RESEARCH PAPER
https://doi.org/10.1071/CP21676

Quantitative evaluation of silicon applications on wheat
response to salinity: changes in photosynthetic pigments,
chlorophyll ﬂuorescence parameters, yield and yieldcomponents
Faride Feghhenabi A, Hashem Hadi A
Lachlan Lake E

For full list of author afﬁliations anddeclarations see end of paper*Correspondence to:
Habib Khodaverdiloo
Department of Soil Science,
Urmia University, Urmia, IranEmail: h.khodaverdiloo@urmia.ac.ir
Handling Editor:
Tina Acuna, Habib Khodaverdiloo B,*, Martinus Th. van Genuchten C,D and

ABSTRACT
Context. Salinity is a major cause of yield loss in wheat globally. Aims and Methods. To
investigate the potential of silicon to minimise the effect of salinity in wheat, experiments wereconducted using outdoor pots subjected to seven salinity treatments. Silicon (as potassium silicate
K2SiO3) was applied as both a priming agent and foliar spray. Selected response functions were used
to quantify wheat response to salinity as affected by silicon application. Key results. Concentrationof chlorophyll a, chlorophyll b and carotenoid decreased by 4.2, 3.6 and 1.4 mg/g FW respectively
with increasing salinity up to an electrical conductivity of 14 dS/m. Increasing salinity levels increased
maximum variable chlorophyll ﬂuorescence yield in a dark-adapted state and decreased thephotochemical quenching coefﬁcient, the nonphotochemical quenching coefﬁcient, nonphotochemical quenching, actual quantum yield of PSII electron transport in the light-adapted
state, and the apparent photosynthetic electron transport rate. The maximal efﬁciency of PSII
photochemistry in the dark-adapted state was not signiﬁcantly inﬂuenced by salinity. The
response functions showed that the salinity threshold value and the salinity at which a given trait
was reduced by 50% (EC50) were 5.7 and 12.1 dS/m, respectively. Conclusions. The combined
treatment of silicon (priming × foliar spray) was found to be the most effective, increasing
salinity threshold value and EC50 by 32 and 2% respectively. Implications. These ﬁndings giveinsight into the effects of salinity on wheat and demonstrate the potential of silicon applications
to promote crop health in saline environments.
Keywords: abiotic stresses, chlorophyll ﬂuorescence, foliar spray, plant production, priming,
reduction function, soil salinity, yield.

Introduction

Received: 28 September 2021
Accepted: 11 March 2022
Published: 4 April 2022
Cite this:Feghhenabi F et al. (2022)
Crop & Pasture Science, 73(10), 1118–1130.
doi:10.1071/CP21676© 2022 The Author(s) (or theiremployer(s)). Published by
CSIRO Publishing.

Wheat (Triticum aestivum L.), belonging to the Poaceae family, is a crop widely cultivatedfor its grains as a worldwide staple food. Wheat is well acclimatised to a broad range ofclimatic and soil conditions; however, salinity stress as a growing problem has a major
impact on wheat yield (Bybordi 2014). Salinity aﬀects almost 20% or around 800 million
hectares of arable land (Hussain et al. 2018) especially in semi-arid and arid agro-ecologicalzones (Shalaby 2018). Soils are classiﬁed as saline when salt accumulates to levels
equivalent to 40 mM sodium chloride (NaCl) or an electric conductivity (EC) of 4 dS/m,
at which point yield starts to decline (Acosta-Motos et al. 2017). Plants display various
physiological, biochemical, and molecular responses to salinity stress. Sensitivity tosalinity stress diﬀers with the stage of development, with more severe yield damage
during reproductive stages than during early vegetative growth (Shin et al. 2020).
Ideally, biomass or grain yield should be the ultimate criterion for salt tolerance(Hopmans et al. 2021). Reductions in yield and yield components under salinity stress

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Crop & Pasture Sciencecan be attributed to lower cell expansion rates, reduced
photosynthesis and increased senescence (Polash et al.
2019). The eﬀects of salinity vary with climate, salinity
intensity, plant species and soil type (Acosta-Motos et al.
2017; Shin et al. 2020). Salinity impairs ionic and osmoticbalances, with the accumulation of salts and the productionof reactive oxygen species (ROS) decreasing theconcentration of photosynthetic pigments such as
chlorophyll and carotenoid followed by the inhibitionof ribulose-1,5-bisphophate and degradation of thephotosynthetic apparatus. Furthermore, increasing salinity
signiﬁcantly reduces the photosynthetic O2 evolution andalso the ﬂuorescence yield, which is associated with alower activity of the PSII complex (Hussain et al. 2019).
Analysing chlorophyll ﬂuorescence is a robust techniquethat provides insights into photosynthetic activity andeﬃciency (Rastogi et al. 2020). Under saline conditions, ageneral decrease in photochemical quenching parameters(FV/Fm, Y(II), qP) and the electron transport rate (ETR)
occurs, but increases in non-photochemical quenchingparameters [qN, NPQ, Y(NPQ)] have also been reported(Acosta-Motos et al. 2015; see Table 1 for meaning ofabbreviations ). The response to salinity stress is correlatedwith decreases in photosystem II (PSII) eﬃciency andincreases in the non-photochemical quenching parameters
as mechanisms to safely dissipate excess energy. Moradi
and Ismail (2007) found that salinity reduced the ETR in asalinity-sensitive rice cultivar, whereas only a slight
reduction in ETR occurred in a salinity-tolerant cultivar;
salinity stress on the non-photochemical quenching parameters caused qN to increase more substantially in salinitytolerant cultivars than in the salinity-sensitive cultivars.
This research investigates the previously unquantiﬁed
reaction of these wheat chlorophyll ﬂuorescence parameters
to increasing salinity.
Silicon (Si) has recently been classiﬁed by the International
Plant Nutrition Institute as a “quasi-essential” element(Hussain et al. 2021). Accumulation of Si in cell tissues canchange the structure of the cell wall and protect theTable 1.

