硬头黄竹地上生物量分配特征及模型构建

doi:10.12302/j.issn.1000-2006.202005008

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南京林业大学学报（自然科学版） ›› 2021, Vol. 45 ›› Issue (1): 189-196.doi: 10.12302/j.issn.1000-2006.202005008

硬头黄竹地上生物量分配特征及模型构建

王路君¹(), 蔡春菊¹^,²^,^*(), 唐晓鹿³, 范少辉¹

1.国际竹藤中心国家林业和草原局/北京市共建竹藤科学与技术重点开放实验室,北京 100102
2.四川长宁竹林生态系统定位观测研究站,四川宜宾 644000
3.成都理工大学生态环境学院,四川成都 610059

收稿日期:2020-05-07 接受日期:2020-06-25 出版日期:2021-01-30 发布日期:2021-02-01
通讯作者: 蔡春菊
基金资助:
国家重点研发计划(2018YFD0600103)

Aboveground biomass allocation patterns and model construction of Bambusa rigida

WANG Lujun¹(), CAI Chunju¹^,²^,^*(), TANG Xiaolu³, FAN Shaohui¹

1. International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration/Beijing for Bamboo & Rattan Science and Technology, Beijing 100102, China
2. Bamboo Forest Ecosystem Observation and Research Station of Sichuan Changning, Yibin 644000, China
3. College of Ecological Environment, Chengdu University of Technology, Chengdu 610059, China

Received:2020-05-07 Accepted:2020-06-25 Online:2021-01-30 Published:2021-02-01
Contact: CAI Chunju

摘要/Abstract

摘要：

【目的】探究硬头黄竹不同龄级、径级地上生物量分配特征,建立全竹龄和不同竹龄地上单株及各器官生物量模型,准确估算硬头黄竹的林分生物量。【方法】选取了硬头黄竹全径级(1.0~7.0 cm)分布的1、2、3年生硬头黄竹各50株,测定各器官和总生物量。采用11种常用生物量模型,分别对硬头黄竹全竹龄和不同竹龄地上单株和各器官生物量进行拟合,筛选最优生物量拟合方程,并应用模型估算不同龄级、径级林分总生物量。【结果】硬头黄竹地上竹秆、竹枝、竹叶生物量占比分别为84.82%、10.84%、4.34%;不同龄级单位面积林分总生物量差异显著,竹龄为1、2、3 a竹生物量占比分别为31.92%、47.15%、20.93%;4.6~5.5 cm径级各器官和总生物量显著高于其他径级,占林分生物量的62.60%。11种生物量模型均可以较好地模拟硬头黄竹地上单株及各器官生物量;优选出全竹龄硬头黄竹地上单株和各器官生物量模型6个,不同竹龄的硬头黄竹地上单株和各器官生物量模型19个(1 a的6个、2 a的7个、3 a的6个)。【结论】硬头黄竹不同龄级、径级各器官生物量占比均为竹秆>竹枝>竹叶,林分生物量主要集中在2龄级、4.6~5.5 cm径级的竹株。全竹龄和不同竹龄地上单株与各器官生物量拟合模型中幂函数的拟合效果最优,其次是多项式函数和指数函数;地上单株与竹秆生物量模型拟合效果受胸径、株高的影响较大,竹枝、竹叶生物量模型拟合效果与胸径关系更密切。全竹龄硬头黄竹地上单株和竹秆生物量模型拟合效果均优于不同竹龄的模型,不同竹龄硬头黄竹地上竹枝、竹叶生物量模型拟合效果均优于全竹龄模型的。

关键词: 硬头黄竹, 龄级, 径级, 分配特征, 生物量模型

Abstract:

【Objective】 We built biometric models to improve the estimation accuracy of aboveground biomass and its allocation patterns in Bambusa rigida for different ages, diameter classes and organs. 【Method】 After selecting B. rigida bamboo pure forests with similar topography, elevation, aspect and slope as the study case, the age and DBH of standing bamboo were investigated. A total of 150 B. rigida individuals(1 a, 2 a, 3 a) with full-diameter classes (1.0-7.0 cm) were selected, and the biomass of different organs and the total biomass were measured. Eleven types of biomass models were applied to model aboveground biomass for individual bamboos and for different organs of different ages of B. rigida. Taking DBH and height of bamboos, and their combined forms (D, D², DH and D²H) as independent variables, the R software was used to run the biomass models. The coefficient of determination (R²), root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the models. Finally, the biomass models with the best statistical performances were selected to estimate the total biomass of different ages and diameter classes. 【Result】 The biomass of the culm, branch and leaves of B. rigida made up 84.82%, 10.84% and 4.34% of the total individual bamboo biomass, respectively. Bamboos with a diameter class of 4.6-5.5 cm made up 62.60% of the stand biomass. All 11 types of biomass models can simulate the aboveground biomass of individual bamboo and organs. A total of 176 fitting equations were obtained, with R² ranging from 0.480 to 0.975, the RMSE ranging from 0.027-0.769 kg, and the MAE ranging from 0.021-0.589 kg. Six models of the aboveground biomass of individual bamboos and organs of all age classes were selected, and 19 models of aboveground biomass of individual bamboos and organs of different age classes were selected (6 of 1 a, 7 of 2 a, 6 of 3 a). 【Conclusion】 The proportion of biomass of different organs in different ages and diameter classes of B. rigida was in the order of culm > branch > leaf. With the increase in age, the proportion of bamboo stalk biomass decreased significantly, while the proportion of bamboo branch biomass increased significantly. In contrast, the proportion of bamboo stalk biomass generally increased, while the proportion of bamboo branches and leaf biomass generally decreased with increasing diameters. The stand biomass was dominated by the age class of 2 a or the diameter class of 4.6-5.5 cm. The fitting effect of the power function was the best, followed by the polynomial function and exponential function. The aboveground biomass of individual bamboos and culms was greatly affected by DBH and plant height, and the biomass of branches and leaves was more closely related to DBH. The model performances of the aboveground biomass models of individual bamboos and culms, regardless of age class, were better than those of different age classes, and the model performances of the aboveground biomass models of branch and leaf, regardless of age class, were better than those of different age classes. In this study, a large number of B. rigida were selected by refining age classes and diameter classes, and the biomass allocation patterns of B. rigida were studied in different diameter classes and age classes. Additionally, various mathematical models were used to construct and select the optimal models. Estimating stand biomass and further obtaining its stock and allocation patterns is important for refining biomass estimation and guiding production and management of bamboo forests.

Key words: Bambusa rigida, age class, diameter class, allocation patterns, biomass model

中图分类号:

S795

王路君,蔡春菊,唐晓鹿,等. 硬头黄竹地上生物量分配特征及模型构建[J]. 南京林业大学学报（自然科学版）, 2021, 45(1): 189-196.

WANG Lujun, CAI Chunju, TANG Xiaolu, FAN Shaohui. Aboveground biomass allocation patterns and model construction of Bambusa rigida[J].Journal of Nanjing Forestry University (Natural Science Edition), 2021, 45(1): 189-196.DOI: 10.12302/j.issn.1000-2006.202005008.

图/表 10

表1

表2

11种常用生物量模型"

模型类型 model type	自变量 independent variable	模型 model
幂函数 power function	D	W=aD^b
	DH	W=a(DH)^b
	D²H	W=a(D²H)^b
多项式函数 polynomial function	D	W=a+bD+cD²
	D²	W=a+bD²+cD⁴
	DH	W=a+bDH+c(DH)²
	D²H	W=a+bD²H+c(D²H)²
指数函数 exponential function	D	W=ae^bD
	D²	W=a $e b D 2$
	DH	W=ae^bDH
	D²H	W=a $e b D 2 H$

表2

图1

图2

表3

表4

表5

表6

表7

表8

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编号 No.	海拔/m altitude	坡度/(°) slope	坡向 aspect	丛密度/ (丛·hm^-2) clump density	丛株数 quantity	平均株高/m mean plant height	平均胸径/cm mean DBH
1	300	30	阳坡	907	7.6	12.03±1.89	4.83±0.73
2	312	35	阳坡	852	8.3	11.78±2.01	4.89±0.79
3	333	33	阳坡	1 500	7.0	12.35±2.45	4.85±0.75
4	290	28	阳坡	1 425	7.2	12.30±2.35	4.85±0.73
5	325	29	阳坡	852	7.5	12.55±2.20	4.90±0.83

函数类型model type	R²	RMSE/kg	MAE/kg
幂函数 power function	0.714~0.975	0.177~0.437	0.136~0.364
多项式函数 polynomial function	0.546~0.940	0.033~0.600	0.026~0.473
指数函数exponential function	0.480~0.845	0.423~0.673	0.324~0.518

自变量 independent variable	单株single bamboo	秆 stalk	枝 branch	叶 leaf
D	0.845~0.950	0.834~0.945	0.750~0.832	0.567~0.724
D²	0.843~0.924	0.837~0.917	0.657~0.760	0.570~0.646
DH	0.818~0.973	0.807~0.975	0.701~0.821	0.546~0.714
D²H	0.660~0.971	0.647~0.971	0.574~0.831	0.480~0.722

竹龄/a bamboo age	器官 organ	函数类型 model type	自变量 independent variable	方程 fitting equation	R²	RMSE/kg	MAE/kg
全竹龄 whole age classes	单株	幂函数	DH	W=0.11 4(DH)¹^.^{411 1}	0.973^**	0.177	0.136
	单株	幂函数	D	W=0.107 0D²^.^{334 1}	0.950^**	0.240	0.185
	竹秆	幂函数	DH	W=0.007 6(DH)¹^.^{489 1}	0.975^**	0.180	0.141
	竹秆	幂函数	D	W=0.081 1D²^.^{450 0}	0.946^**	0.264	0.202
	竹枝	幂函数	D	W=0.015 2D^-4^.^{188 5}	0.832^**	0.355	0.266
	竹叶	幂函数	D	W=0.008 8D¹^.^{545 7}	0.724^**	0.429	0.355
1	单株	幂函数	D²H	W=0.032 9D²H⁰^.^{846 7}	0.954^**	0.233	0.180
	单株	幂函数	D	W=0.114 8D²^.^{308 3}	0.945^**	0.253	0.199
	竹秆	幂函数	D²H	W=0.021 1D²H⁰^.^{909 1}	0.965^**	0.217	0.174
	竹秆	幂函数	D	W=0.081 8D²^.^{467 1}	0.948^**	0.264	0.203
	竹枝	幂函数	D	W=0.014 5D¹^.^{827 2}	0.833^**	0.368	0.273
	竹叶	幂函数	D	W=0.007 9D¹^.^{654 3}	0.769^**	0.408	0.331
2	单株	多项式	D²H	W=0.199 5+0.016 2D²H+ 0.000 004(D²H²)	0.955^**	0.583	0.422
	单株	幂函数	D²	W=2.334 4D⁰^.^{120 0}	0.944^**	0.181	0.138
	竹秆	多项式	D²H	W=0.191 6+0.012 9D²H+ 0.000 000 2(D²H)²	0.964^**	0.456	0.322
	竹秆	幂函数	D²	W=2.415 1D⁰^.^{088 9}	0.938^**	0.198	0.153
	竹枝	幂函数	D	W=0.016 7D²^.¹⁸³	0.774^**	0.365	0.290
	竹叶	多项式	DH	W=0.115 8+0.003 9DH+ 0.000 08(DH)²	0.863^**	0.056	0.043
	竹叶	幂函数	D	W=2.438D⁰^.^{004 8}	0.807^**	0.370	0.298
3	单株	幂函数	DH	W=0.027 6DH¹^.^{258 7}	0.951^**	0.220	0.153
	单株	幂函数	D	W=0.124 3D²^.^{366 3}	0.932^**	0.259	0.169
	竹秆	幂函数	DH	W=0.017 5DH¹^.^{320 2}	0.964^**	0.196	0.137
	竹秆	幂函数	D	W=0.056D²^.^{475 5}	0.940^**	0.253	0.178
	竹枝	幂函数	D	W=0.349 6D¹^.^{786 2}	0.814^**	0.353	0.294
	竹叶	指数函数	D	W=0.009 1e^{0.629 1}^D	0.791^**	0.472	0.387

竹龄 bamboo age	器官organ	R²	RMSE/kg	MAE/kg
全竹龄 whole age	单株single bamboo	0.660~0.973	0.177~0.627	0.136~0.484
	竹秆stalk	0.647~0.975	0.180~0.673	0.141~0.518
	竹枝branch	0.574~0.832	0.057~0.565	0.043~0.411
	竹叶leaf	0.480~0.724	0.033~0.588	0.026~0.492
不同竹龄 different age	单株single bamboo	0.654~0.955	0.233~0.769	0.180~0.589
	竹秆stalk	0.644~0.965	0.217~0.688	0.169~0.544
	竹枝branch	0.633~0.833	0.173~0.601	0.133~0.440
	竹叶leaf	0.535~0.863	0.027~0.444	0.021~0.295

硬头黄竹地上生物量分配特征及模型构建

Aboveground biomass allocation patterns and model construction of Bambusa rigida

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竹龄/a bamboo age	立竹密度/ (株·hm^-2) quantity	林分生物量/(t·hm^-2) biomass
竹龄/a bamboo age	立竹密度/ (株·hm^-2) quantity	秆 stalk	枝 branch	叶 leaf	总生物量 total biomass
1	2 930±456 b	11.408±2.274 b (91.61、33.68)	0.740±0.167 b (5.94、19.71)	0.305±0.064 c (2.45、22.10)	12.560±2.505 b (100、31.92)
2	3 641±580 a	15.658±5.602 a (85.13、46.23)	2.017±0.721 a (10.97、53.69)	0.717±0.256 a (3.90、51.96)	18.407±6.580 a (100、47.15)
3	1 681±334 c	6.805±1.552 c (83.36、20.09)	1.000±0.354 c (12.25、26.62)	0.358±0.154 b (4.39、25.94)	8.163±2.450 c (100、20.93)
总量	8 252±1 488	33.871±8.353 (86.83、100)	3.757±0.909 (9.63、100)	1.380±0.332 (3.54、100)	39.008±9.593 (100、100)

径级/cm diameter	竹林密度/ (株·hm^-2) density	平均株高/m mean height	林分生物量/(t·hm^-2) stand biomass
径级/cm diameter	竹林密度/ (株·hm^-2) density	平均株高/m mean height	秆 stalk	枝 branch	叶 leaf	总生物量 total biomass
1.6~2.5	111±47 e	6.40 e	0.047±0.024 d (80.73 b、0.14 d)	0.008±0.003 c (14.04 a、0.21 c)	0.003±0.001 c (2.74 b、0.22 c)	0.058±0.028 c (100、0.15 c)
2.6~3.5	314±88 d	10.08±2.35 d	0.442±0.118 c (85.75 a、1.30 c)	0.054±0.017 c (10.29 b、1.44 c)	0.021±0.005 c (3.95 a、1.52 c)	0.517±0.140 c (100、1.33 c)
3.6~4.5	1 948±616 b	12.21±2.89 c	5.402±1.830 b (86.19 a、15.95 b)	0.635±0.212 b (10.15 b、16.90 b)	0.228±0.074 b (3.65 a、16.52 b)	6.265±2.116 b (100、16.06 b)
4.6~5.5	4 589±1 742 a	13.73±3.44 b	21.265±6.237 a (87.10 a、62.78 a)	2.311±0.708 a 9.45 b、61.51 a	0.844±0.254 a (3.45 a、61.16 a)	24.420±7.195 a (100、62.60 a)
5.6~6.5	1 081±333 c	15.07±4.10 a	6.715±2.209 b (86.61 a、19.83 b)	0.749±0.233 b (9.73 b、19.94 b)	0.284±0.099 b (3.66 a、20.58 b)	7.748±2.537 b (100、19.86 b)
总量	8 043±1 827	12.02±3.12	33.871±8.353 (86.83、100)	3.757±0.909 (9.63、100)	1.380±0.332 (3.54、100)	39.008±9.593 (100、100)

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