J Theor Biol 317:418–421īaskerville G (1972) Use of logarithmic regression in the estimation of plant biomass. For Ecol Manag 237:29–38īallantyne FT (2013) 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). Can J For Res 37:895–906īalboa-Murias MA, Rodriguez-Soalleiro R, Merino A, Alvarez-Gonzalez JG (2006) Temporal variations and distribution of carbon stocks in aboveground biomass of radiata pine and maritime pine pure stands under different silvicultural alternatives. For Ecol Manag 267:297–308Īntónio N, Tomé M, Tomé J, Soares P, Fontes L (2007) Effect of tree, stand, and site variables on the allometry of Eucalyptus globulus tree biomass. 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.Īlvarez E, Duque A, Saldarriaga J, Cabrera K, de Las Salas G, Valle ID, Lema A, Moreno F, Orrego S, Rodríguez L (2012) Tree above-ground biomass allometries for carbon stocks estimation in the natural forests of Colombia. 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 R 2 a 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. Jackknifing model residuals were used to validate the prediction performance of biomass equations. 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. The model coefficients were simultaneously estimated using seemly unrelated regression (SUR). 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. 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). In 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. We 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.
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