Morphological and pomological characteristics of white mulberry (Morus alba L.) accessions

Scientia Horticulturae 259 (2020) 108827

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Morphological and pomological characteristics of white mulberry (Morus alba L.) accessions

T

Sogand Hashemi, Ali Khadivi

⁎

Department of Horticultural Sciences, Faculty of Agriculture and Natural Resources, Arak University, 38156-8-8349, Arak, Iran

ARTICLE INFO

ABSTRACT

Keywords: White mulberry Phenotypic diversity Breeding Fruit quality Gene pool

White mulberry (Morus alba L.) fruits contain considerable amounts of biologically active ingredients that might be associated with some potential pharmacological activities that are beneficial for health. In the current investigation, morphological and pomological variability of this species was evaluated. The majority of characters showed meaningful variabilities among the studied accessions. Fruit length varied from 14.35 to 26.98 mm with an average of 19.48 and fruit weight ranged between 0.94 and 2.86 g with an average of 1.59. Total soluble solids varied from 7.70 to 25.80% with an average of 16.17. Fruit weight showed positive and significant correlations with leaf and fruit dimensions. Principal component analysis (PCA) described the characters as the seven main components which were able to justify 76.42% of total variance. The dendrogram generated based on the recorded traits revealed two separate clusters and showed the existence of significant variability among the studied accessions. According to the optimum indices for fruit quality in white mulberry, 19 accessions were superior and can be singled out for cultivation. The present findings indicated that the studied accessions may be recognized as a representative gene pool of white mulberry.

1. Introduction

Pubescent, green unisexual flowers emerge with leaves in April to May, blooming axillarily. The M. alba produces lavender white or black fruits which are very sweet but lack of tartness (Yaltirik, 1982). White mulberry as a multipurpose plant may be used especially in pharmaceutical industry and medicine, and food industry. It has been used over the centuries in traditional Chinese medicine as a common agent to treat a variety of conditions including diabetes, atherosclerosis, and cancer as well as for boosting the immune system through potent antioxidant activity (Butt et al., 2008). Different parts of the mulberry plant (fruit, bark, leaf, and root) have drawn interest in their role to treat diabetes (Bantle and Slama, 2006). It is important to characterize the germplasm and identify the diverse parents that can serve as potential allelic resources for hybridization programs to improve yield. Characterization of germplasm is essential to identify individual genotypes and also to gauge extent of variability existing among the accessions. Phenotypic characterization is the first step in the description and classification of the germplasm. Moreover, estimates of genetic variability and relationship between germplasm collections are very useful and facilitate efficient utilization and management of germplasm (Smith and Smith, 1989). A population with more diverse genotypes is of considerable value as the success of any breeding program relies on the genetic variability present in the base population for effective selection and recombination

Mulberry (Morus) is a typical East Asian plant belonging to family Moraceae. It is a perennial tree or shrub and is an economically important plant. It is widely distributed in varied ecological and geographical zones from intensive cultivation in temperate, sub-tropical and tropical areas to natural occurrence in forests throughout the world. This clearly indicates that mulberry contains a high degree of morphological and physiological buffering to changes in the environment. Mulberry can be trained as bush, dwarf or tree and all these types can be maintained with different cultural practices (Benavides et al., 1994). Morus genus has three main species including white (Morus alba), red (M. rubra) and black (M. nigra). Mulberry species tend to hybridize easily, which has led to its considerable genetic variability (Datta, 2002). Morus alba L., commonly called white mulberry, is one of the most important species widely used for the rearing of silkworm (Bombyx mori L.). It is indigenous to China but has spread to various parts of the world. It is woody tree or shrub that can reach 3–10 m in height and 0.50 m in diameter. The bark is gray, thick, with many irregular longitudinal cracks. The ovate winter buds are reddish-brown, bearing grayish brown, imbricate bud scales that are coated with hairs resembling those on the twig surface. ⁎

Corresponding author. E-mail address: [email protected] (A. Khadivi).

https://doi.org/10.1016/j.scienta.2019.108827 Received 25 July 2019; Received in revised form 22 August 2019; Accepted 31 August 2019 0304-4238/ © 2019 Elsevier B.V. All rights reserved.

Scientia Horticulturae 259 (2020) 108827

S. Hashemi and A. Khadivi

Table 1 Descriptive statistics for morphological characters of the studied accessions of white mulberry (M. alba). No.

Character

Abbreviation

Unit

Min.

Max.

Mean

SD

CV (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Tree growth habit Tree growth vigor Trunk diameter Tree canopy density Branching Branch density Branch flexibility Current shoot color Leaf density Leaf length Leaf width Leaf shape Leaf serration type Leaf apex shape Leaf upper surface color Leaf lower surface color Leaf pubescence Leaf lobe presence Petiole length Petiole diameter Fruit strength to branch Fruit length Fruit width Fruit weight Fruit stalk length Fruit stalk diameter Total soluble solid Fruit color Fruit stalk color Fruit taste Fruit drupelet density Fruit shape Fruit pubescence

TrGrHa TrGrVi TruDi TrCaDe Br BrDe BrFl CuShoCo LDe LLe LWi LSh LSeTy LApSh LUpSuCo LLoSuCo LPu LLoPr PetLe PetDi FrStrBr FrLe FrWi FrWe FrStLe FrStDi TSS FrCo FrStCo FrTa FrDrDe FrSh FrPu

