Comprehensive Evaluation of Ecological Functional Traits and Screening of Key Indicators of Leymus chinensis Germplasm Resources from Northern China and Mongolia

Abstract

Leymus chinensis is important for ecological restoration and stock farming in Eurasia. In the context of climate change, excavating L. chinensis germplasm resources with excellent ecological functional traits is important to resist grassland degradation and promote the restoration of degraded grassland ecosystems. In this study, we used 42 L. chinensis germplasm resources (LC01–LC42) from different geographical sources to perform a multidimensional comprehensive evaluation of drought tolerance, rhizome space expansion, and soil improvement abilities. (1) LC07, LC15, LC18, and LC19 exhibited excellent ecological functional traits and could be used in breeding for ecological restoration. They were mainly from eastern and central Mongolia and central Inner Mongolia. (2) Principal component analysis showed that eight principal components with eigenvalues ≥1 were extracted from 26 traits of L. chinensis. The cumulative contribution rate was 80.551%. (3) There was a significant positive correlation between the F value and longitude and a significant negative correlation of the F value with latitude. L. chinensis germplasms from high longitudes and low altitudes may exhibit better comprehensive performance. (4) Plant height, leaf number, tiller number, malonaldehyde, chlorophyll content, dry weight on the ground, maximum one-direction extended distance, and organic matter can be used as key indices to comprehensively evaluate L. chinensis germplasm resources.

1. Introduction

The grasslands of the world have a variety of ecosystem service functions. Their area, which is approximately 5.2 × 10⁵ km², accounts for approximately 40.5% of the total land area (excluding Greenland and Antarctica) [1]. According to satellite remote sensing monitoring data from the early 21st century, around 40% of the world’s grasslands have been degraded [2,3]. This degradation has resulted in billions of dollars in economic losses [4] and serious environmental problems [3,5,6]. Despite numerous ecological restorations initiated by humans in recent decades [7,8,9], this percentage of grassland remains [10], with approximately half of the natural grasslands globally experiencing varying degrees of degradation [11]. In China, more than 90% of grasslands face different degrees of degradation, with over 60% being severely degraded [12]. Grassland degradation is driven by various factors, including overgrazing, eutrophication, land conversion to forestry and crops, land abandonment, invasive species, extreme climate, and altered fire regimes [10].

Currently, the global climate has undergone tremendous changes due to the combustion of fossil fuels and the impact of human activities. Preliminary data for 2022 suggest that the atmospheric CO₂ concentration has exceeded 417.2 ppm, which is more than 50% higher than pre-industrial levels of approximately 278 ppm. [13]. It is expected that the atmospheric CO₂ concentration will double by 2050 [14,15]. Influenced by the high concentration of CO₂ in the atmosphere and the greenhouse effect, the global surface temperature is projected to be 3.3–5.7 °C higher in the period of 2081–2100 compared to that of 1850–1900 (SSP5-8.5, SSP stands for Shared Socioeconomic Pathway) [16]. Numerous studies have indicated that climate change will further exacerbate grassland degradation [17,18,19,20], posing a significant ecological threat to global grasslands. Grassland degradation leads to vegetation destruction, decline in soil organic matter content, and soil erosion [21]. These changes significantly reduce soil microbial abundance, alter soil microbial composition [22], and decrease plant species diversity [23], ultimately resulting in a continuous decline or loss of biodiversity, ecosystem function, and ecosystem services [11]. Therefore, to achieve global sustainable development, it is imperative to enhance the resilience of grassland ecosystems and improve plant adaptability. The international community widely believes that excessive CO₂ emissions are the primary cause of climate change. The Chinese government has set the “Dual-carbon” goal of carbon peaking and carbon neutrality to assist the world in achieving emission reduction targets. Specifically, by reducing industrial carbon emissions and actively promoting ecological restoration efforts, the carbon sequestration potential of grassland ecosystems can contribute to achieving the “Dual-carbon” goal.

According to the third national land survey, China’s grassland area is 2.6 × 10⁸ hm², accounting for approximately 27.5% of the total land area [24]. Compared with the previous value of 4 × 10⁸ hm² [24], the grassland area shows a clear downward trend. This phenomenon indicates that the problem of grassland degradation in China has become more serious. In response to this serious challenge of grassland degradation, strengthening grassland ecological restoration and management is a top priority for the government and academia. The key factor restricting grassland ecological management is the lack of forage seeds with strong resistance that are suitable for large-scale promotion and application. In the past few decades, Chinese scholars have paid more attention to forage agronomic traits in forage resources and breeding, focusing on cultivating new forage varieties with ‘high yield, high quality, and multi-resistance’ [25,26,27]. With the implementation of the strategy of grassland ecological civilization construction in China, the excavation and promotion of grassland ecological functions have become important aspects of grassland ecological management. Ecological function refers to the function of stabilizing the ecological environment, including climate control, carbon sequestration, water conservation, wind-breaking, sand-fixing, and biodiversity maintenance [28,29]. Therefore, in the context of global climate change, to more effectively support the practice of grassland ecological construction, the collection of forage resources and the cultivation of new varieties must be changed from a single forage utilization direction to a multi-functional utilization direction. This means shifting the focus from cultivating new varieties of ‘high yield, high quality, multi-resistance’ to new varieties of forage that consider both production and ecological functions [26,27].

Leymus chinensis (Trin.) Tzvelev, commonly known as sheepgrass, is one of the dominant constructive species in the eastern Eurasian steppe [30]. It has a distribution area of 4.2 × 10⁵ hm² worldwide, of which approximately 50% is in China, especially in northern China [31,32]. L. chinensis has strong ecological adaptability and possesses characteristics such as cold resistance, drought tolerance, barren resistance, saline-alkali resistance, and grazing resistance [33]. It also offers high nutritional value and good palatability for livestock, earning it the title of “first-class forage” among herdsmen [34,35]. Therefore, L. chinensis is listed as the preferred breeding grass species by the National Forestry and Grassland Administration and the Ministry of Agriculture and Rural Affairs of China. In previous studies, new varieties, such as ‘Jisheng 1–4’ [36] and ‘Zhongke 1–2’ [37,38], have been bred with high quality and yield. However, given the current global climate change and the severe degradation of grassland resources, the identification, evaluation, and breeding utilization of L. chinensis resources have shifted from production function to ecology production. L. chinensis is widely distributed in arid and semi-arid areas of northern China. Its strong drought tolerance makes it well suited to the unique climatic conditions of northern China. The asexual reproduction of L. chinensis through its horizontal rhizomes can rapidly expand the population size and living space, forming a robust and dense root network structure. This enables the plant to further explore the soil, obtain water and nutrient sources, and effectively compensate for the ‘three lows’ problem of sexual reproduction [39]. L. chinensis also stabilizes the soil, reducing soil particles and soil erosion [33]. Therefore, L. chinensis can be effectively utilized to control desertification, increase the carbon sink of degraded grassland, and improve the grassland ecological environment in arid and semi-arid areas of northern China [26,34].

In previous studies, scholars have focused more on the ecological characteristics [40,41,42] and biomass [43,44,45] of L. chinensis. However, there have been few reports on the comprehensive analysis of the various ecological functional traits of L. chinensis. Therefore, the purpose of this study was to investigate the drought tolerance of L. chinensis germplasm resources [46], the spatial expansion ability of rhizomes [47], and the effect of soil improvement [48], which had been conducted in the early stage by the team. Principal component analysis (PCA), the membership function method, and cluster analysis were used to comprehensively evaluate the various ecological functional traits, identify L. chinensis materials with excellent ecological functional traits to withstand future global climate change, and provide a material basis for the restoration of L. chinensis grassland degradation in the natural grassland of northern China and the breeding of new varieties of L. chinensis in the future. Additionally, the full subset regression method was used to screen more important indicators in production practice, providing a theoretical and practical basis for the next ecological production breeding.

