Next Article in Journal
Genome-Editing of FtsZ1 for Alteration of Starch Granule Size in Potato Tubers
Previous Article in Journal
Biochemical, Antioxidant Properties and Antimicrobial Activity of Epiphytic Leafy Liverwort Frullania dilatata (L.) Dumort
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Hydrotime Model Parameters Estimate Seed Vigor and Predict Seedling Emergence Performance of Astragalus sinicus under Various Environmental Conditions

1
Key Laboratory of National Forestry and Grassland Administration on Grassland Resources and Ecology in the Yellow River Delta, Qingdao Key Laboratory of Specialty Plant Germplasm Innovation and Utilization in Saline Soils of Coastal Beach, College of Grassland Science, Qingdao Agricultural University, Qingdao 266109, China
2
State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China
3
College of Animal Science and Technology, Qingdao Agricultural University, Qingdao 266109, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Plants 2023, 12(9), 1876; https://doi.org/10.3390/plants12091876
Submission received: 21 March 2023 / Revised: 28 April 2023 / Accepted: 2 May 2023 / Published: 4 May 2023
(This article belongs to the Section Crop Physiology and Crop Production)

Abstract

:
Seed vigor is an important aspect of seed quality. High-vigor seeds show rapid and uniform germination and emerge well, especially under adverse environmental conditions. Here, we determined hydrotime model parameters by incubating seeds at different water potentials (0.0, −0.2, −0.4, −0.6, and −0.8 MPa) in the laboratory, for 12 seed lots of Chinese milk vetch (Astragalus sinicus) (CMV), a globally important legume used as forage, green manure, and a rotation crop. Pot experiments were conducted to investigate the seedling emergence performance of 12 CMV seed lots under control, water stress, salinity stress, deep sowing, and cold stress conditions. Meanwhile, the field emergence performance was evaluated on two sowing dates in June and October 2022. Correlation and regression analyses were implemented to explore the relationships between hydrotime model parameters and seedling emergence performance under various environmental conditions. The seed germination percentage did not differ significantly between seed lots when seeds were incubated at 0.0 MPa, whereas it did differ significantly between seed lots at water potentials of −0.2, −0.4, and −0.6 MPa. The emergence percentage, seedling dry weight, and simplified vigor index also differed significantly between the 12 seed lots under various environmental conditions. Ψb(50) showed a significant correlation with germination and emergence performance under various environmental conditions; however, little correlation was observed between θH or σφb and germination and emergence. These results indicate that Ψb(50) can be used to estimate seed vigor and predict seedling emergence performance under diverse environmental conditions for CMV and similar forage legumes. This study will enable seed researchers, plant breeders, and government program directors to target higher seed vigor more effectively for forage legumes.

1. Introduction

Chinese milk vetch (Astragalus sinicus L.) (CMV) is a globally important forage legume and is especially widely cultivated in East Asia [1,2]. Because of its good adaptability to diverse environments, high quality and yield potential, and excellent palatability, CMV is commonly used as animal feed worldwide and thus plays an important role in developing animal husbandry [3,4,5]. Additionally, the cultivation of CMV improves soil fertility as a result of symbiotic nitrogen fixation, which leads to reduced chemical fertilizer application during crop production [6,7]. Therefore, CMV is an ideal green manure and rotation plant that performs well in environmentally sustainable cropping systems [8,9,10]. Nevertheless, the poor establishment of small-seeded forage legumes is common [11], especially under inappropriate conditions, such as deep sowing, drought, salinity, alkalinity, and cold stress [12], and has become one of the critical constraints encountered by farmers and agricultural companies [13]. Therefore, improving stand establishment in harsh environments has become a major determinant of CMV cultivation and utilization.
The selection of high-quality CMV seeds is an efficient approach to optimizing seedling establishment and thus achieving a better livelihood and environment. Seed vigor is an important aspect of seed quality [14]; it is defined as the sum of the properties of the seed that determine the level of activity and the performance of a seed or seed lot during germination and seedling emergence [15]. Previous research has suggested the presence of a positive linear relationship between seed vigor and seedling emergence in the field [16,17]. Tao et al. found that there was an advantage in using high-vigor CMV seeds to achieve better seedling emergence, whereas low-vigor seeds caused limited seedling emergence [18]. The selection of high-vigor seeds has also been proven to be helpful for seedling emergence in other forage legumes, such as alfalfa (Medicago sativa L.) [19], white clover (Trifolium repens L.) [20], and birdsfoot trefoil (Lotus corniculatus L.) [21]. Thus, more attention should be paid to seed-vigor testing for forage legumes.
The standard germination (SG) experiment, performed according to International Seed Testing Association, is always used to estimate seed quality [22]. Nevertheless, seed vigor describes the comprehensive characteristics of the seeds beyond SG, and seeds from different commercial sources may have similarly high levels of SG in the laboratory; however, under more unpredictable conditions experienced in the field, these same seeds may have strikingly contrasting abilities to establish plants due to differences in their vigor [23]. Consequently, there is an urgent need to develop more precise methods to estimate CMV seed vigor.
There are many ways to test seed vigor, including electrical conductivity [24], DNA replication [25], accelerated aging [26], controlled deterioration [27], and radicle emergence [14,28]. Previously, Tao et al. indicated that an individual count of radicle emergence may have the potential for CMV seed-vigor testing [18]. The methods based on germination characteristics under stress conditions also have the potential for seed-vigor testing. Water potential is a pivotal environmental factor regulating seed germination and emergence [29]. Many studies have suggested that seed germination can be quantified in response to water potential by the hydrotime model [30,31,32]. The hydrotime model was formulated by Gummerson [33] and Bradford [34] and has three parameters that are meaningful from a biological perspective: (1) the constant hydrotime (θH), which is the hydrotime (MPa-hours) to germination; (2) the base water potential (Ψb(g)), which is the base or threshold water potential (MPa) defined for a specific germination fraction, g (%); and (3) germination uniformity (σφb), which is the standard deviation of Ψb(g) [35,36]. The hydrotime model has been shown not only to effectively predict the effect of water potential on progress toward germination but also to provide an understanding of the physiological status of seed lots or seed populations [37,38]. In particular, previous studies have reported the close relationship between hydrotime model parameters and seedling emergence, and thus seed vigor, in many plant species, including alfalfa [39], cotton (Gossypium hirsutum L.) [40], rapeseed (Brassica napus L.) [41], and sugar beet (Beat vulgaris L.) [42]. It has also been reported that seed priming can increase the germination speed under water stress conditions by reducing θH or Ψb(g) [43,44,45,46], which implies that the change in hydrotime model parameters resulting from seed priming may be closely associated with seed vigor and the performance of seeds/seedlings in unfavorable field environments. However, to our knowledge, little literature is available concerning the description of CMV seed germination in response to water potential based on the hydrotime model, and the hypothesis that hydrotime model parameters can be applied to estimate seed vigor and predict the seedling emergence performance of forage legumes, including CMV, has not been validated. Thus, we put forward the following questions in the present research: (1) Does the hydrotime model quantitatively predict seed germination in response to water potential in CMV seed lots with different levels of seed vigor? (2) Can hydrotime model parameters be used as measures to estimate seed vigor and predict CMV seedling emergence performance under diverse environmental conditions? The answers to these questions will provide important parameters for seed-vigor testing and early-warning signs for seed storage.

2. Results

2.1. Seed Germination in Response to Water Potential

The seed germination percentages of 12 seed lots of CMV in response to different water potentials in a laboratory germination test are shown in Table 1. The analysis of variance (ANOVA) indicated that the seed germination percentage (SGP) was significantly influenced by the seed lot, water potential, and their interaction effect (Table 1). Water potential drastically affected seed germination characteristics for both seed lots. With a decrease in water potential, the SGP, germination rate, and germination index decreased significantly. However, the extent of the reduction depended on the seed lot; for instance, from the 0.0 to −0.6 MPa treatments, the SGP of seed lots 2 and 9 decreased from 85.6% to 17.8% and from 90.0% to 51.1%, respectively. When the water potential reached −0.8 MPa, no seed lots germinated to 7.0% (Figure 1, Table 1).
In the non-water stress treatment (0.0 MPa), the germination percentages (standard germination) were high and similar, ranging from 85.6% (lot 2) to 95.6% (lot 12), for the 12 seed lots. In the −0.8 MPa treatment, the SGP also differed little between the 12 seed lots and all lots had values lower than 7%. With the exception of the 0.0 and −0.8 MPa treatments, the SGP differed significantly across the 12 seed lots. For example, in the −0.6 MPa treatment, the germination percentage was significantly affected by the seed lot and ranged from 17.8% (lot 2) to 56.7 (lot 12) (Table 1).
The germination rate and germination index were also significantly influenced by the seed lot in all water potential treatments except for −0.8 MPa, in which low and similar germination rates and germination indexes were observed (Figure 1).

