Differences of morphological and physiological responses of sainfoin varieties/lines under simulated drought stresses

doi:10.21203/rs.3.rs-3336712/v1

Sainfoin is a very important legume forage that is often planted in arid or semi-arid regions. As herbivorous animal husbandry develops, so does the requirement for sainfoin. However, drought is the main factor limiting the growth of sainfoin. The objective of this study is to explore the response mechanisms of sainfoin varieties/lines to drought stress, which provides the theoretical basis for tolerant breeding of sainfoin. In this study, twenty sainfoin varieties/lines were used as the test materials, the effects of drought stress on growth traits, chlorophyll concentration, leaf water content, osmotic adjustment substances, and antioxidant enzyme activities were determined under simulated levels of drought (− 0.5, − 1.0, − 1.5, and − 2.0 MPa) × exposure times (7 days) in pots. The results showed that leaf area, leaf relative water content, root-shoot ratio, root length, root surface area, root volume, root diameter, root tip number, chlorophyll a content, chlorophyll b content, and catalase activity decreased with increasing drought stress, whereas proline, soluble sugar, soluble protein, and malondialdehyde contents increased in response to drought. Root activity, superoxide dismutase, and peroxidase activities increased first and then decreased and reached the maximum at − 1. 0 MPa. Cluster analysis showed that 20 sainfoin varieties/lines could be divided into 3 categories: (1) High tolerance: new line of P1, Onobrychis viciaefolia ‘Gansu’, code of 1626, and the new line of P2; (2) Moderate tolerance: code of 2323-1, the new line of P3, codes of 2323-2, 2208, 2559, 1668, 7456, 13523, 9977, 13709, 03-132 and 1; and (3) Low tolerance: codes of 2049, 9602, 6, and 7. The findings of these results will help to select the most suitable materials for sainfoin and provide some theoretical basis for cultivating new varieties.

Sainfoin

Drought stress

Physiological difference

Cluster analysis

Comprehensive evaluation

Sainfoin (Onobrychis viciaefolia) is mainly distributed in North Africa, West Asia, Central Asia and Europe (Huang et al. 2023). It is a widely cultivated forage crop in arid and semi-arid areas with rich nutrients such as protein, amino acids (Carbonero et al. 2011), crude fat, and crude fiber, and it has numerous superior traits like wide adaptability, high yield, and high tannins, which are used for high-quality feed for ruminants (Cicek et al. 2020). Furthermore, sainfoin can secrete secondary metabolites, such as tannins, flavonoids, and phenolic compounds, which can improve animal health. These metabolites help prevent diseases and pests, reduce bloating, and possess antibacterial properties (Maxin et al. 2020). Although sainfoin exhibits some drought resistance, excessive water deficits can negatively impact its growth and development, leading to significant yield loss and limiting its distribution and promotion. As a result of global warming will exacerbate many areas more frequent and more severe droughts (Zhang et al. 2022). Therefore, there is an increased demand for screening and cultivating new drought-resistant and high-yield varieties suitable for planting (Kim et al. 2023).

The limitation of drought stress on plants is a complex process that depends not only on the degree and duration of stress but also on the developmental stage and sensitivity of plants, leading to significant changes in plant morphology, physiology, biochemistry, and photosynthesis (Alluqmani et al. 2023; Martinez-Goni et al. 2023). Research shows that the plant height, biomass, leaf relative water content (Abdelaal et al. 2022), leaf area and chlorophyll (Chl) content (Roy et al. 2021) decreased gradually with the extension of drought stress time. The reduced availability of moisture has detrimental effects on photosynthetic pigments, as it damages both the photosynthetic machinery and the thylakoid membranes (Abdelhakim et al. 2021). Eventually, the growth of root, stems and leaves of plants is inhibited, resulting in a decrease in plant yield. Additionally, the stress factors of drought induce similar cell physiological changes and alter the internal structure of plants (AlKahtani et al. 2021). Drought stress leads to secondary stress induced by reactive oxygen species (ROS), which is the main cause of oxidative damage to plants and changes the biosynthetic pathway secondary metabolites (Rezende-Silva et al. 2022). When the amount of ROS in plants exceeds their defense mechanisms, it can result in several negative effects such as lipid peroxidation, protein degradation, enzyme inactivation, alteration of the intrinsic properties of biomolecules, and ultimately cell death (Kurutas 2016).

In order to avoid the adverse effects of drought stress, plants have different degrees of changes in morphological characteristics, structure, physiological function, photosynthetic mechanism and molecular aspects in response to stress (Jahan et al. 2021; Mehravi et al. 2023; Zheng et al. 2023). Plants can cope with the oxidative damage to their cells caused by drought stress by significantly increasing the activities of superoxide dismutase (SOD), peroxides (POD), catalase (CAT), and other antioxidant enzymes (Ltaief et al. 2023; Popova et al. 2023; Singh et al. 2023). SOD is the first line of defense against elevated ROS levels, which can catalyze the disproportionation of O₂⁻ to produce H₂O₂, while POD and CAT can further convert H₂O₂, which is indispensable for the degradation of ROS under adversity. Together, SOD, POD, and CAT maintain the balance of ROS and preserve the integrity of the membrane system, exerting the great potential of antioxidant enzymes system to improve drought-induced oxidative stress (Abdelaal et al. 2022; Ru et al. 2023; Singh et al. 2023). Maintaining a high level of antioxidative enzyme activities greatly contributes to alleviating drought stress by enhancing the capacity of plants to withstand oxidative damage (Ma et al. 2020). Moreover, under drought stress conditions, cells accumulate osmoregulatory substances such as proline, soluble sugars, and soluble proteins. This accumulation helps maintain physiological functions by reducing the cellular osmotic potential. It also enhances the plants' tolerance to drought stress (Sattar et al. 2020). Therefore, the drought stress resistance of plants is closely related to the ability of antioxidant enzymes to scavenge ROS and the ability of osmotic regulators to reduce damage (Ma et al. 2020). The research shows that plants with strong drought resistance have higher osmotic adjustment substance content and antioxidant enzyme activity under drought conditions (Khan et al. 2021).

Plant roots are highly sensitive to stress signals; any changes in these signals have a direct impact on overall growth (Echeverria et al. 2021). Numerous studies have demonstrated that drought resistance in plants is achieved through two major mechanisms: drought avoidance and drought tolerance. Drought avoidance involves regulating specific morphological structures or growth rates to reduce water loss and enhance water absorption, while drought tolerance involves the regulation of osmosis, osmotic protection, and antioxidant defense systems to ensure plant viability and productivity under water-deficit conditions (Khodabin et al. 2020; Kopecka et al. 2023; Sustek-Sanchez et al. 2023). In actuality, species-to-species variation in drought stress adaptive mechanisms is significant. Solanum tuberosum seedlings enhance their physiological resistance by increasing the activities of antioxidant enzymes, endogenous hormones, and osmotic adjustment compounds (Lal et al. 2022). The response of Nicotiana tabacum to water stress is influenced by leaf structure, such as spongy tissue and palisade tissue, and stomatal conductance (Khan et al. 2023). The response of Helianthus tuberosus to drought stress is primarily influenced by factors such as leaf area, net photosynthetic rate, and stomatal conductance, which factors work together to reduce water evaporation and enhance stress resistance (Darunee et al. 2022). The high protein content and strong antioxidant properties of Medicago sativa resulted in a decrease in the degree of membrane lipid peroxidation and an improvement in its tolerance (Kang et al. 2023).

