Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Provenance and family variations in early growth of Manchurian walnut (Juglans mandshurica Maxim.) and selection of superior families

  • Qinhui Zhang,

    Roles Conceptualization, Writing – original draft

    Affiliation State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China

    ⨯
  • Su Chen,

    Roles Writing – review & editing

    Affiliation State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China

    ⨯
  • Guanzheng Qu,

    Roles Methodology, Writing – review & editing

    Affiliation State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China

    ⨯
  • Yuchun Yang,

    Roles Formal analysis, Validation

    Affiliation Jilin Provincial Academy of Forestry Sciences, Changchun, China

    ⨯
  • Zhiming Lu,

    Roles Software

    Affiliation Jilin Provincial Academy of Forestry Sciences, Changchun, China

    ⨯
  • Jun Wang,

    Roles Methodology, Validation

    Affiliation Jilin Provincial Academy of Forestry Sciences, Changchun, China

    ⨯
  • Mulualem Tigabu,

    Roles Writing – review & editing

    Affiliation Southern Swedish Forest Research Center, Swedish University of Agricultural Sciences, Alnarp, Sweden

    ⨯
  • Jifeng Liu,

    Roles Investigation

    Affiliation Wanrenhuan Forest Farm of Binxian County, Harbin, China

    ⨯
  • Lianfeng Xu ,

    Roles Project administration, Resources, Supervision

    fangwang614@126.com (FW); XXLLFF2000@126.com (LX)

    Affiliation Qiqihar Branch of Heilongjiang Academy of Forestry, Qiqihar, China

    ⨯
  • Fang Wang

    Roles Conceptualization, Data curation, Funding acquisition

    fangwang614@126.com (FW); XXLLFF2000@126.com (LX)

    Affiliation Jilin Provincial Academy of Forestry Sciences, Changchun, China

    ⨯

Abstract

This study, conducted in China in November 2020, was aimed at exploring the variations in growth traits among different provenances and families as well as to select elite materials of Juglans mandshurica. Thus, seeds of 44 families from six J. mandshurica provenances in Heilongjiang and Jilin provinces were sown in the nursery and then transplanted out in the field. At the age of 5 years, seven growth traits were assessed, and a comprehensive analysis was conducted as well as selection of provenance and families. Analysis of variance revealed statistically significant (P < 0.01) differences in seven growth traits among different provenances and families, thereby justifying the pursuit of further breeding endeavors. The genetic coefficient of variation (GCV) for all traits ranged from 5.44% (branch angle) to 21.95% (tree height) whereas the phenotypic coefficient of variation (PCV) ranged from 13.74% (tapering) to 38.50% (branch number per node), indicating considerable variability across the traits. Further, all the studied traits except stem straightness degree, branch angle and branch number per node, showed high heritability (Tree height, ground diameter, mean crown width and tapering, over 0.7±0.073), indicating that the variation in these traits is primarily driven by genetic factors. Correlation analysis revealed a strong positive correlation (r > 0.8) between tree height and ground diameter (r = 0.86), tree height and mean crown width (r = 0.82), and ground diameter and mean crown width (r = 0.83). This suggests that these relationships can be employed for more precise predictions of the growth and morphological characteristics of trees, as well as the selection of superior materials. There was a strong correlation between temperature factors and growth traits. Based on the comprehensive scores in this study, Sanchazi was selected as elite provenance. Using the top-percentile selection criteria, SC1, SC8, DJC15, and DQ18 were selected as elite families. These selected families exhibit genetic gains of over 10% in tree height, ground diameter and mean crown width, signifying their significant potential in forestry for enhancing timber production and reducing production cycles, thereby contributing to sustainable forest management. In this study, the growth traits of J. mandshurica were found to exhibit stable variation, and there were correlations between these traits. The selected elite provenance and families of J. mandshurica showed faster growth, which is advantageous for the subsequent breeding and promotion of improved J. mandshurica varieties.

Citation: Zhang Q, Chen S, Qu G, Yang Y, Lu Z, Wang J, et al. (2024) Provenance and family variations in early growth of Manchurian walnut (Juglans mandshurica Maxim.) and selection of superior families. PLoS ONE 19(3): e0298918. https://doi.org/10.1371/journal.pone.0298918

Editor: Muhammad Abdul Rehman Rashid, Government College University Faisalabad, PAKISTAN

Received: August 15, 2023; Accepted: January 31, 2024; Published: March 7, 2024

Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This research was funded by the Opening Project of State Key Laboratory of Tree Genetics and Breeding (NO. K2020202) with the corresponding author Fang Wang, who made significant contributions to the conceptualization, data curation, and funding acquisition. Additionally, the Forestry Public Welfare Industry Scientific Research Project, 'Breeding and Utilization of Native Woody Grain and Oil Tree Species in Northeast China' (NO. 201304704), was funded by the corresponding author Lianfeng Xu, who played a key role in project administration, resource management, and supervision.

Competing interests: The authors have declaredthat no competing interests exist.

1. Introduction

The genus Juglans is one of the most important genera of Juglandaceae family, comprises about 21 species, and Juglans mandshurica Maxim. is a deciduous fruit tree belonging to the Juglandaceae family [1, 2]. This tree is native to northern and northeastern China and is mainly distributed in the Changbai and Xiaoxingan Mountains at an altitude ranging from 500 m to 1000 m [3]. The species also grows naturally in Russia, Japan, and Korea and can tolerate temperatures of -50°C, and has important economic values [4, 5]. The J. mandshurica is a versatile wood that is rigid, wear and corrosion-resistant, easily processed, and produces high quality wood suitable for joinery in luxurious furnishings [6, 7]. Besides, its seeds are rich in nutrition and are considered a premium forest food product [8]. Due to its high economic returns and nutritional value, its production and consumption are increasing all over the world [9, 10]. Furthermore, J. mandshurica has high medicinal and health values [11]. For instance, its husk, bark, roots and leaves contain juglone, which has good antitumor [12], antifungal [13], antiviral [14], antioxidant [15], anthelmintic [16] and hypoglycemic activities [17]. They are widely used in pharmaceutical, food, and cosmetic industries.

Understanding and leveraging the variation and interplay of genetic and phenotypic traits hold decisive importance in optimizing the improvement of biological characteristics across various research domains. Against this backdrop, techniques such as correlation analysis, general combining ability analysis, phenotypic coefficient of variation analysis, genetic coefficient of variation analysis, and family heritability analysis have been widely employed to interpret and exploit genetic diversity. Prior studies have laid the foundation for plant breeding by exploring genetic associations between traits, evaluating parental combining effects, and analyzing phenotypic and genetic variations [18, 19]. Recently, researchers have delved deeper into understanding the specific roles of genotype and environmental factors in phenotypic expression and genetic variation using more advanced and refined methods [20]. While these studies have offered valuable insights, there remain unresolved issues and challenges in the specific application of Juglans mandshurica.

