The Chinese fir (Cunninghamia lanceolata) is the largest tree species used for afforestation in China. The purpose of this study was to explore the effects of site quality, stand density, and tree species composition on the growth and yield of mixed Chinese fir forests and to build prediction models for their stand average DBH (diameter at breast height), average height, and volume. Using 430 plots of mixed Chinese fir forests in the Fujian Province of China, the optimal base models for predicting stand average DBH, average height, and volume were selected from the Schumacher, Korf, Logistic, Mitscherlich, and Richards equations. On this basis, the site class index (SCI), stand density index (SDI), and tree species composition coefficient (TSCC) were introduced to improve the model’s performance, and the applicability of the different models was evaluated. The optimal base models for the average DBH, average height, and stand volume of mixed Chinese fir forests all used the Richards equation. The best fitting effect was obtained when the SCI was introduced into parameter a in the average height model, while the inclusion of the TSCC did not improve the model significantly. The fitting effects of the average DBH and stand volume models were both best in the form of $y=a_{1}SC{I}^{a_2}[1-\exp (-b_{1}SD{I}^{b_2})t{]}^c$ when the SCI and SDI were introduced. When the TSCC was further included, the fitting effects of the stand average DBH and volume models were significantly improved, with their R² increased by 47.47% and 58.45%, respectively, compared to the base models. The optimal models developed in this study showed good applicability; the residuals were small and distributed uniformly. We found that the SCI had an impact on the maximum values of the stand average DBH, average height, and volume; the SDI was closely related to the growth rate of the diameter and volume, while the TSCC influenced the maximum values of the stand average DBH and volume. The model system established in this study can provide a reference for the harvest prediction and mixing ratio optimization of mixed Chinese fir forests.

In order to clarify the combined impact of tree species composition, site quality, and stand age on the growth and harvest of mixed forests, the prediction models of average DBH and stand volume for mixed forests were established, respectively. The interval period and tree species composition coefficient (TSCC) were considered as independent variables. These models were then optimized by using the particle swarm optimization algorithm for reparameterization and evaluating their applicability. It was found that after introducing the site quality grade and TSCC, the average stand height prediction model showed a better fitting result. The fit accuracy of the average DBH prediction model and the stand volume prediction model were both improved with the help of the TSCC, mainly because the tree species composition affects the growth rate of the average stand height and average DBH and the maximum growth rate of the stand volume. The degree of the impact can be sorted as Cunninghamia lanceolata > Pinus massoniana > hard broad-leaved tree species (group). Overall, the established growth and harvest prediction models for mixed forests with the interval period and TSCC as independent variables have high fit accuracy and applicability.

In order to solve the problem in that the classical forest land expectation value method ignores the actual forest stock volume when assessing the income at the end of the current production cycle in the forest, and fill the research gap in this area, the technical system of the forest land asset evaluation was enriched. The forest land returns were divided into two parts, i.e., the segmented forest land return price from the growth of the actual forest stand to the end of the growth cycle (B_u1), and the segmented forest land return price for an infinite number of growth cycles after the growth of the actual forest stand to the end of the growth cycle (B_u2). Through structure, the forest land gain price expansion model was obtained, and the stand quality including the average diameter at breast height, average height, stock volume, and outturn of stand as dummy variables were used to construct the growth harvest model related to asset evaluation. Taking Cunninghamia lanceolata forest land as an example, the traditional asset evaluation methods were comparatively analyzed. The residual standard deviation (RSD) of the growth model was less than 10%, the total relative error (TRE) and mean system error (MSE) were within ±10%, the mean prediction errors (MPE) were less than 5%, and the mean percentage standard errors (MPSE) were less than 10%, respectively. Combining the forest land net present value expansion model with the traditional evaluation method, the evaluation value of the forest land assets was subsequently calculated, and accordingly, the forest land asset evaluation prime stand factors were predicated. It was found that the valuation results of the forest land net present value expansion model were consistent with the actual situation. The forest land net present value expansion model can therefore be used for asset evaluation of tree forest land (including natural uneven-aged forest land), bamboo forest land, shrub forest land, and economic forest land, and provide new technical support for forest land asset evaluation.

We developed a model for estimating the carbon storage and volume of entire forest stands at the provincial level, aiming to improve the accuracy of regional productivity assessments. Based on data from the branches, roots, leaves, and trunks of eight dominant tree species (grouped by origin) in Fujian Province, combined with plot-level data, we developed a compatible carbon storage estimation model. This model integrates species composition coefficients and uses stand volume as the independent variable. We estimated the model parameters using a combination of the immune evolutionary algorithm and an improved simplex method, which enhances convergence speed and solution stability compared to the traditional version. The accuracy of the model was validated by cross-model validation and concurrent testing. Applying the model to forest stand data from Wuyishan City, we simulated theoretical logging volumes to demonstrate its practical utility. The results demonstrated that the model exhibited high accuracy in fitting the observed data, with reliable predictions of carbon storage and volume across different forest components. In the case study area, the volume was 21.0521 million cubic meters and the carbon storage was 7.3238 million tons, both of which increased with decreasing interval periods. When logging factors were considered, the increases in carbon storage fluctuated as the interval periods increased and were higher than those when logging factors were not considered. This study confirmed that the developed models were effective for predicting land carbon storage and volume, and the simulation method successfully overcame the challenges associated with model estimation.

Accurate estimation of forest stand carbon storage is critical for assessing ecosystem functions and informing sustainable forest management. Most existing models depend heavily on stand age, a strategy that is often unreliable in natural forests, and they typically ignore species interactions, limiting their applicability across forest types. To overcome these issues, we developed a dynamic carbon storage model based on the Richards equation that replaces stand age with a growth interval period (defined as the time difference between two successive growth stages, T_n = T₂ − T₁) and explicitly incorporates site quality and species composition. This approach enables consistent estimation for both natural and plantation forests. Using field data from six dominant tree species in Wuyishan City, Fujian Province, we calibrated and validated the model through five-fold cross-validation. It achieved high accuracy, with an efficiency coefficient (EA) above 99% and a relative mean absolute error (RMA) under 7%, effectively reflecting how site conditions and mixed-species structures influence carbon dynamics. Total forest carbon storage in the study area was estimated at 7.32 million tons. Simulations show a gradual decline in carbon accumulation over time, consistent with natural growth saturation in aging stands. Scenario analyses further identified practical zones for sustainable harvesting in major plantation types, underscoring the model’s management relevance. The model does not yet include climate variability, disturbances, or below-ground carbon pools. Adding these components in future work would strengthen its utility for regional carbon assessment and support more robust carbon-neutral forestry planning.