integrity of the cell membrane helping to create a physicalbarrier to stress (Nasircilar and Ulukapi 2021). Si improves
the rigidity and erectness of leaves, enhancing thephotosynthetic capacity, and modulates redox-homeostasis
processes (Rastogi et al. 2021). Si increases chlorophyll
content and photosynthesis through improved antioxidant
defence and decreased oxidative damage (Zhang et al.
2018). Rastogi et al. (2021) concluded that Si plays afundamental role in the establishment of a proton (H+)
gradient across the thylakoid membrane, biosynthesis ofchlorophyll and electron transport systems via positiveeﬀects on ionic balance and mineral nutrient uptake(including potassium, magnesium, iron, manganese andphosphorus). Photosynthesis-related proteins such as
photosystem-I, -II, RubisCO, and other chloroplast-related
proteins are regulated by Si during exposure to salineconditions (Liu et al. 2019; Rastogi et al. 2021). Moreover,Si-mediated enhancement of plastocyanin and ferredoxin
gene expression may contribute to increased electrontransport rate and photochemical eﬃciency (Zhang et al.
2018). Therefore, Si-mediated stress resistance mechanisms
can be linked to enhanced growth and biomass production,
increased photosynthesis eﬃciency, balancing ion
homeostasis, activation of the antioxidant system in plants,
elicitation of secondary metabolites related to stressresistance, and regulation of genes involved in various
physiological processes, particularly those associated with
nucleotide binding and transferase activity. Si has also beenshown in Capsicum annuum (Bugwang) to modulate theexpression of vital proteins involved in ubiquitin-mediatednucleosome pathways and carbohydrate metabolisms. Undersaline conditions, Si decreases the apoplastic transport ofsodium and chloride ions (Na+ and Cl−, Liu et al. 2019).
The concentration of available Si for plants is aﬀected by
pH, EC, water status, temperature, and accompanying ionsin the soil. Wheat actively takes up Si, and thus reducing
the availability of Si in the soil as a result of salinity cancause signiﬁcant Si depletion in the plant (Yan et al. 2018).
Foliar spray of silicates induced a higher Si content in silica

Chlorophyll ﬂuorescence (CF) parameters and associated formulas.

Parameter

FormulaDescription

FV

Fm − Fo

Maximum variable chlorophyll ﬂuorescence yield in a dark-adapted state

FV/Fm(Fm − Fo)/Fm00

The photochemical quenching coefﬁcient0

1 − ðF m − F o Þ=ðF m − F o Þ
0

ϕPSII

Non-photochemical quenching coefﬁcient0ðF m − F s Þ=F m
0

The actual quantum yield of PSII electron transport in the light-adapted state0ðF m − F m Þ=F m

NPQ
ETR

The maximal efﬁciency of PSII photochemistry in the dark-adapted state0ðF m − F s Þ=ðF m − F o Þ

qP
qN

0

Non-photochemical quenching

0

ΔF=F m × PPFD × 0.5 × 0.84

Apparent photosynthetic electron transport rate
00Fo, the minimal ﬂuorescence level; Fm, the maximal ﬂuorescence level; F o , the minimal ﬂuorescence level in the light-adapted state; F m , the maximal ﬂuorescence levelin the light-adapted state; Fs, the steady-state value of ﬂuorescence; PPFD, photosynthetic photon ﬂux density.

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F. Feghhenabi et al.

cells together with a signiﬁcant increase in uptake andaccumulation of Si in the leaves of rice (Laane 2018).
Exogenous application of Si through either priming or as afoliar spray has been suggested as a way to promote yieldand stress tolerance (Liu et al. 2019). Priming can stimulate
stress responses and protect seedlings from initial stress
exposure, while foliar spray treatments during early growth
may provide additional protection from later stresses(Subramanyam et al. 2019).
Accordingly, and in addition to providing a quantitative
assessment of wheat response to salinity at maturity, ourstudy aims to determine to what extent Si can buﬀer salinity
damage through modulation of growth and biochemicalcompounds. Our objectives were to explore the quantitative
response of chlorophyll ﬂuorescence, chlorophyll content,
leaf area index, yield, and yield components of wheat exposed
to diﬀerent levels of salinity and determine the eﬃcacy ofremedial Si treatments.

Materials and methods
Plant materials and growing conditions
Healthy uniform-sized seeds of wheat (Triticum aestivum L.)
variety Chamran were prepared by the Cereal Laboratory of
the Agricultural Research Center of West Azerbaijan Province,
Urmia, Iran. The mean 1000-grain weight and seed moisture
content were 35.7 g and 9–10%, respectively. A greenhousebased pot experiment was run outdoors in 2017 and repeatedin 2018. The maximum and minimum temperatures andphotoperiods were, respectively, 23.2°C, 8.7°C and 9.6 h in2017, and 26.5°C, 12.3°C and 10.2 h in 2018. Seed wassown on 30 March 2017 and 23 March 2018. Seeds weresown in PVC pots, 30-cm high with 28-cm diameter, eachpacked with 16.4 kg of farm soil. The loam soil consisted ofsand (46%), silt (33%) and clay (21%), and was non-saline(the EC of the saturation paste was 1.1 dS/m). The soil
contained 18.5% equivalent CaCO3, and had a pH of 7.7.
Pots were watered immediately after sowing with diﬀerent
concentrations of saline water, prepared from Lake Urmia,
Iran (0, 3, 5, 8, 10, 12 and 14 dS/m); watering with saline
solution was maintained until harvest. The amount of saline
water required per pot at 30% of the ﬁeld capacity was 2.4 L.

Si treatments
Si treatments included priming, foliar spray and combination
treatments (priming × foliar spray). The controls were thosewith no Si addition. Si priming was accomplished by soaking
seeds in 1.5 mM potassium silicate (K2SiO3) solutions for 6 h.
The Si concentrations were selected based on previous
research (Feghhenabi et al. 2020). Once primed, the seeds
were rinsed with tap water followed by distilled water anddried back to their original moisture content at room
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Crop & Pasture Sciencetemperature (25°C and 42% relative humidity) as
determined by seed weight. All seeds were subsequently
surface-sterilised in a 1% sodium hypochlorite solution for
10 min. Foliar spray of Si (K2SiO3, 4 mM) was applied
twice: at early and late stages of tillering (31 and 45 days
after sowing) (Bybordi 2015; Alzahrani et al. 2018). Tween
20 (polyoxyethylenesorbitan monolaurate, Sigma Chemicals,UK) at 0.01% (v/v) was used as a surfactant to increase foliar
spray eﬃciency by improving solution penetration. The foliar
sprays were given at the early and the late stages of tillering(31 and 45 days after sowing). The ﬁrst foliar spray wasapplied just before sunset and the second, before dawn.