2.2. Hydrotime Model Analysis for Seed Germination in Response to Water Potential

The hydrotime model parameters calculated from germination data obtained from different water potentials are shown in Table 2. The hydrotime models described the germination of the 12 CMV seed lots well, with r2 values of 0.783 to 0.917 (Table 2). The estimated values of θH, Ψb(50), and σφb differed significantly among seed lots. Seed lot 2 had the highest estimated Ψb(50) (−0.278 MPa), and lot 12 had the lowest Ψb(50) (−0.522 MPa). The estimated value of θH of the 12 lots of CMV ranged from 8.799 to 14.770 MPa·h, and σφb varied from 0.267 to 0.333 (Table 2). We also found that the predicted germination time courses at the five water potentials generally fitted the observed germination data very well (Figure 2).

2.3. Effects of Different Environmental Conditions on Seedling Emergence Performance in Pot Experiments

The effects of different environmental conditions on the seedling emergence performance of 12 CMV seed lots in pot experiments are reported in Table 3. According to the results of ANOVA, the seed lot and environmental conditions had a significant influence on the pot emergence percentage (PEP), seedling dry weight (SDW), and simplified vigor index (SVI) (p < 0.001). Meanwhile, SDW and SVI were significantly affected by the interaction effect between the seed lot and environmental conditions (p < 0.001) (Table 3).
The dynamics of soil water content for control and water stress conditions during the pot experiment are shown in Figure 3. The soil water content was higher in the control condition than in the water stress condition. In the control condition, soil water content in the pot ranged from 8.9% to 24.1% during the pot experiment, with an average value of 17.2%; however, in the water stress condition, soil water content ranged from 5.4% to 20.8%, with an average value of 11.4% (Figure 3).
Overall, PEP, SDW, and SVI decreased significantly with water, salinity, and cold stresses and the deep-sowing treatment as compared to the control conditions and differed significantly across the 12 seed lots of CMV under each environmental condition (Table 3). For instance, for seed lot 6, the PEP under control conditions was 80.0%, while the corresponding values for water stress, salinity stress, deep sowing, and cold stress conditions were 44.4, 46.7, 38.9, and 54.4%, respectively. Among the 12 tested seed lots, the PEP ranged from 54.4 to 83.3%, 28.9 to 52.2%, 26.7 to 53.3%, 15.6 to 43.3%, and 32.2 to 57.8% for control, water stress, salinity stress, deep-sowing, and cold stress conditions, respectively. Generally, seed lot 12 showed the highest pot emergence variables, whereas lot 2 had the lowest (Table 3).

2.4. Seedling Emergence Performance under Field Conditions

There were significant differences in the field emergence percentage (FEP), SDW, and SVI in the two field sowings between the 12 CMV seed lots with similar SG (Figure 4). The observed FEP, SDW, and SVI of all seed lots in the first sowing were higher than those observed in the second sowing. In the first sowing, the FEP, SDW, and SVI for the 12 seed lots ranged from 48.7 (lot 2) to 83.3% (lot 12), 45.8 (lot 4) to 64.4 mg plant−1 (lot 7), and 2318.6 (lot 2) to 5123.9 (lot 12), respectively, whereas the corresponding FEP, SDW, and SVI for the 12 seed lots in the second sowing ranged from 25.3 (lot 2) to 63.3% (lot 3), 21.4 (lot 2) to 48.6 mg plant−1 (lot 9), and 539.6 (lot 2) to 2946.4 (lot 12), respectively (Figure 4).

2.5. Correlation between Hydrotime Model Parameters and Seed Germination and Seedling Emergence Performance under Various Environmental Conditions

Correlation and regression between hydrotime model parameters and seed germination characteristics, emergence percentage, SDW, and SVI under various environmental conditions indicated that Ψb(50) (base water potential) was significantly negatively correlated with the above vigor variables, with the correlation coefficients (r) and determination coefficients (r2) ranging from −0.600 to −0.972 and 0.36 to 0.95, respectively (Table 4, Figure 5). It is evident that seed lots with more negative values of Ψb(50) have higher seed germination and seedling emergence performance under a wide range of environmental conditions. θH (constant hydrotime) was significantly negatively correlated only with the germination rate (r = −0.702), germination index (r = −0.623), and PEP under cold stress (r = −0.630) (p < 0.05). In addition, σφb (standard deviation of Ψb(50)) was significantly positively associated only with SDW (r = 0.666) and SVI (r = 0.616) under salinity stress conditions, and SDW (r = 0.700) and SVI (r = 0.617) in the second sowing under field conditions (p < 0.05) (Table 4).

3. Discussion

Seed germination and seedling emergence are important stages in the establishment of a plant species and the most sensitive ones in the plant’s life cycle under environmental conditions [47]. In the context of global climate change, drought, salinity, and cold are the major adverse factors limiting seed germination and early establishment as well as subsequent growth and productivity [48,49,50]. In addition, burial depth is also one of the primary factors known to influence seedling emergence and establishment [51], and deep sowing resulted in mechanical stress on seeds and seedlings, which ultimately caused poor emergence performance and stand establishment [52]. As is clearly shown in the present study, reduced water potential significantly decreased SGP, GR, and GI in the laboratory (Figure 1, Table 1), and water stress, salinity stress, deep sowing, and cold stress obviously decreased seedling emergence performance in the pot experiment. We also found that the emergence performance of CMV seed lots was the most sensitive to deep sowing (Table 3). In addition, the seedling emergence percentage under field conditions was far lower than the SG in laboratory (Figure 4), which suggests that the unfavorable environmental conditions experienced in the field play a critical role in determining CMV seedling emergence and stand establishment. These results are consistent with earlier research in other forage legumes and not unusual, such as Medicago polymorpha, Medicago truncatula, Medicago littoralis, Trifolium alexandrinum, and Trifolium michelianum [53]. In addition, from a seed structure aspect, reduced seed germination and emergence under stressful conditions may also be due to structural changes in the hilum in the seed coat, which is well known to be the main water intake route during legume seed hydration [54]. On the other hand, we found that the extent of the harmful impact of adverse environmental conditions on seed germination and seedling emergence is seed-lot-dependent, which suggests that the selection of high-vigor seeds is a promising method by which to assuage detrimental environmental conditions. Previously, Wang et al. [11] also reported that high-vigor seeds had advantages in the field establishment of the forage legumes alfalfa and purple vetch (Vicia benghalensis L.).
In this study, we used 12 CMV seed lots collected from different commercial sources, and our results indicate wide variations in seedling emergence performance among these 12 seed lots under diverse environmental conditions, which effectively rank seed lots according to their actual vigor level. All seed lots used in the present experiment were from a single cultivar; thus, any differences in their germination and emergence patterns and stress tolerance were not due to their genotypes. Therefore, from a seed physiology perspective, the difference in the above traits between seed lots was most likely due to different vigor levels caused by seed aging and deterioration [18]. From the start of seed imbibition to germination or emergence, metabolic repair, including DNA repair, is required and is a necessary step for the subsequent events leading to germination and emergence [55]. Low-vigor seeds germinate more slowly and with more difficulty due to a greater need for damage repair, especially under adverse environmental conditions, which ultimately causes different germination and emergence patterns between seed lots [56]. In our study, among all seed lots, as reflected by seedling emergence under pot and field conditions, seed lot 2 had the lowest vigor level and was also the oldest of all seed lots, stored for six years before its use in the present research. In contrast, the youngest lots (stored for one year: lots 11 and 12) are the high-vigor lots (Figure 1, Table 1 and Table 3). This observation coincides with the above-mentioned aging-repair hypothesis; that is, older seed lots had lower vigor levels. However, the high and similar germination percentages under non-water stress conditions (0.0 MPa) (SG) for 12 commercially available seed lots of CMV were, overall, not strongly associated with seedling emergence performance parameters, including the emergence percentage, SDW, and SVI under different environmental conditions; thus, SG is poor in ranking seed-vigor differences among seed lots. This result is in agreement with the findings of Mavi et al. [57], Yan et al. [58], and Luo et al. [59], who stated that seed lots with high and similar SG may exhibit contrasting performance under stressful conditions experienced in the field. The SG test was carried out under favorable conditions in the laboratory and so may not reflect the actual vigor level of the seed lots [22]. Therefore, more precise testing of seed vigor and quality beyond the SG value is an important area of study for seed researchers [60]. Interestingly, the SGP of the 12 CMV seed lots differed significantly under reduced water potentials, suggesting that the germination percentage under appropriate stressful conditions is beneficial in detecting seed vigor. Our results support those of other researchers who have reported that germination at −0.2 MPa water potential can be used to distinguish the seed vigor of the forage grasses Festuca sinensis and Poa crymophila seed lots [61,62]. In our study, −0.2, −0.4, and −0.6 MPa were suitable germination conditions for seed-vigor evaluation, since the highest variations among seed lots were obtained at these levels of water potential (Table 1).
The hydrotime model has been widely used to describe germination dynamics in response to reduced water potential [63,64,65], and the model has allowed a better description of seed germination time courses with reduced water availability in many plant species, such as Stipa spp. [32], Ceratonia siliqua [66], Jatropha curcas [67], and slender wheatgrass (Elymus trachycaulus (Link) Gould subsp. trachycaulus) [68]. Consistent with these experiments, the present study clearly showed that the hydrotime model could also describe the germination time courses of different CMV seed lots at diverse water potentials very well, with r2 values ranging from 0.78 to 0.92 (Figure 2, Table 2). A unique advantage of the hydrotime model for seeds incubated across a range of water potentials is that variation or similarity in germination among seed lots can be ascribed to specific underlying factors, including θH, Ψb(50), and σφb [39,69]. In our study, seed lots with lower Ψb(50) generally had higher seed germination and seedling emergence performance under different environmental conditions than those with higher values of Ψb(50); for example, seed lot 12, with the lowest Ψb(50) (−0.522 MPa), also had the highest emergence performance in most cases. In contrast, seed lot 2, with the biggest Ψb(50) (−0.278 MPa), had the poorest seedling emergence under a wide range of environmental conditions (Table 2 and Table 3). This result parallels the findings of Dahal and Bradford [70], who observed that seeds with lower Ψb(g) generally had higher vigor than those with high values, and this allowed seeds to germinate rapidly in adverse environments. Furthermore, our study revealed very significant correlations between Ψb(50) and seed germination characteristics and seedling emergence performance under diverse conditions (Figure 5, Table 4), which supports our hypothesis that the tolerance of CMV seed lots to unfavorable environmental conditions can be identified by a reduction in Ψb(50). Therefore, Ψb(50) could be considered an ideal indicator when determining seed vigor and predicting emergence performance under various conditions. Accordingly, in the present study, the close association of Ψb(50) with seed vigor and seedling emergence performance is in agreement with the previous findings of Chen et al. [39] in alfalfa, Soltani and Farzaneh [40] in cotton, Soltani et al. [41] in rapeseed, and Bradford and Somasco [71] in lettuce (Lactuca sativa L.). Farzane and Soltani [42] also suggested that Ψb(50) is significantly negatively related to the seed vigor of sugar beet.
However, in the present experiment, a low correlation was observed between θH and σφb and seed vigor. One probable reason is that a lower θH favors rapid and uniform germination, and this happens only when seeds are exposed to favorable conditions. According to the hydrotime definition, when the external water potential approaches or is lower than Ψb(50), seeds will accumulate hydrotime units very slowly, which overrides the advantage of low θH [39]. In addition, no relationship between Ψb(50) and θH was observed, which implies that these two parameters may act independently in regulating seed germination and seedling emergence under diverse conditions. Accordingly, Farzane and Soltani also indicated that Ψb(50) was the only parameter that was significantly related to seedling emergence and seed vigor [42].

