Article

Simulation of Water and Salt Dynamics in the Soil
Profile in the Semi-Arid Regionof Tunisia—Evaluation of the Irrigation Method
for a Tomato Crop
Sabri Kanzari 1,*, Issam Daghari 2, Jiří Šimůnek 3,*, Anis Younes 4, Riadh Ilahy 5,
Sana Ben Mariem 1, Mourad Rezig 1, Béchir Ben Nouna 1, Hassouna Bahrouni 1
and Mohamed Ali Ben Abdallah 1
INRGREF, Laboratory of Rural Engineering, University of Carthage, Ariana 2080, Tunisia;benmeriem.sana@gmail.com (S.B.M.); rezigue_mourad@yahoo.fr (M.R.); b_bechir1106@yahoo.com (B.B.N.);h.bahrouni@gmail.com (H.B.); benabdallah_medali@yahoo.fr (M.A.B.A.)
2 INAT, Tunis 1082, Tunisia; issam.daghari@gmail.com
3 Department of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USA
4 LHyGES, Universite de Strasbourg/EOST/ENGEES, CNRS, 67084 Strasbourg, France; younes@unistra.fr
5 INRAT, Laboratory of Horticulture, University of Carthage, Ariana 2080, Tunisia; bn.riadh@gmail.com* Correspondence: sabri.kanzari@gmail.com (S.K.); jiri.simunek@ucr.edu; Tel.: +1-951-827-7854
1

Received: 4 May 2020; Accepted: 26 May 2020; Published: 3 June 2020

Abstract: In Tunisia, water used for irrigation is often saline, increasing the risk of salinization forsoils and crops. In this study, an experiment was conducted on a tomato crop cultivated on asilty-clay soil irrigated with three different water qualities: 0, 3.5, and 7 dS.m−1. Experimental data
were then used to calibrate and validate the Hydrus-1D model, which simulates water flow andsalt transfer in soils. The successfully-calibrated and validated model was then used to study the
combined effects of the soil osmotic and soil matrix potentials on root water uptake. The values of
the root mean square error (RMSE), the coefficient of determination (CD), the modeling efficiency(EF), and the coefficient of residual mass (CRM) were close to their optimal values for both soil
water content and soil electrical conductivity profiles, indicating the reliability of the model toreproduce water and salt dynamics. Relative yields (Yr), indirectly estimated using actual andpotential root water uptake (transpiration), indicated that the multiplicative stress response model(using the S-shape model) satisfactorily simulates measured yields and reproduces the effects ofirrigation with saline waters on crop yields. An alternative scenario using a reduction of waterrequirements by 50% was investigated to assess an irrigation method with considerable watersavings. As the results show that relative yields, Yr, were only slightly reduced, the crop waterrequirements estimated by CROPWAT 8.0 must have been overestimated. The variation of the soil
salinity in the root zone highlighted a high salinization risk in the short-term when water of 7
dS.m−1 is used for irrigation.
Keywords: soil salinity; saline water; unsaturated soil; root water uptake; Hydrus-1D; Tunisia

1. Introduction
In arid and semi-arid countries such as Tunisia, the use of marginal waters such as saline waters
or treated wastewater is a necessity. However, the use of these waters carries a long-term risk forsoils, groundwater, and crops [1–3]. Irrigation with saline waters generally results in salinization of
the topsoil [4–6], and, in the long term, salts leaching under the effects of rainfall can contaminate
Water 2020, 12, 1594; doi:10.3390/w12061594

