Agricultural Water Management 237 (2020) 106167

Contents lists available at ScienceDirect

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

Impact of long-term recycled water irrigation on crop yield and soil
chemical properties

T

V. Phogata,b,c,*, Dirk Mallantsd, J.W. Coxa,b, J. Šimůneke, D.P. Oliverd, T. Pitta,b, P.R. Petriea,b,faSouth Australian Research and Development Institute, GPO Box 397, Adelaide SA 5001, Australia
The University of Adelaide, PMB1 Glen Osmond SA 5064, Australiac
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
The University of New South Wales, Sydney, NSW 2052, Australiab

A R T I C LE I N FO

A B S T R A C T

Keywords:
Recycled water
irrigation
salinitySAR
ESP
HYDRUS-1D
UNSATCHEM
horticultural crops

The variably-saturated ﬂow and multi-component transport module UNSATCHEM of HYDRUS-1D was used toevaluate the impact of the long-term (2018-2050) application of recycled water (RCW) for irrigating perennial
horticulture (almonds, pistachios), viticulture (grapevines), annual horticulture (carrot, onion, and potato), andpasture crops in representative soils from the Northern Adelaide Plains (NAP), South Australia. The input
parameters for soil hydraulic, soil solution, and cation exchange data were determined for 14 soil proﬁles fromthe NAP region. For a warm-up period from 1970 to 2017, the model used historical climate data and lowsalinity irrigation water. In the subsequent period (2018-2050), irrigation continued with RCW and projected
meteorological conditions were obtained by considering expected future climate change. The average soil watersalinity (ECsw) at the end of the simulation period ranged from 2.9-10.5 dS/m across all soils and crops. Potential
yields of salt-sensitive crops such as annual horticulture and almonds were reduced by 4-32% due to increased
salinity in the soil. Similarly, the model predicted that the sodium adsorption ratio (SAR) and exchangeablesodium percentage (ESP) would increase above threshold values, typically considered as indicative of poor
growing conditions for most crops. Relationships between SAR and ESP were developed for four representativesoils, providing the threshold soil SAR that corresponds to a critical ESP (> 6), which would lead to adverse soil
health and crop growth impacts. Threshold SARs were derived for calcareous (SAR = 4), hard red-brown(SAR = 3.5), sand over clay (SAR = 6), and deep uniform to gradational (SAR = 3) soils. An increase in SAR and
ESP in soils adversely aﬀects soil structural stability and soil water movement, which can severely impact thesustainable crop production in the NAP region. Relationships such as those between SAR and ESP help in
identifying critical soil constraints and assist in devising better guidelines for the sustainable use of recycledwater for irrigated agriculture.

1. Introduction
Increasing demand for food by a burgeoning global human population requires large amounts of freshwater to be allocated to agriculture. However, in many parts of the world, especially under arid andsemiarid climate, water demand has already exceeded water supply
provided by fresh surface and groundwater resources (FAO, 2012;
Famiglietti, 2014; UN Environment, 2019). The scarcity of water supplies, coupled with the need for urban communities to manage large
quantities of water from sewage treatment plants, has led to increasing
interest in the recirculation of this water for irrigating crops.

⁎

Consequently, treated wastewater is recognized as an important alternative water source for supporting crop production (Qadir et al., 2010;
Grattan et al., 2015) and is increasingly being adopted in irrigationwater allocation schemes for arid and semiarid regions where water
scarcity is severe (Hamilton et al., 2007; Otoo and Drechsler 2018).
Generally, usage of RCW can have both positive and negative impacts on crops and soils depending on the water quality, and the extent
and duration of exposure. The beneﬁts include: reducing freshwater
demand, recycling nutrients, and minimizing the discharge of pollutants into waterways (Hanjraa et al., 2012; Hassena et al., 2018). Furthermore, several investigations have reported improvements in soil

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.2020.106167
Received 18 February 2020; Received in revised form 20 March 2020; Accepted 23 March 20200378-3774/ Crown Copyright © 2020 Published by Elsevier B.V. All rights reserved.

Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

2. Materials and Methods

fertility status, nutrient uptake, plant growth and yield potential (e.g.,
Singh et al., 2012; Lal et al., 2015; Minhas et al., 2015; Elfanssi et al.,2018; Hassena et al., 2018). Similarly, some other investigations reported that irrigation with RCW had no detrimental eﬀects on tree
growth and productivity (Parsons et al., 2001; Morgan et al., 2008) nor
upon soil physico-chemical properties (e.g., Andrews et al., 2016).
However, the prolonged and indiscriminate use of RCW can result in
the accumulation of ions in roots and leaves, potentially reaching toxic
levels (Paudel et al., 2016), damage plantations, and decrease the yield(Noshadi et al., 2013). It may further increase soil salinity and SAR,
which can lead to the development of sodicity hazards (high exchangeable sodium) in the soils (Bardhan et al., 2016; Assouline et al.,2016; Qian and Lin 2019; Phogat et al., 2020). These conditions candegrade the soil hydraulic properties and thus aﬀect water movementin the soil (e.g., Assouline and Narkis, 2013; Assouline et al., 2016). In
particular, high ESP in the soil may cause clay dispersion and swelling(Rengasamy and Olsson, 1993), and overall soil structure degradation(Basile et al., 2012). Therefore, understanding the impacts on soils andcrops of long-term irrigation with variable quantity and quality RCW,
potentially exacerbated by hotter and drier climate (IPCC, 2014; CSIRO,
BoM 2016), is essential to develop guidelines for sustainable irrigation
with RCW.
Process-based numerical models are preferred tools to enhance ourunderstanding of the long-term impact of irrigation on soil hydraulicproperties (e.g., Valdes-Abellan et al., 2017), salinity (e.g., Phogat
et al., 2018a) and sodicity risks (e.g., Phogat et al., 2020). Among thesemodels, the multi-component major ion chemistry module UNSATCHEM, integrated into the HYDRUS-1D software package (Šimůnek
et al., 2016), is an advanced numerical tool to predict coupled processesof major ion chemistry, variably-saturated water ﬂow, and solutetransport in soils during transient conditions. While the water ﬂow andsolute transport components of the standard HYDRUS-1D model havebeen used widely (e.g., Šimůnek et al., 2016), fewer applications of the
major ion chemistry module have been reported, mainly because of alack of appropriate experimental soil chemical data (e.g., Gonçalves
et al., 2006; Ramos et al., 2011). Several of the reported studies focusedon assessing the short-term impact of using high SAR water (Suarez
et al., 2006), evaluating ways to reclaim sodic soils (Šimůnek andSuarez, 1997; Kaledhonkar et al., 2006; Wang et al., 2014), assessing
conditions for a sustainable use of coal seam gas water for crop production (Mallants et al., 2017), and undertaking solute transport simulations of irrigation with saline/degraded water (Ramos et al., 2011;
Skaggs et al., 2014). These simulations of the movement of dissolved
solutes, while also accounting for precipitation/dissolution reactions ofsalts in soils, have been a useful complement to ﬁeld studies (Suarez,
2001). Nevertheless, applications with the major ion chemistry module
UNSATCHEM of HYDRUS-1D for assessing long-term risks of irrigation
with RCW to horticultural crops and associated environments are limited.
This modeling study aims to evaluate the impact of long-term(2018-2050) irrigation with RCW on (i) crop yield of horticultural crops(almonds and pistachios), viticulture, pasture (mixed), and annual vegetables (carrot, onion, and potato), and (ii) changes in chemicalcomposition in texturally diﬀerent soils. By applying the actual water
usage to diﬀerent prospective crops considered for expanding irrigatedhorticulture and agriculture in the NAP region in South Australia, amuch-improved understanding of the spatiotemporal soil solution andexchange dynamics allows identifying conditions, including mitigating
measures, required for the sustainable use of recycled water for irrigated crops. The outcome of these long-term simulations, accounting
for future climate predictions, helps in devising better irrigation
guidelines for growing horticultural and vegetable crops.

