Developing additive systems of biomass equations for nine hardwood species in Northeast China

Key messageWe developed two additive systems of biomass equations based on diameter and tree height for nine hardwood species by SUR, and used a likelihood analysis to evaluate the model error structures.AbstractIn this study, a total of 472 trees were harvested and measured for stem, root, branch, and foliage biomass from nine hardwood species in Northeast China. Two additive systems of biomass equations were developed, one based on tree diameter (D) only and one based on both tree diameter (D) and height (H). For each system, three constraints were set up to account for the cross-equation error correlations between four tree component biomass, two sub-total biomass, and total biomass. The model coefficients were simultaneously estimated using seemly unrelated regression (SUR). Likelihood analysis was used to verify the error structures of power functions in order to determine if logarithmic transformation should be applied on both sides of biomass equations. Jackknifing model residuals were used to validate the prediction performance of biomass equations. The results indicated that (1) stem biomass accounted for the largest proportion (62 %) of the total tree biomass; (2) the two additive systems of biomass equations obtained good model fitting and prediction, of which the model Ra2 was >0.89, and the mean absolute percent bias (MAB %) was <35 %; (3) the system of biomass equations based on both D and H significantly improved model fitting and performance, especially for total, aboveground, and stem biomass; and (4) the anti-log correction was not necessary in this study. The established additive systems of biomass equations can provide reliable and accurate estimation for individual tree biomass of the nine hardwood species in Chinese National Forest Inventory.

[1]  Shuo Cai,et al.  Allometric models for aboveground biomass of ten tree species in northeast China , 2013 .

[2]  J. Chambers,et al.  Tree allometry and improved estimation of carbon stocks and balance in tropical forests , 2005, Oecologia.

[3]  A. Kozak.  Methods for Ensuring Additivity of Biomass Components by Regression Analysis , 1970 .

[4]  Chai Ruihai.  Equations for estimating the above-ground biomass of Larix sibirica in the forest-steppe of Mongolia , 2013 .

[5]  J. Navar.  Biomass component equations for Latin American species and groups of species , 2009, Annals of Forest Science.

[6]  G. Baskerville.  Use of Logarithmic Regression in the Estimation of Plant Biomass , 1972 .

[7]  Marcia J. Lambert,et al.  Additive biomass equations for native eucalypt forest trees of temperate Australia , 2004, Trees.

[8]  F. Raulier,et al.  Canadian national tree aboveground biomass equations , 2005 .

[9]  E. Green,et al.  A method of forcing additivity of biomass tables when using nonlinear models , 1985 .

[10]  Matieu Henry,et al.  Using Bayesian Model Averaging to Predict Tree Aboveground Biomass in Tropical Moist Forests , 2012 .

[11]  J. Ewel,et al.  Allometric equations for four valuable tropical tree species , 2006 .

[12]  H. Burkhart,et al.  Biomass partitioning in a miniature-scale loblolly pine spacing trial. , 2009 .

[13]  Raisa Mäkipää,et al.  Biomass and stem volume equations for tree species in Europe , 2005, Silva Fennica Monographs.

[14]  H. Madgwick,et al.  On Estimating the Aboveground Weights of Tree Stands , 1975 .

[15]  M. Hooten,et al.  On the use of log-transformation vs. nonlinear regression for analyzing biological power laws. , 2011, Ecology.

[16]  Bernard R. Parresol,et al.  Development of equations for predicting Puerto Rican subtropical dry forest biomass and volume. , 2006 .

[17]  J. Álvarez-González,et al.  Temporal variations and distribution of carbon stocks in aboveground biomass of radiata pine and maritime pine pure stands under different silvicultural alternatives , 2006 .

[18]  L. Heath,et al.  Methods and equations for estimating aboveground volume, biomass, and carbon for trees in the U.S. forest inventory, 2010 , 2011 .

[19]  Pengxiang Zhao,et al.  Improving the accuracy of tree-level aboveground biomass equations with height classification at a large regional scale , 2013 .

[20]  G. Roi,et al.  Root-system morphology of common boreal forest trees in Alberta, Canada. , 1983 .

[21]  M. Harmon,et al.  Total carbon stocks in a tropical forest landscape of the Porce region, Colombia , 2007 .

[22]  Nellie Clarke Brown.  Trees , 1896, Savage Dreams.

[23]  Shouzheng Tang,et al.  A parameter estimation program for the error-in-variable model , 2002 .

[24]  M. Schoeneberger,et al.  Developing above-ground woody biomass equations for open-grown, multiple-stemmed tree species : Shelterbelt-grown russian-olive , 2007 .

[25]  W. B. Smith,et al.  Allometric Biomass Equations for 98 Species of Herbs, Shrubs, and Small Trees , 1983 .

[26]  G. Xanthopoulos,et al.  Allometric equations for aboveground biomass estimation by size class for Pinus brutia Ten. trees growing in North and South Aegean Islands, Greece , 2011, European Journal of Forest Research.

[27]  A. Kerkhoff,et al.  The Allometry of Coarse Root Biomass: Log-Transformed Linear Regression or Nonlinear Regression? , 2013, PloS one.

[28]  Lianhai Wu,et al.  Biomass structure and allometry of Abies nephrolepis (Maxim) in Northeast China , 2011 .

[29]  K. Niklas,et al.  Canonical rules for plant organ biomass partitioning and annual allocation. , 2002, American journal of botany.

[30]  María Vanessa Lencinas,et al.  Carbon accumulation along a stand development sequence of Nothofagus antarctica forests across a gradient in site quality in Southern Patagonia , 2010 .

[31]  Allometric models for predicting the aboveground biomass of Canada yew (Taxus canadensis Marsh.) from visual and digital cover estimates , 2010 .

[32]  S. Takeda,et al.  Establishment of allometric models and estimation of biomass recovery of swidden cultivation fallows in mixed deciduous forests of the Bago Mountains, Myanmar , 2013 .

[33]  Margarida Tomé,et al.  Effect of tree, stand, and site variables on the allometry of Eucalyptus globulus tree biomass , 2007 .

[34]  Maurizio Mencuccini,et al.  Aboveground biomass relationships for beech (Fagus moesiaca Cz.) trees in Vermio Mountain, Northern Greece, and generalised equations for Fagus sp. , 2003 .

[35]  F. Ballantyne.  Evaluating model fit to determine if logarithmic transformations are necessary in allometry: a comment on the exchange between Packard (2009) and Kerkhoff and Enquist (2009). , 2013, Journal of theoretical biology.

[36]  Richard J. Harper,et al.  Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations , 2010 .

[37]  M. Ter-Mikaelian,et al.  Biomass equations for sixty-five North American tree species , 1997 .

[38]  H. A. Mooney,et al.  Maximum rooting depth of vegetation types at the global scale , 1996, Oecologia.

[39]  Jingyun Fang,et al.  Climatic control of primary forest structure and DBH–height allometry in Northeast China , 2006 .

[40]  Bernard R. Parresol,et al.  Additivity of nonlinear biomass equations , 2001 .

[41]  Shouzheng Tang,et al.  Simultaneous equations, error-in-variable models, and model integration in systems ecology , 2001 .

[42]  R. Briggs,et al.  Forcing additivity of biomass tables: some empirical results , 1984 .

[43]  B. Parresol,et al.  Additivity in tree biomass components of Pyrenean oak (Quercus pyrenaica Willd.) , 2003 .

[44]  Juan Saldarriaga,et al.  Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia , 2012 .

[45]  Dimitris Zianis,et al.  Predicting mean aboveground forest biomass and its associated variance , 2008 .

[46]  Q. Ketterings,et al.  Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests , 2001 .

[47]  Andrew K. Skidmore,et al.  Allometric equations for estimating the above-ground biomass in tropical lowland Dipterocarp forests , 2009 .

[48]  P. Ciais,et al.  Consistent Land- and Atmosphere-Based U.S. Carbon Sink Estimates , 2001, Science.

[49]  R. Birdsey,et al.  National-Scale Biomass Estimators for United States Tree Species , 2003, Forest Science.

[50]  M. Río,et al.  Biomass models to estimate carbon stocks for hardwood tree species , 2012 .

[51]  Huicui Lu,et al.  Short-term effects of harvesting on carbon storage of boreal Larix gmelinii–Carex schmidtii forested wetlands in Daxing’anling, northeast China , 2013 .

[52]  B. Nicoll,et al.  Adaptive growth of tree root systems in response to wind action and site conditions. , 1996, Tree physiology.

[53]  B. Parresol.  Assessing Tree and Stand Biomass: A Review with Examples and Critical Comparisons , 1999, Forest Science.

[54]  Yadvinder Malhi,et al.  Forests, carbon and global climate , 2002, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[55]  Chuankuan Wang,et al.  Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests , 2006 .

[56]  P. Solin.  Cold storage of Norway Spruce cones and its effect on seed viability. , 1970 .

[57]  J. Beauchamp,et al.  Corrections for Bias in Regression Estimates After Logarithmic Transformation , 1973 .

[58]  S. T. Gower,et al.  Direct and Indirect Estimation of Leaf Area Index, fAPAR, and Net Primary Production of Terrestrial Ecosystems , 1999 .

[59]  G. Rubio,et al.  Allometric relations for biomass partitioning of Nothofagus antarctica trees of different crown classes over a site quality gradient , 2010 .

[60]  T. Iehara,et al.  Aboveground biomass equations for individual trees of Cryptomeria japonica, Chamaecyparis obtusa and Larix kaempferi in Japan , 2010, Journal of Forest Research.

[61]  R. B. Jackson,et al.  A Large and Persistent Carbon Sink in the World’s Forests , 2011, Science.

[62]  Juan Majada,et al.  A three level system for estimating the biomass of Castanea sativa Mill. coppice stands in north-west Spain , 2013 .