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Article

Selection of Tomato and Cucumber Accessions for Waterlogging Sensitivity through Morpho-Physiological Assessment at an Early Vegetative Stage

by
Anna Kołton
1,*,
Kinga Kęska
2 and
Małgorzata Czernicka
2
1
Department of Botany, Physiology and Plant Protection, Faculty of Biotechnology and Horticulture, University of Agriculture in Krakow, al. 29 Listopada 54, 31-425 Krakow, Poland
2
Department of Plant Biology and Biotechnology, Faculty of Biotechnology and Horticulture, University of Agriculture in Krakow, al. 29 Listopada 54, 31-425 Krakow, Poland
*
Author to whom correspondence should be addressed.
Agronomy 2020, 10(10), 1490; https://doi.org/10.3390/agronomy10101490
Submission received: 24 August 2020 / Revised: 18 September 2020 / Accepted: 27 September 2020 / Published: 1 October 2020
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

:
Waterlogging anomalies have recently increased, causing a reduction in yield and the loss of billions of dollars. Plant selection for increased tolerance to stress factors requires parameters with high sensitivity, as well as fast and inexpensive measurements. The aim of this study was to select tomato and cucumber accessions that reveal sensitivity and tolerance to waterlogging stress at an early vegetative stage. The selection of effective criteria for assessing plants was also an important issue. A total of 19 cucumber (including four highly homozygous) and 16 tomato accessions were evaluated, and plants with three true leaves were examined. The root zone of stressed plants was waterlogged for 7 days in a deep container. Morphological and physiological characteristics were obtained after 7 days of treatment and used for cluster analysis for discrimination of tolerant and sensitive accessions. Significant decreases in Fv/F0, Fv/Fm, Area, PI ABS, ET0/ABS, and ET0/TR0 parameters, as well as increases in DI0/RC, were observed in sensitive accessions, with no changes in tolerant plants. The OJIP test parameters (Fv/F0, PI ABS, DI0/RC, and Area) were more sensitive in selecting for waterlogging stress than Fv/Fm. The present research can be used in breeding programs. Selected accessions will support a detailed explanation of the physiological differences in response to waterlogging stress in tomato and cucumber plants.

1. Introduction

As a result of climate change, waterlogging events have increased, causing billions of dollars’ worth of crop losses [1,2,3]. The yield loss caused by waterlogging may vary between 15% and 80%, depending on the species (cotton, maize, wheat, rice, and soybean); soil type; and the duration of stress [4,5]. A reduction in the yield of vegetables due to flooding stress has also been observed, in tomato (Solanum lycopersicum L.) by 40% and sweet potato (Ipomea batatas L. Poir) up to 56% [6]. Understanding the morphological, physiological, and molecular mechanisms that underlie waterlogging (WL) tolerance presents a challenge to research. The establishment of selection criteria for WL-tolerant genotypes and breeding of WL-tolerant cultivars are critical for the expansion of cultivation, particularly in areas with frequent and high rainfall. An ideal WL-tolerant cultivar should not only survive waterlogging, but also rapidly recover to the control level [4].
In agricultural soils, waterlogging often occurs because of heavy rainfall, but can also be due to inadequate soil drainage. Taking into account the height of the water surface produced, flooding could be classified as waterlogging when it covers only the roots, or as submergence when water completely covers the plant [7]. The saturation of soil with water reduces gas exchange with the atmosphere, causing the oxygen concentration to decrease rapidly and leading to O2 deficiency (hypoxia) or O2 absence (anoxia). Oxygen diffuses about 10,000 times slower in water than in the air, and this restricts aerobic respiration by the roots [8]. Limited oxygen availability for plants often occurs in hydroponic cultivation in environments without appropriate aeration [9] and also is induced by improper irrigation [10].
Plants growing in waterlogged soils can tolerate oxygen deficiency by shifting from aerobic to anaerobic respiration, although the latter is less efficient for ATP production. Moreover, this process produces harmful metabolic products that could cause plant death, i.e., acetaldehyde or lactic acid [11,12]. Most crops are WL-sensitive; however, the extent of damage depends on the species, the stage of development, and the climatic conditions, as well as on the duration of exposure to stress [1]. Tomato (Solanum lycopersicum L.) and cucumber (Cucumis sativus L.) are classified as sensitive to root hypoxia [13,14,15,16,17,18], although differences amongst genotypes regarding their tolerance to this stress have been reported [19,20,21]. The variability of responses to root hypoxia among genotypes suggests that different strategies have evolved to deal with the stress. Tolerance to waterlogging mostly depends on the ability to develop specialized structures, allowing aeration of the tissues, which includes the formation of aerenchyma, adventitious roots, stem hypertrophic lenticels, and stem cracks [22,23,24,25]. Waterlogging stress induces senescence, resulting in leaf chlorosis, necrosis, and leaf loss [26]. The root system is strongly affected, as evidenced by the reduction in wheat (Triticum aestivum L.) root biomass [27]. Under waterlogging conditions, physiological disturbances are induced in plants, such as stomata closure and reductions in transpiration and photosynthetic rates, leaf water potential and transport of carbohydrates, reduced absorption of nutrients, and hormonal changes [28,29,30].
High sensitivity parameters are used for the selection of plants with increased tolerance to a stress factor. These also need to be fast and inexpensive because analysis of huge plant populations is required. Visual symptoms (visual assessment) parameters related to agronomic characteristics, such as yield or growth, molecular markers, and physiological parameters, are used to assess plant tolerance to stress factors [31,32,33,34]. As early as 1983, researchers used chlorophyll fluorescence to assess the effect of stress on the photosynthetic apparatus of plants, with the authors of the study suggesting their usefulness in plant breeding [35]. Since then, chlorophyll fluorescence has been used in many studies to assess the effect of stress (including waterlogging stress) on green parts of plants, and these have confirmed the usefulness of this method (for example [36,37,38]). Therefore, in our research, we have applied growth parameters as well as chlorophyll fluorescence parameters for the evaluation of plant tolerance to waterlogging stress.
Plenty of studies have demonstrated that plants at the seedling stage were consistently used for, among others, screening genotypes that displayed tolerance to variety of stresses, such as flooding in barley (Hordeum vulgare L.) [37] and hypoxia in cotton (Gossypium hirsutum L.) [39]. In the case of the tomato, plants at the seedling stage were used for the selection of plants tolerant to chilling [40], heat [41], and salinity [42,43], whereas cucumber seedlings, according to the literature, were subjected to submergence in order to select ones tolerant and sensitive to a lack of oxygen [44]. As Zou [34] reported, plants of Brassica napus L. that reveal tolerance to waterlogging at the seedling stage can demonstrate the same tolerance at later developmental stages, and moreover, assessment of tolerance at the seedling stage can be more efficient.
The aim of this study was to evaluate height, weight, leaf number, and chlorophyll fluorescence parameters of tomato and cucumber accessions and their responses to waterlogging stress at an early vegetative stage. Our research could be useful in indicating accessions that may be exploited as potential parental lines in breeding programs to develop waterlogging-tolerant cultivars. Moreover, an important issue is indicating effective selection criteria for the assessment of plant waterlogging stress tolerance. The hypothesis of this study was that accessions differ with tolerance to waterlogging stress. Furthermore, their morphological and physiological characteristics can be used for discrimination of the tolerance of tomato and cucumber plants to waterlogging stress at the seedling stage, with cluster analysis being useful for the indication of more tolerant and sensitive accessions. The presented results have the potential to be further used by breeders and scientists for developing cultivars with improved hypoxia tolerance and increased yield production under waterlogging stress.

