Next Article in Journal
Volumetric Spore Traps Are a Viable Alternative Tool for Estimating Heterobasidion Infection Risk
Previous Article in Journal
Economic and Ecological Impacts of Adjusting the Age-Class Structure in Korean Forests: Application of Constraint on the Period-to-Period Variation in Timber Production for Long-Term Forest Management
Previous Article in Special Issue
The Development of Forest Genetic Breeding and the Application of Genome Selection and CRISPR/Cas9 in Forest Breeding
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 13
Issue 12
10.3390/f13122145
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Growth Rhythm Analysis of Young Stand and Selection of Superior Families in Choerospondias axillaris

by
Guangyou Li
1,
Jianmin Xu
1,
Juan Li
1,
Canzhang Lu
2,
Haifei Lu
1,
Baoying Mai
2,
Mingdao Luo
2 and
Chunjie Fan
1,*
1
Key Laboratory of State Forestry and Grassland Administration on Tropical Forestry, Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou 510520, China
2
Forestry Research Institute of Gaoming, Foshan City, Foshan 528515, China
*
Author to whom correspondence should be addressed.
Forests 2022, 13(12), 2145; https://doi.org/10.3390/f13122145
Submission received: 20 October 2022 / Revised: 8 December 2022 / Accepted: 9 December 2022 / Published: 14 December 2022
(This article belongs to the Special Issue Forest Tree Breeding and Directed Cultivation Techniques)
Browse Figures
Review Reports Versions Notes

Abstract

:
As an important source of medicine, food, and high-quality wood, Choerospondias axillaris has been widely planted in South China. However, few studies of its growth traits and rhythm and concerning the selection of superior provenances/families were developed. In this study, 77 families from five provenances were studied and grouped. Tree height, diameter at breast height (DBH), and crown width within and between rows were measured. The relevance between growth traits and environmental factors was also analyzed. The results showed the height, DBH, and crown width within and between rows were divided into slow, mean, and fast growth periods, which was significantly related with sunshine hours in earlier months. Next, monthly rainfall showed an extremely positive correlation with the increase in tree height, DBH, and plant volume. Then, the monthly volume increment had a significantly positive correlation with five meteorological factors, excluding sunshine hours with increasing months, while the monthly growth of tree height and crown width within and between rows was significantly negatively correlated with sunshine hours. Based on these results, the superior families No. 15, 76, and 56, which originated from the Raoping provenance, were selected for their fast growth and available adaptation. These results provided the reliable growth rhythm of a young C. axillaris stand, which established a basis for fertilizing and forest tending. During these processes, meteorological factors, especially humidity and sunshine duration, had important effects on growth, implying that the external climate should be paid more attention to promote fast growth and prevent diseases and insect pests. Moreover, selecting superior families was helpful to further breeding and plantation.

1. Introduction

Choerospondias axillaris (Roxb.) Burtt et Hill is famous for its fruit, used in wild jujube cake. It is widely grown and planted in South China, and grows on hills, hillsides, or in valleys with an altitude of 300–2000 m and is suitable for soil with deep fertility and good drainage, especially in light, warm, and humid weather [1,2]. It is a popular tree species for landscaping [3] with a high wood quality used for mahogany furniture; it is also a source of plant medicine [4,5,6,7]. Meanwhile, extensive adaptability means it is easily grown in acid, neutral, or calcareous soil. Moreover, it can regenerate naturally and has strong germination ability while possessing a low occurrence level of diseases and insect pests. Additionally, C. axillaris has stronger fast-growing ability and a good afforestation effect after artificial cultivation, with broad development prospects in China for forest classification management [8,9,10].
Growth rhythm is the growth status and trend in a plant in a certain period of time in specific environment, including the beginning and ending time of growth, the length of the growth period, and the speed of the growth rate, which is closely related to the plant’s size during growth [11]. The study of tree growth rhythm [12,13,14,15] can reveal the growth characteristics of an early artificial cultivation period, provide theoretical basis for the management of target trees, and also help to introduce and evaluate germplasm resources, and accelerate the promotion of suitable germplasm in suitable areas as far as possible [16]. Research on the growth rhythm of multiple tree species, such as Larix gmelinii (Rupr.) Kuzen, Picea abies mast, Tapiscia sinensis Oliv., Salix matsudana Pendula, and Zenia insignis Chun, has been developed, including at the seedling and young forest stages [17,18,19,20,21,22]. Ground diameter growth and seedling height conform to the S-shaped growth curve and seedling height and ground diameter are divided into different growth stages [23,24]. During the growth stage, strengthening water and fertilizer management is an important measure to save water, fertilizer, and material resources to achieve a higher input/output ratio.
Meanwhile, growth rhythm can help us when selecting superior families and clones by early growth. Additionally, developing the early selection and genetic variation analysis in tree species was necessary to ascertain superior families and clones and further improve the output value of trees such as Eucalyptus saligna, Cunninghamia lanceolata (Lamb.) Hook., Cryptomeria japonica, and Pinus contorta [25,26,27,28].
Moreover, various tree species have their own fast-growing periods in a year, which are related to genetic correlation and long-term climate. Regarding long-term adaptation to the external climate, the growth rhythm differs among tree species such as Hevea brasiliensis (Willd. Ex A. Juss.) Muell., Quercus rubra L., and Pinus kesiya Royle ex Gordon [29,30,31]. This indicates that meteorological factors have a certain impact on growth [32,33]. The correlation of meteorological factors and growth rhythm can be a useful supplement when selecting superior and stable genotypes [34].
However, there are few studies on the growth characteristics and growth rhythm of the C. axillaris plantations, although some studies on artificial C. axillaris cultivation have been performed. Meanwhile, none of the superior provenances/families suitable for the current planting environment have been selected until now, which limited the large-scale application of C. axillaris plantation. To improve the growth rate and further expand the plantation area, it is urgent to know the growth rhythm of various families and further select superior provenances/families with fast growth and wide adaptability. Hence, 77 families of C. axillaris were used to assess their growth rhythm and select appropriate families/superior plants, which will provide a theoretical basis for young forest tending and management.