2. Materials and Methods

2.1. Experimental Materials

The experimental materials were 42 L. chinensis germplasms from different regions of northern China and Mongolia (Figure 1). The basic information of the sampling points is shown in

Table 1. Basic information of 42 L. chinensis germplasm sources.

Code	Latitude	Longitude	Altitude/m	Soil Regime	Climate Type	Source
LC01	48°32′	119°41′	778	brown soil, dark brown soil	temperate continental climate	Evenk Autonomous Banner of Hulunbuir City, Inner Mongolia
LC02	49°47′	118°41′	613	common chernozem, dark chestnut soil, etc.	temperate continental climate	Chenbaerhu Banner of Hulunbuir City, Inner Mongolia
LC03	47°22′	111°34′	1076	black soil	continental climate	Kent Province, Mongolia
LC04	48°39′	112°17′	1195	black soil	continental climate	Kent Province, Mongolia
LC05	48°35′	113°29′	890	dark brown soil, black soil, saline-alkali soil	temperate continental climate	Eastern Province of Mongolia
LC06	47°52′	114°38′	830	dark brown soil, black soil, saline-alkali soil	temperate continental climate	Eastern Province of Mongolia
LC07	47°30′	115°24′	678	dark brown soil, black soil, saline-alkali soil	temperate continental climate	Eastern Province of Mongolia
LC08	46°36′	115°22′	762	dark brown soil, black soil, saline-alkali soil	temperate continental climate	Eastern Province of Mongolia
LC09	46°35′	121°26′	577	thick black soil, fire black loess	continental monsoon climate	Inner Mongolia Xing’an League Horqin Right Wing Front Flag
LC10	45°50′	120°27′	934	chestnut soil, dark brown soil, chernozem, and meadow soil	temperate continental monsoon climate	Horqin Right Wing Middle Banner, Xing’an League, Inner Mongolia
LC11	44°45′	121°15′	288	meadow soil, chestnut soil, and chernozem	mid-temperate continental monsoon climate	Zhalute Banner, Tongliao City, Inner Mongolia
LC12	43°17′	117°49′	1167	mainly dark chestnut soil, chernozem, and meadow soil.	mid-temperate continental monsoon climate	Keshiketeng Banner, Chifeng City, Inner Mongolia
LC13	43°55′	118°23′	1073	black soil, gray forest soil, brown soil, chestnut soil, meadow soil, aeolian sandy soil	temperate monsoon continental climate	Balin Right Banner, Chifeng City, Inner Mongolia
LC14	46°48	124°31′	144	chernozem, meadow soil, saline-alkali soil, sandy soil	mid-temperate continental climate	Durbert Mongolian Autonomous County, Heilongjiang Province
LC15	47°33′	124°14′	152	chernozem, saline-alkali soil, meadow soil	mid-temperate continental monsoon climate	Fuyu County, Qiqihar City, Heilongjiang Province
LC16	48°20′	122°37′	619	brown coniferous forest soil, dark brown soil, black soil, meadow soil, marsh soil, paddy soil	mid-temperate continental monsoon climate	Hulun Buir City, Inner Mongolia Zhalantun City
LC17	38°37′	112°22′	1879	meadow soil, brown soil, yellow loamy soil	north temperate continental monsoon climate	Jingle County, Xinzhou City, Shanxi Province
LC18	43°38′	116°37′	1237	aeolian sandy soil, chestnut soil, chernozem	mid-temperate semi-arid continental climate	Xilinhot City, Xilin Gol League, Inner Mongolia
LC19	44°51′	118°37′	1016	ash forest soil, light chernozem, meadow soil	mid-temperate arid and semi-arid continental climate	Inner Mongolia Xilin Gol League West Wuzhu Muqin Banner
LC20	44°41′	117°41′	1047	ash forest soil, light chernozem, meadow soil	mid-temperate arid and semi-arid continental climate	Inner Mongolia Xilin Gol League West Wuzhu Muqin Banner
LC21	47°40′	106°45′	1349	dark chestnut soil, chestnut soil, and lowland dark soil	temperate continental climate	Central Province of Mongolia
LC22	46°54′	106°35′	1433	dark chestnut soil, chestnut soil, and lowland dark soil	temperate continental climate	Central Province of Mongolia
LC23	47°38′	107°48′	1706	dark chestnut soil, chestnut soil, and lowland dark soil	temperate continental climate	Central Province of Mongolia
LC24	47°45′	108°48′	1701	black soil	continental climate	Kent Province, Mongolia
LC25	47°24′	110°42′	1043	black soil	continental climate	Kent Province, Mongolia
LC26	46°27′	111°47′	997	chestnut soil	temperate continental climate	Sukhbaatar Province, Mongolia
LC27	46°47′	113°27′	1050	chestnut soil	temperate continental climate	Sukhbaatar Province, Mongolia
LC28	43°38′	115°37′	1145	chestnut soil	mid-temperate semi-arid continental climate	Abaga Banner, Xilinguole League, Inner Mongolia
LC29	41°39′	113°35′	1445	chestnut soil	mid-temperate continental monsoon climate	Shangdu County, Ulanqab City, Inner Mongolia
LC30	41°37′	109°47′	1590	chestnut calcium soil, brown calcium soil	mid-temperate semi-arid continental climate	Darhan Maomingan United Banner, Baotou City, Inner Mongolia
LC31	41°47′	115°22′	1504	pale chernozem, chestnut soil, meadow soil	mid-temperate sub-arid continental climate	Taipusi Banner, Xilinguole League, Inner Mongolia
LC32	44°20′	114°48′	1100	chestnut soil	mid-temperate semi-arid continental climate	Abaga Banner, Xilinguole League, Inner Mongolia
LC33	44°34′	113°15′	1245	brown calcium soil, chestnut soil, gray meadow soil	semi-arid continental climate	Sunite Left Banner, Xilin Gol League, Inner Mongolia
LC34	42°33′	113°48′	1140	chestnut soil, brown soil, gray meadow soil	mid-temperate sub-arid continental monsoon climate	Sunite Right Banner, Xilin Gol League, Inner Mongolia
LC35	42°27′	116°15′	1420	dark chestnut soil, meadow soil, sandy soil	mid-temperate continental climate	Inner Mongolia Xilin Gol League is the blue flag
LC36	42°33′	115°42′	1415	dark chestnut soil, meadow soil, sandy soil	mid-temperate continental climate	Inner Mongolia Xilin Gol League is the blue flag
LC37	46°39′	103°50′	2000	black soil	temperate continental climate	Former Hangai Province, Mongolia
LC38	47°35′	104°34′	1165	dark chestnut soil, chestnut soil, and lowland dark soil	temperate continental climate	Central Province of Mongolia
LC39	48°29′	106°21′	1205	dark chestnut soil, chestnut soil, and lowland dark soil	temperate continental climate	Central Province of Mongolia
LC40	46°51′	102°45′	1696	black soil	temperate continental climate	Former Hangai Province, Mongolia
LC41	47°53′	105°26′	1032	dark chestnut soil, chestnut soil, and lowland dark soil	temperate continental climate	Central Province of Mongolia
LC42	47°50′	102°47′	1541	black soil	temperate continental climate	Houhangai Province, Mongolia

, including 19 germplasm materials in Mongolia, 20 germplasm materials in the Inner Mongolia Autonomous Region, two germplasm materials in Heilongjiang Province, and 1 germplasm material in Shanxi Province.

