Agricultural Water Management 180 (2017) 22–34

Contents lists available at ScienceDirect

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

Evaluation of crop coefﬁcients, water productivity, and water balancecomponents for wine grapes irrigated at different deﬁcit levels by a
sub-surface drip
V. Phogat a,∗ , M.A. Skewes a , M.G. McCarthy a , J.W. Cox a,b , J. Šimůnek c , P.R. Petrie a,daSouth Australian Research and Development Institute, GPO Box 397, Adelaide SA 5001, Australia
The University of Adelaide, PMB1 Glen Osmond SA 5064, AustraliacDepartment of Environmental Sciences, University of California, Riverside, CA 92521, United Statesd
The Australian Wine Research Institute, GPO Box 197, Adelaide SA 5001, Australiaba r t i c l e

i n f oArticle history:
Received 11 November 2015
Received in revised form
28 September 2016
Accepted 19 October 2016
Keywords:
Wine grapes
Sub-surface drip
HYDRUS-2D
Evapotranspiration
Crop coefﬁcients
Water productivity

a b s t r a c t
Accurate estimation of evapotranspiration (ET) and its partitioning into transpiration and evaporation
is fundamental for improving water management practices in water-limited environments and underdeﬁcit irrigation conditions. This investigation was conducted to estimate the water balance and ET components of subsurface drip (SDI) irrigated Chardonnay wine grapes for two seasons (2010–2011 and2011–2012) using a numerical model (HYDRUS-2D). Treatments involved the application of different
volumes [51% (I1 ), 64% (I2 ), 77% (I3 ), and 92% (I4 ) of normal application] of water for irrigation. A modiﬁed version of the FAO-56 dual crop coefﬁcient approach was used to generate daily transpiration andevaporation as inputs to the HYDRUS-2D model. The calibrated and validated model produced estimatesof actual evapotranspiration (ETCact ), actual transpiration (Tpact ), and actual evaporation (Esact ), and deep
percolation under varied irrigation applications. The model-simulated values were then used to estimate
actual crop coefﬁcients (Kcact and Kcbact ), and water productivity of wine grape under different deﬁcit
irrigation conditions.
Seasonal ETCact simulated by HYDRUS-2D for different treatments varied between 239 and 382 mm.
However, seasonal evaporation accounted for 44–59% of seasonal ETCact losses in different treatments. The
modelled daily transpiration rate in I4 treatment (Tp4act ) varied from 0.11–2.74 mm/day. Deep percolationaccounted for 35–40% of the total water applied by rainfall and irrigation. The mean value of actual
crop coefﬁcient (Kcact ) estimated by HYDRUS-2D simulated ETC act over the two seasons was 0.27, whichmatched with other investigations. Similarly, values of Kcbact for initial, mid and end stages were 0.13,
0.27 and 0.14, respectively. Monthly values of evaporation coefﬁcient (Ke ) ranged from 0.1 to 0.32, with amean value of 0.18. Water productivity with respect to ET losses (WPETC ) ranged from 5.9 to 6.2 kg/m3 ofwater use. However, water productivity for transpiration (WPTC ) almost doubled as compared to WPETC
in all treatments. The impact of deﬁcit irrigation on berry juice composition (Brix, pH and titratable
acidity) was lower than the inter-seasonal variability. These results can help develop better irrigation
management strategies for SDI irrigated wine grapes under water scarce conditions.
© 2016 Elsevier B.V. All rights reserved.

1. Introduction
Seasonal management of irrigation water is the most crucial
decision that farmers have to contemplate for sustainable andproﬁtable crop production, particularly in the arid and semiarid
regions of the world where water availability for irrigation is not∗ Corresponding author.
E-mail address: vinod.phogat@sa.gov.au (V. Phogat).
http://dx.doi.org/10.1016/j.agwat.2016.10.016
0378-3774/© 2016 Elsevier B.V. All rights reserved.

assured. The majority of high value horticultural orchards andvineyards in Australia are primarily dependent on irrigation. For
example, 90% of Australian vineyards rely on some assured irrigation arrangement (Australian Bureau of Statistics, 2012). The
allocation of water for irrigation in South Australia is highly inﬂuenced by the amount of seasonal rainfall on the eastern Australianhigh ranges and subsequent storage and ﬂow in the Murray Darling river system. During drought years (2006–2009) the water
allocation was severely reduced, to levels as low as 18% of normal allocations, which had serious repercussions on the sustainable

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

production and resilience of vineyards and orchards. Similar conditions with limited availability of irrigation water occur in otherarid and semi-arid regions of the world. As a consequence, judicious water use, long-term sustainability and increasing efﬁciency,
i.e., improving the yield to water consumption ratio, become major
priorities in viticulture.
Considerable research has been undertaken on various deﬁcit
irrigation techniques, such as regulated deﬁcit irrigation (e.g., Costa
et al., 2007; Romero et al., 2010, 2013; Santesteban et al., 2011;
Edwards and Clingeleffer, 2013; Acevedo-Opazo et al., 2010; Faciet al., 2014), sustained deﬁcit irrigation (e.g., Fereres and Soriano,
2007; Chalmers et al., 2010; Williams, 2010; Williams et al.,2010a,b), and partial root zone drying (e.g., Dry and Loveys, 1998;
McCarthy et al., 2000; Intrigliolo and Castel, 2009; Sadras, 2009;
Romero et al., 2015), in order to evaluate their impact on wateruse, yield, berry composition, and wine attributes. However, mostof these studies were driven by the need to control vine vigour andmaximize fruit quality rather than the need to improve the vineyard
water use efﬁciency (Edwards and Clingeleffer, 2011) and waterproductivity. Moreover, research results on water use with respect
to the application of deﬁcit irrigation techniques under ﬁeld conditions and the long-term impacts on production have been found to
be contradictory (Chaves et al., 2010; Sadras, 2009), which suggests that site speciﬁc factors played a major role in evaluating
the impact of deﬁcit irrigation in these studies. In addition, mostof these studies dealing with deﬁcit irrigation are based on conventional drip or sprinkler irrigation. However, sub-surface drip
irrigation (SDI) could have a different impact on water use, potential
water losses, and overall vine performance compared to conventional drip and other irrigation methods. A few comparative studies(Fandiño et al., 2012; Cancela et al., 2015) showed slightly higher
vine water uptake and crop coefﬁcients under SDI than under conventional drip. Hence, there is need to evaluate the water balancecomponents for wine grapes under SDI, and under varying volumesof irrigation application.
A wide range of methods and techniques have been used for estimating vineyard ET and water use, including lysimeter (Williams
and Ayars, 2005; Netzer et al., 2009), sap ﬂow (Lascano et al., 1992;
Yunusa et al., 1997, 2000, 2004; Ferreira et al., 2012), Bowen ratio(Yunusa et al., 2004; Zhang et al., 2011), surface energy balance(Castellví and Snyder, 2010; Moratiel and Martínez-Cob, 2012),
eddy covariance (Ortega-Farias et al., 2010; Rodríguez et al., 2010),
soil water balance (Singleton and Maudsley, 1996; Fooladmand
and Sepaskhah, 2009), and a combination of ﬁeld measurements(Trambouze et al., 1998; Cancela et al., 2012; Styles et al., 2015).
However, intensive ﬁeld measurements of these agro-hydrologicalﬂuxes require large investment in sensors, labour, and time. On theother hand, precise estimation of the dynamics of evaporation andtranspiration could help in judicious management of scarce water
resources in drip-irrigated high-value cropping systems (Yunusa
et al., 2004; Ortega-Farias et al., 2012). Besides evaporation andtranspiration, reliable understanding of the drainage fraction is
critical for better identiﬁcation of productive and unproductive
fractions of water for SDI, especially under sustained deﬁcit conditions.
Numerical models are ideally suited to evaluate water balancein drip irrigation systems, and to study the importance of and interaction among various water ﬂuxes under sparse vegetation andfor different supplementary irrigation conditions. HYDRUS-2D/3D(Šimůnek et al., 2008, 2016) is a widely used numerical model simulating water ﬂow in soils under cropped conditions. This model
has been previously used to study ET components, for example, foralmond (Phogat et al., 2012, 2013), maize (Ramos et al., 2012), citrus (Phogat et al., 2014), and other crops (Mei-Xian et al., 2013;
González et al., 2015). Recently, Kool et al. (2014a) has successfully
used this model for the estimation of evaporation losses from a23