2.1. Description of the study site
The Northern Adelaide Plains (NAP), South Australia, will experience a considerable expansion of irrigated agriculture and horticulture,
based predominantly on the utilization of increasingly available recycled water (RCW) from the Bolivar treatment plant (The Goyder
Institute for Water Research, 2016). The wastewater is treated using the“Dissolved Air Flotation and Filtration” (DAFF) technique through atertiary process of ﬁltration and disinfection, making it suitable for irrigating horticultural crops. The NAP has a Mediterranean-type climate,which is characterized by hot, dry summers and cool to cold winters.
Long-term (1900-2016) average rainfall in the region amounts to
475 mm (Department of Environment, Water and Natural Resources,
2016) and annual evapotranspiration amounts to 1308 mm. With mostof the rainfall occurring in the winter months, crop production requires
intensive irrigation.
Fourteen representative soil proﬁles were excavated to determine
the relevant physico-chemical properties of the main soil groups (Oliver
et al., 2018). Samples consisted of disturbed soil for the particle size
analysis and undisturbed cores for measurements of the bulk density,
the water content-matric suction relationship, and the saturated hydraulic conductivity. The soil samples were collected from 0-10, 10-30,
and 30-60 cm, and selectively also from lower depths (60-90 and 90120 cm). A detailed description of diﬀerent soil proﬁles and their
physico-chemical properties can be found in Oliver et al. (2018). Undisturbed soil cores collected from the ﬁeld were ﬁrst used to measure
the saturated hydraulic conductivity (Ks) using the constant head
method of Youngs (2001). Subsequently, the soil water retention
characteristic was determined by placing the cores onto saturated
ceramic plates connected to either hanging water columns (0.1, 4 and
8 kPa) or subjected to positive gas (nitrogen) pressures (33, 60, and100 kPa) in sealed chambers. Other soil samples were used to measure
the soil water content at 1500 kPa. More details are available from
Mallants et al. (2019).
2.2. Model description
We used the UNSATCHEM module of HYDRUS-1D (Šimůnek et al.,2016) for evaluating the long-term dynamics of coupled water ﬂow,
solute transport, and major ion geochemistry in the major NAP soil
groups, i.e., calcareous (CAL), hard red brown (HRB), sand over clay
(SOC), and deep uniform to gradational (DUG) soils under diﬀerent
crops (wine grapes, almond, pistachio, pasture, carrot, onion, andcarrot). Initially, the model was warmed up for 48 years from July 1970
to June 2018 using climate parameters from the Bureau of Meteorology(BOM) Station No 023083 at the Edinburgh RAAF site (Latitude-34.7111, Longitude 138.6222, elevation 17 m) to initialize the soil
water balance and to attain equilibrium conditions for chemical species
for each soil-crop combination.
The future climate (2018-2050) data were taken from the Goyder
Institute climate change median climate projections (Charles and Fu,
2015). The median data is based on the downscaled series obtained
from the GFDL – ESM2M Global Climate Model (GCM), one of the six
better performing GCMs, which are deemed to provide more realistic
inputs for impacts and adaptation assessment than those from the six
poorer GCMs. Note that the range of a possible future climate change islarger than that obtained from only using the downscaled results fromthe six better GCMs. A median decrease in annual rainfall by 2050 is
6.8% (relative to the 1986-2005 baseline), the 10th percentile decreaseis 8.8%, and the 95th percentile decrease is 3.5% (for the intermediateemission Representative Concentration Pathway RCP 4.5). RCP 4.5 is a
scenario of long-term, global emissions of greenhouse gases, short-lived
species, and land-use-land-cover, which stabilizes radiative forcing at
4.5 W/m2 (approximately 650 ppm CO2-equivalent) in the year 21002

Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

without ever exceeding that value (Moss et al., 2010). A single climate
future is used rather than a range of climate futures to keep the overall
number of modeling scenarios manageable. As a result, the simulations
present one possible outcome.
A detailed description of the HYDRUS-1D model is available fromŠimůnek et al. (2013) and Šimůnek et al. (2016). The one-dimensional
Richards equation, which assumes that the air phase plays an insigniﬁcant role in liquid ﬂow processes and that water ﬂow due to thermal
gradients can be neglected, is solved in HYDRUS-1D using a Galerkintype linear ﬁnite element scheme (Šimůnek et al., 2013). The governing
one-dimensional water ﬂow equation is described as:

∂θ
∂ ⎡
∂h
=
K (h) ⎛+ cos α ⎞ ⎤ − S ⎜⎛h, z , t ⎞⎟
∂t
∂z ⎢
∂z
⎝
⎠⎥
⎣
⎦
⎠
⎝

Table 1
Estimated average soil hydraulic parameters for diﬀerent soil groups in the
study area (from Mallants et al., 2019).
θrθs(cm3 cm-3)(cm3 cm-3)
Soil Group 1: Calcareous soils (CAL)
Soil depth (cm)

0-15
0.078
0.48
0.035
15-30
0.096
0.482
0.085
30-60
0.07580.485
0.278160-1000.00010.481
0.2305100-200
0.07350.40870.0242
Soil Group 2: Hard red brown soils (HRB)
0-15
0.073
0.469
0.025
15-30
0.101
0.446
0.059
30-60
0.109
0.465
0.282
60-1000.13650.45880.0891
100-200
0.08070.41120.0255
Soil Group 4: Sand over clay soils (SOC)
0-15
0.05870.41270.017115-30
0.082
0.44340.037130-600.10080.38660.024360-1000.10970.41170.025
100-200
0.06950.38120.0259
Soil Group 5: Deep uniform to gradational soils (DUG)
0-15
0.041
0.394
0.0018
15-30
0.126
0.473
0.0014
30-60
0.102
0.442
0.021960-1000.11390.45450.0184100-200
0.07350.40070.0254(1)

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 (positive upwards);
K(h) is the hydraulic conductivity (LT-1) -pressure head function, α isthe angle between the ﬂow direction and the vertical axis (e.g., 1 forvertical ﬂow and 0 for horizontal ﬂow), and S(h,z,t) is a sink term accounting for water uptake by plant roots (L3L-3T-1).
The HYDRUS-1D model will be run with best available soil, crop,
climate, and irrigation management parameters. The soil parameters
are mostly directly measured on representative soil samples or estimated using auxiliary soil data. Most of the crop parameters are fromthe literature, although some calibrated parameters were available forwine grapes. Climate data is in part measured on site (for historicalclimate) and in part derived from downscaled climate projections.
Irrigation management parameters were obtained from surveys. These
data sets are discussed next. The model was thus not speciﬁcally calibrated, as preference was given to use the measured input parameters.
Note that estimating model parameters through calibration using an
inadequate conceptual model could lead to so-called parameter bias
(Enemark et al., 2019).

(2)θ (h) = θs h ≥ 0(3)

K (h) = Ks SeI [1 − (1 − Se1/ m)m]2(4)

where θs is the saturated water content (L L ), θr is the residual watercontent (L3L-3), Ks is the saturated hydraulic conductivity (LT-1), l is ashape factor, and m, α, and n are empirical shape parameters wherem = 1-1/n. Se is the relative saturation (dimensionless), which is deﬁned as
3 -3

Se =

θ − θrθs − θrKs(cm day-1)

L
(-)

1.239
1.208
1.16391.13821.298

207.36181.44146.00267.79
22.91

0.5
0.5
0.5
0.5
0.5

1.217
1.187
1.133
1.1216
1.2196

50.11
40.61
13.98
13.33
12.82

0.5
0.5
0.5
0.5
0.5

1.78781.43971.47451.792
1.2511311.04103.6871.71
190.08
16.02

0.5
0.5
0.5
0.5
0.5

1.58
1.329
1.1886
1.1781
1.2477

39.05
27.65
171
233
17.21

0.5
0.5
0.5
0.5
0.5

cation exchange, and precipitation-dissolution. For the precipitationdissolution of calcite and dissolution of dolomite, either equilibrium or
multicomponent kinetic expressions are used, which include both forward and back reactions. Other precipitation-dissolution reactions
considered involve gypsum (CaSO4.2H2O), hydromagnesite (Mg5(CO3)
4(OH)2.4H2O),
nesquehonite(MgCO3.3H2O),
andsepiolite(Mg2Si3O7.5(OH).3H2O). Since the ionic strength of soil solutions canvary considerably with time and space and often reach high values,
both modiﬁed Debye-Hückel and Pitzer expressions are incorporated
into the model as options to calculate single ion activities.
The geochemical model considered aqueous, gaseous, adsorbed,
precipitated, and complexed phases (Šimůnek and Suarez, 1997;Šimůnek et al., 2013). Partitioning of dissolved major ions between thesolid and solution phases is described using the Equations (6) to (8)(White and Zelazny, 1986) and Gapon selectivity coeﬃcients (Eqs.
9–11). This requires the deﬁnition of the Gapon exchange constants forthe exchange of calcium and magnesium, calcium and potassium, andcalcium and sodium (Gapon, 1933; Šimůnek and Suarez, 1994) givenas:The soil hydraulic characteristics are modelled using the water retention and hydraulic conductivity functions described by the van
Genuchten-Mualem constitutive relationship (van Genuchten, 1980):

θs − θrh<0[1 + (αh)n]mn(-)

θr is the residual water content; θs represents the saturated water content; αrepresents the inverse of an air entry value; n & l are pore distribution parameters, and Ks is the saturated hydraulic conductivity of soils.