Plant roots grow the fastest at the seedling stage, which is most sensitive to soil water demand (Jincya et al. 2021). Therefore, simulating drought conditions in seedlings can accurately reflect the response of root characteristics in different sainfoin materials to drought stress. Polyethylene glycol (PEG) is a commonly used osmotic substance for inducing drought stress in plants by reducing the amount of available water in their roots. Specifically, PEG-6000 with a molecular weight equal to or greater than 6000 is a popular option due to its non-toxic, non-absorbable, and non-metabolized properties (Gong et al. 2022; Qi et al. 2023). Previous research has shown that PEG solutions are a more appropriate method for simulating water deficit in plants and evaluating their potential drought tolerance throughout the entire experimental period (Batool et al. 2022; Li et al. 2022; Wang et al. 2022).

Germplasm resources of sainfoin are scarce in China. By now 3 sainfoin varieties have been officially recognized, of which there are 2 local breeds and 1 domestic foster breed. Sainfoin is mainly distributed in arid and semi-arid regions in China. However, there are few studies on drought tolerance of sainfoin varieties/lines. Therefore, assessing and effectively utilizing these sainfoin resources is important to our goal of developing varieties. It is critical that researchers collect, preserve, and screen for that ideal characteristic to facilitate regional recommendations for proper sainfoin. Here, we examined the seedlings of 20 sainfoin varieties/lines to determine their relative drought tolerance based on growth parameters, morphological indexes, and physiological characteristics.

Plant materials

Sixteen sainfoin lines (codes of 1, 7456, 9602, 2049, 2208, 7, 6, 1668, 2559, 13523, 03-132, 9977, 1626, 2323-2, 13709, and 2323-1) from Russian gene banks and 4 varieties/lines from China (Onobrychis viciaefolia ‘Gansu’ or GS, new lines of P1, P2, and P3 from Gansu Agricultural University, China). The experimental site is located at the Forage Experimental Station, Gansu Agricultural University, Huangyang town, Gansu, China (longitude: 102.40 E; latitude: 37.55 N). The experiment was run on 18 April 2021 using a randomized complete block (RCB) design with four replications. The seeds used in the experiment were collected in June 2020.

Experimental conditions

The first step in this process was to surface sterilize the seeds were sterilized by using 30% H₂O₂ solution and then washed with sterilized distilled water. Any excess surface water was wiped away before planting the seeds in nutritional pots that were 20 cm in depth and 9.2 cm in diameter. The pots were filled with 1 kg of sterilized river sand and placed in a growth chamber with carefully controlled conditions. The plants were exposed to a consistent day/night cycle of 16/8 h, and temperatures were kept at 25/20℃. Also, relative humidity was maintained at 60 ± 5%, and the plants were exposed to 450 µmol m^− 2 s^− 1 of photosynthetic photon flux density. Full-strength Hoagland’s nutrient solution will be poured and renewed every 48 h. The air pump forced the air into the nutrient solution to provide oxygen and mix it to ensure uniform nutrient distribution.

Stress treatments

After 8 weeks of culture, the seedlings of the tested sainfoin varieties/lines were parted into different groups. One group was subjected to gradual drought stress passed adding PEG-6000 solutions to different osmotic potentials of − 0.5, − 1.0, − 1.5, and − 2.0 MPa to full-strength of Hoagland's solution (Hoagland 1950). The other group was well watered without adding PEG-6000, which was fully immersed in the full strength of Hoagland's solution.

Measurement of growth indices

After 7 d of treatment, the leaf area was measured using a portable area meter (LI-3000C, The United States of America) by selecting the third fully developed leaf from the top of the plant (n = 10). After cutting off their shoots from the surface, the length and fresh weight of the samples were measured. Subsequently, the samples were dried in an oven at 65°C until reaching a constant weight, at which point their dry weights were measured. The ratio of the biomass of the roots and the shoots was used to calculate the root-shoot ratio (in terms of dry weight). Roots were washed with distilled water, following which the root surface area, average diameter, length, volume, and root tip number were measured using a Scan Wizard EZ root scanner (Epson Expression 10000XL, Seiko Epson Company, Japan).

Measurement of physiological parameters

The treated plants were washed with distilled water and stored at − 80°C for physiological analysis. The relative water content (RWC) was calculated by the following formula (Liu et al. 2023),\(RWC\left(\%\right)=\left(FW-DW\right)/\left(SW-MD\right)\times 100\). We selected leaves from the seedling, which measured the fresh weight (FW) and were soaked in distilled water for 24 hours, saturated fresh weight (SW) was determined, then, after drying at 65°C to the constant weight, we determined the dry matter mass (DM).

The content of Chl a and Chl b was determined by the acetone ethanol extraction method described by Lichtenthaler and Wellburn (Lichtenthaler et al. 1983). According to the method of Clemensson-Lindell (Clemensson-Lindell 1994), the 0.5 gram root tip was weighed, and the root activity was determined by the 2, 3, 5-triphenyl tetrazolium chloride (TTC) method.

Proline content was determined by acid ninhydrin colorimetry on the basis of the Bates et al (Bates et al. 1973) method. The root tissue was taken 0.5 g with 5 mL sulfosalicylic acid solution and heat in water bath for 10 minutes, then added 2 mL of supernatant to 2 mL of glacial acetic acid and acidic ninhydrin in glass tube. After boiling at 100°C and cooling to room temperature, 4 mL toluene was added to extract. The absorbance of the supernatant was determined at 520 nm. Malondialdehyde content (MDA) was determined according to Heath (Heath et al. 1968) with a method described. The roots of 0.5 g samples were homogenated in 5% (w/v) TCA, and then turned into a centrifuge tube at 3000 r/min for 20 min. The supernatant was taken out and 2 ml of the same volume of 0.67% (w/v) TBA was added, the mixture was boiled at 100°C boiling water bath for 30 min, then quickly cooled with cold water and centrifuged. The supernatant was taken at 450, 532 and 600 nm to measure the absorbance. The content of soluble sugar was determined with anthrone colorimetry according to Ebell (Ebell 1969). The 0.5 g sample and 4 mL 80% ethanol was added, which placed in 80°C water bath and stirred continuously for 40 min and centrifuged at 12000 g for 10 min. We collected the supernatant and took 1 mL of the alcohol extract, added 5 mL of anthrone reagent, boiled in boiling water for 10 min, took out and cool. The supernatant values were measured at 625 nm. The sugar content in the extract was obtained from the standard curve. The content of soluble protein was determined by the Coomassie Brilliant Blue method according to Bradford (Bradford 1976). The roots of 0.5 g samples were homogenated in PBS (pH = 7.8), The homogenate was turned into a centrifuge tube at 4°C and 12000 g for 20 min. The 100 µL enzyme solution was added into 2.9 mL Coomassie Brilliant Blue solution, and the absorbance was measured after 2 min reaction at 595 nm.

