This study aims to construct a stand-level model for estimating the carbon stock of Cunninghamia lanceolata forests applicable to Jiangxi Province based on the national standard “Tree Biomass Models and Related Parameters to Carbon Accounting for Major Tree Species” (GB/T 43648-2024), to improve the accuracy and reliability of the estimation.

Three theoretical growth equations—Logistic, Modified Weibull, and Gompertz—were used as the base models for this study. Two types of power function form, referred to as ND_g^φ and N^δD_g^φ, were introduced to reflect the density of the stand, resulting in six models for estimating carbon stock in variable-density stands. Additionally, the commonly used power-law allometric equation was included in the model comparison framework. The nonlinear weighted least squares method was employed for parameter fitting, and the optimal model was established through model evaluation and testing. To further validate the effectiveness of the constructed model, a systematic comparison was conducted between the estimates from the optimal model and those obtained using traditional methods.

1) The modified Weibull equation, incorporating the power function form ND_g^φ, achieves the highest coefficient of determination (R²=0.973) and the lowest standard error of estimate (SEE=3.004 t·hm^-2). Additionally, it demonstrates minimal values for total relative error (TRE=0.06%), mean systematic error (MSE=-0.07%), mean prediction error (MPE=1.77%), mean percent standard error (MPSE=8.99%), and Akaike Information Criterion (AIC=920.6). TRE and MSE are maintained within ±3%, while MPE and MPSE meet the general accuracy requirements of less than 3% and 15%, respectively. Furthermore, the model's residuals show significant improvements in heteroscedasticity and normality; 2) Compared with the traditional methods, the modified Weibull equation demonstrates the best performance with an R² of 0.978 and a parameter m of 0.950. The parameter n is not significantly different from 0 (P>0.1), and the absolute value of TRE is the lowest at 2.89%. Additionally, the systematic bias between the predicted values and the observed values is not significant (P>0.1). Among traditional methods, the BEF method shows an R² of 0.974 and a parameter m of 0.934. Like the modified Weibull equation, its parameter n is not significantly different from 0 (P>0.1). However, the TRE is 10.57%, indicating a significant systematic underestimation of the predicted values (P<0.001), with an average of 1.427 t·hm^-2 lower than the observed values. In contrast, the CBEF1 and CBEF2 methods yield R² of 0.792 and 0.872, respectively. Their parameter m values are 0.575 and 0.681, with parameter n values of 10.921 and 8.715. The TRE for CBEF1 and CBEF2 methods are -13.17% and -12.36%, respectively, indicating a significant systematic overestimation of the predicted values (P<0.05). On average, the predicted values exceed the observed values by 4.227 t·hm^-2 and 3.673 t·hm^-2, respectively.

The modified Weibull equation, developed according to the national standard, is optimal for estimating carbon stock in Cunninghamia lanceolata forests within the study area. Its estimation results are more accurate and reliable than those of traditional methods, offering greater practical value in application.

The broad-leaved secondary forest is a complex multi-layered mixed-aged forests, and the growth of the basal area determines the stand quality and carbon sink capacity of the forest. The purpose of this study was to investigate the influence of the annual basal area increment (BAI) of individuals in the broad-leaved stratified forests on individual sizes, experimental forests, and forest vertical levels (forest stories).

Three different experimental forests of the broad-leaved secondary forest in southern Jiangxi in China were taken as the research objects. According to the differences in individual-tree height and stand species composition, three experimental forests were divided into three forest stories and six different silvicultural types. The article was adopted the method of quantile regression model that was set 19 quantiles (τ ∈ {0.05, 0.10, 0.15, ..., 0.90, 0.95}), and established the nonlinear regression relationship between the annual basal area increment (BAI) and diameter at breast height (DBH_2021), height-to-diameter ratio (H_D_ratio), forest stories, experimental forests, silvicultural types, etc., and used some indicator, such as AIC, R_τ², MAD and MD, to evaluate each quantile time regression model.

1) There were significant differences in BAI between three experimental forests, but the difference in BAI between the silvicultural plots and the unsilvicultural plots in the same experimental forest was not statistically significant; 2) The quantile regression model when the AIC value was the lowest was log (BAI)_τ - log (DBH_2021) + DBH_2021 + H_D_ratio; when the quantile τ=0.45, the quantile regression model of BAI was most optimal, the fitting coefficient R_τ² of the best quantile regression model of BAI was 0.535 3. When forest stories, experimental forest types, and silvicultural types were considered, the fitting coefficient increased respectively 10%, respectively 0.638 3 (considering forest stories, experimental forest types), 0.638 9 (considering forest stories, silvicultural types); 3) log (DBH_2021), H_D_ratio, forest stories, forest stages, and silvicultural types had positive effects on BAI.

The growth quantile model of annual basal area increment of individual tree based on DBH, height-to-diameter ratio, forest stories, forest stages, and silvicultural types can provide quantitative basis for the quality and efficiency improvement technology of broad-leaved secondary forest, and also provide a technical reference for the sustainable management of forest resources.