Chlorophyll content
Chlorophyll a (Chl a) and b (Chl b) as well as carotenoid (CAR)
content were determined according to Lichtenthaler andWellburn (1983), with slight modiﬁcations, for a period of
2 years. Samples (0.01 mg) of fully matured ﬂag leaves ofwheat at the early ﬂowering stage were taken and extractedwith 95% methanol. Absorption was read using a spectrophotometer (T60UV-Visible-1100 nm, PG Instruments, UK) atwavelengths of 470, 645 and 663 nm. Chlorophyll contents
were calculated using the following equations:
Chl a ðmg=g FWÞ = ½ð12.25 × OD663 Þ − ð2.79 × OD645(1)

Chl b ðmg=g FWÞ = ½ð21.21 × OD645 Þ − ð5.1 × OD663(2)

CAR ðmg=g FWÞ = ½ð1000 × OD470 Þ − 1.82 × Chl a − 85.02
× Chl b =193

(3)

where ODx is the absorbance of the extracts at wavelength x.

Chlorophyll ﬂuorescence
Chlorophyll ﬂuorescence was measured on fully expanded
ﬂag leaves of 50-day-old plants using a portable ﬂuorometer(PAM-2000, Walz, Germany) following Genty et al.’s (1989)
method. After sunset, the ﬂuorometer was connected to aleaf-clip holder with a trifurcated ﬁberoptic and to a computerequipped with data acquisition software. The minimal
ﬂuorescence level (Fo) with all PSII reaction centres open,
and the maximal ﬂuorescence level (Fm) with all PSII
reaction centres closed, were determined on dark-adapted
leaves. Then the leaves were continuously illuminated witha white actinic light at an irradiance of 180 μmol/m2/s tomeasure the steady-state value of ﬂuorescence (Fs), which
occurred around 6 min after the initiation of white actinic
light. The maximal ﬂuorescence level in the light-adapted0state (F m ) was recorded after subjecting the leaves to asecond saturating pulse at 8000 μmol/m2/s. The minimal0
ﬂuorescence level in the light-adapted state (F o ) was
determined by exposing the leaf to far-red light for threewww.publish.csiro.au/cp

Crop & Pasture Science

seconds. We used the light and dark ﬂuorescence data tocalculate the parameters presented in Table 1.

Leaf area index and productivity parameters
Destructive sampling of the plants from three pots was
conducted to estimate the Leaf Area Index (LAI) during the
pre-ﬂowering stage. LAI was calculated by scanning theleaves with an area meter (LI-3100C Area Meter; LI-COR,
Lincoln, NE). Seed yield per plant was calculated using
plants harvested at physiological maturity (ripe grain
stage). Yield components measured included spike number,grains per spike and 1000-grain weight.

Data analysis
Leaf area index, chlorophyll content, yield and yield components were reported as means of six replications, whilechlorophyll ﬂuorescence parameters were reported as meansof three replicates. The data were subjected to analysis ofvariance (ANOVA) using SAS Statistical software ver. 9.4.
Multiple comparisons were performed using the protected
least signiﬁcant diﬀerence (PLSD) test at the P < 0.01 levelof signiﬁcance. Excel 2017 and SigmaPlot ver. 14 (Systat
Software) were used to create the ﬁgures and conduct the
regression analysis.

Quantifying the yield components and yield
response to salinityαðECÞ =

1
1 − bðEC − EC:00 ≤ EC ≤ EC
EC < EC ≤ EC + 1=b
EC > EC + 1=b(4)

1
1 + ðEC=EC50 ÞPαðECÞ =

1
1
1 + ½ðEC − EC Þ=ðEC50 − EC

(5)where EC50 is the soil salinity at which α(EC) is reduced by
50%, and P is a dimensionless shape parameter. For many
crops, the value of P on average was found to be about 3 whenEqn 5 was applied to salinity stress data (van Genuchten and
Gupta 1993). As shown by Steppuhn et al. (2005) andvan Genuchten and Gupta (1993), among others, Eqn 5 was

0 ≤ EC ≤ EC
P

EC > EC(6)

The above macroscopic response functions were ﬁtted to theobserved data by minimising the sum of the squared errors(SSE) between measured (obsi) and model-predicted (predi)
values of the dependent variable using unweighted leastsquares regression:n

SSE = Σ ðpredi − obsi Þ2 ,
i=1

(7)

where n is the number of data-points. The Akaike information
criterion (AIC) was used to compare the performance of themodels; this is because they have diﬀerent numbers ofﬁtting parameters, and more ﬁtting parameters normallywill lead to a better ﬁt (i.e. lower SSE values). Since in ourstudy the number of data-points (n) is small in comparison
to the number of ﬁtting parameters (q), we used thecorrected AIC (Bolster and Hornberger 2007; Razzaghi et al.
2016) as follows:n+ 2ðq + 1Þ +2ðq + 1Þðq + 2Þ
n−q−2

(8)

The response function with the lowest value of AIC wasassumed to be the most accurate. To test if the improved accuracy is statistically sound and justiﬁes the extra parameters,
the probability was calculated using a function used by
Bolster and Hornberger (2007) and Razzaghi et al. (2016):
P=

where α is the speciﬁc trait being considered in relative terms(0 ≤ α ≤ 1) as a function of soil salinity, EC (dS/m), EC* is thesalinity threshold value and b is the slope of the response
function. Since the linearity assumption of Eqn 4 often does
not fully meet actual ﬁeld conditions, van Genuchten and
Hoﬀman (1984) suggested an alternative S-shaped functionof the form:
αEC =

(

AIC = n ln

The most common salinity response function traditionally has
been the linear threshold slope model introduced by Maas andHoﬀman (1977). Written in terms of the soil electricalconductivity, EC, this response function is given by:
8<

found to describe crop salinity tolerance data equally wellor better than Eqn 4. Dirksen et al. (1993) later included athreshold salinity in Eqn 5 to enable a more ﬂexible
description of salinity response data at low salinities:

e0.5Δ
1 + e0.5Δ(9)where Δ is the absolute diﬀerence between the AIC values of
the two response functions being evaluated. Probability (P)values were used to ensure that the decrease in SSE, required
to accept the more accurate response function, is reasonable(Bolster and Hornberger 2007; Razzaghi et al. 2016).

