Impact of Intensive Forest Management Practices on Wood Quality from Conifers: Literature Review and Reflection on Future Challenges
Bibliographic record
Abstract
Abstract Purpose of Review Intensive forest management practices are being implemented worldwide to meet future global demand for wood and wood products while facilitating the protection of natural forest ecosystems. A potential decline in wood properties associated with rapid tree growth makes it essential to quantify the potential impact of intensive management on the process of wood formation and, in turn, on its suitability for various end-uses. Recent Findings Wood produced over short rotations is generally of lower quality because wood properties tend to improve with cambial age (i.e. the number of annual growth rings from the pith). The intensification of silvicultural practices can thus have measurable consequences for the forest products value chain. The use of new planting material from tree improvement programs could offset such effects, but questions arise as to the effects of a changing climate on wood produced from these plantations and the best silvicultural approaches to manage them. Summary Based on these recent findings, we provide reflections on the need for a modelling framework that uses the effects of cambial age, ring width and position along the stem to summarise the effects of tree growth scenarios on wood properties. We then present challenges related to our limited understanding of the effects of several drivers of wood properties, such as climate variation, genetic material, and forest disturbances, among others, and highlight the need for further data collection efforts to better anticipate the quality attributes of the future wood fibre resource. We conclude by providing examples of promising new tools and technologies that will help move wood quality research forward by allowing (1) fast, efficient characterisation of wood properties, and (2) up-scaling predictions at the landscape level to inform forest management decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".