The root samples with 0.5 gram were placed in pre-cooled mortars, and 5 ml sodium phosphate buffer (PBS, pH = 7.8) was added to the ice bath to grind into homogenate. The mixture was transferred into the centrifuge tubes and centrifuged at 4°C, 15000 g for 20 min, which supernatant was collected and stored in separate containers used for the analysis of the enzyme activities of SOD, POD and CAT. The determination of SOD activity was conducted using the NBT method (Giannopolitis CN et al. 1977). The 3 mL volume contained a reaction mixture comprising of the supernatant (50 µL), 0.05 mM PBS (pH = 7.8), H₂O, 130 mM Met, 750 µM NBT, 100 µM EDTA, and 20 µM riboflavin. In this method, the amount of enzyme necessary to achieve a 50% inhibition of NBT reduction, as monitored at a wavelength of 560 nm, was defined as one unit of SOD. To assess CAT activity, the method described by Hadwan was employed (Hadwan 2018). To commence the reaction, 50 µL of enzyme solution was introduced into a reaction mixture consisting of 2.9 mL (50 mM PBS, 0.1 mM EDTA, pH = 7.8). After preheating for 5 minutes in a 25°C water bath, an additional 50 µL of 19 mM H₂O₂ was added. The objective of the experiment was to measure the rate of decline in absorbance at 240 nm per minute, indicative of H₂O₂ consumption. The assessment of the rate of guaiacol oxidation in the presence of H₂O₂ established the activity of the POD, which involved continuous monitoring of the absorption change at 470 nm on a per minute basis. The reaction solution, consisting of 3 mL, comprised 100 mM PBS, 20 mM guaiacol, and 40 mM H₂O₂. Prior to initiating the reaction, the reaction mixture of 2.9 mL was heated for 5 minutes in a water bath at 25°C. Subsequently, the enzyme solution (100 µL) was introduced to commence the reaction (Sigrid K et al. 1990).

Statistical analysis

To eliminate the inherent differences and reflect the drought resistance of twenty sainfoin varieties/lines, the results presented are from relative value (measured value of each index under simulated drought stress/control) (n = 3). Cluster and principal component analysis (PCA) between plant growth parameters and physiological traits of the tested sainfoin varieties/lines were analyzed using SPSS 24.0 software. Correlation analysis was performed using Origin 2022 software. The comprehensive evaluation of sainfoin varieties/lines under drought resistance was performed by subordinate functional analysis.

The calculation formula of each principal component \(U\left({X }_{i}\right)=({X}_{i}-{X}_{min} )⁄({X}_{max}-{X}_{min} )\), the formula used to calculate the indicators that are negatively correlated (MDA) with drought stress \(U\left({X}_{i} \right)=1-({X}_{i}-{X}_{min} )⁄({X}_{max}-{X}_{min} )\), where ‘i’ represents a material, ‘j’ represents an index, ‘U’ is the membership function value, ‘X’ is the index measurement value, X_jmin is the minimum value measured in ‘j’ index. X_jmax is the maximum value measured in ‘j’ index. Index weight \(\left({W}_{i}\right)={F}_{i}/\sum {F}_{i}\) (i=1, 2, n), where W_i is the importance of the ‘i’ comprehensive index in all comprehensive indexes, that is the weight. F_i is the ‘i’ comprehensive index contribution rate of each material. The values drought tolerance comprehensive evaluation \(D=\sum \left(U\left({X}_{i}\right)\times {W}_{i}\right)\) (Tian et al. 2015).

Effects of drought stress on morphological characters of sainfoin seedlings

The relative values of leaf area, root-shoot ratio, root length, root surface area, root diameter, root volume, and root tip number ranged from 0.268 to 0.975, 0.436 to 0.979, 0.550 to 0.987, 0.480 to 0.971, 0.683 to 0.972, 0.449 to 0.933, 0.282 to 0.962 from − 0.5 to − 2.0 MPa, respectively (Table 1). It is thus clear that drought stress inhibited the growth of twenty sainfoin varieties/lines, but the degree of inhibition differed among varieties/lines. After 7 d of treatment, leaf area, root-shoot ratio, root length, root surface area, root volume, root diameter, and root tip number were decreased when compared with their controls. Under − 2.0 MPa, the morphological characteristics of twenty sainfoin varieties/lines were lowest.