Results
Effect of salinity and Si on photosynthetic
pigments
Photosynthetic pigments (Chl a, b and CAR) were foundto decrease linearly with increasing salinity (Fig. 1a–c).
Concentrations of Chl a, b and CAR for the control
dropped by 20, 29, and 28% in the most saline treatment
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F. Feghhenabi et al.
32

Chl a (mg/g FW)

30

Crop & Pasture Science(a)C, y = −0.2688x + 20.214, R2 = 0.77
P, y = −0.2732x + 20.212, R2 = 0.84
F, y = −0.4373x + 24.105, R2 = 0.97
P F, y = −0.3948x + 24.102, R2 = 0.98(b)C, y = −0.2666x + 12.232, R2 = 0.97
P, y = −0.264x + 12.678, R2 = 0.98
F, F, y = −0.0966x + 4.9078, R2 = 0.95 7
P F, y = −0.5306x + 17.409, R2 = 0.98

28
26
24
22
20
18
16
24

Chl b (mg/g FW )

22
20
18
16
14
12

Effect of salinity and Si on chlorophyll
ﬂuorescence

108

CAR (mg/g FW )7

C, y = −0.0846x + 4.6652, R2 = 0.90
P, y = −0.0746x + 4.6163, R2 = 0.93
F, y = −0.0966x + 4.9078, R2 = 0.95
P F, y = −0.0929x + 4.9142, R2 = 0.95

(c)

6
5
4
3

32
ETR (μmol quanta/m2.s)

Priming did not have an eﬀect on Chl a, b and CAR in
the control and salinity treatments, with none of thetreatments having a signiﬁcant eﬀect (P = 0.4195) on the
CAR concentration (Fig. 1a–c). On average, foliar
spray resulted in increases of 18% Chl a and 35% Chl b(Fig. 1a, b) compared to the control, which persisted forEC ≤ 8 dS/m. For EC > 8 dS/m the increase dropped to
11% for Chl a and 21% for Chl b. Since there was nodiﬀerence between the foliar and combination treatments,
we conclude that foliar spray was the primary driverof the increases. The combination and foliar treatments
both lowered the rate of salinity induced decreases in
Chl a and b more than the control and primed treatments.
The faster rate of reduction for Chl a and b in the foliar
spray and combination treatments can be linked tohigher concentrations of Chl a and b at lower salinities(Fig. 1a, b).

C, y = −0.5734x + 28.578, R2 = 0.97
P, y = −0.3374x + 27.402, R2 = 0.94
F, y = −0.2904x + 27.134, R2 = 0.75
P F, y = −0.254x + 27.477, R2 = 0.65

(d)

30
28
26
24
22
20
0

2

4

6

8

10

12

14

Salinity (dS/m)

Fig. 1. Changes of Chl a (a), Chl b (b), CAR (c) and ETR (d) as affectedby different salinity treatments. Bars represent mean values withstandard deviation. Chl a, chlorophyll a; Chl b, chlorophyll b; CAR,
carotenoid; ETR, apparent photosynthetic electron transport rate, C,
control; P, priming; F, foliar spray.

(EC = 14 dS/m) (Fig. 1a–c). For all treatments, Chl aconcentrations were higher than those of Chl b (Fig. 1a, b).
The reductions of 19, 31 and 29% are shown for Chl a, b
and CAR, respectively (Table 2).
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All Si treatments and salinity signiﬁcantly aﬀected theparameters of chlorophyll ﬂuorescence, except FV/Fm(Table 3). Fo increased with intensifying salinity. The Si
treatments were associated with signiﬁcant declines in Fo.
Compared to the control, Fo decreased by 4, 16 and 16%
under the inﬂuence of priming, foliar spray and the
combination treatment, respectively (Table 4). Fm and FV
increased 8.5 and 6.5 units per unit increase in salinity.
Unlike priming, foliar spray and the combination treatment
signiﬁcantly reduced Fm and FV by an average of 14%(Table 4). Many chlorophyll ﬂuorescence parameters showed
little or no eﬀect by priming.
Eventually, the manner in which these two properties (Fmand Fo) changed against diﬀerent salinities led to FV/Fmstability (Table 3). The value of FV/Fm was in the normal
range at all salinity treatments, which could be due tohigher qP levels than qN.
Values of qP, qN, PSII, and NPQ all declined in response
to increasing salinity and were best described by quadratic
regression, except PSII which was linear (Fig. 2a, b). Values
of qP decreased under the inﬂuence of Si treatments;
however, statistically, the eﬀect of the Si treatments was
the same. Unlike qP, qN increased by an average of 20%
with all Si treatments except priming (Fig. 3).
PSII reduced with increasing salinity, with a slope of
0.5% (Fig. 2b). It is expected that with further reductions in
PSII, the plants were subjected to extra stress (Fig. 2b).
Compared to the control, the Si treatments reduced PSII
(Fig. 3). Fig. 3 also shows the reducing trend of NPQ,
which was relatively strong up to EC = 8 dS/m and thenmilder. Statistically, the eﬀect of priming was not signiﬁcant.
Instead, foliar spray and the combination treatment increased
NPQ by up to 22% (Fig. 3).

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Crop & Pasture Science

Table 2. Analysis of variance of photosynthetic pigments, yield, yield components and LAI of wheat as affected by Si treatments of salinity stress fortwo consecutive years (2017–2018).
SOV

d.f.

Mean squares
Chl a

Chl b

CAR

Spike number
n.s.

Grains per spike

1000-grain weight

Grain yield

LAI

n.s.