2. Materials and Methods

2.1. Experimental Setting Area

The experiment was performed in Forestry Research Institute of Gaoming District, Foshan, which is located southwest of Foshan City, Yangmei Jiling (22°47′36″ N, 112° 41′48″ E) (Figure 1) of Yanghe Town. The annual average temperature is 22.5 °C and the annual rainfall is 1681.2 mm. The forest land in the planting area was mainly red soil in mountainous area, with an average altitude of 50 m, soil organic matter content of 17.007 g kg−1, available N, P, and K contents of 89.01, 0.839, and 55.98 g kg−1, respectively, and available B and Zn of 0.165 and 1.000 mg kg−1, respectively, with pH 4.41 (soil:water = 1:2.5). The experimental area’s grown plant species included Ilex asprella (Hook and Arn.) Champ. ex Benth., Desmos chinensis Lour., Mallotus apelta (Lour.) Muell. Arg., and other common herbs, such as Gleichenia linearis Clarke and ferns (Adiantum spp.), among others, before plantation. In this study, a randomized complete block design was adopted, with 77 families, 5 plots, and 8 replicates. Planting holes were 50 × 50 × 40 cm (length × width × height).
Five main meteorological factors including temperature, rainfall, sunshine hours, and humidity were recorded and calculated. As shown in Table 1, the daily average maximum temperature reached the highest from June to July, daily average and minimum air temperature showed the lowest from December to February. The rainfall level was high from May to June and low from November to January, and humidity was high from March to May and low from January to February. The details are listed in Table 1.

2.2. Experimental Design and Materials

The materials of 77 families originated from five provenances, including Anhua, Longchuan, Raoping, Lianshan, and Quanzhou, and the details are listed in Table 2.
For the determination of growth rhythm, all 77 families were selected (see Table 1), and the growth was observed in five trees of each family for no less than one year (a total of 18 observations were conducted to calculate monthly growth increments), including tree height, diameter at breast height (DBH), crown width in and between rows, and growth traits.
The construction of test forest happened in July 2016 and there was an 89.09% preservation rate 3 years after planting, which was enough to develop the determination of growth rhythm.
The tree height (m), diameter at breast height (DBH, mm), crown width (m) within and between rows, growth traits, and the individual tree volume (cm3) were measured and calculated each month. Meanwhile, daily meteorological data were collected from 1 June 2017 to 31 December 2019 at meteorological stations.

2.3. Data Analysis

To investigate the correlation of growth characteristics and growth rhythm of 3-year-old C. axillaris forests with meteorological factors including temperature, rainfall (R), sunshine hours (SH), and air humidity (HU), Excel and SAS software were used for analyzing and calculating. The single plant volume (cm3) [35] was calculated by using formula:
V = H × DBH2 × F
where H, DBH, and F represent tree height (m), DBH (mm), and rate of tree form, respectively. In this study, F value was 0.4.
Linear model was used for analyzing variance of unbalanced data [36]. SAS software version 8.0 [37] was used to analyze the variance of repeated single plant data of all the tested families at 3 years old, with the Duncan test assessing result significance. Meanwhile, SAS software was also used to analyze the Pearson correlation between growth rhythm and meteorological factors.

3. Results

3.1. Growth Traits Showed Significant Differences in Various Families and Superior Families Selection

Five growth traits, including H, DBH, V, CT, and CR showed significant differences in various families with the average values of 3.66 m, 44.34 mm, 3552 cm3, 2.87 m, and 2.91 m, respectively (Table 3). According to their growth and growth rhythm of variance analysis, 77 families were divided into three groups. The first group (Cluster I) included 11 families, which were 15, 76, 56, 49, 17, 5, 48, 18, 71, 12, and 62; these were classified as a fast-growing type with a single plant volume range of 4872–5763 cm3. It was found that all growth traits of the first group were 8.95% higher than the overall average of 77 families, in which the DBH, the single plant volume, the crown width between plants, the crown width between rows was 14.10%, 46.82%, 7.55%, and 8.61% higher than the overall average, respectively.
The secondary group (Cluster II) contained 17 families including 61, 69, 37, 21, 13, 60, 27, 38, 70, 57, 19, 53, 26, 58, 4, 75, and 29, which were classified as a slow-growing type, with a single plant volume range of 1854–2837 cm3. The rest of the families were classified as Cluster III, belonging to the medium growth rate type, with individual plant volume ranging from 2855 to 4562 cm3 (Table 4).

3.2. Two Obvious Peaks Occurred in Tree Height Growth in One Year

According to the height of the various families in each month, the growth trend in height in the C. axillaris families was consistent with corresponding groups. As shown in Figure 2, two obvious growth peaks were observed, from March to May and in August. Meanwhile, slow growth with low values from January to February, June to July, and September to December were also observed. Basically, the growth period of C. axillaris tends to last for a relatively short period in a year (Figure 2A). According to the monthly variation of tree height growth, the growth period can be divided into five stages: lag-growing stage, the first fast-growing stage, the first slow-growing stage, the second fast-growing stage, and the second low-growing stage (Table 5).
It was found that group I and II of C. axillaris had a similar trend in height growth with type III. Differently, type I and II maintained a more fast-growing stage which was from March to May, while type III happened only in April or August (Figure 2A). Type I, with the largest annual height growth of C. axillaris, maintained continuous growth throughout the year. The declining level of growth during the fast-growing period was not as obvious as that in other types, indicating that type I maintained the height growth advantage for a long time. Meanwhile, the growth rate of group II and III in the first fast-growing stage was higher than the growth rate in the second fast-growing stage, while group I showed a faster growth rate in the second fast-growing stage. The fast-growing stage of type III was obviously concentrated in April and August, which was different from that of group I and II.

3.3. DBH Stablely Increased from February to August in C. axillaris Families

The DBH growth showed significantly differences, although it was not the same as the sharp changes in tree height. Similar with the height of trees, two DBH growth peaks were observed in C. axillaris in April and August, respectively (Figure 2B), which could be divided into six stages: lag stage, the first slow-growth stage, the first fast-growing stage, the second slow-growing stage, the second fast-growing stage, and the third slow-growing stage (Table 6).
DBH entered the first and second fast-growing stages at the same time for the three various types, and the change dynamic was almost the same except for the difference in growth value in three types. As shown in Figure 2B, DBH growth in type I was better than that in type II and III. Meanwhile, a stable growth period happened from February to March, and the growth level of the slow-growth period was from June to July, which was significantly higher than that of the first slow-growth period.

