https://orcid.org/0000-0003-4746-9036
Figures
Abstract
Introduction
The effects of salinity memory and its interaction with genetic diversity for drought tolerance and pollination system in terms of morphological, physiological, root characteristics and spectral reflectance indices (SRIs) in tall fescue is still unknown.
Methods
Four tall fescue genotypes (two drought-sensitive and two drought-tolerant) were manually controlled to produce four selfed (S1) and four open-pollinated (OP) progeny genotypes (finally eight progeny genotypes). Then all genotypes were assessed for two years in greenhouse under five salinity treatments including control treatment (C), twice salinity stress treatment (primary mild salinity stress in two different stages and secondary at the end stage) (S1t1S2 and S1t2S2), once severe salinity stress treatment (secondary only, S2), and foliar spray of salicylic acid (SA) simultaneously with secondary salinity stress (H2S2).
Results
Results indicated that obligate selfing (S1) caused to inbreeding depression in RWC, plant growth, catalase activity, root length and the ratio of root/shoot (R/S). Once salinity stress treatment (S2) led to depression in most measured traits, while pre-exposure to salinity (salinity memory) (S1t1S2 and S1t2S2) improved photosynthetic pigments, proline, antioxidant enzymes and R/S.
Conclusion
Salinity memory was more pronounced in drought-sensitive genotypes, while it was more evident in OP than S1 population. Foliar spray of salicylic acid (SA) was almost equally effective in reducing the effects of salinity stress in both populations. The efficacy of application was more pronounced in tolerant genotypes compared to sensitive ones. The possibility of modeling correlated spectral reflectance indices (SRIs) for prediction of different morphological, physiological and root characteristics will be discussed.
Citation: Safari M, Majidi MM (2024) Salt stress memory in tall fescue: Interaction of different stress stages, pollination system and genetic diversity. PLoS ONE 19(9): e0310061. https://doi.org/10.1371/journal.pone.0310061
Editor: Mojtaba Kordrostami, Nuclear Science and Technology Research Institute, ISLAMIC REPUBLIC OF IRAN
Received: March 12, 2024; Accepted: August 20, 2024; Published: September 12, 2024
Copyright: © 2024 Safari, Majidi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: APX, ascorbate peroxidase activity; ARI, anthocyanin reflectance index; C, control treatment; Car, carotenoid content; CAT, catalase activity; Chl a/b, ratio of Chla/Chlb; Chla, chlorophyll a content; Chlb, chlorophyll b content; CRD, crown diameter; CRI, cartenoid reflectance index; DFY, dry forage yield; DFYR, dry forage yield in recovery; G, genotypes; GDVI, green difference vegetation index; GNDVI, green normalized difference vegetation index; H2S2, foliar spray of SA simultaneously with secondary salinity stress; NDRE, normalized difference red edge index; NDVI, normalized difference vegetation index; NWI, normalized water index; OP, open-pollinated; P, pollination system; PH, plant height; PHR, plant height in recovery; POX, peroxidase activity; PRI, photochemical reflectance index; Pro, proline content; PSND, pigment specific normalized different; PSRI, plant senescence reflectance index; PSSR, pigment specific simple ratio; R/S, root to shoot ratio; R/SR, root to shoot ratio in recovery; RA, root area; RARSa, ratio analysis of reflectance spectra; RARSb, ratio analysis of reflectance spectra; RCL, root cumulative length; RDW, root dry weight; RGR, red/ green ratio; RL, root length; RNDVI, red normalized difference vegetation index; RV(A), root volume (Archimedes); RV(G), root volume (Giaroot); RWC, relative water content; RWW, root wet weight; S1, selfed; S1t1, primary mild salinity stress in first time; S1t1S2, twice salinity stress treatment (primary in the first time and secondary at the end stage); S1t2, primary mild salinity stress in second time; S1t2S2, twice salinity stress treatment (primary in the second time and secondary at the end stage); S2, once severe salinity stress treatment (secondary only); SIPI, structure intensive pigment index; SR, simple ratio; SRIs, spectral reflectance indices; T, treatment; Tchl/Car, ratio of Tchl/Car; Tchl, total chlorophyll; WFY, wet forage yield; WFYR, wet forage yield in recovery; WI, water index
Introduction
Tall fescue (Festuca arundinacea) is an important grass species known for its ability to adapt to a variety of abiotic stresses, including salt, drought, and heat. This species is very interesting because it is able to withstand salt and drought stress, which limits plant growth and yields worldwide [1–3]. Recent studies have further explored the adaptive mechanisms of tall fescue to salt stress, highlighting its physiological and molecular responses [4]. Despite extensive research on salinity in plants, the specific mechanisms and factors affecting salt stress memory in tall fescue are poorly understood [2]. Addressing these knowledge gaps is important for developing resistant plant species that can withstand harsh environmental conditions [5]. Although much research has been conducted on the effect of salt stress on different plant species, there are still significant gaps, especially regarding the combined effects of different stages of stress and the effect of genetic background on salt stress memory in tall fescue.
Like the majority of the grass species, the presence of self-incompatibility, leading to cross-pollination, typically directs breeding efforts towards the creation of superior synthetic cultivars and the enhancement of diverse populations [6]. Self-pollination in outcrossing species results in fixing some alleles which may cause inbreeding depression [7]. Inbreeding usually refers to the intermating of closely related individuals, while "inbreeding depression" (ID) denotes the decrease in relative fitness observed in progenies compared to those resulting from outcrossing [8]. The magnitude of ID is depending on the genetic background of the species or population and the interaction of genotype by environment [9, 10]. Nevertheless, selfing can facilitate development of pure lines, a crucial aspect in constructing specialized populations for genetic studies. Also, selfing sometimes improves an attribute by gathering additive homozygous alleles together in progeny. Considering the importance of tall fescue as a hexaploidy and outcrossing grass species widely used for turf and forage purposes, little information is available about the fitness under selfing and cross-pollination and its interaction with stress memory and abiotic stresses.
These toxic effects and osmotic pressure caused by salinity stress significantly reduce the yield of crops cultivated in saline soils [11, 12]. Roots rely on shoots for carbohydrates, while shoots depend on roots for nutrients and water. Consequently, environmental ecological stresses may lead to an increase in the relative biomass of roots compared to shoots [13]. This shift, known as an increase in the Ratio of Root-to-Shoot (RSR), is a common response to salinity stress. Under salt stress, an augmented root biomass can facilitate the retention of toxic ions in root cellular tissues, preventing their translocation to other organs such as leaves. This mechanism contributes to the characteristic resistance of plants to salinity. However, it’s important to note that low water quality can impede plant maturation, leaf enlargement, and alter the link between the root and aerial parts [14]. In contrast to the well-studied changes in root system development under water stress, the understanding of how salinity influences the growth and mass of tall fescue root systems is not yet comprehensive [15, 16].
When plants encounter environmental stresses, their response is intricate and often involves significant alterations in both physiological processes and gene expression patterns [17]. Pre-exposure to environmental stress may produce appropriate reactions and alarm signal (such as physiology and gene expression) memorized by plants [18]. Most of these stress-induced changes are reset to the basal level once the stress is relieved, but some may be stable and carried forward as a form of “stress memory” [19, 20]. Previous research indicates that stress memory induced by pre-exposure to abiotic stresses, such as salt, cold, and hormonal treatments, can enhance the plant’s immune system and enables plants to cope more effectively with the subsequent stress events [18–20]. Ding et al. [18] reported that pre-exposure to drought could induce transcriptional changes in trainable genes in Arabidopsis. Sani et al. [21] expressed that in the primed plant, the level of methylation increased and as a result, the promoter cannot transcribe the gene related to the production of proline limiting enzyme. As a result, this gene will be turned off and proline will be produced. Therefore, a plant that has already experienced once the salinity stress can appear more and faster salt tolerance than a plant that has experienced it for the first time. On the other hand, some reports indicated that the application of plant growth regulators such as salicylic acid (SA) could improve stress tolerance in plants [22, 23]. In plants, salicylic acid (SA) is a signaling molecule synthesized endogenously, and it plays a pivotal role in various aspects of plant physiology, including growth, development, flowering, ripening, defense mechanisms, and responses to abiotic stresses [24, 25]. Applying certain growth regulators externally, such as epibrassinolide, salicylic acid, and glycinebetaine has been found to improve the salt tolerance of Puccinellia distans (weeping alkaligrass), Lolium arundinacea (tall fescue), and Lolium perenne (perennial ryegrass) [26–28].
To evaluate the reaction of plants to abiotic stresses, sampling techniques are mostly considered destructive, cost and time-inefficient and often do not provide real-time updates on the physiological status of plants. Moreover, their application on a large scale is often impractical [29]. A viable alternative to address these limitations is the utilization of remote sensing techniques coupled with visible (VIS)/near-infrared (NIR) spectroscopy. Remote sensing, with its ability to detect biochemical and physiological changes, allows for the indirect estimation of salt stress parameters in a fast manner compared to traditional methods [30, 31]. Salinity stress not only induces significant changes in plant water status but also negatively affects photosynthetic pigments and internal leaf structure. For instance, Poss et al. [32] demonstrated that the water index (WI = (R900/R970)), which incorporates a water absorption band from the NIR region (970 nm), reflects the response of Agropyron elongatum (wheatgrass) growth to salt stress. A meaningful correlation is observed between spectral properties and key plant metrics, such as biomass and leaf area index, in seashore paspalum (Paspalum vaginatum) [33] when cultivated in saline soils. Little information, however, is available in relation to the ability of spectral reflectance techniques to estimate physiological traits, antioxidant enzyme activities, root system and general performance of tall fescue.
The current study addressed important knowledge gaps regarding the effects of salt stress memory in different reproduce systems and genetic backgrounds in tall fescue. Although previous studies have mainly focused on the effects of salinity on plant characteristics and the independent effects of pollination systems or salicylic acid, our study provides the first joint analysis of these factors under different salinity conditions. This study explored the complex relationship between pollination systems (selfed vs. open-pollination), timing of stress memory application, salicylic acid treatment, and genetic variation in response to severe salinity stress, and examined how these factors influence morphological, physiological, root traits, and spectral reflectance indices. This comprehensive analysis is expected to provide new insights into improving salt stress management and address current knowledge gaps in the field.