Provenance tests are crucial for species that have high economic or ecological value [21]. Systematic provenance test can explore the adaptability of different provenance materials to the local environment, quantify the genetic population differentiation, and provide materials for forest breeding and ex-situ protection [22, 23]. Provenance test for J. mandshurica began in the 1980s ~ 1990s [24] by investigating and analyzing phenotypic growth traits of eight J. mandshurica provenances, which resulted in selection of two best provenances in Kuandian and Shulan. Based on a geographical and climatic variation study, Liu profiled the differences in growth and physiological performance of the provenances, and preliminarily classified J. mandshurica into four major provenance zones [25]. Since then, there have been several studies on the provenance of J. mandshurica in China [8, 26–34]. Most of these studies, however, concentrated on seed traits or a few growth traits, other growth data of J. mandshurica seedlings are relatively scarce. In addition, due to challenges posed by late genetic improvement of the J. mandshurica, coupled with long sexual reproduction cycle and low survival rate of vegetative propagation such as cuttings and grafting. These led to a small number and usage rate of improved varieties of J. mandshurica. In addition, excessive logging and man-made destruction of natural forests in recent decades have led to a large reduction in the number of trees [32]. Some available natural distribution areas are even mostly secondary forests, and J. mandshurica trees are on the verge of depletion [35]. At the same time, climate and environmental changes include warming temperatures, late spring frost, drought stress, high temperature stress and salt stress also negatively affected the growth of genus Juglans [36–38]. It is, therefore, important to explore variation patterns of provenances and families to guide collection and conservation of J. mandshurica resources, protection of biodiversity and maintenance of ecological balance. Recent finding shown that although J. mandshurica natural forest is seriously damaged, there is still a high degree of genetic diversity [6]. The result showed that the existing resources of J. mandshurica may still have rich variation, which provided the possibility for the selection of improved varieties of J. mandshurica. On the other hand, the main breeding goal of genus Juglans plants is to cultivate productive new varieties and rootstocks with superior resistance [39–44]. Due to its good drought and cold tolerance and well-developed root system, J. mandshurica is often used as an excellent rootstock for walnuts, which can effectively improve walnut yield and stress resistance [45, 46]. Therefore, preliminary selecting of J. mandshurica resources can provide basic materials for the breeding of high-yield and high-resistance J. mandshurica varieties. This study employed the provenance test to profile 5-year-old J. mandshurica tress in Wanrenhuan forest farm of Binxian county, Heilongjiang Province. The objectives were to: (1) evaluate the variations among different J. mandshurica provenances and families. (2) select elite provenance and families. The existing studies have primarily focused on preliminary insights into the economic, medicinal, and timber value of J. mandshurica, as well as the presence of certain bioactive components. However, these studies have limitations in the in-depth exploration of diversity, the selection of genetic sources, and the evaluation of families for J. mandshurica. They also do not provide comprehensive information on growth characteristics. Therefore, the primary strength of this study lies in the in-depth investigation of the growth traits of J. mandshurica. It involves the assessment of genetic variations and the selection of elite provenance and families, thereby providing robust support for the forest management and timber production of this species.

2. Materials and methods

2.1. Experimental site and plant materials

Seeds of 44 half-sib families (44 individual trees, each tree collected 120 seeds) from six J. mandshurica provenances in Heilongjiang and Jilin provinces, China, were collected. The provenances were Sanchazi (15 families), Hulin (3 families), Daquanzi (9 families), Dongjingcheng (10 families), Yabuli (4 families) and Tieli (3 families) (Table 1). The seeds from open-pollination were sown in autumn 2015, with a sowing area of 1.69 hm2, each family selects 100 healthy seeds for sowing in the nursery, with a germination rate of 74%. 32 seedlings with consistent growth of J. mandshurica were selected per family for afforestation, planted the following year in a randomized complete block design, with 8 trees and 4 blocks at a spacing of 3 m × 4 m (plant spacing and row spacing) in Wanrenhuan Forest Farm of Binxian County, Heilongjiang province (E45°44′20″ ~ 45°57′18″, E128°04′29″ ~ 128°19′13″), and the distance between families is 4 m. The altitude of experimental area is 340 m, with a mean annual temperature of 2.4°C, mean annual rainfall of 577 mm, mean annual sunshine hours of 2090 h, and a frost-free period of 130 d. The geographical location and environmental factors of the provenances are given in Fig 1 and Table 1, and the test forest of Juglans mandshurica given in Fig 2.

thumbnail
Download:
Fig 1. Provenance distribution of Juglans mandshurica collected in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.g001

thumbnail
Download:
Fig 2. Test forest of Juglans mandshurica in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.g002

thumbnail
Download:
Table 1. Family numbers and environmental factors of different provenance of J. mandshurica collected in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.t001

2.2. Trait measurements

In November 2020, we assessed the growth and stem traits of a total of 1408 individual tree (The test forest consists of 44 families, with 32 individual trees per families) in a 5-year-old J. mandshurica provenance test forest. The traits included tree height (m), ground diameter (cm), mean crown width (m), stem straightness degree, tapering, branch angle (°) and branch number per node. The tree height was measured using a Box staff. The ground diameter was measured using a tree caliper. The east-west and north-south crown width were measured using a stainless-steel measuring tape, and the average of two crown width was taken as the mean crown width value. The stem straightness degree was meas ured using grading method and a score from 1 to 5 was assigned, where score 1 was assigned if the stem had more than two obviously bend points in the stem and a score of 5 if the stem was completely straight [47]. Stem straightness degree necessitates the square root transformation of data during the process of conducting analysis of variance (ANOVA). The ratio of ground diameter to tree height was used to calculate tapering [48]. The branch angle was measured using a protractor. Climatic factors were obtained from the National Meteorological Information Center (http://data.cma.cn/). In this study, field site access and sample collection did not require specific permits. The research was conducted with the full consent and cooperation of the relevant site managers and authorities. No permits were necessary due to the non-sensitive nature of the work and the site’s accessibility to researchers. In addition, we collected growth measurement data from the Experimental Forest. These data, referred to as ’Experimental Forest Growth Measurement Data,’ have been included as supporting information in this study.

2.3. Statistical analysis

Mixed linear model was applied to investigate into the differences of provenance, family and block effects on growth traits. The linear model was applied using the following formula [49]. All data were tested for normal distribution and homogeneity of variances. (1) where Xijkl is the observed value of an individual tree l in family j within provenance i growing in block k, μ is the family mean, Bk is the fixed effect of block k, Pi is the fixed effect of provenance i, Fj(i) is the random effect of family j within provenance i, BPik is the fixed interactive effect of block k and provenance i, BFj(i)k is the random interactive effect of block k and family j within provenance i, and eijkl is the random error.

Heritability (h2) among families for each trait was calculated following a formula described by Zhang [50]: (2) where and denotes the variance components of family, family by block interaction, and residual error, respectively. B and N represent the number of blocks and replicates, respectively.

