Next Article in Journal
Maximizing Grains While Minimizing Yield-Scaled Greenhouse Gas Emissions for Wheat Production in China
Previous Article in Journal
Synergy between Zeolites and Leguminous Cover Crops Improved Olive Tree Performance and Soil Properties in a Rainfed Olive Orchard
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Evaluation and Screening of Wild Elymus sibiricus L. Germplasm Resources under Salt Stress

1
College of Grassland Science and Technology, Sichuan Agricultural University, Chengdu 611130, China
2
Sichuan Academy of Grassland Science, Chengdu 611130, China
3
College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Agronomy 2023, 13(11), 2675; https://doi.org/10.3390/agronomy13112675
Submission received: 25 September 2023 / Revised: 20 October 2023 / Accepted: 22 October 2023 / Published: 25 October 2023
(This article belongs to the Section Grassland and Pasture Science)

Abstract

:
Soil salinization is a significant obstacle to improving natural grasslands, also affecting farmland productivity. The main distribution area of high salinity soils is primarily located in western China, with a significant presence in the Qinghai–Tibet Plateau (QTP). Siberian wildrye (Elymus sibiricus L.) has been widely used in ecological restoration and reseeding in the QTP, due to its strong salt and alkali resistance. To comprehensively evaluate its salt tolerance and to screen salt-tolerant and salt-sensitive germplasm resources, 50 wild E. sibiricus accessions were assessed through the measurement of several physiological parameters. These included the relative water content (RWC); relative electrical conductivity (REC); malondialdehyde content (MDA); proline content (Pro); and Na+/K+ ratio after 14 days of salt stress treatment (200 mmol L−1 NaCl). Through principal component analysis (PCA) based on both material sources and physiological indexing, it was found that the salt damage score (SDS) and RWC had a strong positive correlation (p < 0.05), in opposition to other indices. The positive indicators were REC, Pro, Na+/K+, and MDA, and the negative indicators were RWC and SDS. Ultimately, by combining the results of the conventional membership function analysis and the weighted membership function analysis based on major components, three salt-tolerant accessions—including XJ013-28, NM021-13 and XJ012-17—and four salt-sensitive accessions, including XZ008-20, QH013-8, XZ009-20, and QH005-35, were screened out. These results could be effectively used to assess the effects of salt stress on Siberian wildrye and its related species. This study provides a reference for the discovery and breeding of salt-tolerant cultivars among the Elymus species.

1. Introduction

Salinity is a serious abiotic stress that already affects nearly 1.0 × 109 hm2 of land worldwide, including 30% of highly irrigated land [1]. In China, the area of salinized soil covers approximately 1.0 × 108 hm2, representing almost 10% of the world’s total salinized soil area—among which, saline soil, residual saline soil, and potential saline soil account for 37%, 45%, and 18%, respectively [2]. Excessive soil salinity can cause salt injury to plants, which manifests as damage from a variety of secondary stresses, including osmotic stress, ion toxicity, nutrient deficiency, and oxidative stress [3]. High salt content reduces the water content of soil, causes osmotic stress, inhibits the growth and differentiation of plant organs and tissues, and may even lead to plant death [4,5]. Malondialdehyde and relative conductivity are important physiological indicators to measure the degree of cell membrane damage in plants under stress [6,7,8]. When plants are subjected to salt stress, osmotic balance is regulated by increasing free proline content, in order to maintain the stability of enzyme structures and the integrity of the membrane system [9]. Under salt stress, salt-tolerant plants can selectively absorb K+ and transport it to their above-ground organs, simultaneously reducing the transport of Na+ to their above-ground organs, thus maintaining a higher K+/Na+ ratio in order to reduce the damage of salt stress on the above-ground tissues [10,11,12].
The saline–alkali land in China is mainly distributed between Inner Mongolia, Gansu, Qinghai, Tibet, and Xinjiang. The Qinghai–Tibet Plateau (QTP) is a large area of saline soil distribution in China [13]. The introduction and the planting of vegetation restoration are dependent on the biological improvement and utilization of saline soil [14]. Siberian wildrye (Elymus sibiricus L.) is the typical plant of the Elymus genus in the Triticeae of the Poaceae [15]. It is an allotetraploid perennial herb (genome constitute is StStHH, 2n = 4x = 28). It is one of the endemic species of the QTP. It can form dominant species or constructive species in meadow and grassland plant communities. E. sibiricus has been recognized for its remarkable attributes, including elevated crude protein content; desirable palatability; vigorous tillering capacity; superior yield potential; straightforward cultivation techniques; and remarkable resilience towards cold and drought conditions [16,17,18,19]. Moreover, owing to its extensive root system and adaptability, certain E. sibiricus germplasms have shown promising prospects for cultivation in saline–alkali environments [20]. Therefore, understanding the physiological resistance mechanisms of E. sibiricus with regards to salinity tolerance and using it for saline land management and improvement is a cost-effective strategy for saline land management in China.
In this study, the salt response of 50 E. sibiricus wild accessions was evaluated by measuring their relative water content (RWC), relative electrical conductivity (REC), malondialdehyde content (MDA), proline content (Pro), and Na+/K+ ratio after two weeks of salt stress treatment on seedlings. The aims of this study were to improve the salt tolerance evaluation framework of E. sibiricus, to assess the salt tolerance of these E. sibiricus accessions, and to identify robust salt stress tolerant germplasm. This study can provide the genetic resources available for breeding new salt-tolerant E. sibiricus varieties, and offer insights that can inform broader plant breeding and stress tolerance studies of endemic species to the QTP.

