Agricultural Water Management 253 (2021) 106925

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

Borage (Borago officinalis L.) response to salinity at early growth stages asinfluenced by seed pre-treatment
Faride Feghhenabi a, Hashem Hadi a, Habib Khodaverdiloo b, *, Martinus Th. van Genuchten c, da

Department of Plant Production and Genetics, Urmia University, Urmia, Iran
Department of Soil Science, Urmia University, Urmia, Iranc
Department of Earth Sciences, Utrecht University, Utrecht, the NetherlandsdCenter for Environmental Studies, CEA, São Paulo State University, UNESP, Rio Claro, SP, Brazilb

A R T I C L E I N F O

A B S T R A C T

Handling Editor: Dr. B.E. Clothier

Knowledge of plant response to salinity, especially at early growth stages, is critical to optimizing crop pro­duction in salt-affected soils. In this paper we explore quantitatively the effectiveness of seed pre-treatment tolessen the effects of salinity on Borage (Borago officinalis L.) seed germination and subsequent seedling growth.
Two independent experiments were conducted with nine salinity levels and five seed pre-treatments using Lake
Urmia saline water (LUsw), ascorbic acid (Asc), proline (Pro), potassium silicate (K2SiO3) and spermidine (Spd).
The first set of experiments was used to obtain the most effective pre-treatments for seed germination. The
second set of experiments investigated the combined effects of salinity and seed pre-treatment concentration on
the emergence process. Salinity was found to have considerable inhibitory effects on seedling growth, but pretreating the seeds alleviated some of these effects. Seedling response to salinity could be described well using
the threshold-slope salinity response function of Maas and Hoffman (1977) and the S-shaped function of van
Genuchten and Hoffman (1984). A means comparison (using Tukey’s test) showed that the mean salinity
threshold of all seedling traits, EC*, increased markedly (P ≤ 0.001). For example, EC* increased 60% using
K2SiO3 as compared to the control, with Spd and Pro having less of an effect on on EC* than K2SiO3. The mean EC
value at which various seedling traits were reduced by 50% (i.e., EC50) increased from 10.8 dS m− 1 for thecontrol to 12.5, 12.5 and 12.1 dS m− 1 for the Pro, K2SiO3 and Spd pre-treatments, respectively. Statistically, theeffects of Pro, K2SiO3, Spd on EC50 were essentially the same. The effects of Asc, and LUsw on EC50 were notsignificant compared to the control. The increased vigor of seedlings obtained by seed pre-treatment, particularly
when using K2SiO3, should lead to more salt-tolerant plants and higher yield potentials in saline environments.

Keywords:
Germination
Emergence
Seed pre-treatment
Salt-affected soils
Salinity crop response functions

1. Introduction
Soil salinization is a major problem limiting agricultural productivity
in many regions of the world. While salt-affected soils occur in all cli­
matic areas, and at a wide range of altitudes, they are most frequent inarid and semiarid regions. For example, about 7% of the world’s and
55% of Iran’s agricultural land is affected by salinity, with the impacts ofsalinity expected to increase in the near future (Bagheri et al., 2015; Kafi
and Rahimi, 2011). Salinity is one of the most important abiotic stresses
causing reductions in agricultural productivity (Uddin and Juraimi,
2013; Wei et al., 2015). Salinity induces such primary effects as osmotic
and ionic stress, as well as indirectly secondary effects involving
oxidative stress on plants (Li et al., 2008).

The capability of plants to endure and sustain growth in saline en­
vironments (i.e., plant salt tolerance) is vital for the ecological spreadingof plant species and their cultivation in arid, semiarid, and salinityaffected areas. Salt tolerance is an adjustable trait related to many fac­
tors, including especially plant species (Munns and Tester, 2008). In­
vestigations on plant tolerance to salinity stress have involved a range ofmolecular, physiological and morphological factors. Due to increasing
salinity problems worldwide, improved field-scale irrigation and salinity
management practices (Skaggs et al., 2006), as well as development of
more salt tolerant plants (Patade et al., 2009) should be major priorities.
Salinity tolerance during the germination and emergence stages is amajor indication of salt tolerance at later growth stages (Kutlu et al.,2009). Identifying or possibly enhancing crop salt tolerance prior to* Corresponding author.
E-mail address: h.khodaverdiloo@urmia.ac.ir (H. Khodaverdiloo).
https://doi.org/10.1016/j.agwat.2021.106925
Received 3 September 2020; Received in revised form 9 April 2021; Accepted 11 April 2021
0378-3774/© 2021 Elsevier B.V. All rights reserved.