3. Soil hydraulic characteristics

θ (h) = θr +α
(cm-1)(5)

The parameters {θr, θs, KS, α, and n} for diﬀerent soils were derived
from the measurements on soil cores from 14 soil proﬁles from the NAP
as described in Section 2.1. Mean parameter values were then derived
for the 5 most important textural soils groups (Table 1).

0.5Ca + Na − X ↔ Na + Ca0.5 − X(6)0.5Ca + K − X ↔ K + Ca0.5 − X(7)

0.5Mg + Ca0.5 − X ↔ 0.5Ca + Mg0.5 − X(8)The Gapon selectivity or exchange coeﬃcients for reactions (6-8)
are deﬁned as (Šimůnek and Suarez, 1994):

K Ca / Na =Ca − XNa[Na − X ] Ca0.5(9)

K Ca / K =Ca − XK[K − X ] Ca0.5(10)

KMg / Ca =Mg − XCa0.5[Ca − X ] Mg 0.5(11)3.1. Solute transport properties for UNSATCHEM
The major ion chemistry module UNSATCHEM enables the simulation of equilibrium geochemical reactions involving Ca, Mg, Na, K,
SO4, Cl, NO3, H4SiO4, HCO3, and CO2 (Šimůnek and Suarez, 1997;Šimůnek et al., 2013). The model accounts for equilibrium chemical
reactions between these components, such as aqueous complexation,

where [Na], [K], [Mg], and [Ca] are molal activities in the soil solution3Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

Table 2The estimated initial concentrations of adsorbed cations, Gapon selectivity coeﬃcients (from Mallants et al., 2019), and soil solution compositions in diﬀerent soil
groups in the study area.
Soil Type

Adsorbed cation concentration (meq/kg)
Ca-X

CAL
HRB
SOC
DUG

CAL
HRB
SOC
DUG

Mg-X

51.87
35.32
53.17
60.61
80.19
21.36
41.14
22.72
Soil solution composition (meq/L)
Mg2+
Ca2+
3.82
2.31
2.94
3.58
4.41
1.74
1.76
3.79

Gapon coeﬃcients
Na-X

K-X

KCa/NaKCa/K

KMg/Ca

13.71
36.93
3.00
22.72

7.74
15.32
9.02
16.79

0.038
0.033
0.020
0.013

0.957
0.753
1.880
1.313

0.009
0.065
0.014
0.025

Na+
9.91
14.81
3.0
11.5

K+
0.05
0.58
0.07
0.26

Alk
3.22
3.78
1.85
4.7

Cl0.5
2.28
0.44
0.84

SO4211.3
15.34
6.46
11.5

compares well with previous calibrated values for wine grape (Phogat
et al., 2017). Soil speciﬁc information (soil texture, ﬁeld capacity, θfc;
permanent wilting point, θwp; readily available water, RAW; total
available water, TAW) has been drawn from the soil analyses (Oliver
et al., 2018).
The values of daily potential transpiration (Tp) and soil evaporation(Es) thus obtained were used as time-variable boundary conditions in
the HYDRUS model, along with the irrigation schedule for diﬀerent
crops and precipitation received at the site during the simulation
period. The amount and timing of irrigation were also imposed as atime-variable ﬂux boundary.

(dimensionless), and [Na-X], [K-X], [Mg-X], and [Ca-X] are adsorbed
concentrations (mmolc/kg soil).
Initial average concentrations of soil solution species, soil exchange
cations, and estimated Gapon selectivity coeﬃcients in the soil proﬁlescollected from the NAP area are given in Table 2 (for further details of
wet chemistry analyses, see Oliver et al. (2018)). Note that this initialsoil solution is brought into equilibrium with the cation exchange
complex during the model warming up period (1970-2018).
3.2. Estimation of potential evaporation (Es) and potential transpiration(Tp)

3.3. Root water uptake parameters

The HYDRUS-1D/UNSATCHEM (Šimůnek et al., 2016) model requires daily inputs of rainfall as well as potential evaporation (Es) andpotential transpiration (Tp). Therefore, the daily Es and Tp values for all
crops (wine grape, almond, pistachio, pasture, carrot, onion, and potato) for current and future climates were estimated following the FAO56 dual crop coeﬃcient (DCC) approach (Allen et al., 1998).
The FAO-56 DCC methodology requires a considerable amount ofdata for crops, soils, and climate. All relevant information and required
data for this approach for the NAP soils and crops are included inMallants et al. (2019) and Phogat et al. (2020). Daily climate data forthe historic climate (1970-2018) was obtained from the Bureau of
Meteorology (BOM), Edinburg RAAF station, while future climate(2018-2050) data was taken from the Goyder climate change medianclimate projections for the same station (Charles and Fu, 2015).
Apart from daily Es and Tp, the irrigation requirements for diﬀerent
crops (almonds, wine grape, pistachios, pasture, carrot, onion, andpotato) were also estimated following the FAO-56 dual crop coeﬃcient(DCC) approach (Allen et al., 1998), as discussed in detail by Mallants
et al. (2019). For wine grapes, measured LAI values were taken fromsimilar studies (Phogat et al., 2016, 2017) in the study area. Also, values for canopy geometry and fractional cover for wine grape previously
calibrated by Phogat et al. (2016) have been used here. The functional
relation followed in the FAO dual crop coeﬃcient approach (Allenet al., 1998) is given as:
ETC = (Kcb + Ke) ET0

Water extraction S(h,hs,x,z,t) from the soil is computed according to
the Feddes macroscopic approach (Feddes et al. 1978). In this method,
the potential transpiration rate, Tp, is distributed over the root zone
using a normalized root-density distribution function {β(x,z,t)} andmultiplied by dimensionless water α1(h) and salinity α1(hs) stress response functions as:

S (h, hs , x , z , t ) = α1 (h, hs , x , z , t ) Sp (x , z , t )= α1 (h, hs , x , z , t ) β (x , z , t ) Tp (t )(13)This model reduces potential plant root water uptake rates according to the local soil water pressure head, h, and osmotic head, hs, at
any point in the root zone. This model deﬁnes how potential transpiration (Tp) is reduced when the soil is no longer capable of supplying
the amount of water required by plants under prevailing climatic andsoil conditions. The multiplicative model for the uptake reduction due
to the osmotic stress is considered in this study:

α1 (h, hs ) = α1 (h) α1 (hs )(14)

