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Volume 16
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10.3390/f16020353
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Article

Development of Full Growth Cycle Crown Width Models for Chinese Fir (Cunninghamia lanceolata) in Southern China

by
Zheyuan Wu
1,
Dongbo Xie
1,
Ziyang Liu
1,
Linyan Feng
1,
Qiaolin Ye
2,
Jinsheng Ye
3,
Qiulai Wang
3,
Xingyong Liao
4,
Yongjun Wang
4,
Ram P. Sharma
5 and
Liyong Fu
1,*
1
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2
State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University, Nanjing 210037, China
3
Guangdong Forestry Survey and Planning Institute, 338 Guangshanyilu, Guangzhou 510520, China
4
Chengdu Academy of Agriculture and Forestry Sciences, Chengdu 610000, China
5
Institute of Forestry, Tribhuvan University, Kathmandu 44600, Nepal
*
Author to whom correspondence should be addressed.
Forests 2025, 16(2), 353; https://doi.org/10.3390/f16020353
Submission received: 31 December 2024 / Revised: 26 January 2025 / Accepted: 14 February 2025 / Published: 16 February 2025
(This article belongs to the Special Issue Forest Biometrics, Inventory, and Modelling of Growth and Yield)
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Abstract

This study focused on 16,101 Cunninghamia lanceolata trees across 133 plots in seven cities of Guangdong Province, China, to develop a comprehensive full growth cycle crown width (CW) model. We systematically analyzed the dynamic characteristics of CW and its multi-scale influencing mechanisms. A binary basic model, with the diameter at breast height (DBH) and height (H) as core predictor variables, effectively reflected tree growth patterns. The inclusion of age groups as dummy variables allowed the model to capture the dynamic changes in CW across different growth stages. Furthermore, the incorporation of a nested two-level nonlinear mixed-effects (NLME) model, accounting for random effects from the forest block- and sample plot-level effects, significantly improved the precision and applicability of the final model (R2 = 0.731, RMSE = 0.491). This model quantified both macro- and micro-level effects of region and plot on CW. Our findings showed that the two-level NLME model, incorporating tree age groups, optimally accounted for environmental heterogeneity and tree growth cycles, resulting in the best-fitting statistics. The proposed full growth cycle CW model effectively enhanced the model’s efficiency and predictive accuracy for Cunninghamia lanceolata, providing scientific support for the sustainable management and dynamic monitoring of plantation forests.
Keywords: crown width model; mixed-effects model; power function model; Cunninghamia lanceolata; full growth cycle crown width model; mixed-effects model; power function model; Cunninghamia lanceolata; full growth cycle

Share and Cite

MDPI and ACS Style

Wu, Z.; Xie, D.; Liu, Z.; Feng, L.; Ye, Q.; Ye, J.; Wang, Q.; Liao, X.; Wang, Y.; Sharma, R.P.; et al. Development of Full Growth Cycle Crown Width Models for Chinese Fir (Cunninghamia lanceolata) in Southern China. Forests 2025, 16, 353. https://doi.org/10.3390/f16020353

AMA Style

Wu Z, Xie D, Liu Z, Feng L, Ye Q, Ye J, Wang Q, Liao X, Wang Y, Sharma RP, et al. Development of Full Growth Cycle Crown Width Models for Chinese Fir (Cunninghamia lanceolata) in Southern China. Forests. 2025; 16(2):353. https://doi.org/10.3390/f16020353

Chicago/Turabian Style

Wu, Zheyuan, Dongbo Xie, Ziyang Liu, Linyan Feng, Qiaolin Ye, Jinsheng Ye, Qiulai Wang, Xingyong Liao, Yongjun Wang, Ram P. Sharma, and et al. 2025. "Development of Full Growth Cycle Crown Width Models for Chinese Fir (Cunninghamia lanceolata) in Southern China" Forests 16, no. 2: 353. https://doi.org/10.3390/f16020353

APA Style

Wu, Z., Xie, D., Liu, Z., Feng, L., Ye, Q., Ye, J., Wang, Q., Liao, X., Wang, Y., Sharma, R. P., & Fu, L. (2025). Development of Full Growth Cycle Crown Width Models for Chinese Fir (Cunninghamia lanceolata) in Southern China. Forests, 16(2), 353. https://doi.org/10.3390/f16020353

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