commercial vineyard in combination with short term above canopy
measurements of climatic parameters.
The objectives of this study thus were to (a) calibrate and validate HYDRUS-2D under deﬁcit sub-surface drip irrigation of wine
grape, (b) to estimate water balance and actual ET components ofwine grape, and compare them under different SDI deﬁcit irrigation treatments, (c) to estimate the actual crop coefﬁcients of wine
grapes, and (d) to assess yield, wine quality parameters and waterproductivity of Chardonnay wine grape under SDI under different
deﬁcit irrigation treatments, and compare these to other publishedresearch.

2. Materials and methods
2.1. Experimental details
A ﬁeld experiment was conducted in the Markaranka vineyard(34.08◦ S and 139.87◦ E), located near Waikerie in South Australia.
The Chardonnay wine grapes (clone Bernard 95) on Ramsey rootstock were planted in November 2004 at a vine spacing of 2.5 m
and row spacing of 3.35 m, and were irrigated using subsurface
drip (SDI). Monitoring of the experimental site was initiated in
November 2010 and continued for two seasons. The soils at thesite are predominately light textured, ranging from sand to loamy
sand, with the sand content in the range of 84–91%, the clay content from 9 to 13%, and the silt content from 0 to 3%. The climate ischaracterized as dry, with warm to hot summers and mild winters. The total rainfall during the experimental period from 22
September 2010 to 30 June 2011 was 338 mm (Fig. 1), and from
7 September 2011 to 30 June 2012 was 236 mm. Unusually highprecipitations of 95.2 and 68.2 mm occurred on 8 December 2010
and 14 January 2011, respectively, during the ﬁrst season. Similar heavy summer rainfalls of 58.2 mm and 42 mm occurred on 17
December 2011 and 29 February 2012, respectively, during the second season. The grass reference ET (ET0 ) was calculated using a
modiﬁed Penman-Monteith equation, which incorporates a number of weather and energy balance parameters, as described in Allenet al. (1998). Estimated ET0 during 2010–11 and 2011–12 seasons
was 904 and 1055 mm, respectively (Fig. 1). The maximum daily
ET0 was estimated to be 8.4 mm on January 1, 2012. Mild frost
conditions occurred during the winter months. Weather data werecollected from an automated weather station located at Qualco,
4 km from the trial site.
The grapevine growth season at the ﬁeld site started with budburst in late August–late September. Full leaf cover was attained
before ﬂowering, which started in late November and/or early
December. The leaves remained active for 3 months till the end of
March. The fruit yield and yield attributes (number of bunches pervine, average bunch weight) were recorded for all the treatments
plots. Juice from a fresh ﬁfty berry sample was extracted usinga small hand-held press and centrifuged at 3000 rpm for 5 min.
A bench-top refractometer was used to determine Total Soluble
Solids (expressed as o Brix). A 10 mL sample of clear juice was used
for the determination of pH and Titratable acid using an automatedend-point titrator pH 8.2 with 1 M NaOH (Amerine, 1965).

2.2. Irrigation treatments and water content measurements
The irrigation system at the experimental site consisted of apressure compensated drip system (Toro Drip-In® Rootguard® ),with an emitters discharge rate of 1.6 L h−1 and a spacing of 40 cm.
The SDI drip lines were installed at a depth of 25 cm, 25 cm awayfrom the vine line. Water for irrigation was pumped directly from
the Murray River. Irrigation treatments were designed to test the24

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

Fig. 1. Rainfall, irrigation (for treatment I4 ), and reference evapotranspiration (ET0 ) at the experimental site from 22 September 2010 to 30 June 2012.

impact of different SDI volumes on ET components, yield and waterproductivity of Chardonnay wine grape.
Four irrigation treatments with different irrigation event
lengths were established, resulting in applications of about 51 (I1 ),
64 (I2 ), 77 (I3 ), and 92 (I4 ) % of the total amount of irrigation appliedby the grower to the rest of the Chardonnay vineyard. The irrigation
event length in I4 was similar to full irrigation applied by the grower
in the rest of the vineyard. However, different application rates atthe experimental area and in the reminder of the vineyard allowed
92% of full irrigation in I4 treatment. In other treatments (I1, I2, I3 ),timers were ﬁtted on the drip laterals to apply different deﬁcit irrigations. Each treatment consisted of nine vines irrigated using thesame lateral, and all treatments were replicated 5 times. A similar
irrigation application rate, timing and schedule was followed for
all replicates of different treatments. Total irrigation water applied
during 2010–11 amounted to 137, 171, 202, and 240 mm in I1 , I2 ,I3 and I4 treatments, respectively. Corresponding values for different treatments during 2011–12 were 155, 195, 232, and 278 mm,
respectively. The irrigation depths and timing of irrigations for I4
are shown in Fig. 1.
The irrigation in the entire vineyard, including the experimental area, was scheduled by the vineyard manager based on
biweekly soil moisture monitoring using a portable capacitancesensor (Diviner, 2000, Sentek, Stepney, South Australia). Irrigation
was applied when the soil water content reached the reﬁll point,i.e., the soil water content for which the rate of water extraction
would start to diminish. This point occurred when total available
water in the soil proﬁle (100 cm) ranged between 30 and 35 mm
which was estimated using the procedure outlined in McMullen(2000). A regulated deﬁcit irrigation strategy was not applied atany stage of the season. The aggregate irrigation application to thereminder of the vineyard was 260 mm and 302 mm for the 2010–11
and 2011–12 seasons, respectively.
Water content in the soils at the experimental site was monitored at soil depths of 15, 30, 60, and 90 cm. Theta probes were
installed in I1 and I2 treatments, approximately 10 cm away fromthe drip line. The Theta probes were manufactured by Delta-T
Devices Ltd, and were equipped with TBug loggers manufactured
by Measurement Engineering Australia. These probes were calibrated for the experimental site by the gravimetric method. The
probes collected water content data at 15-min intervals. However,the last reading of water content for each day was compared withmodelling output predicted at the same time at the four monitored
depths.

