基于3-PG模型的长白落叶松生物量生长预测

doi:10.11707/j.1001-7488.20210307

Abstract

Abstract:

Objective: This study was executed to predict the biomass growth of Larix olgensis based on 3-PG model in order to provide the basis for studying its growth rules. Method: The data used in this article was obtained from 5 plots of experimental forests continuously observed for 28 years and 24 plots with 3 re-measurements. The stem density, DBH(diameter at breast height), volume and biomass of each plot in different times were calculated using the biomass calculation formula for each component (leaf, stem and root). The physiological parameters of the 3-PG model of L. olgensis were calibrated by density data. And the parameter values were determined based on soil data and meteorological data through parameter calibration, iterative fitting and sensitivity analysis. The result accuracy was examined by calculating coefficient of determination (R²), mean error(ME), mean absolute error(MAE), mean relative error(MRE), and root mean square error(RMSE). Two factors, the canopy quantum efficiency(alpha) and minimum biomass fraction of NPP to roots (pRn), were chosen to procced sensitivity analysis. Then, the growth biomass of L. olgensis was predicted under the conditions of the fertility ratings(FR) being 0.2, 0.4 and 0.6. Result: 1) The prediction results were reliable and the coefficient of determination (R²) was above 0.77. The absolute values of MRE of all the other indicators were within 10.97%, except for the foliage to stem biomass ratio with a value of 25.6%. 2) The sensitivity analysis showed that alpha and pRn were the key parameters of the model with high sensitivities. 3) The predicted biomass growth in this study was consistent with the growth mechanisms of plants under different FRs. At the same time, the biomass of larch was increased with the growth of FR. Conclusion: After the parameter calibration based on field data, the 3-PG model could simulate the biomass growth of L. olgensis well and be used as an effective forest management tool. For the 3-PG model of L. olgensis, alpha and pRn might be the key parameters affecting the prediction results.

Key words: Larix olgensis, 3-PG model, sensitivity analysis, biomass

CLC Number:

S757

Xiaoyun Xia,Yong Pang,Qingfeng Huang,Rong Wu,Dongsheng Chen,Yu Bai. Prediction of Biomass Growth of Larix olgensis Based on 3-PG Model[J]. Scientia Silvae Sinicae, 2021, 57(3): 67-78.

Figures/Tables 10

Table 1

Fig.1

Table 2

Table 3

Key parameters of 3-PG model of Larix olgensis"

类型Indicators	参数Parameter	值Value	来源Source
异速生长关系与分配 Allometric relationships and partitioning	胸径2 cm时叶干生物量比 Foliage to stem biomass ratio at DBH = 2 cm	0.5	拟合值Fitted
	胸径20 cm时叶干生物量分配比 Foliage: stem biomass ratio at DBH = 20 cm	0.4	拟合值Fitted
	干生物量与胸径关系中常数 Constant in the stem biomass v. DBH relationship	0.096 3	黄兴召(2014)
	干生物量与胸径关系中指数 Power in the stem biomass v. DBH relationship	2.443	黄兴召(2014)
	初级生物量分配到根的最大值 Maximum biomass fraction of NPP to roots	0.4	拟合值Fitted
	初级生物量分配到根的最小值 Minimum biomass fraction of NPP to roots	0.2	拟合值Fitted
凋落物与根更新 Litterfall and root turnover	最大凋落物速率 Maximum litterfall rate	0.033	拟合值Fitted
	林龄为0时的凋落物速率 Litterfall rate at age = 0	0.001	默认值Default
	凋落率中间值时的林龄 Age at which litterfall rate has median value	24	拟合值Fitted
	根的月平均凋落速率 Average monthly root turnover rate	0.007	拟合值Fitted
树龄调节 Age modifier	植物生长最大林龄 Maximum stand age used in age modifier	100	拟合值Fitted
	计算林龄对于生长关系式中相对林龄的指数 Power of relative age in function for f_age	5	拟合值Fitted
	林龄对生长影响等于0.5时的相对林龄 Relative age to give f_age = 0.5	0.5	拟合值Fitted
树干死亡与自我疏伐 Stem mortality and self-thinning	最大林龄的死亡率 Mortality rate for large age	0.6	拟合值Fitted
	在1 000株·hm^-2下每株树最大树干质量 Max. stem mass per tree @ 1 000 trees·hm^-2	212	拟合值Fitted
	自然稀疏法则指数Power in self-thinning rule	1.5	默认值Default
	达到郁闭度年龄Age at canopy cover	19	拟合值Fitted
冠层电导 Conductance	冠层量子效率Canopy quantum efficiency	0.056	López-Serrano et al.(2015)