Table 1

Relative values of simulated drought stress on morphological characters of twenty sainfoin varieties/lines after 7 d of treatment
Varieties /lines	Treatment / MPa	LA	R/T	RL	RTSA	ARD	RV	RTN
1	− 0.5	0.932	0.901	0.889	0.923	0.956	0.871	0.913
	− 1.0	0.899	0.772	0.799	0.877	0.912	0.773	0.691
	− 1.5	0.831	0.729	0.653	0.672	0.859	0.635	0.623
	− 2.0	0.726	0.592	0.649	0.647	0.794	0.449	0.446
7456	− 0.5	0.973	0.883	0.913	0.949	0.949	0.855	0.847
	− 1.0	0.651	0.852	0.817	0.796	0.916	0.701	0.652
	− 1.5	0.552	0.815	0.708	0.639	0.835	0.658	0.529
	− 2.0	0.523	0.682	0.614	0.560	0.725	0.570	0.432
9602	− 0.5	0.888	0.940	0.935	0.884	0.956	0.809	0.780
	− 1.0	0.820	0.840	0.807	0.760	0.913	0.676	0.654
	− 1.5	0.655	0.614	0.720	0.642	0.852	0.632	0.522
	− 2.0	0.605	0.585	0.638	0.505	0.752	0.558	0.402
2049	− 0.5	0.805	0.925	0.901	0.918	0.958	0.882	0.802
	− 1.0	0.772	0.863	0.777	0.798	0.908	0.822	0.637
	− 1.5	0.710	0.806	0.667	0.717	0.763	0.608	0.585
	− 2.0	0.450	0.595	0.595	0.603	0.729	0.543	0.448
P1	− 0.5	0.795	0.910	0.903	0.900	0.953	0.930	0.892
	− 1.0	0.751	0.803	0.757	0.725	0.903	0.682	0.715
	− 1.5	0.724	0.701	0.624	0.635	0.860	0.592	0.496
	− 2.0	0.692	0.686	0.550	0.581	0.760	0.532	0.391
2208	− 0.5	0.975	0.941	0.903	0.887	0.949	0.820	0.962
	− 1.0	0.949	0.875	0.731	0.769	0.906	0.685	0.715
	− 1.5	0.804	0.807	0.601	0.650	0.796	0.635	0.528
	− 2.0	0.590	0.762	0.568	0.584	0.709	0.514	0.399
7	− 0.5	0.785	0.939	0.917	0.961	0.966	0.871	0.879
	− 1.0	0.758	0.903	0.789	0.891	0.929	0.675	0.701
	− 1.5	0.686	0.886	0.690	0.881	0.819	0.581	0.617
	− 2.0	0.672	0.779	0.622	0.767	0.716	0.499	0.434
6	− 0.5	0.752	0.936	0.907	0.931	0.932	0.860	0.828
	− 1.0	0.748	0.733	0.807	0.841	0.873	0.757	0.601
	− 1.5	0.691	0.732	0.713	0.825	0.843	0.614	0.500
	− 2.0	0.642	0.671	0.588	0.710	0.727	0.569	0.443
GS	− 0.5	0.903	0.886	0.922	0.886	0.948	0.827	0.941
	− 1.0	0.732	0.854	0.750	0.764	0.901	0.812	0.741
	− 1.5	0.616	0.805	0.649	0.740	0.811	0.689	0.530
	− 2.0	0.586	0.744	0.578	0.658	0.724	0.586	0.378
1668	− 0.5	0.801	0.933	0.930	0.971	0.957	0.933	0.821
	− 1.0	0.749	0.870	0.777	0.911	0.918	0.691	0.664
	− 1.5	0.593	0.810	0.730	0.808	0.837	0.597	0.580
	− 2.0	0.558	0.669	0.641	0.703	0.747	0.478	0.465
2559	− 0.5	0.879	0.973	0.910	0.914	0.961	0.825	0.824
	− 1.0	0.871	0.939	0.795	0.734	0.903	0.783	0.595
	− 1.5	0.825	0.931	0.675	0.633	0.854	0.674	0.456
	− 2.0	0.675	0.759	0.606	0.597	0.736	0.562	0.393
13523	− 0.5	0.832	0.979	0.905	0.917	0.945	0.869	0.767
	− 1.0	0.806	0.830	0.785	0.769	0.919	0.713	0.571
	− 1.5	0.777	0.712	0.693	0.684	0.818	0.586	0.551
	− 2.0	0.478	0.436	0.636	0.604	0.709	0.514	0.423
P2	− 0.5	0.945	0.912	0.916	0.921	0.964	0.784	0.811
	− 1.0	0.905	0.787	0.802	0.800	0.916	0.668	0.553
	− 1.5	0.780	0.777	0.650	0.722	0.843	0.591	0.415
	− 2.0	0.587	0.713	0.581	0.625	0.778	0.544	0.373
03-132	− 0.5	0.957	0.915	0.932	0.962	0.959	0.829	0.907
	− 1.0	0.888	0.882	0.769	0.820	0.916	0.665	0.665
	− 1.5	0.617	0.767	0.697	0.706	0.840	0.539	0.643
	− 2.0	0.588	0.740	0.641	0.598	0.741	0.473	0.366
9977	− 0.5	0.831	0.943	0.901	0.900	0.972	0.763	0.735
	− 1.0	0.815	0.935	0.761	0.721	0.907	0.637	0.466
	− 1.5	0.562	0.798	0.695	0.651	0.825	0.550	0.433
	− 2.0	0.463	0.723	0.591	0.480	0.683	0.501	0.358
1626	− 0.5	0.788	0.921	0.965	0.941	0.964	0.898	0.722
	− 1.0	0.625	0.883	0.823	0.791	0.897	0.880	0.435
	− 1.5	0.507	0.798	0.695	0.700	0.836	0.658	0.357
	− 2.0	0.501	0.607	0.627	0.618	0.741	0.563	0.282
2323-2	− 0.5	0.798	0.945	0.903	0.893	0.968	0.822	0.878
	− 1.0	0.754	0.863	0.805	0.744	0.905	0.719	0.674
	− 1.5	0.730	0.743	0.737	0.666	0.818	0.529	0.501
	− 2.0	0.497	0.601	0.648	0.580	0.708	0.466	0.375
P3	− 0.5	0.729	0.877	0.878	0.901	0.965	0.851	0.854
	− 1.0	0.576	0.821	0.810	0.736	0.889	0.673	0.608
	− 1.5	0.517	0.760	0.723	0.646	0.842	0.621	0.485
	− 2.0	0.268	0.708	0.658	0.579	0.731	0.535	0.387
13709	− 0.5	0.930	0.913	0.913	0.926	0.970	0.902	0.811
	− 1.0	0.821	0.870	0.814	0.797	0.925	0.850	0.565
	− 1.5	0.482	0.859	0.725	0.705	0.882	0.710	0.508
	− 2.0	0.423	0.790	0.657	0.598	0.754	0.465	0.356
2323-1	− 0.5	0.891	0.949	0.987	0.880	0.953	0.871	0.851
	− 1.0	0.841	0.783	0.846	0.717	0.896	0.730	0.737
	− 1.5	0.773	0.750	0.754	0.663	0.826	0.601	0.506
	− 2.0	0.744	0.678	0.647	0.579	0.763	0.476	0.348
CK, without adding PEG-6000. Drought stress treatment, adding PEG-6000 with osmotic potentials of − 0.5, − 1.0, − 1.5, and − 2.0 MPa. Relative value of each index=measured value of each index under simulated drought stress/CK. Numbers along left-hand side match sainfoin codes explained in 2.1 Plant materials. LA: Leaf area; R/T: Root-shoot ratio; RL: Root length; RTSA: Root total surface area; ARD: Average root diameter; RV: Root volume; RTN: Root tip number.

Effects of drought stress on physiological indices of sainfoin seedlings

After 7 d of exposure to drought stress, the relative values of root activity, Chl a, Chl b, free proline, leaf water content, MDA, soluble sugar, and soluble protein contents, SOD, POD, and CAT activities ranged from 0.279 to 7.669, 0.349 to 0.979, 0.298 to 0.993, 1.213 to 14.671, 0.543 to 0.992, 1.039 to 5.083, 1.059 to 2.341, 1.011 to 1.799, 0.712 to 2.370, 1.002 to 4.136, 0.522 to 2.568 from − 0.5 to − 2.0 MPa, respectively (Table 2). When compared with the control seedlings, proline, MDA, soluble sugar, and soluble protein were increased and peaked under − 2.0 MPa, whereas Chl a, Chl b, and CAT were reduced and obtained the minimum under − 2.0 MPa, albeit not to the same extent for twenty sainfoin varieties/lines. During water stress, root activity, SOD and POD activities increased firstly, reaching the peak under − 1.0 MPa, then decreased.