F. Feghhenabi et al.

Agricultural Water Management 253 (2021) 106925

sowing is hence vital. Seed pre-treatment or seed priming is a
pre-planting practice that involves the exposure of seeds to limited hy­
dration using external low-water potentials. Seed pre-treatment has
been shown to enhance germination and emergence under saline con­ditions for a variety of crops (Aloui et al., 2014; Demir Kaya et al., 2006;
Patanè et al., 2009; Pill et al., 1991; Sadeghi and Robati, 2015). Cho­montowski et al. (2020) concluded that the efficiency of seed
pre-treatment is higher in seeds with lower vigor than those with higher
vigor. Since poor seed germination is a major reason limiting thelarge-scale cultivation and production of medicinal plants under salineconditions, seed pre-treatment may well become very attractive. Several
factors affecting the response of seeds to pre-treatment indicate thatsuitable material with precise concentrations used for seed
pre-treatment will improve seed germination under water and salinity
stress conditions (Kazemi and Eskandari, 2012). The effects of different
seed pre-treatments, as an agricultural technique, could explored also
from an ecophysiological point of view. In ecophysiology, the mecha­
nistic understanding of a plant community in response to seed
pre-treatments and salinity can be improved by focusing on such issues
as physiology, biochemistry, biophysics, and molecular biology.
Many compounds have been used for seed pre-treatment. Proline as
an organic osmolyte, in addition to osmotic protection, has been shown
to be a key factor to reduce the harmful effects of salinity, and toaccelerate post-stress recovery through detoxification of reactive oxygen
species (ROS), protection of membrane integrity, and stabilization ofproteins/enzymes from stress (Shakirova et al., 2003). Shafiq et al.
(2018) concluded that seed pre-treatment with proline mediated im­provements in growth and antioxidant enzyme activities contributed tostress tolerance, which partly relied on the ability of plants to absorb
sodium and partition it in wheat roots. As a beneficial plant nutrient,silicon (Si) is known to play a crucial role in regulating and alleviating
many biotic and abiotic stresses, including from drought, salt and heavy
metals, through enhanced membrane stability, production of antioxi­
dants and photosynthetic efficiency, and reductions in oxidative dam­age. As a seed pre-treatment, application of 1.5% Si under water stressconditions improved biological yield and grain yield of wheat (Mukhtar
et al., 2015). Ascorbic acid (Asc), an abundant relatively small moleculein plants, further has been shown to play multiple roles in plant growth.
For example, Ajmal Khan et al. (2006) found that Asc as seed
pre-treatment can improve germination by scavenging undue superox­
ide radicals or singlet oxygen. Spermidine (Spd) is a ubiquitous
low-molecular-weight aliphatic amine involved in various biochemicaland physiological processes related to the regulation of plant growth anddevelopment. Seed pre-treatment with Spd improved seed germination
under different water stress conditions. Improvements in the meta­bolism of starch was suggested as a probable cause for this seed invig­
oration (Li et al., 2014). Halo-priming or seed pre-treatment with salt
solutions such as KCl and CaCl2 is another method to improve tolerance
against salinity stress. Islam et al. (2015) reported that low proline andNa+ contents caused enhanced salt tolerance of halo-primed wheat. Still,
the response to salinity at any growth stage differs among species, andeven among cultivars (Bojovi et al., 2010).
Plant survival during germination is one of the most importantsalinity tolerance indices, while subsequent yield and growth reductions
are criteria for the ultimate salinity tolerance of plants as discussed by
Saadat and Homaee (2015). Models of whole-plant response to envi­
ronmental stresses can be useful for comparing the degree of salinity
tolerance of different species, including their application to practicalfield problems (Maas and Hoffman, 1977; Skaggs et al., 2006; Steppuhn
et al., 2005). Functional descriptions of seed germination and emer­
gence can similarly be effective to quantify seedling emergence andsubsequent plant response to salinity. Several salinity-dependent
reduction functions (Maas and Hoffman, 1977; van Genuchten and
Hoffman, 1984) have been used over the years to quantify whole-plant
response to salinity stress. To our best knowledge, very few studies (e.g.,
Feghhenabi et al., 2020) have used these functions to assess the effects ofseed pre-treatment on the response of plants to salinity in a quantitative
way.
Borage (Borago officinalis L.) is a valuable annual medicinal herb
suitable for cultivation in many countries, including Iran (Chakovari
et al., 2016; Naghdi Badi and Sorooshzadeh, 2011). High salt tolerance
and absorption of minerals by this plant have been mentioned in several
studies (e.g., Asadi-Samani et al., 2014; Ghassemi-golezani et al., 2013;
Torabi et al., 2012). However, these studies did not include a quanti­tative response of Borage to increasing salinity levels such as salinity
threshold values. Therefore, in addition to a quantitative assessment ofBorage response to salinity at early growth stages, our study determines
how seed pre-treatment affects Borage response to salinity after germi­
nation. Specific objectives were to i) determine the tolerance of Borage
to salinity stress with or without different seed pre-treatments, ii)
explore the quantitative response of Borage to salinity with or without
the various seed pre-treatments and, consequently, iii) select the mosteffective seed pre-treatments for moderating the effects of salinity,
including the best function for describing Borage response to salinity at
the seedling stage.
2. Materials and methods
Our study was conducted in 2017 at the Seed Technology Laboratoryof the Faculty of Agriculture and Natural Resources of Urmia University(Urmia, Iran). Borage seeds were provided by PAKAN BAZR Company(Esfahan, Iran). Fig. 1 shows a flowchart of the various experiments thatwere conducted. The pre-experiments showed a mean 100-seed weightof 2.03 g, while seed moisture contents ranged between 5% and 6%.
Borage seeds showed a 100% germination percentage at 25 ºC as
assessed using Petri dishes. The seeds further were surface-sterilized in a
1% sodium hypochlorite solution, rinsed with distilled water, and driedprior to the Phase-1 and Phase-2 experiments.
The main experiments involved two consecutive steps. Phase-1 ex­
periments were used to estimate the final germination rate (GR) of seeds
and the germination percentage (GP). This produced initial data about
the response of seed germination to different concentrations of various
seed pre-treatment agents at the applied salinity levels. The mosteffective concentration of each seed pre-treatment was then selected forthe next step. The Phase-1 experiments hence were used to compare
control (untreated) seeds and pre-treated seeds. For the pre-treatment
experiments we placed subsamples of the seeds into 100-ml flasks.
Table 1 shows the type, concentration and duration of the seed pretreatments. A zero concentration was considered as the control. The
seed pre-treatments and their concentrations were selected based on
many previous studies (Ghassemi-golezani et al., 2013; Ibrahim, 2016;
Kadkhodaie and Bagheri, 2012; Kazemi and Eskandari, 2012; Mukhtar
et al., 2015; Sivritepe et al., 2003; Torabi et al., 2012). After
pre-treatment, the seeds were thoroughly rinsed consecutively with tap
and distilled water, dried with blotting paper and then left overnight atroom temperature. After drying, the moisture contents of the treatedseeds were found to be similar to those of the untreated seeds. Afterbeing surface-sterilized in a sodium hypochlorite solution, the seeds
were used for the experiments 36 h after the pre-treatments.
Saline solutions (0, 2, 4, 6, 8, 10, 12, 14 and 20 dS m− 1) were pre­pared using the saline water of Lake Urmia and distilled water
depending upon target salinity levels. The salinity of each solution wasmeasured using an electrical conductivity meter (EC meter), with results
expressed in dS m− 1 at 25 ºC. Phase-1 experiments were carried out
following a completely randomized design with four replicates. Eachreplicate involved 100 primed seeds placed in covered Petri dishes
containing a single filter paper moistened with 7 ml of each saline water
solution. The Petri dishes were placed in a germinator at 25 ± 2 ◦ C in
the dark. Data were collected every 12 h until no further germination
occurred for 48 h. At the end of the Phase-1 experiments, estimates ofthe final GP and GR values were obtained using methods described byBabakhanzade Sajirani et al. (2011). The results were used to select the2

F. Feghhenabi et al.

Agricultural Water Management 253 (2021) 106925

Fig. 1. Flowchart of the study (Asc, ascorbic acid; Pro, proline; K2SiO3, potassium silicate; Spd, spermidine; LUsw, Lake Urmia saline water).

3

F. Feghhenabi et al.

Agricultural Water Management 253 (2021) 106925(0 ≤ α ≤ 1) as a function of soil salinity, EC (dS m− 1); EC* is the salinity
threshold value, and b is the slope of the response function. Since ex­periments often do not show the linear response to salinity typical of Eq.
(1), van Genuchten and Hoffman (1984) proposed an alternative
S-shaped response function of the form:( )1
α EC =(2)
1 + (EC/EC50 )p

Table 1Treatments at the germination and emergence stages.
Growthstage

Seed pretreatments#

Seed pretreatment
concentrations

Duration ofseed pretreatment

Salinity levels
for alltreatments (dSm− 1)

Germination

ControlAsc–
0, 50, 75,
100 mM0, 12, 17, 20 mM
0, 1, 1.5, 2 mM0, 0.5, 1, 1.5 mM
0, 100, 150,
200 mg L− 1
–
75 Mm
12 mM
1.5 mM
1.5 mM
150 mg L− 1–
24 h0, 2, 4, 6, 8,
10, 12, 14, 20

Pro
K2SiO3
Spd
LUsw
EmergenceControl
Asc
Pro
K2SiO3
Spd
LUsw

in which EC50 is the soil salinity at which α is reduced by 50%, and p is adimensionless shape parameter (about 3 for many crops). Several
studies found Eq. (2) to describe crop salt tolerance data equally well or
better than Eq. (1) (Steppuhn et al., 2005; van Genuchten and Gupta,
1993). Dirksen et al. (1993) later included a threshold salinity in Eq. (2)to enable a more flexible description of salinity response data at low
salinities:⎧
1
0 ≤ EC < EC∗⎪⎪⎪⎨
α(EC) =
(3)
⎪
1
⎪
∗
⎪
⎩
EC
≥
EC
1 + [(EC − EC∗ )/(EC50 − EC∗ ) ]p

2 days
6h
10 h
10 h
–
24 h
2 days
6h
10 h
10 h

0, 2, 4, 6, 8,
10, 12, 14, 20#
Asc, ascorbic acid; Pro, proline; K2SiO3, potassium silicate; Spd, spermidine;
LUsw, Lake Urmia saline water.

most effective concentration for each treatment, further referred to as
the optimal pre-treatment concentration. The reduction slope (thedecline in germination or another trait per unit increase in salinity) andthe y-intercept of the regression line obtained from these results was
subjected to an analysis of variance. Means comparison Tukey-HSD tests
were performed next, with the level of significance at <0.05. A mild
slope reflects greater sustainability of a particular trait in response toincreased salinity levels. Therefore, the concentration that was mosteffective to increase seedling resistance to salinity (i.e., by reducing theslope) should be used in further evaluations (KhoshKholg Sima et al.,2014; Maggio et al., 2004; Steppuhn et al., 2005). Still, one must also
consider the importance of y-intercept.
The Phase-2 experiments were used to examine, for each optimal
pre-treatment, the effects of salinity on subsequent seedling growthduring a period of 14 days. The experiments for this purpose wereconducted in a greenhouse with a 12 ± 1 h photoperiod at
22.5 ± 2.5 ◦C, a 50 ± 10% relative humidity (day/night), and a lightintensity of 700 µmol m− 2 s− 1. Ten germinated seeds were planted at a
depth of 2 cm in PVC pots (16 cm high and 7 cm in diameter and filledwith local farm soil). The seedling density per pot was reduced to four
after emergence. The loam soil used for the experiments (sand, silt andclay percentages were 46%, 33% and 21%, respectively) was non-saline(the electrical conductivity of the saturation paste was 1.1 dS m− 1),contained 18.5% equivalent CaCO3, and had a pH value of 7.7.
The experiments produced estimates of the final maximum emer­
gence percentage (Emax), the rate at which the seedlings reached 50%
emergence (R50 = 1/D50), the vitality index (VI, being the length of theseedlings ×Emax), and the reciprocal of the emergence uniformity(REU=1/EU, where EU = D90 - D10). D10, D50 and D90 represent the
numbers of days needed to reach 10%, 50% and 90% of emergence,
respectively. When the difference between D10 to D90 increases, theemergence uniformity (EU) decreases and REU increases. Seedlingdevelopment rates were evaluated by measuring a range of seedling rootand stem attributes, including lengths and dry weights, 16 days after
planting.
The effects of seed pre-treatment and salinity on the various traits
were evaluated in terms of several salinity stress response functions.
Historically the most popular response function has been the linear
threshold-slope model by Maas and Hoffman (1977), given by⎛ ⎞ ⎧0 ≤ EC < EC∗⎨1α⎝EC⎠ = 1 − b(EC − EC∗ ) EC∗ ≤ EC < EC ∗ +1/b)(1)⎩0EC ≥ EC ∗ +1/b

The above salinity response functions were fitted to the observed
data by minimizing the sum of the squared errors (SSE) between
measured (Oi) and model-predicted (Pi) values using unweighted leastsquares regression:∑n
SSE =(Pi − Oi )2(4)
i=1
where n is number of data-points. Since the reduction functions havedifferent numbers of fitting parameters, the Akaike information criterion(AIC) was further used to compare the performance of the three models.
Since the number of data-points (n) in comparison to the number offitting parameters (q) was relatively small in our study (n exceeded q by
only four or five), we used the corrected AIC (Bolster and Hornberger,
2007; Razzaghi et al., 2016) as follows:
((
)
)
SSE
2(q + 1)(q + 2)
AIC = n ln
+ 2(q + 1) +(5)nn− q− 2The salinity response or reduction function with the lowest value of
AIC was assumed to be the most accurate. To test if the improved ac­
curacy was statistically sound and justified the extra parameters, theprobability (P) was calculated using (Razzaghi et al., 2016):P=

exp (0.5∆)
1 + exp (0.5∆)(6)in which Δ is the absolute difference between the AIC values of the two
reduction functions being evaluated. P values were used to ensure thatthe decrease in SSE, required to accept the more accurate reduction
function, is reasonable (Bolster and Hornberger, 2007; Razzaghi et al.,2016). After verifying the reduction functions, and to compare thevarious seed pre-treatments, the selected function parameters wereanalyzed following a completely randomized design with three
replicates.
3. Results and discussion
3.1. Seed germination as influenced by salinity and seed pre-treatment
Values of the germination percentage (GP) and the germination rate(GR) as affected by salinity are shown in Figs. 2 and 3, respectively, forboth the control and the most effective seed pre-treatment concentra­
tions. Table 2 lists the optimized values of the linear reduction functions
for the optimal pretreatment concentrations as well as those for theother concentrations. Notice that the optimal pretreatment values were
the same for GP and GR, being 1.5 mM for Spd, 75 mM for Asc, 12 mM

where α is the specific trait being considered in relative terms
4F. Feghhenabi et al.

Agricultural Water Management 253 (2021) 106925

Table 2
Influence of the seed pretreatments on seed germination according to the pa­
rameters of regression lines.
Seed pretreatment

Concentration

Asc (mM)

Pro (mM)

K2SiO3 (mM)

Fig. 2. Effect of salinity on germination percentage (GP) of Borage untreated ortreated with the selected most effective seed pre-treatment concentrations (Spd,spermidine; Asc, ascorbic acid; Pro, proline; K2SiO3, potassium silicate; LUsw,
Lake Urmia saline water; Con, control).

Spd (mM)

LUsw(mg L− 1)

GP (%)GR (per day)Slope

yintercept

Slopeyintercept

0
50
75
100
0
12
17
25
0
1
1.5
2
0
0.5
1
1.5
0

-1.76ab-1.88bc-1.72a-1.92c-1.76a-2.15b
-2.94c-2.82c-1.76a-2.50c-2.39b
-2.51c-1.76a-2.23c-2.12b
-2.03b
-1.76a62.61c61.70c67.85a64.28b
62.61c89.22a84.03b85.06b
62.61d78.01c85.19a81.93b
62.61d69.05b
67.45c75.33a62.61c-0.020ab-0.021ab-0.019a-0.022b-0.020a-0.020a-0.023b-0.023b-0.020b-0.019b-0.016a-0.016a-0.020ab-0.021ab-0.021c-0.019a-0.020ab

0.94a
0.79c
0.95a
0.84b
0.94c
1.07a
0.88d
0.98b
0.94a
0.91b
0.95a
0.87c
0.94b
0.90c
0.91c
0.98a
0.94ab50
100
150-2.58b
-2.58b
-2.54b79.51b
78.49b85.75a-0.022b-0.021b-0.018a

0.92b
0.91b
0.94a

All GP and GR data are means. Values for each pretreatment compound followedby the same letter(s) are not significantly different according to Tukey’s test
(P < 0.05). Asc, ascorbic acid; Pro, proline; K2SiO3, potassium silicate; Spd,spermidine; LUsw, Lake Urmia saline water; GP, final germination percentage;GR, germination rate.

impacts of salinity stress on both GP and GR, as depicted by the upward
shift of the curves in Figs. 2 and 3. During seed pre-treatment, the ac­tivity of XTH (xyloglucan endotrans hydrolase) improves to lead to anincrease in the production of endo-β-mannanase, while simultaneously
facilitating causing some cytoskeleton reorganization which is necessary
for cell wall loosening. These modifications have been shown to accel­
erate the germination process (e.g., Lutts et al., 2016). Kazemi andEskandari (2012) concluded that although seed pre-treatment presum­
ably ameliorates seedling growth under saline conditions, seed andseedling performance seem to be affected by pre-treatment in aconcentration-dependent manner. Moreover, the effects of concentra­tion may depend on the type of pre-treatment.
The results in Table 2 show that the most effective concentrations ofSpd, Asc, Pro, K2SiO3 and LUsw improved the GR by 2%, 4%, 5%, 11%
and 13%, respectively. The GP improved by 10%, 11% and 3% at themost effective concentration of Asc, Pro and K2SiO3, respectively, while
the incremental effects of Spd and LUsw on GP were less than 1%. GR
hence was affected more by seed pre-treatment than GP. Results byHamama and Murniati (2010) showed that of various concentrations of
Asc (0, 55, 110, 165, 220, and 275 mM), 55 mM Asc produced the best
results for GP, speed of germination, VI, length of the shoots, primaryand seminal roots, and the number of seminal roots of maize. However,Hua-long et al. (2014) reported that increasing proline concentration asa seed pre-treatment (in the range of 5–45 mM) improved GR and therelative germination energy (RGE) of rice during salinity stress. Our
results are very similar to previous studies. For example, positive effects
of seed pre-treatment with silicon (Feghhenabi et al., 2020; Shi et al.,2014; Soukup et al., 2017; Torabi et al., 2012), polyamines (Farooq
et al., 2008), Spd (Paul et al., 2017), Asc (Sabaghnia et al., 2016) and Pro(Taie et al., 2013) have been reported for seed germination, plant
growth and/or plant salt tolerance.

Fig. 3. Effect of salinity on the germination rate (GR) of Borage untreated ortreated with the selected most effective seed pre-treatment concentrations (Spd,spermidine; Asc, ascorbic acid; Pro, proline; K2SiO3, potassium silicate; LUsw,
Lake Urmia saline water; Con, control).

for Pro, 1.5 mM for K2SiO3, and 150 mg L− 1 for LUsw. The data in
Table 2 indicate a significant effect of seed pre-treatment concentrationon the germination salinity reduction parameters. As reflected by theslope and y-intercept, Asc pretreatment at a concentration of 75 mM wasmost effective in reducing the effects of salinity on both GP and GR. The
Pro data showed that although the 12 mM concentration produced amore negative slope than the control, the y-intercept was higher thanthat for control. K2SiO3 seed pre-treatment at a concentration of 1.5 mM
had the most positive effect on increasing the y-intercept of GP (by 35%
as compared to the control). The highest y-intercept for GR was recorded
for the control and K2SiO3 at a concentration of 1.5 mM. K2SiO3 at aconcentration of 1.5 and 2 mM reduced the slope of GR by 20% ascompared to the control. An Spd concentration of 1.5 mM increased they-intercept of GP and GR more than the other concentrations. This
pretreatment concentration produced a milder slope of GR as comparedto the control and the other SPd concentrations. The most positive effectusing LUsw was found with a concentration of 150 mM as shown by the
y-intercept of GP and GR and the slope of GR. Results for the control
indicated that the negative influence of salinity was higher on GP thanon GR, with a 56% decrease for GP at EC = 20 dS m− 1, compared to a
40% decrease for GR (see also Figs. 2 and 3).
The negative impact of salinity on germination may be due to theinhibiting effects of osmotic stress and/or a reduction in mobilization ofseed food reserves at low salinity, except at higher salinities where ion
toxicity may be the main reason for the negative response (Kha­jeh-Hosseini et al., 2003). Seed pre-treatments moderated the inhibitory

3.2. Seedling emergence as influenced by salinity and seed pre-treatment
Salinity had a negative effect on the growth and development of the5