2. Materials and Methods

2.1. Materials, Resources, and Experimental Design

A total of 50 accessions of Elymus sibiricus were used, with the majority sourced from various regions within China, including the Sichuan (6), Qinghai (8), Tibet (4), Gansu (6), Xinjiang (7), Inner Mongolia (8), Heilongjiang (1), and Hebei (6) provinces. Additionally, a limited number of accessions (4) were obtained from Russia (Table S1).
During the initial phase, the glumes of each accession were meticulously removed, followed by sterilization (75% alcohol for 30 s, 10% sodium hypochlorite for 20 min, and rinsing with sterile water twice). The sterilized seeds were then placed on a petri dish lined with moist, double-layered filter paper for germination and seedling emergence. After the seedlings reached the three-leaf stage, uniform and healthy seedlings were selected and transplanted into pots (5 cm in diameter and 25 cm in height) filled with river sand. One plant was grown in each pot, and each accession was represented by four replicates. A small amount of Hoagland nutrient solution was applied daily to keep the river sand moist. When the plants reached 5–8 tillers, the pots were placed in an uncovered chamber and submerged in a 200 mmol·L−1 NaCl Hoagland solution for 14 days. The water level was carefully monitored, ensuring it reached two-thirds of the pot height while completely immersing the root system. Periodic water supplementation was carried out, to prevent any fluctuations in salt concentration due to evaporation. All these meticulous procedures were executed within the controlled environment of a glass greenhouse situated at the Research Academy of Sichuan Grassland Science in Hongyuan County, Aba Prefecture, Sichuan Province, China. The greenhouse was diligently maintained by an air conditioner at a temperature range of 22–26 °C during the daytime, and 15–18 °C during the nighttime. Following the controlled exposure to saline conditions, bulked leaf samples were systematically collected from each individual plant in every pot, instantly frozen in liquid nitrogen, and securely stored in a −80 °C ultra-low temperature freezer. These samples were intended for subsequent determination of various physiological indicators.

2.2. Measurement Indices

2.2.1. Relative Water Content (RWC)

The leaves were weighed to record the fresh leaf weight (FW). The leaves were then soaked in distilled water for 12 h and the turgid weight (TW) was recorded. Finally, the dry weight of the leaves was measured after drying at 65 °C for 48 h to constant weight (DW) [21]. R W C % = F W D W T W D W × 100 % .

2.2.2. Relative Electrical Conductivity (REC)

Leaves of the same size were selected and immersed in a centrifuge tube containing 10 mL of deionized water for 6 h. The initial conductivity was measured and recorded as S1. The centrifuge tube containing the leaves was then heated in a boiling water bath for 30 min. After cooling to room temperature, the total conductivity was measured and recorded as S2 [22]. R E C % = S 1 S 2 × 100 % .

2.2.3. Malondialdehyde Content (MDA) and Proline Content (Pro)

0.5 g of the leaves kept in a −80 °C refrigerator was taken and ground into powder using a high-throughput tissue grinding instrument. The MDA content in leaves was determined using an MDA assay kit (Solarbio, Beijing, China). Specific procedures and calculations were performed according to the instructions in the kit. Proline was determined by the same method as MDA using a Pro assay kit (Solarbio, Beijing, China).

2.2.4. Na+/K+ Ratio

The leaves were dried and ground to powder, weighed, dissolved in 10 mL of 98% H2SO4 and 3 mL of 30% H2O2 mixture, left for 5 h, and digested using the digestion oven. After digestion, the solution was fixed to 100 mL. A series of KCl and NaCl solutions with different concentrations were prepared and the corresponding standard curves were plotted. The Na+ and K+ ions were analyzed by means of a flame spectrophotometer (FP64 series flame spectrophotometer, INESA Analytical Instrument Co., Ltd., Shanghai, China). The Na+ and K+ ion contents were ascertained employing the aforementioned standard curves, and consequently, the Na+/K+ ratio was determined [23].

2.2.5. Salt Damage Score (SDS)

In this study, the evaluation of SDS was conducted based on a four-level scoring system in accordance with the Agricultural Industry Standard of the People’s Republic of China—Technical Regulations for Salt Tolerance Identification of Rice (NY/T 3692-2020) [24]. These scores were determined by considering the actual growth conditions of the materials. Grade D, weak salt tolerance (1–2 points): tiller growth stopped, most leaves dried up; Grade C, medium salt tolerance (2–3 points): tiller growth was inhibited, most leaves curled up, some leaves elongated; Grade B, strong salt tolerance (3–4 points): tiller growth was close to normal, the old leaves of the plant were yellow and dry especially at the leaf margin and leaf tip, no obvious damage to the new leaves; Grade A, very strong salt tolerance (4–5 points): tiller growth is basically normal, and the plant leaves are basically undamaged.

2.3. Data Statistics and Analysis

IBM SPSS Statistics 27 and GraphPad Prism 9.4 software were used to calculate the mean, standard error and descriptive statistical analysis, analysis of variance (ANOVA), and principal component analysis (PCA) for each accession. Correlation analysis was performed based on the R package ‘cor’. Microsoft Excel 2021 was used to calculate and analyze the membership function. The comprehensive evaluation of salt tolerance was performed using the assigned weights method [25].
Comprehensive evaluation of the membership functions formula:
1 X ( μ ) = X X m i n X m a x X m i n ;   2 X ( μ ) = 1 X X m i n X m a x X m i n
where X is a composite index of E. sibiricus accessions under salt stress, and Xmax and Xmin are the maximum and minimum values of the composite index, respectively. If the indicators are positively correlated with the evaluated properties, such as salt tolerance, use the Formula (1); for negative correlation, use the Equation (2). The values of the membership function are summed up and the mean is obtained and ranked; the greater the mean value, the stronger the evaluation property.
Comprehensive evaluation of the PCA weighted membership function formula:
D = j = 1 n [ μ X j × W ] j
j = 1, 2, 3, ……n;
  • where μ is the expected value; W is the weight value corresponding to variable X (the proportion of the contribution rate of the corresponding variable and the cumulative contribution rate); and D is the comprehensive evaluation value of salinity tolerance obtained from the evaluation of each index under salt stress, with higher values indicating stronger salinity tolerance.

3. Results

The descriptive statistical results of the six indicators showed wide significant variation among the 50 accessions, with coefficients of variation ranging from 9.633% (RWC) to 47.50% (Pro) (Table 1).

