Salt–Alkali Tolerance Evaluation for Bermudagrass and Critical Indicator Screening at the Seedling Stage

Abstract

The adaptability of bermudagrass genotypes to high-pH saline–alkali conditions was investigated through a comprehensive evaluation of 38 genotypes during the seedling stage. For this purpose, two distinct treatments were established: exposure to saline–alkali solution composed of 45% NaCl, 5% Na₂SO₄, 5% NaHCO₃, and 45% Na₂CO₃ (pH 10.0), and exposure to distilled water as control. On 6th day of treatment, eight physiological indicators were measured. Compared with the control, the net photosynthetic rates, leaf water content, and chlorophyll content of the test genotypes decreased under stress. In contrast, the soluble protein content, proline levels, malondialdehyde concentration, and conductivity exhibited an increase. The salt–alkali tolerance coefficients of each indicator ranged from 0.24 to 8.54, and the variable coefficient was from 9.77% to 62.82%. Based on the salt–alkali tolerance coefficients, the comprehensive evaluation value (D) and resistance coefficient (CSAC) for each genotype were calculated. Subsequently, 38 genotypes were classified into three salt–alkali tolerance clusters by hierarchical clustering analysis, with Cluster I consisting of 10 genotypes with the most salt–alkali tolerance, and Cluster II with intermediate tolerance. Cluster III was comprised of 18 genotypes showing the lowest tolerance. The predictive model for assessing salt–alkali tolerance in bermudagrass is (D) = −0.238 + 0.106 × SAC_Chlb + 0.209 × SAC_RWC + 0.015 × SAC_Pro + 0.284 × SAC_Protein + 0.051 × SAC_Pn. Notably, Cluster I genotypes were more vigorous and showed lower damage under saline stress compared to Cluster III. Moreover, stepwise regression analysis pinpointed Chlb, RWC, and Pro as crucial indicators for evaluating salt–alkali tolerance in bermudagrass genotypes.

1. Introduction

Soil salinity is one of the major environmental factors that influence plant growth and development [1]. Globally, there are over 800 million hectares of saline–alkali soil. Due to population growth, industrialization, and environmental degradation, the issue of soil salinization is becoming increasingly severe. Elevated concentrations of Na⁺ and carbonate/HCO₃⁻ in saline–alkali soils increase soil pH and sodium absorption rates, decrease fertility, and cause the deterioration of soil structure [2]. The impacts of soil salinization on plants primarily encompass both salt stress (primarily NaCl and Na₂SO₄) and alkali stress (primarily NaHCO₃ and Na₂CO₃) [3]. The concurrent occurrence of soil salinization and alkaline stress exerts a pronounced inhibitory effect on plant growth [4]. In light of this, studies have indicated that the screening and breeding of plant genotypes with strong salt–alkali tolerance may be an effective means of mitigating the adverse impacts of saline–alkaline stress. Notable progress has been achieved in oats (Avena sativa L.), alfalfa (Medicago sativa L.), and rice (Oryza sativa L.) [5,6,7].

Bermudagrass [Cynodon dactylon (L.) Pers.] is a warm-season turfgrass widely used for lawn construction due to its strong ecological adaptability and rich genetic variation [8]. Significant differences in stress resistance exist among bermudagrass ecotypes. The screening mainly focuses on drought and cold tolerance [9,10], whereas studies on salt–alkali tolerance are limited. Bermudagrass has been proven to grow well under mild saline–alkali stress (10 mM Na⁺ at pH 8.0) and exhibits a survival rate of 15% under severe saline–alkali stress (150 mM Na⁺ at pH 10.6) [11]. Xinjiang, China, being a major saline–alkali distribution region, harbors a diverse range of bermudagrass genotypes. An evaluation of different ecological types may facilitate obtaining exceptional stress-resistant genotypes.

In order to clarify the stress tolerance of various plant genotypes, morphological and physiological indicators serve as effective evaluation tools [12]. When exposed to saline–alkali conditions, plants suffer significant impacts on both photosynthesis and cell membrane structural integrity due to elevated pH levels. To quantify these impacts, some physiological parameters are considered as essential indicators, including the net photosynthetic rate (Pn), which reflects the overall efficiency of photosynthesis [13], and the contents of chlorophyll a (Chla) and chlorophyll b (Chlb), pigments crucial for light absorption and energy conversion during photosynthesis [14]. Moreover, relative water content (RWC) is a crucial parameter for understanding salt–alkali tolerance in plants, as it indicates the plant’s capacity to maintain water balance under stressful conditions [15]. Additionally, membrane damage and cell stability are assessed through measurements of relative electrolyte conductivity (REL) and malondialdehyde (MDA) levels. REL indicates membrane permeability and the degree of membrane damage, while MDA serves as a marker for lipid peroxidation, indicating oxidative stress and cellular damage [16]. The accumulation of osmotic adjustment substances, such as proline (Pro) and soluble proteins (Protein), play a critical role in mitigating the adverse effects of abiotic stress. These osmolytes accumulate in response to stress, aiding in reducing cellular damage by maintaining turgor pressure and protecting cellular structures, thereby enhancing plant tolerance to stressful environments [17]. By analyzing these parameters, a comprehensive evaluation of plant stress tolerance can be carried out, providing valuable insights into the mechanisms of stress adaptation for different genotypes.

The experimental materials for this study consisted of 38 bermudagrass genotypes originating from Xinjiang. These genotypes were subjected to high-pH saline–alkali stress, and the physiological parameters closely related to overall physiology, including Pn, Chla, Chlb, RWC, REL, MDA, Pro, and Protein, were measured. A comprehensive evaluation of the saline–alkali tolerance of these genotypes was subsequently carried out. This methodology facilitated the identification of resistant bermudagrass genotypes and the screening of salt–alkali tolerance indicators, laying a foundation for future research endeavors.

