 °E; BOM
station number 023083) while data for the second period were based on
downscaled climate projections (i.e., median future climate) for
Edinburgh by the general circulation model GFDL ESM2M at RCP(Representative Concentration Pathway) 4.5 (Charles and Fu, 2015).
Rainfall and soil evaporation were not considered in the simulations asthese are controlled by the presence of closed growing structures(greenhouse).

2.6. Modeling scenarios
Initially, a base scenario was run for each crop (tomato, capsicum,
cucumber, and eggplant) using an irrigation schedule for unheatedgreenhouse cultivation used by the growers in the NAP (Awad et al.,2019) and other inputs described above. Although the initial soil ESP
was higher than the Australian standard (ESP ≤ 6%), a base scenario
for each crop was performed to compare the eﬃcacy of management
scenarios on the reduction of the salinity and sodicity hazards relative
to the base scenario. The model was then run for numerous management scenarios developed based on the outcome of the initial scenario.
These scenarios included 4 leaching fractions (LF, in % of excess water
applied), i.e. LF0 (0%), LF0.15 (15%), LF0.2 (20%), and LF0.3 (30%),
and four annual gypsum applications, i.e. G0 (0 meq/kg soil or 0 t/ha),
G10 (10 meq/kg soil or 1.7 t/ha), G15 (15 meq/kg soil or 2.6 t/ha), andG20 (20 meq/kg or 3.4 t/ha). No gypsum was applied during thewarming up period (1970–2018).

2.5. Modeling domain and initial and boundary conditions
A uniform 2 m domain was considered for all crops (tomato, capsicum, cucumber, and eggplant) with two distinct layers (0–15 and 15
to 200 cm). The initial time step of 0.0001 d, the minimum time step of
1 × 10−5 d, and the maximum time step of 0.1 d were used in thisstudy. One hundred and one ﬁnite element nodes were generated forthe 200 cm soil domain using a lower density (0.5) in the surface layer(15 cm) and a higher density (1) at deeper depths. The root density was
assumed to decrease linearly from the soil surface to the maximum
rooting depth of 1 m. The initially uniform pressure head (−100 cm)
condition was assumed, while the initial solution composition was assumed to be equilibrated with the rainwater composition. The measured exchange composition and CEC were used in diﬀerent soil layers(Table 2).
To simulate water movement, the surface boundary condition was
speciﬁed as an atmospheric boundary with the maximum surface waterlayer of 5 cm, i.e., whenever irrigation exceeds the soil’s inﬁltration
capacity water ponds at the soil surface and inﬁltrates overtime whenirrigation stops. A free drainage BC was imposed at the bottom of thesoil proﬁle. Zero precipitation was considered by the model for protected crop conditions. However, irrigation amounts estimated by the
IQ-QC2 model (Awad et al., 2019) for each crop was assumed as a timevariable ﬂux applied at the top boundary. For solute transport, theconcentration ﬂux boundary condition was selected for the upperboundary condition. For the lower boundary condition, the zero gradient boundary condition is the most appropriate, particularly when

3. Results and discussion
3.1. Water balance components
Model-simulated water balance components obtained for the basescenarios reﬂecting current farmers’ irrigation practices for diﬀerent
greenhouse crops are shown in Fig. 1. The annual values of root water
uptake for all crops are close to their average annual irrigation applications (490.2, 653.8, 667.6, and 311.6 mm for tomato, capsicum,
eggplant, and cucumber, respectively). Average annual root water uptake across all years by tomato, capsicum, eggplant, and cucumber was
476, 642, 649, and 303 mm, respectively. However, the corresponding
average drainage component was only 4.6, 5.4, 6.1, and 4.1% of thetotal irrigation application, respectively. Such low values are below therecommended leaching fraction (LF) needed to ﬂush accumulated salts
from the crop root zone. Typically, open ﬁeld crop irrigation designs
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V. Phogat, et al.

Fig. 2. Simulated annual average values of a)pH, b) EC (dS/m), c) SAR, and d) ESP in the
sandy clay loam (solid lines) and sandy loam
(dot points) soils under tomato (t), cucumber(cu), capsicum (ca), and eggplant (eg) crops
irrigated with a blend of recycled water (Rw)
and rain water (Gw) following a temperature
based irrigation schedule.

extracted by suction cups under a greenhouse tomato crop ranged between 6–7 dS/m, which is similar to the results obtained in the current
study. Whether these salinity levels were the result of farmers overirrigating to overcome the salinity problem was not known as leachingpractices were not well documented (Bonachela et al., 2018). However,soil salinization mostly depends on the amount of applied saline water(Meiri, 1984; Shalhevet, 1994). Therefore, a leaching fraction should beapplied well ahead of the salt accumulation in the root environment
reaching hazardous levels (Meiri, 1984; Shalhevet, 1994) or the evaporative demand increases (Fernández et al., 2010) due to climate
variability and/or change. Therefore, a soil with continued high salinity
values above crop thresholds can create considerable osmotic impacts,
which reduces crop water uptake and renders the soil unﬁt for crop
production.
On the other hand, soilless production systems typically adopt alarge leaching fraction (30–40%) (Bonachela et al., 2018), which is afundamental management consideration for these growing systems tomaintain the salinity of the root zone solution at levels, which are not
detrimental to the optimal crop production (Sonneveld, 2000). To avoid
problems caused by the accumulation of salts, a sizable proportion of
the applied nutrient solution has to be drained, leading to the so-called“drain-to-waste” system (van Os, 1995). Therefore, soilless systems
such as hydroponics are associated with a substantial loss of water andcan potentially represent a major input of salts and agrochemicals togroundwater if untreated discharge occurs.

