Abstract
The artificial young forest is an important component of ecosystems, and biomass models are important for estimating the carbon storage of ecosystems. However, research on biomass models of the young forest is lacking. In this study, biomass data of 96 saplings of three tree species from the southern foot of the Qilian Mountains were collected. These data, coupled with allometric growth equations and the nonlinear joint estimation method, were used to establish independent, component-additive, and total-control compatible models to estimate the biomass of artificial young wood of Picea crassifolia (Picea crassifolia Kom.), Sabina przewalskii (Sabina przewalskii Kom.), and Pinus tabulaeformis (Pinus tabuliformis Carr.). The distribution characteristics of the biomass components (branch, leaf, trunk, and root biomass) and the goodness of fit of the models were also analyzed. The results showed that (1) the multiple regression models with two independent variables (MRWTIV) were superior to the univariate models for all three tree species. Base diameter was the best-fitting variable of the univariate model for Picea crassifolia and Pinus tabulaeformis, and the addition of base diameter and crown diameter as variables to the MRWTIV can significantly improve model accuracy. Tree height was the best-fitting variable of the univariate model of Sabina przewalskii, and the addition of tree height and crown diameter to the MRWTIV can significantly improve model accuracy; (2) the two independent variable component-additive compatible model was the best-fitting biomass model. The compatible models constructed by the nonlinear joint estimation method were less accurate than the independent models. However, they maintained good compatibility among the biomass components and enabled more robust estimates of regional biomass; and (3) for the young wood of Picea crassifolia, Sabina przewalskii, and Pinus tabulaeformis, the aboveground biomass ratio of each component to total biomass was highest for leaf biomass (26%–68%), followed by branch (10%–46%) and trunk (11%–55%) biomass, and the aboveground biomass was higher than the underground biomass. In conclusion, the optimal biomass model of artificial young forest at the sampling site is a multivariate component-additive compatible biomass model. It can well estimate the biomass of young forest and provide a basis for future research.
Funder
Project for Transformation of Scientific and Technological Achievements from the Qinghai Province
The Second Tibetan Plateau Scientific Expedition and Research (STEP) program
The 2020 Qinghai Kunlun talents—Leading scientists project
Reference40 articles.
1. Terrestrial carbon sinks in China and around the world and their contribution to carbon neutrality;Yang;Sci. China Ser. D,2022
2. Li, M.Y. Carbon Storages and Carbon Sequestration Potentials of the Terrestrial Ecosystems on the Loess Plateau. Master’s Thesis, 2021.
3. Wang, Y.Y. Coupling Associations between Plant Species Composition Traits and Biomass Carbon Sink Function and Mechanism of Forests in the Greater and Lesser Khingan Mountains. Ph.D. Thesis, 2021.
4. Construction of biomass model of Chinese fir plantation in Guangdong based on Lianqing data;Guo;J. Cent. South Univ. For. Technol.,2022
5. Biomass and carbon density distribution characteristics of Pinus taiwanensis at different forest ages;Sun;J. Shandong Agric. Univ. (Nat. Sci. Ed.),2022
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献