2. Materials and Methods

2.1. Experimental Materials and Growth Conditions

A total of 38 Xinjiang bermudagrass genotypes were propagated and maintained as individual plants (

Table 1. Information on the test genotypes of bermudagrass.

Code	Original Site	Longitude	Latitude	Elevation (m)	Precipitation (mm)	Mean Temperature (°C)
C1	Yining City	81°36′13″ E	43°55′45″ N	639	319.8	8.9
C4	Yining City	81°38′32″ E	43°57′24″ N	671	319.8	8.9
C6	Yining City	81°17′42″ E	43°56′16″ N	620	319.8	8.9
C7	Cha County	81°23′47″ E	43°56′34″ N	594	314.7	9
C8	Zepu County	77°16′21″ E	38°03′56″ N	1280	66.7	11.3
C12	Yuepuhu County	77°07′28″ E	39°05′37″ N	1180	74.7	11.7
C14	Turpan County	–	–	–	15.3	14.6
C15	Kashghar City	76°00′39″ E	39°26′14″ N	1260	67.4	12
C19	Yingjisha County	76°08′01″ E	38°56′47″ N	1280	86.5	8.9
C20	Kashghar City	75°56.520′ E	39°25.899′ N	1280	67.4	12
C23	Atux County	76°38′44″ E	39°45′12″ N	1200	88.5	13.1
C26	Hutubi City	–	–	–
C30	Yining City	81°17′38″ E	43°57′65″ N	–	54.2	9.5
C35	Cha County	81°14′93″ E	43°52′60″ N	597
C36	Cha County	81°12′29″ E	43°51′05″ N	590	314.7	9
C37	Cha County	81°10′59″ E	43°47′23″ N	635	314.7	9
C39	Huocheng County	80°56′17″ E	44°05′55″ N	707	321.2	6.8
C40	Huocheng County	80°46′50″ E	44°02′33″ N	588	321.2	6.8
C41	Huocheng County	80°44′15″ E	44°01′01″ N	588	321.2	6.8
C44	Barkol County	93°28′57″ E	42°41′36″ N	−139	219.8	2
C49	Turpan County	89°32′05″ E	42°54′44″ N	–	15.3	14.6
C50	Hami City	93°28′06″ E	42°41′04″ N	627	–	–
C53	Turpan County	–	–	–	–	–
C65	Xinjiang Agricultural University	–	–	850	230	6.5
C109	Korla City	–	–	–	55.4	11.8
C111	Yining City	–	–	–	–	–
C112	Zepu County	–	–	–	66.7	11.3
C119	Yining City	–	–	–	319.8	8.9
C120	Yining City	–	–	–	319.8	8.9
C133	Kashghar City	–	–	–	67.4	12
C135	Toksun County	–	–	–	54.2	9.5
C136	Hami City	–	–	–	–	–
C137	Markit County	–	–	–	–	–
C142	Yining City	–	–	–	–	–
C143	Zepu County	–	–	–	66.7	11.3
C145	Manas County	–	–	–	225.2	7
C146	Xinjiang Agricultural University	–	–	–	–	–
C147	Xinjiang Agricultural University	–	–	–	–	–

Note: C146, C147, and C65 are breeding lines from Xinjiang Agricultural University, while other codes represent collection sites for different genotypes.

). The experiment was conducted in Xinjiang Agricultural University, located in Urumqi, Xinjiang. In June 2021, a turf plug with a diameter of 12 cm and a soil depth of 5 cm of each genotype was transplanted into plastic pots (20 cm diameter, 15 cm depth, containing a mixture of nutrient loamy soil and vermiculite in a 2:1 ratio (

Table 2. Principal physical and chemical properties of nutrient soil mixed with vermiculite in pots.

EC (ds/m)	pH	Total Phosphorus (g/kg)	Available Nitrogen (mg/kg)	Organic Carbon (g/kg)
3780	7.07	0.51	63.18	104.41

). Eight pots were transplanted for each genotype and maintained for 60 days with regular weeding and trimming. Quick-acting fertilizer (N:P₂O₅:K₂O = 3:1:1) was applied every ten days.

2.2. Salt and Alkali Stress Treatment

A mixture of NaCl, Na₂SO₄, NaHCO₃, and Na₂CO₃ was used to induce salt–alkali stress in plants, with a mass ratio of 9:1:1:9 and a pH of 10.0. The concentrations ranged from 0 to 150 mmol/L (pH 10.0), and each concentration treatment was repeated four times. Before the experiment started, all materials were fully irrigated (to reach the saturated water-holding capacity of the field) and trimmed, leaving a stump height of 4 cm. Subsequently, stress treatment was carried out gradually, with each pot irrigated with 100 mL of saline–alkali solution at concentrations of 0, 30, 60, 90, 120, and 150 mmol/L from the 1st to the 6th day, respectively. The same amount of clear water was used as a control.

2.3. Determination of Net Photosynthetic Rate

The photosynthetic rate (Pn) of the third fully expanded leaf from the top of bermudagrass plants was measured using the Ciras-2 portable photosynthesis instrument. Measurements were taken on a cloudless day between 9:30 and 13:00 across different treatment conditions. For each treatment, four leaves were selected, and each leaf was measured four times.

2.4. Determination of Chlorophyll Content

Chlorophyll a (Chla) and b (Chlb) content were measured with the 95% ethanol method by spectrophotometer Briefly, 0.2 g of bermudagrass leaves were weighed, ground with a mortar and pestled using 95% ethanol, diluted to 10 mL, and centrifuged. The supernatant was then transferred into a cuvette, and absorbance was measured at 649 nm and 665 nm, with 95% ethanol serving as the blank. The concentrations of Chla and Chlb were calculated using the formulas: $\mathrm{Chla}=13.95\times A665-6.88\times A649$, $\mathrm{Chlb}=24.96\times A649-7.32\times A655$.

