Predicting total and component biomass of Chinese fir using a forecast combination method

doi:10.3832/ifor2243-010

Predicting total and component biomass of Chinese fir using a forecast combination method

Xiongqing Zhang^(1-2), Quang V Cao⁽³⁾, Congwei Xiang^(1-2), Aiguo Duan^(1-2), Jianguo Zhang^(1-2)

iForest - Biogeosciences and Forestry, Volume 10, Issue 4, Pages 687-691 (2017)
doi: https://doi.org/10.3832/ifor2243-010
Published: Jul 17, 2017 - Copyright © 2017 SISEF

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Accurate estimates of tree biomass are critical for forest managers to assess carbon stock. Biomass of Chinese fir (Cunninghamia lanceolata [Lamb.] Hook.) in southern China was assessed by three alternative methods. In the Separate model approach, total and component tree biomass was directly predicted from a regression equation as a function of tree diameter and height. In the Additive model approach, total biomass was predicted as the sum of predictions from all component biomass equations. The Forecast Combination method involved combining predictions from the total biomass equation with the sum of predictions from component biomass equations. Results indicated that the Separate model method outperformed the Additive model method in predicting total and component biomass. The drawback of the Separate model method is that the total is not equal to the sum of its components. The Forecast Combination method provided the overall best prediction for total and component biomass, and still ensured additivity of component biomass predictions.

Keywords

Additivity, Biomass Predictions, Cunninghamia lanceolata, Even-aged Plantations, Tree Allometry

Authors’ address

(1)

State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of the State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091 (P. R. China)

(2)

Collaborative Innovation Center of Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing (P.R. China)

(3)

School of Renewable Natural Resources, Louisiana State University, Agricultural Center, Baton Rouge, LA 70803 (USA)

Corresponding author

Jianguo Zhang
xqzhang85@yahoo.com

Citation

Zhang X, Cao QV, Xiang C, Duan A, Zhang J (2017). Predicting total and component biomass of Chinese fir using a forecast combination method. iForest 10: 687-691. - doi: 10.3832/ifor2243-010

Academic Editor

Matteo Garbarino

Paper history

Received: Oct 08, 2016
Accepted: May 16, 2017

First online: Jul 17, 2017
Publication Date: Aug 31, 2017
Publication Time: 2.07 months

Open Access

This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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List of the papers citing this article based on CrossRef Cited-by.

(1)

Bates JM, Granger CWJ (1969)
The combination of forecasts. Operational Research Society 20 (4): 451-468.
CrossRef | Gscholar

(2)

Bi H, Long Y, Turner J, Lei Y, Snowdon P, Li Y, Harper R, Zerihun A, Ximenes F (2010)
Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations. Forest Ecology and Management 259 (12): 2301-2314.
CrossRef | Gscholar

(3)

Bi H, Turner J, Lambert MJ (2004)
Additive biomass equations for native eucalypt forest trees of temperate Australia. Trees 18: 467-479.
CrossRef | Gscholar

(4)

Chave J, Andalo C, Brown S, Cairns MA, Chambers JQ, Eamus D (2005)
Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145 (1): 87-99.
CrossRef | Gscholar

(5)

Dong L, Zhang L, Li F (2014)
A compatible system of biomass equations for three conifer species in Northeast, China. Forest Ecology and Management 329: 306-317.
CrossRef | Gscholar

(6)

Fahey TJ, Woodbury PB, Battles JJ, Goodale CL, Hamburg SP, Ollinger SV, Woodall CW (2010)
Forest carbon storage: ecology, management, and policy. Frontiers in Ecology and the Environment 8: 245-252.
CrossRef | Gscholar

(7)

Goodman RC, Phillips OL, Baker TR (2014)
The importance of crown dimensions to improve tropical tree biomass estimates. Ecological Applications 24: 680-698.
CrossRef | Gscholar

(8)

Jacobs MW, Cunia T (1980)
Use of dummy variables to harmonize tree biomass tables. Canadian Journal of Forest Research 10: 483-490.
CrossRef | Gscholar

(9)

Kozak A (1970)
Methods for ensuring additivity of biomass components by regression analysis. Forestry Chronicle 46: 402-404.
CrossRef | Gscholar

(10)

Lambert MC, Ung CH, Raulier F (2005)
Canadian national tree aboveground biomass equations. Canadian Journal of Forest Research 35 (8): 1996-2018.
CrossRef | Gscholar

(11)

MacFarlane DW (2015)
A generalized tree component biomass model derived from principles of variable allometry. Forest Ecology and Management 354: 43-55.
CrossRef | Gscholar

(12)

Medhurst JL, Battaglia M, Cherry ML, Hunt MA, White DA, Beadle CL (1999)
Allometric relationships for Eucalyptus nitens (Deane and Maiden) Maiden plantations. Trees 14: 91-101.
CrossRef | Gscholar

(13)

Molto Q, Hérault B, Boreux JJ, Daullet M, Rousteau A, Rossi V (2013)
Predicting tree heights for biomass esitmates in tropical forests. Biogeosciences Discuss 10: 8611-8635.
CrossRef | Gscholar

(14)

Newbold P, Zumwalt JK, Kannan S (1987)
Combining forecasts to improve earnings per share prediction and examination of electric utilities. International Journal of Forecasting 3 (2): 229-238.
CrossRef | Gscholar

(15)

Parresol BR (2001)
Additivity of nonlinear biomass equations. Canadian Journal of Forest Research 31: 865-878.
CrossRef | Gscholar

(16)

Parresol BR (1999)
Assessing tree and stand biomass: a review with examples and critical comparisons. Forest Science 45: 573-593.
Online | Gscholar

(17)

Reed D, Green EJ (1985)
A method of forcing additivity of biomass tables when using nonlinear models. Canadian Journal of Forest Research 15: 1184-1187.
CrossRef | Gscholar

(18)

SAS Institute Inc (2009)
SAS/ETS 9.2 user’s guide. SAS Publishing, Cary, NC, USA, pp. 2908.
Gscholar

(19)

Sanquetta CR, Behling AB, Corte APD, Netto SP, Schikowski AB, Do Amaral M (2015)
Simultaneous estimation as alternative to independent modeling of tree biomass. Annals of Forest Science 72: 1099-1112.
CrossRef | Gscholar

(20)

Tang S, Zhang H, Xu H (2000)
Study on establish and estimate method of compatible biomass model. Scientia Silvae Sinicae 36 (1): 19-27. [in Chinese with English abstract]
Gscholar

(21)

Tang X, Lu Y, Fehrmann L, Forrester DI, Guisasola-Rodíguez R, Pérez-Cruzado C, Kleinn C (2016)
Estimation of stand-level aboveground biomass dynamics using tree ring analysis in a Chinese fir plantation in Shitai County, Anhui Province, China. New Forests 47: 319-332.
CrossRef | Gscholar

(22)

Yue C, Kohnle U, Hein S (2008)
Combining tree-and stand-level models: a new approach to growth prediction. Forest Science 54 (5): 553-566.
Online | Gscholar

(23)

Zhang X, Duan A, Zhang J (2013)
Tree biomass estimation of Chinese fir (Cunninghamia lanceolata) based on Bayesian method. PLoS ONE 8 (11): e79868.
CrossRef | Gscholar

(24)

Zhang X, Lei Y, Cao QV (2010)
Compatibility of stand basal area predictions based on forecast combination. Forest Science 56: 552-557.
Online | Gscholar

(25)

Zhang X, Lei Y, Cao QV, Chen X, Liu X (2011a)
Improving tree survival prediction with forecast combination and disaggregation. Canadian Journal of Forest Research 41: 1928-1935.
CrossRef | Gscholar

(26)

Zhang X, Lei Y, Chen X (2011b)
Comparison of weight computation in stand basal area combined model. Scientia Silvae Sinicae 47 (7): 36-41. [in Chinese with English abstract]
Online | Gscholar

(27)

Zhao D, Kane M, Michael D, Teskey R, Clutter M (2015)
Additive tree biomass equations for midrotation loblolly pine plantations. Forest Science 61: 613-623.
CrossRef | Gscholar

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