4. Materials and Methods

4.1. Seed Materials

Samples of 12 seed lots of CMV were obtained from various commercial sources in China. For each lot, sufficient seeds were collected (in excess of 1000 g) to ensure the implementation of subsequent experiments. These seed lots had been produced over several years (Table 5) from diverse regions in China and stored dry at 4 °C before they were used in the present study. The basic information, including the initial seed moisture content, thousand-seed weight, and proportion of hard seeds in the 12 seed lots (ranging from 0.0 to 4.4%), is also presented in Table 5. The experiments described here were conducted from May to December 2022.

4.2. Germination Test

In order to calculate hydrotime model parameters for each seed lot, a germination test for each CMV seed lot was carried out by incubating seeds at 20 °C in an 8 h/16 h light/dark cycle at water potentials of 0.0, −0.2, −0.4, −0.6, and −0.8 MPa. Water potentials were maintained with solutions of polyethylene glycol 6000 (PEG-6000) prepared according to Michel and Kaufmann [72]. For each seed lot and treatment, three replications of 30 seeds were used, and seeds were sown in 120 mm × 120 mm Petri dishes on the top of two layers of filter paper moistened with 13 mL PEG-6000 solutions or distilled water; the Petri dishes were then sealed with parafilm to reduce the speed of evaporation of water. Seeds were transferred to new filter paper with fresh solution every 48 h to ensure relatively constant water potential. Germination (2 mm radicle emergence) was monitored every eight hours for the first 4 days from the beginning of the germination experiment, and then daily for 21 days. The germinated seeds were removed at each counting. The germination percentage was expressed as germinated seeds divided by total sown seeds. The germination rate (d−1) was calculated as previously described by Fallahi et al. [73]:
G R = i = 1 n S i D i
where GR is the germination rate (d−1), Si is daily seed germination, Di is the number of days to n computations, and n is the number of computation days. The germination index was calculated using the following formula [74]:
G I = G t D t
where GI is the germination index, Gt represents the number of germinated seeds on day t, and Dt represents germination days.

4.3. Pot Experiments

Cylindrical plastic pots (inner diameter, 14 cm; height, 12 cm) were used in this part of the experiment. The clay loam soil (silt 34%, clay 37%, sand 29%, pH 7.20, and organic matter 17.39 g kg−1) used in pot experiments was collected from local farmland and sieved through a 2 mm sieve to remove debris. The soil was dried at 120 °C for 24 h and then spread out for drying, followed by heating and homogenization. CMV seeds in 12 seed lots were sown in various environmental conditions, including control (irrigated with distilled water every four days), water stress (irrigated with distilled water every seven days), salinity stress (irrigated with 125 mM NaCl solution every four days), deep sowing (physical stress) (5 cm), and cold stress (10 °C). The abiotic treatments for the control, water stress, salinity stress, and deep sowing were conducted in a greenhouse (20 °C, 60% relative humidity) on the campus of Qingdao Agricultural University (36°19′ N, 120°23′ E, 38.6 m above sea level), Shandong Province, China. The cold stress treatment was performed in a growth chamber. For each seed lot and treatment, three replicates of 30 seeds were sown at a depth of 1 cm (except for the deep-sowing treatment) in pots that contained 950 g of dry soil, which had been treated as described above. After sowing, 200 mL of distilled water or NaCl solution was used to irrigate each pot immediately and every four days during the period of the pot experiment (except for the water stress treatment, in which pots were irrigated every seven days). The pots containing soil and seeds were placed in a random arrangement on a table in the greenhouse or in the growth chamber, and the locations of pots were changed daily to avoid edge effects.
To monitor the soil water content for the control and water stress treatments, two extra pots without seeds were irrigated under the same conditions as those in each irrigation regime of the experiment. These two pots were weighed daily using an electronic balance, and the soil water content was calculated according to the following formula [75]:
S W C % = W w 950 W w × 100
where SWC is the soil water content (%), and Ww is the weight (g) of wet soil weighed every day.
The pot experiments lasted 30 days. At the end of the experiment, the number of emerged seedlings was counted in each pot, and the emergence percentage was calculated. All emerged seedlings in each pot were cut at the soil level and combined before being dried at 65 °C for 48 h and then weighed; the mean SDW was then calculated. The SVI was derived arithmetically by multiplying the emergence percentage with the SDW [76].

4.4. Field Experiments

The field emergence experiments were implemented at the Jiaozhou Experimental Base of Qingdao Agricultural University (36°25′ N, 120°4′ E, 27.0 m above sea level), Jiaozhou, Shandong Province, China. The mean annual temperature and mean annual precipitation were 13.8 °C and 686.0 mm, respectively. The field emergence experiment was conducted twice, once in June 2022 and once in October 2022. During each sowing, a randomized complete block design (RCBD) with three replications was used, and each replication contained 50 seeds. The seeds from each seed lot and each replication were hand-planted in a 1.5 m row, with 50 cm spaces between adjacent rows (seed lots). The seeds were sown at a depth of approximately 1 cm.
The daily mean temperature and daily precipitation data during each experimental period were collected from local meteorological stations (Figure 6); no irrigation was applied during the field trials. For the first sowing, the mean temperature and total precipitation during the field trial period were 25.8 °C and 327.9 mm, respectively, while the counterpart values for the second sowing were 13.4 °C and 6.2 mm, respectively (Figure 6). At 30 days after each sowing, the emerged seedlings were counted, and the emergence percentage was calculated.
After each field experiment, all emerged seedlings from each block and each seed lot were harvested at the ground level and amalgamated before being dried at 65 °C for 48 h and then weighed, and the mean SDW was calculated; the SVI was also calculated as described above.