The coefficient of variation was calculated by using the following formula [51]:

phenotypic coefficient of variation (PCV): (3) genetic coefficient of variation (GCV): (4) where denotes the phenotypic variance of a trait, denotes the additive genetic variance component in maternal half-sib of a trait (), is the mean value of a growth trait.

The Pearson correlation coefficient was used to measure the linear relationship between two variables. The Pearson correlation coefficient (r) is a standard statistical method employed to quantify the correlation between two variables. Its calculation formula is as follows: (5) where σa(xy) is the covariance component between trait or environmental factor x and y, and are the variance component for trait or environmental factor x and y, respectively.

The general combining ability (GCA) was calculated by the formula [52]: (6) where g is the general combining ability of parents, x is the offspring mean of a hybrid combination of the parents for a particular trait, and μ is the offspring mean of all combinations for this trait.

The principal component values and comprehensive score were calculated according to Chen [52]: (7) (8) where Yi is the value of principal component i, αij is the eigenvalue of trait j within principal component i, Xj is the mean of trait j, W is the comprehensive score, ωi the contribution rate of principal component i, n is the number of traits, and p is the number of extracted principal components.

Selecting elite provenance and families with a selection rate of 10%. The real gains of elite provenance and genetic gain of elite families were computed using the following formula [49]:

genetic gains (ΔG1): (9) real gains (ΔG2): (10) where h2 is the heritability of family, W the selection differential, is the family or provenance mean.

The mean analysis, ANOVA, principal component analysis and Pearson correlation analysis were conducted using IBM SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY, USA) [53]. Additionally, variance parameters and general combining ability were calculated. For all statistical analyses, P-values less than 0.05 were considered statistically significant. Provenance distribution map was drawn using ArcGIS, version 10.7 (Esri, Redlands, CA, USA) [54]. Custom data visualization was carried out in the R environment, version 4.0.2, using "corrplot" packages (R Core Team, Vienna, Austria) [55].

3. Results

3.1. Variations in early growth among provenances and families

Significant variations (P<0.01) in growth traits were detected among different provenances and families within provenances (Table 2). Description statistics of growth and stem traits together with GCV, PCV and heritability are shown in Table 3. The GCVs of all the traits ranged from 5.44 to 21.95%, among which tree height, ground diameter and mean crown width were higher than 15%. The PCVs of all the traits ranged from 13.74% to 38.50%, among which the stem straightness degree and branch number per node were more than 30%. The heritability of all traits in the families ranged from 0.260 to 0.908, among which the heritability of all the traits was higher than 0.7, except for the stem straightness degree, branch angle and branch number per node. In addition, the mean value of all growth traits for different provenances and families are shown in S1 and S2 Tables. The results in S1 Table showed that Daquanzi and Sanchazi provenance had more elite growth traits, which was also reflected in S2 Table. Most of the families with elite growth traits from the Daquanzi and Sanchazi provenance.

thumbnail
Download:
Table 2. Variance analysis of different traits among J. mandshurica families in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.t002

thumbnail
Download:
Table 3. Variation parameters of different traits among J. mandshurica families in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.t003

3.2. Correlation analysis

The correlation among the tree height, ground diameter and mean crown width were significant and positively correlated with a coefficient exceeding 0.8 (Fig 3). Stem straightness degree, branch angle and branch number per node had a weak positive correlation with tree height, ground diameter and mean crown width, with a correlation coefficient of not more than 0.3. Among environmental factors, annual temperature had a strong positive correlation with mean crown width, while annual temperature had a negative correlation with tapering, with correlation coefficients of more than 0.8 (Fig 4). Additionally, it was observed that January mean temperature and winter mean temperature had a strong positive correlation with tree height, ground diameter and mean crown width, the magnitude of the correlation coefficients exceeded 0.9. Tapering was strongly positively correlated with altitude (r = 0.84). In addition, stem straightness degree was strongly positively correlated with winter mean rainfall (r = 0.84). No correlation was observed between the remaining traits and the environmental factors.

thumbnail
Download:
Fig 3. Correlation analysis among different growth traits of J. mandshurica in Northeast China.

TH: Tree height; GD: Ground diameter; MCW: Mean crown width; SSD: Stem straightness degree; TA: Tapering; BA: Branch angle; BNN: Branch number per node. **correlation is significant at 1% level; Numbers are the correlation coefficients among different traits.

https://doi.org/10.1371/journal.pone.0298918.g003

thumbnail
Download:
Fig 4. Correlation analysis between different growth traits and environmental factors of J. mandshurica in Northeast China.

TH: Tree height; GD: Ground diameter; MCW: Mean crown width; SSD: Stem straightness degree; TA: Tapering; BA: Branch angle; BNN: Branch number per node; lo: Longitude; la: Latitude; al: Altitude; at: Annual temperature; jamt: January mean temperature; jumt: July mean temperature; smt: Summer mean temperature; wmt: Winter mean temperature; ar: Annual rainfall; jamr: January mean rainfall; jumr: July mean rainfall; smr: Summer mean rainfall; wmr: Winter mean rainfall; ash: Annual sunshine hours; ffd: Frost-free days. **correlation is significant at 1% level, *correlation is significant at 5% level; Numbers are the correlation coefficients between traits and environmental factors.

https://doi.org/10.1371/journal.pone.0298918.g004

3.3. General combining ability

We evaluated the general combining ability (GCA) of different traits among the families (Table 4). The GCAs for tree height ranged from -0.395 (DQ1) to 0.570 (SC1), while that for ground diameter ranged from -0.984 (DQ1) to 1.469 (SC1), from -0.528 (DQ1) to 0.625 (SC1) for mean crown width. In addition, the GCAs for the stem straightness degree ranged from -0.610 (DJC5) to 0.670 (DJC8), from -0.349 (SC13) to 0.322 (YBL13) for tapering, from -3.894 (SC13) to 3.481 (SC8) for branch angle and -0.270 (SC20) to 0.329 (DQ23) for branch number per node.

thumbnail
Download:
Table 4. General combining ability values of different traits among J. mandshurica families in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.t004

3.4. Principal components analysis

We performed principal components analysis (PCA) and three principal components were extracted based on eigenvalues greater than 1 (Table 5 and Fig 5). The eigenvalue for PC1 was 2.826 and the contribution rate was 40.37%. The absolute eigenvalues for the tree height, ground diameter and mean crown width were relatively high at 0.934, 0.942 and 0.920, respectively. On the other hand, the eigenvalue for PC2 was 1.154 and the contribution rate was 16.49%. The absolute eigenvalue for tapering was relatively high, at 0.926. The eigenvalue for PC3 was 1.007 while the contribution rate was 14.39%. We found that the absolute eigenvalues for the branch angle and branch number per node were relatively high, at 0.623 and 0.578, respectively. The cumulative contribution rate of the three principal components was 71.25%, which covered most of the information of growth and stem form traits for the tested families.

thumbnail
Download:
Fig 5. Component matrix of principal component analysis.