Materials and methods
Plant materials and treatments
The grass project was started in 2005 at Isfahan University of Technology. Over the years, 24 tall fescue genotypes have been collected within a large germplasm from numerous locations of Iran and a few other countries. The genotypes were selected based on morphological and drought tolerance indices in field as well as for root and physiological characteristics in pot experiments [34–37]. Finally, two drought tolerance (11M and 21M) and two susceptible genotypes (1M and 3M) were selected (Fig 1) and established in field.
In April 2020, half of the panicles of each genotype (parental plant) were isolated to ensure obligatory selfing, while the other half was left uncovered to allow open-pollination. By the end of the summer, the seeds from the selfed and open-pollinated panicles were harvested separately. Consequently, two populations were created, comprising 4 selfed progeny (S1) and 4 open-pollinated progeny (OP) (half-sib families), serving as the plant materials for the present study.
The seeds of S1 and OP populations were planted in pots in the research greenhouse of the Isfahan University of Technology. The plants were maintained in the greenhouse with 25/18°C (day/night), 14-h photoperiod, 75 ± 5% relative humidity, and an average photosynthetically active radiation of 400 μmol m–2 s–1. The experiment was conducted in autumn 2020 and repeated in 2021 according to a factorial arrangement based on a CRD with two replications. The first, second and third factors were genotypes (8 genotypes), pollination systems (2 levels), and salinity treatments (5 levels as follow), respectively. A mixture of silt loam soil and coarse sand (1:2 v/v) were used to fill the pots (35⨯18 cm). The weight of soil used in each pot was 7500 ± 50 g (pH = 7.4, BD = 1.32 g/cm3, and organic matter = 1.68%). After the complete establishment of the seedlings (about 45 days) salinity treatments (Fig 2) were applied as follows (salinity stress was applied gradually three times a week):
- Control treatment (C): Irrigation was conducted without limitation (normal condition) with common water.
- Twice salinity stress treatment (primary in the first time and secondary at the end stage) (S1t1S2): Primary mild salinity stress (120 mM NaCl saline) was supplied 45 days after establishment for 12 days (S1t1). Then plant allowed to recover for 90 days by common water and after that secondary stress (severe) applied with 200 mM NaCl saline for 20 days.
- Twice salinity stress treatment (primary in the second time and secondary at the end stage) (S1t2S2): Primary mild salinity stress (120 mM NaCl saline) was supplied 90 days after establishment for 12 days (S1t2). Then plant allowed to recover for 45 days by common water and after that secondary stress (severe) applied with 200 mM NaCl saline for 20 days.
- Once salinity stress treatment (secondary only) (S2): Only one severe salinity stress applied 147 days after establishment for 20 days with 200 mM NaCl saline.
- Foliar spray of SA simultaneously with secondary salinity stress (H2S2): This treatment was same as S2 with the difference that foliar salicylic acid (1 mM) was sprayed on the leaves during the stress (three times every three days) (Fig 2).
Irrigation of all pots was done depending on the needs of the plant and without considering the type of salinity treatment until the complete establishment (about 45 days). During this period, the seedlings were cut three times at two-week intervals to strengthen. Then, primary salinity stresses (S1t1 and S1t2) were implemented as mild salinity stress at two different stages as explained above (S1 Fig). Experienced plants with primary salinity stress, after a rest period (recovery), were treated with secondary salinity stress (severe stress) (S1t1S2 and S1t2S2). Once salinity stress treatment (S2) was also performed at this stage. Control treatment (C) was subjected to no stress until the end of the experiment. H2S2 treatment was implemented with secondary salinity stress and simultaneously salicylic acid (1 mM) was sprayed during stress. After the completion of mild salinity stress, some of the physiological traits (RWC, total chlorophyll, and proline) were measured. Several traits (including morphological (crown diameter, plant height, and forage yield) and physiological (RWC, photosynthetic pigments, proline, and antioxidant enzymes)) and spectroscopy were recorded for all treatments immediately after finishing severe stress. Measurement of morphological (plant height and forage yield) and root characteristics were performed at the end of the experiment (S1 Fig).
Justification for the choice of factors and levels
The selection of factors and their levels in this study was carefully considered based on previous studies and the specific objectives of our research:
Genotypes (sensitive vs. tolerant):
Drought-sensitive genotypes: These genotypes were selected to understand how drought-sensitive plants respond to salinity stress, salinity memory and SA treatment. This helps determine possible improvements in tolerance.
Drought-tolerant genotypes: These genotypes was selected to see how natural plants benefit from salinity stress, stress memory and SA treatment, providing a basis for comparison and identification of specific genetic responses.
Pollination systems (selfed (S1) vs. open-pollinated (OP)):
The choice of selfed (S1) and open-pollinated (OP) systems was made to study the effects of genetic variation on salinity stress, salt stress memory, application of SA. Previous research has shown that mating system and genetic variations can significantly influence stress responses.
Salinity stress treatments (C, S1t1S2, S1t2S2, S2, H2S2):
C (control): Served as a control to compare the effects of severe salinity stress.
S1t1S2 (twice salinity stress treatment (primary in the first time and secondary at the end stage)): This treatment allowed us to evaluate the effect of early-stage mild salinity stress on the plant’s ability to withstand subsequent severe stress. It helps to understand if early exposure creates a stress memory that enhances tolerance.
S1t2S2 (twice salinity stress treatment (primary in the second time and secondary at the end stage)): This treatment tested whether the timing of mild stress exposure affected the plant’s stress memory and subsequent tolerance to severe stress, comparing early and later pre-exposure periods.
S2 (once severe salinity stress treatment): Serving as a treatment to understand the impact of severe salinity stress without any pre-exposure. This helps in comparing the effects of stress memory and SA application.
H2S2 (foliar spray of SA simultaneously with secondary salinity stress): Investigated the potential of SA to mitigate severe salinity stress by enhancing physiological and biochemical processes in the plant. This helps to understand the role of SA in stress tolerance.
These choices were designed to provide a comprehensive understanding of the interactions between different salt stress treatments, pollination systems, and genetic backgrounds. This method helps to identify the main factors and processes that lead to stress tolerance and memory of tall fescue.
Measurements
Various morphological, physiological and Vis/NIR spectroscopy characteristics were measured in this study. Plant height (PH), crown diameter (plant width remaining after cut) (CRD) and wet and dry forage yield per plant (WFY and DFY) was recorded following the method of Pirnajmedin et al. [36].
Leaf water status was evaluated by estimating the Relative Water Content (RWC) following the method outlined by Ritchie et al. [38]. Additionally, spectrophotometry was utilized to measure total chlorophyll (Tchl), chlorophyll a (Chla), chlorophyll b (Chlb), and carotenoids (Car) [39]. Moreover, the determination of proline content (Pro) was carried out using the method outlined by Bates et al. [40]. To determine the protein content and evaluate the activity of the enzyme, a sample of 0.1 g of leaves was taken and subsequently preserved by freezing in liquid nitrogen. Catalase (CAT) activity was determined using the method described by Chang and Maehly (1995) [41]. Additionally, Ascorbate peroxidase (APX) and Peroxidase (POX) activity was mesured based on the methods of Nakano and Asada (1981), and Herzog and Fahimi (1973), respectively [42, 43]. Enzyme activities were quantified based on the weight of protein units, and the protein content was determined using bovine serum albumin as the standard, following the methodology outlined in the Bradford method [44].
To assess the root characteristic system, the roots were thoroughly washed to remove soil, and the root wet weight (RWW) was promptly measured. Root length (RL), root volume (RV(G)), root area (RA), and root cumulative length (RCL) were measured using a computer scanner and Gia Roots software [45]. Additionally, root volume (RV(A)) was determined using Archimedes’ law. Root dry weight (RDW) was obtained after drying the roots in an oven at 85°C for 48 hours. Finally, the root-to-shoot ratio (R/S) was calculated.
The Vis-NIR photo-diode array spectrometer (model: LR1 spectrometer, ASEQ Instrument, Vancouver, Canada) was employed to obtain the diffuse reflectance spectra of leaves of each genotype. Three leaves of each genotype and two parts of each leaf, finally six diffuse reflectance spectra for each genotype, were measured and the averaged spectrum of six spectra was used for statistical analysis. All measurements were performed within the confines of the greenhouse, with no alteration made to the tall fescue leaves. The relative reflectance spectrum was then utilized in the calculation of eighteen different indices, as listed in S1 Table for this study.
Statistical analysis
The data underwent normality testing using the Q-Q plot test and were then subjected to analysis of variance (ANOVA) using SAS (2013) [46] to identify differences between years, genotypes, pollination systems, salinity treatments, and their interactions for each variable. Mean comparisons were conducted using the LSD test (P < 0.05) for various traits and spectral reflectance indices. Correlation coefficients between spectral reflectance indices and different traits were calculated using proc CORR in SAS [46]. Principal component analysis (PCA) was conducted on a correlation matrix [47] in order to decrease the numerous dimensions of the data space, utilizing Stat Graphics Centurion XVII [48]. The determination of the variables responsible for the majority of the variations in different traits was accomplished through stepwise multiple linear regression, employing SAS) [46].
Results
Analysis of variance and mean comparisons
Results from the analysis of variance for two years revealed that genotypes (G), pollination systems (P), and salinity treatments (T) had a significant effect (P < 0.05) on most of the measured traits (morphology, physiology and root) (S2–S4 Tables) and indices (not shown). The G×P, G×T, and P×T interaction effects and the triple interaction of G×P×T were also significant for most measured traits (S2–S4 Tables). The mean comparison of tall fescue genotypes, different pollination systems (selfed (S1) and open-pollinated (OP)), and five salinity treatments (C, S1t1S2, S1t2S2, S2, and H2S2) for different traits and indices are given in Tables 1–4.
How did different pollination systems (selfed (S1) and open-pollinated (OP)) affect traits.