Code Code Code Code Code Code Code Code Code mm mm Code Code Code Code Code Code Code mm mm Code mm mm g mm mm % Code Code Code Code Code Code

1 3 1 3 3 3 3 1 3 44.99 35.78 1 1 1 1 1 0 0 17.30 0.67 3 14.35 8.37 0.94 3.82 0.46 7.70 1 1 1 1 1 1

5 7 7 7 7 7 3 1 7 125.56 91.94 5 1 3 3 3 0 5 55.86 2.22 5 26.98 15.09 2.86 12.07 2.01 25.80 3 1 5 5 5 1

3.06 5.41 4.44 4.67 5.41 5.43 3.00 1.00 4.46 81.18 61.96 2.55 1.00 1.60 2.20 1.08 0.00 0.20 31.91 1.49 3.85 19.48 11.46 1.59 7.92 1.10 16.17 1.06 1.00 3.41 4.30 3.58 1.00

1.21 1.78 1.70 1.07 1.71 1.69 0.00 0.00 0.94 19.44 11.71 1.31 0.00 0.92 0.99 0.40 0.00 0.87 8.94 0.34 0.99 2.94 1.72 0.44 1.74 0.35 4.90 0.35 0.00 1.96 1.53 1.63 0.00

39.41 32.87 38.33 22.87 31.53 31.10 0.00 0.00 20.99 23.95 18.90 51.25 0.00 57.50 44.82 37.04 0.00 437.00 28.01 23.08 25.79 15.10 15.01 27.65 21.99 32.01 30.32 32.83 0.00 57.39 35.56 45.59 0.00

breeding. Information on phenotypic and genotypic variability is vital for effective utilization of germplasm accessions for long-term improvement of yield, adaptation, and resistance to pests and diseases. Large number of mulberry germplasm accessions has been maintained in several countries owing to its great economic importance (Banerjee et al., 2007). Mulberry trees in Iran have been generally found in natural cultivation areas and similar accessions are not presented. To our knowledge, no available data are found on the definition of the morphological characteristics of white or common mulberry (M. alba) grown in Iran. Hence, the objective of the present study was to investigate phenotypic and pomological variation of this species. The results of the present investigation can be used for conserving the indigenous germplasm, identifying accessions with valuable characters, and introducing them into the gene bank.

mulberry descriptor provided by the Central Research and Training Institute (CSRTI, 1986). Fruits and leaves were collected for analyses from different branches per tree in the areas of natural growth. For each accession, 30 leaves and 30 fruits were selected for morphological analysis. Samples of adult leaves were taken in summer from outside sections of trees. The fruits were taken during the mature commercialstage depending on the skin color changes, its appearance and taste and then transported to a laboratory for analysis. The characters including leaf length, leaf width, petiole length, petiole diameter, fruit length, fruit width, fruit stalk length, and fruit stalk diameter were measured using a digital caliper. Fruit weight was measured by an electronic balance with 0.01 g precision. Furthermore, tree growth habit, tree growth vigor, trunk diameter, tree canopy density, branching, branch density, branch flexibility, current shoot color, leaf density, leaf shape, leaf serration type, leaf apex shape, leaf upper surface color, leaf lower surface color, leaf pubescence, leaf lobe presence, fruit strength to branch, fruit color, fruit stalk color, fruit taste, fruit drupelet density, fruit shape, and fruit pubescence were measured based on ranking and coding. In addition, total soluble solids (TSS) of fruits were quantified using a refractometer (PAL-1 ATAGO Corporation pocket, Tokyo, Japan) and expressed as °Brix.

2. Materials and methods 2.1. Plant material In the current investigation, morphological and pomological variability among 97 accessions of white mulberry (M. alba) was evaluated. The plant materials were selected from 10 areas of Markazi province in Iran including Amanabad, Amirkabir, Enjedan, Jezenagh, Karavansara, Kerahrood, Khosbijan, Marzegaran, Roodbaran, and Sagh. Markazi province is far from the sea and has great height with a long winter. Its condition is very cold and snowy during the winter, while, its climate is cool in the highlands and warm in the lowlands during the summer.

2.3. Data analysis The mean of the studied traits was calculated and used for statistical analysis. The minimum value, maximum value, standard deviation (SD) and coefficient of variations (CV%; SD/mean×100) were calculated for the measured traits. Analysis of variance for all the traits was performed using one-way ANOVA by SAS software (SAS Inst., 1990). The coefficient of variation (CV%) was determined as a variability index. The correlation between the traits was calculated using Pearson correlation coefficient by SPSS software (Norusis, 1998). The relationships between the accessions were determined using principal component analysis

2.2. Morphological and pomological measurements In total, 33 morphological and pomological characters were used to study the phenotypic variation of the accessions according to the 2