In treatments I3 and I4 , gypsum block sensors (GB-Lite) were
installed for measuring the spatial and temporal water tension
distribution in the soil. These sensors, also known as Watermark sensors, were manufactured by Irrometer and distributed by
Measurement Engineering Australia. The working range for thesesensors is from 0 to 200 kPa. Separate sensors were installed foreach depth (15, 30, 60 and 90 cm) with a 10 cm horizontal offset
along the drip line between adjacent sensors at different depths.
These sensors are pre-calibrated and do not require any calibration speciﬁc to ﬁeld conditions. The GB-Lite’s were ﬁtted with GBugloggers, and logged the soil tension at an interval of 2 h. The soil tension values were converted to water content using the -h relation
estimated for each depth for the experimental site (Phogat et al.,2016). The water content values thus estimated at different depths
and times were compared with the corresponding water content
values simulated by HYDRUS-2D.
2.3. Numerical modelling
HYDRUS-2D (Šimůnek et al., 2008) software simulates twodimensional variably-saturated water ﬂow, heat movement, andtransport of solutes involved in sequential ﬁrst-order decay reactions. The governing two-dimensional water ﬂow equation is
described as follows:

∂
∂
=
∂t
∂x



K(h)

∂h
∂x



+

∂
∂z





K(h)

∂h
+ K(h)∂z− S(h, x, z, t)(1)

where  is the soil water content (L3 L−3 ); t is time (T); h is thesoil water pressure head (L); x is the horizontal coordinate; z is
the vertical coordinate (positive upwards); K(h) is the hydraulicconductivity (L T−1 ), and S (h,x,z,t) is the sink term accounting forwater uptake by plant roots (L3 L−3 T−1 ).
Root water extraction S(h,x,z,t) from the soil is computed according to the Feddes macroscopic approach (Feddes et al., 1978). In thismethod, the potential transpiration rate, Tp , is distributed over theroot zone using the normalized root density distribution function,
ˇ(x,z), and multiplied by the dimensionless water stress response
function, ␣1 (h,x,z) as:
S(h, x, z, t) = ˛1 (h, x, z) ˇ (x, z) Tp (t) ∗ Lp

(2)

where Lp is the surface length associated with transpiration (i.e.
the lateral extent of the area explored by the roots to access water
for transpiration). This model assigns plant root water uptake rates

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

25

according to potential transpiration, the spatial root distribution,and the local soil water pressure head, h, at any point in the rootzone. Eq. (2) deﬁnes the manner, in which potential transpiration(Tp ) is reduced below its potential value when the soil is no longer
capable of supplying the water required by the plant under theprevailing climatic conditions. In the absence of solute stress, thereduction of root water uptake due to the water stress, ␣1 (h,x,z), is
described as:

˛1 (h) =⎧0, h > h1 orh ≤ h4⎪⎪⎪⎪
h − h1⎪⎪, h2 < h ≤ h1⎨
h2 − h1(3)⎪
1, h3 < h ≤ h2⎪⎪⎪⎪
⎪ h − h4⎩,h < h ≤ h4h3 − h43

where h1 , h2 , h3 , and h4 are the threshold model parameters. Water
uptake is at its potential rate when the pressure head is between
h2 and h3 , decreases linearly when h > h2 or h < h3 , and becomeszero when h < h4 or h > h1 . These critical values of pressure heads
for wine grape were taken from Rallo et al. (2012).
The spatial root distribution is deﬁned in HYDRUS-2D according
to Vrugt et al. (2001):ˇ(x, z) = 1 −

z
zm



1−

x
−
e
xm

pz ∗
px ∗
zm |z −z|+ xm |x −x|

Fig. 2. Measured and ﬁtted (van Genuchten, 1980) relationships between volumetric water content () and pressure head (h); and a predicted relationship between
hydraulic conductivity (K) and pressure head (h) for the 15–30 cm soil layer.

(4)

where xm and zm are the maximum width and depth of the root zone(cm), respectively, z* and x* describe the location of the maximum
root water uptake, from the soil surface in the vertical direction (z*)
and from the tree position in the horizontal direction (x*), and px
and pz are empirical coefﬁcients.
Location of maximum root uptake was assumed to be concentrated mainly near the drip emitter where water and nutrients wereapplied, although not right at the dripper (x* = 30 cm, z* = 25 cm).
Soar and Loveys (2007) reported the maximum root density ofdrip-irrigated vines between soil depths of 25 and 50 cm. Similarly,
Styles et al. (2015) reported the maximum root mass density at the
30 cm depth in a drip-irrigated vineyard in South Australia. However, these studies reported variable maximum depth distribution(60–100 cm) of roots in the soil. These parameters (xm , zm , z*, x*,px and pz ) are highly variable in space and time, difﬁcult to measure in the soil, directly inﬂuence the water uptake by plants andconsequently water content distribution in the soil proﬁle. Hence,parameters except maximum rooting depth (zm = 100 cm) and maximum horizontal extension (xm = 150 cm) were optimized duringcalibration by trial and error and by comparing the measured andsimulated water content distribution. No signiﬁcant volume ofroots was found outside of the speciﬁed area in ﬁeld observations.
2.4. Input parameters for HYDRUS
Applying HYDRUS to ﬁeld conditions requires numerous soil,
water, crop, and weather parameters, which are discussed in thefollowing sections.

2.4.1. Soil hydraulic properties
The soil hydraulic properties are modelled using the water
retention and hydraulic conductivity functions described by thevan Genuchten-Mualem constitutive relationship (van Genuchten,1980):
s − r␪(h) = r +␪(h) = s

1 + (˛h)

n m

h<0

(5)
(6)

h<0



K(h) = Ks SeI 1 − 1 − Se1/m

m 2

(7)

where  s is the saturated water content (L3 L−3 ),  r is the residual
water content (L3 L−3 ), Ks is the saturated hydraulic conductivity(L T−1 ), l is a shape factor, and m, ˛, and n are empirical parameters
where m = 1 − 1/n. Se is the relative saturation (dimensionless) andis deﬁned asSe = − rs − r(8)

Water content and pressure potential relations for undisturbed
soil core samples from different depths (0–15, 15–30, 30–60 and
60–100 cm) at 4 locations were measured in a pressure plate apparatus. Values for each depth were determined as the average ofthe 4 sites at that depth. The van Genuchten-Mualem constitutive relationship (van Genuchten, 1980) was ﬁtted to measured
soil water retention data and used to predict the unsaturated
hydraulic conductivity function. Resulting -h and K-h relationships are depicted in Fig. 2 for the 15–30 cm soil depth. The

Table 1
Soil hydraulic parameters for the van Genuchten-Mualem function (van Genuchten, 1980) and the bulk density for different soil layers at the experimental site.
Depths (cm)Soil texture3−3r (cm cm
0–15
15–30
30–60
60–100Sand
Loamy sandLoamy sandLoamy sandBulk density (g cm−3 )Soil hydraulic parameters