2.2. Index and Separate Evaluation Methods

2.2.1. Drought Tolerance

The selected indicators were plant height (PH, cm), leaf length (LL, cm), leaf width (LW, cm), number of leaves (LN), tiller number (NT), chlorophyll content (SPAD), malonaldehyde (MDA, nmol/g·FW), proline content (PRO, μg/g·FW), fresh weight on the ground (SFW, g), dry weight on the ground (SDW, g), underground fresh weight (RFW, g), and underground dry weight (RDW, g). These indicators directly or indirectly reflected the drought resistance of L. chinensis. Drought tolerance was calculated by combining the drought tolerance coefficient and membership function method using a specific calculation formula [46].

The experiment was completed in the greenhouse of the key field scientific observation station of Shaerqin Forage Resources of the Ministry of Agriculture of the Grassland Research Institute of the Chinese Academy of Agricultural Sciences from April to November 2017. L. chinensis seedlings with basically the same growth status and similar developmental stages in the L. chinensis resource nursery were selected and transplanted into flowerpots. Five plants were planted in each pot: two treatments were set for each material, three replicates were set for each treatment, and six pots were transplanted for each material. Transplanting was performed under greenhouse conditions. During the seedling period, 1/2 Hoagland nutrient solution was added to supplement the water to the maximum water-holding capacity of the soil. After the end of the slow seedling period, the drought stress was performed using the secondary drought stress–rehydration method at the seedling stage: normal irrigation control (CK), maintaining 80–100% of the maximum soil water holding capacity; drought stress treatment (T), and no watering during the whole stress period. Water supplementation was performed using the weighing method during drought stress [46].

2.2.2. Rhizome Space Expansion Ability

According to previous results [47], the following traits in principal component 1 had the largest load: extended area (EA, m²), extended distance (ED, m), accumulated extended distance (AED, m), number of rhizome extension directions (NED), and maximum one-direction extended distance (MED, m). Therefore, we used these traits as indicators of the spatial expansion ability of L. chinensis. The spatial expansion ability of rhizomes was calculated by calculating the average membership function value directly after standardizing the original data.

This experiment was performed at the Key Field Scientific Observation Station of Forage Resources of the Ministry of Agriculture of China from 2017 to 2018. Field planting was adopted, and each growth plot was 5 m × 5 m, which provided sufficient growth space and unified management cultivation conditions without interspecific competition. At least 5 m of open space was left on each side of the test site to eliminate the edge effect. Three replicates per L. chinensis were recorded on 22 September 2018 [47].

2.2.3. Effect on Soil Improvement and Hay Yield

Eight indices related to the comprehensive status of soil nutrients were selected for calculation. The indices used were total nitrogen (TN, g/kg), total phosphorus (TP, g/kg), total potassium (TK, g/kg), available nitrogen (AN, mg/kg), available phosphorus (AP, mg/kg), available potassium (AK, mg/kg), organic matter (OM, g/kg), and soil pH. Finally, the hay yield (DW, kg/m²) was selected to compare its effect on yield. The effect of soil improvement was calculated using PCA, which was based on a prior study by Li [48].

This experiment was performed at the experimental demonstration base of the agropastoral ecotone of the Grassland Research Institute of the Chinese Academy of Agricultural Sciences in Inner Mongolia in 2019. The experimental area established an L. chinensis germplasm planting and preservation area in 2014 and has been homogenized for many years before planting. In the establishment of the germplasm preservation area, land leveling, soil testing, fertilization, weed removal, etc., were performed to ensure uniform and homogeneous soil composition. At the beginning of the establishment of the germplasm planting and preservation areas, different sources of L. chinensis were transplanted into each cell. The planting row spacing was 1 m, and the plant spacing was 0.75 m. Each district was separated by a cement board with a specification of 3 m × 3 m. After planting, L. chinensis was sprayed and watered once, and weeds were removed during management. Subsequently, the same field management measures have been maintained, and geographically related climate and soil differences have been excluded. In the second year, L. chinensis in each plot grew into one.

In July 2019, soil samples were collected from three randomly selected sites in the plots after L. chinensis cutting. A soil drill was used to obtain a depth of 20 cm per drill (L. chinensis rhizomes traversed and concentrated in the 20 cm soil layer). The soil sample collection was repeated three times. The collected soil was passed through a 2 mm sieve to remove litter and other animal and plant residues. After mixing evenly, the mixture was brought back to the laboratory for air drying. Hay yield was measured in three 25 cm × 25 cm quadrats randomly selected from each replicate plot. Each experiment was repeated three times and converted into biomass per square meter [48].

2.3. Comprehensive Evaluation Research Methods

The subordinate function values (i.e., standardization) of 26 indices of L. chinensis (the indexes selected in drought tolerance evaluation were converted into drought resistance coefficients) were calculated. Principal components with eigenvalues ≥ 1 were proposed from 26 trait indices, and the scores of each principal component were obtained. The comprehensive values of L. chinensis materials were calculated using the following formula: comprehensive value (F) = a₁·F₁ + a₂·F₂ +⋯+ a_n·F_n, where F₁, F₂, and ⋯F_n are the scores of principal components 1, 2, and ⋯n, respectively. a₁, a₂,⋯a_n are the percentage of variance explained by principal components 1, 2,⋯n, respectively, and n is the number of principal components extracted. L. chinensis materials were sorted according to the scores and comprehensive values of each principal component [47].

2.4. Data Processing

Microsoft Excel 2019 (Microsoft Corp., Redmond, WA, USA) was used to calculate the average value of the data, and IBM SPSS Statistics 25 (IBM Corp., Armonk, NY, USA) and Origin 2021 were used for the correlation analysis, significance test, and PCA. The principal component score and comprehensive evaluation value (F) were calculated, and the linear fitting curve between the geographical factors of the original habitat and the F value was drawn. Euclidean distance and the Ward method were used to cluster the ecological functional trait data of the 42 L. chinensis. Finally, R 4.1.3 (The R Foundation for Statistical Computing) was used to analyze the data of each index and the comprehensive evaluation value. The index with p < 0.05 was selected to perform all-subsets regression on each index and the comprehensive evaluation value with the leap package. The adjusted R² highest-index combination model was subjected to multiple linear regressions with the lm function.

3. Results

3.1. Comprehensive Evaluation of Ecological Functional Traits of L. chinensis

3.1.1. PCA

Based on the membership function values of the 26 indicators involved (including the drought resistance coefficient of 12 indicators related to drought tolerance, five indicators related to rhizome spatial expansion ability, and eight indicators related to soil improvement effect and hay yield), the factor load and contribution rate of each principal component were calculated. Eight principal components with eigenvalues ≥ 1 were identified from the 26 traits of L. chinensis. The eigenvalues were 5.960, 4.207, 3.192, 2.105, 1.958, 1.305, 1.180, and 1.037, which explained 22.922%, 16.179%, 12.277%, 8.096%, 7.530%, 5.019%, 4.539%, and 3.990% of the total variance, respectively. The cumulative contribution rate was 80.551% (

Table 2. Principal component analysis of ecological functional traits of 42 L. chinensis germplasms.