Mean comparison of pollination systems on measured traits showed that open-pollinated (OP) led to enhancement of crown diameter (CRD), plant height (PH), relative water content (RWC), plant height in recovery (PHR), catalase (CAT) activity, root length (RL) and ratio of root/shoot in recovery (R/SR) compared to selfed system (Tables 1–3). While selfing enhanced dry forage yield (DFY), chlorophyll a (Chla), chlorophyll b (Chlb), carotenoid content (Car), total chlorophyll (Tchl), ratio of Chl a/b (Chl a/b), ratio of the TChl/Car (TChl/Car), proline content (Pro), ascorbate peroxidase (APX), peroxidase (POX) activities and most measured root traits compared to open-pollinated (Tables 1–3). The emergence rate of selfed genotypes was lower than open-pollinated ones (S2 Fig), indicating evidence for inbreeding depression in the early stages of growth. However within S1 and OP populations, genotypes showed considerable variation. For example within S1 population, genotypes 1M and 11M had highest emergence rate, and genotypes 3M and 21M had the lowest value of this trait (S2 Fig).
The correlation coefficients among spectral reflectance indices were calculated and are presented in S5 Table. To reduce the number of indices for subsequent analyses, 5 indices, RNDVI, NWI, PRI, PSND, and GNDVI, were removed due to their high correlations with other indices. Results of the mean comparison of pollination systems for spectral reflectance indices indicated that the highest amount of normalized difference vegetation index (NDVI), ratio analysis of reflectance spectra (RARSa), ratio analysis of reflectance spectra (RARSb), structure intensive pigment index (SIPI), normalized difference red edge index (NDRE), plant senescence reflectance index (PSRI) and green difference vegetation index (GDVI) and the lowest amount of PSSR, CRI and ARI were observed in OP compared with S1 genotypes (Table 4).
How did severe salinity stress (S2) affect traits.
The mean comparison of salinity treatments showed that severe final salinity stress (S2) significantly decreased PH, WFY, DFY, PHR, WFYR, DFYR, Chla, Chlb, Car, Tchl, Chl a/b, Tchl/Car and CAT activity (Tables 1 and 2). While Pro and all root traits were increased under S2 treatment (Tables 2 and 3). Results of the mean comparison of salinity treatments for indices represented that severe stress (S2) significantly reduced WI, RARSa, pigment specific simple ratio (PSSR), cartenoid reflectance index (CRI) and red/ green ratio (RGR) compared to control treatment (Table 4).
How did the timing of stress memory application and foliar spray of salicylic acid (SA) affect traits.
In this study, we applied stress memory in two different stages (45 and 90 days after establishment, S1t1S2 and S1t2S2, respectively) to see how the stage of applying affects this phenomenon. The results showed that similar trends were observed in both stages in terms of measured traits except for RWC, PHR, WFYR, DFYR, RL and R/S (Tables 1–3). Pre-exposure to salinity for causing salinity memory (S1t1S2 and S1t2S2) significantly increased PH, WFY, DFY and all physiological traits and R/SR compared to S2 treatment (Tables 1–3). Thus, it can be said that salinity memory was more evident for morphological and physiological traits. Results of the mean comparison for those physiological traits measured immediately after the first salinity stress condition (S1), at two different times, (S1t1 and S1t2) indicated that RWC and Tchl decreased and Pro enhanced compared with control treatment in both pollination systems (OP and S1) (S3a–S3f Fig). These results confirmed that the primary salinity stress was correctly applied and caused salinity memory in the secondary stress. For example, the S1t1S2 treatment in S1 increased RWC compared with the S2 treatment which indicated the existence of salinity memory (Fig 3a). Results of the mean comparison of for indices showed that under S1t1S2 treatment considerably enhanced WI, PSRI, ARI, GDVI and RGR compared to S2 treatment. Similarly, NDVI, RARSa, RARSb, PSSR, SIPI, NDRE, PSRI, CRI and RGR were increased under S1t2S2 treatment (Table 4).
Mean comparison relative water content for the interactions of two different pollination systems (selfed (S1) and open-pollinated (OP)) and five salinity treatments (C, S1t1S2, S1t2S2, S2 and H2S2) during two years (a). Mean followed by the same letter is not significantly different according to LSD test (probability level of 5%). The trend of relative water content for five salinity treatments (C, S1t1S2, S1t2S2, S2 and H2S2) based on the number of day (establishment) during two years (b).
Foliar spray of salicylic acid (SA) significantly improved most measured morphological and physiological traits, RL and R/SR compared to S2 treatment (Tables 1–3). Foliar application of SA at secondary salinity stress (H2S2) significantly enhanced RWC in tall fescue selfed genotypes compared to S2 treatment (Fig 3a). The trend of relative water content for five salinity treatments (C, S1t1S2, S1t2S2, S2 and H2S2) based on the number of day (establishment) during two years is shown in Fig 3b. At the end of the applied S2 stress, on the 165th day, C, S1t2S2, S2 and H2S2 treatments had the highest RWC respectively, and the S1t1S2 treatment had the lowest ones (Fig 3b). Results of the mean comparison of indices indicated exogenous application of SA lead to enhancement of NDVI, RARSa, RARSb, PSSR, SIPI, NDRE, PSRI and RGR under severe salinity stress compared with S2 treatment (Table 4).
Interaction effects of different treatments
Mean comparison for interaction effect of salinity treatments and pollination systems for some morphological, physiological, root characteristics and the indices of spectral reflectance are presented in Tables 5 and 6.
How did severe salinity stress affect different pollination systems in tall fescue.
In open-pollinated genotypes, substantial reductions in PH, WFY, DFY, DFYR, Pro, RV(G), RA, RCL and RDW were observed under severe salinity stress compared to selfed genotypes (Table 5). As a result, it can be concluded that severe salinity stress had more negative effects on these genotypes. Meanwhile, selfed genotypes exhibited notable declines in most spectral reflectance indices (Table 6).
How did different pollination systems influence the effectiveness of stress memory and application of SA in tall fescue.
In open-pollination, results showed significant increases in PH, WFY, DFY, Tchl, RV(G), RA, RCL and R/SR under memory treatments (S1t1S2 and S1t2S2) compared to non-memory treatment (S2) (Table 5). Meanwhile, CAT and APX activities under S1t1S2 treatment and Car and Pro under S1t2S2 treatment were increased. On the other hand, in S1 population WFY, DFY, Car, Tchl, CAT and POX activities improved by salinity memory (S1t1S2 and S1t2S2 treatments) compared to S2. Also, in S1 population, increases in DFYR, APX, R/S and R/SR were observed under S1t2S2 treatment. Therefore, it can be said that salinity memory was more evident in open-pollinated genotypes than corresponding selfed ones. Under secondary salinity stress condition with application of SA (H2S2), in OP population PH, DFY, PHR, CAT activity, RA (G), RA and RCL improved. Application of SA in severe salinity stress condition (H2S2) considerably improved DFY, DFYR, Tchl, APX, R/S and R/SR in selfed genotypes (Table 5). It can be said that the foliar spray of SA was effective in both S1 and OP genotypes. Results of the mean comparison of interaction effect of salinity treatments and pollination systems for indices indicated that salinity memory increased some spectral reflectance indices including WI, PSRI, ARI, GDVI and RGR in OP and WI, PSRI and RGR in S1 under S1t1S2 treatment compared to S2 treatment. Under S1t2S2 treatment enhanced NDVI, RARSa, RARSb, PSSR, SIPI, PSRI and RGR in both OP and S1 genotypes. Foliar spray of SA (1 mM) significantly improved NDVI, RARSa, RARSb, PSSR, SIPI, PSRI and RGR under severe salinity stress in both of OP and S1 compared to S2 treatment (Table 6). Overall, more spectral reflectance indices improved by salinity stress memory as well as application of salicylic acid despite the different pollination systems.
How did severe salinity stress influence genetic diversity in fescue.
Genotypes tolerant to drought are not necessarily resistant to salinity stress. For example, severe salinity stress reduced RWC, WFY, DFYR, and R/S in sensitive genotypes, and also decreased Pro, RV(G), RA, RCL, and RDW in genotypes tolerant to drought (Table 7). While sensitive genotypes showed reductions in most indices including RARSb, SIPI, PSRI, CRI and GDVI compared to tolerant genotypes (Table 8).
What role did genetic diversity play in modulating the stress memory and application of SA in tall fescue.
Under memory treatments (S1t1S2 and S1t2S2) DFY, Car, Tchl, CAT, APX and POX activities more enhanced in drought-sensitive genotypes compared to non-memory treatment (S2) (Table 7). While drought-tolerant genotypes had more Tchl and R/SR under S1t1S2 and S1t2S2 treatments. Thus, salinity stress memory was more pronounced in drought-sensitive genotypes of tall fescue. Foliar spray of SA in severe salinity stress condition significantly improved RWC, DFY, CAT and APX activities in drought-sensitive genotypes and WFY, DFY, DFYR, CAT, RV(G), RA, RCL and RDW in drought-tolerant genotypes (Table 7). The application of salicylic acid was more effective in reducing the effects of salinity stress in drought-tolerant genotypes. The response of spectral reflectance indices showed that WI, PSRI, ARI, GDVI and RGR significantly increased under S1t1S2 treatment compared to S2 treatment in sensitive genotypes and WI, ARI, and RGR in tolerant genotypes (Table 8). NDVI, RARSa, PSSR, SIPI, PSRI, and RGR enhanced in both drought-sensitive and tolerant genotypes under S1t2S2 treatment. Application of SA improved NDVI, RARSa, RARSb, PSSR, SIPI, PSRI and RGR in both drought-sensitive and tolerant genotypes under severe salinity stress (Table 8). Hence, stress memory and the foliar application of salicylic acid (SA) proved effective in mitigating the adverse effects of salinity stress across both drought-sensitive and tolerant genotypes, as indicated by various measured indices.
Principal component analysis (PCA) and genotype selection
To reduce the dimension of variables and drawing the association between characteristics and genotypes, principal component analysis (PCA) was used. Results of PCA revealed that the two first components explained more than 52, 53, 64, 64, and 58% of the variation in C, S2, S1t1S2, S1t2S2 and H2S2 treatments, respectively (S6 Table). In the control treatment, the first principal component (PC1) demonstrated stronger correlations with DFYR, CRD, RV(A), RV(G), RA, RCL, and RDW (S6 Table). Given that elevated values in these characteristics signify enhanced root growth capacity and, consequently, greater yield production for the genotypes, PC1 was designated as the ‘‘Root Characteristic System”. Principal component 2 (PC2) had negative correlations with Chla, Chlb, Car, Tchl, and Tchl/Car and a positive correlation with Chl a/b (S6 Table). Given that reduced values in these characteristics are indicative of higher photosynthetic capacity, PC2 was termed ‘‘Photosynthetic Capacity”. To categorize the genotypes using PCA, a biplot of PC1 and PC2 was created (Fig 4). Consequently, genotypes 11MS1 and 3MS1 exhibited a high potential for both root and yield production. Under S2 treatment, PC1 was positively correlated with DFY, DFYR, RV(A), RV(G), RA, RCL and RDW. Conversely, PC2 exhibited positive correlations with Chla, Car, and Tchl, while demonstrating negative correlations with CRD, RL, and R/SR (S6 Table). Genotypes 11MS1 and 3MS1 were hence identified as suitable genotypes for once severe salinity stress (Fig 4).
