Accurate measurement of stem diameter is essential to forest inventory. As a millimeter-level measuring tool, terrestrial laser scanning (TLS) has not yet reached millimeter-level accuracy in stem diameter measurements. The objective of this study is to develop an accurate method for deriving the stem diameter from TLS data. The methodology of stem diameter measurement by diameter tape was adopted. The stem cross-section at a given height along the stem was determined. Stem points for stem diameter retrieval were extracted according to the stem cross-section. Convex hull points of the extracted stem points were calculated in a projection plane. Then, a closed smooth curve was interpolated onto the convex hull points to simulate the path of the diameter tape, and stem diameter was calculated based on the length of the simulated path. The stems of different tree species with different properties were selected to verify the presented method. Compared with the field-measured diameter, the RMSE of the method was 0.0909 cm, which satisfies the accuracy requirement for forest inventory. This study provided a method for determining the stem cross-section and an efficient and precise curve fitting method for deriving stem diameter from TLS data. The importance of the stem cross-section and convex hull points in stem diameter retrieval was demonstrated.

The relationship of stand top and stand mean height is important for forest growth and yield modeling, but it has not been explored for natural mixed forests. Observations of stand top and stand mean height can present spatial dependence or autocorrelation, which should be considered in modeling. Simultaneous autoregressive (SAR) models, including spatial lag model (SLM), spatial Durbin model (SDM) and spatial error model (SEM), within nine spatial weight matrices were utilized to model the stand top and stand mean height relationship in the mixed Quercus mongolica Fisch. ex Ledeb. broadleaved natural stands of Northeast China, using ordinary least squares (OLS) as a benchmark model. The results showed that there was a high linear relationship between stand top and stand mean height and that there was a positive spatial autocorrelation pattern in model residuals of OLS. Moreover, SEM and SDM performed better than OLS in terms of reducing the spatial dependence of model residuals and model fitting, regardless of which spatial weight matrix was used. SEM was better than SDM. SLM scarcely reduced the spatial autocorrelation of model residuals. Among nine spatial matrices in SEM, rook contiguous matrix performed best in model fitting, followed by inverse distances raised to the second power (1/d²) and local statistics model matrix (LSM).

Terrestrial laser scanning (TLS) can be used as a millimeter-level measurement tool for forest inventories. However, the stem diameter retrieval accuracy in sample plot scanning is not yet convincing. The errors in each step of stem diameter retrieval algorithms must be evaluated. In this study, six numerical calculation methods for the numerical calculation step, i.e., cylinder fitting (CYF), circle fitting (CF), convex hull line fitting (CLF), the proposed caliper simulation method (CSM), closure B-spline curve fitting (SP) and closure Bézier curve fitting with global convexity (SPC), were applied to stem diameter retrieval, and the similarities and differences were evaluated. The ovality, completeness and roughness were used to evaluate the stem slice point cloud quality. A total of 165 stem slice point clouds at breast height collected from three Larix kaempferi plots were used. Compared with the field-measured stem diameters at breast height (DBHs), the root mean square errors (RMSEs) of the CYF, CF, CLF, CSM, SP and SPC methods were 0.30 cm, 0.30 cm, 0.51 cm, 0.51 cm, 0.56 cm and 0.54 cm, respectively. Compared with the SPC method results, the RMSE of the CSM results was 0.05 cm. The results illustrated that the CYF and CF methods performed the same, as did the CLF and CSM methods. Most DBHs retrieved by the CYF and CF methods were smaller than the field-measured DBHs, and most DBHs retrieved by the CLF, CSM, SP and SPC methods were larger than the field-measured DBHs. This study demonstrated that the CYF and CF methods perform the best and are the most robust, and the measurements by a diameter tape and a caliper are similar enough for forestry inventories. Evaluating and preprocessing stem slice point clouds is a potential way to improve stem diameter retrieval accuracy.

Tea breeding increasingly requires a continuous evidence chain from germplasm identity to molecular variation and progeny performance, but cultivar passports, genome resources, transcriptome profiles, metabolite data and breeding records are often managed separately. Here, we present Ningbo TeaBase (NB-TeaBase), an open, cultivar-centered database for robust-bud tea (Camellia sinensis) breeding. The platform uses named cultivars as the organizing unit and integrates germplasm passport information for eight elite cultivars with 23 chromosome-level genomes, whole-genome resequencing variants, RNA-seq expression profiles, untargeted LC-MS/MS metabolite features, annotations and downloadable records. Shared cultivar, gene, variant, pathway and metabolite identifiers allow users to characterize a cultivar across genomic, transcriptomic and metabolic layers and to connect these profiles with breeding records. A parental-prediction module incorporates genome-wide markers and phenotypes from 122 progeny assigned to seven observed parental combinations; it reports genomic estimated breeding values (GEBVs) for candidate crosses and observed-family general and specific combining-ability summaries for seven traits. By combining germplasm documentation, multi-omics evidence and progeny-informed cross ranking, NB-TeaBase supports cultivar evaluation, parent selection, cross prioritization and breeding strategy formulation. Current prediction outputs remain exploratory because the phenotypic training data cover only seven parental combinations and require broader validation.

Camellia japonica is a widely cultivated woody ornamental plant. However, current studies mostly focus on the onset of flowering, neglecting the overall flowering time distribution patterns of the blooming process. In this study, we used uniform 5-year-old potted cuttings of C. japonica ‘Jinjiang Mudan’ to evaluate six candidate distribution models, including normal, log-normal, skew-normal, gamma, Weibull, and exponential, to model flowering time distribution. These candidates were compared to obtain an optimal distribution model using three-fold cross-validation, six evaluation indicators, and three goodness-of-fit tests in the control. The optimal distribution model was used to compare and analyze the different effects of the control, shading, and exogenous hormone treatments. The results showed that the skew-normal distribution model emerged as the most suitable distribution model among the six candidates and captured the flowering time distribution characteristics effectively in all treatments. Shading treatments were found to delay and extend the flowering period, with moderate treatments (50% and 70% shading) demonstrating better performance, extending the flowering period by approximately 40%. In terms of exogenous hormone treatments, BG (a mixture of the 6-BA and GA₃) concentrations could prolong and delay the flowering period. Lower concentrations (100 and 250 mg L⁻¹) of 6-BA and GA₃ were effective in extending the flowering period, with BA250 exhibiting the most pronounced effect, delaying flowering onset by approximately 12% and extending the flowering period by approximately 17%. Considering that this study is based on single-location and single-season trials, these findings provide a valuable methodological resource for quantifying and predicting flowering time distribution in C. japonica, other ornamentals, and crops.