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Modeling stand biomass for Moso bamboo forests in Eastern China

Stand biomass models can be used as basic decision-making tools in forest management planning. The Moso bamboo (Phyllostachys pubescens) forest, a major forest system in tropical and subtropical regions, represents a substantial carbon sink, slowing down the rise of greenhouse gas concentrations in...

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Autores principales: Zhou, Xiao, Yin, Zixu, Zhou, Yang, Zhang, Xuan, Sharma, Ram P., Guan, Fengying, Fan, Shaohui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416116/
https://www.ncbi.nlm.nih.gov/pubmed/37575914
http://dx.doi.org/10.3389/fpls.2023.1186250
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author Zhou, Xiao
Yin, Zixu
Zhou, Yang
Zhang, Xuan
Sharma, Ram P.
Guan, Fengying
Fan, Shaohui
author_facet Zhou, Xiao
Yin, Zixu
Zhou, Yang
Zhang, Xuan
Sharma, Ram P.
Guan, Fengying
Fan, Shaohui
author_sort Zhou, Xiao
collection PubMed
description Stand biomass models can be used as basic decision-making tools in forest management planning. The Moso bamboo (Phyllostachys pubescens) forest, a major forest system in tropical and subtropical regions, represents a substantial carbon sink, slowing down the rise of greenhouse gas concentrations in the earth’s atmosphere. Bamboo stand biomass models are important for the assessment of the contribution of carbon to the terrestrial ecosystem. We constructed a stand biomass model for Moso bamboo using destructively sampled data from 45 sample plots that were located across the Yixing state-owned farm in Jiangsu Province, China. Among several bamboo stand variables used as predictors in the stand biomass models, mean diameter at breast height (MDBH), mean height (MH), and canopy density (CD) of bamboo contributed significantly to the model. To increase the model’s accuracy, we introduced the effects of bamboo forest block as a random effect into the model through mixed-effects modeling. The mixed-effects model described a large part of stand biomass variation (R2 =( )0.6987), significantly higher than that of the ordinary least squares regression model (R2 =( )0.5748). Our results show an increased bamboo stand biomass with increasing MH and CD, confirming our model’s biological logic. The proposed stand biomass model may have important management implications; for example, it can be combined with other bamboo models to estimate bamboo canopy biomass, carbon sequestration, and bamboo biomass at different growth stages.
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spelling pubmed-104161162023-08-12 Modeling stand biomass for Moso bamboo forests in Eastern China Zhou, Xiao Yin, Zixu Zhou, Yang Zhang, Xuan Sharma, Ram P. Guan, Fengying Fan, Shaohui Front Plant Sci Plant Science Stand biomass models can be used as basic decision-making tools in forest management planning. The Moso bamboo (Phyllostachys pubescens) forest, a major forest system in tropical and subtropical regions, represents a substantial carbon sink, slowing down the rise of greenhouse gas concentrations in the earth’s atmosphere. Bamboo stand biomass models are important for the assessment of the contribution of carbon to the terrestrial ecosystem. We constructed a stand biomass model for Moso bamboo using destructively sampled data from 45 sample plots that were located across the Yixing state-owned farm in Jiangsu Province, China. Among several bamboo stand variables used as predictors in the stand biomass models, mean diameter at breast height (MDBH), mean height (MH), and canopy density (CD) of bamboo contributed significantly to the model. To increase the model’s accuracy, we introduced the effects of bamboo forest block as a random effect into the model through mixed-effects modeling. The mixed-effects model described a large part of stand biomass variation (R2 =( )0.6987), significantly higher than that of the ordinary least squares regression model (R2 =( )0.5748). Our results show an increased bamboo stand biomass with increasing MH and CD, confirming our model’s biological logic. The proposed stand biomass model may have important management implications; for example, it can be combined with other bamboo models to estimate bamboo canopy biomass, carbon sequestration, and bamboo biomass at different growth stages. Frontiers Media S.A. 2023-07-27 /pmc/articles/PMC10416116/ /pubmed/37575914 http://dx.doi.org/10.3389/fpls.2023.1186250 Text en Copyright © 2023 Zhou, Yin, Zhou, Zhang, Sharma, Guan and Fan https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Zhou, Xiao
Yin, Zixu
Zhou, Yang
Zhang, Xuan
Sharma, Ram P.
Guan, Fengying
Fan, Shaohui
Modeling stand biomass for Moso bamboo forests in Eastern China
title Modeling stand biomass for Moso bamboo forests in Eastern China
title_full Modeling stand biomass for Moso bamboo forests in Eastern China
title_fullStr Modeling stand biomass for Moso bamboo forests in Eastern China
title_full_unstemmed Modeling stand biomass for Moso bamboo forests in Eastern China
title_short Modeling stand biomass for Moso bamboo forests in Eastern China
title_sort modeling stand biomass for moso bamboo forests in eastern china
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416116/
https://www.ncbi.nlm.nih.gov/pubmed/37575914
http://dx.doi.org/10.3389/fpls.2023.1186250
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