Global near infrared spectroscopy models (multiple-species, multiple-sites) were developed to predict chemical properties of Eucalyptus wood. The sample data set included 186 samples from four data sets (five species) originating from six countries: Eucalyptus urophylla from Argentina, Colombia, Venezuela, and South Africa; Eucalyptus dunnii from Uruguay; Eucalyptus globulus and Eucalyptus nitens from Chile; and Eucalyptus grandis from Colombia. The 186 samples were all preselected from larger collections of 400 to nearly 1800 samples to represent the range of chemical and spectral variation in each data set. The chemical traits modeled were total lignin, insoluble lignin, soluble lignin, syringyl–guaiacyl ratio (S/G), glucose, xylose, galactose, arabinose, and mannose. Single-species models and global multiple-species models were developed for each chemical constituent. For the global model, the R²_cv for total lignin, insoluble lignin and syringyl–guaiacyl ratio were 0.95, 0.96, and 0.86, respectively. An alternate expression of the syringyl–guaiacyl relationship (S/(S+G)) resulted in better near infrared calibrations (e.g., for the global model, R²_cv = 0.95). The global models for sugar content were also very good, but were slightly inferior to those for the lignin related traits, with R²_cv = 0.74 for glucose, 0.89 for xylose, and from 0.72 to 0.91 for the minor sugars. To investigate the utility of the global models to predict chemical traits for species not included in the calibration, three-species calibrations were used to predict each trait in a fourth species data set. The prediction fit statistics ranged from excellent to poor depending on the species and trait, but in general the predictions would be at least moderately useful for most species-trait combinations. For some species-trait combinations with poor initial predictions from the global model, the inclusion of 10 samples from the “new” species into the calibration global model improved the fit statistics substantially. The global calibrations will be useful in tree breeding programs to rank species, families, and clones for important wood chemical traits.

Research Highlights: This study provides a comprehensive set of wood and pulping properties of Acacia crassicarpa A.Cunn. ex Benth. to assess variation and efficient sampling strategies for whole-tree level phenotyping. Background and Objectives: A. crassicarpa is an important tree species in Southeast Asia, with limited knowledge about its wood properties. The objective of this study was to characterize important wood properties and pulping performance of improved germplasm of the species. Furthermore, we investigated within-tree patterns of variation and evaluated the efficiency of phenotyping strategies. Materials and Methods: Second-generation progeny trials were studied, where forty 50-month-old trees were selected for destructive sampling and assessed for wood density, kraft pulp yield, α-cellulose, carbohydrate composition, and lignin content and composition (S/G ratio). We estimated the phenotypic correlations among traits determined within-tree longitudinal variation and its importance for whole-tree level phenotyping. Results: The mean whole-tree disc basic density was 481 kg/m³, and the screened kraft pulp yield was 53.8%. The reliabilities of each sampling position to predict whole-tree properties varied with different traits. For basic density, pulp yield, and glucose content, the ground-level sampling could reliably predict the whole-tree property. With near infrared reflectance spectroscopy predictions as an indirect measurement method for disc basic density, we verified reduced reliability values for breast height sampling but sufficiently correlated to allow accurate ranking and efficient selection of genotypes in a breeding program context. Conclusions: We demonstrated the quality of A. crassicarpa as a wood source for the pulping industry. The wood and pulping traits have high levels of phenotypic variation, and standing tree sampling strategies can be performed for both ranking and high-accuracy phenotyping purposes.