Table 2

Relative values of simulated drought stress on physiological indices of twenty sainfoin varieties/lines after 7 d of treatment
Varieties/lines	Treatment / MPa	RA	RWC	Chl a	Chl b	Pro	MDA	SS	SP	SOD	POD	CAT
1	− 0.5	1.053	0.862	0.979	0.939	2.092	1.900	1.066	1.102	1.219	1.883	1.370
	− 1.0	2.415	0.848	0.878	0.740	3.876	3.075	1.268	1.122	1.783	2.204	1.569
	− 1.5	0.411	0.768	0.712	0.700	5.721	4.070	1.496	1.348	1.375	2.112	1.485
	− 2.0	0.279	0.714	0.597	0.500	8.263	5.083	1.905	1.520	1.092	1.861	1.236
7456	− 0.5	1.904	0.834	0.899	0.893	2.375	1.127	1.059	1.097	1.063	2.298	1.093
	− 1.0	0.876	0.781	0.831	0.669	4.637	1.174	1.461	1.314	1.311	2.308	1.715
	− 1.5	0.638	0.711	0.653	0.521	7.986	1.201	1.875	1.513	1.065	2.075	1.389
	− 2.0	0.353	0.672	0.417	0.368	11.112	1.613	1.961	1.525	0.712	1.762	1.260
9602	− 0.5	2.322	0.985	0.926	0.798	2.042	1.565	1.170	1.103	1.681	3.046	1.506
	− 1.0	5.619	0.920	0.801	0.690	3.389	1.578	1.701	1.272	1.928	3.397	1.776
	− 1.5	1.743	0.897	0.661	0.619	4.842	1.710	2.074	1.527	1.880	2.693	1.528
	− 2.0	1.529	0.759	0.480	0.542	8.015	2.499	2.341	1.603	0.967	1.360	1.295
2049	− 0.5	1.219	0.958	0.925	0.683	2.053	1.240	1.186	1.186	1.285	2.685	1.443
	− 1.0	3.279	0.924	0.802	0.566	3.349	1.500	1.471	1.313	2.164	3.355	1.471
	− 1.5	1.213	0.876	0.618	0.481	6.214	1.512	1.720	1.533	1.762	2.641	1.185
	− 2.0	0.437	0.716	0.447	0.410	9.596	1.778	2.019	1.799	1.056	2.273	1.121
P1	− 0.5	1.553	0.985	0.963	0.849	2.136	1.341	1.187	1.134	1.144	1.262	1.424
	− 1.0	3.098	0.894	0.875	0.658	2.775	1.417	1.320	1.211	1.704	2.947	1.528
	− 1.5	1.019	0.878	0.692	0.572	4.521	1.502	1.466	1.298	1.281	1.899	1.503
	− 2.0	0.509	0.686	0.568	0.394	7.523	1.713	1.663	1.421	0.860	1.829	1.394
2208	− 0.5	1.523	0.931	0.973	0.979	1.780	1.141	1.079	1.193	1.233	1.738	1.280
	− 1.0	2.864	0.909	0.851	0.834	4.103	1.428	1.172	1.216	1.518	3.369	1.304
	− 1.5	0.968	0.815	0.716	0.492	8.454	1.461	1.255	1.236	1.269	1.974	1.232
	− 2.0	0.918	0.693	0.586	0.467	12.114	1.652	1.635	1.539	1.022	1.002	1.068
7	− 0.5	1.707	0.992	0.959	0.993	1.975	1.181	1.184	1.011	1.089	1.716	1.870
	− 1.0	2.995	0.932	0.889	0.907	3.429	1.493	1.285	1.155	1.390	2.004	1.538
	− 1.5	1.972	0.855	0.805	0.702	5.807	1.630	1.547	1.311	1.172	1.605	1.251
	− 2.0	0.983	0.746	0.589	0.708	9.016	1.823	1.869	1.404	1.125	1.124	0.857
6	− 0.5	1.328	0.843	0.888	0.823	1.850	1.066	1.181	1.230	1.386	2.830	2.212
	− 1.0	3.704	0.832	0.847	0.754	3.327	1.163	1.274	1.425	1.583	4.089	2.147
	− 1.5	1.199	0.824	0.747	0.581	6.866	1.348	1.537	1.654	1.427	2.323	1.369
	− 2.0	0.764	0.646	0.508	0.340	9.790	1.618	1.765	1.714	1.513	1.963	1.140
GS	− 0.5	5.135	0.814	0.965	0.927	2.153	1.261	1.224	1.118	1.786	1.986	1.953
	− 1.0	7.669	0.747	0.891	0.725	4.149	1.419	1.273	1.142	2.064	2.061	1.717
	− 1.5	4.086	0.685	0.712	0.686	7.126	1.630	1.532	1.427	1.917	1.699	1.668
	− 2.0	1.694	0.670	0.598	0.620	12.006	2.166	1.960	1.498	1.644	1.262	0.770
1668	− 0.5	1.234	0.953	0.969	0.954	2.119	1.337	1.240	1.111	1.327	1.302	2.088
	− 1.0	2.766	0.861	0.859	0.800	3.024	1.503	1.250	1.534	1.554	1.621	1.489
	− 1.5	1.337	0.847	0.648	0.707	7.295	1.586	1.296	1.615	1.499	1.299	1.175
	− 2.0	1.143	0.665	0.512	0.320	9.656	2.277	1.620	1.693	1.433	1.238	0.936
2559	− 0.5	1.414	0.919	0.942	0.929	1.736	1.276	1.112	1.084	1.082	1.234	2.354
	− 1.0	2.272	0.875	0.823	0.667	3.441	1.467	1.146	1.221	1.568	1.933	2.150
	− 1.5	0.868	0.806	0.586	0.630	5.268	1.807	1.374	1.499	1.196	1.691	1.666
	− 2.0	0.494	0.692	0.508	0.454	7.424	2.129	1.484	1.562	1.505	1.237	1.331
13523	− 0.5	1.364	0.934	0.929	0.879	2.195	1.303	1.168	1.132	1.052	2.174	2.568
	− 1.0	3.609	0.851	0.876	0.713	3.683	1.458	1.459	1.158	1.486	2.689	2.174
	− 1.5	1.780	0.835	0.626	0.638	6.380	1.613	1.589	1.389	1.083	2.464	1.638
	− 2.0	1.166	0.749	0.510	0.508	11.088	2.656	1.958	1.421	1.300	1.939	1.296
P2	− 0.5	3.472	0.881	0.949	0.721	1.741	1.555	1.652	1.081	1.038	1.757	2.487
	− 1.0	7.187	0.837	0.878	0.663	4.900	2.172	1.705	1.170	1.281	1.992	2.061
	− 1.5	4.547	0.620	0.729	0.463	9.165	2.268	1.754	1.390	1.145	1.431	1.875
	− 2.0	2.038	0.543	0.606	0.302	12.427	2.405	2.031	1.468	1.078	1.113	0.806
03-132	− 0.5	1.450	0.928	0.915	0.938	1.960	1.235	1.084	1.045	1.214	2.729	1.314
	− 1.0	3.820	0.829	0.802	0.674	3.122	1.513	1.181	1.124	1.383	3.472	1.267
	− 1.5	1.810	0.818	0.638	0.529	10.069	1.678	1.383	1.320	1.298	1.548	1.086
	− 2.0	0.813	0.577	0.561	0.460	12.428	2.237	1.615	1.483	1.301	1.139	0.638
9977	− 0.5	2.656	0.987	0.871	0.822	2.085	1.387	1.097	1.109	1.138	1.830	1.179
	− 1.0	3.707	0.840	0.805	0.756	5.937	1.511	1.142	1.285	1.687	4.136	1.023
	− 1.5	1.999	0.745	0.611	0.591	10.304	1.597	1.173	1.541	1.384	1.720	0.651
	− 2.0	1.420	0.622	0.449	0.424	14.671	2.218	1.328	1.658	1.180	1.004	0.575
1626	− 0.5	2.302	0.884	0.967	0.741	1.500	1.295	1.145	1.164	1.658	1.643	1.651
	− 1.0	4.629	0.832	0.854	0.696	3.003	1.390	1.318	1.195	1.861	2.917	1.525
	− 1.5	2.963	0.803	0.671	0.578	8.481	1.667	1.455	1.435	1.753	2.075	1.319
	− 2.0	0.783	0.763	0.486	0.451	11.288	1.844	1.664	1.587	1.689	1.418	1.121
2323-2	− 0.5	2.135	0.902	0.924	0.838	1.345	1.334	1.135	1.072	1.480	1.463	1.122
	− 1.0	4.505	0.830	0.820	0.796	3.082	1.551	1.213	1.088	1.778	1.556	1.147
	− 1.5	3.067	0.693	0.671	0.692	6.367	1.661	1.265	1.278	1.721	1.385	0.953
	− 2.0	1.961	0.681	0.523	0.463	8.718	2.589	1.539	1.379	1.628	1.311	0.667
P3	− 0.5	2.108	0.965	0.971	0.882	1.213	1.052	1.097	1.061	1.622	2.072	1.034
	− 1.0	5.254	0.836	0.840	0.813	3.753	1.202	1.168	1.208	2.209	2.881	0.949
	− 1.5	2.423	0.689	0.632	0.577	6.173	1.359	1.217	1.371	2.027	2.308	0.888
	− 2.0	1.411	0.636	0.415	0.305	10.075	1.599	1.495	1.593	1.978	1.682	0.522
13709	− 0.5	1.504	0.919	0.894	0.909	1.892	1.039	1.164	1.172	1.584	2.138	2.161
	− 1.0	2.767	0.899	0.808	0.687	3.061	1.101	1.283	1.192	2.370	3.595	1.907
	− 1.5	1.742	0.707	0.613	0.512	4.339	1.324	1.572	1.328	2.018	2.698	1.560
	− 2.0	0.865	0.554	0.401	0.298	7.396	1.355	1.768	1.439	1.898	1.511	1.382
2323-1	− 0.5	2.052	0.945	0.896	0.817	1.523	1.206	1.310	1.043	1.009	2.702	2.176
	− 1.0	4.404	0.925	0.852	0.807	4.178	1.513	1.504	1.170	1.392	3.979	1.806
	− 1.5	2.056	0.846	0.697	0.643	6.935	1.598	1.618	1.271	1.248	3.144	1.759
	− 2.0	1.304	0.686	0.349	0.583	12.651	2.651	1.782	1.378	1.214	2.073	0.824
CK, without adding PEG-6000. Drought stress treatment, adding PEG-6000 with osmotic potentials of − 0.5, − 1.0, − 1.5, and − 2.0 MPa. Relative value of each index=measured value of each index under simulated drought stress/CK. RA: root activity; Chl a: chlorophyll a; Chl b: chlorophyll b; Pro: proline; RWC: leaf relative water content; MDA: malondialdehyde; SS: soluble sugar; SP: soluble protein; SOD: superoxide dismutase; POD: peroxidase; CAT: catalase. Numbers along left-hand side match sainfoin codes explained in 2.1 Plant materials.

