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.

Long-term monocultures of tea and the excessive use of chemical fertilizer lead to the degradation of soil quality. Improving the soil quality of ex-tea plantations through vegetation restoration is an important task. However, the changes in soil nutrients, fungal communities, and the effects of microorganisms on soil nutrients after reforestation remain unclear. Therefore, in this study, we aimed to explore the effects of Pinus and Chinese fir on soil nutrients and fungal communities in ex-tea plantation areas that were subjected to the reforestation modes of pure forest and mixed forest by measuring soil chemical properties and ITS rRNA gene sequences. The results showed that (1) after reforestation, the relative normalized difference vegetation index (NDVI) of the Mixed forest, Mixed Pine and Mixed Fir areas increased (p < 0.05) compared to that of pure forest; (2) the soil organic carbon (SOC), total nitrogen (TN), and N:P ratios of the mixed forest increased by an average of 54%, 90%, and 299% (p < 0.05) compared to pure forest, whereas the total phosphorus (TP) and available potassium (AK) decreased by an average of 39% and 89% (p < 0.05); and (3) there was no significant difference in the diversity of the fungal communities of the pure and mixed forests, but the fungal phyla Mucoromycota, Glomeromycota, and Rozellomycota were significantly different in the pure and mixed forests. This differing microbial composition led to a significant increase (p < 0.05) in symbiotrophs (ecotomycorhizal, ericoid mycorhizal) in the mixed forest, which was negatively correlated with the soil TP and positively correlated with the TN and the N:P ratio. In addition, there was also a significant decrease (p < 0.05) in complex nutrient types (ectomycorrhizal-fungal parasite-plant saprotroph-wood saprotroph), which were negatively correlated with the SOC and TN, and arbuscular mycorrhizas, which were positively correlated with the TP. Our results show that the chemical properties of soils and the structure of the fungal communities changed significantly due to the reforestation of Chinese fir and Pinus, and the mixed forest mode of reforestation was more conducive to improving the soil quality; therefore, a mixed forest of Chinese fir and Pinus can be used to improve degraded soils in ex-tea planting areas.

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.

Plant-associated microbes play a crucial role in host growth, health, and stress resistance. Host plants significantly influence the assembly of their microbial communities. To examine the influence of plant provenance on bacterial composition and plant growth promoting (PGP) properties, we compared readily culturable bacteria isolated from the root interior and rhizosphere of Tripterygium wilfordii Hook f., a woody Chinese medicinal plant, across three provenances grown in different regions. Regardless of the host growing environment, the abundance of endophytic bacterial colonies followed the trend: Fujian provenances > Hunan provenances > Hubei provenances. A total of 227 isolates were classified into 21 genera from the root interior and 40 genera from the rhizosphere, with Pseudomonas and/or Bacillus as the dominant genera. The taxonomic composition varied across plant provenances and host growing environments. Plants from the same provenance but cultivated in different locations exhibited greater similarity in their endophytic bacterial composition. The majority of the assayed strains displayed one or more PGP traits. Local plants harbored a higher proportion of PGP strains than non-local plants. Our findings indicate that plant provenance significantly influences the composition of root-associated culturable bacteria, particularly endophytic communities. Plants in their native environments may recruit PGP bacteria to enhance their fitness.