2.5. Relative Moisture Content Determination

The relative water content (RWC) of leaves was determined using the saturated weight method. Briefly, 0.2 g of fresh leaves were weighed and immediately immersed in water for 12 h. Subsequently, they were dried in an oven at 105 °C for 30 min, further dried at 85 °C until reaching a constant weight, cooled to room temperature, and then the dry weight was recorded. This process was repeated three times. The RWC was calculated by the following formula: $\mathrm{RWC}={\displaystyle \frac{W_f-W_d}{W_t-W_d}}\times 100\%$, where W_f was the fresh weight of the leaves, W_t was the saturated weight, and W_d was the dry weight of the leaves.

2.6. Determination of Relative Electrolyte Conductivity (REL)

Relative electrolyte conductivity (REL) was determined by measuring the electrical conductivity of bermudagrass leaf extracts and comparing it to that of a known standard electrolyte solution. Briefly, leaf samples were collected, thoroughly rinsed with distilled water to eliminate surface contaminants, cut into small segments, and then immersed in deionized water. Vacuum infiltration was applied to ensure the complete extraction of electrolytes. The initial conductivity of the solution (C1) was measured using a conductivity meter. Thereafter, the samples were boiled for 30 min to release all electrolytes, cooled to room temperature, and the final conductivity (C2) was measured. REL was calculated as the ratio of C1 to C2 and expressed as a percentage.

2.7. Determination of Malondialdehyde Content

The measurement of malondialdehyde (MDA) was conducted using the thiobarbituric acid (TBA) method. Specifically, 0.5 g of plant leaf tissue were weighed and placed into a mortar. Subsequently, 0.1 g of PVP, a small amount of quartz sand, and 5 mL of phosphate-buffer solution with a pH of 7.8 were added to the mortar. The mixture was ground on ice, and the homogenate was transferred into a centrifuge tube, frozen, and centrifuged for 10 min at 10,000× g. The supernatant (hereinafter referred to as the enzyme extract) was then transferred to a test tube and stored at 0–4 °C.

To prepare the MDA reaction solution, 0.6 g of TBA were dissolved in a small amount of 1 M NaOH and diluted to 100 mL with 10% TCA. For MDA determination, 1 mL of enzyme extract was mixed with 2 mL of 0.6% TBA solution, sealed in a boiling water bath for 15 min, cooled rapidly, and centrifuged. The supernatant was collected, and its color was measured at three wavelengths: 600, 532, and 450 nm. For each treatment, the procedure was repeated three times. The results were calculated using the following formula: $\mathrm{MDA}={\displaystyle \frac{(A532-A600)\times V_{\mathrm{z}}-V_1}{0.155\times W\times V_2}}$. In the formula, V_Z represented the total volume of reaction solution (mL), V₁ represented the volume of extraction solution (mL), V₂ represented the volume of extraction solution for determination (mL), W represented the fresh weight of sampled leaves (g), and 0.155 represented the extinction coefficient of MDA (nmol/L).

2.8. Determination of Proline Content

The concentration of proline (Pro) in bermudagrass leaves was determined using the ninhydrin colorimetric method. For each treatment, the procedure was repeated four times. Specifically, 0.3 g of leaf tissue were weighed and placed into a 15 mL test tube. Subsequently, 5 mL of 3% sulfosalicylic acid solution were added, and the mixture was covered and boiled for 20 min. After cooling, 2 mL of the extraction supernatant were transferred to another 15 mL test tube. As a reference, 2 mL of distilled water were added. Then, 2 mL of glacial acetic acid and 3 mL of acidic ninhydrin solution (2.5% concentration) were added, and the mixture was heated for 40 min before adding 5 mL of toluene. The solution was shaken for 30 s and allowed to stand for layer separation. The absorbance was measured at a spectrophotometric wavelength of 520 nm using the reference solution as a blank. A standard curve was plotted to determine the Pro concentration of the test solution. The Pro content of the sample was calculated using the following formula: $\mathrm{Pro}={\displaystyle \frac{C_1-V_0}{V_1-W}}$, where C₁ represents the Pro concentration in the extraction solution (μg/mL) obtained from the standard curve, V₀ represents the total volume of the extraction solution (mL), V₁ represents the volume of the test solution added during measurement (mL), W represents the fresh sample weight (g).

2.9. Determination of Soluble Protein Content

A standard curve for protein concentration was prepared using bovine serum albumin as a standard sample and the Coomassie Brilliant Blue G-250 method. For soluble protein content determination, 1 mL of the extract was mixed with 5.0 mL of Coomassie Brilliant Blue G-250 solution. After thorough mixing, the absorbance was measured at 595 nm. The protein content (mg/g) was calculated using the standard curve according to the following formula: $\mathrm{Protein}={\displaystyle \frac{C\times V_t}{V_s\times W_F\times 1000}}$, where C is the value obtained from the standard curve (μg), and V_t is the total volume. W_F represents the fresh weight of the sample (g). V_S represents the sample volume added during measurement (mL).