0.04
0.04
0.05
0.05

)

3

−3

s (cm cm
0.41
0.39
0.38
0.37

)

−1

␣ (cm
0.03
0.03
0.04
0.04

)

−1

n

Ks (cm d

2.09
2.29
1.70
1.62

561.60216.00259.20172.80

)

l
0.52
0.56
0.41
0.38

1.50
1.57
1.41
1.44

26

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

Fig. 3. Daily transpiration (Tp ) and evaporation (Es ) for wine grapes estimated by modiﬁed FAO-56 for I4 irrigation treatment.

saturated hydraulic conductivity (Ks) and bulk density were measured on undisturbed cores following standard procedures (Klute,1986). The soil hydraulic parameters utilized in HYDRUS-2D simulations are given in Table 1 and also described in section 2.3 andTable 1 of Phogat et al. (2016).
2.4.2. Estimation of evaporation (Es ) and − transpiration (Tp )HYDRUS-2D requires daily estimates of transpiration (Tp ) andevaporation (Es ) as input parameters so that it can simulate corresponding actual values of ET components (ETCact , Tpact , and Esact )for a given soil, climate, crop, and irrigation conditions. In thisstudy, the daily inputs of Tp and Es for HYDRUS-2D were estimatedusing a modiﬁed FAO-56 dual crop coefﬁcient approach (Phogat
et al., 2016), in which the evaporation coefﬁcient (Ke ) for SDI was
adjusted by adopting the new fraction representing the amount ofirrigation depth that moves to the evaporation zone (fI,Es ). Details
about the estimation of daily Es and Tp involving soil, water, climate, and vineyard crop parameters for two seasons (2010–11 and2011–12), are described in section 2.5 of the companion manuscript(Phogat et al., 2016). The daily values of Tp and Es for two seasons(2010–11 and 2011–12) for I4 treatment are shown in Fig. 3. Similarly, the values of these parameters in other treatments (I1 , I2
and I3 ) were estimated by varying the amount of irrigation andcrop related parameters. A similar strategy has been adopted by
González et al. (2015) for modelling soil water dynamics for fulland deﬁcit irrigated maize using HYDRUS-1D.
2.4.3. Initial and boundary conditions
The ﬂow domain and prescribed boundary conditions are illustrated in Fig. 4. The simulated domain was 100 cm deep and 335 cm
wide (the row spacing at the ﬁeld site), perpendicular to a vine row.
The transport domain was discretized into 16,502 elements with avery ﬁne grid around the dripper (0.3 cm) and gradually increasing element spacing farther from the dripper (up to 3 cm). The drip
tubing was depicted as a 1 cm radius circle 25 cm below the surface. SDI irrigation was simulated assuming an inﬁnite line source,which was shown previously (Skaggs et al., 2004; Hanson et al.,2008; Phogat et al., 2012) to be a good representation of the drip
irrigation system.
A time-variable ﬂux boundary condition was speciﬁed aroundthe drip tubing. During water applications, a constant water ﬂux
was imposed on the variable ﬂux boundary equal to the water application rate. Boundary water ﬂux was calculated depending on theemitter discharge rate and a drip line surface area, as described in
Phogat et al. (2012).
An atmosphere boundary condition was speciﬁed at the soil surface. No ﬂow boundary conditions were prescribed at vertical sides

Fig. 4. A schematic view of the simulated domain, displaying boundary conditions,spatial discretization, and the positions of the drip emitter and the theta probe.

and a free drainage boundary condition was speciﬁed at the bottom
boundary. Daily evaporation, Es , transpiration, Tp , (which were both
estimated using the procedure discussed in the previous section)and rainfall were used to deﬁne the atmospheric boundary condition. The initial soil water contents were based on values measured
by the Theta probes (I1 and I2 treatments) and gypsum bocks (I3 andI4 treatments).
Utilizing all input parameters described above, HYDRUS-2D
was calibrated against measured water contents (Theta probe) intreatment I1 during the ﬁrst season (2010–11) by adjusting root
water uptake parameters values as described in section 2.3. Since
soil hydraulic parameters were estimated directly from measured
retention and conductivity values, they were not optimized duringcalibration. Other studies (e.g. Gonçalves et al., 2006) followed asimilar strategy relying on independently measured soil hydraulicparameters. The calibrated model was validated using measured
water contents for treatments I2 (Theta probe), I3 and I4 (gypsum
blocks) during both seasons (2010–11 and 2011–12) using similar
domain characteristics.
2.5. Estimation of crop coefﬁcients and water productivity
Daily values of actual evapotranspiration (ETCact ), transpiration(Tpact ), and evaporation (Esact ) produced by HYDRUS-2D for two
seasons were used to compute the monthly actual crop coefﬁcients(Kcact ), basal crop coefﬁcients (Kcbact ), and evaporation coefﬁcient(Ke ) of wine grape under different deﬁcit irrigation treatments.
Water productivity of grapevine for actual ET losses (WPETC ) andwater productivity for actual transpiration (WPTC ) were estimatedV. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

27

using the average seasonal ETCact and Tpact values, respectively,
obtained from HYDRUS-2D simulations for two seasons, andmeasured average yield data for different treatments. Common
equations for estimating WPETC and WPTC in terms of fruit yield(Perry et al., 2009; Molden et al., 2010) for all treatments are givenas:Y
ETCact

(9)

Y
Tpact

(10)

WPETC =WPTC =

Where, Y is the average fruit yield (kg/ha), and ETCact and Tpact are
the average seasonal actual evapotranspiration (m3 of water/ha)
and actual transpiration (m3 of water/ha), respectively estimatedby HYDRUS-2D in different treatments.
2.6. Statistical analysis
The modelling output during calibration and validation period
was tested by estimating 3 statistical parameters i.e. mean error(ME), mean absolute errors (MAE) and root mean square error(RMSE). These errors were calculated from the measured and simulated water content for both seasons (2010–11 and 2011–12) andat all depths (15, 30, 60 and 90 cm). The following equations were
employed to estimate ME, MAE and RMSE by comparing measured(M) and corresponding HYDRUS-2D simulated (S) values of watercontents as follows:1(Mi − Si )
N
N

ME =

(11)

i=1

1
|Mi − Si |
N
N

MAE =

(12)
Fig. 5. Comparison of measured and simulated water content during calibration (I1treatment, 2010–11) and validation (I1 treatment, 2011–12) at different soil depths.

i=1


 N
1
2
RMSE = (Mi − Si )

(13)