Trait	Principal Component
	1	2	3	4	5	6	7	8
PH	0.223	0.447	0.056	0.563	−0.011	−0.068	−0.109	0.372
LL	0.147	0.375	−0.214	0.430	0.284	−0.213	0.306	0.400
LW	0.095	0.070	0.090	−0.250	0.759	0.263	0.220	−0.018
LN	0.245	−0.408	−0.191	0.244	0.545	0.243	0.219	0.049
NT	−0.251	0.305	0.273	−0.267	−0.199	0.266	0.143	0.496
SPAD	0.083	0.187	0.182	0.230	−0.297	0.681	0.318	−0.203
MDA	0.490	0.010	0.418	0.337	0.136	0.402	−0.070	−0.074
PRO	0.216	0.072	0.574	0.247	0.151	0.213	−0.585	0.168
SFW	−0.056	0.514	0.569	0.065	0.064	−0.242	−0.216	−0.160
SDW	−0.039	0.447	0.757	−0.101	0.253	0.033	−0.001	−0.130
RFW	−0.096	0.584	0.551	−0.113	0.006	−0.256	0.049	0.003
RDW	−0.206	0.370	0.562	−0.457	0.052	−0.077	0.301	−0.008
NED	0.788	−0.391	0.154	−0.097	−0.247	−0.073	0.136	0.016
MED	0.707	−0.435	0.331	0.159	−0.019	−0.244	0.021	−0.002
AED	0.796	−0.419	0.240	−0.102	−0.133	−0.046	0.127	0.065
ED	0.817	−0.416	0.299	0.009	−0.003	−0.164	0.071	−0.003
EA	0.791	−0.426	0.340	0.003	0.007	−0.107	0.115	0.067
TN	0.663	0.530	−0.326	−0.121	0.156	−0.022	0.021	−0.248
TP	0.563	0.509	−0.442	−0.169	−0.098	−0.108	−0.046	0.091
TK	−0.340	0.251	0.167	0.535	−0.161	−0.135	0.419	−0.017
AN	0.666	0.567	−0.261	−0.072	0.107	0.047	0.002	−0.163
AP	0.429	0.600	−0.060	0.093	−0.240	−0.043	0.230	0.075
AK	0.569	0.296	−0.297	−0.117	−0.032	0.248	−0.282	0.302
OM	0.681	0.580	−0.268	−0.108	0.133	−0.036	−0.048	−0.213
pH	−0.257	−0.164	−0.079	0.131	0.725	−0.219	0.000	0.047
DW	0.086	−0.114	0.071	−0.717	0.114	0.058	0.064	0.350
Eigenvalue	5.960	4.207	3.192	2.105	1.958	1.305	1.180	1.037
Contribution rate %	22.922	16.179	12.277	8.096	7.530	5.019	4.539	3.990
Cumulative contribution rate %	22.922	39.101	51.378	59.473	67.003	72.022	76.581	80.551

Note: A bold number indicates that the score is relatively high in this principal component.

). Figure 2 shows the principal component two-dimensional coordinate diagram of the 26 traits of L. chinensis.

The comprehensive evaluation results showed that the original 26 indicators were converted into eight new independent, comprehensive indicators through principal component extraction. Most of the data information of all the original indicators was reflected. For PC1, two groups of traits displayed the highest contributions: ED, AED, NED, EA, and MED, and OM, AN, and TN, which were all fairly correlated in this axis towards the positive side of it. Furthermore, MDA, with a much lower contribution, is fully aligned with this PC1 axis. For PC2, the largest contributions were from SFW, RFW, OM, AP, AN, and TN towards the positive end and from MED, EA, ED AED, NED, and LN towards the negative end. Thus, OM, TN, AN, NED, AED, ED, EA, and MED were all traits that contributed significantly to the variation explained by PC1 and PC2. For PC3, the largest contributions are from PRO, SFW, SDW, RFW, and RDW towards the positive end. For PC4, the largest contributions were from PH and TK towards the positive end and from DW towards the negative end. For PC5, the largest contributions were from LW, LN, and pH towards the positive end. For PC6, the largest contribution was from the SPAD towards the positive end. For PC7, the largest contribution was from PRO towards the negative end. For PC8, the largest contribution was from NT towards the positive end.

3.1.2. Comprehensive Evaluation

Eight principal component factor scores were calculated according to the factor score coefficient matrix and its corresponding principal component. The formulae used were:

F₁ = 0.037X₁ + 0.025X₂ + 0.016X₃ + 0.041X₄ − 0.042X₅ + 0.014X₆ + 0.082X₇ + 0.036X₈ − 0.009X₉ − 0.007X₁₀ − 0.016X₁₁ − 0.035X₁₂ + 0.132X₁₃ + 0.119X₁₄ + 0.133X₁₅ + 0.137X₁₆ + 0.133X₁₇ + 0.111X₁₈ + 0.094X₁₉ − 0.057X₂₀ + 0.112X₂₁ + 0.072X₂₂ + 0.095X₂₃ + 0.114X₂₄ − 0.043X₂₅ + 0.014X₂₆;

(1)

F₂ = 0.106X₁ + 0.089X₂ + 0.017X₃ − 0.097X₄ + 0.073X₅ + 0.045X₆ + 0.002X₇ + 0.017X₈ + 0.122X₉ + 0.106X₁₀ + 0.139X₁₁ + 0.088X₁₂ − 0.093X₁₃ − 0.103X₁₄ − 0.100X₁₅ − 0.099X₁₆ − 0.101X₁₇ + 0.126X₁₈ + 0.121X₁₉ + 0.060X₂₀ + 0.135X₂₁ + 0.143X₂₂ + 0.070X₂₃ + 0.138X₂₄ − 0.039X₂₅ − 0.027X₂₆;

(2)

F₃ = 0.018X₁ − 0.067X₂ + 0.028X₃ − 0.060X₄ + 0.086X₅ + 0.057X₆ + 0.131X₇ + 0.180X₈ + 0.178X₉ + 0.237X₁₀ + 0.173X₁₁ + 0.176X₁₂ + 0.048X₁₃ + 0.104X₁₄ + 0.075X₁₅ + 0.094X₁₆ + 0.106X₁₇ − 0.102X₁₈ − 0.138X₁₉ + 0.052X₂₀ − 0.082X₂₁ − 0.019X₂₂ − 0.093X₂₃ − 0.084X₂₄ − 0.025X₂₅ + 0.022X₂₆;

(3)

F₄ = 0.268X₁ + 0.204X₂ − 0.119X₃ + 0.116X₄ − 0.127X₅ + 0.109X₆ + 0.160X₇ + 0.118X₈ + 0.031X₉ − 0.048X₁₀ − 0.054X₁₁ − 0.217X₁₂ − 0.046X₁₃ + 0.076X₁₄ − 0.049X₁₅ + 0.004X₁₆ + 0.002X₁₇ − 0.058X₁₈ − 0.080X₁₉ + 0.254X₂₀ − 0.034X₂₁ + 0.044X₂₂ − 0.056X₂₃ − 0.051X₂₄ + 0.062X₂₅ − 0.341X₂₆;

(4)