4.5. Statistical Analyses

All statistical analyses were performed using SPSS 26.0 software (SPSS Inc., Chicago, IL, USA). Data for germination and emergence percentages in response to the seed lot and water potential were subjected to ANOVA. Significant differences between treatments or seed lots were compared using Duncan’s multiple comparison test at the p < 0.05 probability level. Proportional data were arcsine-transformed before statistical analysis, and non-transformed data are reported in all tables and figures. All measurements reported are the mean of three replications. Pearson correlation and regression analyses were used to detect the relationships between hydrotime model parameters and the germination percentage, germination rate, germination index, and seedling emergence performance under various environmental conditions.
The hydrotime model is described by the following equation [33,34,77]:
θH = (ΨΨb(g)) tg
where Ψ is the actual water potential (MPa), θH is the hydrotime constant (MPa·h), Ψb(g) is the base water potential (MPa) defined for a specific germination fraction (g), and tg is the time (h) to germination of fraction g (%) of the seed lot.
The normal distribution of Ψb(g) values among seeds in a population is characterized by its median Ψb(50) and standard deviation (σφb), which can be estimated using repeated probit analyses, varying θH until the best fit is reached [40,69,78] as follows:
Probit (g) = [Ψ − (θH/tg) − Ψb(50)]/σφb
which separately models the germination time course at different water potentials for each seed lot.

5. Conclusions

Overall, the present study clearly shows that the hydrotime model can describe the germination time course of CMV seed lots in response to different water potentials very well. The 12 seed lots of CMV differed significantly in their hydrotime model parameters (θH, Ψb(50), and σφb). Additionally, different water potentials and environmental conditions also had a significant influence on seed germination and seedling emergence for each CMV seed lot. Seed germination and emergence characteristics significantly decreased under reduced water potential and abiotic stresses. In addition, the 12 seed lots also significantly differed in their seedling emergence under field conditions. Among the three hydrotime model parameters, correlation and regression analyses indicated that Ψb(50) was the most highly correlated with germination characteristics and seedling emergence performance under different environmental conditions. The lower the Ψb(50) of a lot, the higher its vigor. Thus, it can be concluded that Ψb(50) is an ideal trait to estimate CMV seed vigor and predict seedling emergence under diverse environmental conditions. These findings provide important parameters for seed vigor testing and early-warning signs for CMV seed storage. Future seed treatment and breeding programs for seed vigor could pay attention to reducing Ψb(50) to increase seed vigor and stress tolerance. Moreover, the evaluation of the relationship between stress tolerance characteristics and Ψb(50) might reveal similar significant associations in other plants.

