Research on the Growth Model of a 21-Year-Old Betula alnoides Plantation with Different Planting Densities

doi:10.13466/j.cnki.lyzygl.2023.04.007

Abstract

Abstract:

In order to understand the effect of planting density on the growth model of Betula alnoides plantation,analytical trees were used to analyze its growth dynamics,to enrich the information related to plantation and provide the scientific basis for cultivation management.In Jiangcheng County,Pu'er City,Yunnan Province,based on the investigation for a 21-year-old B.alnoides plantation of established by different initial uneven row spacing(2 m×2 m×5 m,2 m×3 m×5 m,3 m×3 m×5 m and 2 m×2 m×10 m),the best growth models of the trees with different planting densities was analyzed based upon the 12 standard trees selected from each tree measure.The results showed that mean diameters at the breast height(DBHs)under bark,tree heights(THs)and timber volume under bark of the different planting densities for the 21-year-old stand trees were 16.8~17.5 cm,26.7~28.0 m and 0.274 3~0.333 8 m³,respectively,and the trees were still in vigorous growth stage.The peak growth differentiation of DBHs,THs,and timber volume at different planting densities was observed during 9~12,9~11 and 8~15-year-old,respectively,when the planting densities had significant (P=0.025~0.046<0.05,P=0.001~0.006<0.01) effect on the total volume increment.The best fitting predictive models for DBHs,THs and timber volume-ages of the different planting densities were generally dominated by the Richards model and Gompertz model,in which Richards model can be used for the growth analysis and prediction of DBHs,THs,and timber volume of B.alnoides plantation.

Key words: Betula alnoides, uneven row spacing, growth model

CLC Number:

S758.5

WANG Wenjun, LI Lianfang, LI Xiaojun, HOU Haixiong, LIU Xian, ZHANG Heyao, GU Meng, ZHOU Dongmei. Research on the Growth Model of a 21-Year-Old Betula alnoides Plantation with Different Planting Densities[J]. FOREST RESOURCES WANAGEMENT, 2023, 0(4): 53-61.

Figures/Tables 11

Fig.1

Tab.1

Tab.2

Tab.3

Models for growth fitting

模型	方程	变量	参数
Richards	y=a(1- $e - b x$ )^c	x,y	a,b,c
Weibull	y=a(1- $e - (x - b c) d$ )	x, y	a,b,c,d
Logistic	y=a/(1+be^-^cx)	x, y	a,b,c
Gompertz	y=a $e - b e - c x$	x, y	a,b,c
幂函数	y=ax^b	x, y	a,b
二次函数	y=a+bx+cx²	x, y	a,b,c
三次函数	y=a+bx+cx²+dx³	x, y	a,b,c,d

Tab.3

Fig.2

Tab.4

The DHB-age growth model of different planting densities

密度(株行距)	模型	方程	R²	SSE	拐点林龄/a
2 m×2 m×5 m	三次函数	y=-2.6+1.5x-3.0E-02x²+1.6E-04x³	0.969	48.3	—
2 m×2 m×5 m	Richards	y=20.25(1- $e - 0.112 x$ )^1.915	0.968	49.8	5.8
2 m×3 m×5 m	Richards	y=18.75(1- $e - 0.172 x$ )^3.021	0.992	14.9	6.4
3 m×3 m×5 m	Richards	y=22.28(1- $e - 0.101 x$ )^2.062	0.981	32.3	7.2
2 m×2 m×10 m	Gompertz	y=18.83 $e - 3.996 e - 0.177 x$	0.989	19.6	7.8
2 m×2 m×10 m	Richards	y=20.54(1- $e - 0.124 x$ )^2.216	0.988	20.3	6.4

Tab.4

Fig.3

Tab.5

The THs-ages growth model of different planting densities

密度(株行距)	模型	方程	R²	SSE	拐点林龄/a
2 m×2 m×5 m	Gompertz	y=28.933 $e - 3.845 e - 0.186 x$	0.950	248.7	7.2
2 m×2 m×5 m	Richards	y=31.34(1- $e - 0.129 x$ )^2.101	0.950	248.8	5.8
2 m×3 m×5 m	Richards	y=29.91(1- $e - 0.159 x$ )^2.153	0.980	99.5	4.8
3 m×3 m×5 m	Richards	y=30.79(1- $e - 0.151 x$ )^2.132	0.982	89.8	5.0
2 m×2 m×10 m	三次函数	y=-0.838+1.716x+0.024x²-0.002x³	0.971	128.3	—
2 m×2 m×10 m	Richards	y=31.54(1- $e - 0.116 x$ )^1.672	0.971	131.2	4.4

Tab.5

Fig.4

Tab.6

Tab.7

The volume-ages growth model of different planting densities

密度(行间距)	模型	方程	R²	SSE	拐点林龄/a
2 m×2 m×5 m	Richards	y=0.652 9(1- $e - 0.093 x$ )^5.609	0.955	0.025 2	18.5
2 m×2 m×5 m	Gompertz	y=0.53 $e - 9.218 e - 0.127 x$	0.955	0.025 1	17.5
2 m×3 m×5 m	Richards	y=0.550 1(1- $e - 0.118 x$ )^5.735	0.986	0.011 3	14.8
2 m×3 m×5 m	Gompertz	y=0.489 $e - 8.873 e - 0.149 x$	0.986	0.011 4	14.6
3 m×3 m×5 m	Richards	y=0.663 5(1- $e - 0.101 x$ )^5.786	0.981	0.013 4	17.4
3 m×3 m×5 m	Gompertz	y=0.554 $e - 9.326 e - 0.133 x$	0.981	0.013 4	16.8
2 m×2 m×10 m	Richards	y=0.466 3(1- $e - 0.143 x$ )^7.703	0.980	0.015 4	14.3
2 m×2 m×10 m	Gompertz	y=0.435 $e - 10.843 e - 0.166 x$	0.980	0.015 4	14.4

Tab.7

References 27

[1]	雷相东, 李永慈, 向玮. 基于混合模型的单木断面积生长模型[J]. 林业科学, 2009, 45(1):74-79.
[2]	施拥军, 刘恩斌, 周国模, 等. 基于随机过程的毛竹笋期生长模型构建及应用[J]. 林业科学, 2013, 49(9):89-93.
[3]	中国科学院中国植物志编辑委员会. 中国植物志[M]. 北京: 科学出版社, 1979:108.
[4]	曾杰, 郑海水, 翁启杰. 我国西南桦的地理分布与适生条件[J]. 林业科学研究, 1999, 12(5):479-484.
[5]	Giri K, Pandey R, Jayaraj R S C, et al. Regression equations for estimating tree volume and biomass of important timber species in Meghalaya,India[J]. Current Science, 2019, 116(1):75-81. doi: 10.18520/cs/v116/i1/75-81
[6]	Mäkinen Harri, Pekka Nöjd, Pekka Saranpää. Seasonal changes in stem radius and production of new tracheids in Norway spruce[J]. Tree physiology, 2003, 23(14):959-968. pmid: 12952782
[7]	Ahmed A K M, Fu Zhixiang, Ding Changjun, et al. Growth and wood properties of a 38-year-old Populus simonii× P.nigra plantation established with different densities in semi-arid areas of northeastern China[J]. Journal of Forestry Research, 2020, 31(2):497-506. doi: 10.1007/s11676-019-00887-z
[8]	Zhao Dehai, Kane M, Borders B E. Growth responses to planting density and management intensity in loblolly pine plantations in the southeastern USA Lower Coastal Plain[J]. Annals of Forest Science, 2011, 68(3):625-635. doi: 10.1007/s13595-011-0045-7
[9]	Charlton R A, Naghizadeh Z, Ham C, et al. A value chain comparison of Pinus patula sawlog management regimes based on different initial planting densities and effect on wood quality[J]. Forest Policy and Economics, 2020, 111:102067. doi: 10.1016/j.forpol.2019.102067
[10]	Wang Chunsheng, Zhao Zhigang, Hein S, et al. Effect of planting density on knot attributes and branch occlusion of Betula alnoides under natural pruning in southern China[J]. Forests, 2015, 6(4):1343-1361. doi: 10.3390/f6041343
[11]	Tang Cheng, Wang Chunsheng, Pang Shengjiang, et al. Stem taper equations for Betula alnoides in South China[J]. Journal of Tropical Forest Science, 2017, 29(11):80-92.
[12]	李莲芳, 刘永刚, 孟梦, 等. 人工林培育中非均匀密度的概念、理论及其实践基础[J]. 西部林业科学, 2007(1):30-33.
[13]	李莲芳, 孟梦, 方波, 等. 西南桦与高阿丁枫混交幼林生长及其混交匹配性分析[J]. 西部林业科学, 2012, 41(1):17-23.
[14]	云南省气象局. 云南气候图册[M]. 昆明: 云南人民出版社, 1982:3-13.
[15]	孟宪宇. 测树学[M]. 3版. 北京: 中国林业出版社, 2006:171-201.
[16]	魏国余, 覃德文, 孙灿岳, 等. 麻栎人工林生长规律模拟与研究[J]. 西北林学院学报, 2014, 29(4):145-150.
[17]	廖美振, 温红芳, 董南松, 等. 陈山红心杉人工林生长过程及其模型模拟[J]. 中南林业科技大学学报, 2018, 38(9):107-114.
[18]	李春明, 张会儒. 利用非线性混合模型模拟杉木林优势木平均高[J]. 林业科学, 2010, 46(3):89-95.
[19]	聂稳, 江泽平, 刘逸夫, 等. 云杉属4个树种不同分布区生长过程及树皮厚度模拟研究[J]. 西北林学院学报, 2022, 37(5):95-103.
[20]	李春义, 姚光刚, 陈文婧, 等. 甘肃小陇山白皮松生长模型研究[J]. 中南林业科技大学学报, 2018, 38(2):70-75.
[21]	Harms W R, Whitesell C D, DeBell D S. Growth and development of loblolly pine in a spacing trial planted in Hawaii[J]. Forest Ecology and Management, 2000, 126(1):13-24. doi: 10.1016/S0378-1127(99)00079-1
[22]	王春胜, 赵志刚, 曾冀, 等. 广西凭祥西南桦中幼林林木生长过程与造林密度的关系[J]. 林业科学研究, 2013, 26(2):257-262.
[23]	陶玉华, 赵峰, 庞正轰, 等. 西南桦人工林生长规律研究[J]. 西北林学院学报, 2019, 34(1):180-183.
[24]	许林红, 刘际梅, 杨德军. 西南桦树冠与胸径生长关系研究[J]. 四川林业科技, 2018, 39(4):34-36.
[25]	Ferraz Filho A C, Mola-Yudego B, González-Olabarria J R, et al. Thinning regimes and initial spacing for Eucalyptus plantations in Brazil[J]. Anais da Academia Brasileira de Ciencias, 2018, 90(1):255-265. doi: 10.1590/0001-3765201720150453
[26]	徐期瑚, 林丽平, 薛春泉, 等. 基于树高-年龄分级的广东枫香生长模型[J]. 林业资源管理, 2018(5):47-53.
[27]	林丽平, 徐期瑚, 薛春泉, 等. 基于树高—年龄分级的广东木荷生长模型研究[J]. 西南林业大学学报:自然科学, 2018, 38(4):126-132.

造林的株行距	初植密度/(株/hm²)	保存密度/(株/hm²)
2 m×2 m×5 m	1 429	674
2 m×3 m×5 m	1 250	594
3 m×3 m×5 m	833	563
2 m×2 m×10 m	833	533

株行距	胸径/cm			树高/m			材积/m³
株行距	平均	最小值	最大值	平均	最小值	最大值	平均	最小值	最大值
2 m×2 m×5 m	18.5	17.9	19.6	26.8	22.4	29.5	0.326 2	0.258 1	0.381 8
2 m×3 m×5 m	19.6	18.9	20.0	27.3	25.6	28.3	0.389 3	0.346 2	0.423 0
3 m×3 m×5 m	18.7	18.7	18.8	28.0	26.3	29.4	0.375 3	0.351 5	0.419 5
2 m×2 m×10 m	18.9	18.5	19.2	26.7	25.0	29.5	0.368 5	0.331 9	0.417 1

密度(株行距)	林龄/a
密度(株行距)	8	9	10	11	12	13	14	15
2 m×2 m×5 m	0.020 2^b	0.029 6^Bb	0.029 6^Bc	0.053 4^Bb	0.069 2^Bb	0.088 0^Bb	0.107 9^b	0.107 9^b
2 m×3 m×5 m	0.032 5^a	0.049 3^Aa	0.049 3^Aa	0.090 1^Aa	0.112 5^Aa	0.137 5^Aa	0.163 0^a	0.163 0^a
3 m×3 m×5 m	0.024 7^ab	0.034 7^Bb	0.034 7^ABbc	0.063 5^Bab	0.084 1^ABab	0.106 3^ABab	0.131 0^ab	0.131 0^ab
2 m×2 m×10 m	0.023 5^b	0.037 4^ABb	0.037 4^ABb	0.076 8^ABab	0.099 7^ABab	0.125 8^Aab	0.151 3^a	0.151 3^a