F₅ = − 0.006X₁ + 0.145X₂ + 0.387X₃ + 0.278X₄ − 0.101X₅ − 0.152X₆ + 0.069X₇ + 0.077X₈ + 0.032X₉ + 0.129X₁₀ + 0.003X₁₁ + 0.026X₁₂ − 0.126X₁₃ − 0.010X₁₄ − 0.068X₁₅ − 0.002X₁₆ + 0.003X₁₇ + 0.079X₁₈ − 0.050X₁₉ − 0.082X₂₀ + 0.055X₂₁ − 0.123X₂₂ − 0.016X₂₃ + 0.068X₂₄ + 0.370X₂₅ + 0.058X₂₆;

(5)

F₆ = − 0.052X₁ − 0.163X₂ + 0.202X₃ + 0.187X₄ + 0.203X₅ + 0.522X₆ + 0.308X₇ + 0.163X₈ − 0.186X₉ + 0.025X₁₀ − 0.196X₁₁ − 0.059X₁₂ − 0.056X₁₃ − 0.187X₁₄ − 0.035X₁₅ + 0.125X₁₆ − 0.082X₁₇ − 0.017X₁₈ − 0.083X₁₉ − 0.103X₂₀ + 0.036X₂₁ − 0.033X₂₂ + 0.190X₂₃ − 0.027X₂₄ − 0.168X₂₅ + 0.045X₂₆;

(6)

F₇ = − 0.092X₁ + 0. 259X₂ + 0.186X₃ + 0.185X₄ + 0.121X₅ + 0.269X₆ − 0.059X₇ − 0.496X₈ − 0.183X₉ − 0.001X₁₀ + 0.041X₁₁ + 0.255X₁₂ + 0.115X₁₃ + 0.018X₁₄ + 0.108X₁₅ + 0.060X₁₆ + 0.097X₁₇ + 0.018X₁₈ − 0.039X₁₉ + 0.355X₂₀ + 0.001X₂₁ + 0.195X₂₂ − 0.239X₂₃ − 0.041X₂₄ + 0.000X₂₅ + 0.054X₂₆;

(7)

F₈ = 0.359X₁ + 0.385X₂ − 0.017X₃ + 0.047X₄ + 0.478X₅ − 0.196X₆ − 0.072X₇ + 0.162X₈ − 0.155X₉ − 0.125X₁₀ + 0.003X₁₁ − 0.007X₁₂ + 0.015X₁₃ − 0.002X₁₄ + 0.062X₁₅ − 0.003X₁₆ + 0.064X₁₇ − 0.239X₁₈ + 0.088X₁₉ − 0.016X₂₀ − 0.157X₂₁ + 0.072X₂₂ + 0.291X₂₃ − 0.206X₂₄ + 0.045X₂₅ + 0.338X₂₆;

(8)

In the formulae, X₁−X₂₆ represent standardized PH, LL, LW, LN, NT, SPAD, MDA, PRO, SFW, SDW, RFW, RDW, NED, MED, AED, ED, EA, TN, TP, TK, AN, AP, AK, OM, pH, and DW. Taking the contribution rate of principal components as the weight, the comprehensive score F of multiple traits of L. chinensis was calculated using the scores and weight values of the first eight principal components (

Table 3. Main factor scores and comprehensive values of ecological functional traits of 42 L. chinensis germplasms.

Code	Principal Component								F	Rank
	F1	F2	F3	F4	F5	F6	F7	F8
LC01	−1.320	−0.249	0.647	1.604	−0.128	1.109	−0.641	−0.224	−0.156	25
LC02	0.017	0.609	−0.847	0.542	−2.269	0.076	−0.215	−1.193	−0.226	29
LC03	0.294	0.206	−0.906	1.305	−2.232	0.099	0.661	0.156	−0.040	19
LC04	1.470	0.963	−0.542	1.605	−0.400	−2.168	0.880	0.963	0.615	5
LC05	−0.837	2.370	0.951	0.948	−0.129	−1.506	−0.743	−0.621	0.299	9
LC06	−0.920	0.807	1.380	0.748	0.240	−0.477	−2.053	0.339	0.080	15
LC07	1.823	2.447	0.709	0.806	2.564	0.749	0.458	−1.531	1.436	1
LC08	−0.728	−0.294	−0.952	0.595	2.150	0.812	−0.093	−0.659	−0.138	23
LC09	−0.266	0.213	−1.519	0.526	0.816	1.468	1.311	−0.228	0.019	17
LC10	−0.189	−0.189	0.046	−0.354	−0.772	−1.252	−0.065	−0.358	−0.292	34
LC11	−0.029	−0.305	0.497	−0.759	0.189	−1.225	1.326	−0.814	−0.094	20
LC12	−0.171	−2.006	−0.009	−0.253	0.306	−1.392	−0.096	1.160	−0.484	40
LC13	0.786	−1.835	0.592	1.432	0.256	0.950	1.272	−0.142	0.237	11
LC14	0.196	−0.505	0.717	−0.574	−0.767	0.407	−2.145	0.996	−0.112	21
LC15	2.017	−1.075	1.241	−0.458	0.523	−0.026	0.658	0.322	0.601	6
LC16	2.156	0.729	0.354	0.228	−1.748	0.278	−0.775	−0.075	0.643	3
LC17	−1.409	0.275	0.761	−0.369	−1.188	0.723	−0.777	−0.835	−0.418	38
LC18	0.718	1.436	−0.211	−0.975	−0.487	0.278	0.373	0.264	0.368	7
LC19	1.755	−0.808	0.963	0.565	−0.098	3.099	−0.104	−0.079	0.715	2
LC20	0.406	0.429	1.687	0.122	1.247	0.059	−0.256	0.881	0.620	4
LC21	−0.569	0.066	−1.241	0.486	−0.257	1.299	−0.881	1.026	−0.231	30
LC22	1.962	0.614	−1.368	−0.935	−0.968	−0.555	−0.306	−0.409	0.217	12
LC23	−1.045	0.813	−0.428	−0.969	−1.564	1.676	1.350	2.716	−0.128	22
LC24	−0.414	0.212	1.327	−0.895	−0.652	0.329	−0.273	−0.312	−0.034	18
LC25	−0.588	−0.812	0.019	1.180	−0.878	−0.267	1.182	−0.628	−0.272	33
LC26	−0.413	−0.102	1.771	1.878	−0.142	−1.174	0.465	−0.326	0.244	10
LC27	−0.464	1.222	0.128	−0.048	0.411	0.850	0.246	2.011	0.333	8
LC28	0.378	−0.341	−1.034	−0.544	1.295	−0.168	−2.539	0.951	−0.159	26
LC29	−0.232	0.423	−0.623	−0.738	0.640	0.072	−1.211	−1.614	−0.234	31
LC30	0.635	−0.587	−0.819	−1.120	−0.723	−0.632	0.052	−1.897	−0.373	36
LC31	0.203	−0.938	−0.198	−1.159	0.567	0.238	0.977	−0.524	−0.180	27
LC32	0.769	−1.403	−0.061	−1.907	−0.131	0.007	−0.812	−0.435	−0.343	35
LC33	−0.074	−0.841	0.139	−0.516	0.259	−0.963	0.808	−0.519	−0.236	32
LC34	−1.039	1.069	2.121	−2.642	0.179	−0.427	1.793	0.650	0.100	13
LC35	−1.004	−0.783	−0.146	−0.201	0.269	0.037	1.191	−0.449	−0.413	37
LC36	0.078	1.102	−2.169	−1.195	1.055	−0.629	−0.520	0.454	−0.155	24
LC37	−1.239	−0.716	−1.382	1.169	0.064	−0.565	−0.360	−0.562	−0.667	42
LC38	−1.244	1.006	−1.627	0.046	0.859	0.000	1.505	0.200	−0.220	28
LC39	−1.075	−0.772	0.560	−0.196	0.288	−0.519	−0.710	0.118	−0.435	39
LC40	−1.433	−0.708	0.137	−0.426	0.037	0.634	−0.549	−1.353	−0.627	41
LC41	0.618	−1.198	−0.417	0.939	0.829	−1.625	−0.190	2.520	0.057	16
LC42	0.420	−0.544	−0.245	0.508	0.492	0.319	−0.195	0.060	0.082	14

). The comprehensive score F reflects the excellent comprehensive traits of the germplasm. The larger the F value, the better the comprehensive traits. The top ten comprehensive scores of L. chinensis were LC07, LC19, LC16, LC20, LC04, LC15, LC18, LC27, LC05, and LC26. The last ten L. chinensis were LC37, LC40, LC12, LC39, LC17, LC35, LC30, LC32, LC10, and LC25.

