Abstract
Key message
Near-infrared hyperspectral imaging allows to build suitable wood density maps for 6-year-old Eucalyptus grandis trees. Robust age–age correlations from wood density maps suggest feasible early tree selection for wood density.
Abstract
Wood is a heterogenous material whose properties vary over time, making it difficult to predict the wood properties at a given age of trees in the future. The site and climate are also factors affecting wood heterogeneity. To improve the accuracy of early selection of trees in drier sites, it is thus important to study inter-annual variations in wood density in conditions of contrasting water availability. We tested the use of near-infrared hyperspectral imaging (NIR-HSI) to assess inter-annual wood density and predict wood density at a future age to evaluate the accuracy of early selection of Eucalyptus grandis trees for wood density and to see if a drier site influences early selection. We sampled 38 six-year-old trees growing under two different water regimes: (i) 37% throughfall reduction (–W), to simulate a dry site, and (ii) undisturbed throughfall (+ W). NIR-HSI images were used to build high-resolution wood density maps of the whole cross section. After the annual growth rings were delimited, the average wood density at each age and in growth ring was extracted to evaluate juvenile–mature correlations in the wood. The NIR-HSI images calibrated with a locally weighted partial least square regression (LWPLSR) model, using raw spectra, performed well in predicting the wood density of the whole cross section. Correlations for wood density between ages 1–3 and 5–6 were strong (r = 0.85 to 0.94), while correlations between rings 1–3 and 4–5 were moderate to strong (r = 0.51 to 0.87). In − W plots, juvenile–mature correlations were slightly lower than in + W plots. Our results suggest that early E. grandis selection for wood density is feasible to predict wood density at 6 years of age.
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Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
We gratefully acknowledge Rildo M. Moreira and staff of the Itatinga Research Station (ESALQ/USP) for their technical support in sample collection, and staff of the Plateforme d’histocytologie et d’imagerie cellulaire végétale (PHIV) of the Centre de coopération internationale en recherche agronomique pour le développement (CIRAD), and in particular Christelle Baptiste and Jean-Luc Verdeil, for technical support in sample preparation and acquisition of high-resolution images of wood anatomy. We also thank Jean-Paul Laclau for setting up the experimental plantation and for authorization to collect wood samples, and the staff of Instituto de Química-Universidade Estadual de Campinas (UNICAMP), in particular professor Celio Pasquini, for allowing access to the laboratory and equipment for the acquisition of NIR hyperspectral images (INCTAA, FAPESP 2014/50951-4, CNPq 465768/2014-8).
Funding
This research was funded by the Agropolis Foundation under reference ID1203-003 through the «Investissements d’avenir» program (LabexAgro/ANR-10-LABX-0001–01) and Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (444793/2014–3). Roger Chambi-Legoas was supported by a scholarship from Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica (FONDECYT-CONCYTEC, Award Number 239–2018), and by a grant for yearly stays in Montpellier from the CIRAD “Action incitative—Soutien aux doctorants”.
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Chambi-Legoas, R., Tomazello-Filho, M., Vidal, C. et al. Wood density prediction using near-infrared hyperspectral imaging for early selection of Eucalyptus grandis trees. Trees 37, 981–991 (2023). https://doi.org/10.1007/s00468-023-02397-2
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DOI: https://doi.org/10.1007/s00468-023-02397-2