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Article

Phenotypic Diversity and Relationships of Fruit Traits in Persimmon (Diospyros kaki Thunb.) Germplasm Resources

by
Yi Dong
1,2,
Weimin Sun
1,
Zhihui Yue
1,
Bangchu Gong
1,
Xu Yang
1,
Kaiyun Wu
1,
Cuiyu Liu
1 and
Yang Xu
1,*
1
Research Institute of Subtropics Forestry, Chinese Academy of Forestry Sciences, Hangzhou 311400, China
2
College of Forestry, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(9), 1804; https://doi.org/10.3390/agriculture13091804
Submission received: 1 August 2023 / Revised: 8 September 2023 / Accepted: 9 September 2023 / Published: 13 September 2023
(This article belongs to the Section Crop Genetics, Genomics and Breeding)
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Abstract

:
Persimmons (Diospyros kaki Thunb.) are a characteristic Chinese fruit and account for a large planting area in China. The evaluation and selection of persimmons that are astringent without softening play an important role in modern breeding programs. However, the phenotypic variability and diversity of some representative traits in the natural population of persimmon germplasm without softening are still unclear. In this study, 56 traits from 150 samples of D. kaki collected in East Asia were used to evaluate phenotypes and correlations using principal component analysis (PCA) and cluster analysis. The results show that the mean coefficient of variation for the persimmon germplasm traits was 26.19%, and significant variations in phenotypic traits were observed. The frequency distribution maps of most of the characteristics showed obvious normal distribution characteristics. The analysis of the correlations between the traits showed that the most significant positive correlations had correlation coefficients close to 0.7. For example, seed width was highly and significantly correlated with fruit longitudinal and transverse width (0.796 and 0.786), and pith height was highly and significantly correlated with seed length (0.816). Moreover, the correlations between fruit traits and support traits, such as those related to the fruit handle and sepals, were significant, and the coefficients of the correlations between sepal width and fruit traits were 0.671 to 0.739. Fruit water content was negatively correlated with the soluble solids content (−0.623). Principal component analysis reduced 38 traits to 8 principal components, explaining 82.459% of the total variations, which were related to fruit size, seed, pith, color, fruit hardness, and soluble solid content; thus, persimmon germplasm could be divided into three major categories according to the sizes and shapes of the traits, and the first cluster was divided into two subcategories. The first subclass of fruits is medium size and shape; the second subclass of fruits is small in all traits; the second cluster is of high-quality and large fruits, optimal in all traits; and the third cluster is of medium to high-quality and oblate fruits. The results of this study are important for genetic improvement, diversity conservation, and resource utilization regarding persimmons and further research in this regard.

1. Introduction

Diospyros kaki Thunb., also referred to as persimmon, is an important fruit in China and a representative species for economic exploitation. The traditional main producers of persimmons are China, Korea, and Japan [1]. China was the first country to cultivate persimmon trees, and the common use of persimmon fruits was recorded as early as the Han dynasty [2]. China also has the largest cultivated area and highest production of persimmons worldwide and is rich in persimmon germplasm resources [3]. Persimmon fruits can be processed into products, such as dried persimmon [4], jam, persimmon vinegar, and fruit juice [5,6]. Persimmon plants have a high medicinal value [7], and persimmon pedicels and fruits can be used as medicines.
Persimmon fruits are classified as pollination-constant non-astringent (PCNA), pollination-variant non-astringent (PVNA), pollination-variant astringent (PVA), and pollination-constant astringent (PCA) based on their natural de-astringency ability, the number of seeds in the fruit, whether the seeds produce volatile substances, and whether the fruit pulp has brown spots [8,9]. Pollination-constant non-astringent persimmon fruits, which can be eaten fresh without an artificial reduction in astringency, are currently the key category of variety in the persimmon industry and are also a key target for genetic improvement. The majority of persimmon varieties in China are the pollination-constant astringent, such as ‘Mopan’, ‘Shuishi’, and ‘Niuxingshi’. Non-astringent persimmon varieties are often obtained by crossing Japanese non-astringent persimmon parents (J-PCNA × J-PCNA) in the breeding process, but the current breeding of non-astringent persimmons is limited due to inbreeding degradation. A new type of breeding program (C-PCA × J-PCNA) × J-PCNA has thus arisen, i.e., using Chinese astringent persimmons crossed with Japanese non-astringent persimmons, which are then crossed with Japanese non-astringent persimmons. The abundance of astringent persimmon varieties in China provides rich genetic diversity for breeding work. The selection of excellent breeding parents in persimmon resources is critical in new breeding programs, and the artificial removal of astringency often alters the traits of the persimmon itself. Therefore, the evaluation of astringent persimmons without softening plays an important role in modern (C-PCA × J-PCNA) × J-PCNA breeding programs.
Phenotypic traits result from the expression of genetic material, and variation results from the joint action of genetic diversity and environment [10]. The detection of plant genetic variation via morphological or phenotypic traits is the easiest and most direct method [11,12,13,14]. The study of phenotypic diversity can elucidate the structure and laws of population variation and thereby provide an important basis for the formation mechanism of complex traits through the deep excavation of genetic resources [15]. Genetic diversity is characterized by analyzing variations at the DNA level, phenotypes, and physiological characteristics [16,17,18,19]. The genetic diversity in persimmon germplasm resources has recently been reported from a study mainly focusing on the phenotypic diversity of the leaves and some fruit traits [20], the variation in the flavonoid and polyphenol contents [21], and the variation in the ascorbic acid content of the leaves [22]. Ye et al. [23] studied the patterns in fruit color; size; hardness; and soluble solids, tannin, and ascorbic acid contents during the development and maturation of persimmon fruits to provide a theoretical basis for determining the optimal harvesting period for these fruits. Zhang et al. [24] analyzed morphological variations in persimmon fruits to reveal the pattern of fruit phenotypic variation and establish a fruit size and morphological evaluation index. Liang et al. [25] analyzed the genetic diversity of persimmon germplasm regarding the three aspects-phenotype: the main nutrient contents in fruits and leaves, the molecular markers, and the selected superior germplasm with specific traits based on phenotype and nutrient content. Maeda et al. [26] revealed the correlation between traits by quantifying the phenotypic diversity of fruits, seeds, and leaf shapes in persimmon germplasm. Martínez-Calvo et al. and An et al. studied and analyzed the morphological traits of 27 persimmon cultivars and described the variation in 24 flower and fruit morphological traits in persimmon germplasm [27,28]. Guan et al. and Yang et al. analyzed the phenotypic characteristics of persimmon germplasm according to morphology and further explained the genetic and morphological diversity [29,30].
In summary, the exploration of the phenotypic diversity of persimmon resources based on morphology has been limited to a small number of resources or traits. Two articles on the germplasm evaluation by Guan et al. included a large number of samples, but the number of traits measured was small (approximately 20) and these included not only fruit traits but also other tree growth traits, with limited attention given to the fruit traits. Although an article by Fu et al. measured 22 fruit traits, the sample size was small, including only 61 [17]. The genetic improvement in key traits is a conventional and effective breeding approach for crops and economic trees [20], and the establishment and development of morphological traits is an important basis for genetic diversity studies. The establishment of genetic diversity in germplasm resources not only provides valuable resources for future genetic mapping and functional genome research but also facilitates the utilization of the core germplasm and molecular breeding [20,31]. Fruit phenotypic traits widely fluctuate among different persimmon germplasm; therefore, a systematic evaluation is necessary before breeding and processing.
In addition, the phenotypic traits of persimmon germplasm have been evaluated at the fruit ripening stage in studies reported in previous literature. Due to the crisp characteristics of sweet persimmon (PCNA), we need to obtain phenotypic information of the breeding parents without softening and select excellent breeding parents for (C-PCA × J-PCNA) × J-PCNA breeding programs. However, the evaluation of the phenotypic traits of astringent persimmons without softening has not been reported. To better exploit persimmon germplasm resources in China and provide excellent breeding parents for new breeding programs, and for a first large-scale study, we selected 150 persimmon germplasm from the persimmon germplasm repository in Zhejiang Province and determined 56 phenotypic traits, including those related to the fruit, seed, pith, fruit handle, sepal, and fruit pedicel. Our main objectives were (1) to quantify the phenotypic variation and diversity; (2) to explore the differences and connections between the phenotypic traits of persimmon germplasm and to provide a scientific basis for the innovative use of persimmon germplasm resources and breeding; and (3) to clarify the characteristics of each group and accordingly determine the value of each group for use in breeding and determine important representative traits.