Y

1

126.72**39.85**

6.46**

1.17

115.29**4.98

0.23**

6.03**

B

4

1.99n.s.

1.66n.s.

2.10**

4.42n.s.

14.19n.s.

13.64**

0.05*

0.76**

n.s.

n.s.

T

3

111.47**118.40**0.35

61.62**

1150.83**

3.94

1.40**

6.57**

S6

75.59**

92.89**

4.90**

449.45**8011.97**118.40**11.10**

9.68**

n.s.

n.s.

T×S

18

2.06

3.55**

0.03

3.49*

50.16**

0.43**

0.05**

0.23**

Y×T

3

2.33n.s.

1.56n.s.

0.04n.s.

0.66n.s.

1.52n.s.

0.18n.s.

0.003n.s.

0.01n.s.

Y×S

6

0.14n.s.

0.38n.s.

0.08n.s.

0.24n.s.

3.51n.s.

0.55n.s.

0.008n.s.

0.06n.s.

Y×T×S

18

n.s.

0.20

n.s.

n.s.

n.s.

n.s.

n.s.

n.s.

0.04n.s.

Error

108

1.90
7.04

CV (%)

0.15

0.01

0.30

3.20

0.18

0.003

1.57

0.37

1.97

14.06

1.78

0.02

0.11

10.51

14.78

5.14

5.68

4.33

6.64

11.11

*, **Signiﬁcant at 5% and 1%, respectively.
SOV, sources of variation; Y, year; B, block; T, treatment; S, salinity; Chl a, chlorophyll a; Chl b, chlorophyll b; CAR, carotenoid; LAI, leaf area index; CV, coefﬁcient ofvariation; n.s., not signiﬁcant.

Table 3.

Summary of analysis of variance of chlorophyll ﬂuorescence (CF) parameters of wheat as affected by the Si treatments of salinity.

CF parameters

Si treatments

Si treatments × Salinity

Salinity

F-valueP-value

F-valueP-value

F-valueP-valueFo

47.65< 0.0001

117.93< 0.0001

1.65

0.08

Fm

12.21

< 0.0001

22.93< 0.0001

0.41

0.98

FV

9.61

< 0.0001

16.70< 0.0001

0.33

0.99

FV/Fm

0.04

0.99

0.04

1

0.10

1

PSII

6.99

0.00054.09

0.0019

0.72

0.78

ETR

25.10

< 0.0001

189.94< 0.0001

11.66< 0.0001

qP

6.23

0.001

4.83

0.0005

0.74

0.75

qN

20.59

<0.0001

14.11< 0.0001

0.84

0.65

NPQ

20.92

< 0.0001

8.21< 0.0001

0.96

0.52

Detailed information on each CF parameter is presented in Table 1.

As salinity treatments increased, the ETR of all Si
treatments and the control decreased linearly (Fig. 1d). The
Si treatments improved the ETR signiﬁcantly in the moststressful treatment (Fig. 1d). The combination treatment
was the most eﬀective at maintaining ETR with a 56%
reduction in the slope (Fig. 3).
Experimental functions were used to more accurately
study the LAI, yield and yield components, and to model
their response to salinity and the Si treatments (Table 5).
Results show that the van Genuchten and Hoﬀman (1984)
response function had the lowest AIC values, followed by
the Maas and Hoﬀman (1977) and Dirksen et al. (1993)
functions. Mean and median AIC values for the van
Genuchten and Hoﬀman (1984) and the Maas andHoﬀman (1977) functions were −41.4 and −40.8, and−34.8 and −36.9, respectively. The mean and median AIC

values were −26.1 and −24.5 for the Dirksen et al. (1993)
function, respectively. The ﬁrst to third quartile values of
the AIC for the van Genuchten and Hoﬀman (1984)
function were within the range of −49.3 to −37.0, while
they varied from −44.5 to −29.1 for the Maas andHoﬀman (1977) function, and from −34.7 to −19.7 for theDirksen et al. (1993) response function. While there was
generally no statistically signiﬁcant diﬀerence between
either the Maas and Hoﬀman (1977) and the Dirksen et al.
(1993) functions or the van Genuchten and Hoﬀman(1984) and Maas and Hoﬀman (1977) functions in termsof their AIC values (P = 0.053), the van Genuchten and
Hoﬀman (1984) response function provided ﬁts that were
statistically superior (P = 0.015) to the Dirksen et al.
(1993) function. Table 5 lists, for all Si treatments, theﬁtted parameter of the van Genuchten and Hoﬀman
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F. Feghhenabi et al.
Table 4.

Crop & Pasture Science

Effect of Si treatments and salinity on Fo, Fm, and FV.

CF parameters

Control

Si treatments
Priming

Salinity
Priming × Foliar spray

Foliar spray

Fo60.90 ± 0.36a

58.48 ± 0.58b

50.81 ± 1.97c

51.95 ± 0.93c

1.997x ± 40.701

Fm

339.14 ± 8.46a

326.76 ± 7.25a288.76 ± 17.61b

296.38 ± 5.19b

8.4779x ± 249.78

FV

278.24 ± 8.11a

268.29 ± 6.67a

237.95 ± 15.66b

244.43 ± 4.26b

6.4809x ± 209.08

Data are means ± s.d. of three replicates. Means followed by different letters indicate signiﬁcant difference according to PLSD test (P < 0.05). Detailed information oneach CF parameter is presented in Table 1.

0.50

(a)

0.45

ΦPSII

a

qP

qN

a

NPQ

2

qP, y = 0.0003x2 -0.0102x+0.42, R =0.99
qN, y = 0.0006x2 -0.0132x+0.317, R 2 =0.96

b

0.40

0.40

a

a
Parameters

Quenching coefficient

0.45

0.35
0.30
0.25

0.35

b

b

b

b
b

a
0.30
0.25

c
b

b

CONTROLPRIMING

bc
a

0.20

0.20

Photosynthetic parameters

0.50

(b)

Foliar spray priming*foliar spray

Treatments
2

ΦPSII, y = 0.0001x 2 -0.0064x +0.3397, R =1
NPQ, y = 0.0008x2 -0.016x +0.4195, R 2 =0.85

0.45
0.40

Fig. 3. Effect of treatments on some CF parameters. Data are mean ±s.d. Within the same trait, different letters indicate a signiﬁcantdifference by the PLSD test. Description of each CF parameter ispresented in Table 1.

0.35
0.30

Yield components

0.25

Spike number

0.20
0

2

4

6
8
Salinty (dS/m)

10

12

14

Fig. 2. Changes of qP, qN (a) and ϕPSII, NPQ (b) by different salinity
treatments. Each point represents the mean ± s.d. Detailed information
on each CF parameter is presented in Table 1.

(EC50) and Maas and Hoﬀman (EC* and b) response
functions.

Leaf area index
Increasing salinity caused LAI to decrease (Fig. 4a). Values of
EC* and EC50 of LAI for the control were 0.25 and 23.1 dS/m,
respectively. EC* increased up to EC* = 6.3, 5.8 and 8.2 dS/m
by priming, foliar spray and the combination treatment,
respectively, while EC50 declined for all of these treatments.
The reducing slope also increased sharply as a result of the
Si treatments (Fig. 4a). However, the relative LAI derived
from the Si treatments was higher than the control for all
salinity treatments.
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Increasing salinity caused the spike number to decrease forthe control and the Si treatments. Spike number for the control
began to decrease at EC* = 10.9 dS/m and was lowered by 50%(EC50) at EC = 15.8 dS/m (Table 5). The average EC* and EC50
values changed to 11.2 and 14.9 dS/m, respectively, by the Si
treatments. The function parameters showed that priming andthe combination treatment were more eﬀective, especially interms of improving relative spike numbers, than the foliar
spray (Fig. 4b).

Grains per spike
Salinity exhibited a signiﬁcant eﬀect on grains per spike.
Grains per spike of the control did not decreasesigniﬁcantly for EC* < 6 dS/m, after which they decreased
by 7%. For the control, EC50 was 13.2 dS/m (Fig. 4c). The
Si treatments increased the average EC* of grains per spikeby 17%, while priming and combination treatment
decreased EC50 by 2% and increased b by 51% (Table 5).
Priming and combination treatments were also more
eﬀective at increasing EC* than the foliar spray. However,they were not the best in increasing EC50 (Table 5, Fig. 4c).

www.publish.csiro.au/cp

Crop & Pasture Science

Table 5. Optimised parameters of the van Genuchten and Hoffman (1984) and Maas and Hoffman (1977) reduction functions used in this study.
Trait

Spike number

Grains per spike

1000-grain weight (g)Grain yield (g)LAIParameters

Control

Si treatments
Priming

Foliar spray

Priming × Foliar spray

EC*

10.93

11.44

10.91

11.21

b

0.107

0.169

0.107

0.146

EC50

15.81

14.39

15.59

14.85

EC*

5.99

7.14

6.03

7.80

b

0.067

0.096

0.069

0.106

EC50

13.20

12.96

13.35

13.02

EC*

6.42

9.04

13.25

13.03

b

0.023

0.029

0.209

0.158

EC50

21.97

22.01

21.52

21.21

EC*

5.66

6.90

5.84

7.48

b

0.074

0.104

0.080

0.115

EC50

12.11

12.31

12.23

12.35

EC*

0.25

6.32

5.77

8.21

b

0.025

0.080

0.056

0.108

EC50

23.11

14.04

15.15

13.92

EC*, salinity threshold at which the reduction begins; b, reduction slope in response to salinity; EC50, EC at which traits are reduced by 50%; LAI, leaf area index.

1000-grain weight
Values of 1000-grain weight of the control decreasedslightly when EC* < 6.4 dS/m, having a slope of 2.3% andreaching EC50 at 22 dS/m (Table 5). The Si treatments
increased EC* signiﬁcantly (P = 0.041); EC* was increased
up to EC = 9.04, 13.25 and 13.03 dS/m by priming, foliar
spray and the combination treatment, respectively. By
comparison, EC50 was not aﬀected by the Si treatments(Table 5, Fig. 4d).

Grain yield
Salinity reduced grain yield. Yield of the control werereduced after salinity increased past EC* = 5.7 dS/m, after
which yield decreased at a rate of 7%; EC50 was 12.1 dS/m
(Table 5). Priming increased EC* and EC50 by 22 and 2%,
respectively, while foliar spray increased these twoparameters by 3 and 1% (Table 5, Fig. 4e). The combination
treatment increased EC* and EC50 by 32 and 2%, respectively.

Discussion
Effect of salinity and Si on photosynthetic
pigments
Chl a, b and CAR decreased with increasing salinity, which is inagreement with the previous results of Taïbi et al. (2016) on
Phaseolus vulgaris L. and Siddiqui et al. (2014) on Cucurbita
pepo L. Chutipaijit et al. (2011) showed 30, 45 and 36%
reductions in Chl a, b and CAR concentrations for rice plants

subjected to 100 mM NaCl, respectively. Corn exposed tosaline conditions showed that decreased Chl a was the main
driver for decreased PSII exciting capacity (Oxborough2004). A decrease in the chlorophyll concentration of salinitystressed plants is a typical symptom of oxidative stress, thought
to be due the inhibition of chlorophyll synthesis, together withdegradation by the enzyme chlorophyllase (Hanin et al. 2016).
Lower chlorophyll concentrations as a result of either slow
synthesis or fast breakdowns may indicate the presence ofa photoprotection process through reductions in lightabsorbance (Taïbi et al. 2016).
Among treatments, foliar spray was the primary driver ofthe increases. Similar increases in Chl a and b in response
to foliar applied Si have been observed in salinity-stressed
peppers (Abdelaal et al. 2020). The accumulation of salts in
the root zone decreases osmotic and water potential
reducing the available water aﬀecting plant water status as
has been demonstrated in rocket (Hniličková et al. 2017).
In turn, plant water status has been shown to aﬀect
photosynthesis and chlorophyll content in Matricaria
chamomilla L. showing strong correlation between relative
water content and both Chl a and b (Pirzad et al. 2011).
The application of Si has been shown to increase relative
water content in salinity-stressed pepper (Abdelaal et al.
2020) and improve photosynthesis in potato (Kaﬁ et al.
2019). Our increased concentrations of Chl a and b in
response to Si may have been partially a result of improved
plant water status and reduced oxidative damage inconjunction with overall increased plant health as has been
observed also for winter wheat (Stankowski et al. 2021).
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F. Feghhenabi et al.