3.1.3. Cluster Analysis

The drought tolerance, rhizome spatial expansion ability, and soil improvement effect of 42 L. chinensis germplasms were analyzed by cluster analysis. The results are shown in Figure 3. When the Euclidean distance coefficient was 10.5, 42 L. chinensis specimens were clustered into four groups. Clusters 1–4 included 16, 6, 9, and 11 L. chinensis specimens, respectively.

From the geographical distribution of each cluster group (Figure 4), a clear mixed distribution between Clusters 1–4 was evident, with a large number of overlaps. Cluster 2 was surrounded by the other three clusters. Clusters 1 and 3 almost completely overlapped. The number of L. chinensis germplasm in Cluster 1 was the largest, and the distribution range was the widest, which was mainly mixed in the northeast–southwest direction. Clusters 2 and 4 were approximately horizontally mixed in the east–west direction. Among them, the excellent L. chinensis germplasm with a higher comprehensive value is mainly from eastern and central Mongolia and central Inner Mongolia, and the L. chinensis germplasm with a lower comprehensive value is mainly from central Mongolia and western and northern Inner Mongolia.

3.2. Relationship between Comprehensive Evaluation Value of L. chinensis and Its Original Habitat Geographical Factors

The curve fitting analysis of the comprehensive evaluation value (F) with the longitude, latitude, and altitude of the original habitat (Figure 5) showed that the F value had a significant regression with longitude (p < 0.05) and a very significant regression with altitude (p < 0.01). At the same time, the correlation analysis results of the F value and longitude, latitude, and altitude of its original habitat also showed that F values were significantly positively correlated with longitude (p < 0.05) and negatively correlated with altitude (p < 0.01) (

Table 4. Correlation between F value of ecological functional traits and geographical factors of 42 L. chinensis from different geographical sources.

Index	Longitude/°	Latitude/°	Altitude/m	F
Longitude/°	1
Latitude/°	−0.157	1
Altitude/m	−0.731 **	−0.369 *	1
F	0.326 *	0.257	−0.434 **	1

Note: The number of test samples was 42, ** p < 0.01, * p < 0.05.

). That is, the comprehensive ranking of L. chinensis germplasm with high longitude and low altitude was high, the L. chinensis germplasm with low longitude and high altitude ranked lower, and the altitude factor had a great influence on the comprehensive performance of L. chinensis germplasm.

3.3. Index Screening

The all-subsets regression method was used to screen 26 indicators (including the drought resistance coefficient of 12 indicators related to drought tolerance, 5 indicators related to rhizome spatial expansion ability, and 8 indicators related to soil improvement effect and hay yield) (Figure 6). In this figure, the horizontal axis represents the predictive variable, and the vertical axis represents the accuracy of the regression (corrected R²), which shows the change in the regression accuracy when considering different combinations of predictive variables. It can be observed from this figure that when R² = 0.94, two groups of optimal combinations of predictive variables were evident. The first group included PH, LN, NT, SPAD, MDA, SDW, MED, and OM (eight indicators), and the second group included PH, LN, NT, MDA, SDW, MED, AP, and OM (eight indicators). The difference between the two groups is that the second group shows that SPAD can be deleted from the regression model, and AP is added. When R² = 0.90, the combination of predictive variables displayed was PH, LN, NT, SDW, MED, and OM (six indicators); that is, the elimination of SPAD and MDA in the regression model can still produce relatively high prediction accuracy. Therefore, the combination of predictive variables in any of the above groups has a significant impact on the comprehensive evaluation of the ecological functional traits of L. chinensis germplasm resources.

The multiple linear regression equation established using these eight indicators was Y = 1.228PH + 1.117LN + 0.331NT + 0.543SPAD + 0.117MDA + 0.899SDW + 0.004MED + 0.045OM − 4.298 (

Table 5. Multiple regression of each index.

Trait	Estimated Value	Std. Error	t	p
Constant	−4.298	0.257	−16.732	<2 × 10−16 ***
PH	1.228	0.140	8.779	3.80 × 10−10 ***
LN	1.117	0.177	6.309	3.90 × 10−7 ***
NT	0.331	0.056	5.897	1.31 × 10−6 ***
SPAD	0.543	0.160	3.392	0.00182 **
MDA	0.117	0.037	3.143	0.00352 **
SDW	0.899	0.111	8.103	2.37 × 10−9 ***
MED	0.004	0.001	6.353	3.42 × 10−7 ***
OM	0.045	0.004	11.977	1.47 × 10−13 ***

Note: *** p < 0.001, ** p < 0.01.

). The equation shows that the fitting effect is good and the precision is high. These eight indices can be used as important indices for the comprehensive evaluation of ecological functional traits of germplasm resources in production practice in the future.

3.4. Drought Tolerance, Rhizome Space Expansion Ability, and Effect on Soil Improvement Were Evaluated Separately

3.4.1. Drought Tolerance Evaluation

PCA of the drought tolerance coefficients of these 12 traits showed that the cumulative contribution rate of the first five factors could reach 77.797% (

Table 6. Principal component analysis of drought tolerance indexes of 42 L. chinensis germplasms.

Trait	Principal Component
	1	2	3	4	5
PH	0.271	0.599	−0.572	0.128	0.039
LL	0.060	0.431	−0.493	0.674	0.025
LW	0.128	0.211	0.726	0.493	0.172
LN	−0.469	0.539	0.379	0.328	−0.037
NT	0.403	−0.348	−0.084	−0.073	0.492
SPAD	0.125	0.299	−0.126	−0.252	0.779
MDA	0.150	0.757	0.189	−0.316	0.144
PRO	0.355	0.522	0.198	−0.499	−0.313
SFW	0.771	0.049	−0.120	−0.019	−0.354
SDW	0.880	0.152	0.281	0.004	−0.014
RFW	0.838	−0.109	−0.169	0.166	−0.096
RDW	0.719	−0.331	0.289	0.240	0.150
Eigenvalue	3.228	2.083	1.543	1.324	1.159
Contribution rate %	26.899	17.354	12.857	11.032	9.655
Cumulative contribution rate %	26.899	44.253	57.110	68.142	77.797

). Most of the data information of all the original indicators was concentrated. The characteristic root of principal component 1 was 3.228, and the contribution rate was 26.899%. Among them, SFW, SDW, RFW, and RDW had strong loads. The eigenvalue of principal component 2 was 2.083, and the contribution rate was 17.354%. PH, LN, MDA, and PRO had strong loads. The eigenvalue of principal component 3 was 1.543, and the contribution rate was 12.857%. PH and LW had strong loads. The characteristic root of principal component 4 was 1.324, and the contribution rate was 11.032%. The characteristic root of principal component 5 was 1.159, and the contribution rate was 9.655%.