3.4. Growth Rhythm of Crown Width within Row Showed Differences with Crown Width between Rows

The growth rhythm of CT showed a similar result with DBH among three types of C. axillaris families. Meanwhile, a slight change was observed in each type. Two key growth peaks also occurred in April and August, which was important to the growth of crown width (Figure 2C). According to Figure 1, the growth period of CT can be divided into five stages, i.e., delayed growth stage, the first fast-growing stage, the first slow-growing stage, the second fast-growing stage, and the second slow-growing stage (Table 7). The monthly variation of CT and the start of the growth period was basically the same among the three C. axillaris groups. However, CT in type I did not show an obvious advantage. For example, CT of type II was significantly higher than that of other types in the second fast-growing stage. Meanwhile, it was found that type III entered the first slow-growth stage in May, while the other two types entered it in June. After the slow-growth period from September to October, CT growth decreased significantly in the second slow-growth period, with the growth of type I being larger than that of types II and III. However, the growth of type II did not show significant differences in the two fast-growing stages.
Differing from CT, an apparent small growth peak occurred in October with similar growth changes among the three types. There were also two high growth peaks in April and August, although the peak value in April was significantly higher than that in August (Figure 2D). The CR growth period can also be divided into five stages: the lag stage, first fast-growing stage, first slow-growing stage, second fast-growing stage, and second slow-growing stage (Table 8). The start time of both fast-growing stages and the overall dynamics was almost the same among three types of C. axillaris. For type II, the growth peak in the first fast-growing stage was significantly higher than that in the second fast-growing stage and that of other types in the same fast-growing stage. Moreover, the growth level of type III decreased significantly from May to June.

3.5. Rainfall and Sunshine Hours Significantly Affect Growth Traits

To identify the effects of meteorological factors in growth traits of C. axillaris, Pearson correlation analysis was performed between the growth traits and meteorological factors (Table 9 and Table 10). All monthly growth traits were significantly correlated with sunshine hours (SH) (Table 9; p < 0.10, 0.05, 0.01), while the increase in V of a single tree in each month was significantly correlated with temperature, rainfall, and humidity (Table 10). Meanwhile, tree height and DBH were also significantly correlated with rainfall and SH, and crown width and CR were highly correlated with SH.
To easily understand the relationship between C. axillaris growth rhythm and the monthly dynamics of meteorological factors, the chart of the monthly variation of growth traits and various meteorological factors were plotted. The monthly DBH growth was not strongly correlated with air temperature and air humidity, but closely related to SH, with a significant negative correlation (Figure 3). The monthly dynamics of DBH growth and rainfall were generally consistent, particularly from January to June, with DBH growth lagged due to rainfall changes in July, and then remaining consistent. The monthly increment of tree height was basically consistent with the temperature, monthly rainfall, SH, and air humidity trends (Figure 4). Monthly height growth showed a different trend to that of monthly rainfall from January to July, and then the two changed in a similar trend. The growth trend in CT and CR was basically the same, i.e., the increment of all types’ CT in April was high, the increment in August was high but lower than that in April, and the growth increments from January to February and from September to December were relatively low, i.e., the slow-growth stage (Figure 5 and Figure 6). Only for fast-growing C. axillaris (type I), the growth peak of CR in May was higher than that in April. Additionally, the increment between rows in May had a strong relation with values of all meteorological factors during the previous month.

4. Discussion

C. axillaris, as an important tree species used for ecological applications and wood supply, was an essential candidate to develop growth rhythm analysis and selection of superior families. Owing to the lack of systematical and comprehensive research, the development of C. axillaris in South China had severely lagged. Hence, it is necessary to develop the research about growth rhythm and further selecting superior families in C. axillaris. In this study, 77 families from five provenances were used for selecting superior families and growth rhythm analysis. No. 15, 76, and 56 originated from Raoping provenances, which are located in the east of Guangdong province. Hence, it was concluded that families from Raoping provenances were more suitable for planting in Guangdong province. The suitability of these families in various areas needs to be further verified in the next step of the study.
Tree growth trends reflect the adaptability of its genetic characteristics to environmental conditions. If these are mastered, scientific management can be carried out according to the growth characteristics in different periods of seedling growth to accelerate the growth of trees [38]. Dendrobenthamia japonica (DC.) Fang var. chinensis (Osborn.) Fang has a small leaf area and poor stress resistance in the early growth stage, and it requires water and fertilizer management (April to May). Its peak growth period plays a key role in the growth of seedling height and ground diameter. As this period occurs in the summer, i.e., high temperature and drought, it is necessary to strengthen water and fertilizer management [39,40]. In addition, as the growth peak period of ground diameter and seedling height do not coincide, the time of water and fertilizer management in summer should be appropriately extended (June to September); from October to late November, the growth period of D. japonica is in the hardening stage, so fertilization should be stopped, and water management should be executed well.
The C. axillaris growth rhythm showed that the growth peak was mainly in April and August. The monthly increment of tree height, DBH, and single volume were also significantly correlated with the rainfall of the previous month, and water was the limiting factor for the growth of C. axillaris. According to our research, it was obvious that rainfall and fertilizer play an important role in promoting the rapid growth of plants. Recently, a study of the relationships between the growth of C. axillaris. and the climate of long-term plantations in East-Central Thailand was performed [41]. Similarly, the growth of C. axillaris was positively correlated with moisture, which was the main limiting factor and corresponded with the monsoon season from July to October [41]. In T. grandis, a significant correlation between rainfall and the DBH growth of teak also occurred, especially in the rainy season [42]. Contrastingly, there is no obvious correlation between C. acuminata and growth in the rainy season and rainfall, and it seems that increasing rainfall in the dry season may have a larger effect on stem radial growth than that in rainy season [42]. Therefore, it is necessary that early artificial forest tending and topdressing are carried out in combination with rainy weather and forest road conditions in rainy months. However, to gain high-quality timber with straight stems and no knots, the slow-growing period from December to February of the following year is needed [43].
Temperature was also one of the main climatic factors affecting plant growth in a southern subtropical climate. Of course, the promoting effect of rainfall on growth may also include the effect of temperature, because temperature is high in the rainy season, and the curve of water heat coefficient (R/T) is basically consistent with that of rainfall. However, the responses of different species to the periodic changes of rainfall and temperature seem to be quite complex [42]. The change of stem growth includes the real growth and the change of trunk volume caused by the change of water content in plants. This volume change caused by changing water content has been reported in the determination of radial growth of trees in tropical forests. This volume change is caused by changing water content and temperature. The growth in the dry season from October to March is generally the smallest, even negative in October, November, and February, while in December and January there is still a certain amount of growth [44,45]. Similar results were also found in C. axillaris, in which the monthly increment of tree height was basically consistent with the temperature. However, the DBH growth was not strongly correlated with air temperature. This may be caused by the decrease in rainfall in October and November, while plant transpiration is still strong, resulting in the rapid decrease in water content in cells and the shrinkage of trunk volume. Meanwhile, the low DBH value in February may be the result of the real deceleration of plant trunk growth under low temperatures in January and February [46].