Correlation analysis of drought tolerance

Leaf area was significantly positively correlated with root length and root volume, and was highly significantly positively correlated with the root tip number (Fig. 1). Root-shoot ratio exhibited a positive correlation with root total length, root total surface area, average root diameter, root volume and root tip number (Fig. 1). Root length has shown a significant positive correlation with root activity and Chl a, while a very significant positive correlation was found in root total surface area, root average diameter, root volume, root tip number, Chl b and proline (Fig. 1). Chl a was highly significantly positively correlated with Chl b, proline and soluble protein (Fig. 1). The relationship between soluble sugar and soluble protein and catalase was a significant positive correlation (Fig. 1).

Principal component analysis of drought tolerance

The contribution rates of the first five principal components (PC) were 36.08, 13.04, 10.69, 9.66 and 5.83%, respectively (Table 3). Because of the eigenvalues of the five principal components were all greater than 1, indicating that these five principal components contained most of the data, which could be used as an effective factor in data analysis. The PC1 has a higher load capacity in the root activity, root total surface area, the average root diameter and root tip numbers, PC2 has a high load on the CAT and soluble sugar, PC3 has a high load on the MDA and POD, PC4 has a high load capacity on SOD, PC5 has higher load capacity on soluble proteins.

Table 3

The characteristic vector, eigenvalues, contribution rate and cumulative contribution rates of the first five principal components
Index	Principal component
Index	1	2	3	4	5
Leaf area	0.622	0.236	0.026	− 0.002	− 0.262
Root shoot ratio	− 0.213	0.566	− 0.014	0.359	0.010
Root length	0.558	− 0.151	− 0.186	0.579	− 0.189
Root total surface area	0.862	− 0.183	− 0.181	− 0.097	− 0.075
Average root diameter	0.773	0.115	0.182	0.206	− 0.208
Root volume	0.754	− 0.255	− 0.023	0.351	− 0.164
Root tip number	0.768	0.326	− 0.250	0.089	0.204
Root activity	0.903	0.085	− 0.038	− 0.096	− 0.213
Relative water content	0.487	0.441	− 0.206	− 0.357	− 0.177
Chl a	0.682	− 0.386	0.254	− 0.211	0.379
Chl b	0.6403	− 0.348	0.029	− 0.187	0.227
Proline	0.767	− 0.146	0.382	− 0.013	− 0.140
Malondialdehyde	− 0.231	0.131	0.786	0.239	− 0.380
Soluble sugar	0.374	0.722	0.180	0.115	0.345
Soluble protein	0.645	− 0.018	0.419	0.073	0.429
Superoxide dismutase	− 0.261	− 0.057	0.215	0.776	0.282
Peroxidase	− 0.220	− 0.205	0.757	− 0.351	− 0.087
Catalase	0.108	0.809	0.241	− 0.272	0.045
Eigenvalue	6.495	2.347	1.923	1.738	1.050
Contribution rate /%	36.08	13.04	10.69	9.66	5.83
Cumulative contribution rate /%	36.08	49.13	59.81	69.47	75.30

Subordinate function of drought tolerance

Subordinate function values of twenty sainfoin varieties/lines were given in Table 4. The D value of the new line of P1 (2.259) was higher than other varieties/lines, showing that its drought tolerance is the strongest. By contrast, the D values of codes 2049, 9602, 6, and 7 were under 1.800, which were obviously lower than other varieties/lines.