2. Materials and Methods

2.1. Plant Materials

The materials used in this study included 150 persimmon germplasm resources except for some foreign varieties they were from the southern provinces of China. The branches of old trees that were more than 70 years old were collected in the place of origin; the name of the species was recorded, the samples were numbered in order of collection and named according to the place of origin if there was no name, and they were collected and preserved centrally through grafting and propagation. They included 27 pollination-constant non-astringent and pollination-variant non-astringent varieties, such as ‘Fuyu’ and ‘Jiro’; 114 astringent persimmon varieties, including ‘Hongyu’, ‘Qiujianding’, and ‘Denglong’; and some closely related species including ‘Diospyros lotus Linn.’, ‘Diospyros oleifera Cheng.’, ‘Diospyros kaki spp. Jingzao’, and 9 others.
All 150 persimmon germplasms were grafted and stored during the spring of 2014–2015 in a plant nursery in Lanxi City in Zhejiang Province (29°25′ N, 119°51′ E). All the stocks were labeled “YLNO.6”. There were few differences in the terrain and environment of the plant nursery. In addition, each germplasm contained 6 plants with a spacing of 3 m × 4 m. Fruit samples at the maturity stage were collected at the Lanxi City Nursery from September to December 2021. The sample names and sampling times are shown in Table 1. According to Chinese agricultural standards, NY/T 1309-2007 “Technical Code for Evaluating Germplasm Resources-persimmon”, the persimmon fruit surfaces had reached their inherent color, the fruits were slightly hard, and the seed color had become brown, which was defined as the fruit ripening period (Figure 1). In this study, 30 persimmon fruits of each genotype were collected to determine the phenotypic traits of the persimmons that met the sampling criteria detailed in the Materials and Methods sections of the studies by Asakuma et al. [32].

2.2. Trait Measurements

2.2.1. Determination of Fruit Phenotypic Traits

Persimmon fruits with symmetrical growth and no pests or diseases were selected to measure the fruits’ longitudinal and transverse lengths with a Vernier caliper (0~150 mm, with a measuring accuracy of 0.01 mm; Shanghai Anting Scientific Instruments Factory). The schematic diagram is shown in Figure 2a. The fruit shape index is the shape index for the longitudinal section of the fruit. The fruit sectional area was scanned with a leaf area scanner (CI-202 type; Cedar Rapids, Iowa City, IA, USA), and the fruit sectional diagram is shown in Figure 2a. Fruit weight was determined with an electronic balance (YP502N type, Shanghai Precision Scientific Instruments Co., Ltd., Shanghai, China); fruit volume was measured using the drainage method, which calculates the difference in the water surface height and the product of the bottom area of the cylindrical container. Fruit skin and pulp colors were measured 3 times for each fruit, with a portable colorimeter at the equatorial position (CR-400 type; Japan Konica Minolta Co., Ltd., Tokyo, Japan). The values L*, a*, and b* denote the brightness (L* denotes the shade of the color, ΔL+ denotes white bias, and ΔL- denotes black bias; a* denotes the range from red to green, Δa+ denotes red bias, and Δa- denotes green bias; and b* denotes the range from blue to yellow, Δb+ denotes yellow bias, and Δb- denotes blue bias), and h* and c* indicate the color angle and color saturation, respectively. Each fruit was peeled equatorially, and the hardness of the two corresponding surfaces was measured with a digital hardness tester (GY-4 digital display; Zhejiang Top Instruments, Hangzhou, China). The soluble solids content was measured using a digital display saccharimeter at the fruits’ equatorial position (PAL-1 type; Japan ATAGO Company, Saitama, Japan). The fruit water content is the ratio of the difference between the fresh and dry weight of the fruit to the fresh weight.

2.2.2. Determination of Pith and Seed Traits

The base width, top width, base height, and height of the pith were measured, as shown in Figure 2b. The pith weight was measured with an electronic balance, and the pith volume was calculated using the drainage method. The dried and deteriorated seeds in a single fruit were removed, the number of seeds was counted, and the normally developed seeds were selected to measure their lengths, widths, and thicknesses with Vernier calipers. The seed length and width measurement sites are shown in Figure 2c. The seed weight and seed volume are the ratios of the seed gross weight and seed gross volume to the number of seeds, respectively.

2.2.3. Fruit Pedicel, Sepal, and Fruit Handle Trait Measurements

The fruit pedicels were weighed with an electronic balance, and the thicknesses, lengths, and widths were measured with Vernier calipers (Figure 2d). The sepals of each fruit that were selected were intact, well-sized, and representative. The length, width, and height of each sepal were measured. Afterward, the measured fruit pedicels and sepals were depicted on A4 paper, and their areas were scanned with a leaf area scanner. The part of the fruit handle attached to the trunk is the base of the fruit handle, the part attached to the fruit is the top of the fruit handle, and the middle part is the middle of the fruit handle. The length, basal width, middle width, and top width of the fruit handles were measured sequentially with Vernier calipers. The weight of the fruit handle is the ratio of the total weight of the fruit handle to its number for each germplasm.