5. Conclusions

The tree height, DBH, single plant volume, crown width between trees and between rows were significantly different among various families (p < 0.01). All 77 families were classified into fast-growing type, middle-growth type, and slow-growth type. Meanwhile, the families No. 15, 76, and 56 showed a higher trait of growth than the average values, and were selected as superior families which will be used for propagation and plantation in the future. The growth trend in fast-growing, middle growth, and slow-growing C. axillaris (type I, II, and III, respectively) was consistent with the average growth characteristics, although there were differences in the growth rate. Significant differences in tree height and DBH among the three C. axillaris types were observed, especially in tree height. All monthly increments of growth traits were significantly correlated with SH, while the monthly growth of single plant volume accumulation was significantly correlated with other meteorological factors. Meanwhile, tree height and canopy width were significantly negatively correlated with SH. Moreover, the monthly increment of growth traits was higher from April to August, and peak values were apparent in April and August. These results will be helpful to future plantation and forest management of C. axillaris.

Author Contributions

Conceptualization, G.L., J.X. and C.F.; methodology and investigation, G.L., J.L., C.L., H.L., B.M. and M.L.; writing and revision, G.L., J.L. and C.F.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.

Funding

Project supported by the Forestry Science and Technology Innovation Project of Guangdong “Research and demonstration on breeding and high efficient cultivation techniques of Choerospondias axillaris” (No. 2015KJCX008/2018KJCX021), and by the Technical Standard Project of Guangdong “Technical regulation of seedling cultivation of Choerospondias axillaris” (No. 2017-DB-05).

