Root turnover is a key process contributing to soil carbon storage, nutrient cycling, and other ecosystem functions. However, quantifying root turnover rates remains highly uncertain and methodologically challenging. Field rhizotrons were employed to quantify root turnover times using median longevities of five branching orders in a Larix gmelinii plantation. Root images were recorded by scanning the rhizotron windows at a monthly interval during four growing seasons. Root demographic data and branching orders were obtained by analyzing these images using Rootfly software coupled with manual mouse‐tracing of individual roots. Root longevities and turnover estimates were calculated using these data. Roots of different branching orders showed significantly different turnover times. The mean turnover times of the first‐order roots and second‐order roots were 284 and 994 d, respectively. Roots of higher branching orders (third to fifth orders) remained alive at the end of the 4‐yr experimental period, indicating much longer turnover times than the duration of the experiment. Root turnover times increased exponentially with branching orders. Further analysis of these data suggested that root branching orders combined with sampling biases, timing of root cohorts, and longevity distribution patterns crucially influenced root turnover times. The method of combining field glass rhizotrons with electronic scanning permits quantification of root turnover for five branching orders in a temperate forest. The overall result empirically demonstrates the crucial role of branching orders for accurately quantifying root turnover times.

Mixed plantation of Chinese fir (Cunninghamia lanceolata) is an effective artificial forest management for tree productivity. However, the mixing strategies, site conditions, and subsurface properties that affect tree productivity are not yet fully understood. In this study, we conducted a meta-analysis of 96 publications to consolidate insights on the effects of mixing strategies (e.g., planting density, mixing proportion, mixed species, and tree age), site conditions (e.g., mean annual precipitation (MAP), mean annual temperature (MAT), elevation, and total nitrogen (TN) or total phosphorus (TP) of sample sites), and subsurface properties (e.g., soil characteristics, microbial communities, and extracellular enzyme activity) on tree height, diameter at breast height, and individual volume of Chinese fir. We used the Web of Science and China National Knowledge Infrastructure for searching peer-reviewed papers, and the searching words were: (“Cunninghamia lanceolata” OR “Chinese fir”) AND “mix*”. Following the data screening process, the natural logarithm of the response ratio (lnRR) was computed for subsequent analysis. The results showed that introduced companion species generally increased the individual volume of Chinese fir by an average of 20%. Densities ranging from 1200 to 2000 trees per hectare and moderate mixing proportions (1:1 to 3:1) optimized individual tree growth and thereby boosted productivity. Broadleaf species may be beneficial companions, and trees aged 10 to 20 years grew fastest. At sites with low MAT and high MAP, mixed plantations enhanced the tree productivity of Chinese fir. The optimal elevation range for mixed plantations may be 200 to 600 m. Further, mixed plantations significantly changed soil properties by improving soil structure, increasing soil pH and soil water content, and soil total and available N and P, which were crucial for boosting the productivity of Chinese fir. Soil microbial biomass and enzyme activities were also significantly increased by mixed plantations. Overall, these findings highlight the importance of mixing strategies and site conditions in increasing tree productivity of Chinese fir by improving soil physicochemical characteristics, increasing resource availability, and reducing interspecific and intraspecific competition through niche separation.

Rhizosphere effects (REs) play important roles in regulating carbon (C) and nutrient cycling in terrestrial ecosystems. However, little is known about the REs of mature trees in the field, especially at the ecosystem scale. This study aimed to explore the variation and patterns of REs in natural ecosystems. Here, combining soil monoliths with an adhering soil (shaking fine roots) method was adopted to sample paired rhizosphere soil and bulk soil along an altitudinal gradient. Based on the relative REs and the percentage of rhizosphere soil mass, the REs on soil C and net nitrogen mineralization rates (C_min and net N_min) at the ecosystem scale were estimated. Our results showed that the REs on soil processes, soil microbial biomass C and extracellular enzyme activities (β-glucosidase and N-acetyl-glucosaminidase activities), and soil chemical properties (total C, total N, inorganic N, extractable P, K, Ca, Mg, Fe, and Mn) were significantly positive across altitudinal sites, while soil pH was significantly negative. Although the relative REs on investigated variables varied significantly among altitudes, the relative REs did not show a clear trend with the increased altitudes. Across altitudes, the mean magnitude of ecosystem-level REs on C_min and net N_min were 19% (ranging from 4% to 48%) and 16% (ranging from 3% to 34%), respectively. Furthermore, the magnitude of ecosystem-level rhizosphere effects increased linearly with the increased altitudes. The altitudinal patterns of ecosystem-level RE mainly depend on the percentage of rhizosphere soil mass. In conclusion, our results provided a set of new evidence for the REs, and highlighted the need to incorporate REs into land C and N models.

Accurate segmentation of fine roots in field rhizotron imagery is essential for high-throughput root system analysis but remains challenging due to limitations of traditional methods. Traditional methods for root quantification (e.g., soil coring, manual counting) are labor-intensive, subjective, and low-throughput. These limitations are exacerbated in in situ rhizotron imaging, where variable field conditions introduce noise and complex soil backgrounds. To address these challenges, this study develops an advanced deep learning framework for automated segmentation. We propose an improved U-shaped Convolutional Neural Network (U-Net) architecture optimized for segmenting larch (Larix olgensis) fine roots under heterogeneous field conditions, integrating both in situ rhizotron imagery and open-source multi-species minirhizotron datasets. Our approach integrates (1) a Convolutional Block Attention Module (CBAM) to enhance feature representation for fine-root detection; (2) an additive feature fusion strategy (UpAdd) during decoding to preserve morphological details, particularly in low-contrast regions; and (3) a transfer learning protocol to enable robust cross-species generalization. Our model achieves state-of-the-art performance with a mean intersection over union (mIoU) of 70.18%, mean Recall of 86.72%, and mean Precision of 75.89%—significantly outperforming PSPNet, SegNet, and DeepLabV3+ by 13.61%, 13.96%, and 13.27% in mIoU, respectively. Transfer learning further elevates root-specific metrics, yielding absolute gains of +0.47% IoU, +0.59% Precision, and +0.35% F1-score. The improved U-Net segmentation demonstrated strong agreement with the manual method for quantifying fine-root length, particularly for third-order roots, though optimization of lower-order root identification is required to enhance overall accuracy. This work provides a scalable approach for advancing automated root phenotyping and belowground ecological research.