require a 10–20% higher water application for salinity control, depending on the crop, soil, and water quality (Hoﬀman and Shannon,
2007). In some cases, this fraction can be higher than 20% if climate
and soil encourage salinity build-up in the soils and when crops arehighly sensitive to salinity (Hoﬀman and Shannon, 2007). Rhodes andLoveday (1990) suggested a leaching fraction of 20–50% as ideal value
for such conditions, including for irrigation with recycled water. As theleaching fraction is dependent on local conditions of soil, climate, andcrop, the determination of the minimum leaching requirement wasundertaken in this study as one of the management scenarios.
3.2. Soil pH and salinity (ECSW) dynamics under diﬀerent crops
Simulated annual values of pH and ECSW for base scenarios fordiﬀerent crops are shown in Fig. 2 for both soils (S1 and S2). The
subsequent discussion is based on the results for soil S1, while those forS2 are very similar. The pH values decreased gradually, although themagnitude of the reduction was very small (less than 5%). Thesechanges in soil pH were expected due to the lower pH of irrigationwater (6.95–7.4, Table 2), as compared to the initial soil solution(8.4–8.7), which gradually brought down the soil pH to achieve a quasiequilibrium. At the end of the simulation in the year 2050, the average
pH values in the soils varied in a narrow range (8.1–8.4) for all crops.
The ﬁnal pH depends on the amount of irrigation water showing thelowest pH for crops receiving the most water (capsicum and eggplant),
the highest pH is for the crop receiving the least water (cucumber), withthe pH for tomato in between.
On the other hand, there was a similarly sharp increase in the proﬁleaverage ECSW values under all crops. The ECSW build-up was the lowest
under cucumber, followed by tomato; these results are consistent withthe lower amounts of seasonal irrigation applied to those two crops.
Meanwhile, the highest annual ECSW was observed for capsicum andeggplant; this is the result of the higher irrigation application for thesecrops and a longer cropping season (Jan to Oct-Nov) as compared to
cucumber (Jan-May) and tomato (Jan-Sept). The proﬁle average ECSW
in year 2050 rose to 6.5, 7.6, 8.7, and 9.3 dS/m for cucumber, tomato,
capsicum, and eggplant, respectively. These values are above the salinity tolerance thresholds (ECSW was derived from published ECe values
as ECSW = 2×ECe) for these crops, i.e., 5, 5, 3.4, and 2 dS/m, respectively (Maas and Hoﬀman, 1977; Sonneveld and Vogt, 2009).
Bonachela et al. (2018) observed that mean EC of the soil solution

3.3. Soil ESP and SAR dynamics under diﬀerent crops
Similarly, annual average SAR and ESP values (for base scenarios) in
the greenhouse soils also showed an increasing trend for all crops(Fig. 2). Initial (in 2018) average SAR in the soil solution varied from 1
to 1.9 in S1 under diﬀerent crops and increased by 2.2, 1.8, 1.7, and 1.8
times (till 2050) under tomato, capsicum, cucumber, and eggplant
crops, respectively. Similarly, SAR values in sandy loam soil (S2) also
showed a rapid increase (1.8 to 2.8 times) during 33 years of irrigation
with blended water. The ﬁnal values of SAR (in 2050) in sandy clay
loam (S1) soil ranged from 2.6 to 5.1 while the corresponding vales insandy loam (S2) soil varied from 3.3-6.6.
Likewise, the ESP values in sandy clay loam (scl) soil (S1) at the endof the simulation (the year 2050) were 30.8, 27.1, 31.4, and 33.2%
under tomato, capsicum, cucumber, and eggplant, respectively. The
6

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crusting, and reduced porosity in similar soils. However, they found astrong correlation between clay dispersion and both SAR and the total
cation concentration (TCC). In another study, Rengasamy and Marchuk(2011) proposed an alternative ratio (CROSS) to SAR. It includes othercations (Mg2+ and K+) that can induce dispersive soil behavior, whichis critical for characterizing the structural stability of the soils. However, we understand that diﬀerent cations (e.g., Na+, K+, and Mg2+)
have diﬀerent eﬀects on clay dispersion in diﬀerent soils and that theseeﬀects depend on all inherent soil characteristics, which aﬀect thebiogeochemical dynamics of the soil solution and soil exchange.
These results suggest that an irrigation schedule for greenhousecrops in the NAP region without a signiﬁcant leaching fraction and/or
gypsum addition appears not to suﬃce to avoid the buildup of salinity
and sodicity in the soil. Therefore, there is a need to improve irrigation
scheduling with an appropriate leaching fraction (LF) as well as withregular applications of a suitable amount of gypsum (G) for controllingsalinity and sodicity. These management scenarios will be discussed
next. Alternatively, an application of soluble Ca as part of irrigation canalso help in keeping ESP under control. However, rapid leaching ofsoluble Ca or its likely precipitation in the soil as calcite at high pH may
reduce the eﬀectiveness of added soluble Ca.