3.1. Physiological Response of E. sibiricus under Salt Stress

The relative water content (RWC) is often considered as one of the important indicators to evaluate the water content in plants, and it is closely related to the salt tolerance of plants. When E. sibiricus was subjected to the stress environment of 200 mmol·L−1 NaCl for 14 days, the RWC of accession W610304 reached a max of 95.24%, showing the strongest water retention capacity. HB010-3 was relatively weak in terms of water retention, with the lowest relative water content of 68.61% (Figure 1a). Relative electrical conductivity (REC) reflects the cell membrane permeability, and it has been shown that an increase in cell membrane permeability indicates an increase in salt stress damage, and that the change in REC is negatively correlated with plant salt tolerance. Salt stress had a significant effect on the REC of E. sibiricus, and the REC of QH008-33 was the lowest, at 19.47% after 14 days of growth under salt stress, indicating that the plant cell membrane was less damaged. This result indicated that QH008-33 was extremely tolerant to salt stress. On the contrary, the REC of GS014-8 was the highest at 96.97%, indicating that it was extremely sensitive to the salt stress environment (Figure 1b).
SC012-16 had the lowest MDA content of 3.75 nmol·g−1, indicating that it was less damaged by salt stress. On the contrary, W610304 was subjected to salt stress and accumulated a large amount of MDA, measured at 39.4 nmol·g−1, and suffered a higher degree of damage (Figure 2a). Pro not only regulates cytoplasmic osmotic pressure under stress conditions, but also protects cell membranes and enzyme structures and relieves stress pressure [26]. Therefore, Pro content is often used to assess plant tolerance. HB010-3 had the highest Pro content of 49.28 μg·g−1. In contrast, SC015-20 had only 6.28 μg·g−1 (Figure 2b). When plants are in salt-stressed environments, their intracellular ion homeostasis is disrupted. Because Na+ and K+ have similar ionic radii and will compete for binding to ion proteins that transport K+, Na+ inhibits K+ uptake in a saline environment. QH005-35 had the highest Na+/K+ value of 1, indicating that Na+ has the strongest inhibitory effect on K+ in the intracellular environment of this material, and that salt stress has the greatest effect on its intracellular ion balance. In contrast, the E. sibiricus material HB011-27 maintained a very low Na+/K+ value (0.2157) in a 200 mmol·L−1 NaCl solution, indicating that this plant material has an extremely strong ion homeostasis (Figure 2c).
After 14 days of salt stress treatment, the E. sibiricus accessions had different plant phenotypes (Figure 3). XJ013-28 and PI598784 had abundant tillers, less yellow leafage, no obvious leaf curling, and still bright green leaves, so the points of SDS were higher and were rated as grade A (Figure S1). In contrast, the E. sibiricus accessions of NM011-6 and QH005-35 were more damaged, with suppressed tillering and plant growth, more severe leaf wilting and leaf curling, and a high number of needle-like leaves; they thus had lower points of SDS and were rated as grade D, indicating lower tolerance to salt stress.

3.2. Correlation Analysis and Principal Component Analysis

The KMO–Bartlett test result showed that the overall MSA (measures of sampling adequacy) is 0.52 (p = 0.0099), and the MSA value for each parameter is greater than 0.5, which indicates that the dataset is suitable for PCA analysis (Table 2). Correlation analysis of the measured indicators of E. sibiricus under salt stress was performed using the R(v.4.3.1) package ‘cor’. Among the corrplot of the observed indicators of E. sibiricus seedlings (Figure 4), REC and RWC showed a significant negative correlation (R = −0.598, p < 0.05), but all other indicators have a correlation of less than 0.8. Overall, the correlations among the indicators were not strong or significant, indicating that the observed indicators were relatively independent of each other. However, since the performances of E. sibiricus accessions varied under different observation indices, the salt tolerance of the accessions could not be evaluated by a single index. In order to objectively and accurately evaluate the salt tolerance of E. sibiricus, principal component analysis (PCA) was performed by SPSS.
In the process of performing PCA, three principal component factors with eigenvalues greater than 1 were extracted in this study. They possessed a cumulative contribution of 67.28%, indicating that they could cover most of the information of the six measurement indices, and thus could be used as effective components of the analyzed data (Table 3). From the component loadings matrix, combined with the magnitude of the contribution of each principal component, it can be concluded that the RWC, REC, and SDS have higher loadings on the first principal component compared to other measured indicators. This indicates that the first principal component mainly responds to the information of these indicators. MDA and Pro have higher loadings on the second principal component, indicating that the second principal component mainly responds to the information of these two indicators. The third principal component has the highest loadings of Na+/K+. The loadings of Na+/K+ were the highest in the third principal component, indicating that the information of this index was more completely represented. In conclusion, the most important indicators of salt tolerance in E. sibiricus are RWC, REC, and SDS, followed by MDA, Pro, and Na+/K+ (Table 4).
The associations between all physiological parameters under salinity stress conditions were estimated through PCA. The first two PCAs were used to construct an informative plot system based on the evaluated sources of geographical information on E. sibiricus, but each accession could not be clearly identified. The first two PCAs represented about 49.5% of the variability, with the first and the second PCA explaining 31.7% and 17.8% of the total variation between all parameters, respectively. In general, the parameters represented by adjacent or parallel vectors show a strong positive association between each other. It is not a very accurate approach to distinguish individual germplasm by geographical origin as a group (Figure 5). All the parameters measured in this study can be divided into three groups. The first group included Pro and Na+/K+ ratio. The second group included REC and MDA. The third group consisted of RWC and SDS. There was an acute angle between the vectors of these parameters. A strong positive association was found between the characteristics included in the same group. There was also a positive association between the first and second groups. On the other hand, a negative association was observed between the second and third groups (Figure 6).

3.3. Cluster Analysis

After normalization of all data, the 50 germplasm in this study were classified into seven clusters (Figure 7). Cluster 1 consisted of 15 accessions, followed by 12, 9, 2, 8, 2, and 2 in clusters 2, 3, 4, 5, 6, and 7, respectively. The heat map shows the two-way classification of traits and germplasm; seedling traits under salinity stress were divided into three groups. SDS and RWC were found to be positively correlated with the salt stress in E. sibiricus. QH005-35 was found to be the most susceptible to salinity stress. QH006-22, XJ012-17, and PI598784 perform better in some negative indicators like MDA, Pro, REC, and Na+/K+, so they are considered as salt-tolerant varieties.