Table 3

Fig.2

Table 4

Table 5

Fig.3

Fig.4

Fig.5

References 0

	陈东升, 孙晓梅, 李凤日. 基于混合模型的落叶松树高生长模型. 东北林业大学学报, 2013, 40 (10): 60- 64. doi: 10.3969/j.issn.1000-5382.2013.10.013
	Chen D S , Sun X M , Li F R . Models of tree height growth for larch based on mixed model. Journal of Northeast Forestry University, 2013, 41 (10): 60- 64. doi: 10.3969/j.issn.1000-5382.2013.10.013
	崔诗梦, 向玮. 间伐与气候对长白落叶松树轮宽度的影响. 林业科学, 2017, 53 (12): 1- 11. doi: 10.11707/j.1001-7488.20171201
	Cui S M , Xiang W . Effects of thinning and climate factors on Larix olgensis tree ring width. Scientia Silvae Sinicae, 2017, 53 (12): 1- 11. doi: 10.11707/j.1001-7488.20171201
	邓晓华, 张广福, 张怡春, 等. 长白落叶松人工林全林分生长模型的研究. 林业科技, 2003, 28 (1): 10- 12.
	Deng X H , Zhang G F , Zhang Y C , et al. Study on all stand growth model of Larix olgensis. Forestry Science and Technology, 2003, 28 (1): 10- 12.
	高慧淋, 董利虎, 李凤日. 基于混合效应的人工落叶松树冠轮廓模型. 林业科学, 2017, 53 (3): 84- 93.
	Gao H L , Dong L H , Li F R . Crown shape model for Larix olgensis plantation based on mixed effect. Scientia Silvae Sinicae, 2017, 53 (3): 84- 93.
	何丽鸿, 王海燕, 雷相东. 基于BIOME-BGC模型的长白落叶松林净初级生产力模拟参数敏感性. 应用生态学报, 2016, 27 (2): 412- 420.
	He L H , Wang H Y , Lei X D . Parameter sensitivity analysis of simulating net primary productivity of Larix olgensis forest based on BIOME-BGC model. Chinese Journal of Applied Ecology, 2016, 27 (2): 412- 420.
	花利忠, 江希钿, 贺秀斌. 2007. 3-PG模型在华南尾叶桉人工林的应用研究. 北京林业大学学报, 29(2): 100-104.
	Hua L Z, Jiang X D, He X B. 2007. Application of 3-PG model in Eucalyptus urophylla plantations of southern China, 29(2): 100-104. [in Chinese]
	黄华国, 窦宝成, 田园, 等. 2010. 应用过程模型3-PG模拟青海云杉生长. 北京: 中国科技论文在线. http://www.paper.edu.cn/releasepaper/content/201004-1022.
	Huang H G, Dou B C, Tian Y, et al. 2010. Application of the process-based forest growth model 3-PG to Picea crassifolia stand. Beijing: China Science Paper Online. http://www.paper.edu.cn/releasepaper/content/201004-1022. [in Chinese].
	黄兴召. 落叶松人工林生物量和碳储量研究. 北京: 中国林业科学研究院博士学位论文, 2014,
	Huang X Z . Biomass and carbon storage of larch plantation. Beijing: PhD thesis of Chinese Academy of Forestry, 2014,
	刘坤, 曹林, 汪贵斌, 等. 基于3-PG模型的杉木人工林各器官生物量和LAI估算. 西北农林科技大学学报: 自然科学版, 2015, 43 (9): 57- 64.
	Liu K , Cao L , Wang G B , et al. Estimating biomass components and LAI of Chinese fir plantation based on 3-PG model. Journal of Northwest A & F University: Natural Science Edition, 2015, 43 (9): 57- 64.
	马华文, 许翠清, 李海朝. 立地条件对长白落叶松光合特性的影响. 东北林业大学学报, 2008, 36 (8): 4- 7. doi: 10.3969/j.issn.1000-5382.2008.08.002
	Ma H W , Xu C Q , Li H C . Photosynthesis characteristics of Larix olgensis under different site conditions. Journal of Northeast Forestry University, 2008, 36 (8): 4- 7. doi: 10.3969/j.issn.1000-5382.2008.08.002
	孟宪宇. 长白落叶松直径分布收获模型的研究. 北京林业大学学报, 1991, 13 (4): 9- 16.
	Meng X Y . Study on the diameter distribution harvest model of Larix olgensis. Journal of Beijing Forestry University, 1991, 13 (4): 9- 16.
	闵志强. 长白落叶松生物量估测模型研究. 北京: 北京林业大学硕士学位论文, 2010,
	Min Z Q . Study on biomass estimation model of Larix olgensis. Beijing: MS thesis of Beijing Forestry University, 2010,
	孙志虎. 长白落叶松人工用材林长期生产力维持的研究. 哈尔滨: 东北林业大学博士学位论文, 2005,
	Sun Z H . On the Long-Term productivity Maintenance of Larix olgensis timber forest in northeastern China. Harbin: PhD thesis of Northeast Forestry University, 2005,