PC1: principal component 1; PC2: principal component 2; PC3: principal component 3.

https://doi.org/10.1371/journal.pone.0298918.g005

thumbnail
Download:
Table 5. Principal component analysis of different traits among J. mandshurica families in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.t005

3.5. Selection of elite provenance and family

The linear equation for each principal component score was obtained from the results of principal component analysis, and , where x1, x2, x3, x4, x5, x6 and x7 denote the tree height, ground diameter, mean crown width, stem straightness degree, tapering, branch angle and branch number per node, respectively. The contribution rate of each principal component was used as the weight to calculate the comprehensive score, using the following formula: . The final comprehensive scores and ranking of provenances and families are given in Tables 6 and 7. The provenance was selected based of tree height, ground diameter and mean crown width, with a real gain of 5% or more. The real gain for each trait was 6.46%, 6.73%, 7.31%, 4.11%, 0.04%, 0.01% and 0.47% for tree height, ground diameter, mean crown width, stem straightness degree, tapering, branch angle and branch number per node, respectively (Fig 6). We then applied 10% inclusion rate and selected four families: SC1, SC8, DJC15 and DQ18. The mean values of the selected elite families for tree height, ground diameter, mean crown width, stem straightness, tapering, branch angle, and branch number per node were 2.31 m, 5.91 cm, 1.60 m, 4.04, 2.58, 49.85° and 2.20, respectively. The genetic gain achieved after selecting these superior families were 10.47%, 13.51%, 14.83%, 3.51%, 0.94%, 5.49% and 1.72%, for tree height, ground diameter, mean crown width, stem straightness, tapering, branch angle, and branch number per node, respectively (Fig 6).

thumbnail
Download:
Fig 6. Genetic gains of different J. mandshurica traits in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.g006

thumbnail
Download:
Table 6. Comprehensive scores and rankings of different J. mandshurica provenances in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.t006

thumbnail
Download:
Table 7. Comprehensive scores and rankings of different J. mandshurica families in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.t007

4. Discussion

Genetic variation drives evolution of different species, thereby enabling them to adapt to the prevailing environmental conditions [56]. Thus, assessment of genetic variation in different populations is important as a first step in breeding improved genotypes and for effective conservation of forest genetic resources, and the results will be helpful for the breeding of high-quality commercial varieties [57, 58]. The variation in phenotypic traits is the result of the interaction between genetic variation and environmental factors, reflecting the variability of genotypes, populations, ecotypes, and provenance [59]. Our results demonstrated that most of the traits varied significantly among different provenances and families, while the differences among blocks were small, which was consistent with data from previous studies [27, 33, 34, 60, 61]. Similar research results were also obtained in Eucalyptus urophylla and some subtropical pine species [62, 63]. The results also demonstrated that the accessions had comparatively large variation for genetic improvement, and the differences mainly resulted from genetic rather than environmental stimuli. It should be noted that in natural environments different populations often have significant differences due to interaction between genetic and environmental diversity. Thus, analyses of the variations have contributed to a better understanding of the inherent patterns of genetic variation [64].

In this study, the mean values of tree height and ground diameter for 5-year-old trees were higher than previous findings that used 7-year-old J. mandshurica [33] and 5-year-old J. mandshurica [65, 66]. The accessions showed a distinct growth advantage. In comparison to the 1-9-year-old walnut trees in the edge habitats of natural forests, our research material did not exhibit a significant advantage in tree height growth [67]. This phenomenon appears to be influenced by a variety of interrelated factors. Notably, the high-density planting method in artificial forests may exacerbate competition among trees, thereby imposing certain limitations on their growth. Additionally, it is worth noting that nutrient supply in artificial forests is typically relatively inadequate, necessitating supplementary fertilization and management interventions. Moreover, adverse growth conditions such as limited light exposure and water availability should be considered, as they may significantly impact tree height. By enhancing management practices and nutrient supply, there is the potential to elevate the tree height of walnut trees in artificial forests, gradually reducing the disparity in tree height compared to natural forests. The crown is a major primary site of photosynthesis and energy retention for trees, which reflects the viability and competitiveness of tree growth [68]. The mean crown width in this study were 1.33 m, which was slightly lower than the results by Zhao on a 6-year-old J. mandshurica [69]. This variation could be associated with the variety used and the soil fertility [70, 71]. In addition, there are few studies on the traits reflecting the stem form characteristics such as stem straightness degree, tapering, branch angle and branch number per node of the J. mandshurica. Therefore, our study investigated and analyzed these traits to gain a deeper understanding of the growth variation pattern of the J. mandshurica. The form quality of the trees is controlled by their own genetic and environment conditions, which could directly affect the processing, quality, yield, and commercial value of the wood [72]. Larger stem straightness degree and smaller tapering have been shown to be beneficial for the selection of large-size timber, while larger branch angle and branch number per node could be conducive in improving fruit production [73, 74]. ANOVA results showed that the significant differences in stem form traits among the different provenances and families in this study, which could provide materials for directional breeding of J. mandshurica for diverse objectives.

The coefficient of variation is a small-scale variability measure used to reflect the genetic variability of traits in population, and the larger the coefficient of variation, the more favorable the selection of the elite materials [75, 76]. In this study, the phenotypic coefficient of variation (PCV) and genetic coefficient of variation (GCV) were higher than a previous report by You in Hebei province [30], thus indicating obvious differences in geographical provenance. Among the analyzed traits, the coefficient of variation was relatively large for tree height, ground diameter, mean crown width and stem straightness degree, demonstrating that using these traits as the selection index could yield more selection potential. In addition, the genetic coefficients of variation for traits such as tree height, ground diameter, mean crown width, and tapering accounted for more than 65% of the phenotypic coefficient of variation, which was consistent with the findings for Pinus koraiensis [77]. This data indicated that growth variation among families is more controlled by genetic factors, which provides a basis for the selection of elite provenances and families. Besides, heritability is an important indicator in forest breeding research, which reflects the relative roles of genetics and environment in the expression of various traits, while helping to rank the importance of each trait in hybrid breeding, and provide support for early selection of trees [78, 79]. In this study, except for the stem straightness degree, branch angle and branch number per node, the heritability of all the traits in the family was high and could be inherited more stably. The outcomes were better than for 4-year-old, 5-year-old, 6-year-old and 7-year-old J. mandshurica [61, 65, 80]. It has been shown that high genetic control could maximize the genetic gain and facilitate the selection of elite families [81].