CRD, crown diameter; PH, plant height; DFY, dry forage yield; PHR, plant height in recovery; DFYR, dry forage yield in recovery; RWC, relative water content; Chla, chlorophyll a content; Chlb, chlorophyll b content; Car, carotenoid content; Tchl, total chlorophyll; Chla/b, ratio of Chla/Chlb; Tchl/Car, ratio of Tchl/Car; Pro, proline content; CAT, catalase activity; APX, ascorbate peroxidase activity; POX, peroxidase activity; RL, root length; RV(A), root volume (Archimedes); RV(G), root volume (giaroot); RA, root area; RCL, root cumulative length; RDW, root dry weight; R/S, root to shoot ratio; R/SR, root to shoot ratio in recovery.
Under S1t1S2 treatment, PC1 had negative correlations with DFYR, RWC, RV(A), RV(G), RA, RCL and RDW. Consequently, selecting genotypes with low PC1 values can result in the identification of tolerant genotypes with a high root production capacity. Conversely, PC2 exhibited a positive association with PH and PHR, while showing a negative association with CAT and R/S (S6 Table). The biplot analysis of PC1 and PC2 (Fig 4) highlighted that genotypes 11MS1 and 1MS1, characterized by low PC1 and high PC2 values, were identified as preferable choices for first time pre-exposure salinity stress conditions. Under S1t2S2 treatment PC1 had positive correlations with DFY, DFYR, CAT, POX, RV(A), RV(G), RA, RCL and RDW. Therefore, selection based on high PC1 values can lead to tolerant genotypes with high potential for root production and yield. PC2 had positive correlations with Chla, Car, Tchl, Chl a/b, Tchl/Car, R/S and R/SR and a negative correlation with RWC (S6 Table). Therefore, genotypes with high PC2 values are well-suited for salinity stress conditions. As revealed by the biplot analysis of PC1 and PC2 (Fig 4), genotypes 21MS1 and 3MS1 were identified as particularly preferable choices for such conditions.Under H2S2 treatment, PC1 had high and negative correlations with DFYR, RV(A), RV(G), RA, RCL, RDW and R/S. PC2 had high and positive correlations with Chla, Chlb, Car, Tchl and Tchl/Car (S6 Table). Classify the genotypes based on PCA, genotypes 11MS1 and 3MS1 were found to have high potential to root production and yield (low PC1) and high photosynthetic capacity (high PC2) under the foliar spray of SA simultaneously with salinity stress (Fig 4).
Correlation among indices and traits, and regression models
Correlation coefficients between spectral reflectance indices with different traits of tall fescue genotypes (Table 9) showed positive correlations between NDVI, RARSa and RARSb indices with CRD, RL, while negative correlations with Chla, Tchl, Chl a/b and Tchl/Car. Also, RARSb was positively correlated with R/S and R/SR. WI and NWI had positive correlations with PH, Chla, Chlb, Tchl, Chl a/b and Tchl/Car and a negative correlation with RL. PSSR had positive correlations with DFYR and RL. PSND, SIPI and PRI were positively correlated with CRD and RL, while negatively correlated with Tchl/Car. RNDVI, GNDVI and NDRE were significantly and negatively correlated with Chla, Tchl and Chl a/b but positively correlated with CRD and RL. CRI was positively correlated with CRD, DFYR, RL, RV(G), RA, RCL, RDW and R/S, while negatively correlated with Chl a/b. PSRI had positive correlations with CRD, PHR, RL, R/S and R/SR, but ARI had negative correlations with PHR, RL and R/SR and a positive correlation with Tchl/Car (Table 9).
Stepwise multiple linear regression (Table 10) indicated that spectral indices of CRI could be considered as independent variable that explained most of the ratio of root/shoot variation (r = 0.46). RARSb and ARI explained some variation of relative water content (RWC) (r = 0.43). WI and RARSa made a significant multivariate regression model with CAT activity (r = 0.42). The result of stepwise regression also indicated plant height in recovery can explain by SR and ARI (r = 0.40). PSSR and CRI could be regarded as independent variables that account for a portion of the variation in dry forage yield in recovery (r = 0.46) (Table 10).
Discussion
The effect of severe salinity stress and pollination system
Salinity, heat, and water deficit are the most abiotic stresses which cause significant damage on plants [49]. Stress memory, which occurs in some plants, plays an important role in adaptation to these adverse conditions [50]. When subjected to salt stress, plants, as sessile organisms, must initiate responses and adaptations to counter adverse conditions. Consequently, delving into the mechanisms of salt stress response and discovering strategies to enhance plant stress tolerance becomes imperative [51]. Changes in canopy characteristics, including leaf chlorophyll, other photosynthetic pigment contents, dry matter, and plant water status, have been observed to alter in response to the ionic and osmotic components of salinity stress. These changes result in noticeable variability in the spectral reflectance of the canopy, particularly in the visible (VIS) and near-infrared (NIR) domains [52–54]. Different spectral reflectance indices (SRIs) have been formulated as alternative means of selection in breeding trials, replacing the need for destructive and direct selection criteria [55]. A solution to deal with salt stress is using plant growth regulators.
Plant hormones, recognized as a sustainable tool aid in modifying the detrimental repercussions of both abiotic and biotic strains on plants. Salicylic acid a phenolic compound synthesized by root cells in numerous plants, governs physiological processes and diminishes the repercussions of stress, thereby enhancing the harmful consequences of salinity via signaling mechanisms [56]. In this study we evaluated the consequences of salinity memory during two different stages and foliar application of SA under salinity stress compared with a single salinity on two selfed and open-pollinated reproductive systems of tall fescue. Considerable genotypic variation observed among genotypes, pollination systems and salinity treatments in terms of most morphological, physiological, root traits and spectral indices which can facilitate the selection process in this plant.
No inbreeding depression was observed for biomass, photosynthetic capacity, enzymatic antioxidant activities (e.g., APX and POX), and potential of root production which can be attributed to the low heterosis for these traits. This facilitates the development of inbred lines in this open-pollinated plant for further studies. The enhancement of these traits through selfing suggests that associated additive genes become fixed in the selfing system. Consequently, methods founded on recurrent selection may propose an increased response to selection for these traits in tall fescue. Geiger [57] and Waser and Price [58] reported that significant outbreeding depression is sometimes observed in crosses between distant populations of the same species. However, Saeidnia et al. [10] reported that the S1 families exhibited lower performance compared to the OP ones. The stage-specific variation in inbreeding depression could be linked to genes expressed at different stages [59].
In the present study, once severe salinity stress treatment (S2) decreased PH, WFY, DFY, PHR, WFYR, DFYR, Chla, Chlb, Car, Tchl, Chl a/b, TChl/Car, CAT and POX activities. Initially, salt stress causes water loss and growth inhibition, which constitutes the ion-independent response. As salt stress persists, Na+ accumulates to toxic concentrations, leading to premature senescence, known as the ion-dependent response [60, 61]. Fan et al. [51] noted that salt stress adversely affected ion homeostasis in bermudagrass cells, potentially leading to negative impacts on the plant’s growth and the absorption of nutrient elements [62]. A comparable outcome was reported in Arabidopsis under osmotic stress [63], aligning with our findings. Photosynthesis is pivotal for plants, and its efficiency is sensitive to stress conditions [64]. Chlorophyll, as a fundamental component of photosynthesis, directly influences the plant’s photosynthetic efficiency. Salt stress is known to reduce chlorophyll synthesis and increase chlorophyllase-mediated degradation [65]. Fan et al. [51] showed that the Chl a/b ratio decreased in bermudagrass following salt stress treatment. Consistent with our results, Hu et al. [66] reported that the salt stress reduced Chla and Chlb in perennial ryegrass. Abiotic stress could also lead to an overproduction of ROS, resulting in lipid peroxidation and harm to proteins and nucleic acids [66–68]. The findings imply that salt stress has the potential to induce an excess of ROS, and that the function of CAT and POX in eliminating ROS may be significant. However, some studies including Hu et al. [69] reported that the activity of antioxidant enzymes such as APX, POX, CAT and SOD increased in some Lolium perenne L. cultivars after four days’ treatment with 250 mM NaCl. Hence, the impact of stresses on enzyme activity is contingent on the crop species, the type of stress, and the duration and intensity of the stress.
Once severe salinity stress (S2) increased CRD, Pro and all root characteristics. The accumulation of proline is recognized to be linked with stress conditions, particularly those involving osmotic stress and high-temperature stress [70, 71]. Li et al. [72] expressed that the NaCl stress resulted in an increase of Pro content in Festuca valesiaca and Festuca glauca which was in agreement with our results. Water absorption efficiency by plant roots is a vital determinant of salinity resistance, and water absorption relies on the root size and its spatial distribution [73]. Different studies have been conducted on root characteristic system [74–78], whereas the role of salinity, on tall fescue root system growth and mass is still unclear. Hameed and Ashraf (2008) [79] demonstrated that under saline conditions (200 mM NaCl), the grass population originating from saline soil exhibited higher fresh and dry biomass of roots compared to the population from non-saline conditions. This finding is in agreement with the results obtained in this study. The increase in root mass to shoot mass ratio is a frequent reaction to salt stress which was also shown in this study. The increase in root-to-shoot ratio (RSR) is associated with features related to water stress and osmotic effects rather than direct salinity effects. In the context of salinity stress, a larger root mass can facilitate the retention of toxic ions in root cellular tissues and regulate their translocation to plant organs, such as the stem and leaves. This characteristic mechanism contributes to the plant’s resistance to salinity.