3.4. Comprehensive Evaluation of Salt Tolerance

Comprehensive evaluation is a commonly used method for screening adversity-tolerant and sensitive accessions. Methods based on traditional membership function analysis require artificial assignment of correlations between indicators and resistance (positive and negative correlations); but the reality is that the physiology and biochemistry that occurs in response to stress is extremely complicated, and a simple linear relationship is not sufficient. Therefore, in this study, two comprehensive analysis methods—principal component weighted membership function analysis, and fuzzy mathematical membership function analysis—were used to distinguish salt-tolerant and salt-sensitive materials in the comprehensive evaluation of the salt tolerance of E. sibiricus. The weighted membership function algorithm, based on the principal component analysis, was used to calculate the composite scores of salt tolerance indices for E. sibiricus, and the higher the score, the better the germplasm salt tolerance. The top five and bottom five accessions in terms of salt tolerance under salt stress conditions were selected as tolerant and sensitive materials under stress conditions, respectively. These were XJ013-28, NM021-13, SC012-16, XJ012-17, GS002-5 and QH005-35, along with NM003-10, XZ009-20, QH013-8, and XZ008-20 (Table 5).
Furthermore, based on the salinity tolerance index results, the E. sibiricus accessions were rescreened using the fuzzy membership function method in this study, and their salinity tolerance was comprehensively evaluated. Based on the specific affiliation values and ranking of different E. sibiricus accessions, the top five accessions were considered salt-tolerant, including XJ013-28, NM021-13, XJ012-17, QH008-33, and SC010-15, while the bottom five accessions were considered salt-sensitive; these included ID HB009-15, QH005-35, QH013-8, XZ008-20, and XZ009-20 (Table 6).
Therefore, it is necessary to use a combination of both principal component weighted association function analysis and fuzzy association function analysis. In summary, XJ013-28, NM021-13, and XJ012-17 exhibited salt tolerance, while XZ008-20, QH013-8, XZ009-20, and QH005-35 exhibited salt sensitivity when exposed to a salt stress environment. Notably, XJ013-28 had the most robust overall salt tolerance rating, while XZ008-20 had the weakest overall salt tolerance rating.

4. Discussion

Currently, soil salinity is one of the environmental factors affecting plant growth, causing secondary stresses like ionic, osmotic, and oxidative stress, which inhibit the growth and differentiation of plant tissues and organs, causing plant leaves to wilt and curl, and plants to wilt and dwarf [27,28,29,30,31]. Water molecules are abundant in plant leaves, and the amount of water content in leaves is a key factor in determining plant survival, as well as being an important indicator of overall plant water content, which is extremely important in assessing the physiological status of plants under salt stress. Leaf water content was negatively correlated with the salinity of the growing environment—the higher the salinity, the more significant the effect [32]. In an analysis of the salt tolerance of alfalfa, it was found that the variation of leaf water content varied among different varieties of alfalfa, and its salt tolerance was positively correlated with leaf water content [33]. In this study, the relative water content of E. sibiricus leaves varied widely (68.61% to 95.24%) under salt stress, indicating that different genotypic materials showed large differences in relative water content under salt stress, which can provide preparation for the screening of different salt-tolerant germplasm materials. It was found that the Triticosecale rimpaui germplasm accessions with higher salt tolerance could maintain higher relative water content under salt stress, indicating that it is important for plants to maintain and utilize internal water under salt stress [34].
The cytoplasm of plant leaf cells is tightly bound to the cell wall, forming a membrane known as the leaf cell membrane. When plants are in a high salinity environment, this membrane increases its permeability due to varying degrees of damage, leading to extravasation of intracellular electrolytes, resulting in an increase in the conductivity of the external fluid and a consequent increase in the relative conductivity. The accumulation of Malondialdehyde (MDA) indicates a level of damage to membranes and cells. MDA is a measure of membrane damage and is the end product of lipid peroxidation, and its values are useful in assessing the extent of plant damage due to stress [6,7,8]. In the salt stress experiment of Chenopodium quinoa Willd [35], the MDA content of leaves increased significantly, aggravating the degree of membrane ester peroxidation and causing cell membrane damage. The changes in MDA levels reflect the peroxidation of plant cytoplasmic membranes under stress. It is generally believed that the higher the intracellular MDA content, the greater the damage to the membrane system. In the present study, different E. sibiricus materials showed different levels of malondialdehyde response, with a minimum of 3.75 nmol g−1 and a maximum of 40.00 nmol g−1, which provided some basis for the salt tolerance evaluation work. To maintain cell expansion pressure and alleviate osmotic stress, plants also actively accumulate organic substances for osmoregulation. Proline is the most important osmoprotectant for plants to regulate osmotic potential in response to stress [36]. It has been shown that both E. sibiricus and Elymus nutans have adapted to saline environments by accumulating proline [37]. In the present study, the proline accumulation of E. sibiricus in a high salinity environment ranged from 6.28 μg g−1 to 49.67 μg g−1, and the materials with high proline content grew well, indicating that E. sibiricus can regulate cellular osmotic potential and mitigate osmotic damage by accumulating large amounts of proline. Accumulating more proline may be one of the important strategies for E. sibiricus to respond to salt stress.
Excessive sodium ions under salt stress can lead to ion imbalance, which can cause ion toxicity because most salt-intolerant species do not have the ability to translocate Na+, and Na+ is more likely to accumulate in plants compared to CI. While K+ is an important ion involved in plant photosynthesis, osmoregulation, and other processes in plants, too much Na+ inhibits K+ uptake, which in turn affects plant growth. Therefore, plants maintain a low Na+/K+ ratio as a reflection of their high salt tolerance [38]. This phenomenon has been observed in rice [39], oats [40], and alfalfa [41]. The Na+/K+ ratio of the test materials varied from 0.22 to 1, with an average value of 0.61. In general, the salt tolerance of different germplasm differed significantly. The highest Na+/K+ ratio was 1, which was lower than that of oats [42], indicating that E. sibiricus species have strong salt tolerance.
Salt tolerance in plants is a quantitative genetic trait, controlled by multiple genes that reflect the genetics, physiology, and biochemistry of the plant’s own responses. The strength of salt tolerance cannot be accurately evaluated by only one indicator, so a comprehensive evaluation of indicators should be conducted when studying the salt tolerance of plants [36]. The fuzzy mathematical affiliation function method is widely used to evaluate the salt tolerance of many plants because it can eliminate the one-sidedness brought by a single index [40,41,42]. Due to the correlation between different indicators, the salt tolerance information overlaps, and it is necessary to determine the positivity and negativity of the affiliation function, although salt tolerance is a comprehensive trait, and most of the time, it is not a simple linear relationship [43,44]. Therefore, the present study was conducted by principal component analysis. Principal component analysis was used to convert each individual index of E. sibiricus into three independent composite indices that reflect most of the information of the indices. These were combined with the weights to calculate the comprehensive evaluation value of drought tolerance (D values) in order to identify the salt tolerance of E. sibiricus, and then combined with the results of conventional affiliation function analysis based on the measured indices to evaluate the salt tolerance of E. sibiricus. The accuracy of the study results was improved by combining the results of the conventional affiliation function analysis with multiple evaluation methods.

5. Conclusions

In this study, salt tolerance evaluation was conducted on diverse geographical sources of collected E. sibiricus germplasm. Multiple assessment methods were employed, revealing that there was no significant correlation between geographic origin and salt tolerance. This lack of correlation could be attributed to the sampling strategy being random in nature and the limited diversity of accessions. Through comprehensive evaluation, salt-tolerant germplasm was successfully identified, providing a valuable reference for the assessment of highly resistant germplasm in E. sibiricus and its related species. Additionally, in future salt tolerance evaluations, it is recommended to utilize various assessment methods, such as membership functions and PCA, focusing on indicators such as REC, Pro, Na+/K+, MDA, and others. Moreover, this evaluation provides useful resources for the development of grazing pastures and artificial grasslands in challenging environmental conditions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy13112675/s1, Table S1: Basic information of the tested E. sibiricus accessions. Figure S1: The obvious phenotype difference between Lower point of SDS and Higher point of SDS accessions.

Author Contributions

S.C.: software, writing—original draft preparation, visualization, formal analysis, J.F.: software, data curation, writing—original draft preparation, Y.X. (Yi Xiong): validation, investigation, writing—review and editing, Y.X. (Yanli Xiong): validation, writing—review and editing, Y.L.: conceptualization, investigation, J.Z.: methodology, resources, Z.D.: resources, methodology, X.M.: conceptualization, writing—review and editing, supervision, project administration, funding acquisition, L.Y.: supervision, project administration, funding acquisition, resources. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (32271753); National Forestry and Grassland Administration Emergency Science and Technology Leading Project (20220102); Sichuan Provincial Central and Local University Regional Cooperation Project (2023YFSY0012); the Sichuan Province "14th Five-Year Plan" Forage Breeding Research Project (2021YFYZ0013-2); and the Sichuan Forage Innovation Team Program (sccxtd-2020-16).

Data Availability Statement

Data are contained within the article or the Supplementary Materials.

Acknowledgments

We are very grateful to the College of Grassland Science and Technology, Sichuan Agricultural University and Sichuan Academy of Grassland Science for providing us with experimental equipment and venues. Thank you to all authors for their hard work on this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Giordano, M.; Petropoulos, S.A.; Rouphael, Y. Response and Defence Mechanisms of Vegetable Crops against Drought, Heat and Salinity Stress. Agriculture 2021, 11, 463. [Google Scholar] [CrossRef]
  2. Huang, J.; Kong, Y.L.; Xu, Q.S.; Zhu, C.Q.; Zhu, L.F.; Cao, X.C.; Hong, X.Z.; Zhang, J.H. Progresses for Characteristics and Amelioration Measures of Saline Soil. Soils 2022, 54, 18–23. [Google Scholar]
  3. Muchate, N.S.; Nikalje, G.C.; Rajurkar, N.S.; Suprasanna, P.; Nikam, T.D. Plant Salt Stress: Adaptive Responses, Tolerance Mechanism and Bioengineering for Salt Tolerance. Bot. Rev. 2016, 82, 371–406. [Google Scholar] [CrossRef]
  4. Ben, H.A.; Ghneim, H.T.; Ben, R.W.; Dabbous, A.; Ben, S.R.; Brini, F.; Abdelly, C.; Ben, H.K. Early effects of salt stress on the physiological and oxidative status of the halophyte Lobularia maritima. Funct. Plant Biol. 2020, 47, 912. [Google Scholar] [CrossRef]
  5. Sadaqat, S.S.; Li, Z.; Yan, H.; Shi, L.; Zhou, B. Comparative Study of the Effects of Salinity on Growth, Gas Exchange, N Accumulation and Stable Isotope Signatures of Forage Oat (Avena sativa L.) Genotypes. Plants 2020, 9, 1025. [Google Scholar] [CrossRef]
  6. Wang, W.; Zhao, Y.; Wang, W.; Xu, K.; Niu, L.; Yu, L.; Zou, J.; Wang, Z.; Zhang, Y.; Wang, F.; et al. Evaluation of Salt Tolerance Mechanism and Study on Salt Tolerance Relationship of Different Salt-Tolerant Wheat Varieties. Comput. Intell. Neurosci. 2022, 2022, 2395568. [Google Scholar] [CrossRef]
  7. Zuo, Z.F.; Kang, H.; Park, M.Y.; Jeong, H.; Sun, H.; Song, P.; Lee, H.Y. Zoysia japonica MYC type transcription factor ZjICE1 regulates cold tolerance in transgenic Arabidopsis. Plant Sci. 2019, 289, 110254. [Google Scholar] [CrossRef]
  8. Zou, J.; Liu, C.; Liu, A.; Zou, D.; Chen, X. Overexpression of OsHsp17.0 and OsHsp23.7 enhances drought and salt tolerance in rice. J. Plant Physiol. 2012, 169, 628–635. [Google Scholar] [CrossRef]
  9. Wang, Y. Evaluation of Salt Tolerance of Different Types of Tobacco and Physiological Mechanism of Salinity Stress Tolerance at Seeding Stage. Master’s Dissertation, Henan Agricultural University, Zhengzhou, China, 2021. [Google Scholar]
  10. Hu, D.D.; Li, R.F.; Dong, S.T.; Zhang, J.W.; Zhao, B.; Ren, B.Z.; Ren, H.; Yao, H.Y.; Wang, Z.Q.; Liu, Z. Maize (Zea mays L.) responses to salt stress in terms of root anatomy, respiration and antioxidative enzyme activity. BMC Plant Biol. 2022, 22, 602. [Google Scholar] [CrossRef]
  11. Bargaz, A.; Zaman, A.M.; Farissi, M.; Lazali, M.; Drevon, J.J.; Maougal, R.; Georg, C. Physiological Physiological and molecular aspects of tolerance to environmental constraints in grain and forage legumes. Int. J. Mol. Sci. 2015, 16, 18976–19008. [Google Scholar] [CrossRef]
  12. Wang, X.D.; Wang, C.; Ma, Z.H.; Hou, R.F.; Gao, Q.; Chen, Q. Effect of short-term salt stress on the absorption of K+ and accumulation of Na+, K+ in seedlings of different wheat varieties. Acta Ecol. Sin. 2011, 31, 2822–2830. [Google Scholar]
  13. Wei, B.X. Distribution and Cause Analysis of Saline—Alkaline Soil in China. Technol. Soil Water Conserv. 2012, 6, 27–28. [Google Scholar]
  14. Yang, J.S.; Yao, R.J.; Wang, X.P.; Xie, W.P.; Zhang, X.; Zhu, W.; Zhang, L.; Sun, R.J. Research on Salt-affected Soils in China: History, Status Quo and Prospect. Acta Pedol. Sin. 2022, 59, 10–27. [Google Scholar]
  15. Huang, F.; Li, Z.Y.; Li, H.Y.; Shi, W.G.; Bai, W.J.; Liu, L.; Li, J.; Xie, Y.F. Analysis of Morphological Diversity of Elymus sibiricus L. Germplasm Resources. Chin. J. Grassl. 2015, 37, 111–115. [Google Scholar]
  16. Yan, X.B. Study of Genetic Diversity of Elymus Species. Ph.D. Dissertation, China Agricultural University, Beijing, China, 2005. [Google Scholar]
  17. Li, M.F.; Li, X.R.; Li, Y.Z.; Chen, L.L.; Wang, Y.; Li, C.Y.; Chang, D.; Lei, X.; Yan, L.J.; You, M.H.; et al. Identification of Wild Elymus sibiricus Germplasm Resources and Analysis of Variation of Dorsal Hairs in Basal Leaf Sheath. Acta Prataculturae Sin. 2023, 31, 1026–1035. [Google Scholar]
  18. Peng, J.; Sun, X.X.; Shi, H.T.; Chen, Y.J.; Cai, X.R.; Wang, X.; Bai, X.; Li, S.L. Nutritional Value and Molecular Structure Characteristics in Different Fractions of Elymus sibiricus from Qinghai-Tibet Plateau. Chin. J. Anim. Nutr. 2022, 34, 5270–5282. [Google Scholar]
  19. Zeng, H.G.; Yue, J.M.; Zhang, S.H.; Yi, K.; Wei, K.; Yang, S.F.; Liu, G.X.; Li, M.L.; Yang, C.H. Comprehensive Evaluation of Agronomic Traits of 76 Germplasm Resources of Elymus sibiricus. Acta Agrestia Sin. 2022, 30, 3046–3055. [Google Scholar]
  20. Zhang, Z.; Zheng, Y.; Zhang, J.; Wang, N.; Wang, Y.; Liu, W.; Bai, S.; Xie, W. High-Altitude Genetic Selection and Genome-Wide Association Analysis of Yield-Related Traits in Elymus sibiricus L. Using SLAF Sequencing. Front. Plant Sci. 2022, 13, 874409. [Google Scholar] [CrossRef]
  21. Guo, Y.; Liu, Y.; Zhang, Y.; Liu, J.; Gul, Z.; Guo, X.R.; Abozeid, A.; Tang, Z.H. Effects of Exogenous Calcium on Adaptive Growth, Photosynthesis, Ion Homeostasis and Phenolics of Gleditsia sinensis Lam. Plants under Salt Stress. Agriculture 2021, 11, 978. [Google Scholar] [CrossRef]
  22. You, J.; Zhang, J.; Wang, W.R.; Lu, J.; Jia, P.X.; Miao, J.S.; Yang, Y.L. Changes of Physiological Properties in Limonium aureum(Linn.) Hill Seedlings under Salt Stress. Bull. Bot. Res. 2013, 33, 45–50. [Google Scholar]
  23. Wang, N.; Yang, J.; Huang, Q.; Su, G.L.; Zhou, H.; Xu, Q.H.; Dong, H.L.; Yan, G.T. Physiological Salinity Tolerance Mechanism for Transport of K+ and Na+ Ions in Cotton (Gossypium hirsutum L.) Seedlings under Salt Stress. Cotton Sci. 2015, 27, 208–215. [Google Scholar]
  24. Han, L.Z.; Gen, L.Y.; Ma, X.D.; Cui, D.; Wang, C.L.; Zhang, Q.X. Technical Code of Practice for Identification of Salt Tolerance in Rice; NY/T 3692-2020; Ministry of Agiculture and Rural Affairs of the People’s Republic of China: Beijing, China, 2020. Available online: http://www.jgj.moa.gov.cn/nybz/202009/t20200924_6353165.htm (accessed on 24 September 2020).
  25. Liang, S.; Xiong, W.; Yin, C.; Xie, X.; Jin, Y.; Zhang, S.; Yang, B.; Ye, G.; Chen, S.; Luan, W. Overexpression of OsARD1 Improves Submergence, Drought, and Salt Tolerances of Seedling Through the Enhancement of Ethylene Synthesis in Rice. Front. Plant Sci. 2019, 10, 1088. [Google Scholar] [CrossRef] [PubMed]
  26. Zhu, B.; Zheng, S.; Fan, W.; Zhang, M.; Xia, Z.; Chen, X.; Zhao, A. Ectopic overexpression of mulberry MnT5H2 enhances melatonin production and salt tolerance in tobacco. Front. Plant Sci. 2022, 13, 1061141. [Google Scholar] [CrossRef] [PubMed]
  27. Zhao, N.; Sheng, M.; Zhao, J.; Ma, X.; Wei, Q.; Song, Q.; Zhang, K.; Xu, W.; Sun, C.; Liu, F.; et al. Over-Expression of HDA710 Delays Leaf Senescence in Rice (Oryza sativa L.). Front. Bioeng. Biotechnol. 2020, 8, 471. [Google Scholar] [CrossRef]
  28. Zhang, Z.Y.; Li, X.; Wang, Y.; Chang, W.; Gao, X.Q.; Fu, B.Z. Salt Tolerance Evaluation and Salt Tolerance Index Screening of 59 Alfalfa Germplasm Materials at Seedling Stage. Acta Agrestia Sin. 2020, 28, 112–121. [Google Scholar]
  29. Zhang, Z.R.; Zhang, X.W.; Jiang, W.; Wang, B.B.; Sun, H.D.; Wang, Y.S.; Luo, J.X. Responses of Growth and Physiological Characteristics of Three Ground Cover Plants to Salinity Stress. Tianjin Agric. Sci. 2017, 23, 20–24. [Google Scholar]
  30. Han, Z.P.; Zhang, H.X.; Zhou, F. Effect of Salinity on Plant and Adaptation of Plant to Salinity. J. Shanxi Datong Univ. (Nat. Sci.) 2015, 31, 59–62. [Google Scholar]
  31. Wang, Y.F. Responses of Maize Seedling under NaCl Stress and the Regulatory Mechanism of Salt Tolerance. Ph.D. Dissertation, Shenyang Agricultural University, Shenyang, China, 2008. [Google Scholar]
  32. Mi, Y.W.; Wang, G.X.; Gong, C.W.; Cai, Z.P.; Wu, W.G. Effects of Salt stress on growth and physiology of Isatis indigotica seedlings. Acta Pratacult. Sin. 2018, 27, 43–51. [Google Scholar]
  33. Li, Y.; Liu, G.B.; Gao, H.W.; Sun, G.Z.; Zhao, H.M.; Xie, N. A comprehensive evaluation of salt-tolerance and the physiological response of Medicago sativa at the seedling stage. Acta Pratacultu. Sin. 2010, 19, 79–86. [Google Scholar]
  34. Zhang, R.Q.; Chen, H.; Wang, R.Q. Screening on Salt-tolerant Germplasm Resource of Triticale. Seed 2016, 35, 61–64. [Google Scholar]
  35. Liu, W.; Li, J.; Yang, F.; Huang, J.; Wei, Y.; Jin, Q. Effects of Salt Stress on Seed Germination of 40 Quinoa Germplasm and Evaluation of Salt Tolerance. Mol. Plant Breed. 2021, 19, 938–995. [Google Scholar]
  36. Yang, Y.J.; Zhang, H.; Zhou, H.K.; Wang, W.Y.; Yin, H.X.; Yao, B.Q.; Zhao, X.Q. Effect of Salt Stress on Physiological Characteristics of Alpine Grassland Pasture Elymus sibiricus. Acta Agric. Boreali-Occident. Sin. 2015, 24, 156–162. [Google Scholar]
  37. Yang, Y.J.; Zhou, H.K.; Wang, W.Y.; Yin, H.X.; Yao, B.Q.; Yu, X.C.; Zhao, X.Q.; Wang, F.G. Effect of salt stess on the physiological characteristics of Elymus nutans. J. Lanzhou Univ. 2014, 50, 101–106. [Google Scholar]
  38. Zhao, Z.J.; Zhang, H.L.; Wang, M.J.; Zhang, X.M.; Li, L.X. Salt stress-related regulation mechanism of intracellular pH and ion homeostasis in plants. Plant Physiol. J. 2020, 56, 337–344. [Google Scholar]
  39. Porcel, R.; Aroca, R.; Azcon, R.; Ruiz-Lozano, J.M. Regulation of cation transporter genes by the arbuscular mycorrhizal symbiosis in rice plants subjected to salinity suggests improved salt tolerance due to reduced Na+ root-to-shoot distribution. Mycorrhiza 2016, 26, 673–684. [Google Scholar] [CrossRef]
  40. Wang, M.M.; Zhou, X.R.; Liang, G.L.; Zhao, G.Q.; Jiao, R.A.; Chai, J.K.; Gao, X.M.; Li, J.N. A multi-trait evaluation of salt tolerance of 5 oat germplasm lines at lines at seedling stage. Acta Pratacult. Sin. 2020, 29, 143–154. [Google Scholar]
  41. Li, X.; Sha, B.P.; Gao, X.Q.; Wang, Y.; Fu, B.Z. Identification and Comprehensive Evaluation of Salt Tolerance of Alfalfa Germplasm Materials at Germination Stage. Acta Agrestia Sin. 2020, 28, 437–445. [Google Scholar]
  42. Gao, Z.W. Research on the Salt-Alkali Resistance Mechanism of Alfalfa an Oat. Ph.D. Dissertation, Northeast Normal University, Changchun, China, 2011. [Google Scholar]
  43. Peng, Z.; Li, L.; Liu, Y.P.; Liu, H.M.; Jing, R.L. Evaluation of Salinity Tolerance in Wheat (Triticum aestium) Genotypes at Germination and Seedling Stages. J. Plant Genet. Resour. 2017, 18, 638–645. [Google Scholar]
  44. Sheng, W.; Wu, X.C.; Liang, H.; Li, C.Y.; Ma, Y.Y.; Zhang, J.X.; Ding, X.P.; Li, X.Y.; Liu, G.D. Evaluation of salt tolerance in 41 pigeon pea varieties during germination. Pratacult. Sci. 2022, 39, 1607–1617. [Google Scholar]
Figure 1. (a) Effects of salt stress on relative water content (RWC-a) and (b) relative electrical conductivity (REC-b) of E. sibiricus accessions after 14 days. Red dots represent max and min values.
Figure 1. (a) Effects of salt stress on relative water content (RWC-a) and (b) relative electrical conductivity (REC-b) of E. sibiricus accessions after 14 days. Red dots represent max and min values.
Agronomy 13 02675 g001