2.10. Data Statistical Analysis

Microsoft Excel 2021 was used for data collection, and SPSS 26.0 (SPSS Inc., Chicago, IL, USA) was used for the statistical analysis of relevant data. The total difference between each index for control plants and plants exposed to the stress was analyzed using the independent sample t-test. Differences between the genotypes were analyzed using ANOVA, and the salt–alkali tolerance coefficient (D) was calculated according to the following formula: $SAC={\displaystyle \frac{\mathrm{Measured} \mathrm{values} \mathrm{of} \mathrm{corresponding} \mathrm{indicators} \mathrm{under} \mathrm{salt}-\mathrm{alkali} \mathrm{stress}}{ \mathrm{Measured} \mathrm{values} \mathrm{of} \mathrm{corresponding} \mathrm{indicators} \mathrm{without} \mathrm{stress}}}$. Correlation analysis was performed on the SAC value of each indicator, and subsequently, the factor weight coefficient (Vi), the membership function value of each comprehensive indicator [μ(xi)], the salt–alkali resistance comprehensive evaluation value (D), and the comprehensive salt–alkali resistance coefficient value (CSAC) were calculated. The value of the positive index membership function is given by $\mu p(x_i)={\displaystyle \frac{x_i-x_{\mathit{imin}}}{x_{\mathit{imax}}-x_{\mathit{imin}}}}$, and the negative index membership function value is given by: $\mu I(x_i)={\displaystyle \frac{x_{\mathit{imax}}-x_i}{x_{\mathit{imax}}-x_{\mathit{imin}}}}$. Formula: $V_i={\displaystyle \frac{P_i}{{\displaystyle {\sum }_{i=1}^n{P}_i}}}$, $D={\displaystyle {\sum }_{i=1}^n}[\mu (x_i)\times V_{\mathrm{i}}]$, $CSAC={\displaystyle \frac{1}{n}}\sum _{i=1}^n\mu \left(x_i\right)$. Here, P_i represents the contribution rate of the i composite indicator, indicating the importance of the i relative to all parameters. x_i, x_imax and x_imin represent the value of the i composite indicator and the maximum and minimum values, respectively. The D value and CSAC value were used to perform hierarchical clustering analysis using the Euclidean method, which enabled the classification of salt–alkali resistance levels [18].

3. Results

3.1. Physiological Responses of Bermudagrass under Saline–Alkali Stress

Compared with the control group, bermudagrass seedlings under salt stress exhibited significantly higher average REL, protein, Pro, and MDA content (p < 0.01), but lower average leaf RWC, Chla, Chlb, and Pn (Figure 1). Under saline–alkali stress, the average values of Pn, RWC, Chla, Chlb, Pro, Protein, MDA, and REL were observed to be 73%, 70%, 91%, 77%, 325%, 109%, 102%, and 238%, respectively, relative to the levels measured in non-stressed conditions.

3.2. Analysis of Eight Indicators of Salt–Alkali Tolerance Coefficient of Bermudagrass

The salt–alkali tolerance coefficient (relative value) was used to eliminate background differences among genotypes and characterize genotype tolerance (

Table 3. The saline–alkali tolerance coefficient (SAC) of eight indicators of test genotypes.

Code	SACPn	SACChla	SACChlb	SACRWC	SACREL	SACMDA	SACPro	SACProtein
C1	0.88 gh	0.73 p	0.55 r	0.92 e	1.84 m–p	0.32 f	4.55 m	1.06 o
C4	0.81 hij	0.98 fg	1.03 h	1.00 fg	2.14 j–m	2.68 ab	4.38 n	1.02 no
C6	0.92 g	0.98 fg	0.88 j	0.94 ghi	1.91 m–p	0.86 f	3.40 s	0.93 mno
C7	0.67 l	1.05 e	1.37 e	0.79 j	2.80 ef	1.00 b–f	1.83 x	0.91 l–o
C8	0.58 mn	1.16 d	2.02 d	0.89 i	1.99 l–o	0.58 f	3.95 p	0.92 k–n
C12	0.66 l	0.92 i–l	0.76 m	0.42 o	1.99 l–o	0.84 c–f	3.62 r	1.14 k–n
C14	0.68 kl	1.24 c	2.79 b	0.76 jk	2.54 f–i	0.74 ef	7.86 b	1.02 k–n
C15	0.76 ijk	0.89 lm	0.55 r	0.88 e	2.50 e–h	1.06 c–f	1.48 Aa	1.05 j–n
C19	0.99 f	0.61 r	0.24 y	0.75 jk	3.91 b	0.74 f	5.14 j	1.17 i–n
C20	0.52 no	0.79 o	0.37 x	0.79 j	2.97 de	1.13 b–f	4.61 m	1.05 h–m
C23	0.84 hi	1.08 e	1.22 f	0.42 o	1.64 pq	0.62 f	4.84 k	1.06 h–m
C26	1.16 e	0.94 hij	0.63 p	0.42 o	3.19 cd	2.05 a	1.65 yz	0.92 h–m
C30	0.42 pq	0.84 n	0.44 uv	0.74 d	2.74 ef	1.52 a–e	3.11 t	1.05 g–m
C35	0.86 gh	0.90 klm	0.59 q	0.59 lmn	2.69 efg	2.06 b–f	3.89 q	1.03 g–l
C36	0.52 no	0.99 f	1.02 h	0.82 j	2.28 i–l	0.39 f	6.23 f	1.05 f–k
C37	0.64 lm	0.98 fg	0.90 j	0.62 lm	2.04 mn	1.32 b–f	7.41 c	1.01 f–k
C39	0.64 lm	0.89 lm	0.52 st	0.94 ghi	1.40 q	0.37 f	4.60 lm	1.13 f–k
C40	0.86 b	0.57 q	0.46 w	0.57 n	2.03 de	0.72 def	2.04 v	1.00 f–k
C41	0.66 lm	0.99 f	0.97 i	0.71 k	2.14 j–m	0.65 f	5.21 ij	1.04 f–k
C44	1.79 gh	0.67 s	0.40 u	0.54 lmn	2.90 lmn	0.73 f	2.54 w	0.92 e–k
C49	0.27 st	0.95 ghi	0.85 k	0.58 a	2.12 j–m	0.66 f	1.69 y	1.21 e–k
C50	1.45 c	0.77 a	0.88 a	0.96 fgh	2.37 h–k	3.11 b–f	5.40 h	1.10 e–k
C53	0.72 kl	0.88 m	0.69 o	0.88 i	1.46 q	0.33 f	2.71 u	1.29 d–j
C65	2.45 a	0.90 lm	0.52 st	0.79 j	2.92 de	1.97 abc	5.68 g	1.13 d–j
C109	0.34 rs	0.84 n	0.41 w	0.57 mn	3.40 c	1.77 b–f	7.24 d	1.07 c–i
C111	1.10 e	1.07 e	2.04 d	0.99 f	2.50 f–i	3.33 a–d	5.35 hi	1.20 c–h
C112	0.26 t	1.36 b	2.25 c	0.71 k	1.84 m–p	0.67 f	1.52 zAa	1.21 c–h
C119	0.85 gh	0.95 ghi	0.71 n	0.59 lmn	4.85 a	1.03 b–f	1.30 Ab	1.10 b–g
C120	1.29 d	0.91 j–m	0.51 t	0.43 p	2.01 l–o	0.74 b–f	1.04 Ac	1.12 b–g
C133	1.02 f	0.62 r	0.42 v	0.80 j	1.85 m–p	1.40 b–f	4.70 l	1.11 b–g
C135	0.45 op	0.90 klm	0.46 u	0.47 o	2.41 g–j	0.83 c–f	0.69 Ae	1.19 b–g
C136	0.81 hij	0.94 h–k	0.96 i	0.64 l	1.78 nop	1.35 c–f	0.88 Ad	1.17 b–f
C137	0.38 pqr	0.98 fg	0.81 l	0.901 hi	1.69 opq	1.68 b–f	4.25 o	1.29 b–e
C142	0.37 qr	1.06 e	1.11 g	0.68 c	2.95 de	1.97 b–f	4.07 p	1.35 bcd
C143	0.75 jk	0.96 fgh	0.80 l	0.81 j	4.03 b	0.39 def	5.05 ij	1.23 bcd
C145	0.83 hij	0.71 p	0.54 rs	0.41 o	2.07 k–n	1.39 b–f	6.72 e	1.07 bc
C146	0.45 op	0.99 f	1.02 h	0.93 ghi	2.54 f–i	1.02 b–f	8.54 a	1.07 b
C147	0.88 gh	1.04 e	1.12 g	0.46 b	2.06 lmn	1.28 c–f	6.79 e	1.06 a
Max	2.45	1.36	2.79	1.00	4.85	3.33	8.54	1.35
Min	0.26	0.57	0.24	0.41	1.40	0.32	0.69	0.91
Average Value	0.80	0.92	0.89	0.71	2.43	1.19	4.10	1.09
CV (%)	51.80	17.49%	61.99%	25.64%	29.66%	61.66%	50.62%	9.64%