The membership function value of each trait index of 42 L. chinensis germplasm resources was obtained by combining the membership function method and drought tolerance coefficient. The product of the membership function value and weight was determined to obtain the D value and sorted. The stronger the drought tolerance, the greater the D value (Supplementary Materials, Schedule S1). The results showed that the D values ranged from 0.258 to 0.804. The top ten L. chinensis were LC07, LC34, LC20, LC27, LC15, LC19, LC05, LC26, LC24, and LC18. The last ten L. chinensis were LC03, LC02, LC12, LC37, LC30, LC32, LC22, LC10, LC39, and LC28.

3.4.2. Evaluation of Rhizome Space Expansion Ability

The membership function values of EA, ED, AED, NED, and MED of 42 L. chinensis germplasms were obtained. Finally, the average value was calculated to obtain the D value and sorted (Supplementary Materials, Schedule S2). D values ranged from 0.000 to 1.000. The top ten L. chinensis were LC15, LC13, LC16, LC19, LC22, LC41, LC32, LC04, LC12, and LC30. The bottom ten L. chinensis were LC38, LC05, LC23, LC17, LC36, LC08, LC21, LC01, LC37, and LC09.

3.4.3. Evaluation of Soil Improvement Effect

PCA was performed on these eight indicators to reflect the effect of soil improvement. The cumulative contribution rate of the first three factors reached 83.319%, including the main soil nutrient information. The characteristic root of principal component 1 was 4.437, and the variance contribution rate was the largest, reaching 55.467%. Among them, TN, TK, AN, AP, AK, and OM had the largest factor load. The characteristic root of principal component 2 was 1.192, the variance contribution rate was 14.898%, and the TP load was the largest. The characteristic root of principal component 3 was 1.036, the variance contribution rate was 12.954%, and the pH load was the highest (

Table 7. Principal component analysis of soil improvement effect of 42 L. chinensis germplasms.

Trait	Principal Component
	1	2	3
TN	0.925	0.030	0.260
TP	−0.236	0.896	0.062
TK	0.872	0.022	−0.041
AN	0.916	0.026	0.173
AP	0.676	0.514	−0.143
AK	0.705	−0.339	−0.243
OM	0.944	0.008	0.188
pH	−0.286	−0.086	0.905
Eigenvalue	4.437	1.192	1.036
Contribution rate %	55.467	14.898	12.954
Cumulative contribution rate %	55.467	70.365	83.319

).

According to the factor score coefficient matrix and its corresponding principal component, three principal component factor scores were calculated. The formula used was as follows:

F₁ = 0.439X₁ − 0.112X₂ + 0.414X₃ + 0.435X₄ + 0.321X₅ + 0.335X₆ + 0.448X₇ − 0.136X₈;

(9)

F₂ = 0.027X₁ + 0.821X₂ + 0.020X₃ + 0.024X₄ + 0.471X₅ − 0.310X₆ + 0.007X₇ − 0.079X₈;

(10)

F₃ = 0.255X₁ + 0.061X₂ − 0.040X₃ + 0.170X₄ − 0.140X₅ − 0.239X₆ + 0.185X₇ + 0.889X₈;

(11)

where X₁−X₈ represent the standardized soil TN, TP, TK, AN, AP, AK, OM, and pH, respectively. They were substituted into the expression and used to calculate the scores of each principal component. The variance contribution rate of each principal component was used as the weight for the weighted summing of the extracted scores to obtain the comprehensive scores of soil improvement effects of 42 L. chinensis germplasms from different geographical sources (Supplementary Materials, Schedule S3). The top ten L. chinensis were LC07, LC22, LC16, LC04, LC36, LC18, LC03, LC29, LC02, and LC09. The bottom ten L. chinensis were LC12, LC17, LC40, LC01, LC25, LC39, LC37, LC35, LC26, and LC13.

3.5. Comprehensive Evaluation of Ecological Functional Traits of L. chinensis Compared with Drought Tolerance, Rhizome Space Expansion Ability, and Soil Improvement Effect

First, the principal components of the comprehensive evaluation were compared with the principal components of drought tolerance (Table 6), rhizome space expansion ability, and soil improvement effect (Table 7). The first principal component in the comprehensive evaluation was the same as all the indices of rhizome space expansion ability, namely NED, MED, AED, ED, and EA. The second principal component in the comprehensive evaluation was the same as the first principal component of the soil improvement effect, namely TN, TP, AN, AP, and OM. The contribution rate of the first two principal components reached 39.101%. The findings indicate that the rhizome space expansion ability and soil improvement effect are important indicators in the comprehensive evaluation of L. chinensis.

Second, the comprehensive evaluation rankings were compared with drought tolerance, rhizome space expansion ability, and soil improvement effect (

Table 8. Comprehensive evaluation of ecological functional traits of 42 L. chinensis germplasms and comparison of drought tolerance, rhizome spatial expansion ability, and soil improvement effect.

Code	Ecological Functional Traits		Drought Tolerance		Rhizome Space Expansion		Soil Improvement Effects
	F	Rank	D value	Rank	D value	Rank	F	Rank
LC01	−0.156	25	0.431	19	0.181	35	−1.696	39
LC02	−0.226	29	0.288	41	0.325	26	0.881	9
LC03	−0.040	19	0.258	42	0.435	20	1.023	7
LC04	0.615	5	0.416	23	0.633	8	2.397	4
LC05	0.299	9	0.560	7	0.118	41	0.825	11
LC06	0.080	15	0.519	11	0.225	31	−0.979	32
LC07	1.436	1	0.804	1	0.478	17	4.155	1
LC08	−0.138	23	0.509	12	0.156	37	−0.588	29
LC09	0.019	17	0.441	17	0.191	33	0.831	10
LC10	−0.292	34	0.337	35	0.424	21	−0.003	18
LC11	−0.094	20	0.455	13	0.528	12	−0.257	21
LC12	−0.484	40	0.293	40	0.611	9	−2.080	42
LC13	0.237	11	0.453	14	0.763	2	−1.144	33
LC14	−0.112	21	0.371	31	0.507	16	−0.588	28
LC15	0.601	6	0.573	5	1.000	1	0.244	17
LC16	0.643	3	0.421	22	0.763	3	2.541	3
LC17	−0.418	38	0.452	15	0.136	39	−1.808	41
LC18	0.368	7	0.533	10	0.394	22	1.856	6
LC19	0.715	2	0.565	6	0.759	4	0.368	14
LC20	0.620	4	0.692	3	0.512	14	−0.349	23
LC21	−0.231	30	0.374	29	0.178	36	−0.346	22
LC22	0.217	12	0.332	36	0.678	5	3.058	2
LC23	−0.128	22	0.429	20	0.122	40	−0.374	24
LC24	−0.034	18	0.535	9	0.380	23	−0.922	31
LC25	−0.272	33	0.383	26	0.438	19	−1.671	38
LC26	0.244	10	0.541	8	0.458	18	−1.426	34
LC27	0.333	8	0.583	4	0.243	29	0.359	15
LC28	−0.159	26	0.359	33	0.343	24	0.271	16
LC29	−0.234	31	0.372	30	0.238	30	0.905	8
LC30	−0.373	36	0.298	38	0.542	10	0.733	12
LC31	−0.180	27	0.450	16	0.533	11	−0.588	27
LC32	−0.343	35	0.314	37	0.659	7	−0.113	20
LC33	−0.236	32	0.405	25	0.525	13	−0.732	30
LC34	0.100	13	0.696	2	0.297	28	−0.518	26
LC35	−0.413	37	0.406	24	0.311	27	−1.476	35
LC36	−0.155	24	0.379	27	0.145	38	2.120	5
LC37	−0.667	42	0.295	39	0.183	34	−1.534	36
LC38	−0.220	28	0.435	18	0.000	42	0.524	13
LC39	−0.435	39	0.346	34	0.330	25	−1.607	37
LC40	−0.627	41	0.376	28	0.214	32	−1.788	40
LC41	0.057	16	0.364	32	0.662	6	−0.472	25
LC42	0.082	14	0.421	21	0.508	15	−0.032	19