Figure 2. Effects of salt stress on malondialdehyde content (MDA-a) (a), proline content (Pro-b) (b), and Na+/K+ ratio (c) of E. sibiricus accessions after 14 days. Red dots represent max and min values.
Figure 2. Effects of salt stress on malondialdehyde content (MDA-a) (a), proline content (Pro-b) (b), and Na+/K+ ratio (c) of E. sibiricus accessions after 14 days. Red dots represent max and min values.
Agronomy 13 02675 g002
Figure 3. Effects of salt stress on the salt damage score (SDS) of E. sibiricus accessions after 14 days. Red dots represent max and min values.
Figure 3. Effects of salt stress on the salt damage score (SDS) of E. sibiricus accessions after 14 days. Red dots represent max and min values.
Agronomy 13 02675 g003
Figure 4. Corrplot of observed indicators. Red represents positive correlation; blue represents negative correlation. ** = significant at p < 0.01, respectively.
Figure 4. Corrplot of observed indicators. Red represents positive correlation; blue represents negative correlation. ** = significant at p < 0.01, respectively.
Agronomy 13 02675 g004
Figure 5. Principal component analysis (PCA) of labelled groups.
Figure 5. Principal component analysis (PCA) of labelled groups.
Agronomy 13 02675 g005
Figure 6. PCA of physiologic parameters.
Figure 6. PCA of physiologic parameters.
Agronomy 13 02675 g006
Figure 7. Clustered heat map of the performance of E. sibiricus accessions under salt stress.
Figure 7. Clustered heat map of the performance of E. sibiricus accessions under salt stress.
Agronomy 13 02675 g007
Table 1. Descriptive statistical analysis of indicators for 50 E. sibircus accessions.
Table 1. Descriptive statistical analysis of indicators for 50 E. sibircus accessions.
IndicatorsMeanMinMaxStandard DeviationCoefficient of Variation (%)Fp
RWC (%)82.8959.4397.657.9869.6333.55<0.01
REC (%)56.9318.4696.9721.6237.97676.60<0.01
MDA (nmol·g−1)19.613.2842.168.29842.3187.25<0.01
Pro (μg·g−1)26.355.3852.1612.5147.50220.01<0.01
Na+/K+0.60750.210.190631.37111.62<0.01
SDS2.8641.74.20.58220.3279.38<0.01
Table 2. KMO–Bartlett test of principal components.
Table 2. KMO–Bartlett test of principal components.
ItemsMeasures of Sampling Adequacy (MSA)
KMO0.52
Bartlett (p value)0.0099
RWC0.50
REC0.53
MDA0.31
PRO0.69
Na+/K+0.52
SDS0.59
Table 3. Eigenvalues of principal components.
Table 3. Eigenvalues of principal components.
IngredientsEigenvalueContribution Rate (%)Total Contribution Rate (%)
11.96532.74532.745
21.06817.850.545
31.00416.73667.28
40.86914.48781.767
50.76212.69894.465
60.3325.535100
Table 4. Loading matrix of principal components.
Table 4. Loading matrix of principal components.
IndicatorsIngredients
123
W0.4870.2640.249
RWC−0.4080.0990.43
REC0.3980.095−0.325
MDA0.0790.7940.228
PRO0.177−0.5120.354
Na+/K+0.227−0.0620.694
SDS−0.309−0.145−0.216
Table 5. Principal Component Weighted Membership Function Analysis.
Table 5. Principal Component Weighted Membership Function Analysis.
CodeD ValuesOrderCodeD ValuesOrder
XJ 013-280.73451QH 003-190.401626
NM 021-130.66632GS 003-280.388927
SC 012-160.6363GS 011-130.384128
XJ 012-170.61324SC 015-200.383429
GS 002-50.59465HB 009-150.382430
QH 006-220.58926GS 014-80.376331
PI 5987840.58247HB 012-110.375832
GS 003-150.56998XZ 012-160.371333
SC 010-150.54979NM 003-140.370734
SC 018-160.539610NM 032-120.369835
NM 036-30.531211XJ 025-200.354236
QH 008-330.52212NM 008 -10.347337
NM 031-50.493113NM 011-40.345138
QH 001-210.484414GS 013-140.33539
XJ 026-260.477815PI 5987790.330440
SC 011-40.473616XJ 026-350.327741
XZ 009-250.455417NM 011-60.32342
HB 011-270.44218SC 002-40.313743
HB 013-50.436319QH 006-200.307444
XJ 021-330.428320W 6103040.302445
QH 002-30.42721QH 005-350.299546
HB 001-50.424422NM 003-100.2947
PI 3262660.419823XZ 009-200.282248
XJ 003-150.404424QH 013-80.230449
HB 010-30.403325XZ 008-200.17250
Table 6. Analysis of membership functions in fuzzy mathematics.
Table 6. Analysis of membership functions in fuzzy mathematics.
CodeD ValuesOrderCodeD ValuesOrder
XJ 013-280.85471PI 3262660.504126
NM 021-130.73542GS 013-140.484827
XJ 012-170.70733XJ 026-260.483628
QH 008-330.68834QH 002-30.471629
SC 010-150.67065SC 002-40.468630
PI 5987840.64946GS 011-130.457131
QH 006-220.64587HB 001-50.454632
GS 003-150.62998XJ 025-200.444733
XZ 009-250.62689NM 032-120.444734
SC 018-160.62210NM 008-10.430935
GS 002-50.613911SC 015-200.430536
HB 013-50.602412NM 011-40.421137
SC 011-40.600713NM 031-50.412738
SC 012-160.599714NM 003-100.411439
NM 036-30.587115PI 5987790.404540
XJ 026-350.567516GS 014-80.390441
HB 011-270.557417NM 011-60.3942
XJ 021-330.554718HB 012-110.388643
GS 003-280.53819NM 003-140.383544
XZ 012-160.516320QH 006-200.368645
XJ 003-150.515321HB 009-150.367246
W 6103040.515122QH 005-350.318247
QH 003-190.514223QH 013-80.286848
QH 001-210.509624XZ 008-200.282749
HB 010-30.507725XZ 009-200.279750
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Chen, S.; Feng, J.; Xiong, Y.; Xiong, Y.; Liu, Y.; Zhao, J.; Dong, Z.; Ma, X.; Yan, L. Evaluation and Screening of Wild Elymus sibiricus L. Germplasm Resources under Salt Stress. Agronomy 2023, 13, 2675. https://doi.org/10.3390/agronomy13112675

AMA Style

Chen S, Feng J, Xiong Y, Xiong Y, Liu Y, Zhao J, Dong Z, Ma X, Yan L. Evaluation and Screening of Wild Elymus sibiricus L. Germplasm Resources under Salt Stress. Agronomy. 2023; 13(11):2675. https://doi.org/10.3390/agronomy13112675

Chicago/Turabian Style

Chen, Shuming, Junjie Feng, Yi Xiong, Yanli Xiong, Yingjie Liu, Junming Zhao, Zhixiao Dong, Xiao Ma, and Lijun Yan. 2023. "Evaluation and Screening of Wild Elymus sibiricus L. Germplasm Resources under Salt Stress" Agronomy 13, no. 11: 2675. https://doi.org/10.3390/agronomy13112675

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop