Developing Two Additive Biomass Equations for Three Coniferous Plantation Species in Northeast China

Abstract

Accurate quantification of tree biomass is critical and essential for calculating carbon storage, as well as for studying climate change, forest health, forest productivity, nutrient cycling, etc. Tree biomass is typically estimated using statistical models. In this study, a total of 289 trees were harvested and measured for stem, root, branch, and foliage biomass from three coniferous plantation species in northeastern P.R. China. We developed two additive systems of biomass equations based on tree diameter (D) only and both tree diameter (D) and height (H). For each system, 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 seemingly unrelated regression (SUR). The results indicated that stem biomass had the largest relative contribution to total biomass, while foliage biomass had the smallest relative proportion for the three species. The root to shoot ratio averaged 0.27 for Korean pine, 0.25 for larch, and 0.23 for Mongolian pine. The two additive biomass systems obtained good model fitting and prediction performance, of which the model R a 2 > 0.80, and the percent mean absolute bias (MAB%), was <17%. The second additive system (D and H) had a relatively greater R a 2 and smaller root mean square error (RMSE). The model coefficient for the predictor H was statistically significant in eight of the twelve models, depending on tree species and biomass component. Adding tree height into the system of biomass equations can marginally improve model fitting and performance, especially for total, aboveground, and stem biomass. The two additive systems developed in this study can be applied to estimate individual tree biomass of three coniferous plantation species in the Chinese National Forest Inventory.

Development and evaluation of robust tree biomass equations for rubber tree (Hevea brasiliensis) plantations in India

“…being modelled, D 2 H is tree volume, and ε is the error. These models were fitted using ordinary least square regression assuming a power function with multiplicative error structure (Lai et al 2013;Dong et al 2016). For estimating biomass components in the arithmetic domain, the equations were back-transformed to give Y = αX β × CF where X is the single or compound variables, αX β = exp.…”

Effect of Climatic Factors on Stem Biomass and Carbon Stock of Larix gmelinii and Betula platyphylla in Daxing’anling Mountain of Inner Mongolia, China

“…In addition, a high quantity of rainfall and humidity can cause loss of nutrients overflowing [62]. For more accurate precision of tree biomass prediction, incorporating different stand levels of the tree and climate variables could be better [63,64]. at is why in the current study, we select only a simple tree biomass model along with two climatic factors for final stem biomass prediction.…”

Developing growth models for tree plantations using inadequate data – a case for Korean pine in Northeast China

“…Calculation of the carbon stock requires models for the biomass of different parts of the trees, including roots. Such models have already been developed (Dong et al 2016). In addition, there are measurements of the carbon content of different part of Korean pine trees (Yang et al 2017).…”

“…The new models developed in this study could be combined with existing biomass and taper models (Dong et al 2016;Jin et al 2017), timber assortment specifications and economic data to optimize the management of Korean pine plantations in timber productions. The models for stem taper and biomass may be regarded more reliable than the previous model for dominant height, diameter increment, survival and tree height.…”