2.3. Statistical Analysis

Excel 2019 was used for data analysis and for plotting the frequency histograms in this study. SPSS software (version 22.0; SPSS Inc., Chicago, IL, USA) was applied for cluster analysis and plotting. OriginLab2017 software and R3.5.0 were used for plotting the correlation graphs, boxplots, and biplots, and the coefficient of variation (CV) was calculated to evaluate the variability of each identified trait. Pearson correlation coefficients (p < 0.05; p < 0.01) were used to show the correlations between the analyzed traits.

3. Results

3.1. Evaluation of Fruit Phenotypic Traits

A total of 56 traits (related to fruit, seed, pith, fruit pedicel, sepal, and fruit handle) of 150 persimmon germplasm were determined, as shown in Table 2. Details of the means and standard deviations of the 150 germplasm-specific traits and their corresponding histograms are shown in Table S1 and Figure S1. Frequency histograms were constructed for the fruit phenotypic traits, as shown in Figure S2, reflecting the frequency distribution of each trait in the persimmon germplasm. Fruit weight, volume, and longitudinal and transverse area were significant indicators of fruit size. The coefficients of variation ranged from 35.97% to 49.31% and were all greater than 10%, with a high degree of dispersion and normally distributed. The fruit weight and volume of the ‘Taishuu’ were the largest at 264.9 g and 285.73 cm3, respectively. The minimum fruit weight and volume were obtained for ‘Diospyros lotus Junchuxiong-3′ at 2.99 g and for ‘Diospyros lotus Junbiyang’ at 3.47 cm3, respectively. The average fruit shape index value was 0.98, and a few fruits were long in shape, such as ‘Diospyros kaki spp. Jinzao’, with a fruit shape index of 1.64. The fruit skin and pulp color were also measured in this study. The coefficients of variation for fruit skin L* and fruit pulp L* were 6.48% and 10.80%, respectively, indicating a relatively low degree of variation. The L* values for both fruit skin and pulp were greater than 60, and the c* values were 63.34 and 39.24, respectively, indicating that both fruit skin and pulp had a light luster. The coefficient of variation for hardness was 30.54%, reflecting the crispness of the fruit, with a large variation among the persimmon germplasms. The frequency distribution was normal.
The coefficients of variation for seed gross weight, seed gross volume, seed weight, and seed volume were 37.40%, 33.51%, 29.34%, and 29.33%, respectively. The average seed shape index was 1.81, indicating that the seeds were elongated. ‘Shangfushi’ had the highest seed shape index among the persimmon germplasms at 2.82, and ‘Shehuangshi’ had the smallest at 1.11. The average number of seeds per fruit was 4.25. ‘Suichang-4′ contained the highest number of seeds at 6.7, ‘Taiwanzhengshi’ contained the lowest number, and some germplasm did not contain seeds, such as ‘Diospyros kaki spp. Jinzao’, ‘Matsumoto-wase Fuyu’, and ‘Qingyuan-3′. The frequency distribution of seed traits was mainly normal. The piths of the persimmon germplasms were all solid, and the coefficients of variation for pith traits ranged from 23.96% to 50.18% and were all greater than 10%, with abundant variation. The maximum values of persimmon germplasm pith weight and volume were 5.58 g for ‘Maekawa jiro’ and 11.64 cm3 for ‘Jiro’, respectively, and the minimum was 0.07 g for ‘Diospyros lotus Junchuxiong’ and 0.28 cm3 for ‘Y-1′, respectively. The piths were long and variable, and the frequency distribution map shows that the distribution of each trait was more concentrated.
The coefficients of variation for each fruit pedicel trait ranged from 9.56% to 37.63%. The mean values of the fruit pedicel length, width, and shape index (the ratio of pedicel length to width) were 2.75 cm, 2.51 cm, and 1.11, respectively. The values of the length and width were similar, indicating that the fruit pedicels were rounded. In the frequency distribution of fruit pedicel traits, each trait showed normal distribution characteristics. The coefficients of variation for the persimmon germplasm sepal traits ranged from 15.67% to 36.00%, indicating high levels of variation. The sepal areas ranged from 0.77 to 6.48 cm2, and the mean value of the sepal shape index was 1.02 and ranged from 0.74 to 1.64, indicating that the sepal shapes varied from flat to elongated. Most sepals were round in shape. The frequency distribution map of sepal traits showed that the distribution of the sepal length and shape index was more concentrated, and the distribution of the sepal area, width, and height was more even. Some germplasms exhibited extremely shortened fruit handles that could not be measured, such as D. lotus, which includes ‘Junchuxiong’, ‘Junmiyang’, and ‘Juntaian’. The coefficients of variation for fruit handle traits in the remaining germplasms ranged from 18.73% to 36.32%, with mean values of 2.71 mm, 2.93 mm, and 3.27 mm for the top, middle, and basal fruit handle width, respectively, indicating a gradual increase in fruit handle width from the opposite end of the fruit to the fruit end. The frequency distribution plot showed normal distribution characteristics, and the distribution was more even at all levels.

3.2. Correlation Analysis of Phenotypic Traits of Persimmon

Pearson correlation analysis of these traits in persimmon germplasm was performed. The fruit traits were mainly positively correlated with each other, and a few were negatively correlated. Among the 1540 correlation coefficients for each pair of traits, 148 showed significant correlations and 922 showed highly significant correlations. As shown in Figure S3, the strong correlations were concentrated around the sizes and shapes of the traits.

3.2.1. Correlation Analysis of Fruit, Seed, and Pith Traits

Fruit volume, fruit weight, fruit transverse area, and fruit transverse length and width were significantly and positively correlated with seed traits. The seed width was most significantly correlated with fruit transverse and longitudinal widths (0.786 and 0.796). The correlation coefficient between the fruit shape index and the seed shape index was 0.787 (Table S2). The seed gross weight was highly significantly positively correlated with seed gross volume (0.949), and seed weight and seed volume were both significantly correlated with seed width (0.837 and 0.729).
Pith weight, volume, and base and top widths were highly significantly and positively correlated with most of the fruit traits. The correlation coefficients for pith weight and fruit sectional (fruit transverse and longitudinal) width were 0.756 and 0.727, respectively. Pith height and fruit longitudinal length (0.721) were highly significantly correlated. The ratio of pith height to base width (0.789) and the ratio of pith height to top width (0.795) were highly significantly correlated with the fruit shape index. Seed width and seed thickness were highly significantly and positively correlated with other pith-related traits except for pith height, the ratio of pith height to top width, and the ratio of pith height to base width. Pith height was highly significantly correlated with seed length (0.816).