2. Materials and Methods

2.1. Plant Materials and Cultivation

Seeds of 19 cucumber and 16 tomato accessions were provided by Polish breeding companies, i.e., KHiNO Polan, PlantiCo, and Spójnia HiNO (Table 1). Seeds were sown in 40-cell multi-pots; each cell had volume of 0.23 dm3. Cells were fulfilled with peat substrate Klasmann KTS-2 (Germany). According to the manufacturer, the peat substrate contained, as follows (in mg dm−3): 250–500 N, 170–230 P2O5, 320–500 K2O, and 80–120 Mg. The salinity and pH were 2.0 g dm−3 and 5.5–6.5, respectively. Seeds were cultivated in a greenhouse and, after germination, were lit with supplementary radiation (High-Pressure Sodium HPS lamps) to prolong the day length to 16 h. Minimum photosynthetic photon flux density (PPFD) on plant level during the day was 80 ± 20 µmol m−2 s−1 (when only radiation from HPS lamps reached the plants). The ambient temperature during tomato cultivation was 25.1 ± 4.8 °C in the day, and 22.3 ± 6.0 °C in the night. During cucumber cultivation, the daily average temperature was 27.6 ± 6.1 °C, and the night temperature was 24.1 ± 7.1 °C.

2.2. Stress Treatment

Tomato and cucumber seedlings, at the 3–4 fully expanded mature leaf stage, were divided into 2 equal groups: the Control and Stress groups. Before stress treatment, the percent volumetric water content (VWC) was measured using a Delta-T Devices SM150 soil moisture sensor kit (Delta-T Devices Ltd., Cambridge, United Kingdom) and plants were watered to obtain a soil moisture level up to 30%. The root zone of tomato and cucumber plants from the Stress group were waterlogged for 7 days (Figure 1) in a deep tray containing water. Plants from the Control group were watered as needed. The oxygen level in the air and in the water were monitored during the experiment by a Dissolved Oxygen (DO) Meter (HI 2040-02 edge, Hanna instruments, Woonsocket, RI, USA). The oxygen concentration in the water reached a value of 2.6 mg dm−3 (air saturation = 29.2%, temperature = 20 °C) and that level was maintained to the end of the stress treatment, whereas in the air the oxygen concentration was 9.20 mg dm−3.

2.3. Growth Analysis

Before the waterlogging treatment, we labelled 20 random plants from the Control (C) and Stress groups (S) for further analysis. At the 0 time-point and after 7 days of waterlogging, we determined the numbers of leaves on Control and Stress plants. After 7days of treatment, the following parameters were measured: plant height (only shoots) (cm), measured with a ruler, and stem weight (with leaves) (g), determined by a laboratory scale (Ohaus, Parsippany, NJ, USA) (Figure 1). Plant height and weight were presented as a percentage ratio (%), assuming Control values as 100%.

2.4. Chlorophyll a Fluorescence Analysis

Chlorophyll a fluorescence was measured on the third leaf from the top of the plant, after 30 min dark adaptation with a special clip. The analyses were made after treatment with 3500 µmol m−2 s−1 light intensity. In the case of each accession, we performed the measurements on 8 plants from the Control or Stress groups. Chlorophyll a fluorescence was measured using a HandyPea portable fluorometer (Hansatech, King’s Lynn, UK). The fast phase of the fluorescence transient was denoted as OJIP, where the letters indicate characteristic points on the fluorescence induction curve: O is for origin (first measured minimal level), J and I are intermediates, and P is the maximum level of fluorescence curve. For simplicity, the analysis of OJIP fluorescence transient was called the JIP-test [45]. Some of the JIP-test parameters were calculated with formulas from Stirbet and Govindjee [45] and Stirbet et al. [46], as follows: F0 (minimum chlorophyll a fluorescence), Fm (maximum chlorophyll a fluorescence after dark adaptation), Fv (maximum variable fluorescence), Fv/F0 (ratio of the photochemical and non-photochemical processes in photosystem II (PSII), the maximum efficiency of the photochemical processes of PSII), Fv/Fm (the maximum quantum yield of PSII photochemistry), Tfm (time to reach the maximum chlorophyll fluorescence), Area (area above the OJIP transient and Fm line), Fm/F0 (the stable parameter in healthy leaves, value between 4–5), PI ABS (performance index on an absorption basis), ABS/RC (absorbed photon flux per PSII reaction center (RC) or apparent antenna size of an active PSII), TR0/RC (maximum trapped exciton flux per active PSII), ET0/RC (the flux of electrons transferred from the primary electron acceptor (QA) per active PSII reaction center), DI0/RC (the flux of energy dissipated in processes other than trapping per active PSII reaction center), ET0/ABS (quantum yield of electron transport from QA), and ET0/TR0 (efficiency with which a PSII trapped electron is transferred from QA).

2.5. Statistical Analysis

Euclidean distances were computed using Ward’s method between samples from Control and Stress groups of each tomato and cucumber accession. Prior to analysis, we standardized the data. Ward’s method was applied and observations with high values of measured features were clustered together and the same rule was applied with low values of parameter observations. After cluster analysis was performed, we created a dendrogram with a marked cut-off point dividing the analyzed objects into distinct clusters. The cut-off point was set at a clear clustering point and is marked with a colored line in the graph. Differences between clusters and between Control and Stress groups were determined using Student’s t-test. The level of significance was established as p < 0.05. All data analyses were made using STATISTICA 13 (TIBCO Software Inc. (2017) from Statistica (data analysis software system, version 13. http://statistica.io)).

3. Results

The presented results compared the response of 15 cucumber and 16 tomato accessions to waterlogging stress. For clear presentation, we divided the results into two subsections. Additional analyses were made with four homozygous accessions of cucumber and are presented in the Supplementary Materials Section.

3.1. Cucumber

Cluster analysis based on all tested parameters and 30 treatments (15 cucumber accessions each as Control and Stress) were classified into two discrete groups with an Euclidean distance of 20 (Figure 2). Nine treatments were included in cluster 1 and 21 others were included in cluster 2.
The differences between cluster 1 and 2 are presented in Table 2. Cluster 1 consisted of groups with favorable values of determined parameters, in contrast to cluster 2, where groups with less favorable values were clustered. Interestingly, the mean value of two parameters (increase in leaf number and Tfm) were similar in both clusters. Other parameters were significantly different between clusters. A significant decrease in growth, Fm, Fv, Fv/F0, Fv/Fm, Area, Fm/F0, PI ABS, ET0/RC, ET0/ABS, and ET0/TR0 parameters were observed in cluster 2 as compared to cluster 1. As well as an increase in weight, we also observed increases in F0, ABS/RC, TR0/RC, and DI0/RC.
On the basis of Figure 2 and Figure 3, we assigned accessions GM-50 and G404 of both Control and Stress groups in cluster 1, which were close to each other. This meant that their parameters did not change under stress conditions, and thus these accessions could have been considered as tolerant to hypoxia stress. To select one of these, we conducted a comparison of morphological and physiological parameters in Control and Stress groups, followed by statistical analysis (t-test, p < 0.05) (Table 3). As a result, in GM-50, two morphological parameters were changed between control and stressed conditions (the decrease of weight change and height change in stress treatment was observed), whereas in G404, four parameters were statistically disparate (the decrease of weight change, height change, and leaf number, as well as increase of Tfm in stress-treated plants were noticed). According to that analysis, we selected plants from cucumber accession GM-50 as they were more tolerant to oxygen deprivation in the root zone.
The selection of sensitive cucumber accession was based on the analysis of distances between accessions and their Control and Stress groups presented in Figure 2 and Figure 3. Accessions GMG-30 and TYTUS were selected as hypothetically sensitive accessions since their Control groups were assigned to cluster 1, whereas the Stress groups were assigned to cluster 2, indicating dissimilarity in parameter values. TYTUS was chosen as a sensitive cucumber accession due to the number of changed parameters between control and stress conditions, i.e., 13, whereas in GMG-30, only four appeared to be different (Table 4).
The parameters of both the more tolerant (GM-50) and more sensitive (TYTUS) accessions are presented in Figure 4. Control parameters were set as 1 and the parameters of Stress-treated plants were expressed as a percentage of Control. On the presented radar graph, it is easy to notice differences in the response of both genotypes to the given stress.
In the case of cucumber accessions provided by the Polish breeding company, we carried out additional experiments with highly homozygous plants (see the Supplementary Materials Section). Homozygous lines of cucumber plants were classified into two groups according to cluster analysis (Figure S1). Five treatments were included in cluster 1, and three others into cluster two. Cluster 1 consisted of groups with better values of determined parameters, in contrast to cluster 2, where groups with worse values were clustered. Differences between cluster 1 and 2 are presented in Table S1; according to Figures S1 and S2, DH2 and DH1 accessions were classified as more tolerant. As a result, DH2 was chosen as a more tolerant form for further investigation. The selection of accessions sensitive to hypoxia stress was based on the assumption that Control and Stress will be in separate clusters. The most sensitive accession was DH4, because the Control plants were included in Cluster 1 and the Stress plants in Cluster 2. This indicated a significant deterioration of parameters after stress treatment.