The reduction of root water uptake due to the water stress, α1(h), is
described as:⎧ 0, h > h1 or h ≤ h4
⎪ h − h1
⎪ h − h , h2 < h ≤ h12
1
α1 (h) =⎨1, h3 < h ≤ h2
⎪h−h4⎪
, h4 < h ≤ h3
⎩ h3 − h4(12)-1

where ETC is the crop evapotranspiration (LT ), ET0 is the reference
crop evapotranspiration (LT-1), Kcb is the basal crop coeﬃcient, which
represents the plant transpiration component, and Ke is the soil evaporation coeﬃcient. Some of the basic information such as Kcb, crop
duration, plant height, rooting depth, and depletion factor, is available
from FAO-56 (Allen et al., 1998). The details on data requirements andother relevant information for each crop, soil, and climate conditions
can be found in Mallants et al. (2019). In this approach, standard Kcb
values (Allen et al., 1998) of the crops were adjusted for the local climate, taking into consideration crop height, wind speed, and minimum
relative humidity averages. Estimated Kcb’s in the current study(15)

where h1, h2, h3, and h4 are the threshold model parameters. Water
uptake is at the potential rate when the pressure head is between h2 andh3, decreases linearly when h > h2 or h < h3, and becomes zero whenh < h4 or h > h1. These critical values of the pressure head for viticulture and perennial horticulture (e.g., almond) for the study weretaken from previous investigations in South Australia (e.g., Phogat
et al., 2012, 2013, 2017) and other available literature, e.g., Taylor and4

Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

Table 3The estimated average seasonal composition of recycled water (used for irrigation) and rainwater in the study area.
Water type

Season

Ca2+
Mg2+
Na+
K+
Alka
SO42Cl—————————————————————————————meq/L———————————————————————SAR

Recycled water(RCW)

AutumnwinterspringsummerAverage

1.85
1.88
2.02
1.92
1.92
0.17

7.7
7.8
8.5
8.5
8.1
1.1

Rainwater
a

2.44
2.59
3.13
2.84
2.75
0.11

11.25
11.72
13.62
13.12
12.43
0.41

1.02
0.94
0.97
1.04
0.99
0.01

1.80
1.90
2.09
2.08
1.97
0.16

3.77
3.73
3.76
3.77
3.76
0.08

10.27
10.71
12.62
12.44
11.51
0.51

Alkalinity.

atmospheric boundary condition with surface runoﬀ was imposed and afree drainage boundary condition was applied at the bottom boundary.
For solute transport, a concentration ﬂux boundary condition was usedat the soil surface and a zero concentration gradient boundary condition was imposed at the bottom to allow gravitational outﬂow withdrainage water. A constant pressure head of -100 cm was considered toinitiate the model runs. Measured daily rainfall, and calculated daily Es
and Ep for diﬀerent crops were applied during the warmup period(1970-2018). Measured soil solution and exchange parameters(Table 2) were assumed as initial conditions for the multi-component
solute transport module.
While cultivation methods may aﬀect the soil properties over theyears, we assume that the measured soil physical and hydraulic properties reﬂect such tillage eﬀects and remain constant over the simulation period. Furthermore, even though the location of horticultural
crops is not static across multiple years, as long as sprinkler irrigation isapplied, water is rather uniformly distributed over the cropped area in
response to the crop ET demand. This means that salinity distribution israther uniform in space and therefore assuming the same exposure overmany years is justiﬁed. Tree crops, however, have a ﬁxed location
across the years so the salt distribution is likely less uniform, especially
under drip irrigation. The long-term impact of use of RCW will thendepend, among others, on the spatial distribution of the root system
relative to the location of the drippers. Two-dimensional salinity distribution under drip irrigation was demonstrated by Phogat et al.
(2014).

Ashcroft (1972) and the HYDRUS database.
The threshold model uses two variables to simulate the osmotic
stress α1(hs): the osmotic head, below which water is extracted at the
maximum rate, and the slope, i.e., a fractional reduction of water uptake per a unit increase in the osmotic head above the threshold. These
parameters were obtained from Zhang et al. (2002) for wine grapes,
Sanden et al. (2004) for pistachios, and Ayers and Westcot (1985) for
other crops.
3.4. Irrigation water quality
Average seasonal recycled water (RCW) quality data was obtained
from Awad et al. (2019). The mean seasonal composition for RCW was
estimated from raw data from 2002-2017. The ﬁnal values used for themodeling study are given in Table 3. The water quality falls into themedium category for the salinity rating in Australia (ANZECC,ARMCANZ 2000), which means that moderately tolerant crops can begrown using RCW as an irrigation source. The chemical composition of
rainwater was obtained from Cresswell et al. (2010) for the Adelaide
region (Table 3).
In this study two irrigation systems were identiﬁed on the basis of asurvey undertaken among the farmers: overhead sprinklers (potato,
carrots, cauliﬂower, broccoli, cabbage. lettuce) and drippers (almonds,
wine grapes, tomatoes). For sprinkler irrigation, water application isrelatively uniform across the soil surface thus leading to one-dimensional ﬂow and solute migration. The use of the one-dimensional
HYDRUS model is thus appropriate. For drip irrigation, ﬂow and solutetransport is quasi-three dimensional (in the radial direction away fromthe dripper), therefore the use of a one-dimensional model may not be
entirely correct. The discrepancies in both water content and salinity
are likely highest at the edge of the wetting zone surrounding thedripper; how much this impacts crop growth will depend on the spatial
distribution of the rooting system relative to the position of the drippers. Previous work by Phogat et al. (2014) with a two-dimensionalmodel with drip irrigation applied to a mandarin tree clearly showed
the non-homogeneous salt redistribution surrounding the dripper.
Under the dripper, salt levels were the lowest, while highest salinity
was observed at the edge of the wetted soil area. Our one-dimensional
may capture the water and salt distribution just underneath the dripper,
but not at the edges. Estimating the impact of this simpliﬁcation on crop
yield was beyond the scope of this study.

4. Results and Discussion
4.1. Impact of RCW irrigation on pH and salinity (ECsw) dynamics in the
soil
Periodic changes in the proﬁle-averaged pH, ECsw, SAR, and ESP in
diﬀerent soils under recycled water (RCW) irrigation of almonds areshown in Fig. 1. Corresponding chemical variables for other crops
during selected years are listed in Table 4. The initial proﬁle-averaged
pH, which ranged from 7.9 to 8.3 in diﬀerent soils, gradually converged
to a narrow range (8.2-8.3) at the end of the simulation (Fig. 1). Soil pH
in CAL and DUG soils decreased during the early years (3-4 years)
following the application of RCW irrigation, while it increased slightly
in the other two soils (HRB and SOC). The pH remained more or lessconstant after that and attained a steady-state condition in all soils(Fig. 1). For the other crops, RCW irrigation had a similar impact on soil
pH where diﬀerences between initial and ﬁnal values were within a-0.21 to 0.32 range. (data not shown). Quan and Lin (2019) observed a0.25-0.3 increase in the initial soil pH (7.1-7.5) during 11 years of recycled water irrigation of landscape facilities. However, a soil pH
higher than 8 can reduce the micro-nutrient availability to the crops,
which in turn can impact the normal growth and development of trees
and other crop plants. Previous work in the NAP area by Stevens et al.
(2004) also did not observe any signiﬁcant diﬀerences in the soil pH
due to RCW irrigation for periods between 10-28 years.
Concentrations of soluble salts in the soil solution (ECsw) increased

3.5. Modeling domain, initial and boundary conditions
Simulations for all crops were performed for a 200-cm deep soil
proﬁle divided into 100 ﬁnite elements. The ﬁnite element nodes were
distributed with a nodal density of 0.5 at the surface and 1 at thebottom to ensure a ﬁne discretization at the soil surface where majorwater and solute dynamics processes take place. The domain was divided into 5 soil layers (0-15, 15-30, 30-60, 60-100, and 100-200 cm) toaccommodate the measured textural heterogeneity in various soil
groups (CAL, HRB, SOC and DUG) in the NAP area determined by soil
sampling and the particle size analysis. At the soil surface, an5Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

Fig. 1. Temporal changes in the proﬁle-averaged annual values of a) pH, b) ECsw, c) SAR, and d) ESP in diﬀerent soils (CAL = calcareous soils, HRB = hard red
brown, SOC = sand over clay, and DUG = deep uniform to gradational) for almond irrigated with recycled water during 2018-2050.