Table 4

The subordinate function value, weight, D value, D mean and sequence of simulated under drought stress of twenty sainfoin varieties/lines after 7 d of treatment.
Varieties /lines	Treatment /MPa	U (X₁)	U (X₂)	U (X₃)	U (X₄)	U (X₅)	D value	D mean	Order
1	− 0.5	3.857	0.999	1.244	1.099	− 0.140	2.328	1.986	11
	− 1.0	3.276	1.403	1.361	1.141	0.108	2.161
	− 1.5	2.675	1.244	1.373	0.909	0.313	1.833
	− 2.0	2.284	1.275	1.339	0.632	0.492	1.624
7456	− 0.5	3.872	0.637	1.063	0.870	− 0.294	2.205	1.839	15
	− 1.0	3.068	1.014	1.488	1.016	0.121	1.997
	− 1.5	2.610	1.057	1.491	0.893	0.231	1.778
	− 2.0	1.914	1.168	1.270	0.499	0.188	1.378
9602	− 0.5	2.846	0.658	1.286	0.829	− 0.208	1.751	1.530	20
	− 1.0	2.579	1.247	1.522	0.631	− 0.055	1.745
	− 1.5	1.837	1.147	1.611	0.713	0.308	1.423
	− 2.0	1.661	1.146	1.086	0.223	0.325	1.202
2049	− 0.5	3.013	1.084	1.074	0.741	− 0.158	1.867	1.580	19
	− 1.0	2.671	1.500	1.209	0.861	0.081	1.828
	− 1.5	1.887	1.261	1.277	0.854	0.217	1.430
	− 2.0	1.536	1.199	1.255	0.316	0.401	1.193
P1	− 0.5	5.495	1.411	0.912	1.077	− 0.124	3.135	2.559	1
	− 1.0	4.471	1.613	1.360	0.941	− 0.003	2.735
	− 1.5	3.521	1.530	1.393	0.917	0.122	2.277
	− 2.0	3.243	1.404	1.466	0.553	0.138	2.087
2208	− 0.5	4.418	1.328	1.110	1.077	− 0.126	2.633	2.141	6
	− 1.0	3.651	1.459	1.451	0.889	− 0.037	2.320
	− 1.5	2.727	1.373	1.269	0.876	0.101	1.845
	− 2.0	2.580	1.396	1.163	0.704	0.402	1.765
7	− 0.5	3.084	1.159	1.106	1.067	− 0.188	1.958	1.658	18
	− 1.0	2.674	0.982	1.174	1.151	− 0.020	1.764
	− 1.5	2.358	0.903	1.153	1.040	0.106	1.592
	− 2.0	1.849	0.772	1.075	0.978	0.282	1.319
6	− 0.5	3.100	0.908	1.291	1.000	0.021	1.956	1.686	17
	− 1.0	2.635	1.067	1.616	0.711	0.191	1.783
	− 1.5	2.439	0.768	1.410	1.024	0.331	1.659
	− 2.0	1.806	0.779	1.318	0.964	0.448	1.346
GS	− 0.5	5.050	1.309	1.193	0.798	− 0.392	2.888	2.401	4
	− 1.0	4.311	1.309	1.180	0.781	− 0.270	2.539
	− 1.5	3.561	1.235	1.404	0.844	0.042	2.231
	− 2.0	3.066	0.877	1.315	0.935	0.227	1.945
1668	− 0.5	3.600	1.233	1.135	1.144	− 0.033	2.244	1.856	14
	− 1.0	3.165	0.927	1.312	1.158	0.116	2.021
	− 1.5	2.586	0.753	1.320	1.211	0.168	1.725
	− 2.0	2.052	0.761	1.217	0.926	0.368	1.435
2559	− 0.5	3.757	0.947	1.216	0.824	− 0.048	2.239	1.810	16
	− 1.0	3.016	0.966	1.461	0.968	0.092	1.951
	− 1.5	2.542	0.788	1.398	0.934	0.202	1.688
	− 2.0	1.860	0.672	1.378	0.983	0.426	1.362
13523	− 0.5	3.978	0.993	1.183	1.065	0.004	2.383	1.922	13
	− 1.0	3.685	1.005	1.477	1.056	0.113	2.294
	− 1.5	2.706	0.931	1.328	0.848	0.204	1.771
	− 2.0	1.641	0.960	1.011	0.687	0.719	1.240
P2	− 0.5	5.164	1.003	1.138	0.915	− 0.202	2.911	2.422	3
	− 1.0	4.527	0.919	1.159	0.794	− 0.026	2.593
	− 1.5	3.913	0.670	1.267	0.761	0.055	2.273
	− 2.0	3.283	0.220	1.213	0.892	0.159	1.910
03-132	− 0.5	4.141	0.787	1.505	0.821	− 0.178	2.426	1.989	10
	− 1.0	3.331	0.834	1.657	0.661	− 0.111	2.052
	− 1.5	2.996	0.846	1.390	0.843	0.099	1.895
	− 2.0	2.557	0.339	1.168	0.780	0.413	1.582
9977	− 0.5	4.242	1.257	1.084	0.865	− 0.123	2.506	2.009	9
	− 1.0	3.486	0.901	1.736	0.853	0.076	2.188
	− 1.5	2.959	0.571	1.288	0.936	0.218	1.836
	− 2.0	2.393	0.601	0.986	0.637	0.443	1.507
1626	− 0.5	5.051	1.024	1.338	0.923	− 0.068	2.901	2.426	2
	− 1.0	4.171	1.082	1.679	0.699	0.079	2.520
	− 1.5	3.676	0.944	1.720	0.789	0.235	2.289
	− 2.0	3.019	0.964	1.729	0.786	0.429	1.993
2323-2	− 0.5	4.259	1.199	1.286	0.970	0.021	2.557	2.111	7
	− 1.0	3.703	1.355	1.319	0.811	0.135	2.311
	− 1.5	3.099	1.029	1.471	0.705	0.245	1.982
	− 2.0	2.359	0.955	1.248	0.589	0.584	1.594
P3	− 0.5	4.138	1.269	1.209	0.832	− 0.059	2.476	2.063	8
	− 1.0	3.636	1.281	1.383	0.642	0.058	2.247
	− 1.5	2.966	1.003	1.430	0.718	0.176	1.903
	− 2.0	2.404	0.769	1.389	0.878	0.374	1.624
13709	− 0.5	4.330	0.993	1.298	0.715	− 0.122	2.513	1.980	12
	− 1.0	3.464	0.962	1.623	0.843	0.023	2.167
	− 1.5	2.871	0.744	1.379	0.799	0.182	1.817
	− 2.0	2.079	0.732	1.285	0.756	0.244	1.421
2323-1	− 0.5	4.589	1.203	1.168	0.908	− 0.270	2.669	2.236	5
	− 1.0	4.044	1.161	1.431	0.799	− 0.013	2.443
	− 1.5	3.306	1.071	1.487	0.821	0.085	2.093
	− 2.0	2.855	0.552	1.052	0.855	0.196	1.738
Weight		0.479	0.173	0.142	0.128	0.077
CK, without adding PEG-6000. Drought stress treatment, adding PEG-6000 with osmotic potentials of − 0.5, − 1.0, − 1.5, and − 2.0 MPa. U (X₁), U (X₂), U (X₃), U (X₄), U (X₅): The subordinate function values of 5 principal components under drought stress respectively of twenty sainfoin varieties/lines. Weight: Weight of 5 principal components. D value: Comprehensive evaluation values of drought tolerance of twenty sainfoin varieties/lines at seedling stage under drought stress. D mean: The mean of D values. Order: The drought resistance of twenty sainfoin varieties/lines was ranked according to D mean from large to small.

Cluster analysis of drought tolerance

Based on cluster analysis of D value, we categorized twenty sainfoin varieties/lines into three groups: (1) High tolerance: new line of P1, Onobrychis viciaefolia ‘Gansu’, the code of 1626, and the new line of P2; (2) Moderate tolerance: code of 2323-1, the new line of P3, codes of 2323-2, 2208, 2559, 1668, 7456, 13523, 9977, 13709, 03-132 and 1; and (3) Low tolerance: codes of 2049, 9602, 6, and 7 (Fig. 2).

