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A machine learning-based framework for evaluating photosynthetic regulation and carbon sequestration capacity among shelter forest species
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Shumin HAN1, Xing XING2, Rui ZHANG3, Penghao JI1, 4, Runhong GAO1, Yao YAO3, Zhengrong LYU3, Junling YANG3, Feng LI5, Jingliang SONG6
Journal of Central South University of Forestry & Technology | 2026, 46(6) : 140 - 152
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Journal of Central South University of Forestry & Technology | 2026, 46(6): 140-152
A machine learning-based framework for evaluating photosynthetic regulation and carbon sequestration capacity among shelter forest species
Full
Shumin HAN1, Xing XING2, Rui ZHANG3, Penghao JI1, 4, Runhong GAO1, Yao YAO3, Zhengrong LYU3, Junling YANG3, Feng LI5, Jingliang SONG6
Affiliations
  • 1.College Forestry, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China
  • 2.Inner Mongolia Women's Career Development Service Center, Hohhot 010051, Inner Mongolia, China
  • 3.Baotou Forestry and Grassland Work Station, Baotou 014030, Inner Mongolia, China
  • 4.College of Science, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China
  • 5.Wild Animal and Plant Protection Center, Tumt Left Banner Municipal Bureau of Forestry and Grassland, Hohhot 010100, Inner Mongolia, China
  • 6.Han Mountain Forest Farm, Balin Right Banner, Chifeng City, Inner Mongolia, Chifeng 025164, Inner Mongolia, China
Published: 2026-06-25 doi: 10.14067/j.cnki.1673-923x.2026.06.014
Outline
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【Objective】

The middle section of the Yinshan mountains in Inner Mongolia, as a representative area of the Three-North Shelter Forest Program, faces critical challenges in enhancing ecosystem carbon sequestration functions. This study aims to investigate the diurnal variation characteristics of photosynthesis and water use efficiency (WUE) in arbor and shrub species in the Middle Section of the Yinshan Mountains, analyze the influence of environmental factors on photosynthetic regulation, and comprehensively evaluate the ecological adaptability and carbon sequestration functions of different tree species. For the first time, a combination of random forest regression models and cluster analysis is applied to provide scientific insights for improving shelter forest ecosystem functions.

【Method】

Seven arbor species and five shrub species were selected in the middle section of the Yinshan mountains, Inner Mongolia. Photosynthetic parameters were measured using a CI-340 portable photosynthesis system. Spearman's correlation analysis, principal component analysis (PCA), random forest regression modeling, and hierarchical cluster analysis were employed to explore the photosynthetic mechanisms and ecological adaptability of the species.

【Result】

The study revealed distinct diurnal variation patterns in photosynthetic activity among arbor and shrub species. Net photosynthetic rate (Pn) and WUE exhibited significant interspecific differences, with single-peak and double-peak patterns observed. Temperature, stomatal conductance, and transpiration rate were identified as key regulators of photosynthesis, particularly in coniferous species, which demonstrated higher WUE. Random forest regression analysis highlighted the critical roles of temperature and water management in photosynthetic processes. Cluster analysis further revealed marked differences in carbon sequestration capacity and ecological adaptability among species, offering critical guidance for species selection and carbon sequestration assessments.

【Conclusion】

This study underscores the significant differences in photosynthesis and WUE between arbor and shrub species in the middle section of the Yinshan mountains. Temperature, stomatal conductance, and transpiration rate were pivotal in photosynthetic regulation. Unlike traditional linear regression methods, the random forest model effectively captured the nonlinear relationships between environmental variables and photosynthetic rates. The hierarchical cluster analysis, applied for the first time in this context, identified distinct functional groups of species based on their carbon sequestration potential and ecological adaptability. These findings fill a critical gap in understanding photosynthetic mechanisms and carbon sequestration capacity within the Three-North Shelter forest program, providing a novel framework for quantitative carbon sequestration assessment and informing future afforestation and restoration strategies.

the middle section of the Yinshan mountains in Inner Mongolia  /  net photosynthetic efficiency  /  random forest  /  carbon fixation and oxygen release  /  hierarchical cluster analysis
韩淑敏, 星星, 张瑞, 冀鹏浩, 高润红, 姚瑶, 吕整荣, 杨俊玲, 李峰, 宋景良. 基于机器学习的防护林树种光合调控与碳汇能力评估框架. 中南林业科技大学学报, 2026 , 46 (6) : 140 -152 . DOI: 10.14067/j.cnki.1673-923x.2026.06.014
Shumin HAN, Xing XING, Rui ZHANG, Penghao JI, Runhong GAO, Yao YAO, Zhengrong LYU, Junling YANG, Feng LI, Jingliang SONG. A machine learning-based framework for evaluating photosynthetic regulation and carbon sequestration capacity among shelter forest species[J]. Journal of Central South University of Forestry & Technology, 2026 , 46 (6) : 140 -152 . DOI: 10.14067/j.cnki.1673-923x.2026.06.014
Year 2026 volume 46 Issue 6
12
2
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Article Info
doi: 10.14067/j.cnki.1673-923x.2026.06.014
  • Receive Date:2025-05-09
  • Online Date:2026-10-30
  • Published:2026-06-25
Article Data
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History
  • Received:2025-05-09
  • Revised:2025-08-29
Funding
Affiliations
    1.College Forestry, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China
    2.Inner Mongolia Women's Career Development Service Center, Hohhot 010051, Inner Mongolia, China
    3.Baotou Forestry and Grassland Work Station, Baotou 014030, Inner Mongolia, China
    4.College of Science, Inner Mongolia Agricultural University, Hohhot 010018, Inner Mongolia, China
    5.Wild Animal and Plant Protection Center, Tumt Left Banner Municipal Bureau of Forestry and Grassland, Hohhot 010100, Inner Mongolia, China
    6.Han Mountain Forest Farm, Balin Right Banner, Chifeng City, Inner Mongolia, Chifeng 025164, Inner Mongolia, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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