Relative LAI

1.4

Crop & Pasture Sciencesaline conditions were prevented by application of Si as anutrient solution. The proposed mechanisms of this eﬀectof Si are to regulate photosynthesis-related proteins andlower the disruption of the homeostasis of essential metal
ions and as a result, maintain the proteome of chloroplasts(Dhiman et al. 2021; Rastogi et al. 2021). Our results
demonstrate that Si caused photosynthetic pigments to beless damaged. This may be due to competition of Si with
NaCl for intake and the presence of suﬃcient amounts of Si
for assemblage of protein clusters. Thus, Si reduced stress(ROS) in chloroplasts under conditions of salinity stress(Muneer et al. 2014).

(a)

1.2
1.0
0.8
0.6

(b)
Relative spike number

1.2
1.0

Effect of salinity and Si on chlorophyll
ﬂuorescence

0.8
0.6
0.4

Relative grains per spike

(c)
1.0
0.8
0.6

(d)

Relative
1000-grain weight

1.05
1.00
0.95
0.90
0.85
0.80

Relative grain yield

(e)
1.2
1.0
0.8
ControlPriming
Foliar spray
Priming × Foliar spray

0.6
0.4
0.2
0

2

4
6
8
10
Salinity (dS/m)

12

14

Fig. 4. Overall ﬁt of experimental relative LAI values (a), yieldcomponents (b and c) and grain yields (d and e) by the reduction
function given by Eqn 5, with the parameter values listed in Table 5.

In line with the positive results of Si applications in our
experiment, Muneer et al. (2014) concluded that photosynthesis pigment degradation in tomatoes exposed to1126
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The stress-induced eﬀects on light-utilisation eﬃciency of thephotosynthetic apparatus in plants can be detected bychlorophyll ﬂuorescence parameters (Kalaji et al. 2017; Liet al. 2018). Fluorescence rises during the ﬁrst second ofillumination from Fo to Fm and presents as a curve withseveral phases (Dąbrowski et al. 2019). In this study, Fm
increased at a faster rate than Fo as a result of increasing
salinity, and this caused FV to increase further withincreasing salinity. Fm can be used to represent the size ofthe pool of reduced plastoquinone on the reducing side of
PSII. In non-optimal and stressful conditions, the faster rateof increase in Fm probably relates to less eﬃcient electrontransport between the acceptors of PSII and PSI, whichleads to a full reduction of the reaction centres; it has also
been suggested that increased Fm in rice and rapeseed wasdue to reduced blockage of electron transfer from thereaction centre to the quinone pool (Kalaji et al. 2018;
Faseela et al. 2020). Increases in Fo can be due to a partial
reduction of quinone A (QA) in the dark due to increased
reduction of the plastoquinone pool (Kalaji et al. 2017). An
increase in FV is a sign of damage to the PSII reaction
centre, leading to a decrease in absorbed light and asubsequent increase in unused emitted light (Adhikari et al.
2019). The results of chlorophyll ﬂuorescence analysis
showed no signiﬁcant change on FV/Fm. The value of FV/Fm
was in the normal range at all salinity treatments, which
could be due to higher qP levels than qN. Adhikari et al.
(2019) reported that changes in FV/Fm for lettuce at
diﬀerent salinity treatments were not signiﬁcant, suggestinga more robust photoprotective mechanism and healthy
photosynthetic apparatus. However, Jimenez et al. (1997)
concluded that the lack of changes in FV/Fm indicated that
this parameter could not be used as an indicator of salinity
stress in roses. Observed values of qP, qN, PSII, and NPQ all
decreased in response to increasing salinity. The reduction ofthese parameters is consistent with a stress response (Li et al.
2018). Salinity-induced decrease in qP indicates that low PSII
reaction centres were open, which might allow low excitationenergy to be used for electron transport (Zhang et al. 2018).

www.publish.csiro.au/cp

The qN reduction pattern against diﬀerent salinity levels was
similar to that of qP, reﬂecting a decrease in the safe dissipation of excess energy that could induce photo-oxidative damage
in the photosynthetic apparatus (Acosta-Motos et al. 2015).
In contrast, Acosta-Motos et al. (2017) observed an increase
in qP and PSII and a decrease in the non-photochemical
quenching parameters of salinity-treated plants, indicating
an adaptive response to the imposed stress conditions.
The observed reduction in PSII due to increasing salinity
indicated the negative eﬀect of salinity stress on
photoreceptor centres (Kaﬁ et al. 2019). Indeed, decreases in
PSII suggest a reduced capacity to convert photon energy
into chemical energy. The eﬀect of salinity on electrontransport chains and their protein centres results ininhibition of energy transfer from photoreceptor centres toelectron transport chains, and their destruction through anegative feedback, resulting in reductions of PSII (Kaﬁet al. 2019).
Stress-induced suppression in the apparent ETR may
suggest low tolerance of plants to stress (Kalaji et al. 2017);
it is thought that stress-induced reductions in ETR may also
be caused by photochemical down-regulations (Maghsoudi
et al. 2015). Taken together, the results of the chlorophyll
ﬂuorescence parameters demonstrated that light-utilisationeﬃciency and photosynthesis of wheat leaves were
adversely aﬀected by salinity. Si treatments, and inparticular the foliar spray and combination treatment wereeﬀective at reducing the damage to the PSII reaction centre,
and improving photochemical regulation, and enhancing
the safe dissipation of excess energy. This reduced damage
was likely via Si-induced protection of the PSII electrontransfer chain, an increased ETR as well as the promotionof the non-photochemical quenching coeﬃcient. With
Si-mediated FV depletion the PSII reaction centre suﬀered
less damage, increasing the potential to absorb light anddecrease unused emitted light. Increases in nonphotochemical parameters such as qN and NPQ due to Si
treatments particularly foliar spray and combination
treatment indicated the eﬃciency of Si treatments to safely
dissipate excess energy (Anjum 2011).
In this study, Si-mediated increase in ETR under diﬀerent
salinity levels suggests an improved capacity to convert
photon energy into chemical energy (Zhang et al. 2018).
Rastogi et al. (2021) concluded that the numbers of open
PSII reaction centres were higher in Si-treated plants, which
resulted in increased use of excitation energy in theelectron transport chain, thereby improving photochemicaleﬃciency, however Si treatments had no eﬀect on PSII
or qP.