Acknowledgments

The authors gratefully thank Huang Xizhao, Ou Jinwei, Li Weimei, Wang Weiqing, Liang Baozhi, and Zhu Yanglin, respectively from the Research Institute of Tropical Forestry and the Gaomin Forestry Research Institute of Foshan for their painstaking assistance in the growth traits survey. Comments from reviewers are also appreciated.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhu, H. A review on the research of Choerospondias Axillaris. Guangxi For. Sci. 2005, 34, 122–126. [Google Scholar]
  2. The Editorial Committee of Dendrology (Southern Edition). Dendrology; China Forestry Press: Beijing, China, 1994; pp. 580–587. [Google Scholar]
  3. Hau, C.H. Tree seed predation on degraded hillsides in Hong Kong. For. Eco. Manag. 1997, 99, 215–221. [Google Scholar]
  4. Liu, X.; Ding, Y.; Chen, M.; Wen, F.; Xie, X. Research and manufacturing of Choerospondias Axillaris juice. Soft Drink. Industry. 1995, 39, 14–16. [Google Scholar]
  5. Bu, C. Analysis of nutritional components and processing utilization of Choerospondias Axillaris fruit. Wild Plants China 1992, 11, 32–36. [Google Scholar]
  6. Gautam, K.H. The sweet and sour tale of la psi-domesticating and commercialzing Choerpondias axillaris. Agro-For. Today 1997, 9, 12–16. [Google Scholar]
  7. Li, M. By Mongolia Qiwei Pills progress of science. North. Pharm. 2011, 8, 70–71. [Google Scholar]
  8. Zeng, F. Studies on the growth laws of Choerosponsias axillaries Natural Forests. J. Fujian For. Sci Technol. 2001, 28, 13–15. [Google Scholar]
  9. Huang, Y.; Zhuang, X. Seedling Raising Techniques of Native Tree Species in South China; China Forestry Press: Beijing, China, 2007; pp. 158–159. [Google Scholar]
  10. Luo, W.; Guiping, H.E.; Chen, Y.; Sun, H.; Feng, J.; Zhang, J.; Jin, Q. A study on variation of growing traits of young Choerospondias axillaris forest in its geographic provenances and its provenance selection. Acta Agri. Univ. Jiangxiensis 2007, 29, 365–371. [Google Scholar]
  11. Wang, T.; Tigerstedt, P.M. A variation of growth rhythm among families and correlation between growth rhythm and growth rate in Betula pendula Roth. Scand. J. For. Res. 1993, 8, 489–497. [Google Scholar] [CrossRef]
  12. Huang, J.; Guo, J.; Zeng, J. Early selection of Betula alnoides clones and their growth rhythm in Western Yunan of China. For. Res. 2017, 30, 518–524. [Google Scholar]
  13. Lai, M.; Sun, X.; Zhang, S. Growth rhythm of young Larix kaempferi×Larix olgensis hybrid. J. Northeast. Univ. 2014, 42, 13–17, 33. [Google Scholar]
  14. Zhou, X.; Zhou, W.; Lin, W.; Zhou, P.; Chen, X. Observation and analysis on growth rhythms of Toona sinensis of 14 provenances. J. South China Agri. Univ. 2016, 37, 90–96. [Google Scholar]
  15. Wang, X.; Zhang, R.; Xu, Z.; Cheng, Y.; Wu, J.; Zhou, J. Study on growth rhythm of different Zelkova schneideriana provenances at seedling stage. J. Cent. South Univ. For. Technol. 2013, 33, 37–40. [Google Scholar]
  16. Cao, J.; Fang, L. Studies on observation of growth rhythm of young Betula luminifera. For. Res. 2006, 19, 142–145. [Google Scholar]
  17. Lukkarinen, A.J.; Ruotsalainen, S.; Nikkanen, T.; Peltola, H. The growth rhythm and height growth of seedlings of siberian (Larix sibirica Ledeb.) and dahurian (Larix gmelinii Rupr.) larch provenances in greenhouse conditions. Silva Fenn. 2009, 43, 5–20. [Google Scholar] [CrossRef] [Green Version]
  18. Lukkarinen, A.J.; Ruotsalainen, S.; Peltola, H.; Nikkanen, T. Annual growth rhythm of Larix sibirica and Larix gmelinii provenances in a field trial in southern Finland. Scand. J. For. Res. 2013, 28, 518–532. [Google Scholar] [CrossRef]
  19. Danusevicius, D.; Gabrilavicius, R. Variation in juvenile growth rhythm among Picea abies provenances from the Baltic states and the adjacent regions. Scand. J. For. Res. 2001, 16, 305–317. [Google Scholar] [CrossRef]
  20. Zhang, L.; Han, Y.; Liu, Z.; Meng, Y.; Liu, Y.; Kong, D. Study on the growth rhythm of Tapiseia sinensis at seedling stage. Guangdong Agri. Sci. 2018, 45, 47–51. [Google Scholar]
  21. Qin, G.; Song, Y.; Qiao, Y.; Yu, Z.; Peng, L. Annual height growth of salix matsudana at nursery stage and its gray correlation. J. Northeast For. Univ. 2019, 47, 42–45. [Google Scholar]
  22. Kuang, L.; Deng, X.; Chen, S.; Qu, W.; Zhaxi, Q.; Chen, X. Growth rhythm of Zenia insignis seedlings from four provenances. J. South China Agri. Univ. 2014, 35, 98–101, 107. [Google Scholar]
  23. Peng, Y.; Huang, Z.; Shen, W.; Zhu, J.; Hao, H. Analysis on growth rhythm and biomass increment of an introduced variety of Alnus formosana. J. of Central South Univ. of For. Technol. 2014, 34, 7–12. [Google Scholar]
  24. Li, P.; Que, Q.; Wu, L.; Zhu, Q.; Chen, X. Growth rhythms of Toona ciliate seedlings from different provenances. J. South China Agri. Univ. 2017, 38, 96–102. [Google Scholar]
  25. Lan, H.; Huang, X.; Luo, J.; Arnold, R.J. Genetic variation in growth and stem straightness in Eucalyptus saligna trials in Fujian. Aust. For. 2012, 75, 163–174. [Google Scholar]
  26. Wu, H.; Duan, A.; Zhang, J. Long-term Growth Variation and Selection of Geographical Provenances of Cunninghamia lanceolata (Lamb.) Hook. Forests 2019, 10, 876. [Google Scholar] [CrossRef]
  27. Hiraoka, Y.; Miura, M.; Fukatsu, E.; Iki, T.; Yamanobe, T.; Kurita, M.; Isoda, K.; Kubota, M.; Takahashi, M. Time trends of genetic parameters and genetic gains and optimum selection age for growth traits in sugi (Cryptomeria japonica) based on progeny tests conducted throughout Japan. J. For. Res. 2019, 24, 303–312. [Google Scholar] [CrossRef]