In tree breeding, it is often desired to improve comprehensive traits in the target material [82]. Thus, the correlation between the traits is particularly important [83]. Correlation analyses and coefficients could effectively and quantitatively characterize the degree of association among traits and provide space for selection of comprehensive traits [84]. However, data on the correlations among traits in the provenances of J. mandshurica remains limited. Here, we analyzed the correlation among and between traits as well as environmental factors in juvenile J. mandshurica. The data showed large correlation coefficients (r > 0.8) among some growth traits. This finding was similar to that observed in a previous study with growth traits of P. sylvestris clones [85], which indicated strong associations among these traits and were favorable for comprehensive selection. The correlation coefficients among stem straightness degree, tapering, branch angle and branch number per node were small (r < 0.3), and weakly correlated, as in the case of previous findings on P. koraiensis and P. Sylvestris [77, 86]. These data demonstrate that the genetic independence between growth and stem form traits and could be used for the evaluation and selection based on different breeding objectives to improve breeding efficiency. Furthermore, it was also observed in this study that the January mean temperature and winter mean temperature has a strong positive correlation with the growth traits. This indicates that the temperature in cold season has a significant effect on the growth of J. mandshurica at seedling. Similar results were reported by Liu, who studied P. sylvestris. The investigation may provide references for cultivation and promotion of J. mandshurica [87].

Phenotypic variation is an effect of genetic and environmental interactions, and environmental factors play a crucial role in shaping the plant phenotypes, such as phenotypes and nutritional traits in the fruit [88, 89]. The correlations between most traits and geographic factors, such as longitude, latitude and altitude, in our study were insignificant, which is consistent with results by Xia [29]. The study demonstrated that the growth of different J. mandshurica provenances is weakly associated with geographic factors and their changes show a random variation. On the other hand, annual temperature had a strong positive correlation (r > 0.8) with mean crown width. This finding was consistent with the findings on Xanthoceras sorbifolium [90], which indicated that the increase in temperature is conducive to physiological activities such as photosynthesis of J. mandshurica, thus promoting the growth and development of the crown. In addition, the annual temperature showed a strong negative correlation (r = -0.85) with tapering, indicating that the higher the temperature, the relatively more use of biomass, and the tree height growth and tree morphology tends to be slender and tall, which is different from studies that evaluated coniferous tree species such as Larix Mill. [91] and might be due to the difference of the tree species.

Combining ability is one of the breeding objectives, which refers to the relative potential of parental dominance hybrid to progeny [92]. It is widely used in the breeding of cross-pollinated plants, and the data could help in the selection of elite hybrid parents [93]. In this study, the data showed that the general combining ability of different traits varied widely among different families, which was similar with the results from P. koraiensis [94], it was difficult to perform comprehensive selection. Therefore, it was necessary to carry out further selection in combination with PCA. PCA is a common multivariate analysis tool that helps reduce data dimensionality and retain data trends and patterns [95], and is widely used in trait analysis of walnut tree species [96–98]. In our study, the higher eigenvalues for tree height, ground diameter, mean crown width in PC1 could reflect growth patterns. In addition, the higher eigenvalues for tapering in PC2 could represent stem form, while that of branch angle and branch number per node in PC3 could represent the branching features. This result is consistent with the data from Yang on Castanopsis hystrix [99], suggesting that the differences in J. mandshurica family are mainly in growth, stem form and branching. Based on the PCA analysis, we primarily selected one elite provenance (Sanchazi) and four elite families (SC1, SC68, DJC15 and DQ18). The selected families, whose genetic gain in tree height was higher than the results of Chu which used 15-year-old J. mandshurica [31], had obvious advantages in growth and had high genetic gain. The differentiation of materials in our study was large, which has a large potential for genetic improvement and is conducive for early selection new J. mandshurica varieties. The selected families could provide material for regional afforestation.

5. Conclusions

Taken together, our study successfully investigated the variations in growth and stem form traits of 5-year-old J. mandshurica provenances and families within provenances, revealing significant genetic factors at play. We identified elite families, with particular focus on tree height, ground diameter, and mean crown width. This provides a robust foundation for comprehensive multi-trait selection, offering potential for the breeding and promotion of J. mandshurica varieties. This research contributes to the theoretical and breeding basis for the conservation and utilization of valuable resources, as well as the development of the local J. mandshurica cultivation industry.

Supporting information

S1 Table. Average values of different traits among J. mandshurica provenance in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.s001

(DOCX)

S2 Table. Average values of different traits among J. mandshurica families in Northeast China.

https://doi.org/10.1371/journal.pone.0298918.s002

(DOCX)

S1 Data. Test forest growth measurement data.

https://doi.org/10.1371/journal.pone.0298918.s003

(XLSX)

S1 File.

https://doi.org/10.1371/journal.pone.0298918.s004

(ZIP)

S2 File.

https://doi.org/10.1371/journal.pone.0298918.s005

(ZIP)