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(PCA) by SPSS software. To better understand the patterns of variations among the accessions, the distance matrix of morphological data as input data was used for cluster analysis using Ward method by PAST statistics software (Hammer et al., 2001). In addition, a scatter plot was created according to the first component (PC1) and the second component (PC2) using the PAST statistics software. 3. Results and discussion 3.1. Morphological and pomological characteristics The ANOVA (P < 0.01) revealed that most of the measured characters showed meaningful variabilities among the studied accessions. The highest CV was related to leaf lobe presence (437.00%) and followed by leaf apex shape (57.50%), fruit taste (57.39%), and leaf shape (51.25%). In contrast, there were no differences among the accessions in terms of six characters including branch flexibility (intermediate), current shoot color (green), leaf serration type (serrulate), leaf pubescence (absent), fruit stalk color (green), and fruit pubescence (present) (CV = 0.00%) (Table 1). The CV values of morphological characters could indicate the ability to differentiate among the accessions. The characters with lower CVs can be more consistent among the accessions, while the characters with CV values higher than 20.00% can be used as reliable markers for distinguishing the accessions (Farahani et al., 2019). Here, 24 out of 33 the recorded variables (73.00% of them) showed the CV values higher than 20.00%, indicating considerable variations among the accessions investigated (Table 1). In the first step, 20 vegetative characters were recorded to differentiate the accessions. Spreading to upright growth habit was predominant (62 out of 97 accessions). In addition, growth vigor was very high in the majority of accessions (50). Trunk diameter, canopy density, branching, branch density, and leaf density was high to very high in most of the accessions. Leaf lobe was absent in most of the accessions (92) (Table 2). Similarly, Boubaya et al. (2011) observed that the majority of their white mulberries were without leaf lobes (84.00%). Three types of leaf shape including broad (34), broad-oblong (51), and oblong (12) were observed. Boubaya et al. (2011) reported that the majority of their white mulberries have an oval leaf form. Some plants exhibit heterophylly, having more than one shape of leaf. In some cases, heterophylly is thought to be an adaptive mechanism that allows plants to optimally respond to environmental heterogeneity (Nakayama et al., 2017). Leaf apex shape was predominantly acute (68). Boubaya et al. (2011) reported that 52.00% of white mulberries were characterized by

Fig. 1. The pictures of the studied white mulberry (M. alba) accessions pointing out different leaf lobes and fruit shapes.

a triangular leaf apex, 35.00% acute, 9.00% round, and 4.00% blunt. Peris et al. (2014) suggested that leaf apex shape could be utilized for differentiation among mulberry varieties. Leaf length ranged from 44.99 to 125.56 mm with an average of 81.18 and leaf width varied from 35.78 to 91.94 mm with an average of 61.96 (Table 1). Petiole length ranged from 17.30 to 55.86 mm, while the range of petiole diameter was 0.67–2.22 mm. Suman et al. (2018) recorded the range of 88.90–175.00 mm for leaf length,

Table 2 Frequency distribution of the measured qualitative morphological characters (tree, leave and fruit related traits) in the studied accessions of white mulberry (M. alba) according to the mulberry descriptor provided by the Central Research and Training Institute (CSRTI, 1986). Code and frequency (No. of accessions) Character

0

1

3

5

7

Tree growth habit Tree growth vigor Trunk diameter Tree canopy density Branching Branch density Leaf density Leaf shape Leaf apex shape Leaf upper surface color Leaf lower surface color Leaf lobe presence Fruit strength to branch Fruit color Fruit taste Fruit drupelet density Fruit shape

– – – – – – – – – – – Absent (92) – – – – –

Spreading (16) – Low (13) – – – – Broad (34) Acute (68) Light-green (39) Light-green (93) Present on one side (3) – White (94) Little sweet (38) Low (17) Broad (22)

Spreading to upright (62) Intermediate (30) Intermediate (13) Intermediate (23) Intermediate (27) Intermediate (26) Intermediate (27) Broad-oblong (51) Blate (29) Dark-green (58) Dark-green (4) Present on both sides (2) Intermediate (56) Pink (3) Intermediate (1) – Broad-oblong (25)

Upright (19) High (17) High (59) High (67) High (23) High (24) High (69) Oblong (12) – – – – High (41) – Very sweet (58) High (80) Oblong (50)

– Very Very Very Very Very Very – – – – – – – – – –

3

high high high high high high

(50) (12) (7) (47) (47) (1)

4

LLoPr

1 0.07 0.13 −0.19 0.27** 0.23* 0.28** −0.15 0.29** 0.10

TrGrHa TrGrVi TruDi TrCaDe Br BrDe LDe LLe LWi LSh LApSh LUpSuCo LLoSuCo LLoPr PetLe PetDi FrStrBr FrLe FrWi FrWe FrStLe FrStDi TSS

1 0.05 0.46** 0.24* 0.11 0.13 0.25* 0.31** 0.33** −0.14 0.00 0.04 −0.10 0.20* 0.26** 0.22* −0.32** 0.30** 0.24* 0.17 0.14 0.24* 0.07 −0.01 −0.08 0.38** −0.06

TrGrHa TrGrVi TruDi TrCaDe Br BrDe LDe LLe LWi LSh LApSh LUpSuCo LLoSuCo LLoPr PetLe PetDi FrStrBr FrLe FrWi FrWe FrStLe FrStDi TSS FrCo FrTa FrDrDe FrSh

Character

TrGrHa

Character

1 0.40** −0.22* 0.29** 0.27** 0.28** 0.05 0.17 0.11

PetLe

1 0.72** 0.51** 0.96** 0.95** 0.83** 0.35** 0.37** −0.15 0.10 −0.05 0.01 −0.26** 0.31** 0.04 −0.03 −0.03 −0.09 −0.11 0.23* −0.21* −0.05 0.16 −0.10 0.44** 0.12

TrGrVi

1 −0.59** 0.59** 0.61** 0.59** −0.21* 0.59** −0.17

PetDi

1 0.42** 0.75** 0.76** 0.83** 0.53** 0.51** −0.17 0.00 0.05 −0.05 −0.22* 0.49** 0.22* −0.26** 0.20* 0.17 0.13 0.26** 0.07 −0.01 0.06 −0.12 0.55** 0.12

TruDi

1 −0.66** −0.82** −0.57** 0.49** −0.82** 0.31**

FrStrBr

1 0.53** 0.54** 0.44** 0.28** 0.25* 0.01 0.08 −0.14 −0.13 −0.04 0.23* 0.19 −0.32** 0.23* 0.17 0.15 −0.04 0.20* 0.09 0.39** 0.01 0.41** 0.13

TrCaDe

1 0.83** 0.92** −0.17 0.71** −0.13

FrLe

1 0.99** 0.87** 0.36** 0.38** −0.16 0.11 −0.05 0.01 −0.27** 0.32** 0.03 −0.04 −0.04 −0.10 −0.11 0.25* −0.22* −0.04 0.17 −0.10 0.46** 0.12

Br

1 0.83** −0.38** 0.85** −0.27**

FrWi

1 −0.15 0.74** −0.05

FrWe

1 0.86** 0.37** 0.38** −0.16 0.10 −0.04 0.01 −0.28** 0.33** 0.04 −0.05 −0.02 −0.08 −0.09 0.26** −0.19 −0.04 0.17 −0.09 0.47** 0.14

BrDe

Table 3 Bivariate correlations among morphological characters in the studied accessions of white mulberry (M. alba).

1 −0.46** 0.23*

FrStLe

1 0.35** 0.37** −0.20* 0.18 −0.07 0.01 −0.27** 0.35** 0.03 0.00 −0.01 −0.11 −0.06 0.35** −0.20* 0.05 0.10 −0.02 0.37** 0.18

LDe

1 −0.26**

FrStDi

1 0.89** 0.19 −0.23* 0.16 −0.05 0.08 0.81** 0.62** −0.40** 0.41** 0.36** 0.34** −0.05 0.26** 0.04 0.03 −0.16 0.65** −0.11

LLe

1

TSS

1 −0.01 −0.12 0.12 −0.01 0.09 0.74** 0.53** −0.19 0.35** 0.23* 0.27** 0.13 0.10 0.12 −0.05 −0.04 0.65** −0.03

LWi

FrCo

1 −0.19 0.17 −0.09 0.08 0.05 0.20 −0.34** 0.21* 0.31** 0.21* −0.36** 0.32** −0.21* 0.24* −0.30** 0.05 −0.19

LSh

FrTa

1 −0.20 −0.02 −0.07 −0.08 −0.24* 0.30** −0.20* −0.24* −0.13 0.31** −0.28** 0.15 0.01 0.16 −0.23* 0.18

LApSh

FrSh

1 −0.05 0.00 −0.19 0.24* −0.12 −0.17 −0.10 0.13 −0.17 0.16 −0.04 0.17 −0.04 0.12

LLoSuCo

(continued on next page)

FrDrDe

1 0.17 −0.08 0.15 0.02 0.06 0.10 0.07 0.01 −0.07 0.00 −0.17 0.03 −0.15 0.12 −0.02

LUpSuCo

S. Hashemi and A. Khadivi

Scientia Horticulturae 259 (2020) 108827

Scientia Horticulturae 259 (2020) 108827

1 −0.10 0.23*

1 −0.10

FrDrDe

0.04 −0.30** 0.34** −0.27**

−0.09 0.81** 0.03 0.23*

1 −0.10 0.08 −0.06

FrTa FrCo TSS

−0.25 0.29** −0.03 0.36**

For explanation of morphological character symbols, see Table 1. * , **. Correlation is significant at the 0.05 and 0.01 levels, respectively.

−0.09 −0.12 0.41** 0.04 0.06 −0.36** 0.53** −0.23* −0.01 −0.23* 0.53** 0.04 −0.04 0.04 0.10 −0.18 FrCo FrTa FrDrDe FrSh

−0.05 −0.03 0.54** −0.05

−0.04 −0.30** 0.38** −0.21*

−0.15 0.48** −0.53** 0.31**

FrWi LLoPr Character

Table 3 (continued)

PetLe

PetDi

FrStrBr

FrLe

FrWe

FrStLe

*

FrStDi

57.80–119.30 mm for leaf width, and 22.40–46.30 mm for petiole length in M. alba. Moreover, Kalkisim (2013) reported the range of 95.94–130.02 mm for leaf length, 73.19–100.84 mm for leaf width, and 27.55–50.94 mm for petiole length in M. alba. In the second step, the accessions were studied based on 12 pomological characters. Fruit strength to branch was predominately intermediate (56) and followed by high (41). Fruit drupelet density as a good indicator of fruit quality was predominantly high in the majority of accessions (80) (Table 2). One of the most appreciated traits by consumers is fruit size. In addition, fruit size and weight have a direct impact on the sales and acceptance of fruit crops for fresh and processed markets and have a major impact on performance. Fruit length varied from 14.35 to 26.98 mm with an average of 19.48 and fruit width ranged from 8.37 to 15.09 mm with an average of 11.46 (Table 1). Fruit weight ranged between 0.94 and 2.86 g with an average of 1.59. Fruit stalk length varied from 3.82 to 12.07 mm and the range of fruit stalk diameter was 0.46–2.01 mm. Yilmaz et al. (2012) reported the range of 0.66 to 3.07 g for fruit weight in M. alba genotypes from Turkey. Furthermore, Aljane and Sdiri (2016) reported the average of 21.38 mm for fruit length, 13.78 mm for fruit width, 1.58 g for fruit weight, and 6.75 mm for fruit stalk length in M. alba. Moreover, Kalkisim (2013) reported the range of 22.27–30.32 mm for fruit length, 12.50–15.62 mm for fruit width, and 2.02–2.73 g for fruit weight in M. alba. Higher fruit weight is one of the most important desirable fruit characteristics in mulberry breeding programs (Aljane and Sdiri, 2016). Three types of fruit shape were observed that included broad (22), broad-oblong (25), and oblong (50). Fruit color was white in the majority of accessions (94). In addition, fruit taste was very sweet in most of the accessions (58). Aljane and Sdiri (2016) reported that most of the white mulberry genotypes had sweet fruits. The pictures of leaves and fruits of the studied M. alba accessions are shown in Fig. 1. In the third step, TSS as an analytical and sensory property was measured. There was a significant difference among the accessions in terms of this biochemical property and it varied from 7.70 to 25.80% with an average of 16.17. Yilmaz et al. (2012) reported 30.67% as the highest value for TSS in M. alba genotypes from Turkey. Moreover, Kalkisim (2013) reported the range of 8.05–23.28% for TSS in M. alba. It has been reported that TSS content in M. alba was higher than M. nigra and M. rubra (Lale and Ozcagiran, 1996; Ozdemir and Topuz, 1998; Yilmaz et al., 2012). The TSS is an important component of fruit flavor for many fruits including white mulberry. One of the most important biochemical properties of fruits is TSS which is used indirectly in the amount of sugars (Hirsch et al., 2012).

1

FrSh

S. Hashemi and A. Khadivi

3.2. The correlations among characters The significant correlations were observed between some characters (Table 3). Tree growth habit was positively and significantly correlated with trunk diameter (r = 0.46), canopy size (r = 0.24), and leaf density (r = 0.25) and agreed with the previous finding in different mulberry species (Hosseini et al., 2018; Farahani et al., 2019). Tree growth vigor was significantly and positively correlated with trunk diameter (r = 0.72), canopy density (r = 0.51), branching (r = 0.96), branch density (r = 0.95), and leaf density (r = 0.83) and agreed with the previous finding in different mulberry species (Hosseini et al., 2018; Farahani et al., 2019). Leaf length was positively and significantly correlated with tree growth habit (r = 0.31), tree growth vigor (r = 0.35), trunk diameter (r = 0.53), canopy density (r = 0.28), branching (r = 0.36), branch density (r = 0.37), leaf density (r = 0.35), and leaf width (r = 0.89) and coincided with the previous findings in mulberry (Sarkar et al., 1987; Vijayan et al., 1997; Tikadar and Roy, 2001; Bajpai et al., 2015; Hosseini et al., 2018; Farahani et al., 2019). In addition, leaf width showed positive and significant correlations with all the above characters. The existence of close positive correlations among leaf-related characters and also positive correlations between leaf traits and tree vigor and canopy size indicate that more leaf 5

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Table 4 Eigenvectors of principal component axes from the PCA for morphological characters in the studied accessions of white mulberry (M. alba). Component Character Tree growth habit Tree growth vigor Trunk diameter Tree canopy density Branching Branch density Leaf density Leaf length Leaf width Leaf shape Leaf apex shape Leaf upper surface color Leaf lower surface color Leaf lobe presence Petiole length Petiole diameter Fruit strength to branch Fruit length Fruit width Fruit weight Fruit stalk length Fruit stalk diameter Total soluble solid Fruit color Fruit taste Fruit drupelet density Fruit shape Total % of Variance Cumulative %

1 0.21 0.92** 0.81** 0.64** 0.95** 0.95** 0.90** 0.29 0.31 −0.18 0.19 −0.06 0.00 −0.36 0.27 0.00 −0.08 0.03 −0.05 −0.06 0.30 −0.16 −0.05 0.28 −0.10 0.45 0.26 5.49 20.32 20.32

2 0.23 −0.11 0.15 0.27 −0.11 −0.09 −0.07 0.27 0.17 0.25 −0.14 −0.01 −0.12 0.23 0.17 0.58** −0.72** 0.93** 0.88** 0.94** −0.22 0.83** −0.15 −0.01 −0.23 0.43 0.13 4.84 17.92 38.24

3

4

0.12 0.17 0.28 −0.01 0.17 0.17 0.15 0.87** 0.84** 0.15 −0.29 0.16 −0.06 0.12 0.79** 0.54 −0.16 0.18 0.17 0.16 −0.04 0.09 0.15 −0.18 −0.01 0.49 −0.29 3.23 11.96 50.20

−0.01 −0.05 −0.08 0.25 −0.05 −0.04 0.02 −0.01 0.09 −0.20 0.23 −0.23 0.23 0.18 0.09 −0.21 0.33 −0.02 −0.22 0.07 0.26 −0.20 0.88** 0.07 0.86** 0.04 0.42 2.35 8.70 58.90

5 0.15 −0.01 0.16 −0.38 −0.01 0.00 0.15 −0.08 0.10 −0.52 0.27 −0.04 0.05 −0.12 0.04 −0.03 0.36 0.03 −0.17 0.09 0.67** −0.25 0.05 −0.74** 0.11 −0.13 0.38 2.00 7.41 66.30

6

7 **

0.81 −0.09 0.23 0.10 −0.04 −0.03 0.06 0.05 0.10 −0.33 −0.04 0.00 −0.04 0.47 0.06 −0.01 −0.24 0.07 0.12 −0.04 0.03 0.18 0.10 0.02 0.02 0.27 −0.35 1.39 5.16 71.47

0.05 −0.04 0.06 −0.17 −0.03 −0.02 −0.03 0.06 0.05 0.08 −0.31 0.77** 0.66** −0.12 0.07 −0.18 0.16 0.08 −0.02 −0.01 0.07 −0.09 −0.03 0.06 −0.01 0.13 0.21 1.34 4.96 76.42

** Eigenvalues ≥ 0.58 are significant.

expansion leads to stronger aerial growth (Khadivi-Khub and Anjam, 2014). Fruit length was significantly and positively correlated with tree

growth habit (r = 0.30), trunk diameter (r = 0.20), canopy size (r = 0.23), leaf length (r = 0.41), leaf width (r = 0.35), leaf shape (r = 0.21), leaf lobe presence (r = 0.27), petiole length (r = 0.29), and

Fig. 2. Two-dimensional scatter plot based on PC1 and PC2 (38.24% of total variance) for the studied accessions of white mulberry (M. alba). The symbols represent the accessions of each population in the plot including Amanabad (Am), Amirkabir (A), Enjedan (E), Jezenagh (J), Karavansara (Ka), Kerahrood (K), Khosbijan (Kh), Marzegaran(M), Roodbaran (R), and Sagh (S). 6

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Fig. 3. Dendrogram of cluster analysis for the studied accessions of white mulberry (M. alba) based on morphological characters.

7

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S. Hashemi and A. Khadivi

Fig. 4. Bi-plot for the studied populations of white mulberry (M. alba) based on morphological characters.a.

petiole diameter (r = 0.59), while it was negatively correlated with leaf apex shape (r = -0.20) and fruit strength to branch (r = -0.66). Similarly, fruit width showed significant and positive correlations with tree growth habit (r = 0.24), leaf length (r = 0.36), leaf width (r = 0.23), leaf shape (r = 0.31), leaf lobe presence (r = 0.23), petiole length (r = 0.27), and petiole diameter (r = 0.61), while it was negatively correlated with leaf apex shape (r = -0.24) and fruit strength to branch (r = -0.82). Fruit weight showed positive and significant correlations with leaf length (r = 0.34), leaf width (r = 0.27), leaf shape (r = 0.21), leaf lobe presence (r = 0.28), petiole length (r = 0.28), and petiole diameter (r = 0.59) and corresponded with the previous findings (Bajpai et al., 2015; Hosseini et al., 2018; Farahani et al., 2019). The fruit size is determined by several main factors including the total active leaf area per tree (Fishler et al., 1983). In addition, fruit weight was highly and positively correlated fruit length (r = 0.92), fruit width (r = 0.83), fruit stalk diameter (r = 0.74), and fruit drupelet density (r = 0.41), and agreed with the previous findings (Bajpai et al., 2015; Hosseini et al., 2018; Farahani et al., 2019). Furthermore, fruit weight was negatively correlated with fruit strength to branch (r = -0.57). With this regard, it might be concluded that these variables can be exploited either by breeding programs or for facilitating the identification of the accessions during field surveys (Khadivi-Khub et al., 2015). The TSS was positively and significantly correlated with fruit strength to branch (r = 0.31), fruit stalk length (r = 0.23), fruit drupelet density (r = 0.41), and fruit shape (r = 0.23). Moreover, fruit taste showed positive and significant correlations with TSS (r = 0.81) and fruit shape (r = 0.23).

major role to differentiate the accessions investigated. The remaining components (PC4–PC7) included other variables and explained less variability (26.22% of total variance) and thus had low roles to distinguish the accessions. The PCA had been used to evaluate germplasm of mulberry (Chang et al., 2014; Hosseini et al., 2018; Krishna et al., 2018; Farahani et al., 2019) and revealed that fruit traits contributed to differentiate the accessions. 3.4. Phenotypic variation Projection of the studied accessions on the PC1/PC2 plot based on the measured characters is presented in Fig. 2. By starting from negative toward positive values of PC1, the accessions showed gradual increases in tree growth vigor, trunk diameter, canopy density, branching, branch density, and leaf density. Furthermore, by starting from negative to the positive values of PC2, the accessions indicated gradual increases in petiole diameter, fruit length, fruit width, fruit weight, and fruit stalk diameter, and also gradual decreases in fruit strength to branch. A dendrogram generated using Ward method revealed two separate clusters based on the recorded traits (Fig. 3). Cluster I comprised 25 accessions and all the accessions of Amirkabir area were placed into the same sub-cluster. In addition, cluster II included the remaining 72 accessions which formed two sub-clusters. Sub-cluster II-A contained 22 accessions and sub-cluster II-B consisted of 50 accessions. The cophenetic correlation coefficient indicated a high correlation (r = 0.95) between the morphological distance matrix and the cophenetic matrix of dendrogram, indicating the goodness of fit of cluster analysis. The cophenetic correlation coefficient is considered to be a very good representative of the data matrix in the dendrogram if it is 0.90 or greater (Romesburg, 1990). In addition, according to the population analysis (Fig. 4), the studied areas were placed into four groups. The Marzegaran was placed in the first group, while Amirkabir and Kerahrood populations formed the second group. In addition, the populations including Khosbijan, Jezenagh, and Amanabad were placed in the third group and characterized by high values in fruit weight, fruit length, fruit width, and TSS. The fourth group included Enjedan, Karavansara, Roodbaran, and Sagh populations, so that Sagh population showed some similarities to the populations of the third group. The success of any breeding program depends on knowledge about the magnitude of the variability together with the extent to which component characters are inherited (Johnson et al., 1995). Morphological studies showed the existence of significant variability among the studied accessions, concerning tree, leaf, and fruit character related. In general, the present results indicated that the white mulberries studied can be considered as the promising sources in the breeding programs.

3.3. Principal component analysis (PCA) The PCA can clarify the main differences among the accessions studied and also reduce the amount of data. Using the PCA, various characters may be placed in the components. The importance of the components in variance of the measured characters is revealed by the relative variance of each component. The highest amount of variance is justified by the first component and the remaining variances are justified by the subsequent components (Iezzoni and Pritts, 1991; KhadiviKhub and Khalili, 2017). Here, the PCA described the recorded characters as the seven main components which were able to justify 76.42% of total variance (Table 4). The first component (PC1) was positively correlated with tree growth vigor, trunk diameter, canopy density, branching, branch density, and leaf density and explained 20.32% of total variance. Six characters including petiole diameter, fruit strength to branch, fruit length, fruit width, fruit weight, and fruit stalk diameter were found into PC2, which accounted for 17.92% of total variance. The PC3 accounted for 11.96% of total variance and included leaf length, leaf width, and petiole length. These components played a 8

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4. Conclusions

package for education and data analysis. Palaeontol. Electron. 4 (1), 9. http:// palaeoelectronica. Hirsch, G.E., Facco, E.M.P., Rodrigues, D.B., Vizzotto, M., Emanuelli, T., 2012. Physicochemical characterization of blackberry from the Southern Region of Brazil. Ciência Rural 42, 942–947. Hosseini, A.S., Akramian, M., Khadivi, A., Salehi-Arjmand, H., 2018. Phenotypic and chemical variation of black mulberry (Morus nigra) genotypes. Ind. Crops Prod. 117, 260–271. Iezzoni, A.F., Pritts, M.P., 1991. Applications of principal components analysis to horticultural research. Hortic Sci 26, 334–338. Johnson, H.W., Robinson, H.F., Comstock, R.F., 1995. Estimates of genetic and environmental variability in soybean. Agron. J. 47, 313–318. Kalkisim, O., 2013. Determination of the pomological and morphological properties of white mulberry types growing in transition region between mild and continental climates. J. Food Agric. Environ. 11, 568–571. Khadivi-Khub, A., Anjam, K., 2014. Morphological characterization of Prunus scoparia using multivariate analysis. Plant Syst. Evol. 300, 1361–1372. Khadivi-Khub, A., Karimi, S., Kameli, M., 2015. Morphological diversity of naturally grown Crataegus monogyna (Rosaceae, Maloideae) in Central Iran. Braz. J. Bot. 38 (4), 921–936. Khadivi-Khub, A., Khalili, Z., 2017. A breeding project: the selection of promising apricot (Prunus armeniaca L.) genotypes with late blooming time and high fruit quality. Sci. Hortic. 216, 93–102. Krishna, H., Singh, D., Singh, R.S., Kumar, L., Sharma, B.D., Saroj, P.L., 2018. Morphological and antioxidant characteristics of mulberry (Morus spp.) genotypes. J. Saudi Soc. Agri. Sci. https://doi.org/10.1016/j.jssas.2018.08.002. Lale, H., Ozcagiran, R., 1996. A study on the phonological, pomological and fruit properties of mulberry species. Derim 13 (4), 177–182. Nakayama, H., Sinha, N.R., Kimura, S., 2017. How do plants and phytohormones accomplish heterophylly, leaf phenotypic plasticity, in response to environmental cues. Front. Plant Sci. 8, 1717. Norusis, M.J., 1998. SPSS/PC Advanced Statistics. SPSS Inc, Chicago. Ozdemir, F., Topuz, A., 1998. Some chemical composition of mulberries grown in Antalya. Derim 15 (1), 30–35. Peris, N.W., Gacheri, K.M., Theophillus, M.M., Lucas, N., 2014. Morphological characterization of mulberry (Morus spp.) accessions grown in Kenya. Sustain. Agric. Res. 3 (1), 10. Romesburg, H.C., 1990. Cluster Analysis for Researchers. Krieger Publishing, Malabar, FL, USA. Sarkar, A., Roy, B.N., Gupta, K.K., Das, B.C., 1987. Character association in mulberry under close planting. Indian J. Seric. 26, 76–78. SAS® Procedures (1990) Version 6, 3rd ed. SAS Institute, Cary, NC. Smith, J.C.S., Smith, O.S., 1989. The description and assessment of distance between inbred lines of maize: the utility of morphological, biochemical and genetic descriptors and a scheme for the testing of distinctiveness between inbred lines. Maydica 34, 151–161. Suman, K., Thakur, I., Kumari, A., 2018. Variation in growth characteristics of different clones of Morus alba. J. Pharm. Phytochem. 7 (6), 1236–1238. Tikadar, A., Roy, B.N., 2001. Multivariate analysis in some mulberry germplasm (Morus spp.) germplasm accessions. Indian J. Seric. 40 (2), 71–74. Vijayan, K., Tikadar, A., Das, K.K., 1997. Correlation studies in mulberry (Morus spp). Indian J. Genet. 57 (4), 455–460. Yaltirik, F., 1982. Morus. In: In: In: Davis, P.H. (Ed.), Flora of Turkey, vol. 7. Edinburgh University Press, Edinburgh, UK, pp. 641–642. Yilmaz, K.U., Zengin, Y., Ercisli, S., Demirtas, M.N., Kan, T., Nazli, A.R., 2012. Morphological diversity on fruit characteristics among some selected mulberry genotypes from Turkey. J. Anim. Plant Sci. 22, 211–214.