  28. Hayatgheibi, H.; Fries, A.; Kroon, J.; Wu, H.X. Genetic analysis of lodgepole pine (Pinus contorta) solid-wood quality traits. Can. J. For. Res. 2017, 47, 1303–1313. [Google Scholar] [CrossRef] [Green Version]
  29. Zhang, F.; Ya, H.; Li, X.; Mao, C.; Yang, T.; Li, G.; Wu, Y. Growth dynamics of Hevea brasiliensis clones and correlation with meteorological factors. J. Northwest For. Univ. 2019, 34, 91–97. [Google Scholar]
  30. Dong, Q.; Zhou, Y.; Zhang, H.; Cao, S.; Liu, X.; Zhao, X. Introduction experiment of Quercus rubra in Yulin Sandy Land. Prot. For. Sci. Technol. 2018, 11, 13–14, 50. [Google Scholar]
  31. Yang, X.; Xu, J.; Zhu, Y.; Shen, L.; Li, G.; Wu, S.; Luo, Y.; Liu, J.; Chen, L. Growth rhythm for half-sib families of young Pinus kesiya plantations in Southern Yunnan, China. J. Trop. Subtrop. Bot. 2019, 27, 399–407. [Google Scholar]
  32. Pumijumnong, N.; Eckstein, D. Reconstruction of pre-monsoon weather conditions in northwestern Thailand from the tree-ring widths of Pinus merkusii and Pinus kesiya. Trees-Struct. Funct. 2011, 25, 125–133. [Google Scholar] [CrossRef]
  33. Fan, G.; Liu, Y.; Lai, X. Analysis of woody plants phenology variation characteristics in China. J. Meteoro. Sci. 2012, 32, 68–73. [Google Scholar]
  34. Skrøppa, T.; Steffenrem, A. Performance and Phenotypic Stability of Norway Spruce Provenances, Families, and Clones Growing under Diverse Climatic Conditions in Four Nordic Countries. Forests 2021, 12, 230. [Google Scholar] [CrossRef]
  35. Mckenney, D.; Davis, W.J.; Turnbull, J.W. Impact of Australian Tree Species Selection Research in China: An Economic Perspective. For. Ecol. Manag. 1993, 60, 59–76. [Google Scholar] [CrossRef]
  36. Huang, S.; Xie, W. Practical SAS Programming and Forestry Experiment Data Analysis; South China University of Technology Press: Guangzhou, China, 2001; pp. 247–268. [Google Scholar]
  37. Kung, F.H. Estimating Parent Effects in Full-sib Progeny Tests Following Use of an Irregular Mating Design. Silvae Genet. 1978, 27, 196–200. [Google Scholar]
  38. Wang, L. Dynamic Study on Species Height Growth of Evergreen Broad-Leaved Trees of Tiantong in Zhejiang; Nanjing Normal University: Nanjing, China, 2014; pp. 1–52. [Google Scholar]
  39. Yang, L. Study on Leaves Phenology in Spring of Evergreen Broad-Leaved Forest in Tiantong, Zhejiang; East China Normal University: Shanghai, China, 2009; pp. 1–85. [Google Scholar]
  40. Liu, W.; Li, D.; Ji, Q.; Chen, H.; Lai, S.; Gen, S.; Yun, X.; Chen, Q. Leaf nitrogen allocation of evergreen and deciduous broad-leaved tree species and their relationships with photo-synthetic capacity in the two habitats. Ecol. Sci. 2015, 34, 1–8. [Google Scholar]
  41. Surayothee, W.; Buajan, S.; Fu, P.; Pumijumnong, N.; Fan, Z.; Panthi, S.; Finnegan, P.M.; Zhang, Y.; Chen, Y.; Tor-ngern, P.; et al. Growth-Climate Relationships and Long-Term Growth Trends of the Tropical Forest Tree Choerospondias axillaris (Anacardiaceae) in East-Central Thailand. Forests 2021, 12, 1655. [Google Scholar] [CrossRef]
  42. Chen, Z.; Zhang, D.; Guo, Z. Diameter growth of three tree species in the lower subtropical climate. Acta Ecol. Sin. 1999, 19, 939–943. [Google Scholar]
  43. Ouédraogo, D.-Y.; Mortier, F.; Gourlet-Fleury, S.; Freycon, V.; Picard, N. Slow-growing species cope best with drought: Evidence from long-term measurements in a tropical semi-deciduous moist forest of Central Africa. J. Ecol. 2013, 101, 1459–1470. [Google Scholar] [CrossRef]
  44. Breitsprecher, A.; Bethel, J.S. Stem-growth periodicity of trees in a tropicalwet forest of Costa Rica. Ecology 1990, 71, 1156–1164. [Google Scholar] [CrossRef]
  45. Wu, C.; Jiang, B.; Yuan, W.; Shen, A.; Yang, S.; Yao, S.; Liu, J. On the Management of Large-Diameter Trees in China’s Forests. Forests 2020, 11, 111. [Google Scholar] [CrossRef] [Green Version]
  46. Zhou, Y. Relative Contribution of Growing Season Length and Amplitude to Long-Term Trend and Interannual Variability of Vegetation Productivity over Northeast China. Forests 2020, 11, 112. [Google Scholar] [CrossRef]
Forests 13 02145 g001 550
Figure 1. Location of study sites (black circles) in Gaoming of Foshan City, Guangdong, China.
Figure 1. Location of study sites (black circles) in Gaoming of Foshan City, Guangdong, China.
Forests 13 02145 g001
Forests 13 02145 g002 550
Figure 2. Monthly dynamics of growth traits for three types of C. axillaris families. (A): Monthly dynamics of height growth; (B): monthly dynamics of DBH growth; (C): monthly dynamics of crown width in row; (D): monthly dynamics of crown between rows.
Figure 2. Monthly dynamics of growth traits for three types of C. axillaris families. (A): Monthly dynamics of height growth; (B): monthly dynamics of DBH growth; (C): monthly dynamics of crown width in row; (D): monthly dynamics of crown between rows.
Forests 13 02145 g002
Forests 13 02145 g003 550
Figure 3. Dynamics of monthly height growth and meteorological factors including temperature (A); rainfall (B); sunshine hours (C); and relative humidity (D). Meteorological data were supplied by Gaoming Meteorological Bureau, Foshan City, Guangdong Province, China.
Figure 3. Dynamics of monthly height growth and meteorological factors including temperature (A); rainfall (B); sunshine hours (C); and relative humidity (D). Meteorological data were supplied by Gaoming Meteorological Bureau, Foshan City, Guangdong Province, China.
Forests 13 02145 g003
Forests 13 02145 g004 550
Figure 4. Dynamics of monthly DBH growth and meteorological factors including temperature (A); rainfall (B); sunshine hours (C); and relative humidity (D). Meteorological data were supplied by Gaoming Meteorological Bureau, Foshan City, Guangdong Province, China.
