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应用生态学报 ›› 2026, Vol. 37 ›› Issue (1): 145-154.doi: 10.13287/j.1001-9332.202601.008

• 研究论文 • 上一篇    下一篇

基于哑变量与联立方程组的樟子松人工林碳储量生长模型

张丽荣1, 李镐然2, 王奇龙1, 刘丹丹3, 赵雅琪1, 王维芳1*   

  1. 1东北林业大学林学院, 哈尔滨 150040;
    2东北林业大学生命科学学院, 哈尔滨 150040;
    3攀枝花学院, 四川攀枝花 617000
  • 收稿日期:2025-10-03 修回日期:2025-11-11 发布日期:2026-07-18
  • 通讯作者: *E-mail: weifangwang@nefu.edu.cn
  • 作者简介:张丽荣, 女, 2000年生, 硕士研究生。主要从事森林可持续经营研究。E-mail: 2857566620@qq.com
  • 基金资助:
    “十四五”国家重点研发计划项目(2022YFD2201002)

Growth model of carbon storage for Pinus sylvestris var. mongolica plantation based on dummy variables and simultaneous equations

ZHANG Lirong1, LI Haoran2, WANG Qilong1, LIU Dandan3, ZHAO Yaqi1, WANG Weifang1*   

  1. 1College of Forestry, Northeast Forestry University, Harbin 150040, China;
    2College of Life Sciences, Northeast Forestry University, Harbin 150040, China;
    3Panzhihua University, Panzhihua 617000, Sichuan, China
  • Received:2025-10-03 Revised:2025-11-11 Published:2026-07-18

摘要: 为了科学预测樟子松人工林碳储量动态变化,本研究基于黑龙江省樟子松人工林固定监测样地数据,筛选确定Weibull模型为林分碳储量随林龄变化的最优基础模型。在此基础上,通过再参数化方法,构建了包含地位级指数(SCI)和林分密度指数(SDI)的广义模型C1,以及包含SCI和林分胸高断面积(BAS)的广义模型C2。为量化区域影响,在C1模型的基础上引入区域哑变量,建立模型C3;同时,采用非线性似乎不相关回归法,构建了BAS与碳储量的联立方程组,形成碳储量模型系统。结果表明: 优化模型C1、C2和C3的决定系数(R2)分别为0.9856、0.9968和0.9862,均方根误差(RMSE)均低于3 t·hm-2,模型稳定且预测精度高。C1与C2模型比较显示,BAS对碳储量估算的影响大于SDI。基于区域哑变量的C3模型将26个林区划分为3个区域,在林分年龄、SCI和SDI相同条件下,碳储量表现为区域2(完达山山系)>区域1(小兴安岭和张广才岭)>区域3(平原地区),证实区域对碳积累具有显著影响。联立模型系统中各子模型的R2均大于0.98,相对均方根误差(rRMSE)均小于9%,表明模型系统具有良好的通用性与稳定性。本研究构建的独立模型(C1、C2和C3)和模型系统各有侧重,适用于不同实际应用场景的林分碳储量精准预测,为樟子松人工林碳汇评估与经营决策提供科学依据。

关键词: 樟子松人工林, 哑变量, 胸高断面积, 碳储量生长模型, 联立方程组

Abstract: To scientifically predict the dynamics of carbon storage in Pinus sylvestris var. mongolica plantation, we identified the Weibull model as the optimal model for describing the variation of stand carbon storage with stand age, based on data from fixed monitoring plots of P. sylvestris var. mongolica plantation in Heilongjiang Province. Following a reparameterization approach, we further constructed a generalized model C1 incorporating site class index (SCI) and stand density index (SDI), and a generalized model C2 incorporating SCI and stand basal area (BAS). To quantify regional effects, we introduced a regional dummy variable into model C1, resulting in model C3. Then, we established a simultaneous equation system for carbon storage and BAS using nonlinear seemingly unrelated regression, forming the carbon storage model systems. The results showed that the optimized models C1, C2, and C3 had coefficients of determination (R2) of 0.9856, 0.9968, and 0.9862, respectively, with root mean square errors (RMSE) of all models being less than 3 t·hm-2, indicating high model stability and predictive accuracy. A comparison between models C1 and C2 showed that BAS had a greater influence on carbon storage estimation than SDI. Model C3, based on regional dummy variables, categorized 26 forest areas into three regions. Under the same stand mean age, SCI, and SDI conditions, carbon storage followed an order of Region 2 (Wandashan area) > Region 1 (Xiaoxing’anling area and Zhangguangcailing area) > Region 3 (plain area), confirming regional vari-ations in carbon accumulation. In the simultaneous model systems, all sub-models achieved R2 values greater than 0.98 and relative root mean square errors (rRMSE) below 9%, demonstrating strong generalizability and stability of the established system. The independent models (C1, C2 and C3) and the model system developed here have their specific focuses and are suitable for precise prediction of stand carbon storage in different practical application scenarios, providing a scientific basis for carbon sink assessment and management decision-making in P. sylvestris var. mongolica plantations.

Key words: Pinus sylvestris var. mongolica plantation, dummy variable, stand basal area, stand carbon storage growth model, simultaneous equations