Dynamic Changes in Leaf Biomass and the Modeling of Individual Moso Bamboo (Phyllostachys edulis?(Carrière) J. Houz) under Intensive Management

编号 030034404

推送时间 20220523

研究领域 森林经理 

年份 2022 

类型 期刊 

语种 英语

标题 Dynamic Changes in Leaf Biomass and the Modeling of Individual Moso Bamboo (Phyllostachys edulis?(Carrière) J. Houz) under Intensive Management

来源期刊 Forests

期 第344期

发表时间 20220429

关键词 leaf biomass;  Moso bamboo;  modeling schemes;  accuracy comparison;  intensive; 

摘要 Accurate estimations of leaf biomass are required to quantify the amount of material and energy exchanged between vegetation and the atmosphere, to enhance the primary productivity of forest stands, and to assess the contributions of vegetation towards the mitigation of global climate change. The leaf biomass of Moso bamboo (Phyllostachys edulis?(Carrière) J. Houz) changes dramatically during the year owing to changes in the leaves and the growth of new shoots. Furthermore, the relationship between the leaf biomass of Moso bamboo under cutting the top of the culm and the diameter at breast height (D) and culm height is decoupling, which increases the difficulty of estimating leaf biomass. Consequently, an effective method to accurately estimate the leaf biomass of Moso bamboo under cutting the top of the culm is required. In this study, leaf biomass and other factors (age,?D, culm height, crown length, and crown width) were measured for 54 bamboo samples collected from December 2019 to December 2020. Models for predicting the leaf biomass of the Moso bamboo were established using multiple linear regression with two strategies, and their accuracies were evaluated using leave-one-out cross-validation. The results showed that crown length, crown width, and age were highly correlated with leaf biomass, and these were important factors when making estimations. Variation in monthly averaged leaf biomass is significant, with a decreasing trend from January to May and an increasing trend from June to December in off-years. The leaf biomass model that utilized data from the three leaf change periods had a better fit and accuracy, with?R2?values of 0.583–0.848 and prediction errors between 8.59% and 24.19%. The model that utilized data for all months had a worse fit and accuracy, with an?R2?value of 0.228 and prediction error of 46.79%. The results of this study provide reference data and technical support to help clarify the dynamic changes in Moso bamboo leaf biomass, and therefore, aid in the development of accurate simulations.

服务人员 付贺龙

服务院士 唐守正

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