The selection of superior lines is extremely important to improve the utilization rate and economic value of bamboo. In this research, 120 anther-regenerated bamboo lines were planted in the field, and the survival rate reached 84.2% one year after planting. During five years of observations, we continuously measured and recorded the number of shoots and the size of the new bamboo of these regenerated lines. The results showed that there were considerable differences in culm size and growth rate among the different lines. After comprehensive evaluation, we found that one of the lines (P82) had obvious advantages in culm size and growth rate compared with the others. The chromosome ploidy of line P82 and the other three lines (P38, P84, and P34) was detected. It was found that P82 was hexaploid, while the other three lines were dodecaploid. Nutritional components of the P82 shoots were further detected. The results showed that the content of soluble sugar was 1.4%, the content of free amino acid was 3.5 g·kg⁻¹ (FW, fresh weight), and the content of protein was 14.8 g·kg⁻¹ (FW), and there were no significant differences compared with the local wild mature bamboo. Anatomical analysis showed that the vascular bundle size of the line P82 (hexaploid) was significantly larger than that of line P38 (dodecaploid), and the length of parenchyma cells in the culm wall of line P82 was similar to that of line P38, however, the cell width of line P82 was significantly wider than that of line P38. In this study, the breeding of superior lines of regenerated bamboo plants from an anther culture was realized, which provided an example for a new method for selecting superior lines from an anther culture, and also enriched the resources of superior lines of D. latiflorus.

Utilizing bamboo residues for the cultivation of Stropharia rugosoannulata is an ecological practice grounded in the concept of agricultural waste recycling, aiming to improve soil microecology and enhance nutrient cycling in bamboo forests. However, a comprehensive and systematic evaluation of the ecological effects of using bamboo residues as cultivation substrates is lacking. To evaluate soil responses following the cultivation of S. rugosoannulata, a field experiment was conducted using bamboo residues pre-fermented with 4% rapeseed cake. The results showed that cultivating S. rugosoannulata with rapeseed cake-fermented bamboo residues significantly enhanced soil nutrient levels and enzyme activities. Notable increases were observed in soil organic carbon, total nitrogen, available nitrogen, and total potassium, as well as in the activities of sucrase, urease, peroxidase, polyphenol oxidase, and neutral protease. Both bacterial and fungal α-diversity were significantly enhanced, and substantial shifts occurred in the community structure and composition of soil microbiota. Metabolomic analysis revealed that significantly differential metabolites were primarily enriched in five key pathways, including purine metabolism, glycerolipid metabolism, biosynthesis of plant secondary metabolites, and starch and sucrose metabolism. Correlation analyses further revealed that specific microbial taxa (four bacterial genera and seven fungal genera) exhibited strong correlations with soil nutrient indicators, whereas another group of taxa (six bacterial phyla and eight fungal genera) was closely linked to soil enzyme activities. Furthermore, bacterial communities were significantly correlated with metabolite variations after substrate addition. Specifically, Firmicutes showed strong positive correlations with multiple metabolites, whereas Planctomycetes exhibited negative correlations with some of the same metabolites, indicating potential competitive interactions. Based on these findings, this study proposes a preliminary “Microbe–Enzyme–Metabolite–Nutrient” coupling cycle, driven by the synergistic interplay among bamboo residues, hypha–microbiome complex, soil enzymes, and functional metabolites. This mechanism provides a scientific explanation for the soil health improvements observed during S. rugosoannulata cultivation and offers theoretical support for the efficient utilization of bamboo waste and maintenance of forest ecosystem stability.

Bamboo shoot quality is determined by sugars, proteins, water status, and cell-wall components that are conventionally quantified by destructive, low-throughput assays. Here we tested whether visible–near-infrared (VIS–NIR) hyperspectral imaging over 400–1000 nm can estimate five nutritional and structural traits—soluble sugar, protein, moisture, lignin, and cellulose—in Dendrocalamus brandisii shoots, and whether the tissue region from which spectra are taken matters. Spectra were acquired from 102 shoots, and partial least squares regression (PLSR), Ridge regression, and principal component analysis–Ridge regression (PCA–Ridge) were compared under repeated held-out validation stratified by shoot length class. Under this internal validation, PCA–Ridge gave the highest mean test-set R² for soluble sugar (R² = 0.64 ± 0.04) and lignin (R² = 0.51 ± 0.06), PLSR for protein (R² = 0.62 ± 0.05), and Ridge for moisture (R² = 0.52 ± 0.04). Cellulose was not reliably predictable (R² ≈ 0.04). Modeling the outer and inner tissue surfaces separately showed that the most informative region is trait-dependent: the inner tissue surface provided stronger predictive information for protein and moisture, while the outer surface showed a slight advantage for soluble sugar; for lignin, the regional difference was small. Within length classes, soluble sugar was positively correlated with hardness in the 30 cm class (r = 0.728, BH-adjusted p = 0.005). VIS–NIR hyperspectral imaging combined with regularized regression thus offers a workable route to estimating soluble sugar, protein, moisture, and lignin (cellulose was not estimable in the 400–1000 nm window) from a single acquisition in D. brandisii shoots, with trait-specific ROI placement. Models were trained on a single sampling batch that included six shoots of other bamboo species, and require independent D. brandisii-only validation across sites, seasons, and instruments.