Author Contributions

Conceptualization, Q.T., D.C. and J.S.; methodology, Q.T., D.C., M.B., Y.Z. and R.Z.; validation, Q.T. and J.S.; formal analysis, Q.T., D.C., Y.Z. and J.S.; investigation, Q.T., D.C., M.B., Y.Z., R.Z., X.C., X.S., T.N., Y.N. and S.Z.; resources, Q.T. and J.S.; data curation, Q.T., D.C., M.B., R.Z. and J.S.; project administration, Q.T. and J.S.; funding acquisition, Q.T. and J.S.; software, Q.T., D.C., M.B., T.N. and Y.N.; supervision, Q.T., D.C. and J.S.; writing—original draft, Q.T. and D.C.; writing—review and editing, Q.T., D.C., M.B., S.Z. and J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the China Agricultural Research System (CARS-34), the Forage Industrial Innovation Team, Shandong Modern Agricultural Industrial and Technical System (SDAIT-23-01), the Qingdao Agricultural University Doctoral Start-up Fund (663-1121038), the Natural Science Foundation of Shandong Province (ZR2022QC151) and First-class Grassland Science Discipline Program in Shandong Province, China (1619002).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors thank Jipeng Sun and Yanda Lu for their extensive assistance in field management and sample collection. We appreciate the anonymous reviewers for their valuable input.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Naito, Y.; Fujie, M.; Usami, S.; Murooka, Y.; Yamada, T. The involvement of a cysteine proteinase in the nodule development in Chinese milk vetch infected with Mesorhizobium huakuii subsp. rengei. Plant Physiol. 2000, 124, 1087–1095. [Google Scholar] [CrossRef]
  2. Chang, D.N.; Gao, S.J.; Zhou, G.P.; Deng, S.H.; Jia, J.Z.; Wang, E.T.; Cao, W.D. The chromosome-level genome assembly of Astragalus sinicus and comparative genomic analyses provide new resources and insights for understanding legume-rhizobial interactions. Plant Commun. 2022, 3, 100263. [Google Scholar] [CrossRef]
  3. Liu, J.X.; Ye, J.A.; Ye, H.W. The effects of supplementary Chinese milk vetch silage on the growth rate of cattle and their intake of ammoniated rice straw. Anim. Feed Sci. Technol. 1997, 65, 79–86. [Google Scholar] [CrossRef]
  4. Cho, H.; Brotherton, J.E.; Song, H.; Widholm, J.M. Increasing tryptophan synthesis in a forage legume Astragalus sinicus by expressing the tobacco feedback-insensitive anthranilate synthase (ASA2) gene. Plant Physiol. 2000, 123, 1069–1076. [Google Scholar] [CrossRef]
  5. Cho, H.; Widholm, J.M.; Tanaka, N.; Nakanishi, Y. Agrobacterium rhizogenes-mediated transformation and regeneration of the legume Astragalus sinicus (Chinese milk vetch). Plant Sci. 1998, 138, 53–65. [Google Scholar] [CrossRef]
  6. Qiao, J.; Zhao, D.; Zhou, W.; Yan, T.M.; Yang, L.Z. Sustained rice yields and decreased N runoff in a rice-wheat cropping system by replacing wheat with Chinese milk vetch and sharply reducing fertilizer use. Environ. Pollut. 2021, 288, 117722. [Google Scholar] [CrossRef] [PubMed]
  7. Li, Y.X.; Zhou, L.; Li, Y.G.; Chen, D.S.; Tan, X.J.; Lei, L.; Zhou, J.C. A nodule-specific plant cysteine proteinase, AsNODF32, is involved in nodule senescence and nitrogen fixation activity of the green manure legume Astragalus sinicus. New Phytol. 2008, 180, 185–192. [Google Scholar] [CrossRef] [PubMed]
  8. Zhou, X.; Liao, Y.L.; Lu, Y.H.; Rees, R.M.; Cao, W.D.; Nie, J.; Li, M. Management of rice straw with relay cropping of Chinese milk vetch improved double-rice cropping system production in southern China. J. Integr. Agric. 2020, 19, 2103–2115. [Google Scholar] [CrossRef]
  9. Zhong, C.; Liu, Y.; Xu, X.T.; Yang, B.J.; Aamer, M.; Zhang, P.; Huang, G.Q. Paddy-upland rotation with Chinese milk vetch incorporation reduced the global warming potential and greenhouse gas emissions intensity of double rice cropping system. Environ. Pollut. 2021, 276, 116696. [Google Scholar] [CrossRef]
  10. Yang, W.; Yao, L.; Zhu, M.Z.; Li, C.W.; Li, S.Q.; Wang, B.; Dijkstra, P.; Liu, Z.Y.; Zhu, B. Replacing urea-N with Chinese milk vetch (Astragalus sinicus L.) mitigates CH4 and N2O emissions in rice paddy. Agric. Ecosyst. Environ. 2022, 336, 108033. [Google Scholar] [CrossRef]
  11. Wang, Y.R.; Yu, L.; Nan, Z.B.; Liu, Y.L. Vigor tests used to rank seed lot quality and predict field emergence in four forage species. Crop Sci. 2004, 44, 535–541. [Google Scholar] [CrossRef]
  12. Townsend, C.E.; McGinnies, W.J. Establishment of nine forage legumes in the central great plains. Agron. J. 1972, 64, 699–702. [Google Scholar] [CrossRef]
  13. Guo, Z.G.; Liu, H.X.; Wang, S.M.; Tian, F.P.; Cheng, G.D. Biomass, persistence and drought resistance of nine lucerne varieties in the dry environment of west China. Aust. J. Exp. Agric. 2005, 45, 59–64. [Google Scholar] [CrossRef]
  14. Shinohara, T.; Ducournau, S.; Matthews, S.; Wagner, M.H.; Powell, A.A. Early counts of radicle emergence, counted manually and by image analysis, can reveal differences in the production of normal seedlings and the vigour of seed lots of cauliflower. Seed Sci. Technol. 2021, 49, 219–235. [Google Scholar] [CrossRef]
  15. Ellis, R.H. Seed and seedling vigour in relation to crop growth and yield. Plant Growth Regul. 1992, 11, 249–255. [Google Scholar] [CrossRef]
  16. Lv, Y.Y.; Wang, Y.R.; Powell, A.A. Frequent individual counts of radicle emergence and mean just germination time predict seed vigour of Avena sativa and Elymus nutans. Seed Sci. Technol. 2016, 44, 189–198. [Google Scholar]
  17. Akbarpour, M.; Khajeh-Hosseini, M.; Seifi, A. Potential of a single radicle emergence count in predicting field emergence of Desi chickpea seed lots as an alternative vigour test. Seed Sci. Technol. 2019, 47, 319–324. [Google Scholar] [CrossRef]
  18. Tao, Q.B.; Sun, J.P.; Zhang, Y.Q.; Sun, X.T.; Li, Z.Y.; Zhong, S.Z.; Sun, J. Single count of radicle emergence and mean germination time estimate seed vigour of Chinese milk vetch (Astragalus sinicus). Seed Sci. Technol. 2022, 50, 47–59. [Google Scholar] [CrossRef]
  19. Cheshmi, M.; Khajeh-Hosseini, M. Single count of radicle emergence, DNA replication during seed germination and vigour in alfalfa seed lots. Seed Sci. Technol. 2020, 48, 367–380. [Google Scholar] [CrossRef]
  20. dos Santos, L.A.; Carvalho, I.R.; Pinto, C.C.; Szareski, V.J.; Netto, J.F.; de Medeiros, L.R.; Martins, A.B.N.; Bilhalva, N.S.; Marchi, P.M.; Pimentel, J.R.; et al. Electrical conductivity test for measurement of white clover seeds vigor. J. Agric. Sci. 2019, 11, 40–49. [Google Scholar] [CrossRef]
  21. Artola, A.; Castañeda, G.C. The bulk conductivity test for birdsfoot trefoil seed. Seed Sci. Technol. 2005, 33, 231–236. [Google Scholar] [CrossRef]
  22. Cheshmi, M.; Khajeh-Hosseini, M. Effect of temperature on length of the lag period and its relationship with field performance of alfalfa (Medicago sativa) seeds. Seed Sci. Technol. 2018, 46, 317–326. [Google Scholar] [CrossRef]
  23. Finch-Savage, W.E.; Bassel, G.W. Seed vigour and crop establishment: Extending performance beyond adaptation. J. Exp. Bot. 2015, 67, 567–591. [Google Scholar] [CrossRef] [PubMed]
  24. Soleymani, A. Safflower (Carthamus tinctorius L.) seed vigor tests for the prediction of field emergence. Ind. Crops Prod. 2019, 131, 378–386. [Google Scholar] [CrossRef]
  25. Lv, Y.Y.; Mo, Q.; Powell, A.A.; Wang, Y.R. DNA replication during seed germination, deterioration, and its relation to vigor in alfalfa and white clover. Crop Sci. 2018, 58, 1393–1401. [Google Scholar] [CrossRef]
  26. Artola, A.; Castañeda, G.C. Accelerated aging time estimation for birdsfoot trefoil seed. Seed Sci. Technol. 2005, 33, 493–497. [Google Scholar] [CrossRef]
  27. Modarresi, R.; Damme, P.V. Application of the controlled deterioration test to evaluate wheat seed vigour. Seed Sci. Technol. 2003, 31, 771–775. [Google Scholar] [CrossRef]
  28. Luo, Y.; Guan, Y.J.; Huang, Y.T.; Li, J.; Li, Z.; Hu, J. Single counts of radicle emergence provides an alternative method to test seed vigour in sweet corn. Seed Sci. Technol. 2015, 43, 519–525. [Google Scholar] [CrossRef]
  29. Javaid, M.M.; Mahmood, A.; Alshaya, D.S.; AlKahtani, M.D.F.; Waheed, H.; Wasaya, A.; Khan, S.A.; Naqve, M.; Haider, I.; Shahid, M.A.; et al. Influence of environmental factors on seed germination and seedling characteristics of perennial ryegrass (Lolium perenne L.). Sci. Rep. 2022, 12, 9522. [Google Scholar] [CrossRef]
  30. Idris, L.M.; Nulit, R.; Zaman, F.Q.; Arifin, F.K.M. Hydrotime analysis of Amaranthus spp. Seed germination under salinity condition. J. Appl. Res. Med. Aromat. Plants 2020, 17, 100249. [Google Scholar] [CrossRef]
  31. Hu, X.W.; Fan, Y.; Baskin, C.C.; Baskin, J.M.; Wang, Y.R. Comparison of the effects of temperature and water potential on seed germination of Fabaceae species from desert and subalpine grassland. Am. J. Bot. 2015, 102, 649–660. [Google Scholar] [CrossRef] [PubMed]
  32. Zhang, R.; Luo, K.; Chen, D.L.; Baskin, J.M.; Baskin, C.C.; Wang, Y.R.; Hu, X.W. Comparison of thermal and hydrotime requirements for seed germination of seven Stipa species from cool and warm habitats. Front. Plant Sci. 2020, 11, 560714. [Google Scholar] [CrossRef] [PubMed]
  33. Gummerson, R.J. The effect of constant temperatures and osmotic potentials on the germination of sugar beet. J. Exp. Bot. 1986, 37, 729–741. [Google Scholar] [CrossRef]
  34. Bradford, K.J. A water relation analysis of seed germination rates. Plant Physiol. 1990, 94, 840–849. [Google Scholar] [CrossRef] [PubMed]
  35. Farahinia, P.; Sadat-Noori, S.A.; Mortazavian, M.M.; Soltani, E.; Foghi, B. Hydrotime model analysis of Trachyspermum ammi (L.) Sprague seed germination. J. Appl. Res. Med. Aromat. Plants 2017, 5, 88–91. [Google Scholar] [CrossRef]
  36. Shaygan, M.; Baumgartl, T.; Arnold, S. Germination of Atriplex halimus seeds under salinity and water stress. Ecol. Eng. 2017, 102, 636–640. [Google Scholar] [CrossRef]
  37. Bradford, K.J.; Still, D.W. Applications of hydrotime analysis in seed testing. Seed Technol. 2004, 26, 74–85. [Google Scholar]
  38. López, A.S.; López, D.R.; Arana, M.V.; Batlla, D.; Marchelli, P. Germination response to water availability in populations of Festuca pallescens along a Patagonian rainfall gradient based on hydrotime model parameters. Sci. Rep. 2021, 11, 10653. [Google Scholar] [CrossRef]
  39. Chen, X.L.; Wei, Z.C.; Chen, D.L.; Hu, X.W. Base water potential but not hydrotime predicts seedling emergence of Medicago sativa under water stress conditions. PeerJ 2022, 10, e13206. [Google Scholar] [CrossRef]
  40. Soltani, E.; Farzaneh, S. Hydrotime analysis for determination of seed vigour in cotton. Seed Sci. Technol. 2014, 42, 260–273. [Google Scholar] [CrossRef]
  41. Soltani, E.; Adeli, R.; Akbari, G.A.; Ramshini, H. Application of hydrotime model to predict early vigour of rapeseed (Brassica napus L.) under abiotic stresses. Acta Physiol. Plant 2017, 39, 252. [Google Scholar] [CrossRef]
  42. Farzane, S.; Soltani, E. Relationships between hydrotime parameters and seed vigor in sugar beet. Seed Sci. Biotechnol. 2011, 5, 7–10. [Google Scholar]
  43. Tatari, S.; Ghaderi-Far, F.; Yamchi, A.; Siahmarguee, A.; Shayanfar, A.; Baskin, C.C. Application of the hydrotime model to assess seed priming effects on the germination of rapeseed (Brassica napus L.) in response to water stress. Botany 2020, 98, 283–291. [Google Scholar] [CrossRef]
