Zhou Xiao, Li Zhen, Liu Liyang, Sharma Ram P, Guan Fengying, Fan Shaohui
International Center for Bamboo and Rattan, Key Laboratory of National Forestry and Grassland Administration, Beijing, China.
National Location Observation and Research Station of the Bamboo Forest Ecosystem in Yixing, National Forestry and Grassland Administration, Yixing, China.
Front Plant Sci. 2023 Feb 16;14:1139448. doi: 10.3389/fpls.2023.1139448. eCollection 2023.
Bamboo crown width (CW) is a reliable index for evaluating growth, yield, health and vitality of bamboo, and light capture ability and carbon fixation efficiency of bamboo forests. Based on statistical results produced from fitting the eight basic growth functions using data from 1374 in Yixing, Jiangsu Province, China, this study identified the most suitable function (logistic function) to construct a two-level mixed effects (NLME) CW model with the forest block and sample plot-level effects included as random effects in the model. Four methods for selecting sample bamboos per sample plot (largest bamboo, medium-sized bamboo, smallest bamboo, and randomly selected bamboos) and eight sample sizes (1-8 selected bamboos per sample plot) were evaluated to calibrate our NLME CW model. Using diameter at breast height (DBH), height to crown base (HCB), arithmetic mean diameter at breast height (MDBH), and height (H) as predictor variables, the model produced the best fit statistics (Max R, min RMSE, and TRE). This model was further improved by introducing random effects at two levels. The results showed a positive correlation of CW with HCB and DBH and a negative correlation with H. The smallest two bamboo poles per sample plot used to estimate the random effects of the NLME model provided a satisfactory compromise regarding measurement cost, model efficiency, and prediction accuracy. The presented NLME CW model may guide effective management and carbon estimation of bamboo forests.
竹冠幅（CW）是评估竹子生长、产量、健康和活力以及竹林光捕获能力和碳固定效率的可靠指标。基于对中国江苏省宜兴市1374个样本数据拟合八个基本生长函数得出的统计结果，本研究确定了最合适的函数（逻辑函数），以构建一个两级混合效应（NLME）冠幅模型，该模型将林班和样地水平效应作为随机效应纳入其中。评估了在每个样地选择样本竹的四种方法（最大竹、中等大小竹、最小竹和随机选择的竹）以及八个样本量（每个样地选择1 - 8株竹），以校准我们的NLME冠幅模型。使用胸径（DBH）、冠基高（HCB）、胸径算术平均值（MDBH）和高度（H）作为预测变量，该模型产生了最佳拟合统计量（最大R、最小RMSE和TRE）。通过引入两级随机效应进一步改进了该模型。结果表明，冠幅与冠基高和胸径呈正相关，与高度呈负相关。每个样地用于估计NLME模型随机效应的最小两根竹竿在测量成本、模型效率和预测准确性方面提供了令人满意的折衷方案。所提出的NLME冠幅模型可为竹林的有效管理和碳估算提供指导。