	王烁, 董利虎, 李凤日. 人工长白落叶松枝条存活模型. 北京林业大学学报, 2018, 40 (1): 57- 66.
	Wang S , Dong L H , Li F R . Branch survival models of planted Larix olgensis tree. Journal of Beijing Forestry University, 2018, 40 (1): 57- 66.
	吴明钦, 孙玉军, 郭孝玉, 等. 长白落叶松树冠体积和表面积模型. 东北林业大学学报, 2014, 42 (5): 1- 5. doi: 10.3969/j.issn.1000-5382.2014.05.001
	Wu M Q , Sun Y J , Guo X Y , et al. Predictive models o crown volume and crown surface area for korean larch. Journal of Northeast Forestry University, 2014, 42 (5): 1- 5. doi: 10.3969/j.issn.1000-5382.2014.05.001
	解雅麟, 王海燕, 雷相东. 基于3-PG模型的长白落叶松人工林生长和生物量模拟. 南京林业大学学报: 自然科学版, 2018, 42 (1): 141- 148.
	Xie Y L , Wang H Y , Lei X D . Growth and biomass simulation of Larix olgensis plantations based on the 3-PG model. Journal of Nanjing University: Natural Science Edition, 2018, 42 (1): 141- 148.
	赵梅芳. 基于3-PG机理模型的杉木林碳固定及蒸散量模拟研究. 长沙: 中南林业科技大学硕士学位论文, 2008,
	Zhao M F . Simulating Chinese fir plantation carbon storage and evapotranspiration using the 3-PG model. Changsha: MS thesis of Central South University of Forestry and Technology, 2008,
	朱智强, 蒋菊生. 3-PG生长模型及其在橡胶树栽培领域的应用. 热带农业科学, 2010, 30 (7): 4- 7. doi: 10.3969/j.issn.1009-2196.2010.07.002
	Zhu Z Q , Jiang J S . 3-PG growth model and its application in rubber cultivation. Chinese Journal of Tropical Agriculture, 2010, 30 (7): 4- 7. doi: 10.3969/j.issn.1009-2196.2010.07.002
	Almeida A C , Smethurst P J , Siggins A , et al. Quantifying the effects of Eucalyptus plantations and management on water resources at plot and catchment scales. Hydrological Processes, 2016, 30 (25): 4687- 4703. doi: 10.1002/hyp.10992
	Bryars C , Maier C , Zhao D , et al. Fixed physiological parameters in the 3-PG model produced accurate estimates of loblolly pine growth on sites in different geographic regions. Forest Ecology and Management, 2013, 289, 501- 514. doi: 10.1016/j.foreco.2012.09.031
	Coops N C , Hember R A , Waring R H . Assessing the impact of current and projected climates on douglas-fir productivity in British Columbia, Canada, using a process-based model (3-PG). Canadian Journal of Forest Research, 2010, 40 (3): 511- 524. doi: 10.1139/X09-201
	Coops N C , Waring R H , Law B E . Assessing the past and future distribution and productivity of ponderosa pine in the Pacific Northwest using a process model, 3-PG. Ecological Modelling, 2005, 183 (1): 107- 124. doi: 10.1016/j.ecolmodel.2004.08.002
	Dye P J , Jacobs S , Drew D . Verification of 3-PG growth and water-use predictions in twelve Eucalyptus plantation stands in Zululand, South Africa. Forest Ecology & Management, 2004, 193 (1/2): 197- 218.
	Dye P J . Modelling growth and water use in four Pinus patula stands with the 3-PG model. Southern African Forestry Journal, 2001, 191 (1): 53- 63. doi: 10.1080/20702620.2001.10434151
	Forrester D I , Tang X . Analysing the spatial and temporal dynamics of species interactions in mixed-species forests and the effects of stand density using the 3-PG model. Ecological Modelling, 2015, 319, 233- 254.
	Gonzalez-Benecke C A , Jokela E J , Cropper W P , et al. Parameterization of the 3-PG model for Pinus elliottii stands using alternative methods to estimate fertility rating, biomass partitioning and canopy closure. Forest Ecology and Management, 2014, 327, 55- 75. doi: 10.1016/j.foreco.2014.04.030
	Hung T T , Almeida A C , Eyles A , et al. Predicting productivity of Acacia hybrid plantations for a range of climates and soils in Vietnam. Forest Ecology and Management, 2016, 367, 97- 111. doi: 10.1016/j.foreco.2016.02.030
	Jégo G , Thibodeau F , Morissette R , et al. Estimating the yield potential of short-rotation willow in Canada using the 3-PG model. Canadian Journal of Forest Research, 2017, 47 (5): 636- 647. doi: 10.1139/cjfr-2016-0353
	Landsberg J J , Waring R H , Coops N C . Performance of the forest productivity model 3-PG applied to a wide range of forest types. Forest Ecology and Management, 2003, 172 (2/3): 199- 214.
	Landsberg J J , Waring R H . A generalized model of forest productivity using simplified concepts of radiation-use efficiency, carbon balance and partitioning. Forest Ecology and Management, 1997, 95 (3): 209- 228. doi: 10.1016/S0378-1127(97)00026-1
	López-Serrano F R , Martínez-García E , Dadi T , et al. Biomass growth simulations in a natural mixed forest stand under different thinning intensities by 3-PG process-based model. European Journal of Forest Research, 2015, 134 (1): 167- 185. doi: 10.1007/s10342-014-0841-3
	Minunno F , Xenakis G , Perks M P , et al. Calibration and validation of a simplified process-based model for the prediction of the carbon balance of Scottish Sitka spruce (Picea sitchensis) plantations. Canadian Journal of Forest Research, 2010, 40 (12): 2411- 2426. doi: 10.1139/X10-181
	Monteith J L . Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 1972, 9 (3): 747- 766. doi: 10.2307/2401901
	Reineke L H . Perfecting a stand-density index for even-age forests. Journal of Agricultural Research, 1933, 46 (1): 627- 638.
	Sampson D A , Waring R H , Maier C A , et al. Fertilization effects on forest carbon storage and exchange, and net primary production: a new hybrid process model for stand management. Forest Ecology and Management, 2006, 221 (1): 91- 109.
	Sands P J , Landsberg J J . Parameterisation of 3-PG for plantation grown Eucalyptus globulus. Forest Ecology and Management, 2002, 163 (1): 273- 292.
	Stape J L , Ryan M G , Binkley D . Testing the utility of the 3-PG model for growth of Eucalyptus grandis×E. urophylla with natural and manipulated supplies of water and nutrients. Forest Ecology and Management, 2004, 193 (1/2): 219- 234.
	Subedi S , Fox T R . Modeling repeated fertilizer response and one-time midrotation fertilizer response in loblolly pine plantations using FR in the 3-PG process model. Forest Ecology and Management, 2016, 380, 90- 99.
	Waring R H . A process model analysis of environmental limitations on the growth of Sitka spruce plantations in Great Britain. Forestry, 2000, 73 (1): 65- 79.
	Xenakis G , Ray D , Mencuccini M . Sensitivity and uncertainty analysis from a coupled 3-PG and soil organic matter decomposition model. Ecological Modelling, 2008, 219 (1): 1- 16.
	Zhao M , Xiang W , Peng C , et al. Simulating age-related changes in carbon storage and allocation in a Chinese fir plantation growing in southern China using the 3-PG model. Forest Ecology and Management, 2009, 257 (6): 1520- 1531.

龄组	样本数	海拔	胸径	密度	地上生物量	蓄积
Age group	Number	Elevation/m	DBH/cm	Density/(trees·hm^-2)	Aboveground biomass /(t·hm^-2)	Volume/(m³·hm^-2)
幼龄林Young	22	242.85~319.41	10.90~11.40	1 005~2 283	43.00~94.17	57.96~149.80
中龄林Middle	48	242.85~319.41	10.11~17.23	1 005~2 995	45.21~150.86	88.04~293.18
近熟林Near mature	54	242.85~263.32	12.89~19.37	1 005~2 370	79.62~171.74	155.61~356.94
成熟林Mature	57	242.85~272.76	15.85~26.06	500~1 940	90.79~173.11	144.06~336.19

月份 Month	平均最高气温 T_max/℃	平均最低气温 T_min/℃	平均降雨量 PPT/mm	霜冻天数 FD/d	日平均辐射量 DAR/(MJ·m^-2d^-1)
1	-12.2	-23.1	6.4	30	5.73
2	-6.7	-19.3	6.2	30	8.48
3	1.7	-9.7	14.1	30	13.50
4	12.9	0.4	24.4	10.8	15.42
5	20.5	7.9	49.5	0	18.68
6	25.4	14.3	92.1	0	18.57
7	27.3	17.8	108.5	0	17.78
8	25.9	16.2	133.0	0	15.81
9	20.6	8.6	57.1	0	13.32
10	11.5	0.1	30.2	11.4	9.76
11	-0.9	-10.6	10.8	30	6.30
12	-10.3	-19.9	10.2	30	4.80

统计量Statistic	斜率Slope	截距Intercept	R²	ME	MAE	RMSE	MRE(%)
胸径DBH/cm	1.14	-2.07	0.85	0.05	0.91	1.29	0.37
密度Density/(trees·hm^-2)	1.07	-150.31	0.97	29.44	67.60	98.91	1.84
叶干生物量比Ratio of foliage to stem biomass	1.87	-0.03	0.67	0.01	0.01	0.02	20.84
根生物量Root biomass /(t·hm^-2)	0.73	7.92	0.64	-1.85	3.16	4.17	-7.70
干生物量Stem biomass /(t·hm^-2)	1.02	-0.11	0.86	-1.62	10.20	14.30	-1.39
地上生物量AGB/(t·hm^-2)	0.99	3.26	0.86	-2.44	10.54	14.85	-2.00
总生物量Total biomass /(t·hm^-²)	0.95	10.95	0.84	-4.29	12.55	17.97	-2.94
蓄积Volume/(m³·hm^-2)	1.15	13.88	0.82	-19.87	32.34	44.19	-7.87

统计量Statistic	斜率Slope	截距Intercept	R²	ME	MAE	RMSE	MRE(%)
胸径DBH/cm	1.05	-1.76	0.96	1.02	1.13	1.33	6.48
密度Density/(trees·hm^-2)	0.93	187.50	0.89	88.01	104.66	185.34	6.07
叶干生物量比Ratio of foliage to stem biomass	1.75	-0.01	0.84	0.02	0.02	0.02	25.60
根生物量Root biomass/(t·hm^-2)	0.99	2.02	0.77	-1.87	2.19	3.07	-9.12
干生物量Stem biomass/(t·hm^-2)	1.19	-31.03	0.84	-10.69	16.17	19.10	-9.89
地上生物量AGB/(t·hm^-2)	1.18	-35.41	0.84	-10.68	16.17	19.09	-10.97
总生物量Total biomass/(t·hm^-2)	1.16	-29.52	0.83	-58.08	58.39	74.39	-4.34
蓄积Volume/(m³·hm^-2)	0.95	48.06	0.90	-88.71	88.71	104.91	-9.09

[1]	Xiuhong Liu,Chunqian Jiang,Rui Xu,Xiao He,Mengjuan Qi. Comparison of Methods to Construct Compatible Individual Tree Biomass Models — A Case Study of Cyclobalanopsis glauca [J]. Scientia Silvae Sinicae, 2020, 56(9): 164-173.
[2]	Jingyu Wei,Wenyi Fan,Ying Yu,Xuegang Mao. Polarimetric Decomposition Parameters for Artificial Forest Canopy Biomass Estimation Using GF-3 Fully Polarimetric SAR Data [J]. Scientia Silvae Sinicae, 2020, 56(9): 174-183.
[3]	Jundong Rong,Lili Fan,Liguang Chen,Yinghui Zhang,Tianyou He,Lingyan Chen,Kunpeng Song,Yushan Zheng. Impacts on Biomass Allocation and Root Growth of Fokienia hodginsii Seedlings of Different Patterns and Quantities of Nitrogen Application [J]. Scientia Silvae Sinicae, 2020, 56(7): 175-184.
[4]	Wankuan Zhu,Yuxing Xu,Zhichao Wang,Apeng Du. Biomass Estimation Coefficient and Its Impacting Factors for Eucalyptus Plantation in China [J]. Scientia Silvae Sinicae, 2020, 56(5): 1-11.
[5]	Lihu Dong,Yongshuai Liu,Bo Song,Yifei Zhou,Fengri Li. Comparison of Individual Tree Carbon Estimation Approaches [J]. Scientia Silvae Sinicae, 2020, 56(4): 46-54.
[6]	Jinming Wang,Xiangyue Yuan,Zhongjia Chen,Yuwei Zhang,Yanming Wang. Distribution of Temperature Field in Biomass Hot Forming [J]. Scientia Silvae Sinicae, 2020, 56(4): 150-159.
[7]	Jincui Wu,Jun Zhou,Yu Zhang,Xiaoyan Yu,Lei Shi,Lianghua Qi. Carbon Sequestration Value and Its Change of Phyllostachys edulis Forest: A Case Study of Fujian Province [J]. Scientia Silvae Sinicae, 2020, 56(4): 181-187.
[8]	Lü Zhou,Guanglong Ou,Junfeng Wang,Hui Xu. Light Saturation Point Determination and Biomass Remote Sensing Estimation of Pinus kesiya var. langbianensis Forest Based on Spatial Regression Models [J]. Scientia Silvae Sinicae, 2020, 56(3): 38-47.
[9]	Ruyi Sha,Shasha Zhang,Zhan Yu,Fuquan Zhao,Chenggang Cai,Zhuqian Xiao,Jianwei Mao. Advances in Pseudo-Lignin Deposition and Its Effects on Enzymatic Hydrolysis of Cellulose [J]. Scientia Silvae Sinicae, 2020, 56(3): 127-143.
[10]	Xiang Li,Ni Chen,Xuemin Qi,Jie Chu,Junhua Zhang,Delong Chang,Yaya Xu. Composition and Structure Characteristics of Sodium Ethoxide Pretreated Lignocelluloses Biomass [J]. Scientia Silvae Sinicae, 2020, 56(2): 156-163.
[11]	Bin Wang,Xianglin Tian,Tianjian Cao. Uncertainty Analysis of Height Predictions for Young Pinus tabulaeformis Using a Bayesian Approach [J]. Scientia Silvae Sinicae, 2020, 56(11): 73-86.
[12]	Yefan Cao,Laifa Wang,Xizhuo Wang,Jiehong Fan. Pathogenicity of Bursaphelenchus xylophilus to Larix olgensis Seedlings [J]. Scientia Silvae Sinicae, 2020, 56(11): 108-115.
[13]	Lei Deng,Chunyun Zhu,Shichuan Yu,Yinyan Qi,Wenhui Zhang,Sheng Du,Jinhong Guan. Effects of Mingling Intensity on Morphological Characteristics of Fine Roots of a Middle-Aged Picea crassifolia Natural Forests in Qilian Mountains [J]. Scientia Silvae Sinicae, 2020, 56(1): 191-200.
[14]	Liu Chunyang, Shi Tian, Shi Guo, Yang Linfei, Fan Xuefeng, Gao Shuangcheng, Zhang Gaina. Effects of Different Transplanting Periods on Growth of Tree Peony ‘Fengdan’ Seedlings and the Comprehensive Evaluation [J]. Scientia Silvae Sinicae, 2019, 55(8): 54-62.
[15]	Xue Chunquan, Xu Qihu, Lin Liping, He Xiao, Cao Lei, Li Haikui. Biomass Dynamic Predicting for Schima superba in Guangdong Based on Allometric and Theoretical Growth Equation [J]. Scientia Silvae Sinicae, 2019, 55(7): 86-94.

Prediction of Biomass Growth of Larix olgensis Based on 3-PG Model

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 0

Related Articles 15

Recommended Articles

Metrics

Comments