Simulating the canopy photosynthesis of Qinghai spruce (Picea crassifolia Kom.) in the Qilian Mountains, Northwestern China

• Published in: New forests 2022-05, Vol.53 (3), p.511-531
• Main Authors: Liu, Junjie, Wang, Juan, Han, Yanan, Yuan, Shunjie, Gao, Yunfei, Rong, Zhanlei, Zhao, Chuanyan
• Format: Article
• Language: English
• Subjects: Biomedical and Life Sciences; Canopies; Carbon cycle; Carbon sequestration; Forestry; Gas exchange; Herbivores; Leaves; Life Sciences; Mountains; Photosynthesis; Physical characteristics; Picea crassifolia; Primary production; Regression models; Spatial distribution
• ISSN: 0169-4286; 1573-5095
• DOI: 10.1007/s11056-021-09869-8

Qinghai spruce ( Picea crassifolia Kom.) forests play a key role in the carbon sequestration in the Qilian Mountains, Northwestern China. Carbon sequestration is closely related to canopy photosynthesis, which greatly depends on the leaf-level photosynthesis. Quantifying the magnitude of canopy photosynthesis will improve the modeling of forest carbon cycling. Although biochemical models have been widely used to estimate primary production, the effects of canopy partition on the photosynthetic rates and its feed-backs into the carbon cycle are generally not represented. In this study, the difference and connection between the leaf gas exchange indices in the different canopy partitions and the diameter at breast height (DBH), tree height (H), crown diameter (C D ), and living under branch height (B H ) were determined, and a multiple linear regression model was explored. This study was performed to develop a regression model to link morphological and physiological characteristics to canopy photosynthetic rate. The classification methods improved the working efficiency, fitting performance, and prediction accuracy compared with the classical method. The classified DBH method exhibited the best fitting performance, because it had the highest determination coefficient (R 2  = 0.9060). The photosynthetic model of the spruce canopy with the aforementioned factors produced a good simulation (R 2  = 0.9773) and provided data support to further estimate the carbon budget and primary net productivity of the spruce canopy. The developed model was also very useful for updating and modifying forest base maps and registries of spatial distribution of biomass and carbon storage.