Note: This table lists the specific values of multiple indicators related to salt–alkali tolerance capability (SAC) under different codes (e.g., C1). These indicators include SAC_Pn (indicating the SAC of photosynthetic rate), SAC_Chla (indicating the SAC of chlorophyll a content), SAC_Chlb (indicating the SAC of chlorophyll b content), SAC_RWC (indicating the SAC of relative water content), SAC_REL (indicating the SAC of electrolyte leakage rate), SAC_MDA (indicating the SAC of malondialdehyde content), SAC_Pro (indicating the SAC of proline content), and SAC_Protein (indicating the SAC of protein content). Each value in the table is followed by a series of letters (e.g., a, b, c, etc.), which are used to indicate significant differences between different genotypes. A later alphabetical order indicates a lower ranking of the indicator among all samples.

). The results showed that under saline–alkali stress, the number of bermudagrass genotypes with salt–alkali tolerance coefficients ≥100% for Pn, Chla, Chlb, RWC, REL, MDA, Pro, and Protein were, respectively, 7, 1, 8, 11, 36, 20, 38, and 33, which means that salt–alkali tolerance coefficients varied among genotypes, and significant differences existed among genotypes for each indicator (p ≤ 0.05).

The salt–alkali tolerance coefficients for Pn, Chla and Chlb ranged, respectively, from 0.26 to 2.45, from 0.57 to 1.36, and from 0.24 to 2.79, indicating significant differences in tolerance among the genotypes (Figure 2). Under saline–alkali stress, genotypes such as C111 and C147 showed significant increases in Pn, Chla, or Chlb content, whereas the three indicators of most genotypes significantly decreased. C112 and C14 could maintain higher Chla (salt–alkali tolerance coefficients are 1.36 and 1.24, respectively) and Chlb (salt–alkali tolerance coefficients are 2.25 and 2.79, respectively) contents under salt–alkali stress. The chlorophyll degradation of genotypes such as C135, C40, C50, C1 was more severe compared with other genotypes under saline–alkali stress.

The salt–alkali resistance coefficients of REL and MDA ranged from 1.40 to 4.85 and 0.320 to 3.33, respectively (Figure 2). The top 20% of genotypes with the highest REL and MDA salt–alkali resistance coefficients consisted of ten genotypes, including C119 and C143, etc., and ten genotypes, including C111 and C50, etc., respectively, as indicated higher cell membrane damage in these genotypes under salt–alkali stress, with their MDA and REL salt–alkali tolerance coefficients above 1.97 and 2.95, respectively. However, C53, C39, and C1 suffered less damage, with MDA and REL salt–alkali tolerance coefficients below 0.368 and 1.843 respectively. Under saline–alkali stress, some genotypes, including C4 and C111, etc., maintained RWC to support seedling growth, while others, including C145 and C23, had RWC values below 47.10%. Overall, the saline–alkali treatment led to an increase in Pro and protein content, accompanied by a high Pro salt–alkali tolerance coefficient of 8.54.