References

  1. 1. Arab MM, Marrano A, Abdollahi-Arpanahi R, Leslie CA, Cheng H, Neale DB, et al. Combining phenotype, genotype, and environment to uncover genetic components underlying water use efficiency in Persian walnut. J Exp Bot. 2020; 71(3):1107–27. pmid:31639822
  2. 2. Luan F, Wang Z, Yang Y, Ji Y, Lv H, Han K, et al. Juglans mandshurica Maxim.: A review of its traditional usages, phytochemical constituents, and pharmacological properties. Front Pharmacol. 2021; 11:569800. https://doi.org/10.3389/fphar.2020.
  3. 3. Zhang Q, Zhang X, Yang Y, et al. Genetic diversity of Juglans mandshurica populations in northeast China based on SSR markers [J]. Frontiers in Plant Science, 2022, 13: 931578. https://doi.org/10.3389/fpls.2022.931578.
  4. 4. Tian J, Wu Y, Wang Y, Han F, editors. Development and prospects of the walnut industry in China. VI Int Walnut Symp 861; 2009.
  5. 5. Zhou Y, Yang B, Jiang Y, Liu Z, Liu Y, Wang X, et al. Studies on cytotoxic activity against HepG-2 cells of naphthoquinones from green walnut husks of Juglans mandshurica Maxim. Mol. 2015; 20(9):15572–88. https://doi.org/10.3390/molecules200915572.
  6. 6. Zhang Q, Yu S, Pei X, Wang Q, Lu A, Cao Y, et al. Within-and between-population variations in seed and seedling traits of Juglans mandshurica. J For Res. 2022; 33(4):1175–86. https://doi.org/10.3390/molecules200915572.
  7. 7. Zhu H, Zhao Y, Lin S, Yang Y, Zhao S, Sun L, et al. The progress of Juglans mandshurica Maxim. Chin Agric Sci Bull. 2011; 27(25):1–4.
  8. 8. Pang H, Li H, Long Z, Qi Y. Study on seed traits and seedling growth characteristics of Juglans mandshurica Maxim. Seed. 2019; 38(9):89–92. https://doi.org/10.16590/j.cnki.1001-4705.2019.09.089.
  9. 9. Hassani D, Sarikhani S, Dastjerdi R, Mahmoudi R, Soleimani A, Vahdati K. Situation and recent trends on cultivation and breeding of Persian walnut in Iran. Sci Hortic. 2020; 270:109369.
  10. 10. Vahdati K, Sarikhani S, Arab MM, Leslie CA, Dandekar AM, Aletà N, et al. Advances in rootstock breeding of nut trees: objectives and strategies. Plants. 2021; 10(11):2234. pmid:34834597
  11. 11. Chu G, Liang R, Wan C, Yang J, Li J, Wang R, et al. Ultrasonic-Assisted Extraction of Flavonoids from Juglans mandshurica Maxim.: Artificial Intelligence-Based Optimization, Kinetics Estimation, and Antioxidant Potential. Mol. 2022; 27(15):4837. pmid:35956798
  12. 12. Mallavadhani UV, Prasad CV, Shrivastava S, Naidu V. Synthesis and anticancer activity of some novel 5, 6-fused hybrids of juglone based 1, 4-naphthoquinones. Eur J Med Chem. 2014; 83:84–91. pmid:24953027
  13. 13. Sharma N, Ghosh P, Sharma UK, Sood S, Sinha AK, Gulati A. Microwave-assisted efficient extraction and stability of juglone in different solvents from Juglans regia: quantification of six phenolic constituents by validated RP-HPLC and evaluation of antimicrobial activity. Anal Lett. 2009; 42(16):2592–609.
  14. 14. Vardhini SR. Exploring the antiviral activity of juglone by computational method. J Recept Signal Transduction. 2014; 34(6):456–7. pmid:24846583
  15. 15. Chobot V, Hadacek F. Milieu-dependent pro-and antioxidant activity of juglone may explain linear and nonlinear effects on seedling development. J Chem Ecol. 2009; 35(3):383–90. pmid:19263168
  16. 16. Islam AM, Widhalm JR. Agricultural uses of juglone: Opportunities and challenges. Agronomy. 2020; 10(10):1500.
  17. 17. Hosseini S, Huseini HF, Larijani B, Mohammad K, Najmizadeh A, Nourijelyani K, et al. The hypoglycemic effect of Juglans regia leaves aqueous extract in diabetic patients: A first human trial. Daru, J Pharm Sci. 2014; 22(1):1–5.
  18. 18. Zhao X, Hou W, Zheng H, Zhang Z. Analyses of genotypic variation in white poplar clones at four sites in China. Silvae Genet. 2013; 62(4):187–95.
  19. 19. Zhao X, Bian X, Li Z, Wang X, Yang C, Liu G, et al. Genetic stability analysis of introduced Betula pendula, Betula kirghisorum, and Betula pubescens families in saline-alkali soil of northeastern China. Scand J F Res. 2014; 29(7):639–49.
  20. 20. Jiang L, Xu X, Cai Q, Han R, Tigabu M, Jiang T, et al. Variations in growth and photosynthetic traits of polyploid poplar hybrids and clones in Northeast China. Genes. 2022; 13(11):2161. pmid:36421836
  21. 21. Alizoti P, Bastien JC, Chakraborty D, Klisz MM, Kroon J, Neophytou C, et al. Non-native forest tree species in Europe: The question of seed origin in afforestation. For. 2022; 13(2):273.
  22. 22. Sebastian-Azcona J, Hacke U, Hamann A. Xylem anomalies as indicators of maladaptation to climate in forest trees: implications for assisted migration. Front Plant Sci. 2020; 11:208. pmid:32174948
  23. 23. Soolanayakanahally RY, Guy RD, Street NR, Robinson KM, Silim SN, Albrectsen BR, et al. Comparative physiology of allopatric Populus species: geographic clines in photosynthesis, height growth, and carbon isotope discrimination in common gardens. Front Plant Sci. 2015; 6:528. pmid:26236324
  24. 24. Yang S, Liu G, Zhang S, Wang H, Peng H, Yang C. Geographical variation and preliminary selection of the best provenance of Juglans mandshurica. J Northeast For Univ. 1990; 18(S2):72–6.
  25. 25. Liu G, Yang S, Li J. Preliminary division and selection of the best provenance of Juglans mandshurica. J Northeast For Univ. 1991; 10(S2):189–96.
  26. 26. Zhang H, Li A, Zhang H, Zhang L, Zhang J. Juglans mandshurica fruits and seed variation and plus tree selection. J Northeast For Univ. 2017; 45(3):1–7.
  27. 27. Yuan X, Qi Y, Liu Z, Zhou Z, Mao Z. Provenance selection of Juglans mandshurica Maxim. and the effects of environmental factors. Bull Bot Res. 2013; 33(4):468–76.
  28. 28. Chen S, Yang H, Han J, Zhang D, Zhao S, Zhang Z, et al. Provenance variation of seed traits of Juglans mandshurica in Changbai Mountains, northeastern China. J Beijing For Univ. 2015; 37(12):32–40.
  29. 29. Xia D, Xu Z, Hou D, Zhou J, Wang H. Study on genetic variation law of Juglans mandshurica provenances. J Anhui Agric Sci. 2014; 42(31):10956–8.
  30. 30. You H, Guo F, Jin C, Wang L, Meng X, Bi J. Study on seed morphology, quality and seedling growth of Juglans mandshurica from different provenances. Jiangsu Agric Sci. 2020; 48(2):155–8.
  31. 31. Chu X, Zhu H, Zhang H, Zhang L, Zhang Z. Variation among provenances and families of Juglans mandshurica and preliminary selection. J Northeast For Univ. 2010; 38(11):5–14. https://doi.org/10.13759/j.cnki.dlxb.2010.11.014.
  32. 32. Lu X, Xu L, Pang Z, Wen B, Pei X, Zhao L, et al. Variation and selection of seedling growth traits among Juglans mandshurica provenances and families. For Res. 2022; 35(1):20–30.
  33. 33. Zhang H, Deng J, Xu Y. Variation regularity among provenances of Juglans mandshurica and family selection. J Northwest For Univ. 2011; 26(2):91–5.