3.2. Tomato

The Figure 5 illustrates the relationship among tomato accessions on the basis of differences in morphological and physiological parameters. It was observed that tomato plants were grouped into two main clusters with an Euclidean distance of 25 (Figure 5). In cluster one, we included 13 treatments, whereas 19 were included for cluster two.
The differences between cluster 1 and 2 are presented in Table 5. Cluster 1 consisted of groups with favorable values of determined parameters, in contrast to cluster 2, where groups with worse values were included. It can be observed that physiological parameters had the main impact on distance calculations, whereas morphological parameters did not influence the hierarchical process.
Cluster 1 mostly consisted of plants from Control groups of tomato accessions, revealing favorable values of tested parameters, whereas Stress groups were mostly assigned to Cluster 2. However, in Cluster 1, there were control and stress-treated plants from the three accessions (PZ 715, POL 8/15, and POL 7/15); this meant that waterlogging stress did not have negative impact on changes in the parameters. According to this, accessions PZ 715, POL 8/15, and POL 7/15 were considered as more tolerant to oxygen deprivation. Going further in the classification, we conducted a comparison of parameters between Control and Stress groups in the accessions selected above (Table 6). Statistical analysis indicated that in POL 8/15, eight parameters changed under waterlogging stress, whereas in POL 7/15, only two of all estimated parameters were unstable. Therefore, accession POL 7/15 was selected as the most tolerant tomato accession to waterlogging.
When searching for more sensitive accessions, we chose those included in both clusters and with a large Euclidean distance. The Euclidean distance matrix depicts the Control and Stress groups of the PZ 215 accession that were furthest apart and, as a result, were selected as more sensitive (Figure 6). Moreover, we selected PZ 115 Control and Stress groups with large distance. Both selected accessions are compared in Table 7. Controls of both presented accessions were included in cluster 1 and those under Stress treatment in cluster 2. However, more parameters were changed after stress treatment in the case of PZ 215. Figure 6 demonstrates elements of the Euclidean distance matrix of tested accessions and confirms POL 7/15 and PZ 215 as accessions with an opposite response to oxygen deprivation.
As a summary, the radar charts were created for POL 7/15 and PZ 215 tomato accessions, defined as more tolerant and more sensitive, respectively (Figure 7). The radar charts strongly highlighted the differences in response to waterlogging stress in selected tomato accessions. There were statistically significant differences in the weight and height of plants between the Control and Stress groups in POL 7/15, and thus only morphological parameters changed. In case of PZ 215, only 2 of 18 parameters were stable: Fm and ET0/RC (Table 7 and Figure 7).

4. Discussion

The reaction of tomato or cucumber accessions to waterlogging stress is diversified. As we have shown, accessions can be grouped for those whose parameters significantly worsen after stress and those that do not show a significant deterioration in functioning. In the presented research, we focused on the response of the aerial part to stress present within the root system. During hypoxia of the root system, signals to the aboveground part—often found in optimal oxygen conditions—are transmitted within the plant body [47,48,49]. The signal that moves from the root to the aboveground part during hypoxia stress changes the functioning of the shoots. The most important process in the aboveground part of plants is photosynthesis, which generates energy and carbohydrates. Stress conditions affect photosynthesis (see the review in [50]). Chloroplasts, key organelles for photosynthesis, are highly sensitive to many stress factors. The photosynthesis process can be disrupted due to decreases in pigment content, changes in electron transport, or disorders in the activities of enzymes related to CO2 fixation. Moreover, limitations in gas diffusion (CO2 and water) can be observed due to stomata closure. Therefore, it is reasonable to study the intensity of photosynthesis or chlorophyll a fluorescence during stresses involving the root system, such as hypoxia, salinity, or others that interfere in the functioning of the plant. For example, decreases in net photosynthesis as well as a decline in maximal photochemical efficiency of PSII after hypoxia have been observed in sensitive accessions of cotton [39], while in tolerant forms, the changes were not observed. The JIP-test, widely discussed since 1995, is a good tool for comparing stressed and control samples and allows investigation of the effects of almost any stress factor, although only if the stress affects photosynthesis [51]. For example, Kalaji et al. [52] tested the impact of 14 abiotic stress factors on barley by fast chlorophyll fluorescence kinetics and used this method as a crop phenotyping tool. Chlorophyll fluorescence measurement is a fast and non-destructive method to analyze the photosynthetic apparatus [53], allowing for the detection of stress effects before the visible signs are noticed [52]. Thus, in our research, we chose parameters related to plant growth and chlorophyll a fluorescence in the leaves.
After performing statistical analysis, we divided the studied waterlogged and control accessions into clusters. In both species, significant decreases in Fm, Fv, Fv/F0, Fv/Fm, Area, Fm/F0, PI ABS, ET0/RC, ET0/ABS, and ET0/TR0 parameters were observed in cluster 2 as compared to cluster 1. In addition, increases in DI0/RC was observed. According to this information, we conclude that in cluster 1, plants had parameters with more favorable values, and in cluster 2, plants had worse parameters (Table 2, Table 5, and Table S1). The decrease in Fm or Fv/Fm is connected with a lower ability of PSII to reduce the QA primary acceptor [54]. Furthermore, the decrease in Fv/F0 during stress could be an indicator of lower efficiency of photochemical processes in PSII [55]. The Area parameter represents the pool size of electron acceptors in PSII, with this pool size being lower during reductions in electron transport in submergence stress [54]. The lower value of ET0/RC is also indicator of disturbances in electron transport from QA to other acceptors. Panda et al. [54] state that both the donor and the acceptor side of PSII were damaged because of submergence. The PI ABS index includes information about the probability that the chlorophyll a molecule functions as a reactive center in PSII, the efficiency of transfer of the absorbed energy to the reduction of QA, and the probability that an electron moves further than QA. It is a very sensitive parameter that decreases during stress conditions [51]. When the photochemistry of photosynthesis is disrupted by stress factors, the dissipation of absorbed energy increases [56], and this can be observed as increase in the DI0/RC value.
Considering the results of the cluster analysis, we chose a more tolerant and more sensitive accession in both species. The parameters of more tolerant accessions did not deteriorate after appropriate stress, and the Control and Stress group of such plants were in cluster 1. In the case of more sensitive accessions, the parameters significantly deteriorated, and the Control group was in cluster 1 and the Stress-treated group in cluster 2. The changes in OJIP test parameters after submergence stress were more pronounced in sensitive rice (Oryza sativa L.) cultivars than in tolerant ones [54]. Similarly, in our experiments, more significant differences, indicating the deterioration of the photosynthetic apparatus, between Stress and Control plants could be observed in the case of sensitive accessions compared with that in more tolerant accessions (Figure 4 and Figure 7). For example, the Fv/F0, Fv/Fm, Area, Fm/F0, PI ABS, and DI0/RC parameters remained at the same level after stress treatment in the case of more tolerant accessions, but changed in sensitive accessions after stress. Increases in DI0/RC in more sensitive accessions of both species indicated that some of the absorbed energy was dissipated and not used in the photochemistry of photosynthesis. In the case of more tolerant plants, DI0/RC was stable. In agreement with our observation of more sensitive samples, we observed increases in DI0/RC parameter in cucumber plants after hypoxia stress [56]. The decrease in Fv/Fm was noted in more sensitive tomato and cucumber plants in our experiments. In agreement with our results, a decrease in Fv/Fm after waterlogging was also observed in rice [54,57], cucumber [56,58], tomato [59,60], wild tomato (Solanum habrochaites S.Knapp & D.M.Spooner) [61], Arabidopsis (Arabidopsis thaliana L.) [62], cotton [39], and pepper (Capsicum annuum L.) plants [63]. Barik et al. [57] examined the reaction of tolerant and susceptible varieties of rice to submergence and observed that the latter exhibited a greater reduction in Fv/Fm parameters in comparison to tolerant varieties. Similarly, in an experiment with cotton, the Fv/Fm parameter was stable in tolerant varieties and significantly decreased in sensitive varieties under hypoxia [39]. In the case of sorghum after waterlogging stress, the changes in Fv/Fm parameters were not significant, despite the observation of a substantial decrease in the Fv/F0 parameter [64]. In the present experiment, the decrease in Fv/Fm in more sensitive tomato genotypes was about 16%, and for the Fv/F0 parameter, this was about 33% after stress treatment; in sensitive cucumber genotypes, these figures were 2.4% and 11.5%, respectively. This suggests that Fv/F0 is a more sensitive parameter than Fv/Fm, consistent with the findings of Tsimilli-Michael [51]. However, the parameter Fv/Fm is more often described in the literature. Kalaji et al. [52] observed that PI ABS is the most sensitive parameter to different stress conditions. A decrease in this parameter was observed under waterlogging stress in terms of rice [54], cucumber [56], or tomato [59]. In the present experiments, the decrease in PI ABS in the case of sensitive tomato plants was about 56%, and for cucumber plants this was about 28% after stress treatment. This parameter did not change in more tolerant accessions after waterlogging stress. Our observations confirm the high sensitivity and usefulness of this parameter in plant selection to waterlogging stress.
Many studies have presented results of plant morphological observations after waterlogging stress; however, sometimes inconsistent information can be found. Decreases in plant height were observed after waterlogging in cucumber [56] and field bean (Vicia faba L. minor) [65]. Six cotton varieties, sensitive and tolerant to hypoxia stress, were tested by Pan et al. [39], and an inhibition in plant growth was observed in more sensitive varieties. However, plant height in stressed tolerant cotton was similar to untreated plants. In the terms of fresh mass of plants, we observed a decrease in terms of cucumber [58], barley [38], and pepper [63] after stress treatment, but no changes were observed by He et al. [56] in cucumber fresh weight after hypoxia. In our results, some accessions indicated an increase in morphological parameters, whereas others decreased. It is worth mentioning that both selected accessions of cucumber and tomato (tolerant and sensitive) demonstrated a decrease in plant height after stress treatment. The appearance of selected accessions is presented in Figure S3. The inhibition of growth does not seem to be correlated with the activity of PSII.
Using physiological parameters and cluster analysis, Barik and co-workers [57] classified seven rice cultivars into two clusters. Cluster one included submergence-tolerant rice varieties whereas susceptible varieties were included in cluster two. These data indicated the usefulness of cluster analysis in stress tolerance classification. Moreover, in our experiment, cluster analysis helped in classification, although the procedure was slightly different. Cluster analysis can be used in stress tolerance plant classification, as also demonstrated by Cao et al. [40], wherein the authors used cluster analysis to divide tomato genotypes into those that are more or less tolerant to chilling stress.
As part of our cooperation with Polish breeders, we also conducted a sensitivity assessment of homozygous cucumber lines for waterlogging stress and selected more tolerant and more sensitive lines (see the Supplementary Materials Section). Selected cucumber homozygous lines can be used for basic research on stress resistance or for breeding new varieties adapted to new breeding programs. The results can be of use not only to Polish breeders, but also to international breeders. According to previous information, choosing more tolerant accessions maintained better PSII activity.
Two tools were used in the present work: chlorophyll a fluorescence was applied as the main tool to assess the state of the photosynthetic apparatus of stress-treated plants and statistical analysis was used to select sensitive and tolerant accessions on the basis of the obtained empirical data. The selected objects will be used for further analysis related to understanding the mechanisms of the stress response to hypoxia in tomato and cucumber plants. In future research, we plan to evaluate the effect of waterlogging of selected tomato and cucumber plants on the photosynthetic rate, chlorophyll and carotenoid accumulation, and other parameters related to the functioning of leaves. We will also investigate if there are differences between selected accessions in terms of yield quality and quantity during stress.