Under salt–alkali stress, the variation coefficient for eight measured indicators among genotypes spanned from 9.64% to 61.99% (Table 2), highlighting significant variations in salt–alkali tolerance ability across these indicators. The rankings of salt–alkali tolerance coefficients of the eight tested indicators for each genotype have not been consistent (Figure 2), suggesting the difficulty in assessing salt–alkali tolerance based on a single indicator.

3.3. Pearson Correlation Analysis of Salt–Alkali Tolerance of Bermudagrass

Correlation analysis revealed that the eight indicators had correlation coefficients from −0.34 to −0.83 for salt–alkali tolerance, suggesting different sensitivities to salt–alkali stress (Figure 3). Chla and Chlb showed a significant positive correlation (0.83 **), while Chla and Pn had a significant negative correlation (−0.34 *). Other indicators did not display a significant correlation and had minimal information overlap. Therefore, these indicators could be directly used as criteria for comprehensively assessing the salt–alkali tolerance of the test genotypes.

3.4. Comprehensive Evaluation of Salt–Alkali Tolerance of Bermudagrass

Using SAC values, comprehensive evaluation values for 38 bermudagrass genotypes were calculated utilizing the membership function method (

Table 4. D and CSAC value for test genotypes by membership functions.

Code	UPn	UChla	UChlb	URWC	UREL	UMDA	UPro	UProtein	D	RankD	CSAC	RankCSAC
C1	0.28	0.21	0.12	0.87	0.87	1	0.49	0.36	0.455	14	0.525	11
C4	0.25	0.52	0.31	1	0.79	0.22	0.47	0.25	0.452	15	0.476	20
C6	0.3	0.52	0.25	0.89	0.85	0.82	0.34	0.04	0.440	18	0.501	15
C7	0.19	0.61	0.44	0.65	0.6	0.77	0.15	0	0.383	22	0.426	25
C8	0.15	0.75	0.7	0.8	0.83	0.91	0.42	0.02	0.525	6	0.573	6
C12	0.18	0.45	0.21	0.02	0.83	0.83	0.37	0.53	0.355	27	0.428	24
C14	0.19	0.86	1	0.59	0.67	0.86	0.91	0.26	0.654	1	0.668	1
C15	0.23	0.41	0.12	0.79	0.68	0.75	0.1	0.33	0.367	25	0.426	25
C19	0.34	0.06	0	0.58	0.27	0.86	0.57	0.6	0.379	24	0.410	27
C20	0.12	0.29	0.05	0.64	0.54	0.73	0.5	0.32	0.345	28	0.399	29
C23	0.26	0.64	0.39	0.02	0.93	0.9	0.53	0.35	0.433	19	0.503	14
C26	0.41	0.47	0.15	0.03	0.48	0.43	0.12	0.03	0.234	38	0.265	38
C30	0.08	0.34	0.08	0.55	0.61	0.6	0.31	0.32	0.306	34	0.361	31
C35	0.27	0.42	0.14	0.3	0.63	0.42	0.41	0.27	0.319	31	0.358	33
C36	0.12	0.53	0.31	0.69	0.75	0.98	0.71	0.33	0.488	9	0.553	9
C37	0.17	0.52	0.26	0.36	0.82	0.67	0.86	0.23	0.431	20	0.486	18
C39	0.18	0.41	0.11	0.89	1	0.98	0.5	0.5	0.485	10	0.571	7
C40	0.28	0	0.09	0.28	0.82	0.87	0.17	0.21	0.273	37	0.340	36
C41	0.18	0.53	0.29	0.5	0.78	0.89	0.58	0.29	0.442	17	0.505	13
C44	0.7	0.13	0.06	0.22	0.57	0.86	0.24	0.03	0.313	33	0.351	35
C49	0.01	0.48	0.24	0.29	0.79	0.89	0.13	0.68	0.360	26	0.439	23
C50	0.55	0.26	0.25	0.93	0.72	0.08	0.6	0.44	0.483	11	0.479	19
C53	0.21	0.39	0.18	0.8	0.98	1	0.26	0.86	0.503	7	0.585	3
C65	1	0.42	0.11	0.65	0.56	0.45	0.64	0.49	0.541	4	0.540	10
C109	0.04	0.34	0.07	0.27	0.42	0.52	0.83	0.36	0.317	32	0.356	34
C111	0.39	0.64	0.7	0.98	0.68	0	0.59	0.65	0.603	2	0.579	4
C112	0	1	0.79	0.51	0.87	0.88	0.11	0.68	0.546	3	0.605	2
C119	0.27	0.49	0.18	0.31	0	0.77	0.08	0.44	0.297	35	0.318	37
C120	0.47	0.43	0.11	0.04	0.82	0.86	0.04	0.49	0.338	30	0.408	28
C133	0.35	0.07	0.07	0.66	0.87	0.64	0.51	0.47	0.404	21	0.455	21
C135	0.09	0.43	0.09	0.11	0.71	0.83	0	0.63	0.282	36	0.361	31
C136	0.25	0.47	0.28	0.38	0.89	0.66	0.02	0.59	0.382	23	0.443	22
C137	0.06	0.52	0.22	0.85	0.92	0.55	0.45	0.86	0.493	8	0.554	8
C142	0.05	0.62	0.34	0.46	0.55	0.45	0.43	1	0.455	13	0.488	17
C143	0.23	0.5	0.22	0.68	0.24	0.97	0.56	0.72	0.480	12	0.515	12
C145	0.26	0.19	0.12	0	0.81	0.64	0.77	0.38	0.342	29	0.396	30
C146	0.09	0.53	0.31	0.88	0.67	0.77	1	0.37	0.532	5	0.578	5
C147	0.29	0.6	0.35	0.09	0.81	0.68	0.78	0.34	0.443	16	0.493	16
Weights	0.170	0.101	0.189	0.133	0.067	0.076	0.136	0.128

Note: The indicators included normalized photosynthetic rate (U_Pn), normalized chlorophyll a content (U_Chla), normalized chlorophyll b content (U_Chlb), normalized relative water content (U_RWC), normalized relative electrical conductivity (U_REL), normalized malondialdehyde content (U_MDA), normalized proline content (U_Pro), normalized protein content (U_Protein). The column D denotes the comprehensive evaluation value for salt–alkali resistance, with Rank_D representing its ranking among all samples. CSAC stands for the comprehensive salt–alkali resistance coefficient value, and Rank_CSAC indicates its ranking among all samples.