3.2.2. Correlation Analysis of Fruit, Fruit Handle, Fruit Pedicel, and Sepal Traits

Among the fruit traits, all traits except for the fruit transverse shape index were highly significantly and positively correlated with each other. Most of the correlation coefficients were close to 0.7, and the fruit weight was highly correlated with the fruit volume (0.997). Fruit transverse area (0.527), fruit transverse width (0.582), and fruit longitudinal width (0.558) were highly significantly correlated with the fruit handle middle width. In addition, fruit traits except for fruit transverse shape index and fruit shape index were highly significantly correlated with sepal area, sepal length, and sepal width. Sepal width had the highest correlation with fruit traits, with correlation coefficients ranging from 0.671 to 0.739.
The correlation coefficient between fruit pedicel weight and fruit longitudinal area was 0.722. Fruit pedicel traits were highly significantly correlated with sepal area and width, with correlation coefficients ranging from 0.654 to 0.850. Among the sepal traits, sepal area (0.639) and width (0.635) were highly significantly and positively correlated with fruit handle weight. Pedicel weight (0.684) and pedicel thickness (0.690) were highly significantly and positively correlated with fruit handle weight.

3.2.3. Correlation Analysis of Fruit Physicochemical Properties

The fruit’s physicochemical properties included fruit water content, soluble solids content, and fruit hardness. Fruit water content was highly significantly and positively correlated with fruit size, seed, fruit pedicel, sepal, and fruit handle traits and was negatively correlated with all shape index traits and seed number. The soluble solids content was highly significantly and negatively correlated with fruit size, pith, and fruit pedicel traits. In addition, soluble solids content was highly significantly and negatively correlated with fruit water content (−0.623).

3.2.4. Correlation Analysis of Fruit Color Traits

Among the fruit color traits, fruit skin L* was highly significantly correlated with fruit skin b* (0.925) and c* (0.855) and highly significantly correlated with fruit skin b* and c* (0.964). These are consistent with the results for fruit pulp traits, wherein the correlation coefficients between fruit pulp L* and b* and c* were 0.850 and 0.821, respectively. In addition, there was a highly significant correlation between fruit pulp b* and c* (0.997). The values of L*, b*, and c* were significantly and positively correlated with all seed length, fruit handle length, fruit pedicel, and the sepal traits in persimmon germplasm except for the shape index and a negative correlation with seed number for both fruit skin and fruit pulp.

3.3. Principal Component Analysis of Fruit Phenotypic Diversity in Persimmon Germplasm Resources

Principal component analysis (PCA) produces a linear combination of the original variables, and principal component one (PC1) explains the largest part of the total variance information, which means that the relevant variables are explained by the same principal component and different principal components explain the less relevant variables [33,34]. Therefore, based on the analysis of phenotypic traits, 15 phenotypic traits with small coefficients of variation and very similar traits were excluded, and a principal component analysis was conducted on 38 personality traits (Table 3). The top eight principal components with eigenvalues of greater than one were extracted, explaining 82.459% of the total variance.
If the coefficient of the component load matrix after the rotation of the principal component is closer to 1 or −1, this indicates a better explanation of the variables and nomenclature [35]. As shown in Table S3, PC1 extracted the majority of information on the fruit phenotypic traits, which was positively distributed on PC1, with a variance contribution of 39.977%, mainly reflecting the size traits in persimmon germplasms. PC2 mainly responded to seed thickness information, which was negatively distributed. PC3 integrated seed gross weight, gross volume, and seed number, and mainly responded to total seed size information. PC4 represented pith volume, base height, and base width, mainly reflecting pith traits. PC5 integrated seed weight and seed volume, so it was named a seed factor. PC6 reflected fruit pulp a* and fruit skin a*, so it was named a color factor. PC7 integrated fruit hardness, so it was named a hardness factor. PC8 integrated soluble solids content in the fruit, whereby the larger the value of PC8, the higher the soluble solids content in the fruit. The PCA explained the variation distribution of phenotypic traits in the persimmon germplasm resources.
In addition, in the biplot of the principal component analysis (Figure 3), we found that only three traits (FSSC, SQ, and PA) were distributed along the negative half-axis of the horizontal axis, most of the remaining traits were evenly distributed along PC1 on both sides of the positive half-axis, and the hardness traits were distributed near the origin. Most of the 150 germplasms were evenly distributed along the coordinate axes of the first principal component and the second principal component, and no obvious hierarchical structure was formed.

3.4. Cluster Analysis of Fruit Phenotypic Traits in Persimmon Germplasm Resources

Cluster analysis is the process of clustering samples together according to the degree of similarity between quality traits, and then clustering the samples according to the comprehensiveness of the categories [35]. In this study, the persimmon germplasms were clustered into three categories at an Euclidean distance of 12 (Figure 4A). Upon observing the germplasm phenotypic traits, it is not difficult to find that the eight genotypes PV, FTA, FI, SL, FV, PH, FPW and SeA differed significantly in different clusters, so these eight traits were selected for box-and-line plotting (Figure 4B):
The first cluster was divided into two subclasses. The first subclass contained 78 germplasms, and the second subclass contained 26 germplasms. The mean values for the sepal area, fruit pedicel weight, and pith volume in the first subcluster were 3.93 cm2, 1.34 g, and 2.68 cm3, respectively, which were similar to the corresponding values of 4.47 cm2, 1.56 g, and 2.95 cm3 in the third cluster. The second subclass was smaller than the other clusters in all the trait indicators except for the round shape of fruits. The second cluster consisted of only three germplasms, and their phenotypic traits were significantly higher than those of the other clusters, with the sepal area and fruit volume being the largest at 50.46 cm2 and 254.74 cm3, respectively, and the second subclass was the smallest at 7.26 cm2 and 14.76 cm3. The third cluster consisted of 21 germplasms, with mean values for fruit transverse area and fruit volume higher than those in the first cluster at 38.79 cm2 and 167.51 cm3, respectively. The fruit shape index was the lowest among the other clusters at 0.75, and the fruit is oblate. The remaining traits were not significantly different from the first subclass.