N

i=1

Here, N is the number of comparisons.
3. Results and discussion
3.1. Comparison of measured and simulated water contents
Water contents simulated by HYDRUS-2D are compared withtheta probe measured values during calibration (I1 treatment,
2010–11) and validation (I1 treatment, 2011–12) at different
depths (15, 30, 60 and 90 cm) in Fig. 5. A similar comparison forvalidation in I2 treatment for both seasons is presented in Fig. 6.
During calibration a marginal underestimation occurred at 15, 60

and 90 cm during the initial stage, which diminished as the season progressed, while at 30 cm there was good agreement. Close
agreement continued thereafter at all locations, except for a small
underestimation at 15 and 60 cm. However unusually high values ofmeasured water content at 90 cm persisted during the second halfof December 2010 after 100 mm of rainfall from 6–8th December.
This condition might have occurred due to local restricted drainage
which was not captured by the modelling simulation. A similar
mismatch was also observed at an early stage of growth during
validation (I1 , 2011–12). However, it did not follow an excessive
rain event. At other depths water content values showed better
matching, except for sporadic underestimation at 15 cm duringinitial and late stages, and a small over-estimation at 60 cm during the latter half of the crop season. Despite these deviations,

Table 2Range and mean of weekly estimated statistical error parameters (RMSE, MAE and ME) in the measured and simulated water contents during 2010–11 and 2011–12 indifferent treatments.
Irrigation treatment

Year

RMSE

MAE

ME

Range

Mean

Range

Mean

Range

Mean

I1

2010–11
2011–12

0.01–0.07
0.01–0.06

0.03
0.03

0.01–0.06
0.01–0.05

0.03
0.02

(−)0.05–0.04(−)0.03–0.04

0.01
0.01

I2

2010–11
2011–12

0.01–0.05
0.01–0.04

0.03
0.03

0.01–0.05
0.01–0.04

0.02
0.02

(−)0.04–0.04(−)0.04–0.02

0.00
0.00

I3

2010–11
2011–12

0.02–0.10
0.03–0.09

0.05
0.05

0.02–0.09
0.03–0.08

0.04
0.05

(−)0.09–0.01(−)0.08–0.02(−)0.04(−)0.03

I4

2010–11
2011–12

0.03–0.11
0.03–0.09

0.06
0.06

0.03–0.10
0.02–0.09

0.05
0.05

(−)0.10–0.02(−)0.09–(−)0.02(−)0.05(−)0.04

28

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

Fig. 6. Comparison of measured and simulated water content during validation (I2treatment, 2010–11 and 2011–12) at different soil depths.

the model has showed fairly good agreement with measured values, as substantiated by the statistical error parameters. The extent
of deviation between simulated water content and gypsum blockmeasured values in I3 and I4 was marginally higher (not shown),
and is statistically compared and discussed below.
The range and mean values of statistical error parameters (RMSE,
MAE and ME) estimated on a weekly basis for all irrigation treatments (I1 , I2 , I3 and I4 ) during 2010–11 and 2011–12 season are
shown in Table 2. Root mean square error (RMSE) values of weekly
water content during calibration (I1 treatment during 2010–11
season) ranged from 0.01–0.07 cm3 cm−3 , with a mean value of0.03 cm3 cm−3 , while, the weekly MAE and ME values during calibration varied from 0.01–0.06 cm3 cm−3 and −0.05–0.04 cm3 cm−3 ,respectively. On the other hand, the error parameters at different
depths (15, 30, 60 and 90 cm) during calibration varied within an
acceptable range (RMSE ranged from 0.02–0.04 cm3 cm−3 , ME varied from −0.01 to 0.02 cm3 cm−3 and MAE showed much lower
range of 0.02–0.03 cm3 cm−3 ) (Table 3). However, the middle
depths of the rootzone (30 and 60 cm) showed slightly lower error
parameters as compared to upper and lower depths.
Similarly, weekly RMSE, MAE and ME during validation (I1during 2011–12) ranged from 0.01 to 0.06, 0.01–0.05 and−0.03–0.04 cm3 cm−3 , respectively (Table 2). Subsequent validation in I2 treatment during both seasons (2010–11 and 2011–12)
showed similar temporal and spatial variation in error estimates(RMSE, MAE and ME) as obtained in I1 during validation (2011–12).
Crevoisier et al. (2008) reported RMSE values between 0.02 and0.06 cm3 cm−3 during validation at various depths, with higher values at the soil surface. Similarly, Deb et al. (2011) reported RMSE
values similar to our study at various depths for water distribution
simulation under a pecan tree. However, Han et al. (2015) recorded
higher RMSE during calibration than validation for cotton.
Comparison between HYDRUS-2D simulated and gypsum blockmeasured values in I3 and I4 treatments showed marginally higher
values of statistical error parameters (Table 2). The weekly esti-

Table 3
Statistical error parameters (RMSE, MAE and ME) in the measured and simulated water contents at different soil depths in different treatments during 2010–11 and 2011–12.
Error parameters(a) I1 Treatment
RMSE
MAE
ME
(b) I2 Treatment
RMSE
MAE
ME
(c) I3 Treatment
RMSE
MAE
ME
(d) I4 Treatment
RMSE
MAE
ME

Year

Soil depth (cm)
15

30

60

90

2010–11
2011–122010–11
2011–122010–11
2011–12

0.03
0.03
0.03
0.02
0.02
0.02

0.02
0.02
0.02
0.01
0.00
0.00

0.03
0.03
0.03
0.02
−0.01
−0.02

0.04
0.04
0.03
0.02
0.02
0.02

2010–11
2011–122010–11
2011–122010–11
2011–12

0.04
0.03
0.03
0.02
0.02
0.02

0.03
0.02
0.02
0.02
−0.02
−0.02

0.03
0.02
0.03
0.02
0.01
0.01

0.02
0.03
0.02
0.03
0.00
−0.02

2010–11
2011–122010–11
2011–122010–11
2011–12

0.03
0.04
0.03
0.03
−0.01
−0.02

0.05
0.06
0.05
0.05
−0.05
−0.05

0.05
0.05
0.04
0.05
−0.04
−0.05

0.04
0.06
0.04
0.06
−0.04
−0.06

2010–11
2011–122010–11
2011–122010–11
2011–12

0.04
0.04
0.03
0.03
−0.02
−0.02

0.07
0.07
0.06
0.07
−0.06
−0.07

0.05
0.05
0.05
0.05
−0.05
−0.05

0.05
0.06
0.05
0.06
−0.05
−0.06

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

29

Table 4
Seasonal actual evapotranspiration (ETCact ), actual transpiration (Tpact ), and actual evaporation (Esact ) for wine grape under different irrigation treatments estimated by
HYDRUS-2D model.
ETCact (mm)2010–11

