Net Primary Productivity Is Driven by Aridity Index and Phenological Phase in Forest Region of China DOI Open Access
Qinghong Cui, Xiao Xiao,

Zhujun Hong

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 612 - 612

Published: March 31, 2025

Net primary productivity (NPP) is a key indicator for assessing carbon fixation capacity. Understanding the mechanisms of sequestration capacity forest ecosystems critical in context global climate change. Research on influencing factors and driving NPP areas China still insufficient, especially lack systematic analysis role phenology. Forest cover has been increasing recent decades due to natural expansion planted forests. It significant clarify underlying drivers China. To address this issue, we collected annual NPP, biomass, phenology, temperature, precipitation data from 2002 2021, then applied general linear mixed effect model (GLMM) Bayesian structural equation models conduct comprehensive NPP. The results have shown that all exert positive influence through bivariate relationship analysis. GLMM revealed was significantly positively affected by aridity index, Among these, index (AI) (58.39%) temperature (27.21%) were identified as having highest contributions direct indirect effects evaluated using (SEMs), interactions between their regulatory revealed. This study crucial understanding impact change regulating providing strategies effective management.

Language: Английский

Net Primary Productivity Is Driven by Aridity Index and Phenological Phase in Forest Region of China DOI Open Access
Qinghong Cui, Xiao Xiao,

Zhujun Hong

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(4), P. 612 - 612

Published: March 31, 2025

Net primary productivity (NPP) is a key indicator for assessing carbon fixation capacity. Understanding the mechanisms of sequestration capacity forest ecosystems critical in context global climate change. Research on influencing factors and driving NPP areas China still insufficient, especially lack systematic analysis role phenology. Forest cover has been increasing recent decades due to natural expansion planted forests. It significant clarify underlying drivers China. To address this issue, we collected annual NPP, biomass, phenology, temperature, precipitation data from 2002 2021, then applied general linear mixed effect model (GLMM) Bayesian structural equation models conduct comprehensive NPP. The results have shown that all exert positive influence through bivariate relationship analysis. GLMM revealed was significantly positively affected by aridity index, Among these, index (AI) (58.39%) temperature (27.21%) were identified as having highest contributions direct indirect effects evaluated using (SEMs), interactions between their regulatory revealed. This study crucial understanding impact change regulating providing strategies effective management.

Language: Английский

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