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Original Articles

Phenotyping wood properties of Corymbia torelliana x Corymbia citriodora and Eucalyptus dunnii based on NIR spectra

, , , , &
Pages 102-111 | Received 30 Nov 2022, Accepted 19 Jun 2023, Published online: 25 Jun 2023
 

ABSTRACT

Selecting individuals in breeding programs for pulp production, the physical and chemical wood traits must be considered in the genotype selection. In this study, trees from hybrid progeny tests of Corymbia torelliana x Corymbia citriodora (CTOxCCT) and progenies of Eucalyptus dunnii (EDU) were investigated. Sawdust of genetically selected standing trees was collected, classified and prepared for near infrared (NIR) spectra readings. The chemical properties of the selected trees were determined through kraft pulping. Predictive models for each property were developed based on the reference data and NIR spectra. Two approaches for models were developed. The first approach, models were fitted with 25 samples of CTOxCCT, and in the second approach, models were fitted using 61 samples (25 of CTOxCCT and 36 of EDU). The estimated R²cv values were 0.60 and 0.73 for basic chip density, 0.37 and 0.65 for extractive contents, 0.56 and 0.53 for total lignin contents, 0.63 and 0.66 for S/G ratio, and 0.52 and 0.77 for screened pulp yield for the first and second approaches, respectively. All developed models have potential for ranking trees in breeding programs. NIR spectroscopy can potentially be applied as a high-throughput field phenotyping tool where thousands of varieties need to be evaluated.

Acknowledgements

We want to thank the company Klabin for providing the data from their experiments.

Data availability statement

The data that support the findings of this study are available from the corresponding author, L.M., upon reasonable request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

Prof. Dr. Evandro V. Tambarussi and Dr Paulo R. G. Hein are supported by a research productivity fellowship granted by CNPq (grant number 303789/2022-0 and 309620/2020-1, respectively).

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