  44. Romano, A.; Bravi, R. Hydrotime model to evaluate the effects of a set of priming agents on seed germination of two leek cultivars under water stress. Seed Sci. Technol. 2021, 49, 159–174. [Google Scholar] [CrossRef]
  45. Windauer, L.; Altuna, A.; Benech-Arnold, R. Hydrotime analysis of Lesquerella fendleri seed germination responses to priming treatments. Ind. Crops Prod. 2007, 25, 70–74. [Google Scholar] [CrossRef]
  46. Li, R.; Min, D.D.; Chen, L.J.; Chen, C.Y.; Hu, X.W. Hydropriming accelerates seed germination of Medicago sativa under stressful conditions: A thermal and hydrotime model approach. Legume Res. 2017, 40, 741–747. [Google Scholar] [CrossRef]
  47. Maleki, K.; Maleki, K.; Soltani, E.; Oveisi, M.; Gonzalez-Andujar, J.L. A model for changes in germination synchrony and its implements to study weed population dynamics: A case study of Brassicaceae. Plants 2023, 12, 233. [Google Scholar] [CrossRef]
  48. Min, X.Y.; Wang, Q.X.; Wei, Z.W.; Liu, Z.P.; Liu, W.X. Full-length transcriptional analysis reveals the complex relationship of leaves and roots in responses to cold-drought combined stress in common vetch. Front. Plant Sci. 2022, 13, 976094. [Google Scholar] [CrossRef]
  49. Bhat, K.A.; Mahajan, R.; Pakhtoon, M.M.; Urwat, U.; Bashir, Z.; Shah, A.A.; Agrawal, A.; Bhat, B.; Sofi, P.A.; Masi, A.; et al. Low temperature stress tolerance: An insight into the omics approaches for legume crops. Front. Plant Sci. 2022, 13, 888710. [Google Scholar] [CrossRef]
  50. Li, Y.; Sun, Y.; Cui, H.T.; Li, M.N.; Yang, G.F.; Wang, Z.Y.; Zhang, K. Carex rigescens caffeic acid O-methyltransferase gene CrCOMT confer melatonin-mediated drought tolerance in transgenic tobacco. Front. Plant Sci. 2022, 13, 971431. [Google Scholar] [CrossRef]
  51. Wu, H.W.; Asaduzzaman, M.; Shephard, A.; Hopwood, M.; Ma, X.Y. Germination and emergence characteristics of prickly lettuce (Lactuca serriola L.). Crop Prot. 2020, 136, 105222. [Google Scholar] [CrossRef]
  52. Mavi, K.; Light, M.E.; Demir, I.; van Staden, J.; Yasar, F. Positive effect of smoke-derived butanolide priming on melon seedling emergence and growth. N. Z. J. Crop Hortic. Sci. 2010, 38, 147–155. [Google Scholar] [CrossRef]
  53. Müller, F.; Masemola, L.; Britz, E.; Ngcobo, N.; Modiba, S.; Cyster, L.; Samuels, I.; Cupido, C.; Raitt, L. Seed germination and early seedling growth responses to drought stress in annual Medicago L. and Trifolium L. forages. Agronomy 2022, 12, 2960. [Google Scholar] [CrossRef]
  54. Miano, A.C.; Augusto, P.E.D. From the sigmoidal to downward concave shape behavior during the hydration of grains: Effect of the initial moisture content on Adzuki beans (Vigna angularis). Food Bioprod. Process. 2015, 96, 43–51. [Google Scholar] [CrossRef]
  55. Ducatti, K.R.; Batista, T.B.; Hirai, W.Y.; Luccas, D.A.; Moreno, L.A.; Guimarães, C.C.; Bassel, G.W.; da Silva, E.A.A. Transcripts expressed during germination Sensu Stricto are associated with vigor in soybean seeds. Plants 2022, 11, 1310. [Google Scholar] [CrossRef] [PubMed]
  56. Matthews, S.; Khajeh-Hosseini, M. Length of the lag period of germination and metabolic repair explain vigour differences in seed lots of maize (Zea mays). Seed Sci. Technol. 2007, 35, 200–212. [Google Scholar] [CrossRef]
  57. Mavi, K.; Demir, I.; Matthews, S. Mean germination time estimates the relative emergence of seed lots of three cucurbit crops under stress conditions. Seed Sci. Technol. 2010, 38, 14–25. [Google Scholar] [CrossRef]
  58. Yan, H.F.; Yu, X.D.; Jia, W.; Mao, P.S. Length of the lag period of germination predicts the vigour differences and field emergence potential in Italian ryegrass (Lolium multiflorum) seed lots. Seed Sci. Technol. 2017, 45, 238–242. [Google Scholar] [CrossRef]
  59. Luo, Y.; Lin, C.; Fu, Y.Y.; Huang, Y.T.; He, F.; Guan, Y.J.; Hu, J. Single counts of radicle emergence can be used as a fast method to test seed vigour of indica rice. Seed Sci. Technol. 2017, 45, 222–229. [Google Scholar] [CrossRef]
  60. Mao, P.S.; Zhang, X.Y.; Sun, Y.; Zhang, W.X.; Wang, Y.W. Relationship between the length of lag period of germination and the emergence performance of oat (Avena sativa) seeds. Seed Sci. Technol. 2013, 41, 281–291. [Google Scholar] [CrossRef]
  61. Venuste, M.; Chen, D.L.; Hu, X.W. Detection of seed vigour differences in Festuca sinensis seed lots. Seed Sci. Technol. 2022, 50, 61–75. [Google Scholar]
  62. Venuste, M.; Li, D.M.; Jia, P.; Hu, X.W. Various vigour test methods to rank seed lot quality and predict field emergence in two forage grasses. Seed Sci. Technol. 2022, 50, 345–356. [Google Scholar]
  63. Patanè, C.; Saita, A.; Tubeileh, A.; Cosentino, S.L.; Cavallaro, V. Modeling seed germination of unprimed and primed seeds of sweet sorghum under PEG-induced water stress through the hydrotime analysis. Acta Physiol. Plant. 2016, 38, 115. [Google Scholar] [CrossRef]
  64. Bakhshandeh, E.; Gholamhossieni, M. Quantification of soybean seed germination response to seed deterioration under PEG-induced water stress using hydrotime concept. Acta Physiol. Plant. 2018, 40, 126. [Google Scholar] [CrossRef]
  65. Krichen, K.; Mariem, H.B.; Chaieb, M. Ecophysiological requirements on seed germination of a Mediterranean perennial grass (Stipa tenacissima L.) under controlled temperatures and water stress. S. Afr. J. Bot. 2014, 94, 210–217. [Google Scholar] [CrossRef]
  66. Cavallaro, V.; Barbera, A.C.; Maucieri, C.; Gimma, G.; Scalisi, C.; Patanè, C. Evaluation of variability to drought and saline stress through the germination of different ecotypes of carob (Ceratonia siliqua L.) using a hydrotime model. Ecol. Eng. 2016, 95, 557–566. [Google Scholar] [CrossRef]
  67. Windauer, L.B.; Martinez, J.; Rapoport, D.; Wassner, D.; Benech-Arnold, R. Germination responses to temperature and water potential in Jatropha curcas seeds: A hydrotime model explains the difference between dormancy expression and dormancy induction at different incubation temperatures. Ann. Bot. 2012, 109, 265–273. [Google Scholar] [CrossRef]
  68. Schellenberg, M.P.; Biligetu, B.; Wei, Y. Predicting seed germination of slender wheatgrass [Elymus trachycaulus (Link) Gould subsp. trachycaulus] using thermal and hydro time models. Can. J. Plant Sci. 2013, 93, 793–798. [Google Scholar] [CrossRef]
  69. Allen, P.S.; Thorne, E.T.; Gardner, J.S.; White, D.B. Is the barley endosperm a water reservoir for the embryo when germinating seeds are dried? Int. J. Plant Sci. 2000, 161, 195–201. [Google Scholar] [CrossRef] [PubMed]
  70. Dahal, P.; Bradford, K.J. Effects of priming and endosperm integrity on germination rates of tomato genotypes. II. Germination at reduced water potential. J. Exp. Bot. 1990, 41, 1441–1453. [Google Scholar] [CrossRef]
  71. Bradford, K.J.; Somasco, O.A. Water relations of lettuce seed thermoinhibition. I. Priming and endosperm effects on base water potential. Seed Sci. Res. 1994, 4, 1–10. [Google Scholar] [CrossRef]
  72. Michel, B.E.; Kaufmann, M.R. The osmotic potential of polyethylene glycol 6000. Plant Physiol. 1973, 51, 914–916. [Google Scholar] [CrossRef]
  73. Fallahi, H.; Aghhavani-Shajari, M.; Mohammadi, M.; Kadkhodaei-Barkook, R.; Zareei, E. Predicting of flixweed (Descurainia sophia (L.) Webb ex Prantl) germination response to temperature using regression models. J. Appl. Res. Med. Aromat. Plants 2017, 6, 131–134. [Google Scholar] [CrossRef]
  74. Tao, Q.B.; Lv, Y.Y.; Mo, Q.; Bai, M.J.; Han, Y.H.; Wang, Y.R. Impacts of priming on seed germination and seedling emergence of Cleistogenes songorica under drought stress. Seed Sci. Technol. 2018, 46, 239–258. [Google Scholar] [CrossRef]
  75. Yang, X.J.; Baskin, C.C.; Baskin, J.M.; Liu, G.Z.; Huang, Z.Y. Seed mucilage improves seedling emergence of a sand desert shrub. PLoS ONE 2012, 7, e34597. [Google Scholar] [CrossRef]
  76. Zhao, H.J.; Liu, Q.L.; Fu, H.W.; Hu, X.H.; Wu, D.X.; Shu, Q.Y. Effect of non-lethal low phytic acid mutations on grain yield and seed viability in rice. Field Crops Res. 2008, 108, 206–211. [Google Scholar] [CrossRef]
  77. Cheng, Z.; Bradford, K.J. Hydrothermal time analysis of tomato seed germination responses to priming treatments. J. Exp. Bot. 1999, 50, 89–99. [Google Scholar] [CrossRef]
  78. Huarte, R. Hydrotime analysis of the effect of fluctuating temperatures on seed germination in several non-cultivated species. Seed Sci. Technol. 2006, 34, 533–547. [Google Scholar] [CrossRef]
Figure 1. Seed germination rate and germination index of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) in response to water potential. Three replications were performed for each treatment of each seed lot, and each point is the average value of three replications.
Figure 1. Seed germination rate and germination index of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) in response to water potential. Three replications were performed for each treatment of each seed lot, and each point is the average value of three replications.
Plants 12 01876 g001
Figure 2. Germination time courses of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.). The symbols are the observed data, and the curves are the results of fitting by the hydrotime model. Three replications were performed for each treatment of each seed lot, and each point is the average value of three replications. Numbers in brackets refer to the seed lot.
Figure 2. Germination time courses of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.). The symbols are the observed data, and the curves are the results of fitting by the hydrotime model. Three replications were performed for each treatment of each seed lot, and each point is the average value of three replications. Numbers in brackets refer to the seed lot.
Plants 12 01876 g002
Figure 3. Dynamics of soil water content under control and water stress conditions during pot experiment.
Figure 3. Dynamics of soil water content under control and water stress conditions during pot experiment.
Plants 12 01876 g003
Figure 4. Field emergence percentage, seedling dry weight, and simplified vigor index of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) in two field sowings. Three replications were performed for each seed lot, and each point is the average value of three replications. Different lowercase letters indicate significant differences among different seed lots at the p < 0.05 probability level according to Duncan’s multiple comparison test.
Figure 4. Field emergence percentage, seedling dry weight, and simplified vigor index of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) in two field sowings. Three replications were performed for each seed lot, and each point is the average value of three replications. Different lowercase letters indicate significant differences among different seed lots at the p < 0.05 probability level according to Duncan’s multiple comparison test.
Plants 12 01876 g004