2011–12

Tpact (mm)2010–11

2011–12

Esact (mm)2010–11

2011–12ETC1act
ETC2act
ETC3act
ETC4act

289.7
324.9
355.5
382.3

239.2
273.7
305.6
334.1

Tp1actTp2actTp3actTp4act

134.4
162.6
191.2
213.1

98.1
121.0
144.7
164.2

Es1actEs2actEs3actEs4act

155.3
162.3
164.3
169.2

141.1
152.7
160.9
169.9

mated RMSE, MAE and ME values in these treatments varied
from 0.02 to 0.11, 0.02–0.10, −0.10–0.02 cm3 cm−3 , respectively(Table 2). However, the error estimates at different depths (Table 3)
in I3 and I4 treatments were well within the reported range e.g.
MAE in I3 and I4 varied from 0.03–0.05 and 0.03–0.07 cm3 cm−3 ,respectively (Table 3). Li et al. (2015) reported RMSE between
0.02–0.07 cm3 cm−3 for soil water dynamics in drip irrigatedtomato-corn intercropping, while, Mei-Xian et al. (2013) showed an
RMSE of 0.03–0.05 cm3 cm−3 for drip irrigated cotton. The ME valuesin I3 and I4 treatments ranged between −0.10 and 0.02 cm3 cm−3
which is similar to González et al. (2015) for water dynamics forfull and deﬁcit irrigated maize.
Despite a similar deviation to other studies in I3 and I4 treatments, a general overestimation of water content was observed,
which is shown by the consistent negative ME values at all
depths (Table 3). This can be attributed to various causes, possibly
including different root distribution characteristics due to higher
water application in I3 and I4 compared to treatments I1 and I2 ,which was assumed constant in all treatments. Modifying theseparameters and calibrating the model for each treatment would
probably improve HYDRUS-2D predictions. Other causes for deviations between measured and simulated water contents explainedby Ramos et al. (2012) relate to ﬁeld measurement, model input,
and model structural errors, and could also be assumed for thecurrent investigation. The measurement of soil water content by
sensors is also not free of errors, due to numerous assumptions
and inherent complexities in the soil (e.g., Rosenbaum et al., 2010;
Ganjegunte et al., 2012; Evett et al., 2012) which may contributea similar extent of deviations in the water contents as obtained bymodelling predictions. Finally, the varied water applications in different treatments could lead to dynamic spatial root growth, which
is not considered in the present modelling investigation.

3.2. Actual ET components simulated by HYDRUS-2D
Seasonal actual evapotranspiration ﬂuxes of wine grape estimated by HYDRUS-2D (ETCact ) for different irrigation treatments
during 2010–11 and 2011–12 seasons are shown in Table 4. Total
seasonal ETCact in the I4 treatment (92% irrigation) varied from
338 to 382 mm over two seasons, which is between 32 and 42%
of ET0 . Williams (2010) showed similar ETC /ET0 relation for Cabernet Sauvignon planted at similar spacing. However, a reduction ofirrigation by 15, 28, and 41% in the I3 , I2 and I1 treatments reduced
ETCact by only 8, 16, and 26%, respectively. Seasonal ETCact of similar
magnitude as in I4 was observed by Cancela et al. (2015) for SDI
simulated with the SIMDualKc model under different climate, soil
and vine settings. Yunusa et al. (1997) also reported similar ET as in
I4 for own-rooted and grafted Sultana grapevine under surface dripin Australian conditions. However, Williams et al. (2010a) reported
much higher ETC (574–829 mm) for lysimeter-planted Thompson
seedless grape vine under surface drip, with more than double theamount of irrigation applied compared to I4 in our study.
Simulated average daily ETCact rates over two seasons were 1.11,
1.26, 1.39, and 1.50 mm/day in I1 , I2 , I3 , and I4 treatments, respectively. Cancela et al. (2015) reported slightly higher daily ETCact(1.7–1.9 mm/day) under SDI than I4 in our study. However, theFig. 7. Simulated daily actual transpiration rates (mm/day) of the Chardonnay wine
grape during 2010–11.

average grape vine ET rate from nine international ﬁeld experiments is 3 mm/day (Teixeira et al., 2007), which represents varied
rootstocks and mostly surface irrigation conditions. Other studies (e.g., Yunusa et al., 1997; Trambouze and Voltz, 2001; Fandiñoet al., 2012; Shapland et al., 2012) showed average daily ETC varying
between 2 and 2.2 mm/day. However, average daily ETC rates similar to our study (in I4 treatment) have been reported by Zhang et al.
(2010) and Zhang et al. (2011) under surface drip irrigation. Thesestudies show that the site speciﬁc variability in plant water uptake
reﬂects numerous climate, soil, irrigation, and plant parameters,
which have tremendous impact on water uptake patterns of wine
grape. The average seasonal ETCact rate in our study had a strong
linear correlation (y = 0.003x + 0.475, R2 = 0.99) with the amount ofirrigation applied in different treatments.
Simulated annual actual transpiration (Tpact ) ranged from134.4–213.1 mm during 2010–11 while the corresponding values during 2011–12 were only 98.1–164.2 mm (Table 4). The
Tpact /ETCact ratio ranged from 0.46–0.56 during 2010–11 and from
0.41–0.49 during 2011–12 for different treatments, which compares well with Cancela et al. (2015) for SDI and Yunusa et al.
(1997) for surface drip irrigation of Sultana grapevine. The modelled daily transpiration rate in I4 treatment (Tp4act ) varied from
0.11–2.74 mm/day (Fig. 7). Peak values of Tp4act were observed during the mid-growth period (December–January) while low values
were observed during initial and late stages of the vine growth.
Similar seasonal variation in daily transpiration was also observedfor other treatments (not shown).
Simulated seasonal actual evaporation (Es act ) ranged from155.3–169.2 mm during 2010–11 and from 141.1–169.9 mm during 2011–12 (Table 4) in different treatments, indicating a relatively
low effect of irrigation treatment. However, seasonal evaporation accounted for 44–59% of seasonal ETCact losses in different
treatments. For sparse vegetation such as grape vine, evaporation
may constitute a large fraction of ET due to the considerable area
exposed to the atmosphere (Kool et al., 2014b). It is worth notingthat differences between seasonal values of evaporation in various
treatments were low during both years. The seasonal evaporationin I4 increased by 14.1 and 18–8 mm only as compared to I1 during

30

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

Fig. 8. Simulated seasonal deep water percolation for wine grapes under different
irrigation treatments during 2010–11 and 2011–12.

Fig. 9. Monthly values of actual single crop coefﬁcients (Kcact ) and actual basal crop
coefﬁcients (Kcbact ) for wine grapes, estimated from HYDRUD-2D simulated actual
ETc (ETcact ) and actual Tp (Tpact ).

2010–11 and 2011–12 season, respectively. This suggests that theeffect of deﬁcit irrigations on the evaporation losses under SDI irrigation is relatively minor. Although, evaporation losses of similar
magnitude as obtained in I4 have been observed for vineyards undersurface drip in this area with similar vine spacing (Yunusa et al.,
1997; Styles et al., 2015) but under different climatic conditions.
On the other hand, Fandiño et al. (2012) reported that evaporation
was only 8–15% of ET, while Cancela et al. (2015) observed this fraction to be 27% under SDI. Both studies considered that the wetted
fraction of the surface (fw ) was only 0.01, compared to 0.22 in ourstudy. Additionally, active ground cover in these studies probably
reduced evaporation losses from the soil surface.
HYDRUS-2D simulated annual deep percolation below the 1 msoil depth ranged from 185.1 to 198.2 mm during 2010–11, and158.5–185.9 mm during 2011–12, in different treatments (Fig. 8).
Similar seasonal percolation was recorded in all the treatments,
except I4 where 7 and 13% higher percolation was estimated during
2010–11 and 2011–12 seasons, respectively, as compared to themean percolation in other treatments. This suggests that most of
the leaching losses were contributed by rainfall, and irrigation had
little impact on deep percolation.
3.3. Crop coefﬁcients
Daily values of ETCact and Tpact produced by HYDRUS-2D for two
seasons were used to compute the monthly actual crop coefﬁcients(Kcact ) and actual basal crop coefﬁcients (Kcbact ) of wine grape for I4
treatment (Fig. 9), which represents near to full irrigation. However,the crop conditions may not represent the FAO deﬁnition of wellwatered, as stress is routinely applied in wine grapes to manage