5. Conclusions

Chlorophyll a fluorescence can be used for the selection of plant accessions sensitive to waterlogging stress.
Not all parameters of the OJIP test seem to have the same sensitivity; Fv/F0, PI ABS, as well as DI0/RC or Area appear to be better in selecting sensitivity to waterlogging stress than Fv/Fm.
From the tested accessions, we selected GM-50, POL 7/15, and DH2 as more tolerant, whereas TYTUS, PZ 215, and DH4 were determined to be more sensitive for waterlogging stress.

Supplementary Materials

The following are available online at https://www.mdpi.com/2073-4395/10/10/1490/s1, Figure S1: Results of cluster analysis for cucumber homozygous lines using the Euclidean distance on the basis of morphological and physiological traits (Ward’s hierarchical algorithm); green line indicates the cut-off point. Table S1: The mean values of parameters determined for each cluster and statistical comparison between clusters estimated in cucumber homozygous lines (bold p-values mean statistically significant differences between cluster 1 and cluster 2). Figure S2: Euclidean distance between Control and Stress groups of each cucumber homozygous lines. White bars indicate accessions from control and stress treatments that were classified into cluster 1; light grey bars indicate accessions from Control and Stress treatments that were classified into different clusters; black bars indicate accessions from Control and Stress treatments that were classified into cluster 2. Figure S3: Accessions GM-50, POL 7/15, and DH2 selected as more tolerant, and TYTUS, PZ 215, and DH4 determined to be more sensitive for waterlogging stress. C: Control plants, S: Stress-treated plants.

Author Contributions

Conceptualization, A.K. and M.C.; methodology, A.K.; formal analysis, A.K. and K.K.; investigation, A.K., M.C., and K.K.; data curation, A.K. and K.K.; writing—original draft preparation, A.K., M.C., and K.K.; writing—review and editing, A.K., M.C., and K.K.; visualization, A.K., K.K., and M.C.; funding acquisition, M.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Polish Ministry of Agriculture and Rural Development (no. HORhn-801-PB-12/15, 2015–2020) and by the Ministry of Science and Higher Education of Poland (SUB/2020–050012-D011).