4. Discussion

This study on the phenotypic assessment of germplasm resources can serve as a basis for potential germplasm mining and the genetic improvement of varieties [36,37]. The evaluation of astringent persimmons without softening plays an important role in modern (C-PCA × J-PCNA) × J-PCNA breeding programs. The development and utilization of excellent persimmon germplasm depend on the identification and evaluation of large-scale germplasm resources. Germplasm phenotypic traits are the easiest and most direct way to detect genetic variation and reveal biodiversity in plants [38]. The selection of breeding materials not only focuses on one trait but also considers other related traits. One of the most critical aspects that determines a fruit’s economic value and has a direct impact on its ability to compete in the market is the fruit’s quality [39]. Both the intrinsic qualities and the appearance of the fruit are considered to be part of the fruit’s quality [17]. Color, shape, and size are the appearance qualities of the fruit that are important indices for evaluating the quality of commodities and the characteristics of varieties, and they serve as a basis for satisfying consumers’ demands for a “good look”; fruit hardness and water content are internal qualities, which impact customers’ purchasing decisions [17,40]. Thus, a comprehensive analysis of multiple traits is necessary to improve the efficiency and accuracy of selection in breeding and meet consumers’ requirements for high-quality persimmon fruits [41].
In this study, 56 fruit phenotypic traits from 150 persimmon germplasm resources with consistent establishment and cultivation management conditions were comprehensively analyzed. Previous studies have revealed a wide range of variation (ranging from 11.06% to 50.88%) in persimmon fruit traits, and our results also show coefficients of variation in phenotypes ranging from 5.10% to 56.78% [17,24,25]. The large coefficients of variation indicate a large diversity of phenotypic traits in persimmon germplasms, which can provide rich test materials for genetic improvement and variety selection. In addition, this study found that fruit weight, fruit volume, fruit hardness, seed, fruit pedicel, and fruit handle traits were significantly and normally distributed, which is consistent with the results of previous persimmon germplasm studies for fruit volume, seed thickness, and fruit size and shape [24].
When selecting traits with strong correlations, the improvement in one trait can affect the other traits. Most of the traits involved in this study were significantly correlated. The fruit transverse and longitudinal lengths were highly significantly and positively correlated with fruit weight and fruit volume, which is consistent with the conclusions reported in previous studies on Physalis peruviana L. fruits [42], Solanum lycopersicum [43], and Malus pumila Mill. [44]. Fruit weight and volume were highly significantly correlated with seed weight and seed volume, with correlation coefficients ranging from 0.475 to 0.697. The correlation between seed and fruit traits was highly significant. This is consistent with the findings from Cucurbita moschata [45], Litchi chinensis [46], Siraitia grosvenorii [47], and Malus Sylvestris [48], and their seed and fruit traits have highly significant positive correlations. Maeda et al. [26] found a significant correlation between fruit and seed shape index in the F1 progeny of persimmon germplasm. Barbera et al. [49] reported that the seed content (number and weight) in fruits of the two major cactus pear species “Gialla” and “Rossa” was significantly correlated with fruit size. Weetman et al. [50] found a significant correlation between the seed shape index and fruit shape index (r = 0.60) in 28 American watermelon varieties. Some researchers speculate that there is some synchronization and correlation between the growth and development processes of seeds and fruits [51], that these two parts may exchange information via hormones, and that seeds may be an effective source for producing substances that promote fruit growth [52]. Moreover, this study found that most pith traits were significantly correlated with seed traits, which was probably due to the pith being the seed-bearing site, providing space for seed growth, and, at the same time, acting as a nutrient transport tissue for seed and fruit development.
This study also found that the correlations for persimmon fruit growth were not only between the different parts of the fruit but also between the fruit and its supporting tissues. Our results show that fruit handle width was highly significantly and positively correlated with fruit size traits, such as fruit transverse and longitudinal length, which is consistent with previous studies on the morphological diversity in Diospyros kaki [24], Cerasus pseudocerasus [53], and Citrus clementina [54]. These relationships may be partly explained by the fruit handle width being an external connective tissue of the fruit and its nutrient transport and weight support functions for fruit development. Sepals are important respiratory organs for persimmon fruit growth and development [55]. This is supported by Geng’s report, which showed that the removal of persimmon sepals can reduce the size of the fruit, indirectly proving that sepals may act as a functional leaf for fruit growth [56]. Highly significant correlations between sepal traits and fruit weight and fruit shape were also found in our paper. There are many examples of similar correlations between sepal traits and fruit traits in previous studies. For example, the sepal traits from fruits of Dipterocarpaceae were significantly positively correlated with fruit weight and fruit transverse and longitudinal length [57]. Zhong et al. found a correlation between Luffa cylindrica fruit sepal and fruit size traits [58].
Fruit water content was highly significantly and positively correlated with fruit size, seed, fruit pedicel, sepal, and fruit handle traits. In addition, the soluble solids content was highly significantly and negatively correlated with the above traits, which is consistent with the significant negative correlation between the water content and soluble solids content (with a correlation coefficient of −0.8) found from the correlation analysis of persimmon by Zhao et al. [59]. This also hints at the difficulty of combining a selection of varieties and parents with both large and good quality fruits in quality breeding.
Clustering analysis and principal component analysis of germplasm using a combination of phenotypic traits of persimmon germplasms can distinguish germplasms with close genetic distances from numerous germplasms with complex genetic backgrounds. Cluster analysis based on morphological and quantitative taxonomy has been widely used in crops, such as Triticum aestivum [60], Pyrus [61], Citrus jambhiri [62], and Passiflora edulis fruit [63]. Principal component analysis can reduce the dimensionality of a dataset by concentrating the information of several dispersed groups of variables into a few composite indicators. In recent years, this technique has been widely used in the evaluation of many crop germplasm resources [64,65,66], such as Cerasus pseudocerasus [67], Pyrus [68], and Castanea mollissima [69]. In studies on persimmon [25,70], cluster analysis combined with principal component analysis has been used to classify germplasm into different taxa that fit this study’s purpose. This helped select desirable germplasm resources by understanding the characteristics of each group.
In this study, the principal component analysis of phenotypic traits revealed that the first and second principal components explained 53.078% of the persimmon germplasms, indicating that the first two components, representing size indicators and seed traits, extracted the vast majority of information on phenotypic traits. The cumulative contribution of the first eight principal components reached 82.46%. This is close to the cumulative variance contributions of 88.441% and 83.948% from the persimmon germplasm obtained by previous authors [71,72]. The first eight principal components’ efficacy factors were summarized as fruit size trait, seed thickness, total seed size trait, seed volume and weight, pith trait, seed trait, color trait, fruit hardness, and soluble solids content. This conclusion is consistent with the results of the correlation analysis. A total of 150 germplasms were not significantly divergent in the biplot of the principal component analysis, suggesting that phenotypic traits in persimmon germplasm are mainly controlled by genes.
The traits screened with principal component analysis can be verified using clustering analysis of the germplasm in grouping situations. In this experiment, three clusters were obtained, and the characteristics of each cluster were clarified with cluster analysis to locate the values of the different clusters in breeding. The second cluster contained the largest fruit size and seed, other traits among all clusters, the highest-quality and largest fruit, and only three germplasms, which is conducive to variety selection and accelerates the subsequent promotion of cultivation. The second subcategory of the first cluster contained small, round fruits, while the third cluster contained medium–high fruit quality and a flat, round fruit shape, both of which can be used for the selection of persimmon fruits with special characteristics. The cluster analysis classified the 128 persimmon germplasms into three main categories, wherein factors such as fruit transverse area, fruit volume, sepal area, and seed length, which had greater influences on the cluster analysis, were also the phenotypic traits with greater contributions from the principal components. Therefore, the results of the principal component analysis were further validated.

5. Conclusions

The present study was the first to determine the phenotypic traits of persimmon germplasm in hard fruit, which are rich in variation and provide diverse germplasm resources for subsequent variety selection. The correlations between traits in persimmon germplasm showed that most of the persimmon fruit traits were significantly correlated with each other, as were fruit and seed traits, and pith and seed traits. The principal component analysis showed that fruit size and seed traits contributed to more than half of the total variation, and they thus played an important role in the fruit-rich variation. These findings are consistent with the results of the cluster analysis, wherein the fruits were clustered into three categories of different sizes and shapes, with the second cluster having significantly more fruit traits than the first and third clusters. This cluster is important for the genetic improvement and biological study of high-quality and large fruits of persimmon germplasm and to meet customers’ demand for high-quality fruits. Rich and high-quality genetic diversity in seed resources underpins targeted breeding and mastering the differences between different samples is beneficial to improve breeding efficiency and clarify breeding direction.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture13091804/s1, Table S1: Means and standard deviations of 56 traits in 150 persimmon germplasm; Figure S1: Characterization of 56 traits among 150 persimmon germplasm; Figure S2: Frequency histograms of phenotypic traits in persimmon germplasm; Figure S3: Correlation analysis of fruit phenotypic traits; Table S2: Correlation analysis among persimmon fruits phenotypic traits; Table S3: Rotated component matrix of PCA (principal component analysis).

Author Contributions

Conceptualization, Y.X.; methodology, Y.D., W.S., Z.Y., B.G., X.Y., K.W., C.L. and Y.X.; software, Y.D. and Y.X.; validation, Y.D. and W.S.; formal analysis, Y.D. and Y.X.; investigation, Y.D., Z.Y., B.G., C.L. and K.W.; writing—original draft preparation, Y.D. and W.S.; writing—review and editing, Y.D., Y.X., B.G., X.Y. and K.W.; visualization, Y.D. All authors have read and agreed to the published version of the manuscript.

Funding

The study was financially supported by the Fundamental Research Funds of Key R&D Plants of Zhejiang Province [2021C02038] and Key Agricultural New Varieties Breeding Projects funded by the Zhejiang Province Science and Technology Department [2021C02066-10].

Data Availability Statement

Not applicable.

Acknowledgments

The survey and sample collection work was kindly supported by numerous local forestry sectors, such as Fuyang Forestry Bureau and Lanxi Forestry Bureau. We are particularly grateful to Plant Nursery of Lanxi City for their efforts in maintaining living plant materials for this study.

Conflicts of Interest

The authors declare that there are no conflict of interest.

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Figure 1. Five fruit diagram of persimmon (Diospyros kaki) at maturity.
Figure 1. Five fruit diagram of persimmon (Diospyros kaki) at maturity.
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Figure 2. Measurement of fruit phenotypic traits. (a) fruit traits. (b) pith traits. (c) seed traits. (d) sepal, fruit handle, and pedicel traits.
Figure 2. Measurement of fruit phenotypic traits. (a) fruit traits. (b) pith traits. (c) seed traits. (d) sepal, fruit handle, and pedicel traits.
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Figure 3. Biplot of the principal component (PC) analysis based on 38 traits in 150 persimmon germplasm.
Figure 3. Biplot of the principal component (PC) analysis based on 38 traits in 150 persimmon germplasm.
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Figure 4. Cluster analysis of fruit phenotypic traits in persimmon germplasm. (A); Q-type clustering analysis of fruit phenotypic traits. (B); box plot of 8 traits in three clusters of 150 persimmon germplasm resources. PV; pith volume, FTA; fruit transverse area, FI; fruit shape index, SL; seed length, FV; fruit volume, PH; pith height, FPW; fruit pedicel weight, SeA; sepal area.
Figure 4. Cluster analysis of fruit phenotypic traits in persimmon germplasm. (A); Q-type clustering analysis of fruit phenotypic traits. (B); box plot of 8 traits in three clusters of 150 persimmon germplasm resources. PV; pith volume, FTA; fruit transverse area, FI; fruit shape index, SL; seed length, FV; fruit volume, PH; pith height, FPW; fruit pedicel weight, SeA; sepal area.
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Table 1. Basic information of 150 persimmon germplasm resourses.
Table 1. Basic information of 150 persimmon germplasm resourses.
No.NameOriginSamplingNo.NameOriginSampling
1FuyuJapan20 November 202139HanagohoJapan1 November 2021
2JiroJapan1 October 202140Luotian 2Luotian County, Hubei Province12 November 2021
3Matsumoto-wase FuyuJapan20 November 202141NiuyanshiLuotian County, Hubei Province17 October 2021
4LieyusuoJapan13 October 202142SifangtianshiLuotian County, Hubei Province1 November 2021
5SurugaJapan3 November 202143HongyushiYongjia County, Zhejiang Province25 October 2021
6DiJapan13 October 202144QiujiandingChangan District, Shanxi City11 November 2021
7FujiwaragoshoJapan20 November 202145DenglongshiDangshan County, Anhui Province1 October 2021
8RuoshanxicilangJapan18 October 202146XiuningbianshiNingxiu County, Anhui Province25 October 2021
9ShougatsuJapan18 October 202147AnxiniuxinshiAnxi County, Fujian Province20 October 2021
10IzuJapan1 October 202148Anxi ChengseshiAnxi County, Fujian Province7 November 2021
11WanyusuoJapan3 November 202149YangshuoNiuxinshiYangshuo County, Guangxi Province5 November 2021
12Maekawa jiroJapan17 October 202150YuanxiaoshiZhaoan County, Fujian Province28 November 2021
13AmahyakumeJapan1 November 202151Guangzhou daniuxinshiGuangzhou City, Guangdong Province20 October 2021
14ShangxizaoshengfuyouJapan1 October 202152Guangzhou dahongshiGuangzhou City, Guangdong Province28 November 2021
15ShangzongJapan17 October 202153Yongshun TezaoshiYongshun County, Hunan Province20 October 2021
16TaishuuJapan29 September 202154Haian XiaofangshiHaian City, Jiangsu Province1 October 2021
17SinamiItaly1 November 202155YueshiSongyang County, Zhejiang Province7 November 2021
18Luotian 1Luotian County, Hubei Province18 October 202156BianshiSongyang County, Zhejiang Province25 October 2021
19ZGuangzhou City, Guangdong Province17 October 202157JinhongshiLanxi City, Zhejiang Province9 October 2021
20ChangshanChangshan County, Zhejiang Province11 October 202158Chun’an 15Chunan County, Zhejiang Province6 November 2021
21Kaihua 1Kaihua County, Zhejiang Province4 October 202159Qingyuan 1Qingyuan County, Zhejiang Province9 November 2021
22Kaihua 2Kaihua County, Zhejiang Province5 November 202160Qingyuan 2Qingyuan County, Zhejiang Province19 October 2021
23Kaihua 3Kaihua County, Zhejiang Province27 November 202161Qingyuan 3Qingyuan County, Zhejiang Province19 October 2021
24Jiangshan 2Jiangshan City, Zhejiang Province9 November 202162Qingyuan 4Qingyuan County, Zhejiang Province7 November 2021
25Longyou 1Longyou County, Zhejiang Province23 October 202163Qingyuan 6Qingyuan County, Zhejiang Province6 November 2021
26Longyou 3Longyou County, Zhejiang Province23 October 202164Liandu 1Liandu District, Lishui City10 November 2021
27Wuyi 1Wuyi County, Zhejiang Province9 November 202165Liandu 2Liandu District, Lishui City27 November 2021
28Wuyi 3Wuyi County, Zhejiang Province4 November 202166Liandu 3Liandu District, Lishui City7 November 2021
29Qingtian 2Qingtian County, Zhejiang Province27 November 202167Liandu 4Liandu District, Lishui City31 October 2021
30TongpenshiFuyang District, Hangzhou City10 November 202168Liandu 6Liandu District, Lishui City6 October 2021
31Linan 2Linan District, Hangzhou City4 November 202169Suichang 1Suichang County, Zhejiang Province5 November 2021
32Xiaoshan 1Xiaoshan District, Hangzhou City9 November 202170Suichang 4Suichang County, Zhejiang Province23 October 2021
33Yuhang 1Yuhang District, Hangzhou City3 October 202171Songyang 1Songyang County, Zhejiang Province11 November 2021
34Yuhang 3Yuhang District, Hangzhou City9 November 202172Songyang 2Songyang County, Zhejiang Province6 November 2021
35Chunan 1Chun’an County, Zhejiang Province14 October 202173Songyang 4Songyang County, Zhejiang Province27 November 2021
36Chunan 4Chun’an County, Zhejiang Province31 October 202174Songyang 5Songyang County, Zhejiang Province8 November 2021
37Chunan 6Chun’an County, Zhejiang Province23 October 202175Yunhe 1Yunhe County, Zhejiang Province10 November 2021
38Chunan 9Chun’an County, Zhejiang Province10 November 202176Yunhe 2Yunhe County, Zhejiang Province10 November 2021
77DashuishiLanxi City, Zhejiang Province20 October 2021115Jiande 1Jiande City, Zhejiang Province10 October 2021
78NiunaishiPanan County, Zhejiang Province27 November 2021116Jiande 6Jiande City, Zhejiang Province9 October 2021
79HuoshiFuyang District, Hangzhou City4 October 2021117Jiande 7Jiande City, Zhejiang Province9 October 2021
80XiaobaxianshiGuanghzou City, Guangdong Province8 November 2021118Yuanling 1Yuanling County, Hunan Province28 November 2021
81PutiangoushiPutian City, FujianProvince27 November 2021119Yuanling 3Yuanling County, Hunan Province28 November 2021
82GongchengyueshenshiGongcheng County, Guangxi Province25 October 2021120ZenjimaruJapan26 October 2021
83AnxiyoushiAnxi County, FujianProvince8 November 2021121NishimurawaseJapan2 October 2021
84ShuishiHangzhou City, Zhejiang Province5 November 2021122AkagakiJapan2 October 2021
85HouzishiGutian County, Fujian Province5 November 2021123DongyangyiJapan4 October 2021
86BoaibayuehuangBoai County, Henan Province2 October 2021124ShanfushiJapan4 October 2021
87ChengjiangniuxinshiChengjiang City, Yunnan Province5 November 2021125Y-19Lanxi City, Zhejiang Province4 October 2021
88Guangzhou jixinshiGuanghzou City, Guangdong Province7 November 2021126SoshuJapan4 October 2021
89HexihuoguanXian City, Shanxi Province25 October 2021127Panan 14Panan County, Zhejiang Province11 November 2021
90ChaoyangyuanxiaoshiChaoyang District, Shantou City9 November 2021128Qujiang 1Qujiang District, Quzhou City16 October 2021
91Tonglu 3Tonglu County, Zhejiang Province11 November 2021129Qujiang 2Qujiang District, Quzhou City8 October 2021
