Economic Evaluations of Tree Improvement for Planted Forests: A Systematic Review
Bibliographic record
Abstract
This paper reviews the literature on the economic evaluations of tree improvement for planted forests and investigates whether or not using improved reforestation stock from tree improvement programs is a good investment. The main findings from systematic web-based searches show that (1) tree improvement is an effective tool to improve forest productivity and to realize financial returns; (2) economic gains from wood production with selection for breeding traits (e.g., high-volume yield or height growth) are the main reasons forest managers adopt new biotechnologies in tree improvement; (3) cost-benefit analysis is the primary empirical approach for estimating the economic effects of tree improvement for planted forests; and (4) there is very little literature on estimating the non-market benefits (e.g., improved watershed protection, amenities, or conservation of genetic diversity) that tree improvement brings, using non-market valuation techniques. The recent introduction of new biotechnologies in tree improvement, such as genomics-assisted tree breeding (GATB), can achieve genetic gains in selected traits more quickly and effectively than traditional breeding approaches, providing economic incentives for forest managers to use better quality stock for planted forests. Therefore, we suggest that future research should (1) consider the additional benefit, extra research and development costs, and time saved by applying new biotechnologies in tree improvement (e.g., GATB) in the cost-benefit analysis; (2) investigate the trade-offs between timber volume and wood quality traits and assess the economic effects of new biotechnologies in tree improvement along different stages of the forestry supply chain; and (3) explicitly account for the non-market trait values for the targeted breeding traits (e.g., drought/pest resistance) so that tree improvement programs can contribute to sustainable production systems. Economic analyses along these lines could help policy makers, forest managers, and forest company owners better understand the trade-offs of alternative breeding objectives and make economically efficient investment decisions for planted forests.
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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 | 0.011 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".