Acknowledgments

The authors would like to thank Urszula Pieniążek for excellent technical assistance. Explicit thanks are addressed to Polish seed and breeding companies, i.e., KHiNO Polan, PlantiCo, and Spójnia HiNO for providing the breeding stocks for analysis.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Bailey-Serres, J.; Lee, S.C.; Brinton, E. Waterproofing crops: Effective flooding survival strategies. Plant Physiol. 2012, 160, 1698–1709. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Pachauri, R.K.; Meyer, L.A. (Eds.) Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014; 151p, Available online: https://www.ipcc.ch/site/assets/uploads/2018/05/SYR_AR5_FINAL_full_wcover.pdf (accessed on 20 August 2020).
  3. FAO. The Future of Food and Agriculture—Trends and Challenges. Rome, Italy. 2017. Available online: http://www.fao.org/3/a-i6583e.pdf (accessed on 20 August 2020).
  4. Patel, P.K.; Singh, A.K.; Yadav, D.; Hemantaranjan, A.; Tripathi, N. Flooding: Abiotic constraint limiting vegetable productivity. Adv. Plants Agric. Res. 2014, 1, 96–103. [Google Scholar]
  5. Kaur, G.; Singh, G.; Motavalli, P.P.; Nelson, K.A.; Orlowski, J.M.; Golden, B.R. Impacts and management strategies for crop production in waterlogged or flooded soils: A review. Agron. J. 2020, 112, 1475–1501. [Google Scholar] [CrossRef] [Green Version]
  6. Rao, R.; Li, Y. Management of flooding effects on growth of vegetable and selected field crops. HortTechnology 2003, 13, 610–616. [Google Scholar] [CrossRef]
  7. Sasidharan, R.; Bailey-Serres, J.; Ashikari, M.; Atwell, B.J.; Colmer, T.D.; Fagerstedt, K.; Fukao, T.; Geigenberger, P.; Hebelstrup, K.H.; Hill, R.D.; et al. Community recommendations on terminology and procedures used in flooding and low oxygen stress research. New Phytol. 2017, 214, 1403–1407. [Google Scholar] [CrossRef] [Green Version]
  8. Bailey-Serres, J.; Voesenek, L.A.C.J. Flooding stress: Acclimations and genetic diversity. Annu. Rev. Plant Biol. 2008, 59, 313–339. [Google Scholar] [CrossRef] [Green Version]
  9. Que, F.; Wang, G.; Feng, K.; Xu, Z.S.; Wang, F.; Xiong, A.S. Hypoxia enhances lignification and affects the anatomical structure in hydroponic cultivation of carrot taproot. Plant Cell Rep. 2018, 37, 1021–1032. [Google Scholar] [CrossRef]
  10. Bansal, R.; Srivastava, J.P. Effect of waterlogging on photosynthetic and biochemical parameters in pigeonpea. Russ. J. Plant Physiol. 2015, 62, 322–327. [Google Scholar] [CrossRef]
  11. Braun, K.P.; Cody, R.B., Jr.; Jones, D.R.; Peterson, C.M. A structural assignment for a stable acetaldehyde-lysine adduct. J. Biol. Chem. 1995, 270, 11263–11266. [Google Scholar] [CrossRef] [Green Version]
  12. An, Y.; Qi, L.; Wang, L. ALA pretreatment improves waterlogging tolerance of fig plants. PLoS ONE 2016, 11, e0147202. [Google Scholar] [CrossRef]
  13. Ahsan, N.; Lee, D.G.; Lee, S.H.; Lee, K.W.; Bahk, J.D.; Lee, B.H. A proteomic screen and identification of waterlogging regulated proteins in tomato roots. Plant Soil 2007, 295, 37–51. [Google Scholar] [CrossRef]
  14. Li, J.; Sun, J.; Yang, Y.; Guo, S.; Glick, B.R. Identification of hypoxic-responsive proteins in cucumber roots using a proteomic approach. Plant Physiol. Biochem. 2012, 51, 74–80. [Google Scholar] [CrossRef] [PubMed]
  15. Xu, X.; Wang, H.; Qi, X.; Xu, Q.; Chen, X. Waterlogging-induced increase in fermentation and related gene expression in the root of cucumber (CucumissativusL.). Sci. Hortic 2014, 179, 388–395. [Google Scholar]
  16. He, L.; Li, B.; Lu, X.; Yuan, L.; Yang, Y.; Yuan, Y.; Du, J.; Guo, S. The effect of exogenous calcium on mitochondria, respiratory metabolism enzymes and ion transport in cucumber roots under hypoxia. Sci. Rep. 2015, 5, 11391. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Xu, X.; Ji, J.; Ma, X.; Xu, Q.; Qi, X.; Chen, X. Comparative proteomic analysis provides insight into the key proteins involved in cucumber (Cucumissativus L.) Adventitious root emergence under waterlogging stress. Front. Plant Sci. 2016, 7, 1515. [Google Scholar] [CrossRef] [Green Version]
  18. Hou, Y.; Jiang, F.; Zheng, X.; Wu, Z. Identification and analysis of oxygen responsive microRNAs in the root of wild tomato (S. habrochaites). BMC Plant Biol. 2019, 19, 100. [Google Scholar] [CrossRef] [Green Version]
  19. Kuo, C.G.; Chen, B.W. Physiological responses of tomato cultivars to flooding. J. Am. Soc. Hort. Sci. 1980, 105, 751–755. [Google Scholar]
  20. Ezin, V.; Pena, R.D.L.; Ahanchede, A. Flooding tolerance of tomato genotypes during vegetative and reproductive stages. Braz. J. Plant Physiol. 2010, 22, 131–142. [Google Scholar] [CrossRef]
  21. Safavi-Rizi, V.; Herde, M.; Stöhr, C. RNA-Seq reveals novel genes and pathways associated with hypoxia duration and tolerance in tomato root. Sci. Rep. 2020, 10, 1692. [Google Scholar] [CrossRef]
  22. Justin, S.H.F.W.; Armstrong, W. The anatomical characteristics of roots and plant response to soil flooding. New Phytol. 1987, 106, 465–495. [Google Scholar] [CrossRef]
  23. Jackson, M.B. Plant survival in wet environments: Resilience and escape mediated by shoot systems. In Wetlands: Functioning, Biodiversity, Conservation and Restoration. Ecological Studies (Analysis and Synthesis); Bobbink, R., Beltman, B., Verhoeven, J.T.A., Whigham, D.F., Eds.; Springer: Berlin/Heidelberg, Germany, 2006; Volume 191, pp. 16–36. [Google Scholar]
  24. Voesenek, L.A.C.J.; Colmer, T.D.; Pierik, R.; Millenaar, F.F.; Peeters, A.J.M. How plants cope with complete submergence. New Phytol. 2006, 170, 213–226. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Shimamura, S.; Yamamoto, R.; Nakamura, T.; Shimada, S.; Komatsu, S. Stem hypertrophic lenticels and secondary aerenchyma enable oxygen transport to roots of soybean in flooded soil. Ann. Bot. 2010, 106, 277–284. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Zeng, F.; Shabala, L.; Zhou, M.; Zhang, G.; Shabala, S. Barley responses to combined waterlogging and salinity stress: Separating effects of oxygen deprivation and elemental toxicity. Front. Plant Sci. 2013, 4, 313. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Herzog, M.; Striker, G.G.; Colmer, T.D.; Pedersen, O. Mechanisms of waterlogging tolerance in wheat–a review of root and shoot physiology. Plant Cell Environ. 2016, 39, 1068–1086. [Google Scholar] [CrossRef]
  28. Irfan, M.; Hayat, S.; Hayat, Q.; Afroz, S.; Ahmad, A. Physiological and biochemical changes in plants under waterlogging. Protoplasma 2010, 241, 3–17. [Google Scholar] [CrossRef]
  29. Pimentel, P.; Alamada, R.D.; Salvatierra, A.; Toro, G.; Arismendi, M.J.; Pinto, M.T.; Sagredo, B.; Pinto, M. Physiological and morphological responses of Prunus species with different degree of tolerance to long-term root hypoxia. Sci. Hortic. 2014, 180, 14–23. [Google Scholar] [CrossRef]
  30. Ren, B.; Zhang, J.; Dong, S.; Liu, P.; Zhao, B. Effects of waterlogging on leaf mesophyll cell ultrastructure and photosynthetic characteristics of summer maize. PLoS ONE 2016, 11, e0161424. [Google Scholar] [CrossRef] [Green Version]
  31. Stoddard, F.L.; Balko, C.; Erskine, W.; Khan, H.R.; Link, W.; Sarker, A. Screening techniques and sources of resistance to abiotic stresses in cool-season food legumes. Euphytica 2006, 147, 167–186. [Google Scholar] [CrossRef]
  32. Zhou, M. Improvement of plant waterlogging tolerance. In Waterlogging Signalling and Tolerance in Plants; Mancuso, S., Shabala, S., Eds.; Springer: Berlin/Heidelberg, Germany, 2010; pp. 267–285. [Google Scholar]
