Development and application of the 3-PGCj model for predicting stand growth of Japanese cedar (Cryptomeria japonica) plantations
Journal
Trees, Forests and People
Journal Volume
20
Start Page
100856
ISSN
2666-7193
Date Issued
2025-06
Author(s)
Abstract
Japanese cedar (Cryptomeria japonica), widely planted for its high-quality timber, lacks reliable growth models for long-term forest management under climate change. This study develops the 3-PGCj model, an extension of the 3-PG framework, to predict C. japonica plantation growth. The model improves upon the original 3-PG by incorporating density-dependent mortality, canopy development, and site-specific fertility ratings (FR). It uses climate data to simulate growth, estimate biomass allocation through allometric equations, assess mortality via zero-inflated Poisson modeling, and simulate canopy dynamics. Parameterization was based on long-term data from 23 C. japonica sites (ages 69–107 years), supplemented by literature and a plant trait database. The 3-PGCj model performed well, with RMSE and MAPE values of 203 st ha-1 (21.1%) for stand density and 2.6 cm (8.2%) for quadratic mean diameter at breast height (qDBH), and high R2 values (0.95 for qDBH and 0.94 for stand density). The results revealed higher planting densities led to earlier attainment of the maximum mean annual increment and current annual increment, key for optimizing management decisions like thinning and rotation ages. However, environmental and climatic conditions can cause variation in optimal timings across plantation areas. Calibration of FR improved model accuracy, demonstrating the influence of site conditions and climatic factors on stand growth, with higher FR values observed at higher elevations and frequent fog. This study highlights the potential of hybrid models to deepen understanding of forest dynamics under climate variability and provide valuable insights for sustainable forest management.
Subjects
Climate variability
Fertility rating
Forest management
Japanese cedar plantation
Mortality
Physiological Principles in Predicting Growth (3-PG)
Self-thinning
Stand growth modeling
Publisher
Elsevier BV
Description
Article number: 100856
Type
journal article