Chinese fir (Cunninghamia lanceolata) is a significant timber species that has been broadly cultivated in southern China. A shoot blight disease on Chinese fir seedlings was discovered in Fujian, China and a fungus was then consistently associated with the symptoms. This fungus was determined to be causing this disease, among others by fulfilling Koch’s postulates. Based on morphological characteristics and multilocus phylogenetic analyses with the sequences of the internal transcribed spacer, partial glyceraldehyde-3-phosphate dehydrogenase gene, partial translation elongation factor 1-α gene, and partial 28S large subunit ribosomal RNA gene, the fungus was identified as Bipolaris oryzae. These characteristics and phylogenetic analyses clearly support that this pathogen is different from B. sacchari, which was, until now, considered to be the causal agent of a similar blight on Chinese fir in Guangdong, China. The fungus was also shown to be strongly pathogenic to rice, one of the most susceptible hosts to B. oryzae. Crop rotation involving rice is often carried out with Chinese fir in southern China, a practice that most likely increases the risk of shoot blight on C. lanceolata. To our knowledge, shoot blight caused by B. oryzae is reported for the first time in a gymnosperm species.

Studying population genetic structure and diversity is crucial for the marker-assisted selection and breeding of coniferous tree species. In this study, using RAD-seq technology, we developed 343,644 high-quality single nucleotide polymorphism (SNP) markers to resolve the genetic diversity and population genetic structure of 233 Chinese fir selected individuals from the 4^th cycle breeding program, representing different breeding generations and provenances. The genetic diversity of the 4^th cycle breeding population was high with nucleotide diversity (P_i) of 0.003, and H_o and H_e of 0.215 and 0.233, respectively, indicating that the breeding population has a broad genetic base. The genetic differentiation level between the different breeding generations and different provenances was low (F_st < 0.05), with population structure analysis results dividing the 233 individuals into four subgroups. Each subgroup has a mixed branch with interpenetration and weak population structure, which might be related to breeding rather than provenance, with aggregation from the same source only being in the local branches. Our results provide a reference for further research on the marker-assisted selective breeding of Chinese fir and other coniferous trees.

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is the most commercially important conifer in China, and the Nanjing Forestry University – Fujian Province Chinese fir Cooperation (NJFU – Fujian Cooperation) breeding program has advanced it into the third cycle of selection and breeding. In this paper, we estimated genetic parameters from four sites for 80 half-sib families and summarized previous estimates of genetic parameters in Chinese fir with an objective to propose optimal breeding strategy. Heritability averaged 0.20 and 0.14 for height and diameter at breast height (DBH), respectively, for the four sites. A significant genotype–environment interaction (G × E) for growth was also observed among the four sites, with the greatest interactions between a marginal site and the three central sites in the Fujian Province Chinese fir plantation region. The average estimated type-B genetic correlation between the marginal site and the three central sites was 0.08 for height and –0.09 for DBH. However, the highly productive families were among the most stable across the four sites. The results from this study in combination with summarized genetic parameters from literature were used to discuss and propose an optimal breeding strategy for the third generation of the breeding program for Chinese firs in Fujian Province.

Accurate and timely information on the number of densely-planted Chinese fir seedlings is essential for their scientific cultivation and intelligent management. However, in the later stage of cultivation, the overlapping of lateral branches among individuals is too severe to identify the entire individual in the UAV image. At the same time, in the high-density planting nursery, the terminal bud of each seedling has a distinctive characteristic of growing upward, which can be used as an identification feature. Still, due to the small size and dense distribution of the terminal buds, the existing recognition algorithm will have a significant error. Therefore, in this study, we proposed a model based on the improved network structure of the latest YOLOv5 algorithm for identifying the terminal bud of Chinese fir seedlings. Firstly, the micro-scale prediction head was added to the original prediction head to enhance the model’s ability to perceive small-sized terminal buds. Secondly, a multi-attention mechanism module composed of Convolutional Block Attention Module (CBAM) and Efficient Channel Attention (ECA) was integrated into the neck of the network to enhance further the model’s ability to focus on key target objects in complex backgrounds. Finally, the methods including data augmentation, Test Time Augmentation (TTA) and Weighted Boxes Fusion (WBF) were used to improve the robustness and generalization of the model for the identification of terminal buds in different growth states. The results showed that, compared with the standard version of YOLOv5, the recognition accuracy of the improved YOLOv5 was significantly increased, with a precision of 95.55%, a recall of 95.84%, an F1-Score of 96.54%, and an mAP of 94.63%. Under the same experimental conditions, compared with other current mainstream algorithms (YOLOv3, Faster R-CNN, and PP-YOLO), the average precision and F1-Score of the improved YOLOv5 also increased by 9.51-28.19 percentage points and 15.92-32.94 percentage points, respectively. Overall, The improved YOLOv5 algorithm integrated with the attention network can accurately identify the terminal buds of densely-planted Chinese fir seedlings in UAV images and provide technical support for large-scale and automated counting and precision cultivation of Chinese fir seedlings.

We used spatial, global trend and post-blocking analysis to examine the effectiveness of a progeny trial in a tree breeding program for Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) on a hilly site with an environmental gradient from hill top to bottom. Diameter at breast height (DBH) and tree height data had significant spatial auto-correlations among rows and columns. Adding a first-order separable autoregressive term more effectively modelled the spatial variation than did the incomplete block (IB) model used for the experimental design. The spatial model also accounted for effects of experimental design factors and greatly reduced residual variances. The spatial analysis relative to the IB analysis improved estimation of genetic parameters with the residual variance reduced 13 and 19% for DBH and tree height, respectively; heritability increased 35 and 51% for DBH and tree height, respectively; and genetic gain improved 3–5%. Fitting global trend and post-blocking did not improve the analyses under IB model. The use of a spatial model or combined with a design model is recommended for forest genetic trials, particularly with global trend and local spatial variation of hilly sites.