Fig. 3. Relationship between annual values of SARsw and ESP in the clay loam(S1) and sandy loam (S2) soils.

initial ESP values (9.5–16.6%, Table 3) in the soil were also high. They
had gradually increased over the model initial equilibration period(1970–2018) and varied for diﬀerent crops in response to the amount ofirrigation water applied. An increase in ESP during 33 years of cropping(2018–2050) in absolute values ranged from 9 to 11, which was anincrease of 39–55% compared to the corresponding values during 2018.
A rapid increase in ESP leads to values much higher than the critical
ESP (> 6), which can lead to the development of sodic soils under
Australian conditions (Northcote and Skene, 1972). It can worsen thesodicity risk in the existing sodic soils. Similar changes in soil pH, ECsw,
SAR, and ESP were also observed in the sandy loam soil (S2) for all
crops (Fig. 2, dot points). However, the magnitude of these parameters
was diﬀerent for diﬀerent crops.
A strong logarithmic relationship was observed between SARsw and
ESP in both soils (Fig. 3). Initially, ESP increased by a factor of two or
more when SARsw risen from 2 to 6. Subsequently, the ESP build-up
occurred at a lower rate and reached a similar value as SAR for SARsw
of 18-19. ESP then decreased with a further increase in SARsw. Numerous studies have attempted to establish relations between SARiw(SAR of irrigation water)/SARe (SAR of the saturation extract) and ESPof the soils in diﬀerent parts of the world (USSL Staﬀ, 1954; Paliwal andGandhi, 1976; Shainberg et al., 1980; Ghafoor et al., 1988; Evangelou
and Marsi, 2003; Seilsepour et al., 2009). Chi et al. (2011) observed
similar logarithmic relations between ESP vs SARe and ESP vs SAR1:5 asin our study and found insigniﬁcant diﬀerences between the two.
Contrary, the higher ESP build up at lower SARsw levels observed inour study diﬀered from an almost linear relation up to SAR 40 in someother studies (e.g., USSL, 1954; Paliwal and Gandhi, 1976; Ghafoor
et al., 1988). However, most of these relations were based on SAR of
irrigation water (SARiw), which is diﬀerent from SAR of soil solution(SARsw). Normally, the salt concentration in soils is highly dynamic
and much higher than the salt concentration in irrigation water, which
may have aﬀected the impact of the interaction between solution andexchange phases (USSL, 1954). Interestingly, Rengasamy et al. (1984)
observed for the Australian surface soils (0–15 cm) linear relation between SAR1:5 and ESP (ESP = 1.95 and SAR1:5 + 1.8), which is similar
to our current study with lower SARsw values (up to 6). Subsequently,
Stevens et al. (2003) extended this relation for NAP soils by including
the data from the 0−100 cm soil depth and found a curvilinear, rather
than linear, relation. Thus, it is clear that approximate relationships
between SAR and ESP vary with site-speciﬁc clay contents, mineralogy,salinity of the equilibrium solution, and the saturation percentage of thesoil solution.
The data in Fig. 3 further reveals that SARsw of 2–3 can potentiallylead to the development of ESP higher than given the value provided by
the Australian standard (ESP ≥ 6) for the development of sodicity hazards in the soil. Rengasamy et al. (1984) also suggested that a SAR (1:5
soil : water ratio) larger than 3 is associated with clay dispersion,

3.4. Management scenarios
Additional scenarios were performed with diﬀerent leaching fractions (LF0.15, LF0.2, and LF0.3) and annual gypsum applications (G10,
G15, G20) for tomato. The results show that an increased leaching
fraction without additions of gypsum produces a small increase in soil
pH (Fig. 4). Gypsum additions at an annual rate of 10 meq/kg soil (G10,
1.7 t/ha) combined with any non-zero leaching fraction reduced soil pH
at all depths by 7.5–9.4 %. However, a further increase in the gypsum
application (G15, G20), regardless of leaching fractions, had only asmall impact on subsequent reductions (0.4- 0.8%) in soil pH. Overall,
average pH had been reduced to 7.7–7.8, which is a suitable range foran optimal nutrient availability for crops (Roques et al., 2013).
Therefore, an annual gypsum application of 10 meq/kg soil (1.7 t/ha)
combined with LF 0.15-LF 0.3 seems adequate for maintaining soil pH
within a suitable range (6.0–8.0).
Similarly, the ECSW in the soil decreased below the tomato threshold(ECSW =5 dS/m) for all non-zero LFs, which suggests that a minimum
LF of 0.15 is adequate to maintain the soil salinity below the threshold(Fig. 4). As expected, applications of gypsum increased the soluble ion
concentration in the soil, leading to an increase in the ECSW at all
depths, although it was still below the crop threshold.
It is worth noting that increasing the LF alone didn’t reduce SAR and
ESP in the soil to acceptable levels. Clearly, these impairments require
additional soil amendments such as applications of gypsum to reduce
their impact on soils and crops. The results show that an addition of
gypsum at an annual rate of 10 meq/kg soil (1.7 t/ha) along with aleaching fraction LF of 0.15 reduced SAR and ESP below thresholds
within the top 30 cm soil depth. However, this combination was unable
to lower SAR and ESP below threshold levels at the 60–90 cm depth. A
further increase in gypsum additions to 15 (G15) and 20 meq/kg (G20)
combined with a leaching fraction LF of 0.15 also did not further reduce
SAR and ESP in the soil at deeper depths. Only when a minimum LF of
0.2 was combined with an annual addition of gypsum of at least
10 meq/kg soil (1.7 t/ha) did average SAR and ESP drop below
thresholds at greater soil depths.
Other scenarios were also explored, in which leaching fractions lessthan 0.15 were used together with diﬀerent gypsum additions (G10,
G15, and G20). While ECSW dropped below its thresholds, these scenarios were unable to lower ESP below the critical threshold (data notshown). Therefore, it is suggested that for shallow-rooted soil-grownvegetables in the NAP region, additions of gypsum at a rate of 10 t/hawith an LF of 0.15 is able to keep the soil free from the salinity andsodicity build-up under greenhouse conditions. On the other hand, for7

