Research on the Main Influencing Factors and Variation Patterns of Basal Area Increment (BAI) of Pinus massoniana

Zhuofan Li, Cancong Zhao, Jun Lu (), Jianfeng Yao (), Yanling Li, Mengli Zhou and Denglong Ha
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Zhuofan Li: College of Tourism, Xinyang Normal University, Xinyang 464000, China
Cancong Zhao: College of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Jun Lu: Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
Jianfeng Yao: Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem, Zhengzhou 450046, China
Yanling Li: College of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China
Mengli Zhou: College of Forestry, Henan Agricultural University, Zhengzhou 450046, China
Denglong Ha: Affairs Center, Henan Jigongshan National Nature Reserve, Xinyang 464000, China

Sustainability, 2025, vol. 17, issue 15, 1-20

Abstract: Understanding the environmental drivers of radial growth in the Pinus massoniana (lamb.) is essential for improving forest productivity and carbon sequestration in subtropical ecosystems. This study used the basal area increment (BAI) as an indicator of radial growth to investigate the main factors affecting the radial growth rate of P. massoniana and the changes in BAI with these factors. A total of 58 high quality tree ring series were analyzed. Six common methods were used to comprehensively analyze the importance of nine factor variables on the BAI, including tree age, competition index, average temperature, and so on. Generalized additive models (GAMs) were developed to explore the nonlinear relationships between each selected variable and the BAI. The results revealed the following: (1) Age and Competition Index was identified as the primary driving force; (2) BAI increased with Age when tree age was below 69 years; (3) from the overall trend, the BAI of P. massoniana decreased with the increase in the Competition Index. These findings provide a scientific basis for developing management plans for P. massoniana forests.

Keywords: Pinus massoniana; variable selection; machine learning; climate change (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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