). Normalized inverse membership functions were employed for salt–alkali tolerance REL and MDA content, while standardized membership functions were used for other indicators. D and CSAC values were calculated for each genotype. The D and CSAC values of 38 genotypes ranged from 0.234 to 0.654 and 0.265 to 0.668, with average values of 0.418 and 0.466, respectively. The ranking results were consistent, especially for salt–alkali-resistant and salt–alkali-sensitive genotypes, with stable ranking positions.

3.5. Cluster Analysis of Salt–Alkali Tolerance of Bermudagrass

Based on the D and CSAC values, the Euclidean hierarchical clustering method was used to perform cluster analysis on the salt–alkali tolerance of test bermudagrass genotypes (Figure 4). The 38 test genotypes were divided into three clusters: Cluster I (10 genotypes, including C14, C53, C39, C137, C36, C111, C65, C112, C146 and C8), Cluster II (10 genotypes, including C147, C37, C23, C41, C6, C50, C142, C4, C143 and C1), and Cluster III (18 genotypes). Cluster I, Cluster II, and Cluster III accounted for 26.32%, 26.32%, and 47.37% of total genotypes, respectively.

Based on D value, CSAC value, and the cluster analysis results of various genotypes, C14, C53, C39, C137, and C36 exhibited higher salt–alkali resistance, while C119, C135, C40, C44, and C109 had weaker resistance. A comparison between saline–alkali-tolerant (Cluster I) and salt–alkali-sensitive (Cluster III) genotypes for different indicators (

Table 5. The difference in the salt–alkali tolerance coefficient for indicators between Cluster I and Cluster III.

Code	Cluster I	Cluster III	F P
SACPn	0.78 ± 0.40	0.81 ± 0.14	0.03
SACChla	1.05 ± 0.03	0.83 ± 0.02	13.45 *
SACChlb	1.37 ± 0.68	0.59 ± 0.07	13.75 **
SACRWC	0.86 ± 0.01	0.61 ± 0.02	23.54 **
SACREL	2.12 ± 0.26	2.68 ± 0.64	4.00
SACMDA	1.11 ± 0.92	1.17 ± 0.21	0.05
SACPro	5.07 ± 0.46	3.01 ± 0.40	6.44 *
SACProtein	1.13 ± 0.01	1.07 ± 0.01	2.11

Note: The values in the salt–alkali-resistant (Cluster I) and salt–alkali-sensitive (Cluster III) were the mean value of SAC ± standard deviation. F is F-distribution, P is probability, * and ** indicate significance at the 0.05 and 0.01 levels of value, respectively.

) revealed that Cluster I genotypes had significantly higher Chlb, RWC, Chla, and Pro levels than Cluster III genotypes by 130.94%, 41.33%, 25.51%, and 68.39%, respectively, under saline–alkali stress.

3.6. Stepwise Regression Analysis to Screen Key Indicators for Salt–Alkali Tolerance Evaluation

The critical indicators for identifying bermudagrass saline–alkali tolerant genotypes were obtained by stepwise regression analysis (

Table 6. Model prediction of saline–alkali stress resistance in bermudagrass genotypes.

Model	Stepwise Regression	R2	F Value	p Value
(I)	(D) = −0.238 + 0.106 × SACChlb + 0.209 × SACRWC + 0.015 × SACPro + 0.284 × SACProtein + 0.051 × SACPn	0.930	85.033 **	0.000
(II)	(CSAC) = 0.045 + 0.078 × SACChlb + 0.180 × SACRWC −0.039 × SACREL + 0.013 × SACPro + 0.241 × SACProtein	0.874	44.283 **	0.000

** indicate significant at the 0.01 level.

). Based on the membership function results, the optimal regression model was established using the D value and CSAC value of each indicator. The fitting curve R² reached 0.930 and 0.874, respectively, with p < 0.01. The critical indicators for D value analysis were Chlb, RWC, Pro, Protein, and Pn, and the critical indicators for CSAC value analysis were Chlb, RWC, REL, Pro, and Protein. The original SAC of 38 genotypes were input into the following regression model:

(I) D = −0.238 + 0.106 × SAC_Chlb + 0.209 × SAC_RWC + 0.015 × SAC_Pro + 0.284 × SAC_Protein + 0.051 × SAC_Pn

(II) CSAC = 0.045 + 0.078 × SAC_Chlb + 0.180 × SAC_RWC − 0.039 × SAC_REL + 0.013 × SAC_Pro + 0.241 × SAC_Protein.

The discriminant analysis assessed the agreement between the predicted model values and hierarchical clustering results. The agreement between models (I) and (II) with membership functions was 84.20%. Model (I) exhibited a higher R² value of 0.930 compared to model (II),which had an R² value of 0.874. Therefore, model (I) is the best model to determine the salt–alkali tolerance of bermudagrass seedlings.