for other crops (Table 4). Interestingly, ECsw was always below 3 dS/min the surface soil zone (0-30 cm) during the entire simulation period(2018-2050), which is well below the critical threshold salinity forvegetables, almond, and clover (Maas and Hoﬀmann, 1977). However,
the average proﬁle ECsw values are higher than the salinity threshold forvegetables (carrot, potato, and onion; ECsw = 2-3.4 dS/m), mixed pasture clover (3 dS/m), and almonds (3 dS/m), similar to that for wine
grapes (4.2 dS/m), but much lower than those for mixed pasture ryegrass (11.2 dS/m) and pistachio (18.8 dS/m).
It is generally accepted that soils with ECe larger than 4 dS/m (≈ECsw of 8 dS/m) are considered as saline soils (U.S. Salinity Lab. Staﬀ,
1994). The previous work by Stevens et al. (2003) for the NAP soils
considered ECe of 3 dS/m as a salinity threshold for vegetable crops.
ECsw higher than the crop salinity threshold can adversely aﬀect thenormal growth, development, and yield of horticultural and vegetable
crops. Hence, leaching of soluble salts with low-salinity water is essential to bring the eﬀective rootzone salinity below the crop salinity
threshold. Among diﬀerent soils, CAL and HRB displayed high salinity
at lower depths, SOC had the lowest ECsw, and DUG had values in between those of the other soils.
Pistachio is a highly salinity tolerant crop that is potentially themost suitable for cultivation in salt-aﬀected soils (Sepaskhah and
Maftoun, 1981). Ferguson et al. (2002) and Sanden et al. (2004)
showed the viability of using saline water with an EC of 8 dS/m, whichshowed no impact on the pistachio yield. Notably, the salinity toleranceof pistachio, similarly as of many other woody species, have been associated with a small growth reduction (Sepaskhah and Maftoun, 1988;
Picchioni et al., 1990), a small decrease in the photosynthetic activity(including a high chlorophyll index, chlorophyll ﬂuorescence, andchlorophyll content) and the transpiration rate (Walker et al., 1988;
Karimi and Maleki Kuhbanani 2015; Momenpour and Imani 2018),
smaller amounts of salt ions transported to the leaves (Walker et al.,
1987; Picchioni et al., 1990), the ability to maintain relative high K+/
Na+ and Ca2+/Na+ ratios needed for osmotic adjustment and enzymatic activities (Chelli-Chaabouni et al., 2010; Hajiboland et al., 2014),
and a smaller nut yield reduction (Sanden et al., 2004; Ferguson et al.,rapidly as a result of irrigation with RCW of all crops (Fig. 1; Table 4).
In almonds, the average proﬁle ECsw in the soil almost attained a steadyvalue within 2-3 years. Final ECsw values (in 2050) varied from 4.33 to5.15 dS/m across diﬀerent soils. Similarly, the corresponding values
under wine grape, pistachios, pasture (mix), carrot, onion, and potato
ranged from 4.0-4.7, 3.8-6.7, 5.5-6.4, 5.8-6.6, 6.1-6.2, and 4.0-9.5 dS/m, respectively (Table 4). These results corroborate well with Stevens
et al. (2004), who reported that ECe values ranged between 2.8 and6.9 dS/m for 10-28 years of RCW irrigation of diﬀerent crops in thesame region.
However, maximum salinity values, irrespective of the crop, wereobserved during 2034, which was projected to be a drought year withan annual rainfall of 247 mm. Compared to other crops, the average
proﬁle ECsw build up in the soil under potato was higher in CAL and
HRB soils (Table 4). This was probably due to a potato-oats (rain-fed)
cropping sequence adopted in the study. After 7 years of irrigation(2025), ECsw in these soils was twice as high as in the other two soils
under potato and other crops. This pattern continued until the end of
the simulation (2050). Overall, the salinity build-up in the CAL and
HRB soils in 2050 was double than observed in the other two soils(Table 4). Therefore, the use of RCW irrigation can have a varied andsigniﬁcant impact on the soil salinity build-up under diﬀerent cropping
sequences, which subsequently may have a dramatic impact on crop
yields. However, responses of crops to salinity are inﬂuenced by several
factors, including climate, irrigation, or agronomic management. For
example, leaching of soluble salts using low-salinity water is one way tobring the eﬀective rootzone salinity below the crop threshold value toattain optimal yield, and thus to control the harmful impact of salts
associated with recycled water irrigation.
On the other hand, ECsw showed an increasing trend from the soil
surface to lower depths, irrespective of soil and crop type. For example,
in almond, ECsw attained a value larger than 5 dS/m everywhere below
a depth of 40 cm after 7 years of irrigation (Fig. 2). At deeper depths(> 80 cm), ECsw increased quickly to a value of more than 5 dS/m in all
soils after about 5-7 years of irrigation (see Fig. 2, the ﬁrst column).
Similar patterns of spatiotemporal changes in ECsw were also observed
6Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

Table 4
Annual values of proﬁle average ECsw, SAR and ESP in diﬀerent soils (CAL = calcareous, HRB = hard red brown, SOC = sand over clay, and DUG = deep uniform togradational) under diﬀerent crops irrigated with recycled water.
Year

Wine grape
2018
2026
2034
2042
2050
Pistachios
2018
2026
2034
2042
2050
Pasture
2018
2026
2034
2042
2050
Carrot
2018
2026
2034
2042
2050
Onion
2018
2026
2034
2042
2050
Potato
2018
2026
2034
2042
2050

