• 研究简报 •

浙江省毛竹竹秆生物量模型

1. 浙江农林大学省部共建亚热带森林培育国家重点实验室 浙江农林大学环境与资源学院 杭州 311300
• 收稿日期:2019-01-22 出版日期:2019-11-25 发布日期:2019-12-21
• 通讯作者: 汤孟平
• 基金资助:
国家林业局林业公益性行业项目"浙江省主要林地立地质量和生产力评价"(20150430303)

Stem Biomass Models of Phyllostachys edulis in Zhejiang Province

Qianyong Shen,Mengping Tang*

1. State Key Laboratory of Subtropical Silviculture, Zhejiang Agriculture and Forestry University School of Environmental and Resources Science, Zhejiang Agriculture and Forestry University Hangzhou 311300
• Received:2019-01-22 Online:2019-11-25 Published:2019-12-21
• Contact: Mengping Tang
• Supported by:
国家林业局林业公益性行业项目"浙江省主要林地立地质量和生产力评价"(20150430303)

Abstract:

Objective: The stem biomass of moso bamboo (Phyllostachys edulis) were accurately measured in sample plots. Proper prediction variables and models were determined on the basis of establishment and comparison among different biomass models with different variables. And the research is carried out to accurately estimate the stem biomass and provide a theoretical basis for the site quality assessment and efficient cultivation for bamboo forest in Zhejiang Province. Method: Firstly, mensuration of 216 sample bamboos harvested from 10 counties that distributed in eastern, southern, western, northern, and central part of Zhejiang Province was carried out. Secondly, the diameter at breast height (D), bamboo age(A), and internode length of bamboo at breast height (L) were introduced. Three different stem biomass models were fitted based on the allometric growth equations and all the sample information. Then, the model fitting method was selected by error structure that decided by the likelihood analysis. Finally, the most suitable stem biomass model was determined on the basis and analysis of the fitting goodness and prediction accuracy of the three different bamboo stem biomass models. Result: The moisture content of bamboo stem decreased with years and the mean water content at the age of degree Ⅴ was 24% lower than that at degree Ⅰ. While bamboo stem biomass accounted for an increase in the proportion of above-ground biomass year by year and that at degree Ⅴ was more than 80%. The error structure of biomass models was determined to be multiplicative based on the likelihood analysis, thus the log-transformed model for fitting was required. 3) Upon accuracy inspection, the coefficient of determination (Ra2) for model (M1) W=0.104 6D2.257 8 was 0.774 2, lower than that of the binary model (M2)W=0.052 0D2.205 2A0.4457 based on D-A and the trigram model (M3) W=0.026 5D2.143 9A0.449 5L0.262 9 based on D-A-L, whose value were up to 0.89. Meanwhile, the standard error of the estimate (SEE) and the mean absolute error (MAE) of model (M3)were the minimum. The three log-transformed models predicted well among different diameter classes as the prediction error were all close to 0. Over all the model M3 performed optimally among different classes. Conclusion: This study conducts the anti-log transformation of the log-transformed model without correction, for it can reduce the prediction accuracy. The binary and trigram models perform better than the unary model in the fitting goodness and prediction accuracy. Thus the optimum model is W=0.026 5D2.143 9A0.449 5L0.262 9, based on the variable D-A-L.