The morphological analysis is considered as a first approach towards the assessment of genetic diversity in a plant species. In the current investigation, morphological and pomological variability of white mulberry accessions in Iran was characterized. Most of the characters showed considerable differences among the accessions. According to the optimum indices for fruit quality in white mulberry, 19 accessions including Jezenagh-16, Jezenagh-14, Amanabad-1, Marzegaran-1, Jezenagh-20, Jezenagh-3, Jezenagh-9, Amanabad-3, Jezenagh-21, Sagh-3, Jezenagh-17, Jezenagh-19, Jezenagh-18, Sagh-4, Sagh-5, Khosbijan-4, Jezenagh-13, Khosbijan-1, and Jezenagh-8 were superior and can be singled out for cultivation. Furthermore, the present findings indicated that the studied accessions may be recognized as a representative gene pool of white mulberry. References Aljane, F., Sdiri, N., 2016. Morphological, phytochemical and antioxidant characteristics of white (Morus alba L.), red (Morus rubra L.) and black (Morus nigra L.) mulberry fruits grown in arid regions of Tunisia. J. New Sci. 35 (1), 1940–1947. Bajpai, P.K., Warghat, A.R., Yadav, A., Kant, A., Srivastava, R.B., Stobdan, T., 2015. High phenotypic variation in Morus alba L. along an altitudinal gradient in the Indian trans-Himalaya. J. Mountain Sci. 12, 446–455. Banerjee, R., Roychowdhuri, S., Sau, S., Das, B.K., Ghosh, P., Saratchandra, P., 2007. Genetic diversity and interrelationship among mulberry genotypes. J. Genet. Genom. 34 (8), 691–697. Bantle, J.P., Slama, G., 2006. Nestle Nutrition Workshop Series: Clinical and Performance Program Edition in: Nutritional Management of Diabetes Mellitus and Dysmetabolic Syndrome Vol 11. Wood head Publishing Limited, Cambridge. Benavides, J.E., Lachaux, M., Fuentes, M., 1994. Efecto de la aplicación de estiércol decabra en el suelo sobre la calidad y producción de biomasa de morera (Morus sp.). Arboles y arbustos forrajeros en América Central 2, 495–502. Boubaya, A., Marzougui, N., Ferchichi, A., Salah, M.B., 2011. Morphological and chemical diversity among South Tunisian mulberry tree (Morus spp.) cultivars. Acta Bot. Gall. 158 (3), 375–385. Butt, M.S., Nazir, A., Sultan, M.T., Schroen, K., 2008. Morus alba L. nature’s functional tonic. Trends Food Sci. Technol. 19, 505–512. Chang, L.Y., Li, K.T., Yang, W.J., Chang, J.C., Chang, M.W., 2014. Phenotypic classification of mulberry (Morus) species in Taiwan using numerical taxonomic analysis through the characterization of vegetative traits and chilling requirements. Sci. Hortic. 176, 208–217. CSRTI, 1986. Mulberry Descriptor. Central Sericultural Research and Training Institute Central Silk Board, Srirampuram, India. Datta, R.K., 2002. Mulberry Cultivation and Utilization in India. Mulberry for Animal Production; Food and Agriculture Organization of the United Nations, Rome, Italy ISBN 92-5-104568-2. Farahani, M., Salehi-Arjmand, H., Khadivi, A., Akramian, M., 2019. Chemical characterization and antioxidant activities of Morus alba var. nigra fruits. Sci. Hortic. 253, 120–127. Fishler, M., Goldschmidt, E.E., Monselise, S.P., 1983. Leaf area and fruit size in girdled grapefruit branches. J. Am. Soc. Hortic. Sci. 108, 218–221. Hammer, Ø, Harper, D.A.T., Ryan, P.D., 2001. PAST: paleontological statistics software

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