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V. Phogat, et al.

Fig. 4. Model simulated annual proﬁle average
for a) pH, b) ECsw, c) SAR, and d) ESP in the
sandy clay loam soil (S1) under diﬀerent
leaching fractions (LF0, LF0.15, LF0.2, andLF0.3) and annual gypsum application (G0,
G10, G15 and G20) scenarios for tomato cultivation
undergreenhouseconditions.
Horizontal black line represents the ECsw
threshold for tomato crop and ESP thresholdfor sodic soils.

also inﬂuence the eﬀectiveness of gypsum in reducing ESP in the soil
were not evaluated in this study.
Overall, the results from the current modeling study suggest that along-term irrigation schedule without signiﬁcant leaching fraction may
lead to salinity and sodicity problems. There is clearly a need to apply aleaching fraction of 15–20% for leaching the salts below the crop rootzone. Even blended water used for irrigation has a strong tendency tobuild up sodicity in the soil. Therefore, annual applications of gypsum
at 1.7–2.6 t/ha (or preferably based on a soil test) or similar Ca amounts
from other sources should be applied to overcome the sodicity hazard in
the soil. The survey conducted by Awad et al. (2019) found that many
growers apply annually organics/compost and Ca through nutrient
solutions. Both of these additions help in alleviating sodicity hazards inthe soil. However, continued monitoring of the salinity and sodicity
status of the soil is an essential aspect of soil management that allows
one to intervene well before issues occur.
The results obtained from the current study are speciﬁc for theconsidered crops, soils, climate, and water qualities. Several otherfactors involved in growing protected crops were not considered in thisstudy, such as diﬀerent substrates, eﬀects of ventilation, anddeep-rooted crops, a combination of an LF of 0.2 with an annual
gypsum addition of 10 meq/kg soil or higher would contribute tomaintaining a sustainable production under long-term greenhouseconditions.
Time series of proﬁle average pH, ECSW, SAR, and ESP values obtained for LFs of 0 and 0.2 and diﬀerent annual gypsum additions (0,10, 15 meq/kg soil) for tomato are shown in Fig. 5. The scenario with
G10 and 0.2 L F had initial average proﬁle ESP values higher than thethreshold (ESP = 6). These ESP values then gradually decreased tovalues similar to those obtained for the scenario with annual additionsof gypsum of 15 meq/kg soil (G15) at the end of the simulation in theyear 2050. Similarly, average SAR values initially increased and subsequently decreased below the threshold, pH was reduced by 8.7% to
an average value around 7.8, and ECSW was much lower than the tomato tolerance threshold (5 dS/m). Similar results were obtained forthe sandy loam soil (S2) with cucumber (Fig. 6). Apparently, theamount of irrigation (312 mm for cucumber versus 490 mm for tomato)and soil properties also inﬂuenced the eﬀectiveness of LF and gypsum
additions for reclaiming the soils. Other factors such as purity of
gypsum, particle size, uniformity of application and mixing in the soil

Fig. 5. Eﬀects of the leaching fraction (LF0 and
LF0.2) and the gypsum application [0 (G0), 10(G10), and 15 (G15) meq/kg soil] on changes
in annual proﬁle average a) pH, b) ECsw, c)
SAR, and d) ESP in the sandy clay loam soil
(S1) during 2018–2050 under the tomato cultivation in greenhouse conditions. Horizontal
black line represents the ECsw threshold fortomato crop and ESP threshold for sodic soils.

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V. Phogat, et al.

