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Article

Screening of Germplasm and Construction of Evaluation System for Autotoxicity Tolerance during Seed Germination in Cucumber

by
Jie Li
1,2,
Jian Li
2,3,
Ping Yang
2,
Hongbo Fu
2,
Yongchao Yang
2 and
Chaowei Liu
1,*
1
College of Life Science, China West Normal University, Nanchong 637000, China
2
College of Biological and Agricultural Sciences, Honghe University, Mengzi 661100, China
3
College of Horticulture, Gansu Agricultural University, Lanzhou 730070, China
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(5), 1081; https://doi.org/10.3390/agronomy14051081
Submission received: 10 April 2024 / Revised: 14 May 2024 / Accepted: 16 May 2024 / Published: 19 May 2024
(This article belongs to the Topic Agronomy, Soil Health and Climate Change: Challenges and Solutions)
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Abstract

:
Due to the widespread use of intensive cropping patterns, the problem of continuous cropping obstacle, which is dominated by autotoxicity, has been becoming more and more prominent. Although many methods have been proposed to overcome the continuous cropping obstacle of cucumber, no study has reported the screening and evaluation of cucumber germplasm resistant to autotoxicity. In this study, 28 physiological indices related to the cucumber bud stage under cinnamic acid (CA) treatment were determined. In total, 45 cucumber cultivars were classified into three groups using principal component analysis and cluster analysis, and a model for evaluating cucumber resistance to autotoxicity was developed. The evaluation model was validated using autotoxicity-tolerant and non-autotoxicity-tolerant cultivars. The results showed that the growth of non-autotoxicity-tolerant cultivars was significantly inhibited compared to autotoxicity-tolerant cultivars. This indicated that the evaluation model of cucumber autotoxicity tolerance is reliable. The results of this study provide a valuable reference for the application of cucumber autotoxicity-tolerant germplasm resources and the development of autotoxicity-tolerant genes.

1. Introduction

A decrease in crop yield and quality occurs when the same crop is grown continuously on a piece of land, a phenomenon known as continuous cropping obstacle [1]. Continuous cropping obstacle has become increasingly serious with the widespread use of intensive land production methods, especially in facility-based cropping [2]. Cucumber (Cucumis sativus L.) is a particularly common continuous monoculture greenhouse vegetable [3]. Although the continuous monoculture model improves land use efficiency, this model poses a major challenge to the sustainability of cucumber facility agriculture [4]. The main causes of barriers to continuous cultivation are soil nutrient imbalances, soil acidification, autotoxicity, and microbial ecological imbalances [5,6]. Autotoxicity, among other things, is an important cause of impediments to continuous cultivation. Autotoxicity occurs as a result of the release of specific chemicals such as cinnamic acid (CA), p-hydroxybenzoic acid (PHBA), and benzoic acid (BA) into the surrounding area [7,8]. These chemicals are actually produced through the volatilization and decomposition of plant leachate, root exudates, and plant residues in the above-ground portion of the plant [9]. Among them, CA is the most toxic to cucumber, adversely affecting plant growth and development through a series of physiological and biochemical changes [10,11,12]. The most common ways to overcome continuous cropping obstacles in current cultivation methods are crop rotation and grafting. But crop rotation is more time-consuming. For example, melon usually requires a 3–4-year crop rotation to have significant relief from this continuous cropping barrier [13]. The same grafting has been shown to adversely affect crop quality [14,15]. Therefore, exploring a new approach to effectively mitigate the continuous cropping obstacle of cucumber will be important for sustainable production of cucumber in facilities.
Principal component analysis and cluster analysis have been increasingly used in the screening of crop germplasm resources [16]. These methods are used for dimensionality reduction when there are more evaluation indices, i.e., removing redundancy and leaving the main information that can be maintained, thus realizing the purpose of data compression, denoising, and feature extraction [17]. The evaluation model can be constructed according to the extracted scores of each principal component, and the comprehensive scores can be calculated and ranked [18]. For example, Men et al. [19] used principal component analysis to evaluate the salt tolerance of different cucumber cultivars at the seedling stage and screened the underground dry weight, strong seedling index, and root–crown ratio as representative evaluation indices to construct the evaluation system. Fu et al. [20] classified 12 cucumber cultivars into four categories of strong heat tolerance, moderate heat tolerance, heat tolerance, and heat sensitivity using principal component analysis and subordinate function analysis. Several indicators, such as internode length, relative conductivity, and the content of chlorophyll a, were screened and used to construct an evaluation system. Miao et al. [21] carried out a comprehensive evaluation of the cold hardiness of cucumber at the germination and seedling stages using principal component analysis, affiliation function analysis, and cluster analysis. Chen et al. [22] combined principal component analysis and subordinate function analysis to comprehensively identify and evaluate the salinity tolerance of cereal (Setaria italica L. Beauv) during the germination period, screened the evaluation indices, and established a system for evaluating the salinity tolerance of cereal during the germination period. Integrated evaluation methods are well established in these studies on the screening of different resistant cultivars of crops under adversity stress.
Plants have a set of defense mechanisms when subjected to autotoxicity stress [23,24]. Previous studies have emphasized that cucumber autotoxicity tolerance is influenced by genetic factors and environmental conditions [25]. Consequently, efficient production depends on the selection of resistant cultivars, as well as on suitable environmental and management conditions. Nonetheless, comprehensive evaluations of autotoxicity tolerance across diverse cucumber cultivars remain limited, and no studies have been reported on the screening and evaluation of cucumber germplasm for autotoxicity resistance. In this study, we comprehensively evaluated the autotoxic resistance of different cucumber cultivars and constructed an evaluation system under CA-induced autotoxic stress.
Understanding the genetic and environmental factors influencing autotoxicity tolerance can inform variety selection, crop management practices, and breeding programs aimed at developing cucumber cultivars with enhanced resilience to autotoxicity. Consequently, by elucidating the autotoxicity resistance profiles of different cucumber cultivars, this study provides valuable insights for farmers, breeders, and researchers. Ultimately, these findings contribute to the advancement of sustainable cucumber production practices and the promotion of food security.

2. Materials and Methods

2.1. Materials

Cucumber (Cucumis sativus L.) cultivars were purchased from the National Resource Germplasm Bank, Tianjin Kerun Cucumber Research Institute, and Vegetable Research Institute of Beijing Academy of Agriculture and Forestry Sciences (Table 1, Supplementary Table S1). Cinnamic acid (CA, 99.5%) was purchased from Shanghai Yuanye Biotechnology Co., Ltd. (in Shanghai, China).

2.2. Treatment

In a pre-test, to determine the CA concentration that causes plants not to die but to be stressed in cucumber seeds [24], the seeds of each variety were selected and soaked in warm water, and then soaked in 20 mL of different concentrations of CA solution (the concentration of CA solution was 0, 0.2, 0.4, 0.6, 0.8, 1.0, and 1.2 mmol·L−1, respectively) for 6 h. The seeds were placed in a petri dish with two layers of filter paper (the filter paper was soaked in different concentrations of CA solution) and finally placed at a temperature of 28 °C and a humidity of 65%. The artificial climate (model BIC-400, made by Shanghai Boxun Industrial Co., LTD, the company is in Shanghai, China) with 200 µmol m−2s−1 light intensity was used to germinate the seeds for 5 days (the filter paper was kept soaking in CA solution during the germination process). Each treatment consisted of 20 seeds and was repeated three times. The number of germinations was recorded per day. After 5 days, root fresh weight, bud fresh weight, root length, and bud length were measured at the root neck of the cucumber seedlings. It was found that when the concentration of CA solution was increased to 0.8 mmol·L−1, the indices of cucumber seedlings were inhibited to varying degrees. Therefore, 0.8 mmol·L−1 CA solution was used as the autotoxicity stress concentration in this study (data not submitted).
In the main experiment, the seeds of 45 cucumber cultivars (Table 1) were soaked in CA solution (0.8 mmol·L−1) and deionized water (CK) for 6 h, respectively. The follow-up process was consistent with the pre-test method. The seeds were placed in an artificial climate for germination, and the number of germinations per day during the germination process was recorded. Each treatment was repeated three times, with 20 seeds per repetition. After 5 days, root length, shoot length, root fresh weight, and shoot fresh weight were measured, and then the amount required to determine antioxidant enzyme, amylase activity, and MDA content was weighed and frozen in liquid nitrogen and placed in a −80 °C refrigerator for subsequent analysis.
In order to further verify the reliability of the initial evaluation result, resistant and non-resistant cultivars, as determined from the initial study, were selected for an additional test using the root application of CA. When the cotyledons were fully expanded, the roots were treated with 1.2 mmol·L−1 CA. Here, we found significant differences in plant phenotypes after 14 days of CA application, so root length, leaf area, stem diameter, and plant height were measured after 14 days. All samples had three biological replicates.

2.3. Determination Method

2.3.1. Determination of Growth Index

Seeds were defined as germinated when the bud length exceeded 1/2 of the seed length. The germination number was counted after treatment for 2–5 days, and the germination potential and germination rate were calculated according to Xiao et al. [26]. After 5 days, the root length, bud length (5 plants were randomly measured in each culture dish, and the average value was obtained), root fresh weight, and bud fresh weight were measured (each treatment of each repeat was a culture dish, first weighed according to the dish, and finally the average fresh weight of each plant was calculated).

2.3.2. Determination of Physiological Indicators

Malondialdehyde (MDA) content was determined by the thiobarbituric acid (TBA) colorimetric method [27], superoxide dismutase activity (SOD) and peroxidase activity (POD) were determined by the method of [28], catalase activity (CAT) was determined by the method of [29], and amylase activity was determined by the 3,5-dinitrosalicylic acid method [30].

2.4. Statistical Analysis

The calculation formula of the relevant indicators is as follows:
Germination potential (%) = the number of germinated seeds on the second day/the total number of seeds × 100%;
The germination rate (%) = the number of germinated seeds on the 5th day/the total number of seeds × 100%;
The relative value of index (%) = CA/CK × 100%;
The index toxicity rate refers to the method of Chen et al. [22], where the index toxicity rate (%) (increase rate or inhibition rate) = |CK − CA|/CK × 100% (in the formula, the index toxicity rate, increase rate, and inhibition rate all have one meaning, and the numerator represents the absolute value of the difference between CK and CA).
The membership function is an important hierarchical evaluation method with the advantage of high comprehensiveness. It has been increasingly used in research on crop germplasm resources [31,32].
Membership function value calculation formula:
μ (Xj) = (Xj − Xmin)/(Xmax − Xmin) j = 1, 2, 3, …, n
In the formula, μ (Xj) represents the membership function value of the jth comprehensive index, Xj represents the jth comprehensive index value, Xmax represents the maximum value of the jth comprehensive index, and Xmin represents the minimum value of the jth comprehensive index.
Index weight calculation formula:
W j = P j / ∑ j = 1 m P j   j = 1 , 2 , 3 ,   … ,   n
In the formula, Wj represents the weight of the jth comprehensive index in all comprehensive indices, and Pj represents the contribution rate of the jth comprehensive index.
According to the membership function value and the weight of each comprehensive index, the comprehensive evaluation value (D) of the self-toxicity tolerance of different cucumber cultivars was calculated:
D = ∑ j = 1 m [ μ X j × W j ]   j = 1 ,   2 ,   3 ,   … ,   n
The data were analyzed using Excel 2019 software. SPSS 23.0 software was used for significant difference analysis (p < 0.05), correlation analysis, principal component analysis, cluster analysis, and stepwise regression analysis [12,20,22].

3. Results

3.1. Correlation Analysis of Various Indicators of Different Cucumber Cultivars under CA Stress

Correlation analyses were performed after the determination of 28 indicators, and a matrix of correlation coefficients was obtained (Supplementary Table S2). Among them, 90 groups of indices reached a significant or extremely significant positive correlation, and 94 groups reached a significant or extremely significant negative correlation. The relative values of germination rate, germination potential, root length, bud length, root fresh weight, bud fresh weight, root–shoot ratio, α-amylase activity, β-amylase activity, and total amylase activity were opposite to their corresponding toxic rate or inhibition rate. The relative values of MDA content, POD activity, SOD activity, and CAT activity showed the same trend as the increase rate. Among them, the increase rate of MDA content was significantly or extremely significantly positively correlated with the toxicity rate of germination, root length, bud length, root fresh weight, and bud fresh weight, and extremely significantly negatively correlated with the increase rate of POD, SOD, and CAT activity. The increase rate of SOD activity was significantly negatively correlated with the toxicity rate of germination and bud length, and significantly positively correlated with the toxicity rate of root fresh weight. These results indicate that cucumber seeds treated with 0.8 mmol·L−1 CA solution showed reduced POD, SOD, and CAT activities, elevated MDA accumulation, and elevated toxicity rates of germination, root length, stem length, root fresh weight, and stem fresh weight during germination compared to clear water treatment. From the above analyses, it can be seen that there is a complex relationship between the 28 indicators of the 45 cultivars. Therefore, systematic analyses and comprehensive evaluations are needed after the dimensionality reduction process.

3.2. Principal Component Analysis of Various Indices of Different Cucumber Cultivars under CA Stress

In order to comprehensively evaluate the autotoxicity resistance of different cucumber cultivars, principal component analysis was performed on the 28 indicators in this study. The eigen value indicates the magnitude of the amount of variation in the original data in each direction and can be used to determine the number of principal components. The condition that the eigen value was greater than 1 was used to determine the principal component in the data statistics [33]. The results retained 9 principal components with characteristic roots greater than 1, and the cumulative contribution rate reached 91.13%, which met the minimum standard of principal component analysis (cumulative contribution rate ≥ 80%) (Table 2). The scores of 9 principal components were obtained using principal component analysis, which were Z1–Z9 (Table 3).

3.3. Comprehensive Evaluation of Autotoxicity Tolerance of Different Cucumber Cultivars

The membership function value μ (X) of each comprehensive trait index was calculated using Formula (1), and the weight of 9 principal components was calculated using Formula (2) according to the contribution rate of principal components. According to the weight of 9 principal components and the membership function value μ (X) of comprehensive traits, the comprehensive score of each variety was calculated and ranked using Formula (3) (Table 4). A cluster analysis of the comprehensive score D value was carried out. When the Euclidean distance was 12.5, the 45 cucumber cultivars were divided into three different groups. These comprised: (i) two cultivars, ‘Jinyan4’ and ‘Jifengcuilvwang’, with a comprehensive score D of 0.770–0.777; (ii) three cultivars with a general tolerance to autotoxicity, ‘Zhongnong6’, ‘Chaoguan1’ and ‘Jufengxinxing’, with a comprehensive score D of 0.548–0.677; and (iii) 40 self-intolerant cultivars such as ‘Jinchun5’, ‘Jinyou1’ and ‘Jinyun7’, with a comprehensive score D of 0.272–0.499 (Figure 1).

3.4. Construction of an Autotoxicity Tolerance Evaluation System of Cucumber Cultivars

Stepwise regression analysis was performed with the comprehensive score D as the dependent variable and 28 indicators as the independent variables. Regression analysis equation: D′ = 0.0932 + 0.001X1 + 0.00019X4 − 0.00064X8 + 0.00005X9 − 0.00028X11 + 0.00031X13 − 0.0002X14 + 0.00005X15 + 0.0001X16 − 0.00013X17 + 0.00017X18 − 0.0004X20 − 0.00006X21 + 0.00012X23 + 0.00012X24 + 0.00041X25 + 0.00042X26 + 0.0004X27 + 0.00034X28 (R2 = 0.9998, p < 0.01). Regression analysis excluded nine indicators, including relative germination potential, relative root length, relative root fresh weight, relative shoot fresh weight, germination toxicity rate, shoot length toxicity rate, root–shoot ratio toxicity rate, and β-amylase activity and total amylase activity inhibition rate among the 28 indicators, indicating that these nine indicators have a low contribution rate in the evaluation of cucumber resistance to autotoxicity in this study. The relative germination rate (X1), relative bud length (X4), germination potential toxicity rate (X8), root length toxicity rate (X9), relative root–shoot ratio (X11), root fresh weight toxicity rate (X13), bud fresh weight toxicity rate (X14), MDA content increase rate (X15), relative MDA content (X16), α-amylase activity inhibition rate (X17), α-amylase activity inhibition rate (X18), β-amylase activity inhibition rate (X20), total amylase activity inhibition rate (X21), POD activity increase rate (X23), relative POD activity (X24), SOD activity increase rate (X25), relative SOD activity (X26), CAT activity increase rate (X27), relative CAT activity (X28), and other 19 indicators reached a significant level (p < 0.01). It can be used as a representative evaluation index for the evaluation system. The linear analysis of the comprehensive score D and the regression value D′ of the 45 cucumber cultivars showed that R2 = 0.9998 (Figure 2). At the same time, the regression accuracy of the comprehensive score D and the regression value D′ was analyzed. The estimation accuracy of the regression value and the original value was above 99.0% (Supplementary Table S3), which proved that the retained indices can be used as indices for evaluating cucumber autotoxicity tolerance.
We validated the results of the comprehensive evaluation, which showed that (i) CA treatment did not significantly inhibit the growth of ‘Jinyan4’, the growth of ‘Jufengxinxing’ was significantly inhibited, but the growth of ‘Jufeng9’ was more significantly inhibited (Figure 3); (ii) CA treatment did not significantly inhibit plant height, stem diameter, leaf area, and root length of ‘Jinyan4’; the growth of ‘Jufengxinxing’ was inhibited, but the inhibition of ‘Jufeng9’ was more significant (Table 5). This result coincided with the comprehensive evaluation results, indicating that the constructed cucumber autotoxicity tolerance evaluation system is usable.

4. Discussion

Autotoxicity is one of the constraints to the sustainable development of facility cucumber [2]. From the cultivar point of view, the discovery of cultivars with strong autotoxicity tolerance is important for overcoming the continuous cropping obstacle. Significant changes in the external morphology and physiological metabolism of cucumber were observed under autotoxic stress [7,34,35]. In this study, we conducted germination tests after treating the seeds of 45 cucumber cultivars with 0.8 mmol·L−1 cinnamic acid and found that the degree of phenotypic and physiological metabolic changes was inconsistent. There were some index changes that were relatively significant, but there were also some index changes that were not significant. In a high-volume cultivar screening exercise, these indices of insignificant variation were meaningless in the screening process. Previous work on varietal screening of cucumber cultivars for salt tolerance [19], heat tolerance [36], and cold tolerance [21] has also pointed out that the occurrence of changes in the response indices to stress varied among the cultivars. Therefore, we used a comprehensive analytical approach based on multiple indicators in order to rapidly and accurately characterize autotoxicity tolerance in cucumber cultivars. This work was of great importance for the subsequent screening of larger quantities of cultivars.
In autotoxic stress, plants respond to stress through physiological metabolism, and these physiological metabolic processes are interrelated. For example, enhanced SOD, POD, and CAT activity in cucumber seedlings under autotoxic stress resulted in lower levels of MDA and less cell membrane damage [34]. Here, in our work of screening 45 cucumber cultivars, we can understand the correlation between these indices through correlation analysis by referring to the previous research results to analyze the rationality of our data. This will help to ensure that reliable data support is provided for the subsequent screening work. In the results of correlation analysis of this study, we found that SOD, POD, CAT activity and MDA content were elevated during cucumber seed germination under cinnamic acid treatment, which was slightly different from the findings of [34] and similar to those of [10,11]. It was possible that this result was due to the large difference between our cinnamic acid treatment concentration and theirs. In addition, α-amylase activity, total amylase activity, and β-amylase activity of cucumber seeds under autotoxic stress were inhibited to varying degrees during the germination process, which was in general agreement with previous studies [12]. The results of the correlation analysis indicate that our data were reasonable, which was a guarantee for our subsequent work.
Principal component analysis has been increasingly used in the screening of crop germplasm resources [37,38,39,40,41]. This method was suitable for large sample sizes and index sizes in screening exercises. In this study, principal component analysis regrouped the 28 indices into 9 principal components, and the cumulative contribution of the 9 principal components reached 91.13% (when the cumulative contribution was ≥80%, it was usually considered that these principal components better represented the characteristics of the original data). This result indicates that the reintegrated 9 principal components represent the characteristics of the 28 indices. The score value of each cultivar in each principal component was calculated based on the principal components. This method was limited to the evaluation of the contribution rate of each factor, or it cannot synthesize all principal components for comprehensive evaluation [42]. The affiliation function analysis method can synthesize the effects of multiple factors and make the assessment results more comprehensive and objective [43]. For example, Fu et al. [20] and Miao et al. [21] screened heat-tolerant and cold-tolerant cucumber cultivar germplasm by combining principal component analysis and affiliation function analysis. In this study, the composite evaluation value of each final cultivar was calculated based on the weights and affiliation function values of the 9 principal components. Although the composite evaluation value was calculated, it was not sufficient to visualize the degree of autotoxicity tolerance of these cultivars. Cluster analysis, on the other hand, provides a more intuitive view of the degree of autotoxicity tolerance of these cultivars. For example, Chen et al. [22] used cluster analysis to visualize the salt tolerance of different cereal seeds during germination. In this study, 45 cucumber cultivars were visually categorized into three different autotoxicity tolerance groups by cluster analysis. It is not possible to discern which specific indices contribute to the composite evaluation value in the whole set of evaluation systems described above. The advantage of stepwise regression analysis was that it was able to find the best indicator among a large number of indicators, thus avoiding the overfitting problem [44]. In this study, 19 indices were found to contribute to the composite evaluation value to a large extent after stepwise regression analysis. An evaluation equation for cucumber autotoxicity tolerance was fitted based on the 19 indices. The coefficient of determination of the equation R2 = 0.9998 indicates that the fitted evaluation equation is reasonable. The previous work on the evaluation and screening of germplasm resources ended with the composite evaluation results, but there was a lack of validation of these results [45]. In this study, we conducted a result validation to ensure the reliability of the results. We selected resistant and non-resistant cultivars identified in the preliminary study for additional trials of root-applied CA. The results showed that CA treatment did not significantly inhibit the growth of ‘Jinyan4’, and the growth of ‘Jufengxinxing’ was significantly inhibited, but the inhibition of ‘Jufeng9’ was more significant. This validation indicates that the cucumber autotoxicity tolerance evaluation system we constructed was reliable. The prediction of cucumber autotoxicity tolerance using this evaluation system can greatly simplify the identification work and provide a basis for the selection and breeding of autotoxicity-tolerant cucumber cultivars.

5. Conclusions

In this study, two cucumber cultivars with strong autotoxicity tolerance were screened out using a comprehensive evaluation method, namely “Jinyan4” and “Jifenglvwang”; the autotoxicity tolerance evaluation equation of the cucumber cultivars was fitted using a stepwise regression analysis: D′ = 0.0932 + 0.001X1 + 0.00019X4 − 0.00064X8 + 0.00005X9 − 0.00028X11 + 0.00031X13 − 0.0002X14 + 0.00005X15 + 0.0001X16 − 0.00013X17 + 0.00017X18 − 0.0004X20 − 0.00006X21 + 0.00012X23 + 0.00012X24 + 0.00041X25 + 0.00042X26 + 0.0004X27 + 0.00034X28. The final autotoxicity tolerance validation test of the cucumber cultivars verified that the evaluation equations were feasible. Autotoxicity-tolerant cucumber cultivars and equations for evaluating autotoxicity tolerance in cucumber cultivars provide valuable insights to farmers, breeders, and researchers in overcoming the continuous cropping obstacle.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy14051081/s1, Table S1: Cucumber cultivar characteristics; Table S2: Correlation coefficient of each index; Table S3: Accuracy analysis of regression equation.

Author Contributions

J.L. (Jie Li) and J.L. (Jian Li): Investigation, methodology, data curation, writing—original draft. P.Y.: Data curation, formal analysis. H.F. and Y.Y.: Investigation, methodology. C.L.: Supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the project of Yunnan Young and Middle Aged Academic and Technical Leaders Reserve Talents (202205AC160056); Yunnan Fundamental Research Projects (202401AT070059); and the Fundamental Research Funds of China West Normal University (19E046).

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

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Agronomy 14 01081 g001
Figure 1. Cluster analysis of the autotoxicity tolerance of different cucumber cultivars. Red indicates 2 cultivars with high autotoxicity tolerance; blue indicates 3 cultivars with average autotoxicity tolerance, and green indicates 40 cultivars with low autotoxicity tolerance.
Figure 1. Cluster analysis of the autotoxicity tolerance of different cucumber cultivars. Red indicates 2 cultivars with high autotoxicity tolerance; blue indicates 3 cultivars with average autotoxicity tolerance, and green indicates 40 cultivars with low autotoxicity tolerance.
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Figure 2. Linear analysis of the comprehensive score values (D) and regression values (D′) of 45 cucumber varieties.
Figure 2. Linear analysis of the comprehensive score values (D) and regression values (D′) of 45 cucumber varieties.
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Figure 3. Seedling phenotypes of three cultivars differing in autotoxicity tolerance under cinnamic acid (CA) stress. CK: Cucumber seedlings treated with 0 mmol·L−1 CA. CA: Cucumber seedlings treated with 1.2 mmol·L−1 CA. Note: Cucumber seedlings were treated when the cotyledons were fully expanded, and the treatments were photographed for up to 14 days.
Figure 3. Seedling phenotypes of three cultivars differing in autotoxicity tolerance under cinnamic acid (CA) stress. CK: Cucumber seedlings treated with 0 mmol·L−1 CA. CA: Cucumber seedlings treated with 1.2 mmol·L−1 CA. Note: Cucumber seedlings were treated when the cotyledons were fully expanded, and the treatments were photographed for up to 14 days.
Agronomy 14 01081 g003
Table 1. Information on cucumber cultivars.
Table 1. Information on cucumber cultivars.
No.CultivarsBreeding Institutes
(Production Unit)		No.CultivarsBreeding Institutes
(Production Unit)
1Jinchun5TJKRCRI24LiangyouF17-3TJYLT Co., Ltd.
2Jinyou1TJKRCRI25Chaoguan1SDSGSASI Co., Ltd.
3Jinyun7TJKRCRI26Bona203TJDRC
4Jinyou35TJKRCRI27Jufeng9SDSGCGASI Co., Ltd.
5Jinyou3TJKRCRI28JufengxinxingSDSGCGASI Co., Ltd.
6Jinyun4TJKRCRI29TianyuanbaimawangziF1JXSRLMA Co., Ltd.
7Jinlü10TJCRI30XianyoudaL307TJXYDSI Co., Ltd.
8Jinza2TJKRCRI31Shanglizaoshubai CucumberHNZZNZSI Co., Ltd.
9Jinyan4TJKRCRI32Guifeicui CucumberHVRIHBP
10Xinjinchun4TJCRI33Jieza4JLVFRI
11Zhongnong6CAAS34Shengyou68YNAAS
12Zhongnong106CAAS35Shengyou8-9YNAAS
13ZhongnonglüxiuCAAS36Shengyou58YNAAS
14Zhonnong116CAAS37Lüyan3F1WHGDFACT Co., Ltd.
15Zhongshou35F1SDSGXXRH Co., Ltd.38LüguanA6SDAAS
16Funong1SDSGHWSI Co., Ltd.39LüguanA7SDAAS
17Funong4SDSGHWSI Co., Ltd.40Deruite8TJDRC
18Fuyang2F1SDSGHLSI Co., Ltd.41Jingyanhanbao5BJVRI
19JingyanlülinglonBJVRI42Zhongnongcuiyu2SDAAS
20Shenglv10-9F1BJVRI43Junyou66LNVRI
21Jinyou48TJKRCRI44JifengcuilüwangJLVFRI
22Jinyou10TJKRCRI45DeerLD-3TJDETSI Co., Ltd.
23Jinyou401TJKRCRI
TJKRCRI: Tianjin Kerun Cucumber Research Institute; CAAS: Chinese Academy of Agricultural Sciences; TJCRI: Tianjin Cucumber Research Institute; SDSGXXRH Co., Ltd.: Shandong Shouguang Xinxinran Horticulture Co., Ltd. (in Shouguang, China); SDSGHWSI Co., Ltd.: Shandong Shouguang Hongwei Seed Industry Co., Ltd. (in Shouguang, China); SDSGHLSI Co., Ltd.: Shandong Shouguang Hongliang Seed Industry Co., Ltd. (in Shouguang, China); BJVRI: Beijing Vegetable Research Institute; TJYLT Co., Ltd.: Tianjin Yilian Technology Co., Ltd. (in Tianjin, China); SDSGSASI Co., Ltd.: Shandong Shouguang Spring and Autumn Seed Industry Co., Ltd. (in Shouguang, China); TJDRC: Tianjin Derui Company; SDSGCGSI Co., Ltd. (in Tianjin, China): Shandong Shouguang Chunguang Seed Industry Co., Ltd. (in Shouguang, China); JXSRLMA Co., Ltd.: Jiangxi Shangrao Limin Agriculture Co., Ltd. (in Shangrao, China); TJXYDSI Co., Ltd.: Tianjin Xianyouda Seed Industry Co., Ltd. (in Tianjin, China); HNZZNZZSI Co., Ltd.: Hunan Zhuzhou Nongzhizi Seed Industry Co., Ltd. (in Zhuzhou, China); JLVFRI: Jilin Vegetable and Flower Research Institute; HVRIHBP: Horticultural Vegetable Research Industry of Hebei Province; YNAAS: Yunnan Academy of Agricultural Sciences; WHGDFACT Co., Ltd.: Wuhan Garden Dafeng Agricultural Science and Technology Co., Ltd. (in Wuhan, China); SDAAS: Shandong Academy of Agricultural Sciences; LNVRI:Liaoning Vegetable Research Institute; TJDETSI Co., Ltd.: Tianjin Deerte Seed Industry Co., Ltd. (in Tianjin, China).
Table 2. Eigen values, contributions of principal components, and cumulative contributions.
Table 2. Eigen values, contributions of principal components, and cumulative contributions.
Principal ComponentⅠⅡⅢⅣⅤⅥⅦⅧⅨ
Eigen value6.304.723.802.852.401.601.451.341.05
Contribution (%)22.5116.8513.5610.198.565.735.204.793.74
Cumulative contribution (%)22.5139.3652.9363.1271.6877.4082.6087.3991.13
Table 3. The principal component score values of different cultivars.
Table 3. The principal component score values of different cultivars.
No.Z1Z2Z3Z4Z5Z6Z7Z8Z9
125.0617.42638.38254.5912.661104.66924.86159.744220.965
218.1435.19523.29236.26722.81282.50638.62060.271244.565
320.1066.1598.84018.73131.420106.923−3.28774.545177.678
46.1409.353−0.79733.57946.41093.77252.939124.199261.966
54.63025.6410.58023.25927.77384.42065.26889.463282.263
68.77918.23512.64710.03216.385112.00947.91296.187264.947
720.353−2.83222.8109.59839.25491.38822.13933.793202.477
811.58321.2136.50724.61824.42887.66545.35059.247258.950
953.65442.63177.09273.365−36.330151.127−35.911114.178250.063
1020.279−13.93731.1854.97336.790141.979−1.26931.758283.087
1135.29732.06852.41173.23015.017140.915−39.853112.753267.594
1227.595−1.3190.46512.97037.583116.15815.64865.776273.294
1316.3504.35319.45513.5479.686121.62916.17582.469212.177
1431.1989.38534.78443.26721.17183.46312.34728.200217.949
159.99823.46317.34119.54721.810111.04959.24193.036296.614
1617.22613.19818.2855.41617.541107.228−11.65796.365213.958
17−6.6364.91112.516−2.63040.44782.83498.76581.877252.072
1821.50019.23921.33734.85019.004102.0014.06183.530241.118
1935.1230.09929.74542.40428.056110.56014.82524.686282.031
2017.657−2.3523.10810.90532.07483.21840.80861.667232.877
2128.140−10.47825.62658.7908.009122.645−17.82862.115213.980
2213.2032.80223.01136.42018.58555.88716.36630.800183.931
2319.2717.18131.25816.30321.05198.98559.23737.830205.656
2417.113−23.2175.56252.89237.118136.00548.68055.290235.530
2528.40525.61846.46458.47124.524141.735−52.733121.390281.987
2616.66212.27213.58528.48427.28475.53243.48738.557251.527
274.291−11.47213.5134.56230.17080.34916.29274.389248.032
2844.1477.68624.11040.10820.64683.78143.21773.211273.856
2923.4270.0128.21541.29619.22278.43925.45438.368262.882
305.929−2.21017.08221.77328.84497.23234.27056.345220.762
3130.6241.3335.58821.94131.14375.1275.99325.278209.296
3220.212−24.5426.13816.18946.06379.03248.91536.956217.611
3331.32610.30913.28255.562−22.35073.81059.32276.822261.528
3413.265−4.65312.03830.15817.611124.91855.40389.765262.421
3524.083−1.52218.527−0.54322.51155.90740.08361.543166.249
3623.600−7.23626.3310.28455.52197.287−7.840−4.808265.569
3719.42924.763−0.44531.26525.62060.6326.24756.591217.513
3824.582−6.89211.8706.82542.94963.23825.63639.514244.030
3911.183−14.40219.21321.07147.45959.794−20.79132.884209.853
4030.32511.6687.4949.37951.59583.78532.29534.515345.673
4131.269−7.39116.2639.86930.455104.61430.01340.989282.867
4225.056−4.8485.7274.28750.63692.91362.51536.378346.776
4318.087−4.80525.27035.15060.89170.402−23.89013.793331.124
4443.01714.97786.984110.42817.877159.654−76.240110.890319.256
4525.2244.9379.63221.41237.91794.13012.13656.727234.813
Table 4. Membership function value and comprehensive evaluation value of different cucumber cultivars.
Table 4. Membership function value and comprehensive evaluation value of different cucumber cultivars.
No.µ(X1)µ(X2)µ(X3)µ(X4)µ(X5)µ(X6)µ(X7)µ(X8)µ(X9)Assessment Values (D)Rank
10.5260.4760.4460.5060.4010.4700.5780.5000.3030.4808
20.4110.4430.2740.3440.6080.2570.6560.5040.4340.41815
30.4440.4570.1100.1890.6970.4920.4170.6150.0630.38731
40.2120.5050.0000.3200.8510.3650.7381.0000.5300.40126
50.1870.7470.0160.2290.6590.2750.8090.7310.6430.40223
60.2560.6370.1530.1120.5420.5410.7090.7830.5470.40520
70.4480.3230.2690.1080.7770.3420.5620.2990.2010.37335
80.3020.6810.0830.2410.6250.3060.6950.4970.5140.40521
91.0001.0000.8870.6720.0000.9180.2300.9220.4640.7771
100.4460.1580.3640.0670.7520.8300.4280.2830.6470.39028
110.6960.8430.6060.6710.5280.8190.2080.9110.5610.6773
120.5680.3460.0140.1380.7600.5810.5250.5470.5930.41318
130.3810.4300.2310.1430.4730.6340.5280.6770.2540.38433
140.6280.5050.4050.4060.5910.2660.5060.2560.2860.4807
150.2760.7150.2070.1960.5980.5320.7740.7580.7220.45612
160.3960.5620.2170.0710.5540.4950.3690.7840.2640.39827
170.0000.4380.1520.0000.7900.2601.0000.6720.4750.30642
180.4670.6520.2520.3320.5690.4440.4590.6850.4150.47110
190.6930.3670.3480.3980.6620.5270.5200.2290.6410.4996
200.4030.3300.0440.1200.7040.2630.6690.5150.3690.34439
210.5770.2090.3010.5430.4560.6430.3340.5190.2640.42713
220.3290.4070.2710.3450.5650.0000.5290.2760.0980.33740
230.4300.4720.3650.1670.5900.4150.7740.3310.2180.41914
240.3940.0200.0720.4910.7550.7720.7140.4660.3840.36736
250.5810.7470.5380.5400.6260.8270.1340.9780.6410.6184
260.3860.5480.1640.2750.6540.1890.6840.3360.4720.40125
270.1810.1950.1630.0640.6840.2360.5290.6140.4530.27245
280.8420.4800.2840.3780.5860.2690.6830.6050.5960.5485
290.4990.3660.1030.3890.5710.2170.5810.3350.5350.39029
300.2080.3320.2040.2160.6700.3980.6310.4740.3020.32941
310.6180.3850.0730.2170.6940.1850.4700.2330.2380.38532
320.4450.0000.0790.1660.8470.2230.7150.3240.2850.30343
330.6300.5190.1600.5150.1440.1730.7750.6330.5280.45611
340.3300.2960.1460.2900.5550.6650.7520.7330.5330.38830
350.5100.3430.2200.0180.6050.0000.6650.5140.0000.34638
360.5020.2580.3090.0260.9450.3990.3910.0000.5500.37934
370.4320.7340.0040.3000.6370.0460.4710.4760.2840.40322
380.5180.2630.1440.0840.8150.0710.5820.3440.4310.35737
390.2960.1510.2280.2100.8620.0380.3170.2920.2420.28544
400.6130.5390.0940.1060.9040.2690.6200.3050.9940.4719
410.6290.2550.1940.1110.6870.4700.6070.3550.6460.41816
420.5260.2930.0740.0610.8950.3570.7930.3191.0000.41119
430.4100.2940.2970.3341.0000.1400.2990.1440.9130.40224
440.8240.5881.0001.0000.5581.0000.0000.8970.8480.7702
450.5280.4390.1190.2130.7640.3690.5050.4770.3800.41817
Weight0.2470.1850.1490.1120.0940.0630.0570.0530.041
Table 5. Morphological changes in the seedlings of three cucumber cultivars treated with 1.2 mmol·L−1 cinnamic acid.
Table 5. Morphological changes in the seedlings of three cucumber cultivars treated with 1.2 mmol·L−1 cinnamic acid.
Cultivars and TreatmentsPlant Height (cm)Stem Diameter (mm)Leaf Area (cm2)Root Length (cm)
Jinyan4CK13.87 ± 0.36 a5.30 ± 0.24 ab22.49 ± 0.59 a8.90 ± 0.36 a
CA13.17 ± 0.36 a4.77 ± 0.14 bc20.95 ± 0.77 a7.93 ± 0.46 ab
JufengxinxingCK13.97 ± 0.42 a5.43 ± 0.36 a22.17 ± 0.68 a8.70 ± 0.78 a
CA10.90 ± 0.32 b4.23 ± 0.05 cd17.00 ± 0.93 b6.43 ± 0.14 b
Jufeng9CK13.63 ± 0.58 a5.10 ± 0.18 ab22.63 ± 0.99 a8.63 ± 1.05 a
CA8.10 ± 0.45 c4.03 ± 0.14 d10.07 ± 0.46 c4.13 ± 0.74 c
Different lowercase letters in the same column indicate a significant difference between the treatments at p < 0.05. Data are the mean ± standard error of the mean of at least three different replicates of each treatment.
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Li, J.; Li, J.; Yang, P.; Fu, H.; Yang, Y.; Liu, C. Screening of Germplasm and Construction of Evaluation System for Autotoxicity Tolerance during Seed Germination in Cucumber. Agronomy 2024, 14, 1081. https://doi.org/10.3390/agronomy14051081

AMA Style

Li J, Li J, Yang P, Fu H, Yang Y, Liu C. Screening of Germplasm and Construction of Evaluation System for Autotoxicity Tolerance during Seed Germination in Cucumber. Agronomy. 2024; 14(5):1081. https://doi.org/10.3390/agronomy14051081

Chicago/Turabian Style

Li, Jie, Jian Li, Ping Yang, Hongbo Fu, Yongchao Yang, and Chaowei Liu. 2024. "Screening of Germplasm and Construction of Evaluation System for Autotoxicity Tolerance during Seed Germination in Cucumber" Agronomy 14, no. 5: 1081. https://doi.org/10.3390/agronomy14051081

APA Style

Li, J., Li, J., Yang, P., Fu, H., Yang, Y., & Liu, C. (2024). Screening of Germplasm and Construction of Evaluation System for Autotoxicity Tolerance during Seed Germination in Cucumber. Agronomy, 14(5), 1081. https://doi.org/10.3390/agronomy14051081

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