The Citing articles tool gives a list of articles citing the current article. The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).
A review of the resource efficiency and mechanical performance of commercial wood-based building materials
Maximilian Pramreiter, Tobias Nenning, Christian Huber, Ulrich Müller, Benjamin Kromoser, Paul Mayencourt and Johannes Konnerth Sustainable Materials and Technologies 38 e00728 (2023) https://doi.org/10.1016/j.susmat.2023.e00728
Using X-ray CT Scanned Reconstructed Logs to Predict Knot Characteristics and Tree Value
Design of a Total Revenue Forecasting Tool to Estimate the Economic Output of Hardwood Logs
Juan J. González, Henry Quesada, Sailesh Adhikari, Brian Bond and Shawn Grushecky Forest Products Journal 70(4) 439 (2020) https://doi.org/10.13073/FPJ-D-20-00035
The economic impact of fire management on timber production in the boreal forest region of Quebec, Canada
Baburam Rijal, Frédéric Raulier, David L. Martell and Sylvie Gauthier International Journal of Wildland Fire 27(12) 831 (2018) https://doi.org/10.1071/WF18041
Value-added forest management planning: A new perspective on old-growth forest conservation in the fire-prone boreal landscape of Canada
Development of lumber volume recovery correction models for stem deformations of natural black spruce trees
Chuangmin Liu, Shu Yin Zhang, Jean-Claude Ruel, Alain Cloutier and Tadeusz Rycabel Scandinavian Journal of Forest Research 22(5) 415 (2007) https://doi.org/10.1080/02827580701594135
Effect of stand and tree attributes on growth and wood quality characteristics from a spacing trial with Populus xiaohei
Ze-Hui Jiang, Xiao-Qing Wang, Ben-Hua Fei, Hai-Qing Ren and Xing-E. Liu Annals of Forest Science 64(8) 807 (2007) https://doi.org/10.1051/forest:2007063
Predicting the lumber volume recovery of Picea mariana using parametric and non-parametric regression methods