【Objective】This research aims to explore the feasibility of utilizing terrestrial laser scanning (TLS) technology for acquiring stem form characteristics of trees and to evaluate and select elit of Korean pine (Pinus koraiensis), providing a theoretical basis for the comprehensive evaluation of superior provenances.【Method】Using 26 P. koraiensis provenances from the provenance test forest in Maoershan, Heilongjiang Province, as experimental materials, TLS was employed to scan sample plots. Data preprocessing was conducted using LiDAR360 software, and parameters such as tree height, diameter at breast height (DBH), and diameters at different heights extracted by TLS were evaluated. Four stem form parameters (breast-height form factor, breast-height form quotient, experimental form factor, and height-to-diameter ratio) were calculated. Additionally, taper equations based on provenance effects were constructed using diameters at different heights extracted by TLS to describe stem form variations among different provenances.【Result】TLS achieved high accuracy in extracting individual tree parameters. The extraction accuracy for DBH (coefficient of determination R2= 0.979 6) was superior to that for tree height (R2=0.811 4) and volume (R2=0.911 9). As the stem height increased, the R2 for diameters at relative heights gradually increased, reaching its peak at 0.08h(R2=0.984 0, where h represents tree height). Beyond 0.08h, the R2 for diameters at relative heights gradually decreased. Variance analysis of stem form indicators for the 26 P. koraiensis provenances showed that all indicators exhibited significant differences among provenances (P<0.05). Introducing provenance variables into the base model improved the fitting accuracy, and constructing a nonlinear mixed model with replication as a random effect further enhanced model precision. By plotting stem form graphs for different provenances, it was observed that the trends in stem form variation were generally consistent across provenances, but growth rates differed.【Conclusion】TLS demonstrated high accuracy in extracting individual tree parameters. All stem form indicators showed significant differences among provenances, indicating good potential for selection. Based on stem form indicators and the optimal taper equation incorporating provenance effects, a group of superior provenances was identified, providing a foundation for genetic improvement and widespread utilization of P. koraiensis.
【Objective】This study aims to identify optimal agroforestry management patterns for Pinus koraiensis plantations and establish a scientific foundation for decision-making in compound management practices.【Method】Three agroforestry systems (forest-fungi, forest-medicine, and forest-vegetable) within P. koraiensis plantations were investigated. Quadrat surveys and high-throughput sequencing were integrated to assess species diversity, soil nutrient dynamics, enzymatic activities, and microbial community structures. Full-length 16S rRNA and ITS amplicon sequencing were utilized to characterize bacterial and fungal communities across soil depths of (0,10] and (10,20] cm.【Result】(1)During initial management stages, species diversity indices in agroforestry systems were lower than those in the control (monoculture plantation).(2)The forest-fungi and forest-vegetable systems exhibited higher soil nutrient accumulation (e.g., organic carbon, total nitrogen), whereas forest-vegetable and forest-medicine systems demonstrated superior nutrient conversion efficiency.(3)Soil catalase activity ranked as forest-fungi > forest-medicine > forest-vegetable, while cellulase activity peaked in the forest-medicine system, with significantly higher values in the (0,10] cm layer than in the (10,20] cm layer (P< 0.05). No significant differences were observed in sucrase or acid phosphatase activities among systems. (4)The α-diversity of soil fungal and bacterial communities was significantly different in richness,and not significantly different in biodiversity,microbial communities across all systems were dominated by mortierellomycota, ascomycota, actinobacteriota, and proteobacteriota, with ascomycota and actinobacteriota identified as key taxa driving nutrient utilization under agroforestry practices. (5)Partial Mantel test revealed than the dominant factors influencing bacterial and fungal communities were soil organic carbon,total nitrogen(TN)and plant Simpson index(P<0.001).In the forest-funus system,both catalase activity and cellulase activity exhibited significant effects on fungal communities,whereas in the forest-vegetable system,soil organic carbon content and C to N ratio,along with the plant Simpson index in control plots,showed notable impacts on fungal communities(P<0.05).Additionally,the plant Shannon index and soil sucrase activity in control plots were indentified as the primary drivers of bacterial community composition(P<0.05).【Conclusion】Agroforestry management initially disrupts understory communities and soil ecosystems, but gradually enhances species richness, nutrient cycling efficiency, and microbial functional stability.
【Objective】A system of stand-level growth models with diameter-class disaggregation was developed for Korean pine (Pinus koraiensis) plantations in Heilongjiang Province to optimize stand management strategies, providing model support for developing management schedules and enhancing forest quality.【Method】Based on the remeasurement data from 218 permanent plots in Korean pine plantations in Heilongjiang Province during 1980—2023, a model system consisting of models for mortality, dominant height, stand basal area and diameter-class disaggregation was constructed. The Weibull function was used to disaggregate the predictions over diameter classes. The parameters of the growth models were estimated using the Gauss-Newton method and seemingly unrelated regression. The method of moments was used to recover the diameter distribution parameters for the diameter-class disaggregation. To verify the applicability of the model system, the study used the differential evolution (DE) algorithm with a model system to perform stand-level management optimization to find the rotation length that maximized wood production in different site indices (11.2, 14.2, 16.0 m).【Result】The components of the dynamic growth model explained over 90% of the variation in the modelling data. The final critical errors for stand mortality and basal area model obtained using seemingly unrelated regression were 16.769% and 17.685%, respectively. When applying the method of moments for parameter estimation of the Weibull equation, the pass rate of the Kolmogorov-Smirnov test was 96.946%. When using the growth with diameter-class disaggregation for predicting stand volume, using an existing taper model, the critical error was 14.612%. The optimization results indicated that, for the three stands, the thinning is by 1-3 years later as site index improves by 2 m, with the final harvest age ranging from 72 to 75 years.【Conclusion】The growth model constructed in this study satisfies the basic assumptions of path-invariance, consistency, and causality during management simulations, thereby allowing reliable growth simulations. Integrating the model with the DE algorithm provides effective forest management prescriptions, offering useful advise for the management decisions of Korean pine plantations.