  34. 34. Li G, Zhang X, Zhang H, Lan S, Kang Y. Variation regularity on seedling height growth of Juglans mandshurica in different provenances and families. For Sci Technol. 2004; 29(4):1–3.
  35. 35. Luo Y, Yang Y, Zhao S, Ren S, Xu C, Zhu A. Production area division of secondary forest of Juglans mandshurica in the central and eastern regions of Northeast China. Chin J Ecol. 2022; 41(12):2299–305.
  36. 36. Fallah M, Vahdati K, Hasani D, Rasouli M, Sarikhani S. Breeding of Persian walnut: Aiming to introduce late-leafing and early-harvesting varieties by targeted hybridization. Sci Hortic. 2022; 295:110885.
  37. 37. Arab MM, Brown PJ, Abdollahi-Arpanahi R, Sohrabi SS, Askari H, Aliniaeifard S, et al. Genome-wide association analysis and pathway enrichment provide insights into the genetic basis of photosynthetic responses to drought stress in Persian walnut. Hortic Res. 2022; 9:1–24. pmid:35928405
  38. 38. Arab MM, Marrano A, Abdollahi-Arpanahi R, Leslie CA, Askari H, Neale DB, et al. Genome-wide patterns of population structure and association mapping of nut-related traits in Persian walnut populations from Iran using the Axiom J. regia 700K SNP array. Sci Rep. 2019;9(1):6376.
  39. 39. Manthos I, Rouskas D. Introduction of a new interesting walnut cultivar “Leto”. Plants. 2021; 10(12):2738. pmid:34961209
  40. 40. Michler CH, Pijut PM, Jacobs DF, et al. Improving disease resistance of butternut (Juglans cinerea), a threatened fine hardwood: a case for single-tree selection through genetic improvement and deployment [J]. Tree Physiology, 2006, 26(1): 121–128. https://doi.org/ 10.1093/treephys/26.1.121.
  41. 41. Sütyemez M, Bükücü ŞB, Özcan A. ‘Helete Güneşi’, a new walnut cultivar with late leafing, early harvest date, and superior nut traits. Agric. 2021; 11(10):991.
  42. 42. Sütyemez M, Bükücü ŞB, Özcan A. Pomological and phenological description of the new ‘Bahri Koz’ cultivar of walnut (Juglans regia L.) and its nutritional value. J Nuts. 2022; 13(2):131–9.
  43. 43. Sütyemez M, Özcan A, Bükücü ŞB. A superior genetic source for late leafing in walnut ‘Ahir Nut’. Horticultural Science. 2022;49(4):205–12.
  44. 44. Vahdati K, Arab MM, Sarikhani S, Sadat-Hosseini M, Leslie CA, Brown PJ. Advances in Persian walnut (Juglans regia L.) breeding strategies. Adv Plant Breed Strategies: Nut Beverage Crops. 2019; 4:401–72.
  45. 45. Liu B, Liang J, Tang G, Wang X, Liu F, Zhao D. Drought stress affects on growth, water use efficiency, gas exchange and chlorophyll fluorescence of Juglans rootstocks. Sci Hortic. 2019; 250:230–5.
  46. 46. Ji X, Tang J, Fan W, Li B, Bai Y, He J, et al. Phenotypic differences and physiological responses of salt resistance of walnut with four rootstock types. Plants. 2022; 11(12):1557. pmid:35736708
  47. 47. Zhao X, Bian X, Liu M, Li Z, Li Y, Zheng M, et al. Analysis of genetic effects on a complete diallel cross test of Betula platyphylla. Euphytica. 2014; 200(2):221–9.
  48. 48. Ou J, Wu Z, Kang Y. Effects of mixing proportion on the growth, stem form quality and spatial utilizataion ability of Manglietia yuyuanensis in mixed forests with Cunninghamia lanceolata and M. yuyuanensis. J Nanjing For Univ. 2020; 44(1):89–96.
  49. 49. Pan Y, Liang D, Guo J, Wang F, Wang F, Li S, et al. Variance analyses on growth traits of Larix kaempferi in different seed sources. J Beijing For Univ. 2018;40(11):19–27. https://doi.org/10.13332/ j.1000-1522.20170478.
  50. 50. Zhang H, Zhang Y, Zhang D, Dong L, Liu K, Wang Y, et al. Progeny performance and selection of superior trees within families in Larix olgensis. Euphytica. 2020; 216:1–10. https://doi.org/.1007/s10681-020-02596-9.
  51. 51. Metougui ML, Mokhtari M, Maughan PJ, Jellen EN, Benlhabib O. Morphological variability, heritability and correlation studies within an argan tree population (Argania spinosa (L.) Skeels) preserved in situ. Int J Agric For. 2017; 7:42–51.
  52. 52. Chen L. The researches on seedling characters difference of Fokienia hodginsii from different provenances and excellent provenance selection [dissertation]. Fuzhou: Fujian Agriculture and Forestry University; 2022.
    • 53. George D, Mallery P. IBM SPSS statistics 26 step by step: A simple guide and reference: Routledge; 2021.
      • 54. Law M, Collins A. Getting to know ArcGIS desktop: ESRI press; 2018.
        • 55. Davies TM. The book of R: a first course in programming and statistics: No Starch Press; 2016.
          • 56. Cardoso JG, Andersen MR, Herrgård MJ, Sonnenschein N. Analysis of genetic variation and potential applications in genome-scale metabolic modeling. Front Bioeng Biotechnol. 2015; 3(13):1–12. pmid:25763369
          • 57. Sarikhani S, Vahdati K, Ligterink W. Biochemical properties of superior Persian walnut genotypes originated from southwest of Iran. Int J Hortic Sci Technol. 2021; 8(1):13–24.
          • 58. Papi R, Spanos K, Kyriakidis D. Genetic variation of Fraxinus angustifolia natural populations in Greece based on nuclear and chloroplast microsatellite markers. Eur J For Res. 2012; 131(4):1151–61. https://doi.org/10.007/s10342-011-0586-1.
          • 59. Zhang L, Lu X, Zhou Q, Deng J. Morphological variability between geographical provenances of walnut fruit (Juglans mandshurica) in the eastern Liaoning province, PR China. Pol J Environ Stud. 2021; 30(5):4353–64.
          • 60. Wang W. Multi-trait variation analysis of Juglans mandshurica grafting seedlings [dissertation]. Haerbin: Northeast Forestry University; China. 2022.
            • 61. Dai W. Variation analysis and early selection of Juglans mandshurica provenance and family. For Invest Des. 2023; 52(2):58–62.
            • 62. Hodge GR, Dvorak WS. Provenance variation and within-provenance genetic parameters in Eucalyptus urophylla across 125 test sites in Brazil, Colombia, Mexico, South Africa and Venezuela. Tree Genet Genomes. 2015; 11(3):57.
            • 63. López-Upton J, Donahue J, Plascencia-Escalante F, Ramirez-Herrera C. Provenance variation in growth characters of four subtropical pine species planted in Mexico. New For. 2005;29(1):1–13.
            • 64. Ming J, Gu W. Phenotypic variation of Syringa oblata Lindl. For Res. 2006; 19(2):199–204. https://doi.org/10.3321/j.issn:1001-498.2006.02.013.
            • 65. Liu J, LI Z, Zhang H, Song L, Yang Y. Analysis of variation in growth traits of different species and families of Juglans mandshurica. J Northeast For Univ. 2023; 51(8):9–17+64.
            • 66. Hu R, Ju Y, Zhang S. Study on the growth of young trees in mixed forests between hard-wood and conifer species. J For Res. 1991; 19(S1):115–20.
            • 67. Yuan Y, Xie X, Xu L, Yu J, Cai Z, Wang Q. Photosynthetic Physiological Characteristics of Naturally Regenerated Juglans mandshurica Saplings at Different Ages. Forest Engineering. 2023; 39(4):29–37.