Studying plant growth under water deficit conditions is crucial for comprehending how plants react to drought stress at a holistic level (Kuromori et al. 2022). Drought stress has been found to the inhibit cell expansion, reduce organic synthesis, and affect the distribution of assimilates, ultimately leading to inhibition of plant growth (Jabbari et al. 2013). Prior studies have established that roots are essential organs, which provide both water and nutrients for plants. The morphological characteristics of a plant's root system can directly reflect its growth and ability to adapt to external environments (De-Jesus-Garcia et al. 2020). Drought stress has an obvious inhibitory effect on seedling growth (Zhang et al. 2019). Under drought conditions, shoot growth is restricted, resulting in a greater distribution of assimilates to the roots and an acceleration of root growth. As a consequence, the root-shoot ratio is increased (Bacher et al. 2021; Xu et al. 2015). Our experiments also showed that the root-shoot ratio was diminished in samples from twenty sainfoin germplasm resources, as evidenced by decreases in leaf area, as well as root length, root surface area, root diameter, root volume, and root tip number. Especially at − 2.0 MPa, the relative value was minimized. This leads to a decrease in root water absorption (Wang et al. 2018). This suggests that plants usually limit their growth to reduce water consumption and ensure their survival under drought stress (Xue et al. 2022). However, the amplitude of those reductions varied by varieties/lines, which is consistent with other findings (Rellán-Álvarez et al. 2016; Motte et al. 2019). Root activity can reflect the metabolic capacity of the root system, which has a direct impact on plant tolerance (Wen et al. 2018). Here, root activity was increased under − 1. 0 MPa but was reduced under − 1. 5 and − 2. 0 MPa. The decrease in root activity is caused by a weakened ability to metabolize, which ultimately affects the plant's ability to tolerate stress (Katuwal et al. 2020). We found that plants could adapt to drought stress by increasing the root activity before − 1. 0 MPa, but this adaptation was limited. In addition, in this study, root length was significantly positively correlated with root activity and Chl a, and highly significantly positively correlated with root total surface area, root average diameter, root volume, root tip number, Chl b and proline. The root-shoot ratio was significantly positively correlated with root total length, root total surface area, root average diameter, root volume and root tip number, further indicating that root morphological characteristics and root activity were important response mechanisms of sainfoin under drought stress.

The leaves are highly sensitive to stress, which can cause them to dehydrate, curl, and wilt. The relative water content of leaves is a key indicator of a plant's stress resistance, and researchers often rely on it to evaluate the plant's tolerance (Corso et al. 2020; Zhou et al. 2022). Photosynthetic pigments in leaves produce organic matter by absorbing light energy (Zhou et al. 2022). We found that drought stress reduced levels of Chl a, Chl b, and RWC in the tested sainfoin varieties/lines. Drought conditions can affect the chloroplast structure and the photosynthetic process (Chen et al. 2016). Drought limits plant growth by reducing the production of photosynthetic pigments and reducing the level of photosynthesis (Mircea et al. 2023). Similar findings regarding the impact of alkalinity on Chl have been observed in Medicago sativa (Krouma 2010). It indicated that the structure of the chloroplast was damaged and the activity of Chl synthesis decreased drought stress (De la Haba et al. 2014).

Due to limitations in research conditions, studies on the underground parts of plants are often less extensive than those aboveground parts. However, it has been found that plants respond to drought by adjusting their root growth and physiological characteristics (Zhang et al. 2021). It is common for plants to use soluble sugars, soluble proteins, and amino acids (such as proline) as osmotic adjustment substances, and the accumulation of these osmolytes is a highly regulated process that can vary depending on the plant species and environmental conditions (Fang et al. 2015). The presence of these osmolytes in plants enables them to withstand water stress and sustain growth and productivity even in unfavorable conditions (Wang et al. 2020). Osmotic regulators can maintain cell volume and stabilize metabolic function under low water potential (Batool et al. 2020). The degree of osmolyte accumulation varied with the degree of stress and sainfoin. Our results showed that the levels of proline, soluble sugar, and soluble protein all increased as the degree of water stress increased. The data presented here suggest that osmolytes may be a key factor in sainfoin's ability to adapt to drought conditions. Specifically, this accumulation appears to support cell turgor, promote stability of the cell membrane, and protect against damage to macromolecules caused by drought stress. However, some studies have pointed out that the contents of proline, soluble sugar, and soluble protein decreased severe drought stress (Li et al. 2015). In addition to being an osmotic regulator, proline also acts as an active oxygen scavenger, ensuring the activity of antioxidant enzymes to some extent (Catola et al. 2016).

Abiotic stress can induce the production of ROS in plant cells, which can cause oxidative damage to membrane lipids (Ma et al. 2020). To resist oxidative damage, plants produce an antioxidant defense system and other mechanisms (Siddiqui et al. 2015). Plants synthesize enzymatic antioxidant substances, such as POD, SOD, and CAT, to reduce the accumulation of ROS. Generally, the stronger the enzyme activity, the greater the enzyme's ability to scavenge (Gao et al. 2020; Chen et al. 2016). Our research showed that the activities of POD and SOD were stimulated under − 1.0 MPa, but decreased with increasing stress, while CAT activity was reduced in the tested sainfoin varieties/lines. Recent studies suggest that antioxidant enzymes may play a crucial role in protecting sainfoin from the adverse effects of drought stress. It can maintain cell membrane permeability and avoid oxidative damage to membrane lipids, but with the aggravation of stress, the activity of antioxidant enzymes will decrease (Huang et al. 2023). This may induce ROS-mediated oxidative damage, particularly if the extent of membrane lipid peroxidation cannot be alleviated (Xu et al. 2011). It has been observed that there is a negative correlation between MDA and antioxidant enzyme activity, indicating that the drought-tolerant sainfoin has stronger enzyme activity, which can counteract the excess ROS and maintain the balance of ROS metabolism (Mahmood et al. 2020).

Drought stress has been observed to cause membrane lipid peroxidation, resulting in increased cell membrane permeability and ultimately causing irreversible cell damage (Xiong et al. 2021). In the field of oxidative stress and redox signal transduction, MDA serves as a marker of lipid peroxidation and is the ultimate decomposition product of plant membrane lipid peroxidation (Han et al. 2022). MDA levels serve as a useful measure of membrane damage (Ahmad et al. 2014). In our subject paper, we discovered an increase in MDA concentrations in the tested sainfoin varieties/lines compared to control amounts. This finding suggests that the plants were either directly or indirectly harmed by drought stress. Despite this, it is worth noting that the increments were relatively smaller in the highly tolerant sainfoin. We observed that the more drought-tolerant sainfoin (new lines of P1 and P2, Onobrychis viciaefolia ‘Gansu’, and 1626) to maintain the stability of cell membranes, sainfoin roots synthesized substantial quantities of enzymatic antioxidants and osmotic adjustment solutes to counteract the various oxidants generated by drought stress.

The effect of water stress on plant growth, osmotic adjustment, and protective enzyme activity of sainfoin seedlings were studied using PEG-6000 solution. In this subject paper, the drought resistance of twenty sainfoin varieties/lines was evaluated comprehensively using subordinate functional analysis and cluster analysis of the test data. Those groupings included (1) High tolerance: new line of P1, Onobrychis viciaefolia ‘Gansu’, the code of 1626, and the new line of P2; (2) Moderate tolerance: code of 2323-1, the new line of P3, codes of 2323-2, 2208, 2559, 1668, 7456, 13523, 9977, 13709, 03-132 and 1; and (3) Low tolerance: codes of 2049, 9602, 6, and 7. These results will significantly contribute to the selection of sainfoin that will have the most appropriate level of tolerance to drought stress when grown in specific regions.

Funding. The study was supported by the Gansu Science and Technology Plan (Nos. 22YF7NA112), Regional Science Fund Projects (Nos. 32360339), and the Ministry of Finance and Ministry of Agriculture and Rural Affairs: National Modern Agricultural Industry Technology System (CARS-34).

Competing Interests. The authors declare no conflict of interest.

Author Contributions. Lili Nan designed the research, Yuheng Yao, Lili Nan, and Kun Wang performed the research, Yuheng Yao and Lili Nan analyzed data and wrote the paper.

Data Availability. Data sharing is not applicable as no new data were generated or analyzed during this study.

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No competing interests reported.

Differences of morphological and physiological responses of sainfoin varieties/lines under simulated drought stresses

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