4. Discussion

4.1. Key Indicators for Evaluating Salt–Alkali Tolerance of Bermudagrass Genotypes

Reliable screening indicators are essential for effectively determining the salt–alkali tolerance of bermudagrass. Under saline–alkali stress, tolerant plants exhibit significantly lower REL values [19]. Pro content acts as a critical molecular switch for salt stress resistance and a crucial indicator for enhancing salt–alkali tolerance [17]. In high-salt environments, Pro accumulation enhances salt tolerance and protects plants from salt stress damage, consistent with the screening of salt–alkali-resistant bermudagrass genotypes in this study. Bai et al. propose that chlorophyll and photosynthesis could be used as essential indicators for assessing oat’s (Avena sativa L.) salt–alkali tolerance [6]. Chlorophyll is sensitive to external pH and is a sensitive indicator of change. This study found that chlorophyll b (Chlb) is particularly essential for selecting salt–alkali-tolerant bermudagrass genotypes, which is in agreement with the findings reported by Ashraf et al. [20]. Because chlorophyll b is less susceptible to degradation than chlorophyll a, plants can remain green and maintain photosynthesis for a while after stress, even though their relative water content (RWC) decreases [21]. Salt–alkali stress reduces Pn and stomatal conductance. Chlb absorbs the blue-violet light band more efficiently than the red light band of Chla, enabling bermudagrass to maintain photosynthesis in adverse environments [22]. Salt–alkali-tolerant bermudagrass may adjust the chlorophyll a/b ratio to inhibit chlorophyll reductase production, maintain Chlb level and improve photosynthetic utilization efficiency under salt–alkali stress, according to Sikder et al. [23]. RWC is suitable for determining the salt tolerance of rice [24], and these findings support the importance of RWC in screening salt-tolerant genotypes, which aligns with the results of this study on bermudagrass salt-tolerant genotypes. Therefore, Chlb, RWC, and Pro content can be critical indicators for evaluating salt–alkali tolerance in bermudagrass.

4.2. Establishment of a Salt–Alkali Tolerance Evaluation Model for Bermudagrass Genotypes

The trends in a single indicator cannot accurately and intuitively reflect the growth ability of different genotypes under salt–alkali stress [25]. Wu et al. (2019) used the membership function fuzzy comprehensive evaluation method to evaluate the salt tolerance of 549 Brassica napus varieties. Similarly, in this study, based on the SAC values of the Pn, Chla, Chlb, RWC, REL, MDA, Pro, and Protein of 38 bermudagrass genotypes, the fuzzy evaluation method was used to evaluate their salt–alkali tolerance. The Euclidean hierarchical clustering method was employed to classify genotypes into salt–alkali-tolerant, intermediate, and sensitive types. A fuzzy comprehensive evaluation can enhance the accuracy of salt–alkali resistance, but the testing indicators are numerous and time-consuming. Therefore, simplifying the evaluation model and screening key indicators can improve accuracy and reduce workload. The stepwise regression analysis model is essential for screening resistance indicators of different genotypes [26]. It is widely used to screen for the critical indicators of drought [10], cold [9], heat [27], and salt–alkali resistance genotypes of different plant genotypes. To conveniently and reliably evaluate the salt–alkali tolerance of bermudagrass genotypes, we established a model using stepwise regression analysis. We performed stepwise regression analysis on the membership function results of the two evaluation methods and the SAC value, respectively. The model’s consistency with clustering results and monitoring R² value confirm that (D) = −0.238 + 0.106 × SAC_Chlb + 0.209 × SAC_RWC + 0.015 × SAC_Pro + 0.284 × SAC_Protein + 0.05 × SAC_Pn can be a predictor for the salt–alkali tolerance evaluation of bermudagrass seedlings.

4.3. Differences in Salt–Alkali Tolerance among Bermudagrass Genotypes

Through research, 10 saline–alkali tolerant genotypes were screened out of the 38 tested genotypes, including C14, C53, C39, C137 and C36. These genotypes exhibited high comprehensive scores (D and CSAC value range from 0.484 to 0.654 and 0.540–0.668) under salt–alkali stress. They exhibit high Chlb, Chla, RWC, and Pro levels, indicating they can maintain high Pn and RWC levels during saline–alkali stress. In contrast, 18 salt–alkali-sensitive genotypes, including C119, C135, C40, C44 and C109 (D and CSAC value are 0.234–0.404 and 0.265–0.455, respectively), exhibited lower Chla, Chlb, Pro and protein content while having higher MDA and REL levels under saline–alkali stress. This study demonstrates significant variations in salt–alkali tolerance among the test genotypes and lays a material and theoretical foundation for future research on salt–alkali tolerance breeding and its mechanisms.

5. Conclusions

This study evaluated the salt–alkali tolerance of 38 bermudagrass genotypes native to Xinjiang by exposing them to 150 mmol/L mixed saline–alkali solution with pH 10.0. Eight indicators were comprehensively analyzed with membership functions, and genotypes were categorized using the Euclidean hierarchical clustering method. The materials under stress exhibited a decrease in photosynthetic rates, leaf water content, and chlorophyll levels compared to the control group. Conversely, there was an elevation in soluble protein content, proline, malondialdehyde, and conductivity. The results classified the genotypes into three distinct clusters: salt–alkali tolerant (Cluster I, ten genotypes, including C14, C53, C39, C137 and C36), intermediate (Cluster II, ten genotypes), and sensitive (Cluster III, 18 genotypes, including C26, C40, C135, C119 and C30). Furthermore, stepwise regression analysis identified Chlb, RWC, and Pro as key indicators for assessing salt–alkali tolerance in bermudagrass genotypes. Additionally, a prediction model for evaluating the salt–alkali tolerance of bermudagrass seedlings was developed: (D) = −0.238 + 0.106 × SAC_Chlb + 0.209 × SAC_RWC + 0.015 × SAC_Pro + 0.284 × SAC_Protein + 0.051 × SAC_Pn.