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Estimation of Biomass Dynamics and Allocation in Chinese Fir Trees Using Tree Ring Analysis in Hunan Province, China

Author

Listed:
  • Xiaojun Xu

    (College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China
    These authors contributed equally to this work.)

  • Fengfeng Ma

    (Ministry of Education Key Laboratory of Silviculture and Conservation, Beijing Forestry University, Beijing 100083, China
    Hunan Academy of Forestry, Changsha 410018, China
    These authors contributed equally to this work.)

  • Kangying Lu

    (School of Ecological Engineering, Guangdong Eco-Engineering Polytechnic, Guangzhou 510520, China)

  • Baoqi Zhu

    (College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China)

  • Shuaichen Li

    (College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China)

  • Kangqi Liu

    (College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China)

  • Qianmin Chen

    (College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China)

  • Qingfen Li

    (College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China)

  • Cheng Deng

    (College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou 510642, China)

Abstract

Studying tree biomass dynamics and allocation is crucial to understanding the forest carbon cycle and the adaptation of trees to the environment. However, traditional biomass surveys are time-consuming and labor-intensive, so few studies have specifically examined biomass formation in terms of the increase in individual tree biomass, and the role that tree age and site conditions play in this process, especially tree roots, is unclear. We studied the tree ring characteristics of 87 sample trees (8–40 years old) from 29 Chinese fir plantations with different site conditions and measured the biomass of their stems, crowns, and roots. The biomass increment at various age stages during tree growth was determined via using tree ring analysis, and a generalized additive mixed model (GAMM) was used to analyze biomass formation and allocation, as well as the specific impact of site conditions on them. The results showed that the biomass increment of Chinese fir trees first increased and then decreased with age, and improving site conditions delayed the carbon maturation of the trees. The proportion of stem biomass increased with age, while the proportion of crown biomass decreased and the proportion of root biomass increased and then decreased. The effect of the site conditions on the tree biomass allocation showed a nonlinear trend. Tree ring analysis provides a feasible and effective method for assessing tree growth and biomass dynamics. Forest managers can use the findings of this study to scientifically optimize the management of increasing forest carbon sequestration.

Suggested Citation

  • Xiaojun Xu & Fengfeng Ma & Kangying Lu & Baoqi Zhu & Shuaichen Li & Kangqi Liu & Qianmin Chen & Qingfen Li & Cheng Deng, 2023. "Estimation of Biomass Dynamics and Allocation in Chinese Fir Trees Using Tree Ring Analysis in Hunan Province, China," Sustainability, MDPI, vol. 15(4), pages 1-14, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3306-:d:1065068
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    References listed on IDEAS

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    1. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    2. X. Lin & D. Zhang, 1999. "Inference in generalized additive mixed modelsby using smoothing splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 381-400, April.
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