  33. Zhou, M. Accurate phenotyping reveals better QTL for waterlogging tolerance in barley. Plant Breed. 2011, 130, 203–208. [Google Scholar] [CrossRef]
  34. Zou, X.; Hu, C.; Zeng, L.; Cheng, Y.; Xu, M.; Zhang, X. A Comparison of screening methods to identify waterlogging tolerance in the field in Brassica napus L. during plant ontogeny. PLoS ONE 2014, 9, e89731. [Google Scholar] [CrossRef] [Green Version]
  35. Smillie, R.M.; Hetherington, S.E. Stress tolerance and stress-induced injury in crop plants measured by chlorophyll fluorescence in vivo: Chilling, freezing, ice cover, heat, and high light. Plant Physiol. 1983, 72, 1043–1050. [Google Scholar] [CrossRef] [PubMed]
  36. Smethurst, C.F.; Shabala, S. Screening methods for waterlogging tolerance in lucerne: Comparative analysis of waterlogging effects on chlorophyll fluorescence, photosynthesis, biomass and chlorophyll content. Funct. Plant Biol. 2003, 30, 335–343. [Google Scholar] [CrossRef] [PubMed]
  37. Pang, J.Y.; Zhou, M.H.; Mendham, N.J.; Shabala, S. Growth and physiological responses of six barley genotypes to waterlogging and subsequent recovery. Aust. J. Agric. Res. 2004, 55, 895–906. [Google Scholar] [CrossRef]
  38. Bertholdsson, N.O. Screening for barley waterlogging tolerance in nordic barley cultivars (Hordeumvulgare L.) using chlorophyll fluorescence on hydroponically-grown plants. Agronomy 2013, 3, 376–390. [Google Scholar] [CrossRef] [Green Version]
  39. Pan, R.; Jiang, W.; Wang, Q.; Xu, L.; Shabala, S.; Zhang, W.Y. Differential response of growth and photosynthesis in diverse cotton genotypes under hypoxia stress. Photosynthetica 2019, 57, 772–779. [Google Scholar] [CrossRef] [Green Version]
  40. Cao, X.; Jiang, X.; Wang, X.; Zang, Y.; Wu, Z. Comprehensive evaluation and screening for chilling-tolerance in tomato lines at the seedling stage. Euphytica 2015, 205, 569–584. [Google Scholar] [CrossRef]
  41. Zhou, R.; Yu, X.; Kjær, K.H.; Rosenqvist, E.; Ottosen, C.O.; Wu, Z. Screening and validation of tomato genotypes under heat stress using Fv/Fm to reveal the physiological mechanism of heat tolerance. Environ. Exp. Bot. 2015, 118, 1–11. [Google Scholar] [CrossRef]
  42. Dasgan, H.Y.; Aktas, H.; Abak, K.; Cakmak, I. Determination of screening techniques of salinity tolerance in tomatoes and investigation of genotype responses. Plant Sci. 2002, 163, 695–703. [Google Scholar] [CrossRef]
  43. Raza, M.A.; Saeed, A.; Munir, H.; Ziaf, K.; Shakeel, A.; Saeed, N.; Munawar, A.; Rehman, F. Screening of tomato genotypes for salinity tolerance based on early growth attributes and leaf inorganic osmolytes. Arch. Agron. Soil Sci. 2017, 63, 501–512. [Google Scholar] [CrossRef]
  44. Qi, X.; Chen, R.; Xu, Q.; Chen, X. Preliminary analysis of cucumber submergence tolerance at seedling stage. China Veg. 2011, 4, 7. [Google Scholar]
  45. Stirbet, A.; Govindjee. On the relation between the Kautsky effect (chlorophyll a fluorescence induction) and photosystem II: Basics and applications of the OJIP fluorescence transient. J. Photochem. Photobiol. B Biol. 2011, 104, 236–257. [Google Scholar] [CrossRef] [PubMed]
  46. Stirbet, A.; Lazár, D.; Kromdijk, J.; Govindjee. Chlorophyll a fluorescence induction: Can just a one-second measurement be used to quantify abiotic stress responses? Photosynthetica 2018, 56, 86–104. [Google Scholar] [CrossRef]
  47. Jackson, M.B. Long-distance signalling from roots to shoots assessed: The flooding story. J. Exp. Bot. 2002, 53, 175–181. [Google Scholar] [CrossRef] [PubMed]
  48. Dat, J.F.; Capelli, N.; Folzer, H.; Bourgeade, P.; Badot, P.M. Sensing and signalling during plant flooding. Plant Physiol. Biochem. 2004, 42, 273–282. [Google Scholar] [CrossRef]
  49. Sasidharan, R.; Hartman, S.; Liu, Z.; Martopawiro, S.; Sajeev, N.; van Veen, H.; Yeung, E.; Voesenek, L.A. Signal dynamics and interactions during flooding stress. Plant Physiol. 2018, 176, 1106–1117. [Google Scholar] [CrossRef] [Green Version]
  50. Ashraf, M.H.P.J.C.; Harris, P.J. Photosynthesis under stressful environments: An overview. Photosynthetica 2013, 51, 163–190. [Google Scholar] [CrossRef]
  51. Tsimilli-Michael, M. Revisiting JIP-test: An educative review on concepts, assumptions, approximations, definitions and terminology. Photosynthetica 2019, 58, 275–292. [Google Scholar] [CrossRef] [Green Version]
  52. Kalaji, H.M.; Rastogi, A.; Živčák, M.; Brestic, M.; Daszkowska-Golec, A.; Sitko, K.; Alsharafa, K.Y.; Lotfi, R.; Stypiński, P.; Samborska, I.A.; et al. Prompt chlorophyll fluorescence as a tool for crop phenotyping: An example of barley landraces exposed to various abiotic stress factors. Photosynthetica 2018, 56, 953–961. [Google Scholar] [CrossRef] [Green Version]
  53. Maxwell, K.; Johnson, G.N. Chlorophyll fluorescence—A practical guide. J. Exp. Bot. 2000, 51, 659–668. [Google Scholar] [CrossRef]
  54. Panda, D.; Rao, D.N.; Sharma, S.G.; Strasser, R.J.; Sarkar, R.K. Submergence effects on rice genotypes during seedling stage: Probing of submergence driven changes of photosystem 2 by chlorophyll a fluorescence induction OJIP transients. Photosynthetica 2006, 44, 69–75. [Google Scholar] [CrossRef]
  55. Roháček, K. Chlorophyll fluorescence parameters: The definitions, photosynthetic meaning, and mutual relationships. Photosynthetica 2002, 40, 13–29. [Google Scholar] [CrossRef]
  56. He, L.; Yu, L.; Li, B.; Du, N.; Guo, S. The effect of exogenous calcium on cucumber fruit quality, photosynthesis, chlorophyll fluorescence, and fast chlorophyll fluorescence during the fruiting period under hypoxic stress. BMC Plant Biol. 2018, 18, 180. [Google Scholar] [CrossRef] [PubMed]
  57. Barik, J.; Panda, D.; Mohanty, S.K.; Lenka, S.K. Leaf photosynthesis and antioxidant response in selected traditional rice landraces of Jeypore tract of Odisha, India to submergence. Physiol. Mol. Biol. Plants 2019, 25, 847–863. [Google Scholar] [CrossRef] [PubMed]
  58. Ma, Y.H.; Guo, S.R. 24-epibrassinolide improves cucumber photosynthesis under hypoxia by increasing CO2 assimilation and photosystem II efficiency. Photosynthetica 2014, 52, 96–104. [Google Scholar] [CrossRef]
  59. Hüther, C.M.; Martinazzo, E.G.; Rombaldi, C.V.; Bacarin, M.A. Effects of flooding stress in ‘Micro-Tom’ tomato plants transformed with different levels of mitochondrial sHSP23.6. Braz.J. Biol. 2017, 77, 43–51. [Google Scholar] [CrossRef] [Green Version]
  60. Mauro, R.P.; Agnello, M.; Distefano, M.; Sabatino, L.; Primo, A.S.B.; Leonardi, C.; Giuffrida, F. Chlorophyll fluorescence, photosynthesis and growth of tomato plants as affected by long-term oxygen root zone deprivation and grafting. Agronomy 2020, 10, 137. [Google Scholar] [CrossRef] [Green Version]
  61. Lin, H.H.; Lin, K.H.; Syu, J.Y.; Tang, S.Y.; Lo, H.F. Physiological and proteomic analysis in two wild tomato lines under waterlogging and high temperature stress. J. Plant Biochem. Biotechnol. 2016, 25, 87–96. [Google Scholar] [CrossRef]
  62. Xu, L.; Pan, R.; Shabala, L.; Shabala, S.; Zhang, W.Y. Temperature influences waterlogging stress-induced damage in Arabidopsis through the regulation of photosynthesis and hypoxia-related genes. Plant Growth Regul. 2019, 89, 143–152. [Google Scholar] [CrossRef]