Fig. 6. Eﬀects of the leaching fraction (LF0 and
LF0.2) and the gypsum application [0 (G0), 10(G10), and 15 (G15) meq/kg soil] on changes
in annual proﬁle average a) pH, b) ECsw, c)
SAR, and d) ESP in the sandy loam soil (S2)during 2018–2050 under the cucumber cultivation in greenhouse conditions. The black line
represents the ESP threshold values for cucumber.

evaporative cooling. These factors can also impact the water use and its
management, and related issues such as salinity and sodicity developments. The concentration of nutrients, the quality of soil substrate, andlocal issues such as leachate management may also impact the plantwater requirement and its eﬀective management. Simulation tools suchas those used in this study can account for these additional conditions
and thus help develop guidelines for practitioners aimed at maintaining
sustainable production systems.

with irrigation waters with high SAR and ESP suggest that annual
gypsum application at a rate of 1.7 t/ha together with a leaching fraction of at least 20% was adequate for managing this hazard. In otherwords, management options that implement both a signiﬁcant leaching
fraction and gypsum amendments are required. Finally, the long-termmonitoring of highly eﬃcient greenhouse production systems is essential for early identiﬁcation of irrigation induced soil salinity andother associated issues.

4. Conclusions

Declaration of Competing Interest
None.

Protected agriculture is a popular cultivation system, especially forvegetable crops. These are highly eﬃcient systems in term of the consumption of energy and other inputs such as water and fertilizers.
However, suitable water is often scarce in regions where these production systems exist, such as in the vicinity of urban, arid, or semi-arid
environments. Therefore, in most cases, low-quality water is used directly or in combination with other available water resources such as
harvested rainwater. Unfortunately, low quality waters are often usedwithout knowing the harmful impacts on crop productivity, soil health,and the wider environment.
This study used the multi-component major ion chemistry module
UNSATCHEM of the HYDRUS-1D model to evaluate the eﬀects of longterm (2018–2050) irrigation with blended water for a range of soilgrown crops under unheated greenhouse conditions. The results revealed that irrigation schedules for future climate projections that do
not apply a signiﬁcant leaching fraction might lead to a high salt buildup and ESP development in the soil. The soil solution salinity (ECSW)
can increase to 6.5–9 dS/m at year 2050 and ESP can rise to 27–33% for
all crops considered. These conditions could render the soil unﬁt for
crop production and could potentially degrade the associated environment. Therefore, appropriate management options should be implemented to keep the irrigation-induced harmful impacts under control.
The study evaluated the eﬃcacy of increased leaching and gypsum
additions to control salinity and sodicity. For this purpose, the UNSATCHEM module was extended with a new capability to allow annual
gypsum applications. Management scenarios with diﬀerent leaching
fractions for salinity control showed that 15–20% more water per irrigation would be required to keep the salinity under control for soilgrown greenhouse vegetables. Results obtained in various scenarios

Acknowledgments
Authors acknowledge the ﬁnancial support by The Goyder Institutefor Water Research for this work under the project “Sustainable
Expansion of irrigated Agriculture and Horticulture in Northern
Adelaide Corridor (Project Number ED-17-01).”
References
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. Crop evapotranspiration: guidelines
for computing crop water requirements. FAO Irrigation and Drainage Paper 56. FAO,Rome.
AUSVEG, 2019a. Greenhouse Vegetables- an Overview. Accessed on 23 September, 2019.
https://ausveg.com.au/biosecurity-agrichemical/crop-protection/greenhousevegetables/#greenhouse.
AUSVEG, 2019b. Protected Cropping- Review of Research and Identiﬁcation of R&D Gaps
for Levied Vegetables. Accessed on 23 September, 2019. https://ausveg.com.au/biosecurity-agrichemical/crop-protection/greenhouse-vegetables/#greenhouse.
Awad, J., Vanderzalm, J., Pezzaniti, D., Olubukoa Esu, O.-O., van Leeuwen, J., 2019.
Sustainable Expansion of Irrigated Agriculture and Horticulture in Northern Adelaide
Corridor: Source Water options/water Availability, Quality and Storage
Consideration. Goyder Institute for Water Research Technical Report Series No.xx/
18, Adelaide, South Australia.
Blanco, F.F., Folegatti, M.V., 2004. Evaluation of evaporation measuring equipments for
estimating evapotranspiration within a greenhouse. Rev. Bras. Eng. Agri. Ambiental
8, 184–188.
Bonachela, S., Fernández, M.D., Cabrerac, F.J., Granados, M.R., 2018. Soil spatiotemporal
distribution of water, salts and nutrients in greenhouse, drip-irrigated tomato crops
using lysimetry and dielectric methods. Agric. Water Manage. 203, 151–161.
Bonachela, S., González, A.M., Fernández, M.D., 2006. Irrigation scheduling of plastic
greenhouse vegetable crops based on historical weather data. Irrig. Sci. 25 (1), 53–62.
Boulard, T., Wang, S., 2000. Greenhouse crop transpiration simulation from external
climate conditions. Agric. For. Meteor. 100, 25–34.
Braga, M.B., Klar, A.E., 2000. Evaporation and evapotranspiration in the ﬁeld and in