Figure 5. Regression analysis of Ψb(50) with germination characteristics, emergence percentage, seedling dry weight, and simplified vigor index of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) under various environmental conditions. * Indicates significance at p < 0.05 probability level, ** indicates significance at p < 0.01 probability level, and *** indicates significance at p < 0.001 probability level. SGP: seed germination percentage; GR: germination rate; GI: germination index; PEP: pot emergence percentage; SDW: seedling dry weight; SVI: simplified vigor index; FEP: field emergence percentage; Ψb(50): base water potential for 50% of seeds to germinate. SGP, GR, and GI are the overall means with five levels of water potential and three replications. Three replications were performed for each treatment of each seed lot, and the regression analysis was performed using the average value of three replications of emergence data.
Figure 5. Regression analysis of Ψb(50) with germination characteristics, emergence percentage, seedling dry weight, and simplified vigor index of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) under various environmental conditions. * Indicates significance at p < 0.05 probability level, ** indicates significance at p < 0.01 probability level, and *** indicates significance at p < 0.001 probability level. SGP: seed germination percentage; GR: germination rate; GI: germination index; PEP: pot emergence percentage; SDW: seedling dry weight; SVI: simplified vigor index; FEP: field emergence percentage; Ψb(50): base water potential for 50% of seeds to germinate. SGP, GR, and GI are the overall means with five levels of water potential and three replications. Three replications were performed for each treatment of each seed lot, and the regression analysis was performed using the average value of three replications of emergence data.
Plants 12 01876 g005
Figure 6. Daily mean temperature and precipitation during two field emergence experimental periods in Jiaozhou, Shandong Province, China.
Figure 6. Daily mean temperature and precipitation during two field emergence experimental periods in Jiaozhou, Shandong Province, China.
Plants 12 01876 g006
Table 1. Seed germination percentage (%) of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) in response to water potential.
Table 1. Seed germination percentage (%) of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) in response to water potential.
Seed LotWater Potential (MPa)
0.0−0.2−0.4−0.6−0.8
190.0 abc58.9 ef36.7 cd31.1 de0.0 c
285.6 c51.1 f28.9 d17.8 e0.0 c
396.7 a87.8 ab57.8 ab48.9 abc1.1 c
493.3 abc62.2 ef48.9 bc35.6 cd1.1 c
591.1 abc81.1 bc47.8 bcd38.9 bcd0.0 c
692.2 abc81.1 bc55.6 abc51.1 abc3.3 abc
788.9 bc86.7 abc52.2 abc45.6 abcd3.3 ab
893.3 ab76.7 cd50.0 bc44.4 abcd0.0 c
990.0 abc77.8 cd58.9 ab51.1 ab6.7 a
1093.3 abc68.9 de43.3 bcd37.8 bcd0.0 c
1190.0 abc82.2 bc60.0 ab45.6 abcd2.2 c
1295.6 ab91.1 a68.9 a56.7 a6.7 ab
Analysis of variance
Source of varianceDegrees of freedomSum of squaresMean squareFp
Seed lot (SL)111.5820.14415.653<0.001
Water potential (WP)428.0777.019764.101<0.001
SL × WP440.8920.0202.206<0.001
Different lowercase letters within a column indicate significant differences at the p < 0.05 probability level according to Duncan’s multiple comparison test. Germination percentage data were arcsine-transformed before statistical analysis, and non-transformed data are shown in the table. Three replications were performed for each treatment of each seed lot, and each point is the average value of three replications.
Table 2. Estimated hydrotime model parameters for 12 seed lots of Chinese milk vetch (Astragalus sinicus L.).
Table 2. Estimated hydrotime model parameters for 12 seed lots of Chinese milk vetch (Astragalus sinicus L.).
Seed LotθH (MPa·h)Ψb(50) (MPa)σφbr2
114.171−0.3350.3090.885
214.770−0.2780.2760.906
313.606−0.4850.2830.876
410.086−0.3770.2700.844
511.819−0.4430.2860.917
68.897−0.4410.3050.783
713.618−0.4790.3190.859
814.114−0.4500.2910.847
913.763−0.4750.3220.838
1012.814−0.3980.2670.866
1113.636−0.4890.3330.879
128.799−0.5220.2990.833
θH is constant hydrotime, Ψb(50) is base water potential for 50% of seeds to germinate, and σφb is standard deviation of Ψb(50). The coefficient of determination (r2) shows the goodness of fit of the model. Three replications were performed for each treatment of each seed lot, and the above hydrotime model parameters were calculated using the average value of three replications of germination data.
Table 3. Effects of different environmental conditions (control, water stress, salinity stress, deep sowing, and cold stress) on seedling emergence percentage, seedling dry weight, and simplified vigor index of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) in pot experiments.
Table 3. Effects of different environmental conditions (control, water stress, salinity stress, deep sowing, and cold stress) on seedling emergence percentage, seedling dry weight, and simplified vigor index of 12 seed lots of Chinese milk vetch (Astragalus sinicus L.) in pot experiments.
Seed LotControl ConditionsWater StressSalinity StressDeep SowingCold Stress
PEP (%)SDW (mg plant−1)SVIPEP (%)SDW (mg plant−1)SVIPEP (%)SDW (mg plant−1)SVIPEP (%)SDW (mg plant−1)SVIPEP (%)SDW (mg plant−1)SVI
166.7 ab49.9 abc3280.1 de35.6 cd26.1 cd924.5 def38.9 bcd28.4 efg1088.3 cd28.9 cd15.3 b432.3 de41.1 cd22.7 cdef924.9 e
254.4 b39.7 d2153.4 f28.9 d15.4 f433.5 g26.7 d24.1 g631.2 e15.6 e9.0 c135.2 f32.2 e17.7 f561.1 f
377.8 a53.9 ab4175.9 ab43.3 abc34.6 ab1484.9 bc41.1 bc30.9 def1262.6 bcd33.3 abcd22.1 a729.4 c44.4 bc25.0 bcde1099.1 de
473.3 a45.3 cd3306.8 de41.1 bc20.7 e849.4 f44.4 abc26.3 fg1173.8 bcd31.1 bcd14.7 b453.2 de48.9 abc22.0 def1079.3 de
577.8 a43.4 cd3360.9 cde45.6 abc23.2 de1045.5 de43.3 abc33.7 cde1465.6 bc32.2 bcd12.9 b408.3 de50.0 abc27.0 bcd1350.1 bc
680.0 a50.2 abc4029.1 abc44.4 abc29.9 bc1324.0 c46.7 abc27.3 fg1262.0 bcd38.9 abc21.9 a844.7 b54.4 ab22.0 def1194.5 cd
774.4 a56.9 a4213.2 ab47.8 ab32.5 ab1540.0 b51.1 ab40.8 ab2093.2 a40.0 ab25.2 a1008.3 a53.3 ab29.5 b1538.2 b
872.2 ab42.4 cd3048.9 e41.1 bc26.4 cd1081.2 d42.2 abc35.6 bcd1497.3 b32.2 bcd16.1 b508.7 d52.2 abc27.3 bcd1425.7 bc
981.1 a47.3 bcd3846.3 bcd37.8 bcd23.1 de869.3 ef38.9 bcd37.2 abc1434.6 bc26.7 d13.1 b346.5 e43.3 bcd21.7 def927.8 e
1066.7 ab42.8 cd2848.3 e35.6 cd23.9 de848.0 f34.4 cd26.8 fg919.6 de25.6 d16.2 b396.8 de43.3 bcd20.4 ef886.1 e
1178.9 a50.2 abc3943.0 abcd46.7 ab32.9 ab1533.1 b48.9 ab39.2 abc1912.9 a41.1 ab24.7 a1011.0 a52.2 abc28.3 bc1471.9 b
1283.3 a55.6 a4629.4 a52.2 a36.8 a1906.0 a53.3 a42.2 a2241.6 a43.3 a25.6 a1108.4 a57.8 a39.0 a2235.9 a
ANOVA
SV df PEP SDW SVI
SL 11 *** *** ***
EC 4 *** *** ***
SL × EC 44 ns *** ***
*** Indicates significance at p < 0.001 probability level; ns indicates not significant at p < 0.05 probability level. Different lowercase letters within a column indicate significant difference at p < 0.05 probability level according to Duncan’s multiple comparison test. Emergence percentage data were arcsine-transformed before statistical analysis, and non-transformed data are shown in the table. Three replications were performed for each treatment and each seed lot, and each point is the average value of three replications. PEP: pot emergence percentage; SDW: seedling dry weight; SVI: simplified vigor index; ANOVA: analysis of variance; SV: source of variance; df: degrees of freedom; SL: seed lot; EC: environmental condition.
Table 4. Pearson correlation coefficient (r) between overall mean of germination percentage, germination rate, and germination index (overall mean of five levels of water potential and three replications), emergence percentage, seedling dry weight, and simplified vigor index under different environmental conditions (control, water stress, salinity stress, deep sowing, and cold stress) and in two field sowings with hydrotime model parameters (θH, Ψb(50), and σφb) for 12 Chinese milk vetch (Astragalus sinicus L.) seed lots.
Table 4. Pearson correlation coefficient (r) between overall mean of germination percentage, germination rate, and germination index (overall mean of five levels of water potential and three replications), emergence percentage, seedling dry weight, and simplified vigor index under different environmental conditions (control, water stress, salinity stress, deep sowing, and cold stress) and in two field sowings with hydrotime model parameters (θH, Ψb(50), and σφb) for 12 Chinese milk vetch (Astragalus sinicus L.) seed lots.
Experiment ConditionsVariableθH (MPa·h)Ψb(50) (MPa)σφb
Germination testGermination percentage (%)−0.463−0.972 (<0.001)0.424
Germination rate (day−1)−0.702 (0.011)−0.847 (0.001)0.234
Germination index−0.623 (0.030)−0.929 (<0.001)0.312
PotControl conditions
Emergence percentage (%)−0.531−0.900 (<0.001)0.476
Seedling dry weight (mg plant−1)−0.268−0.642 (0.024)0.546
Simplified vigor index−0.458−0.858 (<0.001)0.563
Water stress
Emergence percentage (%)−0.557−0.853 (<0.001)0.391
Seedling dry weight (mg plant−1)−0.290−0.807 (0.002)0.476
Simplified vigor index−0.415−0.845 (0.001)0.462
Salinity stress
Emergence percentage (%)−0.552−0.780 (0.003)0.504
Seedling dry weight (mg plant−1)−0.031−0.820 (0.001)0.666 (0.018)
Simplified vigor index−0.284−0.834 (0.001)0.616 (0.033)
Deep sowing
Emergence percentage (%)−0.523−0.803 (0.002)0.518
Seedling dry weight (mg plant−1)−0.347−0.742 (0.006)0.485
Simplified vigor index−0.423−0.754 (0.005)0.529
Cold stress
Emergence percentage (%)−0.630 (0.028)−0.776 (0.003)0.351
Seedling dry weight (mg plant−1)−0.375−0.734 (0.007)0.341
Simplified vigor index−0.508−0.763 (0.004)0.335
FieldFirst sowing
Emergence percentage (%)−0.567−0.825 (0.001)0.519
Seedling dry weight (mg plant−1)−0.191−0.600 (0.039)0.527
Simplified vigor index−0.469−0.810 (0.001)0.566
Second sowing
Emergence percentage (%)−0.437−0.884 (<0.001)0.397
Seedling dry weight (mg plant−1)−0.383−0.741 (0.006)0.700 (0.011)
Simplified vigor index−0.490−0.883 (<0.001)0.617 (0.033)
θH is constant hydrotime, Ψb(50) is base water potential for 50% of seeds to germinate, and σφb is standard deviation of Ψb(50). Numbers in brackets indicate the significance of the coefficients. Three replications were performed for each treatment of each seed lot, and the correlation analysis was performed using the average value of three replications of emergence data.
Table 5. Production year, storage period, initial seed moisture content (SMC), thousand-seed weight (TSW), and proportion of hard seeds (HS) in 12 seed lots of Chinese milk vetch (Astragalus sinicus L.).
Table 5. Production year, storage period, initial seed moisture content (SMC), thousand-seed weight (TSW), and proportion of hard seeds (HS) in 12 seed lots of Chinese milk vetch (Astragalus sinicus L.).
Seed LotProduction YearStorage Period (Years)SMC (%)TSW (g)Proportion of HS (%)
1201848.763.3592.2
2201668.523.3922.2
3201758.693.3830.0
4201758.613.3891.1
5201938.693.5012.2
6201848.483.3841.1
7201848.573.4624.4
8201938.863.4312.2
9201938.883.4802.2
10201758.723.4270.0
11202119.103.3903.3
12202118.933.4152.2
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Tao, Q.; Chen, D.; Bai, M.; Zhang, Y.; Zhang, R.; Chen, X.; Sun, X.; Niu, T.; Nie, Y.; Zhong, S.; et al. Hydrotime Model Parameters Estimate Seed Vigor and Predict Seedling Emergence Performance of Astragalus sinicus under Various Environmental Conditions. Plants 2023, 12, 1876. https://doi.org/10.3390/plants12091876