  63. Yang, B.Z.; Liu, Z.B.; Zhou, S.D.; Ou, L.J.; Dai, X.Z.; Ma, Y.Q.; Zhang, Z.Q.; Chen, W.C.; Li, X.F.; Liang, C.L.; et al. Exogenous Ca2+ alleviates waterlogging-caused damages to pepper. Photosynthetica 2016, 54, 620–629. [Google Scholar] [CrossRef]
  64. Zhang, F.; Zhu, K.; Wang, Y.Q.; Zhang, Z.P.; Lu, F.; Yu, H.Q.; Zou, J.Q. Changes in photosynthetic and chlorophyll fluorescence characteristics of sorghum under drought and waterlogging stress. Photosynthetica 2019, 57, 1156–1164. [Google Scholar] [CrossRef] [Green Version]
  65. Pociecha, E.; Kościelniak, J.; Filek, W. Effects of root flooding and stage of development on the growth and photosynthesis of field bean (Viciafaba L. minor). Acta Physiol. Plant. 2008, 30, 529–535. [Google Scholar] [CrossRef]
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Figure 1. Scheme presenting the parameters measured during the experiment.
Figure 1. Scheme presenting the parameters measured during the experiment.
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Figure 2. Results of cluster analysis for cucumber accessions using the Euclidean distance on the basis of morphological and physiological traits (Ward’s hierarchical algorithm); green line indicates the cut-off point.
Figure 2. Results of cluster analysis for cucumber accessions using the Euclidean distance on the basis of morphological and physiological traits (Ward’s hierarchical algorithm); green line indicates the cut-off point.
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Figure 3. Euclidean distance between Control and Stress groups of each cucumber accession. White bars indicate accessions from control and stress treatments that were classified into cluster 1; light grey bars indicate accessions from control and stress treatments that were classified into different clusters; black bars indicate accessions from control and stress treatments that were classified into cluster 2.
Figure 3. Euclidean distance between Control and Stress groups of each cucumber accession. White bars indicate accessions from control and stress treatments that were classified into cluster 1; light grey bars indicate accessions from control and stress treatments that were classified into different clusters; black bars indicate accessions from control and stress treatments that were classified into cluster 2.
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Figure 4. Radar charts comparing 18 traits estimated in Control and Stress plants of two cucumber accessions, GM-50 and TYTUS. Parameters from the Control group were set as 1 and parameters of the Stress-treated plants were expressed in relation to the Control. Asterisks indicate significant differences between Control and Stress according to Student’s t-test and p < 0.05, calculated separately for each parameter.
Figure 4. Radar charts comparing 18 traits estimated in Control and Stress plants of two cucumber accessions, GM-50 and TYTUS. Parameters from the Control group were set as 1 and parameters of the Stress-treated plants were expressed in relation to the Control. Asterisks indicate significant differences between Control and Stress according to Student’s t-test and p < 0.05, calculated separately for each parameter.
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Figure 5. Results of cluster analysis for tomato accessions using the Euclidean distance on the basis of morphological and physiological traits (Ward’s hierarchical algorithm); green line indicates the cut-off point.
Figure 5. Results of cluster analysis for tomato accessions using the Euclidean distance on the basis of morphological and physiological traits (Ward’s hierarchical algorithm); green line indicates the cut-off point.
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Figure 6. Euclidean distance between Control and Stress groups of each tomato accession. White bars indicate accessions from control and stress treatments that were classified into cluster 1; light grey bars indicate accessions from control and stress treatments that were classified into different clusters; black bars indicate accessions from control and stress treatments that were classified into cluster 2.
Figure 6. Euclidean distance between Control and Stress groups of each tomato accession. White bars indicate accessions from control and stress treatments that were classified into cluster 1; light grey bars indicate accessions from control and stress treatments that were classified into different clusters; black bars indicate accessions from control and stress treatments that were classified into cluster 2.
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Figure 7. Radar charts comparing 18 traits estimated in Control and Stress plants of two tomato accessions POL 7/15 and PZ 215. Parameters from the Control group were set as 1 and parameters of Stress-treated plants were expressed in relation to Control. Asterisks indicate significant differences between Control and Stress plants according to Student’s t-test and p < 0.05, calculated separately for each parameter.
Figure 7. Radar charts comparing 18 traits estimated in Control and Stress plants of two tomato accessions POL 7/15 and PZ 215. Parameters from the Control group were set as 1 and parameters of Stress-treated plants were expressed in relation to Control. Asterisks indicate significant differences between Control and Stress plants according to Student’s t-test and p < 0.05, calculated separately for each parameter.
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Table
Table 1. Description of plant material used in the study.
Table 1. Description of plant material used in the study.
Cucumis sativus L.Solanum lycopersicum L.Origin
AccessionBreeding StatusAccessionBreeding Status
GROT
MARKUS
TYTUS
B1F1
B2F1
DH1
DH2
DH3
DH4		F1 cultivar
F1 cultivar
F1 cultivar
Parthenocarpic cultivar
Parthenocarpic cultivar
Double haploid line
Double haploid line
Double haploid line
Double haploid line		POL 1/15
POL 2/15
POL 3/15
POL 4/15
POL 5/15
POL 6/15
POL 7/15
POL 8/15		F1 cultivar
Breeding line BC
Breeding line
Breeding line
Breeding line
Breeding line
Breeding line BC
Breeding line		KHiNO Polan, PL
PGZ-1
GMG-30
GM-50
G404
G598		Breeding line
Breeding line
Breeding line
F1 cultivar
F1 cultivar		PZ 115
PZ 215
PZ 315
PZ 415
PZ 515
PZ 615
PZ 715
PZ 815		Cultivar
Cultivar
F1 cultivar
Cultivar
F1 cultivar
Cultivar
Cultivar
Cultivar		PlantiCo, PL
NOE1
NOE2
NOE3
NOE4
NOE5		F1 cultivar
F1 cultivar
F1 cultivar
F1 cultivar
F1 cultivar		 Spójnia HiNO
Table
Table 2. Mean value of each parameter determined for both clusters (cucumber plants). Bold p-values indicate statistically significant differences between cluster 1 and cluster 2, estimated with Student’s t-test and p < 0.05.
Table 2. Mean value of each parameter determined for both clusters (cucumber plants). Bold p-values indicate statistically significant differences between cluster 1 and cluster 2, estimated with Student’s t-test and p < 0.05.
ParameterCluster 1Cluster 2p-Value
% weight change971030.0000
% height change93880.0136
Relative leaf number1.111.050.1955
F04444640.0000
Fm249623970.0000
Fv205219320.0000
Fv/F04.654.220.0000
Fv/Fm0.820.800.0000
Tfm1721710.8759
Area27,03922,3020.0000
Fm/F05.655.220.0000
PI ABS1.110.790.0000
ABS/RC3.293.540.0000
TR0/RC2.712.840.0000
ET0/RC1.151.070.0000
DI0/RC0.590.700.0000
ET0/ABS0.350.310.0000
ET0/TR00.430.380.0000
Table
Table 3. Mean value of each parameter determined in two cucumber accessions considered as more tolerant to waterlogging. Bold p-values indicate statistically significant differences between Control and Stress plants of each accession separately estimated with Student’s t-test and p < 0.05.