ECsw

SAR

ESP

CAL

HRB

SOC

DUG

CAL

HRB

SOC

DUG

CAL

HRB

SOC

DUG

0.5
4.6
5.3
4.6
4.7

0.4
4.1
4.6
4.1
4.2

0.4
3.7
4.1
4.1
4.0

0.4
4.1
4.5
4.0
4.1

1.3
7.1
10.9
13.0
14.1

0.9
5.5
9.5
12.0
14.2

1.5
8.8
11.2
12.0
12.9

1.8
10.0
14.4
14.3
14.7

19.5
31.6
37.8
40.1
41.5

28.1
38.2
45.1
48.1
49.7

7.9
18.5
22.7
24.5
26.0

24.0
34.1
35.7
34.9
34.5

0.5
6.7
7.3
6.9
6.7

0.3
4.5
4.8
4.8
4.7

0.4
4.5
4.9
5.2
5.4

0.5
4.1
4.1
3.7
3.8

1.9
13.2
17.2
18.4
19.4

1.3
11.4
15.2
15.4
15.8

2.0
11.4
14.0
15.4
16.1

2.9
12.2
13.9
13.3
13.5

21.4
40.9
46.2
46.8
46.4

29.7
46.0
50.6
50.7
50.8

8.9
23.6
28.5
30.7
31.3

24.5
34.5
33.8
32.7
31.6

0.7
6.3
6.7
6.5
6.2

0.5
5.4
5.8
5.6
5.5

0.5
5.4
6.1
6.0
5.6

0.8
6.4
6.9
6.6
6.4

2.0
13.7
16.7
17.6
18.3

1.3
12.4
16.0
16.3
16.8

2.6
12.8
15.6
16.3
16.7

3.1
16.6
18.1
18.4
18.7

21.8
40.7
44.5
44.2
43.3

30.3
46.9
51.0
51.1
51.3

9.7
25.4
29.8
30.4
30.1

25.5
36.9
37.6
37.1
36.4

0.8
6.0
6.5
6.3
6.3

0.6
5.3
5.9
5.5
5.8

0.7
5.6
6.9
6.5
6.6

1.0
6.0
6.6
6.2
6.5

2.3
13.9
16.7
17.5
18.4

1.4
12.6
16.1
16.1
16.9

2.9
12.9
16.0
16.7
17.7

3.6
16.4
18.0
18.1
18.9

21.9
40.9
44.3
44.0
43.0

30.4
47.3
51.4
51.3
51.5

10.0
25.5
30.0
30.6
30.5

25.8
37.0
37.6
36.8
36.1

4.7
5.9
6.6
5.9
6.2

3.0
5.7
6.4
5.5
6.1

1.9
5.4
6.7
5.7
6.2

6.6
5.8
6.5
6.0
6.2

1.0
8.3
12.7
14.5
15.8

0.8
6.1
10.2
13.0
15.7

1.5
9.5
12.4
13.3
14.8

1.4
11.2
16.8
17.1
17.6

20.2
34.5
40.5
42.3
43.5

28.6
39.7
46.3
49.6
51.5

8.3
19.8
24.2
26.3
28.3

23.7
37.8
39.6
39.0
38.7

2.1
8.9
10.6
9.1
9.5

0.8
7.3
8.7
7.5
8.2

1.0
3.1
4.2
3.3
4.0

2.0
4.5
5.2
4.8
5.0

1.3
8.8
14.1
16.9
19.2

1.0
6.8
11.5
14.8
17.9

1.9
9.3
10.9
11.1
12.1

1.8
12.8
15.5
15.3
15.6

20.3
35.2
42.5
45.4
47.5

28.8
40.8
47.8
51.9
53.6

8.5
19.1
21.9
23.0
23.9

23.5
36.5
36.9
36.1
35.6

greater than 15 in all soils and comparatively higher values in DUG and
CAL soils (Fig. 2). Stevens et al. (2003) reported an increase in SAR in
the top soil by a factor of three relative to virgin soil, which is similar toour simulations for similar RCW SAR (8.7 for Stevens et al. versus 8.1
for this study).
An increasing soil SAR triggers an exchange of cations between thesoil solution and soil cation exchange complex, which increases the soil
ESP. SAR values greater than 13 indicate a potential for sodic soil development (US Salinity Lab Staﬀ, 1954). High SAR values suggest thatthe long-term use of irrigation with recycled water can induce sodicity
hazards, which could have a severe impact on the sustainable production of pastures, and perennial and annual horticultural crops. Soil SAR
is a dynamic property that depends on the composition of the soil solution when the measurements are made. However, the soil-speciﬁc
inherent bio-geochemical and ion exchange reactions can have a great
impact on the composition of the soil solution and exchange.
Although the proﬁle-averaged SAR varied in a rather narrow range(12.1-19.5) in diﬀerent soil-crop combinations, these values are muchhigher than the threshold values reported in Rangasamy et al. (1984),
i.e., SAR1:5 > 3, which may result in adverse soil impacts. Based on the
FAO (FAO, 1985) criteria for sodicity, SAR values predicted in ourstudy are in the range of the light to moderate or moderate to highsodicity hazard class. This suggests that the use of recycled water irrigation can lead to the development of high ESP levels in the soil. HighESP causes reductions in the hydraulic conductivity (McNeal, 1968,1974); for the NAP soils that would be in the range of 20 to 40%(Mallants et al., 2019). The precise extent of any adverse impacts due tohigh SAR is determined by factors such as concentrations of soluble2010). Picchioni et al. (1990). The high salt tolerance of pistachio is
attributed to the ability to store large amounts of Na in the roots andbasal stem. Maintaining normal growth under salinity stress is associated with the ability of plants to reduce water losses and ensure an
osmotic adjustment at the cellular level (Munns, 2002). However, otherstudies (Saandatmand et al., 2007; Karimi et al., 2011) reported thathigh salinity stress could negatively aﬀect the photosynthesis rate, themorphology of leaves, and the nutrient balance of pistachio trees. The
maximum soil salinity value reached during the simulations(ECsw = 13.7 dS/m at 120-200 cm depth in 2047; data not shown),
which is still below the crop threshold, suggests that pistachio could bea viable option for RCW irrigation in the NAP area.
4.2. Changes in sodium adsorption ratio (SAR)
The long-term use of recycled water for irrigation resulted in aconsiderable increase in SAR for all soils and all crops (Figs. 1,2,
Table 4). For example, after 7-8 years of irrigation, SAR in all soil
(except for HRB) under almond increased to a value larger than 10(Fig. 1). Similarly, for other crops, SAR increased rapidly and its proﬁleaveraged values ranged between 5.5 and 16.6 (Table 4) during the sametime (after 7-8 years of irrigation). Subsequently, SAR continued toincrease with the use of RCW irrigation for almost, attaining a constantvalue after about 15 years of RCW irrigation. Note that SAR at lower
depths (> 100 cm) in hard clay and calcareous soils had a delayed response to RCW irrigation (Fig. 2). The proﬁle-averaged SAR values atthe end of the simulation were between 10 and 20 in all soils and for all
crops (Table 4). SAR values at soil depths larger than 50 cm were7

Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

Fig. 2. Predicted changes in ECsw (dS/m), SAR and ESP in diﬀerent soils (CAL = calcareous soils, HRB = hard red brown, SOC = sand over clay, and DUG = deep
uniform to gradational) during 2018-19 (a, b, c), 2025-26 (d, e, f), 2033-34 (g, h i), 2041-42 (j, k, l), and 2049-50 (m, n, o) for almonds irrigated with recycled water.

8

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V. Phogat, et al.

degradation of the soil structure, which ultimately negatively aﬀects
crop growth and yield, and long-term sustainability of crops. Therefore,
irrigation with recycled water should be supplemented with an adequate amount of soil amendments such as gypsum and organic matter/compost to avoid the harmful impacts on soils and crops.
4.4. Yield-salinity relationshipThe modeling results presented in previous sections clearly indicated that irrigation with RCW increased the soil solution salinity(ECsw) for all investigated crops. To estimate the eﬀect of increased
salinity on crop yield, the yield-salinity relations were drawn following
the Maas and Hoﬀman (1977) piecewise linear regression. This model isgiven as:Fig. 3. Relative yield (%) and proﬁle-averaged salinity (ECsw1) relationship(Maas and Hoﬀman, 1977) for diﬀerent crops. The green color in the background shows the model-simulated proﬁle-averaged ECsw1 in diﬀerent soils fordiﬀerent crops irrigated with recycled water.

Yr = 100 − b (ECsw1 − ECt )where Yr is the relative yield, ECsw1 is the proﬁle-average soil salinity at
a given time, ECt is the crop tolerance threshold salinity, and b is adecrease in yield in percent in response to per unit increase in salinity(dS/m) above the threshold value. Calculations based on this model
only indicate the impact of soluble salts on the possible reduction in thepotential yield. Furthermore, high ESP can lead to an additive or
multiplicative eﬀect on the yield reduction due to its adverse impact onsoil physical conditions of the soil. The latter eﬀect was not quantiﬁed
here.
The extent of average proﬁle salinity (ECsw1) in soils under diﬀerent
crops ranged from 2.9 to 10.6 dS/m (the green background in Fig. 3),
with an overall mean of 5.5 dS/m across all soils and crops. Thesesalinities developed over 32 years of recycled water irrigation in the
NAP soils. These values correspond very well with some of the previous
studies in the NAP region, which assessed the impact of the long-term(10-28 years) use of recycled water for irrigation (Stevens et al., 2003,
2004). However, their study focused on analyses of soils (unirrigatedvirgin soils, RCW irrigated soils, and groundwater irrigated soils) that
had received diﬀerent irrigation waters, while the impact on crop yields
was not investigated.
The model-simulated annual proﬁle average salinity (ECsw1) for
diﬀerent crops was used to estimate the reduction in the potential yield(Fig. 3). The yield reduction for the 10th %tile, mean, and 90th %tile
proﬁle-averaged salinities for diﬀerent soils are shown in Table 5. Pistachios and pasture (rye grass) would not experience a signiﬁcant yield
loss because they are tolerant crops. A yield reduction of almonds due
to recycled water irrigation varied from 9 to 22%, depending on the soil
type. A reduction in wine grapes is comparatively low (0-5.7%) due to ahigher tolerance to salinity. A comparison of almond yields grown in
the NAP (Pitt et al., 2017) and Riverland regions (Phogat et al., 2013;
average yield of 8 years) revealed that the almond yield in the NAP
region was only 50% of that in the Riverland region. The major difference between the two sites was the water quality used for irrigation,
climate, and the soil type (more sandy in Riverland). In the Riverland,almonds are irrigated with the River Murray water, which has a much
lower salinity (0.4 dS/m; Phogat et al., 2018a) than recycled water,
groundwater, or blended water (0.8-1.9 dS/m; Phogat et al., 2018b)
used in the NAP region. The current hypothesis is that the use of RCW in
the NAP region (Pitt et al., 2017) with its high soil salinity (soil proﬁleECSe = 2-7.5 dS/m) might have contributed greatly to the reduction in
the almonds yield.
The yield reduction in clover pasture ranged from 12 to 23.8%.
Therefore, under mixed pasture conditions (rye grass and clover), RCW
irrigation can potentially impact the clover crop. Similarly, among
annual horticulture crops, almost one-third of the potential yield inonion (23.5-34.9%) and potato (23.5-34.9%) could be lost due to salinity stress. The corresponding yield loss for carrot and brassicas ranged
from 19.6 to 32.4 and from 5.8 to 14.7%, respectively.
Salinity impacts on crops are inﬂuenced by climate, irrigation, andagronomic management (Rhodes and Loveday 1990). Generally, mostsalts, the leaching fraction, rainfall amounts, and the type and content
of clay in the soil (ANZECC, ARMCANZ 2000).

