Seemingly Unrelated Mixed-Effects Biomass Models for Black Locust in West Poland

编号 010028501

推送时间 20210405

研究领域 森林生态 

年份 2021 

类型 期刊 

语种 英语

标题 Seemingly Unrelated Mixed-Effects Biomass Models for Black Locust in West Poland

来源期刊 forest

期 第285期

发表时间 20210323

关键词 Robinia pseudoacacia;  carbon sequestration;  model’s additivity; 

摘要 Information about tree biomass is important not only in the assessment of wood resources but also in the process of preparing forest management plans, as well as for estimating carbon stocks and their flow in forest ecosystems. The study aimed to develop empirical models for determining the dry mass of the aboveground parts of black locust trees and their components (stem, branches, and leaves). The research was carried out based on data collected in 13 stands (a total of 38 sample trees) of black locust located in western Poland. The model system was developed based on multivariate mixed-effect models using two approaches. In the first approach, biomass components and tree height were defined as dependent variables, while diameter at breast height was used as an independent variable. In the second approach, biomass components and diameter at breast height were dependent variables and tree height was defined as the independent variable. Both approaches enable the fixed-effect and cross-model random-effect prediction of aboveground dry biomass components of black locust. Cross-model random-effect prediction was obtained using additional measurements of two extreme trees, defined as trees characterized by the smallest and largest diameter at breast height in sample plot. This type of prediction is more precise (root mean square error for stem dry biomass for both approaches equals 77.603 and 188.139, respectively) than that of fixed-effects prediction (root mean square error for stem dry biomass for both approaches equals 238.716 and 206.933, respectively). The use of height as an independent variable increases the possibility of the practical application of the proposed solutions using remote data sources. View Full-Text

服务人员 王璐

服务院士 蒋有绪

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