What question did this study set out to answer?

This study aims to understand how wood anatomy influences mechanical properties in Chinese fir by using machine learning methods.

May 10, 2026Open Access

Wood anatomy-guided machine learning linking macromechanics in plantation-grown Chinese fir

SLShoujia LiuChinese Academy of Forestry WZWeihui ZhanChinese Academy of Forestry ZCZheng ChangChinese Academy of Forestry

Key Points

This study aims to understand how wood anatomy influences mechanical properties in Chinese fir by using machine learning methods.
Developed an interpretable SHAP-based framework for analyzing anatomical effects.
Trained five machine learning models using synthetic data generated by three models to predict MOE, MOR, and CSP.
Evaluated model performance comparing synthetic versus real data.
CopulaGAN–XGBoost achieved 75% accuracy for MOE prediction; Gaussian Copula–AdaBoost reached 80% for MOR; Gaussian Copula–Random Forest attained 91% for CSP.
Key anatomical traits affecting mechanics identified include tracheid length, wall thickness, and microfibril angle, with density being the most influential trait.
Latewood versus earlywood contributions show varying influence on mechanical properties.

Abstract

Abstract Understanding how anatomy shapes wood mechanics is essential for grading and breeding. This study develop an interpretable-SHAP-based framework providing the first conditionally independent decomposition of anatomical effects on Chinese fir ( Cunninghamia lanceolata ) performance. To mitigate data scarcity, synthetic-data generated by three generative models were evaluated for correlation, distribution and prediction. Five machine-learning models were trained on synthetic data to predict modulus of elasticity (MOE), modulus of rupture (MOR), and compressive strength parallel to grain (CSP). All generative methods produced realistic data, with Gaussian Copula performing best. The best accuracy was achieved by CopulaGAN–XGBoost (MOE, 75 %), Gaussian Copula–AdaBoost (MOR, 80 %), and Gaussian Copula–Random Forest (CSP, 91 %), outperforming models trained on real data (58 %, 64 %, 68 %). SHAP analysis identified tracheid length (15.6 %, 6.0 %, 8.3 %), wall thickness (13.8 %, 12.6 %, 7.5 %), and microfibril angle (3.4 %, 2.7 %, 4.5 %) as key traits, with microfibril angle showing the strongest interactions. Latewood versus earlywood contributions were 24.1 % versus 16.7 % (MOE), 16.6 % versus 39.9 % (MOR), and 18.1 % versus 28.8 % (CSP). Density was the most influential trait: strength (MOR, CSP) was driven by density and earlywood traits, while stiffness (MOE) depended on density and overall anatomy. These findings provide interpretable guidance for wood quality assessment, material grading and plantation improvement.

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