Figure 4. Dynamics of monthly DBH growth and meteorological factors including temperature (A); rainfall (B); sunshine hours (C); and relative humidity (D). Meteorological data were supplied by Gaoming Meteorological Bureau, Foshan City, Guangdong Province, China.
Forests 13 02145 g004
Forests 13 02145 g005 550
Figure 5. Dynamics of monthly crown width in rows and meteorological factors including temperature (A); rainfall (B); sunshine hours (C); and relative humidity (D). Meteorological data were supplied by Gaoming Meteorological Bureau, Foshan City, Guangdong Province, China.
Figure 5. Dynamics of monthly crown width in rows and meteorological factors including temperature (A); rainfall (B); sunshine hours (C); and relative humidity (D). Meteorological data were supplied by Gaoming Meteorological Bureau, Foshan City, Guangdong Province, China.
Forests 13 02145 g005
Forests 13 02145 g006 550
Figure 6. Dynamics of monthly crown width between rows and including temperature (A); rainfall (B); sunshine hours (C); and relative humidity (D). Meteorological data were supplied by Gaoming Meteorological Bureau, Foshan City, Guangdong Province, China.
Figure 6. Dynamics of monthly crown width between rows and including temperature (A); rainfall (B); sunshine hours (C); and relative humidity (D). Meteorological data were supplied by Gaoming Meteorological Bureau, Foshan City, Guangdong Province, China.
Forests 13 02145 g006
Table
Table 1. The effect of five meteorological factors in the period of investigating.
Table 1. The effect of five meteorological factors in the period of investigating.
Growth Survey MonthAverage Daily
Temperature
(°C)		Daily Maximum
Temperature
(°C)		Daily Minimum
Temperature
(°C)		Rainfall
(mm)		Sunshine Hours
(h)		Daily Air
Humidity
(%)
2017.728.0536.2023.30151.80145.5083.17
2017.828.1338.4021.70260.40230.7081.67
2017.927.4836.2025.4025.00182.2085.08
2017.1023.3133.8024.3052.40216.1078.56
2017.1118.9928.7020.9042.7079.2081.77
2017.1214.7124.1015.300.10180.6066.28
2018.113.1925.0017.0057.8100.1082.13
2018.213.8029.0016.904.20112.4076.46
2018.313.8029.001.608.60112.4076.46
2018.419.2330.105.3049.40124.7082.62
2018.521.5931.407.9053.6070.6084.32
2018.627.2932.4224.3039.194.7827.29
2018.727.2935.7021.80587.80143.4085.23
2018.827.2836.2022.90213.40181.9087.96
2018.926.3035.7025.20153.80165.3086.02
2018.1022.1532.3021.7023.70147.5080.89
2018.1119.6729.5020.4016.90131.4085.88
2018.1214.6228.4018.706.5070.5085.56
2019.114.6228.405.2011.2070.5085.56
2019.214.1526.205.401.60105.5084.34
2019.317.6229.308.8085.0051.4088.86
2019.418.9829.8010.90140.9057.6090.97
2019.523.5233.2015.80292.8071.8092.97
2019.624.6235.2016.80379.5067.7091.27
2019.727.7036.1021.40339.10143.7088.60
2019.827.9437.9023.50237.70156.8088.28
2019.927.7737.5023.20350.40180.9088.50
2019.1025.9235.5016.90145.50239.8083.02
2019.1123.7134.8015.1027.10203.6080.11
2019.1219.2130.5011.300.00255.9075.14
Table
Table 2. The provenances and families sourced in the trial.
Table 2. The provenances and families sourced in the trial.
Family CodesQuantity of FamilyProvenanceLatitude (N)Longitude (E)Altitude (m)
1–44Hunan Anhua28°24′111°12′650
5–95Guangdong Longchuan24°12′115°18′500
10–40, 52–7757Guangdong Raoping23°43′116°59′30
41–477Guangdong Lianshan24°35′112°05′360
48–514Guangxi Quanzhou25°55′112°05′200
Table
Table 3. Growth performance of C. axillaris families at 3 years old.
Table 3. Growth performance of C. axillaris families at 3 years old.
FamiliesHeight (m)DBH
(mm)		Mean Volume (cm3·tree−1)Crown Width in Rows (m)Crown Width between Rows (m)
153.97 ± 1.43 ABCDEFG50.21 ± 21.34 ABCDE5763 ± 64 A2.88 ± 1.05 ABCDE2.93 ± 1.05 ABCDEF
764.03 ± 1.18 ABCDEF52.07 ± 19.43 ABC5720 ± 57 AB3.12 ± 0.89 ABCD3.16 ± 0.70 ABCDEF
564.33 ± 0.29 A55.98 ± 9.94 A5459 ± 16 ABC3.27 ± 0.50 ABC3.47 ± 0.76 AB
494.13 ± 0.94 ABC53.09 ± 14.83 AB5440 ± 40 ABCD3.16 ± 0.65 ABCD3.30 ± 0.73 ABCD
173.88 ± 1.16 ABCDEFG51.20 ± 19.56 ABCD5380 ± 49 ABCDE3.36 ± 1.22 AB3.38 ± 0.98 ABC
53.91 ± 1.16 ABCDEFG47.77 ± 20.3 ABCDEFG5170 ± 66 ABCDEF2.97 ± 0.53 ABCDE3.16 ± 0.67 ABCDEF
484.02 ± 1.31 ABCDEF48.51 ± 18.07 ABCDEF5109 ± 55 ABCDEFG3.14 ± 0.94 ABCD3.19 ± 0.90 ABCDEF
184.08 ± 1.00 ABCD50.24 ± 14.60 ABCDE4958 ± 47 ABCDEFG3.02 ± 0.71 ABCDE3.04 ± 0.85 ABCDEF
713.88 ± 0.93 ABCDEFG50.28 ± 17.61 ABCDE4942 ± 39 ABCDEFG2.93 ± 0.92 ABCDE2.94 ± 0.94 ABCDEF
124.06 ± 0.89 ABCDE50.69 ± 14.39 ABCDE4909 ± 33 ABCDEFG3.17 ± 0.67 ABCD3.27 ± 0.79 ABCDE
623.67 ± 1.43 BCDEFGHIJ50.97 ± 15.94 ABCD4872 ± 42 ABCDEFG3.02 ± 0.79 ABCDE3.00 ± 1.00 ABCDEF
⋮⋮⋮⋮⋮⋮
43.15 ± 0.55 IJ37.23 ± 10.32 GF1980 ± 11 H2.56 ± 0.73 DE2.51 ± 0.70 F
753.19 ± 0.41 HIJ36.16 ± 9.50 GF1856 ± 26 H2.60 ± 0.34 CDE2.56 ± 0.51 EF
293.11 ± 0.50 J35.80 ± 11.52 G1854 ± 13 H2.37 ± 0.73 E2.53 ± 0.77 F
Mean3.66 ± 0.8444.34 ± 14.653552 ± 33042.87 ± 0.792.91 ± 0.81
F value2.782.322.741.992.33
Note: The same letter within a column indicates significant difference between means for each trait at p < 0.01 using Duncan’s test.
Table
Table 4. Classification of C. axillaris families based on growth performance.
Table 4. Classification of C. axillaris families based on growth performance.
TypesFamily CodesHeight
(m·a−1)		DBH
(mm·a−1)		Mean
Volume
(cm3·a−1·tree−1)		Crown Width
in Rows
(m·a−1)		Crown Width
between Rows
(m·a−1)
Ⅰ15, 76, 56, 49, 17, 5, 48, 18, 71, 12, 621.33 ± 0.3616.87 ± 5.745247 ± 3241.03 ± 0.281.05 ± 0.29
Ⅱ73, 24, 11, 9, 46, 63, 52, 10, 45, 42, 47, 68, 36, 50, 67, 32, 25, 51, 41, 16, 20, 64, 31, 65, 55, 59, 54, 40, 44, 35, 33, 28, 2, 30, 6, 66, 74, 34, 22, 43, 7, 8, 23, 72, 14, 1, 39, 77, 31.23 ± 0.2714.81 ± 4.883540 ± 490.96 ± 0.270.97 ± 0.28