The results showed that under severe salinity stress, open-pollinated genotypes displayed negative effects on certain traits, while in sensitive genotypes, the stress effect also manifested in some traits and indices. The impact of salinity stress on pollination systems and genetic diversity in tall fescue highlighted that selecting and breeding salinity-tolerant genotypes can play a crucial role in enhancing plant resilience, preserving genetic diversity, and maintaining reproductive efficiency in saline environments.
Consequence of salinity memory
Recent publications indicated that the plants pre-exposed to abiotic stresses can achieve the potential to display a stronger and faster activation of their defense system in response to the subsequent stress challenges as “stress memory” [80, 81]. Short-term stress memory predominantly results from temporary alternation in physiological and biochemical metabolites. Once the stress subsides, plants revert to their previous growth status, essentially "forgetting" the stress event [82]. In contrast, long-term stress memory, governed by epigenetic mechanisms, has the potential to endure throughout the entire life of the plant that experienced the stress and may even be transmitted to its offspring [83]. In the current research we applied stress memory in two different stages (45 and 90 days after establishment, S1t1S2 and S1t2S2, respectively) to see how the stage of applying affects this phenomenon. The results showed that similar trends were observed in both stages in terms of measured traits with the exception of RWC, PHR, WFYR, DFYR, RL and R/S. Both S1t1S2 and S1t2S2 induced stress memory by increasing PH, WFY, DFY, all physiological traits (including photosynthesis pigments, proline content and activity of enzymes) and R/SR compared to S2 treatment. When plants experience pre-exposure to stress, they can generate stable signals and physiological responses that may persist as "stress memory" [78]. These results was consistent with previous studies that have shown similar memory effects in other plant species under different stress conditions [10, 18, 21, 50, 84]. Hu et al. [85] reported that multiple and short time salt stress/recovery treatments, resulted to salt stress memory in ryegrass. Hu et al. [85] showed that higher H2O2 and O2− accumulation in root hair and higher leaves electrolyte leakage level in control plants than in pre-treated. Higher accumulation of H2O2 might be attributed to the super transcriptional abundance of CAT in treated root, which was consistent with our findings in relation to CAT activity measured in leaves. Hu et al. [78] documented that pre-acclimation treatment inhibited the decline at various steps of OJIP (Chl a fluorescence induction transient) and energy transport fluxes in the active Photosystem II reaction center (PSII RC) for tall fescue genotypes. The plant response to abiotic stress involves alterations in the accumulation of various metabolites. Increased or decreased production of certain metabolites can contribute to enhanced tolerance to abiotic stress in plants [86–88], which was examined as a potential factor in stress memory responses. Furthermore, there are reports indicating that heat stress memory correlates with notable shifts in leaf metabolite profiles [78].
Our results suggest that the combined analysis of different stress stages, pollination systems, and genetic diversity provided a comprehensive view of salt stress memory in tall fescue. In particular, we found that interactions between these factors significantly influence mechanisms of salt stress response and highlight the importance of testing multiple factors simultaneously. For example, the results of the interaction effect of salinity treatments and pollination systems showed that salinity memory was more evident in open-pollinated than selfed in terms of traits. Also, salinity stress memory was more pronounced in drought-sensitive genotypes. The improved performance in drought-sensitive genotypes indicated a potential for using salinity memory as a strategy to enhance stress tolerance in more vulnerable plants. Mendanha et al. [89] and Amini et al. [84] in wheat stated that less productive genotypes (in terms of grain not biomass) had better response to stress memory which was in agreement with our reports. These findings highlights the need for a multifaceted approach to studying plant stress responses, beyond a single analysis to better understand complex interactions.
Effects of salicylic acid
In the current study, spraying tall fescue genotypes with SA under severe salinity stress treatment (H2S2) caused enhancement PH, WFY, DFY, PHR, DFYR, most photosynthesis pigments (including Chlb, Car, Tchl and Tchl/Car), CAT, APX activities, RL and R/SR compared with untreated plants under the same salinity stress condition (S2). Our findings that foliar application of salicylic acid (SA) mitigates the effects of salinity stress was in line with previous research by Kentucky bluegrass and tall fescue, which demonstrated improved stress tolerance with SA application [28, 37]. This supports the hypothesis that SA plays a crucial role in enhancing stress responses in plants. Given that salt stress inhibits plant growth by negatively impacting several physiological and biochemical processes such as photosynthesis, antioxidant capacity, and ion homeostasis [90], it is proposed that the observed improvement in growth of salt-stressed plants following SA treatment could be attributed to SA-induced alterations in these processes. For instance, El-Tayeb [91] demonstrated that the SA-induced growth enhancement might be attributed to the increase activity of antioxidants. Consequently, photosynthesis, a pivotal determinant of plant growth and yield [92], could have been augmented due to SA application, consistent with the findings of this investigation. SA application was also shown to boost photosynthetic rates in various crops, such as barley [93], soybean [94], wheat [95], and maize [96]. The results of the interaction effect of salinity treatments and pollination systems represented the application of salicylic acid was equally effective in reducing the effects of salinity stress in both S1 and OP. The foliar spray of SA was more effective in tolerant genotypes compared to sensitive genotypes in terms of evaluated traits.
Selection of superior genotypes
In the once severe salinity stress condition (S2), PC1 showed positive correlations with root traits, DFY, and DFYR, while PC2 exhibited positive correlations with Chla, Car, and Tchl. Genotypes 11MS1 and 3MS1, characterized by high PC1 and low PC2 values, were identified as preferable for once severe salinity stress conditions. These genotypes displayed an extensive root system and higher yield but lower photosynthetic capacity.
Under the S1t1S2 treatment, PC1 showed negative correlations with root traits. PC2 exhibited positive correlations with PH and PHR. Low PC1 indicated an extensive root system. High PC2 indicated greater yield production capacity. Therefore, genotypes 11MS1 and 1MS1 (low PC1, high PC2) were identified as preferable for first time pre-exposure to salinity stress. Genotypes 11MOP and 21MOP (susceptible) had less extensive root systems and lower yields. Under S1t2S2 treatment, PC1 had positive relevance with DFY, DFYR, CAT, POX activities and root characteristics and PC2 had positive correlations with photosynthetic pigments. Hence genotype 21MS1 (with high PC1 and PC2) was identified as tolerant genotype for this condition. Under H2S2 treatment, PC1 had negative correlations with DFYR and root characteristics and PC2 had positive correlations with photosynthetic pigments. Therefore, genotypes 11MS1 and 3MS1 (with low PC1 and high PC2) were identified as genotypes that positively responded to the application of salicylic acid to reduce the effect of severe salinity stress.
In general, genotype 11M was identified as a suitable genotype not only in respect to stress memory potential (for first time pre-exposure salinity stress) but also as a salinity-tolerant genotype (for once severe salinity stress conditions). Genotypes 11MS1 and 3MS1 were also found to have a high potential to root and photosynthetic capacity under the foliar spray of SA simultaneously with salinity stress. Saeidnia et al. [7, 10] reported that principal component analysis is a powerful multivariate method for identifying superior genotypes when a complex set of variables have been measured.
Spectral reflectance indices (SRIs)
Two types of spectral reflectance indices (SRIs) provide alternative indirect selection tools for descriptive traits: vegetation-SRIs and water-SRIs. The former is composed of various wavelengths in the visible (VIS: 400–700 nm) and red-edge (NIR: 700–850 nm) regions [55]. Recent reports indicate that the wavelengths of 420–470 nm (blue region) effectively characterizes the absorption features of photosynthetic parameters and have been recognized as regions sensitive to salinity stress in many crops [97–99]. The current research results showed that S2 treatment reduced SRIs including WI, RARSa, PSSR, CRI, and RGR compared with control treatment. There have been limited studies investigating the use of spectral reflectance indices (SRIs) for detecting variations in phenotypic parameters under saline conditions. Reduced chlorophyll content in stressed plants results in diminished chlorophyll absorption, which manifests as declining reflectance values in the red-edge zone of the spectra [100] which was consistent with our results regarding some indices. The decline in chlorophyll levels may stem from the interference of Na+ and Cl- ions with enzymes involved in chlorophyll biosynthesis or from disruptions in the integration of chlorophyll molecules into stable complexes [101]. Water index (WI) has been demonstrated to predict relative water content, leaf water potential, stomatal conductance, and canopy temperature with sufficient water stress [102]. Peñuelas et al. [103] showed the usefulness of using WI to assess the effect of salinity on barley. Gao and Li [104] demonstrated that salinity stress in tall fescue can be reflected by parameters at various levels of physiological processes. However, these parameters exhibit uneven sensitivity, making them less suitable as indicators for salinity tolerance selection in breeding. In this study, at least three indices calculated from single leaf reflectance spectrum (mSR750/705, mND750/705, and SI710/760) were identified as sensitive for differentiating salinity stress from untreated plants. It is noted that some wavelengths used in the SRIs that yielded significant results in prior studies may not be universally applicable across all environmental conditions. The lack of a universal relationship between published SRIs and measured parameters may stem from differences in crop types, growth stages, and levels of stress [29].
Foliar application of SA and stress memory alleviated the damage of salinity stress by promoting physiological characteristics which can determine through the function spectral reflectance indices. Our results showed that genotypes (G), pollination systems (P) and their interaction with five salinity treatments (T) had significant effects on most SRIs emphasizing the possibility of selection of genotypes with the positive response to salinity tolerance in this germplasm. In the present study, under S1t1S2 treatment enhanced WI and some vegetation-SRIs in OP and WI, PSRI and RGR in S1 and under S1t2S2 treatment increased most vegetation-SRIs in both OP and S1. Under H2S2 treatment improved most vegetation-SRIs in both OP and S1. So, application of salicylic acid and salinity memory in both OP and S1 genotypes were equally effective in reducing the harmful effects of salinity stress by improving some spectral reflectance indices. Under S1t1S2 treatment improved WI, PSRI, ARI, GDVI and RGR in sensitive genotypes and WI, ARI and RGR in tolerant genotypes. Under S1t2S2 treatment enhanced most vegetation-SRIs in both sensitive and tolerant genotypes. Under H2S2 treatment increased most vegetation-SRIs in both sensitive and tolerant genotypes. Thus, salinity memory and foliar application of SA were effective in order to reduce the harmful effects of salinity stress in both sensitive and tolerant genotypes by improving some spectral reflectance indices.
Correlation between SRIs with traits and stepwise multiple linear regression (SMLR)
Recent advancements in high-throughput tools have empowered plant breeders, allowing them to accelerate the pace of genetic gain by thoroughly evaluating traits that were traditionally challenging to assess rapidly and nondestructively. The present study explores the potential utilization of spectral reflectance indices as alternative selection tools for traits typically measured destructively in tall fescue. Results of phenotypic correlation coefficients between spectral reflectance indices with different traits revealed that WI and NWI had significantly positive correlations with PH and photosynthetic pigments (Chla, Chlb, Tchl, Chl a/b and Tchl/Car). While some vegetation-SRIs (including NDVI, RARSa, RARSb and others) had negative correlations with some photosynthetic pigments. According to these results, high values of WI and NWI and low values NDVI, RARSa and RARSb will lead to select genotypes with higher photosynthetic capacity. We also were found positive relationships between CRI with root traits, CRD and DFYR. Various investigations have highlighted a noteworthy association between spectral characteristics and plant biomass, along with leaf area index, in crops such as eggplant (Solanum melongena L.) [105] and seashore paspalum (Paspalum vaginatum Swartz) [33] when grown in saline soils.
To identify the most influential spectral reflectance indices (SRIs) explaining variability in each destructively measured trait across genotypes, we employed stepwise multiple linear regression (SMLR) with all SRIs as independent variables. The SMLR model identified the Chlorophyll Reflectance Index (CRI) from vegetation-SRIs as the most influential SRI explaining much of the variation in the root/shoot ratio. For dry forage yield in recovery, indices from vegetation-SRIs (PSSR and CRI) exhibited high explanatory power, while combined indices from both vegetation-SRIs and water-SRIs were the best predictors of Catalase (CAT) activity. In stepwise regression for relative water content, RARSb and ARI, and for plant height in recovery, SR and ARI were identified as the most informative indices. This indicates that vegetation-specific spectral reflectance indices (SRIs), which combine data from the blue, green, and red-edge spectral regions are suitable for assessing similar vegetation-related characteristics across the crop canopy. Conversely, indices derived from water-SRIs, which integrate information from NIR and SWIR regions, act as supplementary tools to vegetation-SRIs, enabling the evaluation of plant traits associated with alterations in plant water status and internal leaf structure. Earlier research has underscored the efficacy of SRIs that amalgamate data from the blue, green, red, and red-edge wavebands in phenotyping diverse vegetation parameters, encompassing above-ground biomass and morphological traits linked to growth status [106, 107]. On the contrary, indices that merge information from NIR and SWIR wavebands prove effective in indirectly estimating yield, water content, and various plant physiological characteristics [108–110].
Despite the comprehensive analysis performed in this study, several limitations should be acknowledged. First, the experiments were conducted under controlled greenhouse conditions, which may not fully replicate field conditions. Second, the genetic diversity of the tall fescue genotypes used in this study was only four, which may not represent the genetic variability of the species. Future studies should include different genotypes and conduct field trials to confirm these findings under natural conditions.
Conclusions
The findings of this study suggest that salinity stress can significantly impact the morphological, root, and physiological functions of tall fescue genotypes, consequently affecting plant growth and biomass production. Therefore, selecting and breeding salinity-tolerant genotypes can play a crucial role in enhancing plant resilience, preserving genetic diversity, and maintaining reproductive efficiency in saline environments. The significant genetic variation between pollination systems (selfed and open-pollinated) indicated that any changes in plant natural mating systems could clearly change the germplasm genetic structure. Pre-exposed to an early salinity caused significant results in the subsequent plant stages with the emphasis of physiological traits. The results showed that similar trends were observed in both stages of pre-exposure salinity in terms of measured traits with the exception of RWC, PHR, WFYR, DFYR, RL and R/S. However, salinity memory was more evident in open-pollinated than selfed population. Additionally, salinity memory was more pronounced in drought-sensitive genotypes of tall fescue, allowing them to exhibit a faster and more protective response to periodic salinities. Foliar application of salicylic acid improved tall fescue genotype’s tolerance to salinity stress by promoting yield productivity, photosynthesis pigments (Chla, Chlb and Tchl), non-enzyme (e.g., Car), enzymatic antioxidant (e.g., CAT and APX) activities, root length and ratio of root/shoot in recovery. The results of PCA indicated the superiority of genotype 11M under most salinity treatments and can be used in the development of synthetic varieties and further breeding programs. Comparing various spectral reflectance indices demonstrated vegetation-SRIs (RARSa, PSSR and RGR) were sensitive to explain important traits. Further research is required to validate these indices across various genotypes and physiological attributes, establishing them as a model for the rapid screening of salinity-tolerant materials in breeding projects.
Supporting information
S1 Fig. Different stages for mesuring various traits (morphological, physiological and root) in five salinity treatments (C, S1t1S2, S1t2S2, S2 and H2S2) in tall fescue genotypes.
https://doi.org/10.1371/journal.pone.0310061.s001
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S2 Fig. Mean comparison emergence rate for the interactions of four tall fescue genotypes (1M, 3M, 11M and 21M) and two different pollination systems (selfed (S1) and open-pollinated (OP)) before salinity stress during two years.
Mean followed by the same letter is not significantly different according to LSD test (probability level of 5%).
https://doi.org/10.1371/journal.pone.0310061.s002
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S3 Fig.
Mean comparison of relative water content for the interaction of two different pollination systems (selfed (S1) and open-pollinated (OP)) and two salinity treatments (C and S1) in the first time (a) and in the second time (b) during two years. Mean comparison total chlorophyll for the interaction of two different pollination systems (selfed (S1) and open-pollinated (OP)) and two salinity treatments (C and S1) in the first time (c) and in the second time (d) during two years. Mean comparison of proline content for the interaction of two different pollination systems (selfed (S1) and open-pollinated (OP)) and two salinity treatments (C and S1) in the first time (e) and in the second time (f) during two years. Mean followed by the same letter is not significantly different according to LSD test (probability level of 5%).
https://doi.org/10.1371/journal.pone.0310061.s003
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S1 Table. The formula of different spectral reflectance indices, which were used in this study.
https://doi.org/10.1371/journal.pone.0310061.s004
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S2 Table. Mean squares of morphological traits and relative water content in four tall fescue genotypes and two different pollination systems (selfed (S1) and open-pollinated (OP)) in five salinity treatments (C, S1t1S2, S1t2S2, S2 and H2S2) evaluated during two years.
https://doi.org/10.1371/journal.pone.0310061.s005
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S3 Table. Mean squares of physiological traits in four tall fescue genotypes and two different pollination systems (selfed (S1) and open-pollinated (OP)) in five salinity treatments (C, S1t1S2, S1t2S2, S2 and H2S2) evaluated two years.
https://doi.org/10.1371/journal.pone.0310061.s006
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S4 Table. Mean squares of root traits in four tall fescue genotypes and two different pollination systems (selfed (S1) and open-pollinated (OP)) in five salinity treatments (C, S1t1S2, S1t2S2, S2 and H2S2) evaluated during two years.
https://doi.org/10.1371/journal.pone.0310061.s007
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S5 Table. Correlation coefficients among spectral reflectance indices based on the average of two years for eight genotypes (1MOP, 1MS1, 3MOP, 3MPS1, 11MOP, 11MS1, 21MOP and 21MS1), five salinity treatments (C, S1t1S2, S1t2S2, S2 and H2S2) and two replications (n = 80).
https://doi.org/10.1371/journal.pone.0310061.s008
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S6 Table. The first two principal components (PC) loadings for the measured traits on 8 tall fescue genotypes in five treatment levels (C, S1t1S2, S1t2S2, S2 and H2S2) during two years.
https://doi.org/10.1371/journal.pone.0310061.s009
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References
- 1. Lee KW, et al. Transgenic expression of MsHsp23 confers enhanced tolerance to abiotic stresses in tall fescue. Asian Australas J Anim Sci. 2012; 25(6):818–823. pmid:25049632
- 2. Gao Y, Li D. Growth responses of tall fescue (Festuca arundinacea Schreb.) to salinity stress. Eur J Hortic. Sci. 2014; 79(3):123–128.
- 3. Kosmala A, Perlikowski D, Pawłowicz I, Rapacz M. Changes in the chloroplast proteome following water deficit and subsequent watering in a high-and a low-drought-tolerant genotype of Festuca arundinacea. J Exp Bot. 2012; 63(17):6161–6172. pmid:23045610
- 4. Esmailpourmoghadam E, Salehi H, Moshtaghi N. Differential gene expression responses to salt and drought stress in tall fescue (Festuca arundinacea Schreb.). Mol Biotechnol. 2023. pmid:37742296
- 5. Ponce KS, Guo L, Leng Y, Meng L, Ye G. Advances in sensing, response and regulation mechanism of salt tolerance in rice. Int J Mol Sci. 2021; 22(5):2254. pmid:33668247
- 6. Majidi MM, Mirlohi A, Amini F. Genetic variation, heritability and correlations of agro-morphological traits in tall fescue (Festuca arundinacea Schreb.). Euphytica. 2009; 167(3): 323–331.
- 7. Saeidnia F, Majidi MM, Hosseini E. Simultaneous effect of water deficit and mating systems in fennel (Foeniculum vulgare mill.): Genetics of phytochemical compositions and drought tolerance. Agric Water Manag. 2023; 277:108122.
- 8. Henter HJ. Inbreeding depression and haplodiploidy: experimental measures in a parasitoid and comparisons across diploid and haplodiploid insect taxa. Evol. 2003; 57(8):1793–1803. pmid:14503621
- 9. Hedrick PW, Kalinowski ST. Inbreeding depression in conservation biology. Annu Rev Ecol Syst. 2000; 31(1):139–162.
- 10. Saeidnia F, Majidi MM, Mirlohi A, Spanani S, Karami Z, Abdollahi Bakhtiari M. A genetic view on the role of prolonged drought stress and mating systems on post-drought recovery, persistence and drought memory of orchardgrass (Dactylis glomerata L.). Euphytica. 2020; 216:1–17.
- 11. Munns R, Tester M. Mechanisms of salinity tolerance. Annu Rev Plant Biol. 2008; 59:651–681. pmid:18444910
- 12.
Lauchli A, Grattan S. Plant Growth and Development Under Salinity Stress. In: Jenks MA, Hasegawa PM, Jain S.M. (eds) Advances in Molecular Breeding Toward Drought and Salt Tolerant Crops. Springer, Dordrecht. 2007; 1–32.