AMA Style

Tao Q, Chen D, Bai M, Zhang Y, Zhang R, Chen X, Sun X, Niu T, Nie Y, Zhong S, et al. Hydrotime Model Parameters Estimate Seed Vigor and Predict Seedling Emergence Performance of Astragalus sinicus under Various Environmental Conditions. Plants. 2023; 12(9):1876. https://doi.org/10.3390/plants12091876

Chicago/Turabian Style

Tao, Qibo, Dali Chen, Mengjie Bai, Yaqi Zhang, Ruizhen Zhang, Xiaofei Chen, Xiaotong Sun, Tianxiu Niu, Yuting Nie, Shangzhi Zhong, and et al. 2023. "Hydrotime Model Parameters Estimate Seed Vigor and Predict Seedling Emergence Performance of Astragalus sinicus under Various Environmental Conditions" Plants 12, no. 9: 1876. https://doi.org/10.3390/plants12091876

APA Style

Tao, Q., Chen, D., Bai, M., Zhang, Y., Zhang, R., Chen, X., Sun, X., Niu, T., Nie, Y., Zhong, S., & Sun, J. (2023). Hydrotime Model Parameters Estimate Seed Vigor and Predict Seedling Emergence Performance of Astragalus sinicus under Various Environmental Conditions. Plants, 12(9), 1876. https://doi.org/10.3390/plants12091876

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

Article Metrics

Back to TopTop