            • 68. Fu Y. Developing individual tree crown width models for Larix principis-rupprechtii [dissertation]. Changsha: Central South University of Forestry and Technology; 2017.
              • 69. Zhao Y, Hou X. Effect of soil preparation of slope seperated level ditch on the growth and development of walnut in Loess hills. Shanxi For Sci Technol. 2011; 40(1):13–7. https://doi.org/10.3969/j.issn.1007-726X.2011.01.005.
              • 70. Ali A, Lin S, He J, Kong F, Yu J, Jiang H. Tree crown complementarity links positive functional diversity and aboveground biomass along large-scale ecological gradients in tropical forests. Sci Total Environ. 2019; 656:45–54. pmid:30502734
              • 71. Lincoln NK, Cho A, Dow G, Radovich T. Early growth of breadfruit in a variety× environment trial. Agron J. 2019; 111(6):3020–7.
              • 72. You J, Zhang W, Deng L. Evaluation of the tree form quality of middle-aged Pinus tabuliformis plantation under different canopy densities in Huanglong Mountains, Northwest China. Chin J Appl Ecol. 2015; 26(7):1945–53.
              • 73. Du R, Qu J, Zhao Z, Han M, Fan C. Effects of branch drawing on the leaf growth, fruit quality and yield of Gala apple. J Northwest For Univ. 2009; 24(2):71–4.
              • 74. Li C. Studies on clone selection and silviculture effect for sawlog of Eucalyptus grandis × urophylla [dissertation]. Nanning: Guangxi University; 2007.
                • 75. Bogdan S, Katičić-Trupčević I, Kajba D. Genetic variation in growth traits in a Quercus robur L. open-pollinated progeny test of the Slavonian provenance. Silvae Genet. 2004; 53(5/6):198–201.
                • 76. Wang D, Formica MK, Liu S. Nonparametric interval estimators for the coefficient of variation. Int J Biostat. 2018; 14(1):1–15. pmid:29676993
                • 77. Wang B, Zhao X, Wang H, Jiang G, Shen G, Wang L, et al. Variance analysis of growth characteristics of 30 Pinus koraiensis half-sib families. J Northeast For Univ. 2019; 47(4):8–11+20.
                • 78. Hajnajari H, Chashnidel B, Vahdati K, Ebrahimi M, Nabipour A, Fallahi E. Heritability of morphological traits in apple early-ripening full-sib and half-sib offspring and its potential use for assisted selection. HortScience. 2012;47(3):328–33.
                • 79. Jaisankar I, Sankaran M, Singh D, Damodaran V. Genetic variability and divergence studies in pod and seed traits of Pongamia pinnata (L.) Pierre., accessions in Bay Islands. J For Res. 2014; 25(2):351–8.
                • 80. Han Y, Wu L, Yu Q, Chen H, Yang Y, Wang F. Variation analysis on growth traits of 39 Juglans mandshurica families. J Jilin For Sci Technol. 2020; 49(6):1–4.
                • 81. Munthali C, Chirwa PW, Akinnifesi FK. Genetic variation among and within provenances of Adansonia digitata L.(Baobab) in seed germination and seedling growth from selected natural populations in Malawi. Agrofor Syst. 2012;86(3):419–31.
                • 82. Liang D, Wang B, Song S, Wang J, Wang L, Wang Q, et al. Analysis of genetic effects on a complete diallel cross test of Pinus koraiensis. Euphytica. 2019; 215(2):1–12. https://doi.org/10.1007/s10681-019-2414-5.
                • 83. Lin S. Growth and wood density of 25-year-old Masson’s pine: inter-family variation and selection. For Res. 2010; 23(6):804–8.
                • 84. Yousuf M, Saleem M. Correlation analysis of S1 families of maize for grain yield and its components. Int J Agric Biol. 2001; 3(4):387–8.
                • 85. Li J, Han X, Ma Y, Li Y, Wang L, Han X. Variation analysis of growth traits and coning quantity of Pinus sylvestris var. mongolica clones. Bull Bot Res. 2020; 40(2):217–23.
                • 86. Zhelev P, Lust N. Provenance study of Scots pine (Pinus sylvestris L.) in Belgium. I. Evaluation of phenoptypical traits. Silva Gandavensis. 1999; 64:24–36.
                • 87. Liu J, Wei G, Shi C, Zhao T, Qian Y. Suitable distribution area of drought-resistant afforestation tree species in north China based on MaxEnt model. J Beijing For Univ. 2022; 44(7):63–77. https://doi.org/10.12171/j.1000−1522.20210527.
                • 88. Nicotra AB, Atkin OK, Bonser SP, Davidson AM, Finnegan EJ, Mathesius U, et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 2010; 15(12):684–92. pmid:20970368
                • 89. Li Z, Wang W, Zhang H, Liu J, Shi B, Dai W, et al. Diversity in fruit morphology and nutritional composition of Juglans mandshurica Maxim in northeast China. Front Plant Sci. 2022;13:820457.
                • 90. Zhang Y, Ao Y, Liu J, Zhao L, Zhang Y. Differences in growth characters of Xanthoceras sorbifolium from different distribution areas and analysis on its correlation with geographical-climatic factors. J Plant Resour Environ. 2019; 28(3):44–50, 7.
                • 91. Liu C, Fang W, Cai Q, Ma S, Jiang X, Ji C, et al. Allometric relationship between tree height and diameter of larch forests in China. Acta Sci Nat Univ Pekin. 2017; 53(6):1081–8.
                • 92. Golabadi M, Golkar P, Eghtedary A. Combining ability analysis of fruit yield and morphological traits in greenhouse cucumber (Cucumis sativus L.). Can J Plant Sci. 2015; 95(2):377–85.
                • 93. Khan R, Dubey R. Combining ability analysis for nutritional quality and yield in maize (Zea mays L.). The bioscan. 2015;10(2):785–8.
                • 94. Zhang Q, Wang H, Jiang G, Shen G, Wang L, Li Y, et al. Variation analysis and selection of Pinus koraiensis half-sib families. Bull Bot Res. 2019; 39(4):557–67.
                • 95. Wood RL, Jensen T, Wadsworth C, Clement M, Nagpal P, Pitt WG. Analysis of identification method for bacterial species and antibiotic resistance genes using optical data from DNA oligomers. Front Microbiol. 2020; 11:257. pmid:32153541
                • 96. Bükücü Ş, Özcan A, Sütyemez M, Yildirim E. Determination in the phenological difference levels of seedlings of some walnut genotypes (Juglans regia L.). Appl Ecol Environ Res. 2020; 18(3):4807–15.
                • 97. Song N, Zhang J, Zhao F. The PCA index for measuring functional diversity and its application to Juglans mandshurica communities in the Beijing mountains, China [J]. International Journal of Biomathematics, 2017, 10(07): 1750097.
                • 98. Rezaei Z, Khadivi A, ValizadehKaji B, et al. The selection of superior walnut (Juglans regia L.) genotypes as revealed by morphological characterization [J]. Euphytica, 2018, 214: 1–14.
                • 99. Yang H, Liao H, Yang X, Zhang W, Pan W. Genetic variation analysis of growth and form traits of Castanopsis hystrix in the second generation seed orchard. J South China Agric Univ. 2017;38(5):81–5.