- 13. Lux A, Rost TL. Plant root research: the past, the present and the future. Ann Bot. 2012; 110(2):201–204. pmid:22966495
- 14. Shalhevet J, Huck MG, Schroeder BP. Root and shoot growth responses to salinity in maize and soybean. Agron J. 1995; 87(3):512–516.
- 15. Razzaghi F, Plauborg F, Jacobsen SE, Jensen CR, Andersen MN. Effect of nitrogen and water availability of three soil types on yield, radiation use efficiency and evapotranspiration in field-grown quinoa. Agric Water Manag. 2012; 109:20–29.
- 16. Koevoets IT, Venema JH, Elzenga JTM., Testerink C. Roots withstanding their environment: exploiting root system architecture responses to abiotic stress to improve crop tolerance. Front Plant Sci. 2016; 7:1335. pmid:27630659
- 17. Molinier J, Ries G, Zipfel C, Hohn B. Transgeneration memory of stress in plants. Nature. 2006; 442(7106):1046–1049. pmid:16892047
- 18. Ding Y, Fromm M, Avramova Z. Multiple exposures to drought ’train’ transcriptional responses in Arabidopsis. Nat Commun. 2012; 3(1):740. pmid:22415831
- 19. Goh CH, Nam HG, Park YS. Stress memory in plants: a negative regulation of stomatal response and transient induction of rd22 gene to light in abscisic acid-entrained Arabidopsis plants. Plant J. 2003; 36(2):240–255. pmid:14535888
- 20. Liu N, Fromm M, Avramova Z. H3K27me3 and H3K4me3 chromatin environment at super-induced dehydration stress memory genes of Arabidopsis thaliana. Mol Plant. 2014; 7(3):502–513. pmid:24482435
- 21. Sani E, Herzyk P, Perrella G, Colot V, Amtmann A. Hyperosmotic priming of Arabidopsis seedlings establishes a long-term somatic memory accompanied by specific changes of the epigenome. Genome Biol. 2013; 14(6):1–24. pmid:23767915
- 22. Askari E, Ehsanzadeh P. Effectiveness of exogenous salicylic acid on root and shoot growth attributes, productivity, and water use efficiency of water-deprived fennel genotypes. Hortic Environ Biotechnol. 2015; 56:687–696.
- 23. Ullah A, Sun H, Hakim , Yang X, Zhang X. A novel cotton WRKY gene, GhWRKY6-like, improves salt tolerance by activating the ABA signaling pathway and scavenging of reactive oxygen species. Physiol Plant. 2018; 162(4):439–454. pmid:29027659
- 24. Miura K, Tada Y. Regulation of water, salinity, and cold stress responses by salicylic acid. Front Plant Sci. 2014; 5:4. pmid:24478784
- 25. Wani SH, Kumar V, Shriram V, Sah SK. Phytohormones and their metabolic engineering for abiotic stress tolerance in crop plants. Crop J. 2016; 4(3):162–176.
- 26. Scalia R, Oddo E, Russo G, Saiano F, Grisafi F. Effectiveness of glycinebetaine foliar application in relieving salt stress symptoms in two turf-grasses. Grassl Sci. 2014; 60(2):92–97.
- 27. Sun S, An M, Han L, Yin S. Foliar application of 24-epibrassinolide improved salt stress tolerance of perennial ryegrass. HortScience. 2015; 50(10):1518–1523.
- 28. Arghavani M, Savadkoohi S, Mortazavi SN. Salinity tolerance of Kentucky bluegrass as affected by salicylic acid. J Ornam Plants. 2017; 7(4):237–245.
- 29. El-Hendawy S, et al. Ability of modified spectral reflectance indices for estimating growth and photosynthetic efficiency of wheat under saline field conditions. Agron. 2019; 9(1):35.
- 30. Hu Y, Hackl H, Schmidhalter U. Comparative performance of spectral and thermographic properties of plants and physiological traits for phenotyping salinity tolerance of wheat cultivars under simulated field conditions. Funct Plant Biol. 2016; 44(1):134–142. pmid:32480552
- 31. Sytar O, et al. Applying hyperspectral imaging to explore natural plant diversity towards improving salt stress tolerance. Sci Total Environ. 2017; 578:90–99. pmid:27524726
- 32. Poss J, Russell W, Grieve C. Estimating yields of salt-and water-stressed forages with remote sensing in the visible and near infrared. J Environ Qual. 2006; 35(4):1060–1071. pmid:16738391
- 33. Lee G, Carrow RN, Duncan RR. Photosynthetic responses to salinity stress of halophytic seashore paspalum ecotypes. Plant Sci. 2004; 166(6):1417–1425.
- 34. Pirnajmedin F, Majidi MM, Gheysari M. Root and physiological characteristics associated with drought tolerance in Iranian tall fescue. Euphytica. 2015; 202:141–155.
- 35. Pirnajmedin F, Majidi MM, Gheysari M. Survival and recovery of tall fescue genotypes: association with root characteristics and drought tolerance. Grass Forage Sci. 2016; 71(4):632–640.
- 36. Pirnajmedin F, et al. Persistence, recovery and root traits of tall fescue genotypes with different flowering date under prolonged water stress. Euphytica. 2017; 213:1–15.
- 37. Pirnajmedin F, Majidi MM, Taleb H, Saeidi G, Shojaiefar S. Genotypic-specific response to exogenous applied salicylic acid in tall fescue under different irrigation conditions. Crop Sci. 2020; 60(2):1123–1130.
- 38. Ritchie SW, Nguyen HT, Holaday AS. Leaf water content and gas-exchange parameters of two wheat genotypes differing in drought resistance. Crop Sci. 1990; 30(1):105–111.
- 39. Lichtenthaler HK, Welburn WR. Determination of total carotenoids and chlorophylls a and b of leaf extracts in different solvents. Biochem Soc Trans. 1990; 11:591–592.
- 40. Bates LS, Waldren RA, Teare I. Rapid determination of free proline for water-stress studies. Plant Soil. 1973; 39:205–207.
- 41. Change B, Maehly A. Assay of catalases and peroxidases. Meth Enzymol. 1955; 2:764–775.
- 42. Nakano Y, Asada K. Hydrogen peroxide is scavenged by ascorbate-specific peroxidase in spinach chloroplasts. Plant Cell Physiol. 1981; 22(5):867–880.
- 43. Herzog V, Fahimi HD. A new sensitive colorimetric assay for peroxidase using 3, 3′-diaminobenzidine as hydrogen donor. Anal Biochem. 1973; 55(2):554–562. pmid:4750690
- 44. Bradford MM. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem. 1976; 72(1–2):248–254. pmid:942051
- 45. Galkovskyi T, et al. GiA Roots: software for the high throughput analysis of plant root system architecture. BMC Plant Biol. 2012; 12(1):1–12. pmid:22834569
- 46.
SAS Institute. The SAS system for windows. Release 9.4. Cary, NC: SAS Institute, Inc. 2013.
- 47.
Johnson RA, Wichern DW. Applied multivariate statistical analysis. Prentice Hall Inter, Inc, New Jersey. 2007.
- 48.
Statgraphics. ‘Statgraphics version 15.2.11.’ (Statpoint Technologies: The Plains, VA, USA). 2007.
- 49. Adem GD, Roy SJ, Zhou M, Bowman JP, Shabala S. Evaluating contribution of ionic, osmotic and oxidative stress components towards salinity tolerance in barley. BMC Plant Biol. 2014; 14:1–13. pmid:24774965
- 50. Nourbakhsh V, Majidi MM, Abtahi M. Drought stress memory in orchard grass and the role of marker-based parental selection for physiological and antioxidant responses. Plant Physiol Biochem. 2023; 204: 108061. pmid:37847971
- 51. Fan J, Xu J, Zhang W, Amee M, Liu D, Chen L. Salt-induced damage is alleviated by short-term pre-cold treatment in bermudagrass (Cynodon dactylon). Plants. 2019; 8(9):347. pmid:31540195
- 52. Zhang L, Zhou Z, Zhang G, Meng Y, Chen B, Wang Y. Monitoring cotton (Gossypium hirsutum L.) leaf ion content and leaf water content in saline soil with hyperspectral reflectance. Eur J Remote Sens. 2014; 47(1):593–610.
- 53. Lara MA, et al. Hyperspectral imaging to evaluate the effect of irrigation water salinity in lettuce. Appl Sci. 2016; 6(12):412.
- 54. El-Hendawy S, Hassan W, Refay Y, Schmidhalter U. On the use of spectral reflectance indices to assess agro-morphological traits of wheat plants grown under simulated saline field conditions. J Agron Crop Sci. 2017; 203(5):406–428.
- 55. El-Hendawy S, et al. Combining genetic analysis and multivariate modeling to evaluate spectral reflectance indices as indirect selection tools in wheat breeding under water deficit stress conditions. Remote Sens. 2020; 12(9):1480.
- 56. Noreen S, Ashraf M. Alleviation of adverse effects of salt stress on sunflower (Helianthus annuus L.) by exogenous application of salicylic acid: growth and photosynthesis. Pak J Bot. 2008; 40(4):1657–1663.
- 57.
Geiger HH. Epistasis and heterosis. In Proceedings of the Second International Conference on Quantitative Genetics, Raleigh 395–3991988. (Sunderland, MA, 1988).
- 58. Waser NM, Price MV. Optimal outcrossing in ipomopsis aggregat a: seed set and offspring fitnes. Evol. 1989; 43(5):1097–1109. pmid:28564159
- 59. Husband BC, Schemske DW. Evolution of the magnitude and timing of inbreeding depression in plants. Evol. 1996; 50(1):54–70. pmid:28568860
- 60. Sheldon AR, Dalal RC, Kirchhof G, Kopittke PM, Menzies NW. The effect of salinity on plant-available water. Plant Soil. 2017; 418:477–491.
- 61. Negrão S, Schmöckel S, Tester M. Evaluating physiological responses of plants to salinity stress. Ann Bot. 2017; 119(1):1–11. pmid:27707746
- 62. Hamamoto S, Horie T, Hauser F, Deinlein U, Schroeder JI, Uozumi N. HKT transporters mediate salt stress resistance in plants: from structure and function to the field. Curr Opin Biotechnol. 2015; 32:113–120. pmid:25528276
- 63. Akilan S, et al. Evaluation of osmotic stress tolerance in transgenic Arabidopsis plants expressing Solanum tuberosum D200 gene. J Plant Interact. 2019; 14(1):79–86.