4.3. Impact on Exchangeable sodium percentage (ESP)The initial measured ESP values in the soils ranged between 7.9 and
24% (Oliver et al., 2018) and varied slightly during the warm-up period(1970- 2018) when low-salinity water was used. However, a rapid increase in ESP occurred in response to the use of RCW for irrigation(Figs. 1,2, Table 4). While the proﬁle-averaged ESP (21.8) in the CAL
soil doubled, the corresponding ESP in the SOC soil increased roughly 3
times relative to its initial value, reaching 28.7 after 32 years of irrigation (Fig. 1). Such increases in ESP were observed for all soil-crop
combinations (Table 4). At the end of the simulation (2050), the corresponding proﬁle-averaged ESP across all crops ranged between 40.747.5, 48.9-53.6, 23.9-31.3, and 31.6-38.7% in the CAL, HRB, SOC, andDUG soils, respectively (Table 4). An increase in ESP relative to its
initially measured values in response to RCW irrigation varied from
18.8-27.1, 18.6-24.7, 15.4-22.3, and 7.1-15.0% in the CAL, HRB, SOC,
and DUG soils, respectively. The maximum ESP ranging between 40
and 60 was observed in the HRB soil at the end of the simulation(Fig. 1). This demonstrates that the use of RCW over the years results ina rapid increase in ESP, which may render the soil unﬁt for crop production. Calculated ESP values were above their threshold value (< 6 isnon-sodic) in all soils at all depths.
Normally, shallow depths had lower ESP compared to deeper soil
layers (Fig. 2). For example, the average initial ESP in the HRB soil inthe surface layer (0-15 cm) under almond was 17.7%, which increased
to 56.6% in the 120-200 cm layer at the end of the simulation. ESP at
soil depths larger than 40 cm typically was between 40 and 60 in all
soils. However, the HRB and CAL soils showed comparatively higher
ESP (> 35) than other soils. Irrigation with recycled water tends toincrease the Na content on the soil exchange complex. An increase in
the Na content on the soil exchange complex relative to Ca and Mg may
induce soil swelling and dispersion of clay and organic matter, impact
soil water movement (water logging), and degrade the soil structure.
These conditions may decrease oxygen concentrations and ultimately
impact the plant growth. This condition is therefore considered to be asigniﬁcant barrier to a sustainable irrigation practice. The amount ofdispersed clay is also aﬀected by soil mineralogy, soil solution constituents (e.g., salinity), and organic matter (NRMMC EPHC, 2006).
Therefore, soil amendments such as gypsum, compost or other organic
materials would be required to control ESP below its threshold(ESP < 6) and provide a congenial environment for normal crop
growth.
The range of SAR and ESP observed in this study can have severe
impacts on the clay dispersion, clogging of macropores, and9Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

Table 5
The reduction in the potential crop yield (%) due to proﬁle-averaged salinity (10th %tile, mean, 90th %tile) in calcareous (CAL), hard red brown (HRB), sand over clay
(SOC), and deep uniform to gradational (DUG) soils under recycled water irrigation.
Soil

10th %tile

Mean

90th %tile

Cal
HRD
SoC
DuG
Cal
HRD
SoC
DuG
Cal
HRD
SoC
DuG

Almond

15.1
9.1
11.5
15.0
17.6
11.2
15.1
17.9
22.2
15.2
20.0
22.2

Grape

0.0
0.0
0.0
0.0
1.7
0.0
0.0
0.0
5.7
2.5
1.6
2.3

Pista

Carrot

0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

22.9
19.6
22.2
24.4
27.1
23.1
27.7
27.4
31.1
26.8
32.4
31.2

Onion

26.8
24.9
23.5
27.5
30.5
27.5
28.5
30.7
33.6
31.4
34.9
33.2

Potato

23.4
15.2
0.0
5.9
34.5
25.1
0.6
8.0
42.9
32.1
3.8
10.5

Pasture(Clover)

(Rye)

17.2
12.0
13.8
18.7
19.2
13.9
16.1
20.1
22.9
17.1
19.9
23.8

0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0

classiﬁcation of sodic soils has an ESP threshold of 15 (US Salinity Lab
Staﬀ, 1954). This diﬀerence is attributed to several factors including
diﬀerences in electrolyte concentrations, pH, organic matter contents,
and the clay type and contents, and the way such diﬀerences in soil
chemistry aﬀect the critical ESP, above which clay dispersion and hydraulic conductivity reduction occur (Rengasamy and Olsson, 1991).
Measuring ESP is complicated and time-consuming, and therefore
alternative parameters, which can better characterize sodic soils, havebeen sought after. Many studies (e.g., Rengasamy et al., 1984) suggested SAR as an alternative indicator for sodic soils, since SAR ishighly correlated with the soil ESP and can be more conveniently
measured. Numerous studies have attempted to ﬁnd a relation between
SARe (SAR of the saturation extract) and ESP of the soils in diﬀerent
parts of the world (US Salinity Lab Staﬀ, 1954; Paliwal and Gandhi,
1976; Shainberg et al., 1980; Evangelou and Marsi, 2003; Seilsepour
et al., 2009; Chi et al., 2011). These approximate relationships vary
according to site-speciﬁc soil parameters, such as the clay content andmineralogy of the soils. Similarly, Rengasamy et al. (1984) extended
this relation between SAR1:5 and ESP for Australian soils, similar to
those in the current study, to facilitate the soil classiﬁcation (Fig. 5).
Similarly, Chi et al. (2011) found that there were non-signiﬁcant differences in the ESP development from the approximate log relations
with SARe or SAR1:5.
As for the soils of the NAP region, Rengasamy et al. (1984) found a
linear relationship (ESP = 1.95*SAR + 1.85, R2 = 0.82) between
SAR1:5 and ESP, whereas Stevens et al. (2003) reported a curvilinear(ESP = 5.36*SAR0.65, R2 = 0.83) relationship (Fig. 4). Stevens et al.
(2003) hypothesized that the inclusion of the soil analysis of deeper
layers (up to 1 m) might change the nature of the relationship as theformer study only analyzed samples from surface layers (0-45 cm). We
attempted to draw similar relationships to understand the impact of
RCW irrigation on sodicity development in the soils. A strong relationship was found between the irrigation-induced SAR and the resulting soil ESP (Fig. 4). Both linear, as well as curvilinear regressions
models, showed good agreement, with an R2 of 0.89 and 0.92, respectively.
Since the simulations provided suﬃcient SAR/ESP data for all soil
types, we have derived separate relationships between SAR and ESP fordiﬀerent NAP soils (Fig. 5). There was a good curvilinear relationshipbetween SAR and ESP with R2 values of 0.91, 0.93, and 0.94 for the Cal,
HRB, and SoC soils, respectively. However, the relation for the DuG
soils was poor (R2 = 0.29). The initially high ESP of these soils may
have inﬂuenced the rate of Na+ exchange between the soil solution andthe exchange phase, which may, in turn, have resulted in the lower ESP
build-up in response to recycled water irrigation. More importantly, therelations in Fig. 5 suggest that diﬀerent SAR thresholds exist, which
could lead to soil ESP exceeding the threshold value of ESP = 6. Indeed,

Fig. 4. A comparison of relationships between model-simulated sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) (average data
for all soils) with published relations previously developed for the NAP soils.

vegetable crops suﬀer a 10% yield reduction at an ECe of 2.7 ± 0.8 dS/
m (ECsw1 ≈ 2 ECe) (ANZECC, ARMCANZ 2000). Therefore, appropriate
management of irrigation induced soil salinity is essential for sustainable crop production in the NAP soils. The eﬀectiveness of mitigation
scenarios is reported in Mallants et al. (2019) and will not be discussed
here.