9Agricultural Water Management 227 (2020) 105845

V. Phogat, et al.

water requirements. Irrig. Sci. 25 (2), 171–181.
Nguyen, B.D., 2019. Eﬃcient Practices in Low Technology Greenhouses- Surviving As a
Small Family Farm. Project Report Submitted to Nuﬃeld Australia for Project No
1724, Supported by Horticulture Innovation Australia, Sydney, Australia.
Nikolaou, G., Neocleous, D., Katsoulas, N., Kittas, C., 2017. Modelling transpiration of
soilless greenhouse cucumber and its relationship with leaf temperature in a
Mediterranean climate. Emirates J. Food Agric. 29 (12), 911–920.
Northcote, K.H., Skene, J.K.M., 1972. Australian Soils With Saline and Sodic Properties.
Soil Publication No. 27. Commonwealth Scientiﬁc and Industrial Research
Organisation, Australia.
Paliwal, K.V., Gandhi, A.P., 1976. Eﬀect of salinity, SAR, Ca: Mg ratio in irrigation waterand soil texture on the predictability of exchangeable sodium percentage. Soil Sci.
122 (2), 85–90.
Pardossi, A., Tognoni, F., Incrocci, L., 2004. Mediterranean greenhouse technology.
Chronica Hortic. 44, 28–34.
Parks, S.E., Worrall, R.J., Low, C.T., Jarvis, J.A., 2009. Initial eﬀorts to improve themanagement of substrates in greenhouse vegetable production in Australia. Acta
Hort. 819, 331–336.
Phogat, V., Cox, J.W., Šimůnek, J., 2018. Identifying the future water and salinity risks toirrigated viticulture in the Murray-Darling Basin, South Australia. Agric. Water
Manage. 201, 107–117.
PIRSA Spatial Information Services, 2017. Northern Adelaide Plains Intensive
Horticulture Comparison 2008 / 2016. Primary Industries and Regions SA,
Government of South Australia.
Qiu, R., Du, T., Kang, S., Chen, R., Wu, L., 2015. Assessing the SIMDualKc model for
estimating evapotranspiration of hot pepper grown in a solar greenhouse in
Northwest China. Agric. Syst. 138, 1–9.
PCA, 2019. Our Industry: Protected Cropping Australia. Accessed on July 4, 2019.
http://www.protectedcroppingaustralia.com/?page_id=94.
Ramos, T.B., Šimůnek, J., Goncalves, M.C., Martins, J.C., Prazeres, A., Castanheira, N.L.,
Pereira, L.S., 2011. Field evaluation of a multicomponent solute transport model insoils irrigated with saline waters. J. Hydrol. 407, 129–144.
Rayment, G.E., Lyons, D.J., 2011. Exchangeable bases and CEC (method 15D2). In:
Rayment, G.E., Lyons, D.J. (Eds.), Soil Chemical Methods - Australasia. CSIRO
Publishing, Melbourne, Australia, pp. 318–321.
Reca, J., Trillo, C., Sánchez, J.A., Martínez, J., Valera, D., 2018. Optimization model for
on-farm irrigation management of Mediterranean greenhouse crops using desalinatedand saline water from diﬀerent sources. Agric. Syst. 166, 173–183.
Rengasamy, P., Greene, R.S.B., Ford, G.W., Mehanni, A.H., 1984. Identiﬁcation of dispersive behaviour and the management of red-brown earths. Aust. J. Soil Res. 22,
413–431.
Rengasamy, P., Marchuk, A., 2011. Cation ratio of soil structural stability (CROSS). Soil
Res. 49, 280–285.
Rhodes, J.D., Loveday, J., 1990. Salinity in irrigated agriculture. In: Stewart, B.A.,
Nielsen, D.R. (Eds.), Agronomy Monograph No. 30. ASA Publishers, Madison, pp.
1089–1142.
Roques, S., Kendall, S., Smith, K., Newell Price, P., Berry, P., 2013. A Review of the nonNPKS Nutrient Requirements of UK Cereals and Oilseed Rape. Research Review No.
78. Agriculture and Horticulture Development Board Accessed on July 4, 2019.
https://cereals.ahdb.org.uk/media/679184/rr78.pdf.
Seilsepour, M., Rashidi, M., Khabbaz, B.G., 2009. Prediction of soil exchangeable sodium
percentage based on soil sodium adsorption ratio. Am. Eurasian J. Agric. Environ. Sci.
5 (1), 1–4.
Shainberg, I., Oster, J.D., Wood, J.D., 1980. Sodium calcium exchange in montmorillonite
and illite suspensions. Soil Sci. Soc. Am. J. 44, 960–964.
Shalhevet, J., 1994. Using water of marginal quality for crop production: major issues.
Agric. Water Manage. 25, 233–269.
Shi, W.M., Yao, J., Yan, F., 2009. Vegetable cultivation under greenhouse conditions leads
to rapid accumulation of nutrients, acidiﬁcation and salinity of soils and groundwatercontamination in South-Eastern China. Nutr. Cycling Agroecosyst. 83, 73–84.
Skaggs, T.H., Suarez, D.L., Corwin, D.L., 2014. Global sensitivity analysis for
UNSATCHEM simulations of crop production with degraded waters. Vadose Zone J.
13 (6), 1–13. https://doi.org/10.2136/vzj2013.09.0171.
Šimůnek, J., Suarez, D.L., 1994. Two-dimensional transport model for variably saturated
porous media with major ion chemistry. Water Resour. Res. 30 (4), 1115–1133.
Šimůnek, J., Suarez, D.L., 1997. Sodic soil reclamation using multicomponent transportmodeling. ASCE J. Irrig. Drain. Eng. 123 (5), 367–376.
Šimůnek, J., Šejna, M., Saito, H., Sakai, M., van Genuchten, M.Th., 2013. The Hydrus-1D
Software Package for Simulating the Movement of Water, Heat, and Multiple Solutes
in Variably Saturated Media, Version 4.17, HYDRUS Software Series 3. Department ofEnvironmental Sciences, University of California Riverside, Riverside, California,
USA.
Šimůnek, J., van Genuchten, M.T., Šejna, M., 2016. Recent developments and applications of the HYDRUS computer software packages. Vadose Zone J. 15, 25.
Sonneveld, C., 2000. Eﬀects of Salinity on Substrate Grown Vegetables and Ornamentals
in Greenhouse Horticulture. Thesis. Wageningen University, The Netherlands.
https://doi.org/10.1007/978-90-481-2532-6. 151 pp.
Sonneveld, C., Vogt, W., 2009. Plant Nutrition of Greenhouse Crops. Springer Science andBusiness Media B.V., Dordrecht, The Netherlands.
Stanghellini, C., Pardossi, A., Sigrimis, N., 2007. What limits the application of wastewater and/or closed cycle in horticulture? Acta Hortic. 747, 323–330.
Suarez, D.L., 2001. Sodic soil reclamation: modelling and ﬁeld study. Aust. J. Soil Res. 39,
1225–1246.
Suarez, D.L., Wood, J.D., Lesch, S.M., 2006. Eﬀect of SAR on water inﬁltration under asequential rain–irrigation management system. Agric. Water Manage. 86, 150–164.
Thompson, R.B., Martinez-Gaitan, C., Gallardo, M., Gimenez, C., Fernandez, M.D., 2007.