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groundwater [7,8]. On the other hand, multiple authors have shown that the salinization
phenomenon in the topsoil can be cyclic, and the effect of salinity on crops can be attenuated withproper management that involves appropriate irrigation for salt leaching [9–11]. However, in arid
regions, reduction in agricultural water use is part of the water resource management policy. This,requires lower irrigation doses and involves irrigation strategies such as deficit irrigation and partial
root-zone drying. If one of these techniques is associated with the use of brackish water, the risk ofsalinization can become dramatic, especially for a strategic crop like tomatoes (in Tunisia).
Numerical models are important and useful tools for assessing the risk of soil salinization andits adverse effects on crop yields. The Hydrus-1D model [12], widely used for this purpose,
simulates the movement of water and solutes by numerically solving the Richards andadvection-dispersion equations, respectively. The model also considers the simultaneous effects ofthe soil osmotic and matrix potentials on root water uptake. The model does not directly simulate
crop yield but calculates a relative yield as a ratio of actual and potential root water uptake(transpiration) [13]. Several authors have applied the Hydrus-1D model to study water and salt
dynamics in agricultural soils and their effects on crops in semi-arid regions, as well as developed
management scenarios and long-term forecasts using this model (e.g., [14,15]).
In this context, the objectives of this study are (a) to calibrate and validate the Hydrus-1D model
for simulating water and salt dynamics in a soil cultivated with a tomato crop irrigated with saline
waters and (b) to estimate crop water requirements using this calibrated and validated model.
2. Materials and Methods
2.1. Field Experiment and Irrigation Management
The field experiment was carried out in the city of Ariana (36°50′40.791″ N, 10°11′13.795″ E) in
Tunisia, a semi-arid Mediterranean region. The Ariana region has a semi-arid Mediterranean climate
with mild, wet winters and dry and hot summers. The average annual temperature is 18.7 °C, withthe winter minimum of 6 °C and the summer maximum exceeding 45 °C. The annual average
rainfall is 450 mm, and the average annual reference evapotranspiration is 1100 mm. The soil at thesite is a Fluvisol with a loamy-clay texture. The groundwater level is beneath 8 m. A tomato crop
(Rio Grande) (Petoseed Saticoy, California, USA) was cultivated with a planting density of 1750
plants/ha. The crop was planted on 17 April 2017 and harvested on 3 August 2017.
Tomato water requirements were estimated from 10-year climate data (2007–2017). Meanvalues of meteorological variables were used to estimate reference evapotranspiration using thePenman–Monteith equation [16]. The crop coefficient (Kc) for three crop stages were taken from [16]to calculate actual evapotranspiration. For the initial stage Kc = 0.6, for the medium stage Kc = 1.15,
and for the final stage Kc = 0.8. CROPWAT 8.0 software (FAO, Rome, Italy) [17] was used to establish
irrigation scheduling. CROPWAT 8.0 estimated water requirements of the tomato crop to be 720
mm.
Surface irrigation was used to deliver water to plants (30 plants for each treatment) in three
qualities:

Freshwater (FW) with a salinity of 0 dS m−1;
Saline water with a salinity of 3.5 dS m−1;
Highly saline water with a salinity of 7 dS m−1.

2.2. The Hydrus-1D Model
2.2.1. Model Inputs
1.

Soil Hydraulic Properties
The soil hydraulic properties were measured at the Laboratory of Rural Engineering(INRGREF, Ariana, Tunisia) using soil samples collected in March 2017 at depths of 20, 40, 60, and 80
cm, with three replicates for each depth. The pressure plate apparatus [18] was used to measure thevolumetric water content at multiple pressure heads for each soil sample. The collected experimental

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data were fitted to the van Genuchten retention model [19] using the RETC software (U.S.
Department of Agriculture, Riverside, USA) [20] to estimate the residual water content θr, thesaturated water content θs, and the two shape parameters α and n. Saturated hydraulic conductivity(Ks) was determined by a falling head permeameter method. The values of these parameters fordifferent soil depths are given in Table 1.
Table 1. Soil hydraulic parameters [19] for different soil depths.
Depth (cm)
0–20
20–40
40–60
60–80

θr (cm3.cm−3)
0.01
0.09
0.09
0.09

θs (cm3.cm−3)
0.50
0.48
0.50
0.47

α (cm−1)
0.012
0.015
0.010
0.012

n
1.23
1.32
1.42
1.39

Ks (cm.d−1)
12.60
8.90
12.60
12.50

2.

