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

被引:3
|
作者
Bronisz, Karol [1 ]
Bijak, Szymon [1 ]
Wojtan, Rafal [1 ]
Tomusiak, Robert [1 ]
Bronisz, Agnieszka [1 ]
Baran, Pawel [1 ]
Zasada, Michal [1 ]
机构
[1] Warsaw Univ Life Sci SGGW, Inst Forest Sci, Nowoursynowska 159, PL-02776 Warsaw, Poland
来源
FORESTS | 2021年 / 12卷 / 03期
关键词
Robinia pseudoacacia; carbon sequestration; model’ s additivity; ROBINIA-PSEUDOACACIA; ABOVEGROUND BIOMASS; EMPIRICAL FORMULAS; DRY BIOMASS; TREE; EQUATIONS; ADDITIVITY; HEIGHT; BIRCH; PINE;
D O I
10.3390/f12030380
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
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.
引用
收藏
页数:13
相关论文
共 50 条
  • [1] Seemingly Unrelated Mixed-Effects Biomass Models for Young Silver Birch Stands on Post-Agricultural Lands
    Bronisz, Karol
    Mehtatalo, Lauri
    FORESTS, 2020, 11 (04):
  • [2] Taper models for black locust in west Poland
    Bronisz, Karol
    Zasada, Michal
    SILVA FENNICA, 2020, 54 (05) : 1 - 18
  • [3] Inference for seemingly unrelated linear mixed models
    Wang, Lichun
    Yang, Yang
    JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2024, 228 : 80 - 94
  • [4] Linear mixed-effects models for estimating biomass and fuel loads in shrublands
    Pearce, H. G.
    Anderson, W. R.
    Fogarty, L. G.
    Todoroki, C. L.
    Anderson, S. A. J.
    CANADIAN JOURNAL OF FOREST RESEARCH, 2010, 40 (10) : 2015 - 2026
  • [5] Mixed-effects models in psychophysiology
    Bagiella, E
    Sloan, RP
    Heitjan, DF
    PSYCHOPHYSIOLOGY, 2000, 37 (01) : 13 - 20
  • [6] Some Remarks on Comparison of Predictors in Seemingly Unrelated Linear Mixed Models
    Guler, Nesrin
    Buyukkaya, Melek Eris
    APPLICATIONS OF MATHEMATICS, 2022, 67 (04) : 525 - 542
  • [7] Some remarks on comparison of predictors in seemingly unrelated linear mixed models
    Nesrin Güler
    Melek Eriş Büyükkaya
    Applications of Mathematics, 2022, 67 : 525 - 542
  • [8] Comparison of Fixed- and Mixed-Effects Approaches to Taper Modeling for Scots Pine in West Poland
    Bronisz, Karol
    Zasada, Michal
    FORESTS, 2019, 10 (11):
  • [9] Coefficients of Determination for Mixed-Effects Models
    Zhang, Dabao
    JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS, 2022,
  • [10] Stochastic Differential Mixed-Effects Models
    Picchini, Umberto
    De Gaetano, Andrea
    Ditlevsen, Susanne
    SCANDINAVIAN JOURNAL OF STATISTICS, 2010, 37 (01) : 67 - 90