Leaf area index
LAI is one of the most important traits used in modelling ofphysiological processes such as photosynthesis andrespiration. LAI decreases due to injury and/or death ofCrop & Pasture Scienceleaves due to ionic toxicity from increasing salinity. Leaf
injury and/or death result from elevated salt loads in theleaves when the loads exceed the capability of salt
compartmentalisation in the vacuoles, resulting incytoplasmic toxicity (Munns et al. 2006) and eventuallydeath of the plant (Polash et al. 2019). Ouhibi et al. (2014)
demonstrated that salinity resulted in a restriction of leaf
development. This restriction concerned mainly the total
leaf surface area, leaf number, biomass deposition, and leaf
thickness. Decreased LAI was compensated by Sitreatments. The Si-mediated positive eﬀect on plant growth(e.g. increased dry weight but also LAI) under stress could
be attributed to the ability of Si to modify cell wall
metabolism by improving cell enlargement due toenhancing tissue extensibility (Alzahrani et al. 2018).

Yield components
Grain per spike is most commonly the yield component that ismodulated due to stress and may also be reduced withincreasing salinity. Indeed, relative stability of grain size/
weight against the variability of grain number provided the
ﬂexibility as a trait with its own genetic control (Sadras
2007), which was also observed in some way in our results.
The results of EC50 showed that the changes of 1000-grain
weight were less than other yield components.
Furthermore, comparison of EC* and EC50 for yieldcomponents showed that the 1000-grain weight stability
under stress conditions was provided more by adjusting the
spike number than the grains per spike. In this regard,
Saberi et al. (2013) concluded that under saline conditions,among the yield components, 1000-grain weight stability
played a more important role in maintaining grain yields
than spike number and grains per spike. However, very
high salt concentrations in the soil during grain ﬁlling
reduced grain size of cereals (Munns and Gilliham 2015).
Among yield components, EC* of 1000-grain weight wasmore aﬀected by Si treatments. The relative stability of
1000-grain weight along with the eﬀect of Si treatments,
particularly priming and combination treatment, contributed
to the constancy of grain yield under increasing salinity.

Grain yield
Salinity reduced grain yield relative to control after
EC* = 5.7 dS/m. This result is consistent with theassumption that the salinity of a soil should be larger than
4 dS/m (Acosta-Motos et al. 2017). The combination
treatment and priming increased EC* and EC50. Abdel Latef
and Tran (2016) reported that application of Si as apriming agent improved the growth of stressed plants,
which was accompanied by the enhancement in levels ofphotosynthetic pigments, soluble sugars, soluble proteins,
total free amino acids and K+, as well as the activities
of superoxide dismutase (SOD), catalase (CAT), and1127

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F. Feghhenabi et al.

peroxidase (POD) enzymes. Alzahrani et al. (2018) showedthat application of Si as a soil solution improved growth inboth control wheat plants and plants exposed to 200 mM
NaCl through enhanced gas exchange, tissue water andmembrane stability. Beneﬁcial eﬀects and higher yields by
Si can be correlated with the ability of Si to activate genes
associated with aquaporin, antioxidant system and proteins
involved in antioxidant enzymes, synthesis of amino acids
and photosynthesis (Liu et al. 2019). The success of priming
has been attributed to the triggering of pre-germination
metabolism of seeds (Saddiq et al. 2019). Our results
indicate that proper timing of Si applications is important,with foliar applied Si being less eﬀective than Si applied asa priming agent.

Conclusions
Our results characterise the eﬀects of increasing salinity on
photosynthetic pigments, chlorophyll ﬂuorescence, yieldand yield components in wheat. The reduction inphotosynthetic pigments occurred at lower salinities than
did overall yield and several yield components (including
LAI). Reductions or variations in photosynthetic pigments
and parameters of chlorophyll a ﬂuorescence at lower
salinities did not negatively impact yield. We found that Si
applied as a foliar spray was more eﬀective at maintaining
the concentration of Chl a and b, while foliar spray and combination treatment signiﬁcantly improved most chlorophyll
ﬂuorescence parameters. Although the combined Si treatment
was the most eﬀective at reducing yield loss from salinity,
Si applied as seed primer was the driver of this reduction
and could be considered the single most eﬀective treatment
for reducing the yield loss associated with salinity. Howeverat the highest concentrations of salt, the positive eﬀects of Si
application were lost. Our research provides valuable insight
into the eﬀects of salinity and Si applications on wheat
photosynthetic components, yield and yield components,
with applications of Si either as priming or combination
with foliar spray showing promise as a tool for reducing
grain yield losses when soil salinities are less thanEC = 14 dS/m. Given the salinity level-dependent positiveeﬀect of Si application, future research should focus on
separating the osmotic and ionic phases of salinity andinvestigate the eﬀect of Si on both phases at the physiological,
biochemical, and molecular levels.

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Data availability. The data that support this study are available in the article.
Conﬂicts of interest. The authors declare that there are no conﬂicts of interest.
Declaration of funding. This research did not receive any speciﬁc funding.
Author afﬁliations
Department of Agronomy and Plant Breeding, Urmia University, Urmia, Iran.

A
B

Department of Soil Science, Urmia University, Urmia, Iran.

C

Department of Earth Sciences, Utrecht University, Utrecht, Netherlands.
Center for Environmental Studies, CEA, São Paulo State University, Rio Claro, SP, Brazil.

D
E

South Australian Research and Development Institute, School of Agriculture, Food and Wine, The University of Adelaide, Waite Campus, Urrbrae, SA, Australia.

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