- 64. Baker NR. A possible role for photosystem II in environmental perturbations of photosynthesis. Physiol Plant. 1991; 81(4):563–570.
- 65. Santos CV. Regulation of chlorophyll biosynthesis and degradation by salt stress in sunflower leaves. Sci Hortic. 2004; 103(1):93–99.
- 66. Hu T, Li HY, Zhang XZ, Luo HJ, Fu JM. Toxic effect of NaCl on ion metabolism, antioxidative enzymes and gene expression of perennial ryegrass. Ecotoxicol Environ Saf. 2011; 74(7):2050–2056. pmid:21813179
- 67. Kowaltowski AJ, Castilho RF, Vercesi AE. Mitochondrial permeability transition and oxidative stress. FEBS Lett. 2001; 495(1–2):12–15. pmid:11322939
- 68. Zago E, et al. Nitric oxide-and hydrogen peroxide-responsive gene regulation during cell death induction in tobacco. Plant Physiol. 2006; 141(2):404–411. pmid:16603664
- 69. Hu L, Li H, Pang H, Fu J. Responses of antioxidant gene, protein and enzymes to salinity stress in two genotypes of perennial ryegrass (Lolium perenne) differing in salt tolerance. J Plant Physiol. 2012; 169(2):146–156. pmid:22088275
- 70. Ashraf M, Foolad MR. Roles of glycine betaine and proline in improving plant abiotic stress resistance. Environ Exp Bot. 2007; 59(2):206–216.
- 71. Cvikrová M, Gemperlová L, Dobrá J, Martincová O, Prásil IT, Gubis J, et al. Effect of heat stress on polyamine metabolism in proline-over-producing tobacco plants. Plant Sci. 2012; 182:49–58. pmid:22118615
- 72. Li K, Li H, Zhao Y, Bian X, Meng Z. Effects of NaCl stress on two blue fescue varieties (Festuca glauca). Front Agric China. 2010; 4: 96–100.
- 73. Gupta B, Huang B. Mechanism of salinity tolerance in plants: physiological, biochemical, and molecular characterization. Int J Genomics. 2014; 2014. pmid:24804192
- 74. Hays K, Barber J, Kenna M, Mccollum T. Drought avoidance mechanisms of selected bermudagrass genotypes. HortScience. 1991; 26(2):180–182.
- 75.
Taylor HM. A program to increase plant available water through rooting modification. 463–472. In: W. Bohm (ed.). Root ecology and its practical application: A contribution to the investigation of the whole plant. Verlag Gumpenstein, Irdning, Austria. 1983.
- 76. Beyrouty C, West C, Gbur E. Root development of bermudagrass and tall fescue as affected by cutting interval and growth regulators. Plant Soil. 1990; 127:23–30.
- 77. Bonos SA, Rush D, Hignight K, Meyer WA. Selection for deep root production in tall fescue and perennial ryegrass. Crop Sci. 2004; 44(5):1770–1775.
- 78. Hu T, Liu SQ, Amombo E, Fu JM. Stress memory induced rearrangements of HSP transcription, photosystem II photochemistry and metabolism of tall fescue (Festuca arundinacea Schreb.) in response to high-temperature stress. Front Plant Sci. 2015; 6:403. pmid:26136755
- 79.
Hameed M, Ashraf M. Panicum antidotale: A potential grass for salt affected soils. In: Kafi M. & Khan M.A. (eds) Crop and forage production using saline waters. NAM S and T Centre, Daya Publishing House, New Dehli. 2008.
- 80. Backhaus S, Kreyling J, Grant K, Beierkuhnlein C, Walter J, Jentsch A. Recurrent mild drought events increase resistance toward extreme drought stress. Ecosyst. 2014; 17:1068–1081.
- 81. Vu WT, Chang PL, Moriuchi KS, Friesen ML. Genetic variation of transgenerational plasticity of offspring germination in response to salinity stress and the seed transcriptome of Medicago truncatula. BMC Evol Biol. 2015; 15:1–14. pmid:25884157
- 82. Crisp PA, Ganguly D, Eichten SR, Borevitz JO, Pogson BJ. Reconsidering plant memory: Intersections between stress recovery, RNA turnover, and epigenetics. Sci Adv. 2016; 2(2): e1501340. pmid:26989783
- 83. Sun C. et al. Exploration of epigenetics for improvement of drought and other stress resistance in crops: A review. Plants. 2021; 10(6):1226. pmid:34208642
- 84. Amini A, Majidi MM, Mokhtari N, Ghanavati M. Drought stress memory in a germplasm of synthetic and common wheat: antioxidant system, physiological and morphological consequences. Sci Rep. 2023; 13(1):8569. pmid:37237176
- 85. Hu T, Jin Y, Li H, Amombo E, Fu J. Stress memory induced transcriptional and metabolic changes of perennial ryegrass (Lolium perenne) in response to salt stress. Physiol Plant. 2016; 156(1):54–69. pmid:25913889
- 86. Bruce TJ, Matthes MC, Napier JA, Pickett JA. Stressful “memories” of plants: evidence and possible mechanisms. Plant Sci. 2007; 173(6):603–608.
- 87. Sanchez DH, Szymanski J, Erban A, Udvardi MK, Kopka J. Mining for robust transcriptional and metabolic responses to long-term salt stress: a case study on the model legume Lotus japonicus. Plant Cell Environ. 2010; 33(4):468–480. pmid:19781009
- 88. Urano K, Kurihara Y, Seki M, Shinozaki K. ‘Omics’ analyses of regulatory networks in plant abiotic stress responses. Curr Opin Plant Biol. 2010; 13(2):132–138. pmid:20080055
- 89. Mendanha T, Rosenqvist E, Hyldgaard B, Ottosen CO. Heat priming effects on anthesis heat stress in wheat cultivars (Triticum aestivum L.) with contrasting tolerance to heat stress. Plant Physiol Biochem. 2018; 132:213–221. pmid:30216779
- 90. Ashraf M. Some important physiological selection criteria for salt tolerance in plants. Flora Morphol., Distrib Funct Ecol Plants. 2004;199(5):361–376.
- 91. El-Tayeb M. Response of barley grains to the interactive effect of salinity and salicylic acid. Plant Growth Regul. 2005; 45:215–224.
- 92.
Natr L, Lawlor DW. Photosynthetic plant productivity. In Photosynthesis Handbook, (2nd Ed). Pessarakli M. C.R.C. Press, New York, USA. 2005; 501–524.
- 93. Pancheva T, Popova L, Uzunova A. Effects of salicylic acid on growth and photosynthesis in barley plants. J Plant Physiol. 1996; 149(1–2):57–63.
- 94. Khan W, Prithiviraj B, Smith D. Photosynthetic response of corn and soybeen to foliar application of salicylates. J Plant Physiol. 2003; 160(5):485–492. pmid:12806776
- 95. Singh B, Usha K. Salicylic acid induced physiological and biochemical changes in wheat seedlings under water stress. Plant Growth Regul. 2003; 39:137–141.
- 96. Khodary S. Effect of salicylic acid on the growth, photosynthesis and carbohydrate metabolism in salt stressed maize plants. Int J Agric Biol. 2004; 6(1):5–8.
- 97. Rud R, Shoshany M, Alchanatis V. Spectral indicators for salinity effects in crops: A comparison of a new green indigo ratio with existing indices. Remote Sens Lett. 2011; 2(4):289–298.
- 98. Jaleel CA, Lakshmanan G, Gomathinayagam M, Panneerselvam R. Triadimefon induced salt stress tolerance in Withania somnifera and its relationship to antioxidant defense system. S Afr J Bot. 2008; 74(1):126–132.
- 99. Zhang TT, et al. Using hyperspectral vegetation indices as a proxy to monitor soil salinity. Ecol Indic. 2011; 11(6):1552–1562.
- 100. Main R, Cho MA, Mathieu R, O’kennedy MM, Ramoelo A, Koch S. An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS J Photogramm Remote Sens. 2011; 66(6):751–761.
- 101. Husaini AM, Abdin MZ. Overexpression of tobacco osmotin gene leads to salt stress tolerance in strawberry (Fragaria × ananassa Duch.) plants. Indian J Biotechnol. 2008; 7(4):465–471.
- 102. Peñuelas J, Filella I, Biel C, Serrano L, Save R. The reflectance at the 950–970 nm region as an indicator of plant water status. Int J Remote Sens. 1993; 14(10):1887–1905.
- 103. Peñuelas J, Isla R, Filella I, Araus JL. Visible and near-infrared reflectance assessment of salinity effects on barley. Crop Sci. 1997; 37(1):198–202.
- 104. Gao Y, Li D. Detecting salinity stress in tall fescue based on single leaf spectrum. Sci Hortic. 2012; 138:159–164.
- 105. Leone A, Menenti M, Buondonno A, Letizia A, Maffei C, Sorrentino G. A field experiment on spectrometry of crop response to soil salinity. Agric Water Manag. 2007; 89(1–2):39–48.
- 106. Zerner M, Rebetzke G, Gill G. Genotypic stability of weed competitive ability for bread wheat (Triticum aestivum) genotypes in multiple environments. Crop Pasture Sci. 2016; 67(7):695–702.
- 107. Babar M, Reynolds M, Van Ginkel M, Klatt A, Raun W, Stone M. Spectral reflectance to estimate genetic variation for in-season biomass, leaf chlorophyll, and canopy temperature in wheat. Crop Sci. 2006; 46(3):1046–1057.
- 108. Prasad B, Carver BF, Stone ML, Babar M, Raun WR, Klatt AR. Genetic analysis of indirect selection for winter wheat grain yield using spectral reflectance indices. Crop Sci. 2007; 47(4):1416–1425.
- 109. Kim DM, et al. Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis. Sci Rep. 2015; 5(1):15919. pmid:26531782
- 110. Wang C, et al. Extraction of sensitive bands for monitoring the winter wheat (Triticum aestivum) growth status and yields based on the spectral reflectance. PLoS One. 2017; 12(1): e0167679. pmid:28060827