Solute Transport Parameters
Soil dispersivity λ (cm) was estimated using laboratory experiments on undisturbed soilcolumns [21]. The distribution coefficient Kd for total dissolved solutes was measured using batch
experiments [22]. The bulk density (BD) was measured for each soil layer using the cylinder method.
The measured solute transport parameters for different depths are summarized in Table 2.
Table 2. Solute transport parameters for different soil depths.
Depth (cm)
0–20
20–40
40–60
60–80

BD (g.cm‒3)
1.43
1.46
1.39
1.51

λ (cm)
5.80
5.40
4.60
2.80

Kd (cm3.g−1)
0.14
0.20
0.30
0.35

3.

Crop Measurements and Models
The plant height, rooting depth, and the leaf area index (LAI) were measured every ten days
from the planting date to the harvest date with three replicates for each parameter (Table 3). The LAI
was estimated by the analysis of the scanned leaves (5 to 10) images using Mesurim Pro [23]. The
measured values of the relative crop yield were determined by a direct weighting using an accurate
digital balance.
The S-shape [24] and threshold and slope models [25] were used to simulate the effect of the soil
matrix and osmotic potentials on root water uptake, respectively. It was considered that the effectsof saturation and salinity were multiplicative. The threshold and slope parameters were 4.59 dS.m‒1
and 5%, respectively, as suggested by [25], and the two parameters of the S-shape model were P50 =-800 cm and P3 = 3.
Table 3. Tomato crop parameters for each water quality scenario.
Water quality
Freshwater

3.5 dS.m‒1

7 dS.m‒1

Parameter
Crop Height (cm)LAI
Root Depth (cm)Crop Height (cm)LAI
Root Depth (cm)Crop Height (cm)LAI
Root Depth (cm)

10
5
0.35
5
4.5
0.35
4
4
0.35
4

20
6
1
10
5.5
0.98
8
6
0.96
7

Day After Planting
30
40
50
9
14
33
2
2.5
2.75
15
20
25
8
10
18
1.98
2.25
2.2
12
16
20
10
11
13
1.96
2.2
1.8
11
14
18

60
35
2.8
30
22
2.2
24
15
1.5
21

70
38
2.85
35
25
2.2
28
19
1.5
25

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Soil sampling was carried out on the first day (initial) of the experiment, and then after 30, 50,
70, and 109 days (final) at depths of 20, 40, 60, and 80 cm. Soil water content and salt content were
measured using the gravimetric method and the saturated paste extract, respectively.
Hydrus-1D calibration was performed for the experiment carried out with fresh water. The twoparameters α and n were slightly adjusted during the water movement calibration process and Kd
during the solute transport calibration process; all three parameters were calibrated for the two soil
layers of 20–40 cm and 40–60 cm. The simulation duration was 109 days, and the output dates
corresponded with sampling days. Irrigation/rainfall and evapotranspiration were used to define
the atmospheric boundary condition for the soil surface (Figure 1), and a free drainage boundary
condition was used at the bottom boundary. Only two rainfall events were recorded on days 49 and50, with a total amount of 40 mm. The concentration flux boundary condition was used at bothboundaries for solute transport. Hydrus-1D validation was performed using the two otherexperiments with saline waters. The same parameters obtained by calibration for experiments withfreshwater were used as input during validation, except for the irrigation water quality.

Figure 1. Daily evapotranspiration, rainfall, and irrigation during the experiments.

2.2.3. Statistical Evaluation
The Hydrus-1D results were evaluated graphically by plotting measured and simulated soil
water content and soil salinity as a function of soil depth and statistically by calculating the root
mean square error (RMSE), the coefficient of determination (CD), the modeling efficiency (EF), andthe coefficient of residual mass (CRM) [26]. RMSE values show how much the simulations under- or
overestimated the measurements. CD represents the ratio between the scatter of simulated values tothe average measured value. The EF value compares simulated values to the average measured
value. A negative EF value indicates that the average measured value gives a better estimate thansimulated values. CRM is a measure of the tendency of the model to overestimate or underestimate
measurements. Positive CRM values indicate that the model underestimates measurements, andnegative CRM values indicate a tendency to overestimate measurements. For a perfect fit between
observed and simulated data, values of RMSE, CD, EF, and CRM should equal 0, 1, 1, and 0,
respectively.
𝑛𝑛�∑𝑖𝑖=1(𝑠𝑠𝑖𝑖 − 𝑚𝑚𝑖𝑖 )²𝑛𝑛
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 (%) =
× 100
𝑚𝑚
�

(1)

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𝐶𝐶𝐶𝐶 =𝐸𝐸𝐸𝐸 = 1 −∑𝑛𝑛𝑖𝑖=1(𝑚𝑚𝑖𝑖 − 𝑚𝑚� )²∑𝑛𝑛𝑖𝑖=1(𝑠𝑠𝑖𝑖 − 𝑚𝑚� )²∑𝑛𝑛𝑖𝑖=1(𝑠𝑠𝑖𝑖 − 𝑚𝑚𝑖𝑖 )²∑𝑛𝑛𝑖𝑖=1(𝑚𝑚𝑖𝑖 − 𝑚𝑚
� )²

𝐶𝐶𝐶𝐶𝐶𝐶 =∑𝑛𝑛𝑖𝑖=1(𝑠𝑠𝑖𝑖 − 𝑚𝑚𝑖𝑖 )𝑛𝑛. 𝑚𝑚
�

(2)

(3)
(4)

where si are simulated values, mi are measured values, 𝑚𝑚
� is the average value of observed data, andn is the number of observations.
3. Results
3.1. Water and Salts Movement
Figures 2 and 3 show variations in soil water content and salts profile, respectively, for the threetreatments with fresh and saline waters. The soil water content profiles show that:--

Soil water contents increased with time in all soil horizons compared to the initial soil watercontents due to irrigation events.
The soil water content in the soil surface layer (0–40 cm) reached its maximum value after 50days, and then it gradually decreased. In the treatments with saline water, the soil watercontent reached higher values, especially in the 20–40 cm layer.
Variations in the soil water content are lower in the lower part of the soil profile (40–80 cm). Soilwater contents reached 0.45 m3.m−3 in the three treatments.
The soil salts profiles measured in the three treatments show that:-

The salts were continuously leached from the soil profile irrigated with fresh water.
Desalination was more important in the surface layer than in deeper layers.
The opposite phenomenon was observed in the other two treatments with saline waters, i.e.,
continuous accumulation of salts in the soil profile. Soil salinity reached 6.7 dS.m−1 at the soil
surface and 4.6 dS.m−1 deeper in the soil profile of the treatment with irrigation water quality of
3.5 dS.m1. Maximum salinity values of 11 dS.m−1 and 6 dS.m−1 were, respectively, measured at
the soil surface and the bottom of the soil profile in the treatment with irrigation water qualityof 7 dS.m−1.

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Figure 2. Measured soil water content profiles for treatments with (a) fresh, (b) saline (3.5 dS.m−1),
and (c) saline (7 dS.m−1) water at 0, 30 50, 70, and 109 days.

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Figure 3. Measured soil salts profiles for treatments with (a) fresh, (b) saline (3.5 dS.m−1), and (c)saline (7 dS.m−1) water at 0, 30 50, 70, and 109 days. Different scales are used on the horizontal axis.

3.2. Hydrus-1D Calibration and Validation
3.2.1. Soil Water Profiles
The measured and simulated soil water profiles for the treatment irrigated with freshwater areplotted in Figure 4. The simulated soil water content values are close to the measured values. The

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Hydrus-1D model tends to slightly underestimate measured water contents, with RMSE (%) values(for the whole profile) of 5.4, 5.3, 2.6, and 4.9%, at 30, 50, 70, and 109 days, respectively. The CD, EF,
and CRM indicators are close to their optimal values and demonstrate a good agreement between
simulated (using calibrated parameters) and observed water contents.

Figure 4. Measured and simulated soil water content profiles for the treatment with the irrigationwater quality of 0 dS.m−1 at 30, 50, 70, and 109 days.

The calibrated parameters were then used in model validation simulations. The measured andsimulated soil water content profiles for the treatments with saline water of 3.5 dS.m−1 and 7 dS.m−1
are depicted in Figures 5 and 6, respectively.

Figure 5. Measured and simulated soil water content profiles for the treatment with the irrigationwater quality of 3.5 dS.m−1 at 30, 50, 70, and 109 days.

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Figure 6. Measured and simulated soil water content profiles for the treatment with the irrigationwater quality of 7 dS.m−1 at 30, 50, 70, and 109 days.

Graphically, the simulated values of soil water contents are close to those measured at eachsampling depth and each sampling date in both validation experiments. Slight deviations from themeasured values were observed in the layers between 20 and 40 cm on days 70 and 109. Thesegraphical results are confirmed by the RMSE, CD, EF, and CRM values calculated for each profile(Table 4) with statistical indicators having less than optimal values on days 70 and 109, confirming
model deviations from measured values.
Table 4. The statistical indicators for measured and simulated soil water content profiles at different
sampling dates for treatments irrigated with saline waters.
Water Quality
RMSE (%)

CD

EF

CRM

0 dS.m−1

30 days
5.40

50 days
5.30

70 days2.60

109 days
4.90

3.5 dS.m−1

5.00

7 dS.m−1

5.10

3.30

8.10

7.00

4.30

10.40

0 dS.m−1

7.50

3.5 dS.m−1

0.68

0.67

0.68

0.95

0.91

1.62

3.33

2.60

7 dS.m−1

1.13

0.69

5.03

1.81

0 dS.m−1

0.95

0.85

0.5

0.65

3.5 dS.m−1

0.96

0.93

0.97

−0.07

−0.28
−0.21

0.77

7 dS.m−1
0 dS.m−1

0.02

0.01

0.02

0.04

3.5 dS.m−1

−0.02

0.01

0.01

0.02

7 dS.m−1

0.02

0.04

−0.02

0.05

0.4

The simulated cumulative bottom flux, calculated by Hydrus-1D, was 24 cm for the scenario
irrigated with FW, 31 cm for the scenario irrigated with the 3.5 dS.m−1 water, and 38 cm for the
scenario irrigated with the 7 dS.m−1 water. The leached amounts of water increased because of
reduced root water uptake due to the effects of the saline water used for irrigation.
3.2.2. Soil Salts Profiles
The salinity profiles measured during the tomato crop experiment with the irrigation water
quality of 0 dS.m−1 are plotted together with simulated values of soil salts contents in Figure 7.

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Overall, a good fitting is observed between measured and simulated (using calibrated parameters)
values of soil salts contents at the four sampling dates. The soil electrical conductivity is generallyslightly overestimated by the model, with RMSE values (for all depths per date) of 1.6, 4.3, 5.1, and9.6%, respectively. The CD, EF, and CRM values indicate that Hydrus-1D was able to reproduce thesalts dynamic in the soil satisfactorily.

Figure 7. Measured and simulated soil salts profiles for the treatment with the irrigation water
quality of 0 dS.m−1 at 30, 50, 70, and 109 days.

The validation results for the salts profiles for the two treatments with saline waters are given in
Figures 8 and 9. The measured and simulated salts content profiles are similar at each sampling
depth and each sampling date. The graphical results are confirmed by the statistical indicators(Table 5).

Figure 8. Measured and simulated soil salts profiles for the experiment with the irrigation water
quality of 3.5 dS.m−1 at 30, 50, 70, and 109 days.

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Figure 9. Measured and simulated soil salts profiles for the experiment with the irrigation water
quality of 7 dS.m−1 at 30, 50, 70, and 109 days.
Table 5. The statistical indicators for measured and simulated soil salts profiles at different sampling
dates for treatments irrigated with saline waters.
Water Quality
RMSE (%)

CD

EF

CRM

0 dS.m−1

30 days
5.40

50 days
5.30

70 days2.60

109 days
4.90

3.5 dS.m−1

5.00

3.30

8.10

7.00

7 dS.m−1

5.10

4.30

10.40

7.50

0 dS.m−1

0.91

0.87

0.97

0.91

3.5 dS.m−1

0.98

0.92

0.98

0.90

7 dS.m−1

1.05

1.00

1.00

0.97

0 dS.m−1

0.99

0.99

0.99

0.99

3.5 dS.m−1

0.99

0.97

0.99

0.98

7 dS.m−1

0.99

0.99

0.99

0.99

0 dS.m−1

−0.01

−0.03

−0.09

3.5 dS.m−1

0.04

0.05

−0.05
−0.01

7 dS.m−1

0.01

0.01

0.01

−0.01

0.01

3.2.3. Crop Yields
The relative crop yield (Yr) cannot be estimated directly by the Hydrus-1D model. According to[8], the relative crop yield (Yr) can be estimated as the ratio between the actual and potential (ormaximum) root water uptake (transpiration), and these two values are available in the model. Figure
10 displays measured and simulated relative crop yields as a function of the irrigation water quality(ECw). According to [27], Yr = Ya/Ymax, where Ya is the measured yield, and Ymax is the maximum yield.
In our case, Ymax corresponds to the weight obtained in the experiment with freshwater irrigation andis equal to 2.53 kg/plant.

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Figure 10. The measured and simulated tomato relative yields (Yr) as a function of irrigation waterelectrical conductivity (ECw).

As shown in Figure 10, the relative yield was reduced as the salinity of the irrigation water
increases. The model was able to reproduce the observed decrease in relative yield, although not tothe same extent as observed. Measured relative yields, Yr, were reduced only slightly whenirrigation water with ECw = 3.5 dS.m−1 was used and nearly by half with ECw = 7 dS.m−1. In contrast,
simulated yields were overestimated by the model in both treatments irrigated with saline waters.
The observed effects of the salinity stress were larger than those simulated. The parameters (either
the threshold or the slope) of the salinity stress response function would have to be adjusted toobtain a better correspondence between simulated and measured relative yields. Note that themodel predicted, due to the saturation stress, a slight reduction in relative yield even for theexperiment irrigated with fresh water.
3.3. Evaluation of the Irrigation Method
The success of the calibrated and validated Hydrus-1D model in simulating water and salt
dynamics for experiments with the tomato crop under field conditions, also motivated employing
the model for evaluating alternative irrigation management strategies.
Figure 11 depicts relative yield, Yr, simulated using Hydrus-1D with the S-shape model for
scenarios with three irrigation water qualities when 50% less irrigation water was used. Since thetomato yields decreased only slightly when freshwater was used, the irrigation doses calculated by
the CROPWAT software must be grossly overestimated [28,29]. The results of Figure 12 show asubstantial reduction in the relative yield when the high salinity water was used for irrigation. These
results are in agreement with those of Saad et al. [30], who observed a reduction in tomato fruit yieldof 40% using irrigation of 50% of ETc and water of 9 dS.m−1.
Figure 12 shows the simulated root zone soil salinity (ECe) as it increases over the season withthe salinity of the irrigation water. ECe exceeds 8 dS.m−1 after three months of irrigation with themost saline water (ECw = 7 dS.m−1), which increases the risk of soil salinization. On the other hand,soil salinity in the root zone remains under 4.5 dS.m−1 when moderately saline water is used (ECw =3.5 dS.m−1). Such root zone salinity can be tolerable to tomato crops, reducing relative yield by only
about 15%.

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Figure 11. The simulated relative yields (Yr) of tomato for scenarios with different electricalconductivities of irrigation water (ECw) and with a 50% irrigation reduction.

Figure 12. Simulated rootzone soil salinity for scenarios with different water qualities and a 50%
reduction in irrigation water.

4. Discussion
One of the major weaknesses of numerical models is the lack of calibration and validation infield conditions [31]. In this study, a field experiment involving water and solute dynamics with root
water uptake allowed for the successful calibration and validation of the Hydrus-1D model. The
RMSEs for measured and simulated values of water contents and salt concentrations were low andshowed the ability of the model to reproduce water and salt profiles, and subsequently to evaluate
the effects of irrigation with brackish waters on the soil and tomato crop [32–34]. Irrigation withsaline water with ECw = 3.5 dS.m−1 can be tolerable to the crop and induces a low soil salinization
risk. However, saline water with ECw = 7 dS.m−1 reduces Yr nearly by half and increases the soil
salinity in the short-term [35] and could be disastrous for the crop after several irrigation cycles [36].
This risk is aggravated when the leaching fraction is reduced in the case of a deficit irrigation
strategy [31]. The use of a drip irrigation system associated with proper management practices suchas increasing irrigation frequency [37] or controlled deficit irrigation [38], can mitigate these effects
on both soil and crops. Replication of this experiment over at least three years can provide a strong
statistical significance of the effects of brackish water on tomato crop yields.
The used modeling approach was based on the simulation of the overall salinity. An alternative
geochemical approach that considers the multicomponent solute reactions and transport (asimplemented in the Unsatchem module of HYDRUS-1D [12]) for the calculation of soil electricalconductivity may be more appropriate for optimizing the management of irrigation with highly
saline waters [11,39].

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5. Conclusions
In arid and semi-arid regions like Tunisia, the scarcity of water resources of good quality has
led to the use of brackish water in agriculture. Salts accumulated in the soil due to irrigation withthese waters have negative consequences for both soil and crop yields. Experiments carried out on atomato crop irrigated with three water qualities (0 dS.m−1, 3.5 dS.m−1, and 7 dS.m−1) allowed thestudy the variations of water and salt dynamics in the soil. Observed soil salt profiles indicatedprogressive soil salinization when the plot was irrigated with water of 7 dS.m−1, with soil salinity
reaching a value of 8 dS.m−1 at the soil surface. The effects of irrigation with saline waters on thetomato crop were studied using a modeling approach with Hydrus-1D. The study of the scenario
where the crop water requirements were reduced by half showed that, in all three experiments, thecrop yields were not significantly affected. The irrigation amounts of tomato crop estimated by
CROPWAT 8.0 were overestimated. This overestimation could have been caused by the use of crop
coefficients not adapted to tomato under the local conditions of the current study.
Author Contributions: Conceptualization, S.K. and B.B.N.; data curation, S.K., R.I., and H.B.; formal analysis,
S.K., J.Š., and S.B.M.; funding acquisition, R.I., B.B.N., and M.A.B.A.; investigation, S.K., I.D., and M.R.;
methodology, S.K., R.I., and M.R.; project administration, S.K., M.R., and B.B.N.; resources, S.K., H.B., and
M.A.B.A.; software, S.K., S.B.M., and B.B.N.; supervision, B.B.N.; validation, I.D., A.Y., M.R., H.B., and
M.A.B.A.; visualization, I.D., J.Š., A.Y., R.I., S.B.M., M.R., H.B,. and M.A.B.A.; writing—original draft, S.K. andJ.Š.; writing—review and editing, I.D., J.Š., and A.Y. All authors have read and agreed to the published versionof the manuscript.
Funding: This research received no external funding.
Acknowledgments: The authors would like to appreciate the valuable comments by anonymous reviewers,
which helped to improve the manuscript.
Conflicts of Interest: The authors declare no conflict of interest.

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