). LC07 and LC18 had strong drought tolerance and good soil improvement effects and ranked at the top in the comprehensive ranking. LC15 and LC19 had strong drought tolerance and strong rhizome space expansion ability and also ranked at the top. LC04 and LC16 had strong rhizome space expansion ability and good soil improvement effect and ranked high in the comprehensive ranking. The results also showed that LC12 and LC39 were poor in drought tolerance, soil improvement effect, and comprehensive evaluation. LC17 showed poor performance in rhizome space expansion ability, soil improvement effect, and comprehensive evaluation. Overall, LC37 performed poorly.

Therefore, the L. chinensis germplasms with excellent ecological functional traits were LC07, LC15, LC18, and LC19, and the poor L. chinensis germplasms were LC12, LC17, LC37, and LC39.

4. Discussion

4.1. Comprehensive Evaluation and Methods of L. chinensis and Other Germplasm Resources

It is important for the breeding of L. chinensis to study drought tolerance, rhizome spatial expansion ability, and comprehensive evaluation of the soil improvement effect of L. chinensis germplasm. However, most researchers have focused on single traits, such as drought tolerance [49] and saline-alkali tolerance [50] of L. chinensis, with less attention to the comprehensive evaluation of multidimensional ecological functional traits, such as ‘drought tolerance + rhizome spatial expansion ability + soil improvement effect’. This study used PCA and cluster analysis to comprehensively evaluate a variety of ecological functional traits of 42 L. chinensis germplasms and screened L. chinensis germplasms with excellent ecological functions (LC07, LC15, LC18, and LC19). In a study of saline-alkali tolerance of L. chinensis [51], LC07, LC18, and LC19 showed higher saline-alkali tolerance. In addition, LC15 was a specific strain selected [47]. Therefore, LC07, LC15, LC18, and LC19 could be used as backbone parents in subsequent breeding work.

In the evaluation of germplasm resources, PCA can be used to summarize many indexes that are related to each other with a few comprehensive variables, which can reduce the dimension [52]. Cluster analysis can be classified based on the similarity of the data. Currently, there are many studies on the comprehensive evaluation of these methods [53,54]. At the same time, the comprehensive evaluation of multidimensional germplasm resources has been widely used in a variety of plants. Wang et al. [55] comprehensively evaluated the agronomic traits, chemical composition, green tea quality, and stress resistance of 47 tea germplasms and selected nine excellent varieties. This is consistent with the results of the present study. Li et al. [56] comprehensively analyzed the botanical characteristics, agronomic traits, taste properties, and disease resistance of 45 soybeans. Four germplasm resources with excellent comprehensive traits and high yields were identified by PCA and cluster analysis. Peng et al. [57] evaluated the agronomic and quality traits of 17 pepper germplasms and divided them into four groups and identified high-quality materials. Ma et al. [58] conducted a comprehensive evaluation of the relationship among yield, agronomic traits, and quality traits of six garlic varieties and identified three high-quality materials.

4.2. Relationship between Geographical Factors of Original Habitat and Comprehensive Evaluation of Ecological Functional Traits of L. chinensis Germplasm

L. chinensis, a typical zonal distribution plant, is widely distributed in northern China and Mongolia. This study found that in situ geographical factors play an important role in the evaluation of L. chinensis germplasm resources. Some studies have found that geographical and climatic factors affect the resistance of Oryza meyeriana [59]. Li et al. [60] showed the leaf phenotypic variation of Tetracentron sinense Oliv. is the gradient along longitude, latitude, and altitude. In the evaluation of wild Bermudagrass germplasm resources, there were great differences in morphology and photosynthetic performance at different latitudes and longitudes [61], consistent with the results of this study. This study showed that L. chinensis germplasms with high longitude and low altitude may have better comprehensive performance, whereas L. chinensis germplasms with low longitude and high altitude may have poor comprehensive performance. Wu et al. [62] showed that the production performance of Elymus sibiricus in high-altitude areas was relatively low. Wang et al. [63] found that the leaves were often larger, longer, and thicker under low-altitude conditions. These findings are consistent with the results of the present study. In addition, the results of this study also identify altitude as the key factor affecting the comprehensive evaluation of L. chinensis germplasm, consistent with the results of other studies [64,65,66,67,68].

4.3. Screening of Key Indicators for Comprehensive Evaluation of Ecological Functional Traits of L. chinensis Germplasm

There are many methods for screening comprehensive evaluation indicators, and they have been widely used in research, such as correlation analysis and PCA [69,70], analytic hierarchy process [55], stepwise regression [71], and all-subsets regression method [72]. Compared with stepwise regression, the all-subsets regression method considers more variable combinations, which can ensure that the selected model belongs to the optimal regression model. The results of this study show that PH, LN, NT, MDA, SPAD, SDW, MED, and OM could be used as key indicators for the comprehensive evaluation of ecological functional traits of L. chinensis in the later stage. This could simplify the evaluation of comprehensive ecological functional traits of L. chinensis and reduce the workload of field identification to promote the process of ecological improvement and restoration of degraded grassland. In the identification of multidimensional trait indexes of tea germplasm resources, 29 indices were simplified into 12 key evaluation indices [55]. One study has found that PH and SDW are the key indicators to comprehensively judge the salt tolerance of peanut varieties [73]. Another study found that PH can be used as one of the comprehensive evaluation indicators of low nitrogen tolerance during the entire growth period of rice [74]. In potatoes, it was found that PH and LN can be used as important indicators of drought tolerance. At the same time, MDA was also eliminated in the index screening [75], consistent with the results of this study.

5. Conclusions

In this study, based on the comprehensive evaluation of three ecological functional traits of drought tolerance, rhizome space expansion ability, and soil improvement effect of 42 L. chinensis germplasms from different geographical sources, the germplasm resources with excellent comprehensive traits and good resistance to future changing climate conditions were identified as LC07, LC15, LC18, and LC19. These germplasms could be developed and utilized as the backbone parents for the subsequent breeding of new varieties of L. chinensis. The relationship between the comprehensive evaluation value (F) and geographical factors of the original habitat was analyzed using linear fitting and correlation analysis. The F value was significantly positively correlated with longitude (p < 0.05) and significantly negatively correlated with altitude (p < 0.01). That is, the comprehensive performance of L. chinensis germplasm with high longitude and low altitude was better, and the comprehensive performance of L. chinensis germplasm with low longitude and high altitude was poor. Index screening was performed using the all-subsets regression method. PH, LN, NT, MDA, SPAD, SDW, MED, and OM could be used as key indices to comprehensively evaluate the ecological functional traits of L. chinensis.