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Agricultural Water Management 253 (2021) 106925

seedlings during the Phase-2 experiments. Fig. 4 shows results for thefour most important traits we investigated. We note that all plots in
Fig. 4 are relative to the values of the control (i.e., no seed pre-treatment,
and without any subsequent salinity stress). Each value hence reveals
the combined effects of salinity and seed pre-treatment.
The response of seedlings to salinity was different for all salinity
levels. In actuality, most traits responded in a similar fashion toincreased salinity (Fig. 4). They showed a negligible decrease up to somesalinity threshold, after which salinity had a much more significant ef­
fect. For example, Emax, R50 and REU of the control declined to about
95%, 65% and 55%, respectively, at EC = 14 dS m− 1. Among theseedling properties, root dry weight proved to be the most sensitive tosalinity, showing reductions of 93% at a salinity of 14 dS m− 1 (Fig. 4 andTable 3).
While most seed pre-treatments reduced the negative effects ofsalinity (Fig. 4), some pre-treatments produced lower amounts of thetraits at relatively mild salinities (notably Asc for relative stem dryweight at salinities less than 6 dS m− 1; see Fig. 4d). Seed pre-treatments
had positive effects in two ways: i) by inhibiting the rate of salinityinduced reductions of the traits (e.g. of Emax for all pre-treatments
except K2SiO3 and seedling stem dry weight), and ii) by delaying theinitial decline of the measured traits (e.g. of R50, seedling root dry
weights). For some of the traits, several seed pre-treatments had a pos­
itive effect in terms of both aspects (e.g. for VI derived from Pro). In thisregard, Chomontowski et al. (2020) also concluded that the main effects
of seed pre-treatment were enhancement of seed viability, accelerationof germination, and seedling emergence. Overall, the general patterns of
the traits in terms of their response to salinity were not changed by seed
pre-treatment. For all seed pre-treatments, the traits decreased only
marginally at relatively low salinities, whereas the rate of decrease andthe threshold EC values from which the trait started to decrease were not
the same among the seed pre-treatments. These effects are assessed andquantified next in terms of the modeling approach.
The three salinity response functions given by Eqs. (1), (2) and (3)were used to describe changes in the traits in response to salinity under
the influence of the seed pre-treatments. As an example, Fig. 5a shows
the overall fit of the functions to the VI vs. salinity data for the control
and the Spd treatments. In general, the van Genuchten and Hoffman(1984) response function had the lowest AIC values, followed by theMaas and Hoffman (1977) function (Fig. 5b). While those two functions
statistically did not show significant differences in terms of their AIC
values, they performed significantly better than the Dirksen et al. (1993)
function (Figs. 5b and 6). Based on this finding, we focus further only on
the van Genuchten and Hoffman (i.e., EC50) and Maas and Hoffman (EC*and b) parameters (Table 3). Averages of their functional parameters arelisted in Table 3 (to be discussed in detail next).
3.3. Quantitative representation of the effects of seed pre-treatment onplant response to salinity
Generally, by raising salinity levels, values of the various seed
emergence characteristics all decreased for both the control and all pretreatments. This reflects the fact that salinity slows down the overall
growth process. Although growth reduction in most plants is a common
effect of salinity stress (Skaggs et al., 2006; Chen and Arora, 2011), theactual biochemical and physiological processes responsible for the re­ductions are still not well understood (Munns and Tester, 2008; Noreen
et al., 2010). Our study confirmed that pre-treatment of the seeds alle­viated the adverse effects of salinity. Parameters of selected Maas andHoffman (1977) and van Genuchten and Hoffman (1984) salinity
response functions as given in Table 3 are now used to compare theeffects of seed pre-treatment on various indices of the emergence ofBorage seedlings.
The maximum emergence percentage (Emax) of the control started todecrease at EC = 5.5 dS m− 1 (EC*), with the decline reaching 50%(EC50) at EC = 10.5 dS m− 1. EC* values for Emax as affected by Asc, Pro,

Fig. 4. Effect of salinity on relative values of Emax (a), R50 (b), VI (c) and stem
dry weight (d) of Borage untreated or treated with the selected most effective
seed pre-treatment concentrations (Spd, spermidine; Asc, ascorbic acid; Pro,
proline; K2SiO3, potassium silicate; LUsw, Lake Urmia saline water;Con, Control).

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Table 3
Influence of selected concentrations of seed pre-treatments on optimized parameters of the Maas and Hoffman (1977) and van Genuchten and Hoffman (1984) salinity
response functions used in this study for seedling and emergence attributes.
Trait

Parameter

Seed pre-treatment
Control

Asc

Pro

K2SiO3

Spd

LUsw

Emax (%) (final maximum emergence rate)

EC*
b
EC50
EC*
b
EC50
EC*
b
EC50
EC*
b
EC50
EC*
b
EC50
EC*
B
EC50
EC*
b
EC50
EC*
b
EC50

5.5ab
0.101a10.5a
2.9c
0.065b10.1c
3.8a
0.063ab
11.6ab4.0b0.110b8.2c
3.5c
0.044c
14.3a
4.2bc
0.056b12.2c
1.5c
0.061c
8.2c
4.4bc
0.061a11.4b

4.6b
0.077a10.8a
4.0bc
0.089a11.1bc0.4b
0.066a8.1d
3.7b
0.091bc
9.1b
6.5a
0.074b13.5a
2.3d
0.066ab
10.8d
3.9b
0.082b10.7b3.6c
0.038c
14.1a

6.9ab
0.084a12.9a
4.3b
0.066b12.7a0.0b0.048b10.1bcd
5.8a0.101bc
10.7a
5.1b
0.047c
14.3a
6.5a
0.071a13.6ab4.5b0.063c12.0a
6.1a
0.057ab
13.6a

7.9a
0.113a12.3a
5.8a
0.092a11.7ab3.0a0.069a11.1abc
5.7a0.130a10.7a
7.6a
0.114a13.2ab5.0b0.056b14.4a
7.2a
0.133a12.4a
5.6ab
0.053ab
14.4a

6.3ab
0.078a12.5a
5.1ab
0.085ab
11.6b
3.3a0.050b
12.8a
5.3a
0.143a9.7b
3.7c
0.067b11.9b
4.9b0.067a13.5ab4.3b0.125a10.9b
5.1ab
0.050b13.7a

6.0ab
0.067a12.5a
3.8bc
0.071ab
11.7ab0.0b0.064ab
8.7 cd
4.0b0.089c9.5b
3.9c
0.069b11.5b3.7c
0.056b
12.9bc1.7c
0.089b7.9c
5.9a
0.060a13.3a

R50 (per day) (rate at which seedlings reached 50%)
REU (per day) (reciprocal of emergence uniformity)
VI (vitality index)
Root length (cm)Stem length. (cm)
Root dry weight (g)Stem dry weight (g)EC*, salinity threshold at which the reduction begins; b, reduction slope in response to salinity; EC50, EC at which traits are reduced by 50%; p, a dimensionlessempirical shape parameter; Con, control; Asc, ascorbic acid; Pro, proline; K2SiO3, potassium silicate; Spd, spermidine; LUsw, Lake Urmia saline water. All data aremeans. Values for each pretreatment compound followed by the same letter(s) are not significantly different according to Tukey’s test (P < 0.05).

Fig. 6. P values calculated to test at what probability level the salinity response
function with the lowest AIC is the most accurate function (M&H: Maas andHoffman; DR: Dirksen et al.; vG: van Genuchten and Hoffman).

K2SiO3, Spd and LUsw were 4.6, 6.9, 7.9, 6.3 and 6.0 dS m− 1, respec­tively. EC50 and b were not affected significantly by seed pre-treatments(Table 3). Results by Carrillo-Reche et al. (2018) showed that seed
pre-treatment has a significantly positive effect on seedling performancewith an increased final emergence of 11%. Cell cycle pre-activation ofcell cycle is a likely reason for the better performance of pre-treatedseeds over un-treated seeds (Lutts et al., 2016).
Higher values of the 50% emergence rate (R50) is the primary factor
that ensures overall seedling performance. Islam et al. (2015) elaborated
that emergence rate is one of the crucial contributors of seed vigor undersalinity stress. The highest EC* value for R50 was obtained for K2SiO3-­
treated seedlings (EC* = 5.8 dS m− 1), and the lowest value for thecontrol seedlings (EC* = 2.9 dS m− 1). Seed pre-treatment hence
decreased the sensitivity of the seedlings to salinity by increasing EC* forR50. R50 of the control seedlings decreased to 50% (EC50) at EC= 10.1 dS m− 1, while EC50 values for Asc, Pro, K2SiO3, Spd and LUsw
were more than EC50 of the control (Table 3). Nonetheless, b for R50 was

Fig. 5. Overall fit of the applied reduction functions to the experimental data
for the vitality index (VI) versus salinity for the control and the spermidine(Spd) treated seeds (top), and comparison of the Akaike information criterion(AIC) among the reduction functions used (bottom).

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Agricultural Water Management 253 (2021) 106925

the highest for the Asc and K2SiO3 pre-treatments. Seed pre-treatment
increased the average EC* and EC50 values of R50 by 54% and 16%,
respectively. This shows that seed pre-treatment was more effective at
low salinities than at high salinities. Any factor that affects the emer­
gence rate also has the potential to change yield. By increasing the
emergence rate, the possibility of using growth factors increases andmore yield is achieved. Therefore, seed pre-treatment has the potential
to increase yield under saline conditions by accelerating and synchro­nizing germination and seed emergence, and subsequently enhancing
plant growth, improving stress resistance, and increasing the use effi­
ciency of nutrients and water (Lutts et al., 2016).
The uniformity of seedling emergence (EU) is an important charac­
teristic for field management. Nonuniformity in seedling emergence has
been shown to decrease yields. EU is the time from 10% to 90% of final
emergence (Liu et al., 2017). Reciprocal values of the emergence uni­
formity (REU) were fitted to the reduction functions of the seed
pre-treatment scenarios; they indicated that EC*was reduced by Asc, Pro
and LUsw, while EC* for K2SiO3 and Spd was statistically on par with thecontrol. However, the REU obtained for Spd had EC50 values higher thanthose of the control, while the other seed pre-treatments had mostly
lower EC50 values. On the other hand, the slope b of REU decreased for
the Spd and Pro seed pre-treatments, but increased or remained the samefor the other pre-treatments compared to the control. The positive effectof seed pre-treatments on EC* of Emax and R50 was not observed on EC*of the reciprocal of the emergence uniformity (REU). The positive effectof seed pre-treatments on EC* of Emax and R50 and the lower EC* for REU
by some seed pre-treatments showed that seed pre-treatment is more
effective in reducing the time required for 50% seedling emergence,
than improving 90% seedling emergence.
The vitality index (VI) is a composite indicator accounting for three
attributes (root length, stem length and Emax). Being one of the primary
determinants of high yield, VI is directly and negatively affected bysalinity (Rajabi Dehnavi et al., 2020). The vitality index is one of themost essential indicators of improved crop management, particularly of
optimal crop establishment and increasing the plant’s ability to tolerate
different stress (He et al., 2019). The Pro, K2SiO3 and Spd pre-treatments
were found to delay the decline of VI (EC*) to higher salinities. Asc and
LUsw had no significant effect on EC*. However, K2SiO3 along with Spd
increased the slope b of VI, while Asc, Pro and LUsw decreased the valueof b. All seed pre-treatments had a positive effect only on EC50 of VI, with
K2SiO3 and Pro (both 31% higher than the control) being the mosteffective. Sarika et al. (2013) reported that seed pre-treatment improved
the seed quality and showed improved seedling length and seedling dryweight, which in turn improved higher seedling vigor, germination rates
and mean germination times.
The highest root length EC* was obtained with the Asc, Pro andK2SiO3 pre-treatments (which were 84%, 46% and 116% higher than thecontrol, respectively). However, EC50 did not change significantly with
the Asc, Pro and K2SiO3 seed pre-treatments because of a simultaneous
increase in the reduction slopes, b’s. EC* and EC50 of stem length for thecontrol were 4.2 and 12.2 dS m− 1, respectively. A possible reason for the
reductions in stem and root length is decreased turgor pressure at higher
salinities, which reduces cell enlargement and shortens shoots (Izzoet al., 1991). Reductions in stem length were delayed by the Pro, K2SiO3and Spd pre-treatments relative to the control. Furthermore, the stem
length average EC50 values of the pre-treatments (except Asc) wereabout 11% higher than the control. The highest EC* and EC50 values
were obtained with Pro and K2SiO3, respectively. However, K2SiO3
along with LUsw had no significant effect on b of stem length. The otherseed pre-treatments increased b.
Of all measured traits, root dry weight had the highest susceptibility
to salinity (according to EC* for the control). However, all seed pretreatments (except LUsw) were able to improve EC* and EC50 of root
dry weight. The highest EC* value was obtained with K2SiO3. Moreover,K2SiO3 along with Pro had the highest EC50. The increasing effect of allseed pre-treatments (except Pro) was observed on b. Indeed, all seed pre-treatments except Pro had a negative effect on b. EC* and EC50 values ofstem dry weight for the control were 4.4 and 11.4 dS m− 1, respectively.
Seed pre-treatments increased the EC* and EC50 values of stem dryweight (except EC* for Asc). The highest EC* values of stem dry weight
were for Pro. The effect of all seed pre-treatments on EC50 was statisti­cally similar and higher than the control. Moreover, all seed pretreatments except LUsw decreased b of stem dry weight. Among these,
Asc was the most effective seed pre-treatment with a 37% reduction.
When averaged over all traits, seed pre-treatment increased themean salinity threshold, EC*. The most effective pre-treatment was
K2SiO3, which increased the mean EC* values to 5.9 dS m− 1 (60%),
compared to 3.7 dS m− 1 for the control (Table 3). The effect of Spd andPro on the EC* was less than that of K2SiO3. The mean EC value at which
various traits were reduced by 50% (i.e., EC50) increased from
10.8 dS m− 1 for the control to 11.2, 12.5, 12.5, 12.1 and 11.2 dS m− 1 for
the Asc, Pro, K2SiO3, Spd and LUsw pre-treatments, respectively. Sta­tistically, the effects of Pro, K2SiO3, and Spd on EC50 were the same.
Moreover, EC50 of Asc, and LUsw were at the same level as the control.
In general, the effects of seed pre-treatments on most traits weremore positive on EC*and EC50, as compared to b. According to Table 3,
the most effective results in reducing the impacts of salinity were ob­tained with Pro, K2SiO3 and Spd. However, Fig. 3 indicates that mostseed pre-treatments particularly K2SiO3 and Spd had a positive effect onenhancing seedling attributes, especially VI, at most salinity levels. Ul­
timately, also from an economical perspective, one may conclude thatapplication of K2SiO3 is most effective of all seed pretreatments. Our
results about the positive effects of seed pre-treatment are in line withfindings by Anaya et al. (2015) for broad bean, Moghanibashi et al.
(2015) for sunflower, Patanè et al. (2009) for sweet sorghum, andFeghhenabi et al. (2020) for wheat.
4. Conclusions
Our results show that seed pre-treatment alleviates the negative ef­
fects of salinity on seed germination and establishment of Borage (Bor­ago officinalis L.) as evaluated in terms of indices such as germination
percentage (GP), germination rate (GR), final maximum emergence(Emax), the vitality index (VI) and seedling growth. Our analysis in termsof the salinity response functions provided quantitative insight about thetype of results to be expected. Furthermore, the salinity response func­
tions are potentially very useful for evaluating proposed or anticipatedchanges in operating conditions. According to the functional parameters(especially EC* and EC50) and the relative values of the seedling attri­butes, seed pre-treatment with Pro, K2SiO3 and Spd induced the morepositive changes in seedlings. Overall in terms of economics, applicationof K2SiO3 is the most appealing.
From a practical point of view, when saline water is available for
irrigation at sowing, it may be useful to use pre-treated seeds. Moreover,reduction functions can help to predict the critical salinity thresholdvalue and the rate of reduction at each salinity level. However, morestudies are required to better understand relationships between different
seed pre-treatment materials and salinity stress, and whether the posi­tive effects of seed pre-treatments on early growth are also applicableduring later growth stages. Similar analyses, also at the larger field
scales and for different soil types, hence may be needed to obtain moredefinite conclusions.
Declaration of Competing Interest
The authors declare that they have no conflict of interest.
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