greenhouse oriented north/south and east/west directions. Irrigation 5, 222–228 (inPortuguese).
Charles, S.P., Fu, G., 2015. Statistically Downscaled Projections for South Australia – Task
3 CSIRO Final Report. Goyder Institute for Water Research Technical Report SeriesNo. 15/1 Adelaide, South Australia.
Chi, C.M., Zhao, C.W., Sun, X.J., Wang, Z.C., 2011. Estimating exchangeable sodium
percentage from sodium adsorption ratio of salt-aﬀected soil in the Songnen Plain ofNortheast China. Pedosphere 21 (2), 271–276.
Colombani, N., Mastrocicco, M., Giambastiani, B.M.S., 2015. Predicting trends in alowland coastal aquifer: Comacchio (Itlay). Water Resour. Manage. 29, 603–618.
Department of Environment, Water and Natural Resources, 2016. Non-prescribed Surface
Water Resources Assessment – Adelaide and Mount Lofty Ranges Natural Resources
Management Region, DEWNR Technical Report 2016/34. Department ofEnvironment, Water and Natural Resources, Government of South Australia,
Adelaide.
Evangelou, V.P., Marsi, M., 2003. Inﬂuence of ionic strength on sodium-calcium exchangeof two temperate climate soils. Plant Soil 250, 307–313.
FAO, 2017. Good Agricultural Practices for Greenhouse Vegetable Production in theSouth East European Countries: Principles for Sustainable Intensiﬁcation of
Smallholder Farms. Food and Agriculture Organization of the United Nations, Plant
Production and Protection Paper No 230. FAO, Rome. http://www.fao.org/3/ai6787e.pdf.
Farias, J.R.B., Bergamaschi, H., Martins, S.R., 1994. Evapotranspiration in plastic
greenhouses. Braz. J. Agrometeorol. 2, 17–22 (in Portuguese).
Feddes, R.A., Kowalik, P.J., Zaradny, H., 1978. Simulation of ﬁeld water use and crop
yield. Simulation Monographs. Pudoc, Wageningen, The Netherlands.
Ferguson, K., Hall, B., Parker, J.B., Webb, K., Burﬁeld, T., Cavallaro, D., Parks, S., 2012.
Improving Greenhouse Systems and Production Practices (Greenhouse Production
Practices Component). Final Report of Project No. VG07144. Horticulture Australia
Ltd, Sydney, Australia.
Fernandez, M.D., Baeza, E., Cespedes, A., Perez-Parra, J., Gazquez, J.C., 2009. Validationof on-farm crop water requirements (pRho) model for horticultural crops in an unheated plastic greenhouse. Acta Hortic. 807, 295–300.
Fernández, M.D., Bonachela, S., Orgaz, F., Thompson, R.B., López, J.C., Granados, M.R.,
Gallardo, M., Fereres, E., 2010. Measurement and estimation of plastic greenhousereference evapotranspiration in a Mediterranean climate. Irrig. Sci. 28, 497–509.
Gallardo, M., Fernández, M.D., Giménez, C., Padilla, F.M., Thompson, R.B., 2016. Revised
VegSyst model to calculate dry matter production, critical N uptake and ET c ofseveral vegetable species grown in Mediterranean greenhouses. Agric. Syst. 146,
30–43.
Gallardo, M., Giménez, C., Martínez-Gaitán, C., Stöckle, C.O., Thompson, R.B., Granados,
M.R., 2011. Evaluation of the VegSyst model with muskmelon to simulate crop
growth, nitrogen uptake and evapotranspiration. Agric. Water Manage. 101,
107–117.
Gapon, E.N., 1933. Theory of exchange adsorption in soils. J. General Chem. USSR. 3,
144–152.
Gavilán, P., Lorite, I.J., Tornero, S., Berengena, J., 2006. Regional calibration ofHargreaves equation for estimating reference ET in a semiarid environment. Agric.
Water Manage. 81, 257–281.
Gonçalves, M.C., Šimůnek, J., Ramos, T.B., Martins, J.C., Neves, M.J., Pires, F.P., 2006.
Multicomponent solute transport in soil lysimeters irrigated with waters of diﬀerent
quality. Water Resour. Res. 42 (8), 1–17.
Harmanto, V.M., Salokhe, M.S., Babel, H.J., Tantau, 2005. Water requirement of drip
irrigated tomatoes grown in greenhouse in tropical environment. Agric. Water
Manag. 71, 225–242.
Heuer, B., Meiri, A., Shalevet, J., 1986. Salt tolerance of eggplant. Plant Soil 95, 9–13.
Hoﬀman, G.J., Shannon, M.C., 2007. Salinity. In: In: Lamm, F.R., Ayers, J.E., Nakayama,
F.S. (Eds.), Micro-Irrigation in Crop Production Vol. 13. Elsevier B.V., pp. 131–160.
Itenﬁsu, D., Elliott, R.L., Allen, R.G., Walter, I.A., 2003. Comparison of reference evapotranspiration calculation as part of the ASCE standardization eﬀort. J. Irrig. Drain.
Eng. ASCE 129 (6), 440–448.
Kaledhonkar, M.J., Keshari, A.K., van Der Zee, S.E.A.T.M., 2006. Relative sensitivity of
ESP proﬁle to spatial and temporal variability in cation exchange capacity and porewater velocity under simulated ﬁeld conditions. Agric. Water Manage. 83, 58–68.
Kim, J.H., Choi, C.M., Lee, J.S., Yun, S.G., Lee, J.T., Cho, K.R., Lim, S.J., Choi, S.C., Lee,
G.J., Kwon, Y.G., Kyung, K.C., Uhm, M.J., Kim, H.K., Lee, Y.S., Kim, C.Y., Lee, S.T.,
Ryu, J.S., 2008. Characteristics of groundwater quality for agricultural irrigation inplastic ﬁlm house using multivariate analysis. Korean J. Environ. Agric. 27 (1), 1–9(in Korean with English abstract).
Maas, E.V., Hoﬀman, G.J., 1977. Crop salt tolerance: current assessment. J. Irrig. Drain.
Div. Am. Soc. Civil Eng. 103 (IR2), 116–134.
Machado, R.M.A., Serralheiro, R.P., 2017. Soil salinity: eﬀect on vegetable crop growth,management practices to prevent and mitigate soil salinization. Horticulturae 3, 30.
https://doi.org/10.3390/horticulturae3020030.
Mallants, D., Šimůnek, J., Torkzaban, S., 2017. Determining water quality requirementsof coal seam gas produced water for sustainable irrigation. Agric. Water. Manage 189,
52–69.
Mallants, D., Phogat, V., Oliver, D., Ouzman, J., Beirgadhar, Y., Cox, J., 2019. Sustainable
Expansion of Irrigated Agriculture and Horticulture in Northern Adelaide Corridor:
Task 2, Goyder Institute for Water Research Technical Report Series No. 18‐02,
Adelaide, South Australia.
Meiri, A., 1984. Plant response to salinity: experimental methodology and application to
the ﬁeld. In: Shainberg, I., Shalhevet, J. (Eds.), Soil Salinity Under Irrigation:
Processes and Management. Ecological Studies 51. Springer-Verlag, Berlin, pp.
284–297.
Möller, M., Assouline, S., 2007. Eﬀects of a shading screen on microclimate and crop10