canopy growth and to enhance the fruit quality suitable for betterwine. This is reﬂected in the low volumes of irrigation applied tothe site relative to historical district practices.
The Kc and Kcb coefﬁcients were computed from the simulated
actual crop water use values, hence they are represented as Kcact (Kcactual) and Kcbact (Kcb actual), which are different from standard
potential values (Pereira et al., 2015). The comparison of Kcact,ini(0.37) and Kcbact,ini (0.13) values (average of August and September)showed a much higher contribution of evaporation during thisperiod, which corresponds to the bud burst and initial growth stage.
The maximum Kcact (0.64) and Kcbact (0.37) were recorded in themonth of November which coincides with the maximum growthstage and a large leaf area (Williams et al., 2003). The mean Kcact
value over the two seasons was 0.37, which was slightly higher thanreported by Yunusa et al. (1997) in this region. Similarly, Cancela
et al. (2015) reported average Kcact for SDI irrigated grapevine equal
to 0.45.
Values of Kcbact for initial (August–September), mid(October–January) and end (February–April) stages were 0.13,
0.27 and 0.14, respectively. Cancela et al. (2012) reported similar
Kcbactmid (0.25) for vine under SDI, while other studies (Intrigliolo
and Castel, 2009; Zhang et al., 2011) reported much higher Kcbactmid(0.47–0.5) under surface drip irrigation. Similarly, our Kcbactend was
lower than the values reported by the above studies. However,Fandiño et al. (2012) showed matching Kcbactend values. Similarly,
the Kcact and Kcbact values in our study are much less than thetabulated values reported by Allen et al. (1998) and Allen andPereira (2009) for wine grape under standard and stress conditions.
Under high stress conditions, Allen and Pereira (2009) suggested
applying a stress coefﬁcient (Ks ) with values ranging from 0.5 to
1.0. However, in our study simulated Ks varied from 0.1 to 1.0,
indicating a far greater level of stress at this trial site. Many otherstudies (Yunusa et al., 1997; Williams and Ayars, 2005; McClymont
et al., 2009; Netzer et al., 2009; Edwards and Clingeleffer, 2011)
have shown that ET0 and canopy cover explain a large degreeof variation in grapevine transpiration. Poblete-Echeverría andOotega-Farias (2013) reported 11 and 19% lower values of Kcact
and Kcbact than FAO-56 for grapevines.
Lower Kcact values than those given in FAO-56 have beenreported for other crops, e.g., Er-Raki et al. (2009) found Kcact values
measured by eddy covariance to be 20% less than those reported in
the FAO-56 for an irrigated citrus orchard under drip. Similarly,
Dragoni et al. (2004) showed a signiﬁcant overestimation (over15%) of the basal crop coefﬁcients by the FAO-56 method compared to measurements (sap ﬂow). In contrast, Benli et al. (2006)
and Paço et al. (2006) reported basal crop coefﬁcients that werehigher than the tabulated ﬁgures. Such deviations in crop coefﬁcients from the generic values are bound to occur as a result ofvariable crop conditions due to insufﬁcient or non-uniform irrigation, variable crop density and canopy cover, soil salinity and/or
agronomic management (Pôças et al., 2015).
Monthly Ke values ranged from 0.1 to 0.32, with a seasonal meanof 0.18. Higher Ke values were recorded during the early half of thevine growth, due to a higher rainfall, in comparison to the latter halfof the vine season. Netzer et al. (2009) reported, based on drainage
lysimeter measurements with 80% canopy cover that the average
seasonal Ke for a drip irrigated vineyard remained below 0.15. Similarly, Yunusa et al. (2004) reported a low Ke value of 0.12 frommicro-lysimeter measurements in a drip irrigated vineyard. On theother hand, a high Ke (0.46) value was reported by Lascano et al.
(1992) for a ﬂood irrigated vineyard. A high Ke value of 0.55 was
also reported by Stevens (2002) for a micro-jet wetted soil surface. This suggests that the proportion of the wetted surface areaby rainfall and irrigation highly inﬂuences the magnitude of evaporation losses. This also implies that under a sparsely vegetatedsurface cover such as grapevine irrigated with SDI system, rainfall

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

31

Table 5
Number of bunches/vine, average bunch weight, grape yield and water productivity (WP) of wine grape in relation to ETCact (WPETC ) and Tpact (WPTC ) for different irrigation
treatments (ETC act and Tp act average of two seasons, from Table 4).
Treatments

Bunches/vine

Average bunch weight (g)Yield (kg/ha)

ETCact (m3 /ha)Tpact (m3 /ha)

WPETC (kg/m3 )WPTC (kg/m3 )

I1
I2
I3
I4

145a
163ab
172b
179b

89.70a95.36a91.93a101.40a15620a18570ab
18840ab
21100b

2640a
3000b
3310c
3580d

1160a
1420b
1680c
1890d

5.92a
6.19a
5.69a
5.89a

13.46a13.08a11.22a11.16aWithin columns, means followed by different letters are signiﬁcantly different according to the Fisher’s least signiﬁcant difference (LSD) test (P ≤ 0.05).

Table 6
Berry juice composition parameters (◦ Brix, pH and TA) under different irrigation treatments during 2010–11 and 2011–12.
Treatments

I1
I2
I3
I4
Mean

◦

Brix

pH

TA(g/L)2010–11

2011–12Mean

2010–11

2011–12Mean

2010–11

2011–12

Mean

18.8a
18.3a
18.2a
18.6a
18.5

18.7a
19.2a
19.5a
19.3a
19.2

18.8
18.8
18.9
19.0

3.56a
3.50ab
3.51ab
3.46b
3.51

3.30a
3.28a
3.30a
3.25a
3.28

3.43
3.39
3.41
3.36

5.51a
5.68a
5.77ab
6.04b
5.75

7.85a
7.91a
7.76a
8.78a
8.08

6.68
6.80
6.77
7.41

Within columns, means followed by different letters are signiﬁcantly different according to the Fisher’s least signiﬁcant difference (LSD) test (P ≤ 0.05).

