Breeding of sugi (Cryptomeria japonica) started with the selection of first generation plus-trees in the 1950s in Japan. The initial aim of the breeding program was to improve growth performance, and the aim has been extended to tolerance to adverse meteorological conditions, amelioration of wood properties, mitigation of pollinosis, and adaptation to climate change. These changes have arisen in response to shifts in social demands from timber production to management of diverse forest functions over time. Currently, the focus of C. japonica breeding is focusing on the selection and deployment of second-generation plus-tree clones and establishment of breeding populations for the third-generation plus-trees. In this review, these activities and achievements are described, and prospects of the future breeding initiatives, including the feasibility of breeding for climate change, are discussed.

Along with progress in sequencing technology and accumulating knowledge of genome and gene sequences, molecular breeding techniques have been developed for predicting the genetic potential of individual genotypes and for selecting superior individuals. For Japanese cedar (Cryptomeria japonica (L.f.) D.Don), which is the most common coniferous species in Japanese forestry, we constructed a custom primer panel for target amplicon sequencing in order to simultaneously determine 3034 informative single nucleotide polymorphisms (SNPs). We performed primary evaluation of the custom primer panel with actual sequencing and in silico PCR. Genotyped SNPs had a distribution over almost the entire region of the C. japonica linkage map and verified the high reproducibility of genotype calls compared to SNPs obtained by genotyping arrays. Genotyping was performed for 576 individuals of the F₁ population, and genomic prediction models were constructed for growth and wood property-related traits using the genotypes. Amplicon sequencing with the custom primer panel enables efficient obtaining genotype data in order to perform genomic prediction, manage clones, and advance forest tree breeding.

To clarify inheritance of static bending properties in relation to elastic and plastic regions, air-dry density, microfibril angle of the S₂ layer in latewood tracheid (MFA), and static bending properties (modulus of elasticity [MOE], modulus of rupture [MOR] and bending work) were examined for juvenile wood of 18 full-sib families in 20-year-old Cryptomeria japonica. Heritability of all traits ranged from 0.12 (bending work) to 0.51 (air-dry density). Based on the results from principal component analysis (PCA) and cluster analysis, the families were classified into four groups with different types of load-deflection curves, suggesting that both elastic properties and deflection in plastic region differed among families. Furthermore, families included in a group were produced from specific parents, suggesting that deflection in plastic region as well as elastic property is inheritable. It can be concluded that mating parents may affect elasticity and plasticity of offspring.

As climate change progresses, the breeding of drought-tolerant forest trees is necessary. Breeding drought-tolerant trees requires screening for drought stress using a large number of individuals and a high-throughput phenotyping method. The aim of this study was therefore to establish high-throughput methods for evaluating the clonal stress responses to drought stress using infrared thermography and chlorophyll fluorescence methods in Cryptomeria japonica. The stomatal conductance index (Ig), maximum photochemical quantum yield of photosystem II (Fv/Fm), and axial growth of four plus-tree clones of C. japonica planted in pots were measured weekly for 85 days after irrigation was stopped. The phenotypic trait responsivity to drought stress was estimated by a nonlinear mixed model and by introducing the cumulative water index, which considers the past history of the soil water environment. These methods and procedures enabled us to evaluate the clonal stress responses in C. japonica and could be applied to large-scale clone materials to promote the breeding program for drought tolerance.

With the use of terrestrial laser scanning (TLS) in forest stands, surveys are now equipped to obtain dense point cloud data. However, the data range, i.e., the number of points, often reaches the billions or even higher, exceeding random access memory (RAM) limits on common computers. Moreover, the processing time often also extends beyond acceptable processing lengths. Thus, in this paper, we present a new method of efficiently extracting stem traits from huge point cloud data obtained by TLS, without subdividing or downsampling the point clouds. In this method, each point cloud is converted into a wireframe model by connecting neighboring points on the same continuous surface, and three-dimensional points on stems are resampled as cross-sectional points of the wireframe model in an out-of-core manner. Since the data size of the section points is much smaller than the original point clouds, stem traits can be calculated from the section points on a common computer. With the study method, 1381 tree stems were calculated from 3.6 billion points in ~20 min on a common computer. To evaluate the accuracy of this method, eight targeted trees were cut down and sliced at 1-m intervals; actual stem traits were then compared to those calculated from point clouds. The experimental results showed that the efficiency and accuracy of the proposed method are sufficient for practical use in various fields, including forest management and forest research.