Agricultural Water Management 227 (2020) 105845

V. Phogat, et al.

Villarreal-Guerrero, F., Kacira, M., Fitz-Rodríguez, E., Kubota, C., Giacomelli, G.A.,
Linker, R., Arbel, A., 2012. Comparison of three evapotranspiration models for agreenhouse cooling strategy with natural ventilation and variable high pressure
fogging. Scientia Hortic. 134, 210–221.
Wang, J., Bai, Z., Yang, P., 2014. Mechanism and numerical simulation of multicomponent solute transport in sodic soils reclaimed by calcium sulfate. Environ.
Earth Sci. 72, 157–169.
White, N., Zelazny, L.W., 1986. Charge properties in soil colloids. In: Sparks, D.L. (Ed.),
Soil Physical Chemistry. CRC Press, BOCA Raten, Florida.

Identiﬁcation of irrigation and N management practices that contribute to nitrateleaching loss from an intensive vegetable production system by use of a comprehensive survey. Agric. Water Manage. 89, 261–274.
United States Salinity Lab. Staﬀ, 1954. Diagnosis and Improvement of Saline and Alkali
Soils. Handbook No. 60. United States Department of Agriculture, Washington, DC.
van Genuchten, M.Th., 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Soc. Am. J. 44, 892–898.
van Os, E.A., 1995. Engineering and environmental aspects of soilless growing systems.
Acta Hortic. 396, 25–32.

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