Profitability of continuous-cover forestry in Norway spruce dominated peatland forest and the role of water table
Bibliographic record
Abstract
Continuous-cover forestry (CCF) is expected to reduce the negative environmental impacts of peatland forestry in comparison with rotation forestry (RF), but the unknown profitability of CCF on peatlands limits its application in practice. The profitability of CCF was analyzed by simulating management scenarios with a process-based ecosystem model, EFIMOD, which was complemented to describe the interplay between tree growth and water table depth, which is typical of peatland forests. A variety of harvest intervals and post-harvest basal areas for a mature Norway spruce (Picea abies (L.) Karst.) dominated stand was simulated on a nutrient-rich peatland site. Conventional RF was simulated for comparison. CCF provided a higher profit than RF. The best financial performance was obtained with a 15-year harvest interval regardless of interest rate, although the overall profitability of CCF depended on the interest rate used. Ditch network maintenance was needed to maintain the stand growth only when the post-harvest basal area was smaller than 10 m 2 ·ha −1 . There were many CCF scenarios in which the difference in the net present value of harvest revenues was within 10% compared with the best CCF scenario. Hence, there are many relatively profitable CCF harvesting alternatives for forest management in boreal spruce-dominated peatland forests.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".