seems to make a major contribution towards evaporation losses, as
irrigation under SDI wets less area on the soil surface exposed toevaporation as compared to other methods.
3.4. Yield, water productivity and berry juice quality of winegrape
The grape yield, bunches per vine, and average bunch weight
all increased with an increase in water application, except for the
average bunch weight in I3 (Table 5). The yield in I1 (51% irrigation) was reduced signiﬁcantly (by 26%) as compared to I4 (92%irrigation). Other studies in the same region of Australia (Stevens
et al., 2008, 2010) showed that vines grafted to a range of rootstocks displayed a similar tolerance to reductions in irrigation (35%
reduction in irrigation caused up to 30% reduction in yield). On theother hand, McCarthy et al. (1997) found that stopping supplementary irrigation reduced the yield of grafted Shiraz vines by 50% on
average over four seasons. Williams (2010) reported a 62% yield
reduction with 0.25 ETC irrigation as compared to 1.25 ETC . Hence,different rootstocks and scions respond differently to water stressand to other factors such as soil type.
Water productivity of wine grape was estimated by dividing
measured yield by the seasonal values of actual evapotranspiration(ETCact ) and transpiration (Tpact ) obtained from HYDRUS-2D simulations in respective treatments (Table 5). Interestingly, productivity
in relation to ET losses (WPETC ) varied non-signiﬁcantly in a narrow
range from 5.7 to 6.2 kg/m3 , i.e., 56.9–61.9 kg/mm, across irrigation
applications, with no clear pattern relative to irrigation treatment.
Water productivity relative to transpiration (WPTC ) was approximately double that relative to WPETC in all treatments, whichindicates high Es losses in this study. However, for this measure,there was a clear relationship between a reduced water applicationand increased water productivity, with the most severely stressed
treatment (I1 ) returning WPTC values 20.6% higher than those forthe well-watered treatment (I4 ). This indicates that the amount oftranspiration is directly linked to water productivity, whereas theinclusion of evaporation into the equation can mask this effect.
Stevens et al. (2010) reported that reduced irrigation did notaffect the irrigation water use index, whereas other investigations (Fereres and Soriano, 2007; Williams et al., 2010b) reported
that deﬁcit irrigation does increase water use efﬁciency for woody
perennial crops. However, these studies used different measuresof water use for estimating water use efﬁciency. Comparable

grapevine water productivities against ETC were recorded in otherstudies for similar amounts of water application (Williams et al.,2010b) under SDI (Williams, 2010). However, low water productivity (1.8 kg/m3 ) of grapevine under basin irrigation has beenreported (Atroosh et al., 2013), indicating large undesirable water
loss under such systems.
The values of berry juice quality parameters (◦ Brix, pH and
Titratable acidity) for different treatments during 2010–11 and2011–12 are shown in Table 6. The mean pH decreased and titratable acidity (TA) increased with increase in the amount of irrigation
except I3 treatment. The mean pH of berry juice in I1 treatment
increased by 6% and mean TA decreased by 10% as compared to I4
treatment. However, there was almost no effect on soluble solids(◦ Brix). On the other hand, season to season variation among ◦ Brix,
pH and TA were higher than the variation due to irrigation application. There are conﬂicting reports on the impact of water deﬁcit onwine quality parameters in the literature. Many studies (Salon et al.,2005; Girona et al., 2009; Intrigliolo and Castel, 2010; Santesteban
et al., 2011) reported a decrease in the acidity due to increased levelof deﬁcit which is the additive result of a reduced synthesis of malic
and tartaric acids resulting from lower assimilation rates (Esteban
et al., 1999; De Souza et al., 2005; Salon et al., 2005). However,
Acevedo-Opazo et al. (2010) reported no impact of regulated deﬁcit
irrigation on acidity and pH, and signiﬁcant impact on soluble
solids. Reynolds et al. (2005) also reported similar impact on soluble
solids. Intrigliolo and Castel (2009) reported increased pH of mustas a result of irrigation. However, other studies (Roby et al., 2004;
Junquera et al., 2012) showed little effect of deﬁcit irrigation on theconcentration of soluble solids. Similarly, Edwards and Clingeleffer(2013) observed little impact of regulated deﬁcit irrigation (RDI) on
soluble solids and pH, but TA increased by 10–15% in well wateredvines which is similar to our study. Bellvert et al. (2016) recommended that RDI and deﬁcit irrigation (DI) strategies should not be
followed due to their negative effect on acidity and desirable aromas in sparkling base wines (cv Chardonnay). Cox (2010) illustrated
varied and diverse impact of deﬁcit irrigation on berry composition
in a range of rootstocks and scions suggesting a major role of the
genotypes. In spite of all of the above, the climatic conditions andother environmental factors during the growing season can also
inﬂuence the physiological and wine quality attributes explaining
the inter-annual and water deﬁcit variability as observed in thisinvestigation.

32

V. Phogat et al. / Agricultural Water Management 180 (2017) 22–34

4. Conclusions
Understanding the ET ﬂuxes and crop coefﬁcients of wine
grape under SDI has a signiﬁcant role in managing scarce water
resources and enhancing the water productivity and efﬁciency of
the irrigation system. This study investigated the partitioning of
evapotranspiration components of wine grapes under SDI with different volumes of irrigation (51, 64, 77 and 92% of full) using a
numerical model (HYDRUS-2D) and determined actual crop coefﬁcients, which were compared with the FAO-56 tabulated values,
and other published values. Values of actual evapotranspiration
components (ETCact and Tpact ) produced by the calibrated and validated model were used to estimate water productivity of wine
grapes under different irrigation treatments.
Total seasonal ETCact in the I4 treatment (92% irrigation) varied
from 338 to 382 mm over the two seasons. However, the reduction
in simulated ETC act in the 51% irrigation (I1 ) was only 26% as compared to I4 , which could mean enhanced root water uptake by wine
grape under deﬁcit conditions. A similar response was observed in
fruit yield across irrigation treatments over the two seasons, indicating wine grapes are relatively robust in coping with sustainedwater stress. Actual crop coefﬁcients (Kcact and Kcbact ) derived fromHYDRUS simulations were lower than the tabulated values from
Allen et al. (1998), but within the range of values reported in theliterature. Low values of actual crop coefﬁcients were likely causedby the stress due to low levels of irrigation and high levels of deep
drainage. Drainage ﬂuxes were much higher than expected andaveraged 189 and 169 mm during the two seasons, respectively.
While water productivity relative to evapotranspiration(WPETC ) showed no clear effect of irrigation amount, water productivity relative to transpiration (WPTC ) did show a clear andconsistent effect, with the lowest irrigation treatment (I1 ) returning values over 20% greater than the well-watered treatment (I4 ).
Doubling of WPTC in all treatments compared to WPETC indicatessigniﬁcant evaporation losses in this study. Water productivity values obtained in this study were comparable to similar studies, andmuch higher than studies evaluating surface irrigation.
We believe that information obtained in our study can be utilized for developing better management practices for wine grapes.
It can be used to further improve the estimates of ET components
for wine grapes under SDI condition.
Acknowledgements
The work was conducted by the South Australian Research andDevelopment Institute, a member of the Wine Innovation Cluster atthe Waite Precinct in Adelaide, and is supported by Australian grape
growers and winemakers through their investment body, the Grapeand Wine Research and Development Corporation, with matching funds from the Australian Government. We would also like toacknowledge the support of Treasury Wine Estates and their staff
at the Markaranka vineyard for facilitating site access and support
and for installing the SDI trial used for this project.
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