Ⅲ61, 69, 37, 21, 13, 60, 27, 38, 70, 57, 19, 53, 26, 58, 4, 75, 291.15 ± 0.2113.50 ± 3.862532 ± 3390.91 ± 0.230.90 ± 0.23
Mean1.22 ± 0.2814.78 ± 4.883562 ± 9170.96 ± 0.260.97 ± 0.27
Table
Table 5. Height growth stages for three types of C. axillaris families.
Table 5. Height growth stages for three types of C. axillaris families.
Growth StageMonthGrowth Level in One Month (m)
Rapid Growth
Type Ⅰ		Mean Growth
Type Ⅱ		Slow Growth
Type Ⅲ
Lag-growing stageJanuary–February0.00 ± 0.00 B0.12 ± 0.04 C0.08 ± 0.06 D
First fast-growing stageMarch–May0.16 ± 0.11 A0.18 ± 0.13 A0.13 ± 0.14 B
First slow-growing stageJune–July0.19 ± 0.14 A0.16 ± 0.10 B0.10 ± 0.08 BC
Second fast-growing stageAugust0.23 ± 0.18 A0.17 ± 0.12 A0.17 ± 0.10 A
Second slow-growing stageSeptember–December0.08 ± 0.08 B0.07 ± 0.06 C0.05 ± 0.04 CD
Table
Table 6. Growth stage divisions of DBH for three growth types in C. axillaris.
Table 6. Growth stage divisions of DBH for three growth types in C. axillaris.
Growth StageMonthGrowth Level in One Month (m)
Rapid Growth
Type Ⅰ		Mean Growth
Type Ⅱ		Slow Growth
Type Ⅲ
Lag-growing stageJanuary0.00 ± 0.00 E0.00 ± 0.00 E0.00 ± 0.00 D
First slow-growing stageFebruary–March1.91 ± 0.98 CD1.78 ± 1.06 C1.37 ± 0.64 BC
First fast-growing stageApril4.58 ± 3.24 A4.35 ± 1.97 A3.52 ± 1.61 A
Second slow-growing stageMay–July3.14 ± 1.72 BC2.67 ± 1.75 C2.17 ± 1.54 B
Second fast-growing stageAugust3.78 ± 2.25 AB3.23 ± 1.97 B3.63 ± 1.83 A
Third slow-growing stageSeptember–December1.20 ± 0.74 DE1.28 ± 1.28 D1.20 ± 0.95 CD
Table
Table 7. Growth stage divisions of crown width in rows for three types of C. axillaris families.
Table 7. Growth stage divisions of crown width in rows for three types of C. axillaris families.
Growth StageMonthGrowth Level in One Month (m)
Rapid Growth
Type Ⅰ		Mean Growth
Type Ⅱ		Slow Growth
Type Ⅲ
Lag-growing stageJanuary–February0.07 ± 0.06 B0.08 ± 0.09 C0.24 ± 0.34 B
First fast-growing stageMarch–April0.32 ± 0.15 A0.31 ± 0.15 A0.25 ± 0.13 A
First slow-growing stageMay–June0.24 ± 0.16 B0.18 ± 0.14 B0.12 ± 0.10 B
Second fast-growing stageJuly–August0.31 ± 0.20 A0.31 ± 0.22 A0.25 ± 0.17 A
Second slow-growing stageSeptember–December0.12 ± 0.12 B0.11 ± 0.11 BC0.07 ± 0.07 B
Table
Table 8. Growth stage divisions of crown width between rows for three types of C. axillaris families.
Table 8. Growth stage divisions of crown width between rows for three types of C. axillaris families.
Growth StageMonthGrowth Level in One Month (m)
Rapid Growth
Type Ⅰ		Average Growth
Type Ⅱ		Slow Growth
Type Ⅲ
Lag-growing stageJanuary–February0.07 ± 0.06 C0.10 ± 0.10 D0.16 ± 0.21 B
First fast-growing stageMarch–April0.27 ± 0.09 A0.30 ± 0.14 A0.23 ± 0.10 A
First slow-growing stageMay–June0.27 ± 0.22 B0.23 ± 0.18 C0.10 ± 0.08 B
Second fast-growing stageJuly–August0.23 ± 0.14 AB0.23 ± 0.13 B0.21 ± 0.12 A
Second slow-growing stageSeptember–December0.12 ± 0.12 C0.10 ± 0.11 D0.06 ± 0.05 B
Table
Table 9. The correlation analysis between growth traits of C. axillaris and meteorological factors of the previous month.
Table 9. The correlation analysis between growth traits of C. axillaris and meteorological factors of the previous month.
Factors Mean
Monthly Air
Temperature 		Mean Monthly
Highest Air
Temperature 		Mean Monthly
Lowest Air
Temperature 		Monthly
Rainfall 		Monthly
Sunny Hours 		Relative Air
Humidity
Height0.26250.30980.24730.14180.4155 *0.1927
DBH0.20930.26180.18820.08660.4018 *0.1535
Volume0.28300.36080.24040.02820.5842 ***−0.0426
Crown width in rows0.29400.36060.25690.11400.5021 **0.0709
Crown width between rows0.29660.35360.26720.11900.4564 **0.1293
Note: * significant level at p < 0.10; ** p < 0.05; *** p < 0.01. r (18,0.01) = 0.561, r (18,0.05) = 0.444, r (18,0.10) = 0.378.
Table
Table 10. The correlation analysis between monthly growth level of C. axillaris and meteorological factors of the previous month.
Table 10. The correlation analysis between monthly growth level of C. axillaris and meteorological factors of the previous month.
Factors Mean
Monthly Air
Temperature 		Mean Monthly
Highest Air
Temperature 		Mean Monthly
Lowest Air
Temperature 		Monthly
Rainfall 		Monthly
Sunny Hours 		Relative Air
Humidity
Monthly height growth0.11830.07040.11670.4679 **−0.4203 *0.2619
Monthly DBH growth0.30820.23960.32690.6056 ***−0.32390.3657
Monthly volume0.4521 **0.3942 *0.4776 **0.5359 **−0.17790.6901 ***
Monthly crown width between tree−0.2136−0.2157−0.2282−0.0499−0.30490.0211
Monthly crown width between row−0.1891−0.2121−0.2017−0.0265−0.4608 **0.1825
Note: * significant level at p < 0.10; ** p < 0.05; *** p < 0.01. r (18,0.01) = 0.561, r (18,0.05) = 0.444, r (18,0.10) = 0.378.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Li, G.; Xu, J.; Li, J.; Lu, C.; Lu, H.; Mai, B.; Luo, M.; Fan, C. Growth Rhythm Analysis of Young Stand and Selection of Superior Families in Choerospondias axillaris. Forests 2022, 13, 2145. https://doi.org/10.3390/f13122145

AMA Style

Li G, Xu J, Li J, Lu C, Lu H, Mai B, Luo M, Fan C. Growth Rhythm Analysis of Young Stand and Selection of Superior Families in Choerospondias axillaris. Forests. 2022; 13(12):2145. https://doi.org/10.3390/f13122145

Chicago/Turabian Style

Li, Guangyou, Jianmin Xu, Juan Li, Canzhang Lu, Haifei Lu, Baoying Mai, Mingdao Luo, and Chunjie Fan. 2022. "Growth Rhythm Analysis of Young Stand and Selection of Superior Families in Choerospondias axillaris" Forests 13, no. 12: 2145. https://doi.org/10.3390/f13122145

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop