Biomass Functions and Carbon Content Variabilities of Natural and Planted Pinus koraiensis in Northeast China

Abstract

The population of natural Korean pine (Pinus koraiensis) in northeast China has sharply declined due to massive utilization for its high-quality timber, while this is vice versa for Korean pine plantations after various intensive afforestation schemes applied by China’s central authority. Hence, more comprehensive models are needed to appropriately understand the allometric relationship variations between the two origins. In this study, we destructively sampled Pinus koraiensis from several natural and plantation sites in northeast China to investigate the origin’s effect on biomass equations. Nonlinear seemingly unrelated regression with weighted functions was used to present the additivity property and homogenize the model residuals in our two newly developed origin-free (population average) and origin-based (dummy variable) biomass functions. Variations in biomass allocations, carbon content, and root-to-shoot ratio between the samples obtained from plantations and natural stands were also investigated. The results showed that (1) involving the origin’s effect in dummy variable models brought significant improvement in model performances compared to the population average models; (2) incorporating tree total height (H) as an additional predictor to diameter at breast height (D) consistently increase the models’ accuracy compared to using D only as of the sole predictors for both model systems; (3) stems accounted for the highest partitioning proportions and foliage had the highest carbon content among all biomass components; (4) the root-to-shoot ratio ranged from 0.18–0.35, with plantations (0.28 ± 0.04) had slightly higher average value (±SD) compared to natural forests (0.25 ± 0.03). Our origin-based models can deliver more accurate individual tree biomass estimations for Pinus koraiensis, particularly for the National Forest Inventory of China.

Estimation of biomass in various components of Pinus koraiensis based on Bayesian methods

“…Different model types affect the efficiency, bias, and other numerical values of models. Various allometric biomass models have been employed to estimate forest biomass (Chen, 1981;Wang, 2006;Ma and Li, 2008;Dai et al, 2013;Dong et al, 2014;Widagdo et al, 2021;Xie et al, 2022b), particularly the models W ∼ aD b and W ∼ a(D 2 H) b . For example, the MEF of a model (M1) with only the DBH as an explanatory variable explained 96.8% and 95.0% of the variation of branch and leaf biomass, respectively.…”

“…Even within the same species, growth relationships can vary significantly across different locations, contradicting the universal scaling rules predicted by metabolic scaling theory for diverse species and biological communities (Li et al, 2005;Muller-Landau et al, 2006;Návar, 2009). Probability distributions can be used to overcome this limitation (Dong et al, 2014;Dogn et al, 2015;Widagdo et al, 2021;Xie et al, 2022b). Specifically, the probability distribution of scaling coefficients can assess the range of variation in these coefficients across different locations, providing prior information for Bayesian inference.…”

Research on the Temporal and Spatial Distributions of Standing Wood Carbon Storage Based on Remote Sensing Images and Local Models

“…The Liangshui Nature Reserve, as a natural forest area, is characterized by important strategic significance in terms of a natural ecosystem balance in the context of achieving carbon neutrality. When using stand factor, terrain factor, remote sensing factor, and other data for prediction and carbon storage analysis, a lot of manpower and material resources required for manual census can be saved, and the results provide important reference value for the monitoring of natural ecosystems and establishment of management plans for protected areas [24].…”

“…Carbon storage (Mg/ha) was calculated based on the aboveground biomass in the study area using the continuous function method for the biomass conversion coefficient of the main stand types [23]. The calculated biomass was multiplied by the carbon content coefficient to obtain carbon storage [24]. The carbon storage conversion coefficients of different tree species in the study area are shown in Table 1:…”

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“…Even within the same species, growth relationships can vary significantly across different locations, contradicting the universal scaling rules predicted by metabolic scaling theory for diverse species and biological communities (Li et al, 2005;Muller-Landau et al, 2006;Návar, 2009). Probability distributions can be used to overcome this limitation (Dong et al, 2014;Dogn et al, 2015;Widagdo et al, 2021;Xie et al, 2022b). Specifically, the probability distribution of scaling coefficients can assess the range of variation in these coefficients across different locations, providing prior information for Bayesian inference.…”