92Tonglu 6Tonglu County, Zhejiang Province28 October 2021130Qujiang 3Qujiang District, Quzhou City21 October 2021
93Tonglu 8Tonglu County, Zhejiang Province7 October 2021131Changshan 1Changshan County, Zhejiang Province5 October 2021
94Tonglu 9Tonglu County, Zhejiang Province11 November 2021132Changshan 2Changshan County, Zhejiang Province10 October 2021
95Tonglu 10Tonglu County, Zhejiang Province31 October 2021133Changshan 3Changshan County, Zhejiang Province4 November 2021
96Yunhe 3Yunhe County, Zhejiang Province19 October 2021134GanshuJapan4 October 2021
97Longquan 1Longquan City, Zhejiang Province6 November 2021135Y-63Lanxi City, Zhejiang Province4 November 2021
98Longquan 2Longquan City, Zhejiang Province8 November 2021136Fuyo MutationMaoming City, Guangdong Province3 November 2021
99Jingning 1Jingning County, Zhejiang Province8 November 2021137HanagoshoJapan3 November 2021
100Shaoxin 1Shaoxing City, Zhejiang Province27 November 2021138Z 9Lanxi City, Zhejiang Province31 October 2021
101Shaoxin 2Shaoxing City, Zhejiang Province19 October 2021139Jinzaoshi 1Songyang County, Zhejiang Province28 November 2021
102Pujiang 2Pujiang County, Zhejiang Province7 November 2021140Jinzaoshi 2Songyang County, Zhejiang Province4 November 2021
103Pujiang 3Pujiang County, Zhejiang Province6 November 2021141Jinzaoshi 3Songyang County, Zhejiang Province6 November 2021
104Deqing 4Deqing County, Zhejiang Province1 October 2021142Junchuxiong 3Chuxiong county, Yunnan Province28 October 2021
105Deqing 5Deqing County, Zhejiang Province11 October 2021143JunchuxiongChuxiong county, Yunnan Province21 October 2021
106Anji 3Anji County, Zhejiang Province27 November 2021144JunbiyangBiyang county, Henan Province10 October 2021
107Anji 4Anji County, Zhejiang Province10 November 2021145untaianTaian city, Shandong Province4 November 2021
108Chaoxianshagu2Korea1 October 2021146Y-1Ganzhou City, Jiangxi Province31 October 2021
109ChaoxianpanshiKorea26 October 2021147FangshiFuyang District, Hangzhou City23 October 2021
110TaiwanzhengshiTaiwan9 November 2021148Y-2Ganzhou City, Jiangxi Province6 November 2021
111TaiwanhongshiTaiwan4 October 2021149Y-3Ganzhou City, Jiangxi Province2 December 2021
112HongguanghuiItaly28 October 2021150Y-4Ganzhou City, Jiangxi Province16 October 2021
113XitiaoJapan4 October 2021115Jiande 1Jiande City, Zhejiang Province10 October 2021
114RibenhongshiJapan16 October 2021116Jiande 6Jiande City, Zhejiang Province9 October 2021
Table 2. Summary statistics of the 56 phenotypic traits studied in the 150 persimmon germplasm resources.
Table 2. Summary statistics of the 56 phenotypic traits studied in the 150 persimmon germplasm resources.
TraitsMeanMaxMinSDRCV/%
Fruit weight (FW)/g99.40264.92.9949.02261.9149.31
Fruit volume (FV)/cm3105.21285.733.4751.21282.2648.68
Fruit transverse area (FTA)/cm226.0050.463.0610.3947.4039.96
Fruit transverse length (FTL)/cm5.798.552.091.306.4622.42
Fruit transverse width (FTW)/cm5.638.7321.266.7322.45
Fruit transverse shape index (FTI)1.031.380.690.100.699.24
Fruit longitudinal area (FLA)/cm225.1558.212.959.0555.2635.97
Fruit longitudinal length (FLL)/cm5.558.721.991.166.7320.88
Fruit longitudinal width (FLW)/cm5.798.472.081.266.3921.78
Fruit shape index (FI)0.981.640.690.180.9517.96
Fruit water content (FWC)/%73.648161.433.7619.575.10
Fruit soluble solid content (FSSC)/Brix°19.8038.8810.94.6527.9823.50
Fruit hardness (FH)/kg/cm38.1715.181.352.4913.8330.54
Fruit skin lightness (SKl)63.6671.6846.674.1325.016.48
Fruit skin red-green color (SKa)17.6133.68−3.456.1737.1335.04
Fruit skin yellow-blue (SKb)60.2571.5334.636.6536.9011.03
Fruit skin color angle (FSh)/°69.6182.76−62.516.32145.2623.45
Fruit skin color saturation (FSc)63.3473.4634.946.7538.5210.66
Pulp lightness (Pl)66.1476.0339.917.1436.1210.80
Pulp red-green color (Pa)6.4414.35−3.483.6517.8356.78
Pulp yellow-blue (Pyb)38.4452.5915.457.2537.1418.86
Pulp color angle (Pch)/°65.2885.15−82.2731.46167.4248.20
Pulp color saturation (Pc)39.2453.7915.997.2337.8018.44
Seed gross weight (SGW)/g3.636.80.761.366.0437.40
Seed gross volume (SGV)/cm33.897.240.591.306.6533.51
Seed weight (SW)/g0.901.750.150.261.6029.34
Seed volume (SV)/cm30.982.080.230.291.8529.33
Seed length (SL)/mm20.2527.1110.213.4216.9016.91
Seed width (SWI)/mm11.5018.045.731.9112.3116.65
Seed thickness (ST)/mm5.297.422.650.814.7715.27
Seed shape index (SI)1.812.821.110.401.7122.11
Seed quantity (SQ)4.257.311.436.3033.58
Pith weight (PW)/g2.055.580.070.925.5144.63
Pith volume (PV)/cm32.6611.640.281.3311.3650.18
Pith height (PH)/mm26.4446.8211.326.5035.5024.60
Pith base height (PBH)/mm6.8118.593.012.0615.5830.32
Pith top width (PT)/mm12.9327.984.14.4623.8834.48
Pith base width (PB)/mm6.3611.462.441.539.0223.96
Ratio of height to base width of pith (RHB)2.356.340.810.985.5341.85
Ratio of height to top width of pith (RHT)4.5710.182.021.598.1634.89
Fruit Pedicel weight (FPW)/g1.242.880.10.472.7837.63
Pedicel area (PeA)/cm24.998.810.851.337.9626.56
Pedicel length (PeL)/cm2.753.911.560.412.3514.84
Pedicel width (PeW)/cm2.513.40.990.372.4114.92
Fruit Pedicel thickness (FPT)/mm7.2510.522.31.418.2219.50
Pedicel shape index (PeSI)1.111.580.950.110.639.56
Sepal height (SH)/cm1.022.140.190.371.9536.00
Sepal area (SeA)/cm23.676.480.771.065.7128.73
Sepal length (SeL)/cm2.293.261.30.391.9616.97
Sepal width (SeW)/cm2.283.260.920.392.3417.07
Sepal shape index (SSI)1.021.640.740.160.9015.67
Fruit handle weight (FHW)/g0.120.220.020.040.2036.32
Fruit handle length (FHL)/mm12.5526.163.933.6722.2329.24
Fruit handle top width (FHTW)/mm2.714.41.640.522.7619.22
Fruit handle middle width (FHMW)/mm2.934.591.690.562.9019.22
Fruit handle base width (FHBW)/mm3.274.921.830.613.0918.73
Table 3. Total variance explained of PCA (Principal component analysis).
Table 3. Total variance explained of PCA (Principal component analysis).
ComponentTotalVariance/%Cumulative/%ComponentTotalVariance/%Cumulative/%
115.19139.97739.977200.1750.46297.556
24.97813.10053.078210.1730.45598.010
32.9227.68860.766220.1620.42798.438
42.1545.66866.435230.1390.36498.802
51.8974.99271.427240.0960.25299.054
61.6824.42775.853250.0720.19099.244
71.3253.48679.339260.0690.18399.427
81.1863.12082.459270.0620.16299.589
90.9822.58585.044280.0460.12099.710
100.8082.12787.171290.0290.07699.786
110.7481.96889.139300.0220.05799.843
120.6001.57990.718310.0150.04099.883
130.5291.39292.110320.0140.03799.920
140.4691.23493.344330.0100.02799.947
150.3760.98994.333340.0060.01699.964
160.3340.87895.211350.0050.01499.978
170.2820.74195.952360.0040.01199.989
180.2390.62996.580370.0020.00599.995
190.1950.51397.094380.0020.005100.000
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Dong, Y.; Sun, W.; Yue, Z.; Gong, B.; Yang, X.; Wu, K.; Liu, C.; Xu, Y. Phenotypic Diversity and Relationships of Fruit Traits in Persimmon (Diospyros kaki Thunb.) Germplasm Resources. Agriculture 2023, 13, 1804. https://doi.org/10.3390/agriculture13091804

AMA Style

Dong Y, Sun W, Yue Z, Gong B, Yang X, Wu K, Liu C, Xu Y. Phenotypic Diversity and Relationships of Fruit Traits in Persimmon (Diospyros kaki Thunb.) Germplasm Resources. Agriculture. 2023; 13(9):1804. https://doi.org/10.3390/agriculture13091804

Chicago/Turabian Style

Dong, Yi, Weimin Sun, Zhihui Yue, Bangchu Gong, Xu Yang, Kaiyun Wu, Cuiyu Liu, and Yang Xu. 2023. "Phenotypic Diversity and Relationships of Fruit Traits in Persimmon (Diospyros kaki Thunb.) Germplasm Resources" Agriculture 13, no. 9: 1804. https://doi.org/10.3390/agriculture13091804

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