Table 3. Mean value of each parameter determined in two cucumber accessions considered as more tolerant to waterlogging. Bold p-values indicate statistically significant differences between Control and Stress plants of each accession separately estimated with Student’s t-test and p < 0.05.
ParameterGM-50 ControlGM-50 Stressp-ValueG404 ControlG404 Stressp-Value
% weight change100850.0000100930.0244
% height change100670.0000100650.0000
Relative leaf number1.051.330.15291.300.780.0010
F04234390.09074374640.0696
Fm248425690.1993245325210.2838
Fv206021300.2541201620570.5182
Fv/F04.884.850.82374.664.460.3244
Fv/Fm0.830.830.72300.820.820.3969
Tfm1761790.77881641860.0351
Area29,86829,2640.635927,05126,6480.7991
Fm/F05.885.850.82265.665.460.3243
PI ABS1.131.210.38601.261.100.2351
ABS/RC3.403.310.20753.243.370.2383
TR0/RC2.822.740.13172.662.740.2851
ET0/RC1.231.210.58591.201.210.7823
DI0/RC0.580.570.68290.580.630.2378
ET0/ABS0.360.370.83370.370.360.4029
ET0/TR00.440.440.82270.450.440.4775
Table
Table 4. Mean value of each parameter determined in two cucumber accessions considered as more sensitive to waterlogging. Bold p-values indicate statistically significant differences between Control and Stress plants of each accession, separately estimated with Student’s t-test and p < 0.05.
Table 4. Mean value of each parameter determined in two cucumber accessions considered as more sensitive to waterlogging. Bold p-values indicate statistically significant differences between Control and Stress plants of each accession, separately estimated with Student’s t-test and p < 0.05.
ParameterGMG-30 ControlGMG-30 Stressp-ValueTYTUS ControlTYTUS Stressp-Value
% weight change1001000.5298100990.8431
% height change100720.0000100620.0000
Relative leaf number0.861.230.08381.521.050.0082
F04134560.06054504690.1380
Fm244124050.6090252723710.0049
Fv202919490.3058207719020.0023
Fv/F04.934.420.05504.634.100.0057
Fv/Fm0.830.810.08200.820.800.0035
Tfm1731590.20011721790.4821
Area26,47424,3960.134127,27722,7710.0044
Fm/F05.935.420.05495.635.100.0057
PI ABS1.300.990.03571.040.750.0323
ABS/RC3.243.510.05993.283.410.0802
TR0/RC2.692.820.10822.692.730.4889
ET0/RC1.211.170.16761.110.970.0139
DI0/RC0.550.690.07340.580.680.0052
ET0/ABS0.380.340.02470.340.290.0161
ET0/TR00.450.420.02860.420.360.0200
Table
Table 5. Mean value of each parameter determined for both clusters (tomato plants). Bold p-values indicate statistically significant differences between cluster 1 and cluster 2, estimated with Student’s t-test and p < 0.05.
Table 5. Mean value of each parameter determined for both clusters (tomato plants). Bold p-values indicate statistically significant differences between cluster 1 and cluster 2, estimated with Student’s t-test and p < 0.05.
ParameterCluster 1Cluster 2p-Value
% weight change1021020.798161
% height change94840.493144
Relative leaf number1.151.000.557965
F04525110.000249
Fm237422600.002449
Fv192217490.000067
Fv/F04.283.610.000000
Fv/Fm0.810.770.000074
Tfm1882090.015459
Area19,70216,0860.000004
Fm/F05.254.460.000001
PI ABS1.000.650.000000
ABS/RC3.233.850.000323
TR0/RC2.602.880.000195
ET0/RC1.070.990.015152
DI0/RC0.620.980.001677
ET0/ABS0.330.270.000000
ET0/TR00.410.350.000000
Table
Table 6. Mean value of each parameter determined in three tomato accessions considered as more tolerant to waterlogging. Bold p-values indicate statistically significant differences between Control and Stress plants of each accession separately estimated with Student’s t-test and p < 0.05.
Table 6. Mean value of each parameter determined in three tomato accessions considered as more tolerant to waterlogging. Bold p-values indicate statistically significant differences between Control and Stress plants of each accession separately estimated with Student’s t-test and p < 0.05.
ParameterPOL 7/15
Control		POL 7/15
Stress		p-ValuePOL 8/15
Control		POL 8/15
Stress		p-ValuePZ 715
Control		PZ 715
Stress		p-Value
% weight change1001170.00051001030.61681001050.0282
% height change100700.0239100720.0255100810.0537
Relative leaf number1.101.050.80100.951.000.76991.000.680.0671
F04414470.60304294530.21314394960.0002
Fm225923170.5088241623240.1287229224680.0413
Fv181818690.5695198718710.0507185319720.1355
Fv/F04.174.200.92114.654.220.04114.223.990.1858
Fv/Fm0.800.810.53910.820.800.04170.810.800.1699
Tfm1781910.36191872130.16961771960.3126
Area19,87420,1810.855820,19417,8790.059219,53019,2830.8493
Fm/F05.175.200.92155.655.220.04115.224.990.1862
PI ABS1.130.950.35831.320.940.01080.900.820.3596
ABS/RC3.153.180.81642.973.370.00033.493.350.1196
TR0/RC2.512.560.37182.442.710.00022.822.670.0495
ET0/RC1.041.050.84691.081.110.47941.191.080.0013
DI0/RC0.650.620.60560.530.660.00350.670.680.9076
ET0/ABS0.340.330.77650.360.330.07260.340.320.1381
ET0/TR00.420.410.70110.440.410.10280.420.400.1901
Table
Table 7. Mean value of each parameter determined in two tomato accessions considered as more sensitive to waterlogging. Bold p-values indicate statistically significant differences between Control and Stress plants of each accession separately estimated with Student’s t-test and p < 0.05.
Table 7. Mean value of each parameter determined in two tomato accessions considered as more sensitive to waterlogging. Bold p-values indicate statistically significant differences between Control and Stress plants of each accession separately estimated with Student’s t-test and p < 0.05.
ParameterPZ 115 ControlPZ 115 Stressp-ValuePZ 215 ControlPZ 215 Stressp-Value
% weight change1001090.01601001090.0332
% height change1002110.0001100110.0000
Relative leaf number0.000.200.05270.530.160.0160
F04544910.17414566770.0069
Fm250222740.0065237122110.0645
Fv204817830.0051191515340.0053
Fv/F04.543.800.01494.222.820.0014
Fv/Fm0.820.780.03280.810.680.0034
Tfm2102590.07751832440.0000
Area19,30915,8320.086420,17113,3320.0010
Fm/F05.544.800.01495.223.820.0014
PI ABS1.100.730.02010.970.430.0008
ABS/RC3.463.890.04483.205.130.0009
TR0/RC2.823.000.08662.583.220.0000
ET0/RC1.211.150.09751.040.970.3251
DI0/RC0.640.890.04450.621.910.0051
ET0/ABS0.360.310.03130.330.230.0019
ET0/TR00.430.390.04660.410.310.0036

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Kołton, A.; Kęska, K.; Czernicka, M. Selection of Tomato and Cucumber Accessions for Waterlogging Sensitivity through Morpho-Physiological Assessment at an Early Vegetative Stage. Agronomy 2020, 10, 1490. https://doi.org/10.3390/agronomy10101490

AMA Style

Kołton A, Kęska K, Czernicka M. Selection of Tomato and Cucumber Accessions for Waterlogging Sensitivity through Morpho-Physiological Assessment at an Early Vegetative Stage. Agronomy. 2020; 10(10):1490. https://doi.org/10.3390/agronomy10101490

Chicago/Turabian Style

Kołton, Anna, Kinga Kęska, and Małgorzata Czernicka. 2020. "Selection of Tomato and Cucumber Accessions for Waterlogging Sensitivity through Morpho-Physiological Assessment at an Early Vegetative Stage" Agronomy 10, no. 10: 1490. https://doi.org/10.3390/agronomy10101490

APA Style

Kołton, A., Kęska, K., & Czernicka, M. (2020). Selection of Tomato and Cucumber Accessions for Waterlogging Sensitivity through Morpho-Physiological Assessment at an Early Vegetative Stage. Agronomy, 10(10), 1490. https://doi.org/10.3390/agronomy10101490

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