4.5. Sodicity development
The NAP soils are inherently sodic with a high Na content in the soil
solution and on the exchange complex, especially at deeper depths(Matheson and Lobban 1974). Even the virgin soils have been shown tohave SAR as high as 44 at lower depths (Stevens et al., 2003). However,
RCW containing SARw (SAR of water) of 8-12.2 can have a signiﬁcant
impact on an increase in the soil SAR. The data in the current study
showed that a rapid increase in the soil solution SAR occurred due tothe use of RCW for irrigation. Overall average increases in the proﬁleaveraged SAR values as a result of RCW irrigation in the CAL, HRB,
SOC, and DUG soils were equal to 17.4, 15.8, 13.3, and 13.8, respectively, and corresponding increase ESP values in response to RCW irrigation was 22.7, 21.6, 19.4 and 10.5%, respectively (Table 4).
It is well understood that high SAR of the soil solution can potentially modify the cation dynamics in the soil solution as well as on thesoil exchange complex. High SAR ultimately leads to the developmentof high ESP in the soils. However, there is no universally accepted relationship that can deﬁne the intricate dynamics between the soil solution and the soil cation exchange complex. Similarly, there is nouniversal understanding of the critical ESP value to categorize the sodic
soils. In Australia, a sodic soil is deﬁned as the soil, which has an ESP
larger than 6 (Northcote and Skene, 1972). However, the American
10Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

Fig. 5. Relationships between model-simulated sodium adsorption ratio (SAR) and exchangeable sodium percentage (ESP) for diﬀerent NAP soils.

such as harvested rainwater, to realize long-term sustainability of RCW
irrigation (Mallants et al., 2019). Stevens et al. (2004) suggested aleaching fraction of 20-50% as ideal for RCW water used in the NAP
region. Other studies (e.g., Kumar and Kookana, 2006) expressed similar concerns with winery wastewater, containing salinity and SAR
problems. Overall, the long-term use of recycled water for irrigation canpotentially increase soil salinity and sodicity hazards beyond thethreshold, thereby rendering the soil unﬁt for cultivation. Therefore,
adequate management options must be explored to use these waters
sustainably for crop production in the NAP region.
The results are broadly applicable to the two irrigation systems:
overhead sprinkler and drippers. Note that the modelling itself did notaccount for diﬀerences in irrigation method, as it was a one-dimensional model. A two-dimensional model would be required to incorporate any small-scale spatial eﬀects of diﬀerent irrigation methods
on water and salt distribution in soil (see e.g. Phogat et al. 2014). Extrapolation to other regions where RCW is currently used or may be
used in the future is possible, however care should be taken to adjust
the ﬁndings for local conditions of irrigation water quality, soil types,
climate, and crops.

the SAR thresholds (SARth) that would cause ESP to exceed its threshold
are as follows: SARth = 4 for calcareous soils, SARth = 3.5 for hard red
brown soils, SARth = 6 for sand over clay soils, and SARth = 3 for deep
uniform to gradational soils.
Rengasamy et al. (1984) suggested a common SAR > 3, irrespective of the soil type, associated with clay dispersion, crusting, and reduced porosity in similar soils. However, they found a strong 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 other cations(Mg and K) that induce the dispersive behavior in the soil, which is
critical for characterizing the structural stability of the soils. However,
we understand that the degree of dispersivity due to diﬀerent cations(e.g., Na+, K+, and Mg++) may not be a basic soil property, but may
depend on all inherent soil characteristics, which aﬀect the biogeochemical dynamics of the soil solution and soil exchange. However, ourstudy was not aimed at deriving any such critical thresholds for evaluating the dispersive behavior of diﬀerent soils. We suggest that further
studies are undertaken to thoroughly understand the critical relative
concentrations of various cations (i.e., SAR) in diﬀerent NAP soils that
could impact the structural stability, clay dispersion and a reduction in
the hydraulic conductivity of the soils. Such information can help devise soil/location-speciﬁc guidelines for reducing the harmful impactsof the use of RCW for irrigation.
One of the main concerns with SAR of irrigation water is its eﬀect on
modifying the soil cation exchange complex, resulting in high ESP,
which, in turn, has a dramatic impact on the structural stability and thehydraulic properties of soil. Excellent hydraulic conductivity needs tobe maintained to enable appropriate leaching requirements to movesalts below the root zone. Good soil structure also aids root development, improving plant health, and drought resistance. Based on themodeling results, it is suggested that the use of RCW for irrigation may
need frequent applications of soil amendments such as gypsum, organic
matter/compost, and/or combination of such products, such that soil
ESP can be managed to maintain values below the threshold(ESP < 6%) for sustainable crop production. Similarly, low concentrations of soluble salts in the soils need to be maintained byadopting appropriate leaching, preferably, with low-salinity water,

5. Conclusions
This study uses the multicomponent UNSATCHEM module of
HYDRUS-1D to evaluate the impact of the long-term (2018-2050) use of
RCW for irrigating wine grapes, almonds, pistachios, pasture, carrot,
onion, and potato crops in diﬀerent NAP soils (calcareous, hard red
brown, sand over clay and deep uniform to gradational). Simulated
data revealed that irrigation with recycled water can potentially increase the soil solution salinity (ECsw), sodium adsorption ratio (SAR),
and exchangeable sodium percentage (ESP) in the soil. The average
end-of-simulation ECsw in the soil proﬁle under diﬀerent crops in the10th to 90th %tile range varied from 2.9 to 10.6 dS/m. Usually, theaverage ECsw in the upper soil layers (< 30 cm) remained lower than
4 dS/m for almonds, wine grapes, pistachios, and pasture, while underannual horticulture (carrot, onion, potato), salinity may rise to between
4.87 and 9.5 dS/m due to upward movement of salts during the covercrop season. Average soil salinity at lower depths (> 30 cm) ranged
11Agricultural Water Management 237 (2020) 106167

V. Phogat, et al.

from 3.6 to 10.8 dS/m for all crops, including pasture, viticulture, andperennial horticulture.
Increased salinity in the soil reduced the potential yield of almondby 12- 20% in diﬀerent soils, with a higher yield loss in the hard red
brown soils, followed by the calcareous soils. However, no yield loss
was observed for wine grapes, perennial pastures, and pistachios, asthese are relatively salinity tolerant crops. However, annual horticultural crops (carrot, onion, potato) showed yield losses from 4 to 32%
due to increased salinity associated with RCW irrigation.
The use of RCW for irrigation also has a strong impact on the soil
solution and cation exchange dynamics, which increased the SAR in thesoils. After 32 years of irrigation, the simulated proﬁle average valuesof SAR were 17.4, 15.8, 15.5, and 16.3 in the calcareous, hard red
brown, sand over clay, and deep uniform to gradational soils, respectively. These values are higher than the threshold SAR reported in many
previous studies, which are associated with an increase in the ESP of thesoils. The model predicted proﬁle average ESP in the soil increased inabsolute values by 22.7, 21.6, 19.4, and 10.5 respectively, in the calcareous, hard red brown, sand over clay, and deep uniform to gradational soils. These values are much higher than the accepted ESP
thresholds (ESP > 6) for Australian soils. Thus, it is suggested thathigh SAR and ESP build-up in the soils as a result of RCW irrigation todiﬀerent crops could adversely impact the physical properties of soils
including clay dispersion, porosity and hydraulic conductivity, andstructural stability of the soil. Therefore, adequate management options
must be put in place such that adverse impacts of the use of recycledwater can be minimized, realizing a long-term sustainable crop production.

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6. Declaration of Competing Interest
The authors have no aﬃliation with any organization with a direct
or indirect ﬁnancial interest in the subject matter discussed in the
manuscript.
Acknowledgments
Authors acknowledge the ﬁnancial support provided by the Goyder
Institute for Water Research for this work; under the project“Sustainable Expansion of irrigated Agriculture and Horticulture in
Northern Adelaide Corridor (Project Number ED-17-01).”
Appendix A. Supplementary data
Supplementary material related to this article can be found, in the
online version, at doi:https://doi.org/10.1016/j.agwat.2020.106167.
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