Willow production during 12 consecutive years—The effects of harvest rotation, planting density and cultivar on biomass yield
Bibliographic record
Abstract
Abstract Willow biomass produced in short rotation coppice systems can potentially be used as biomass feedstock in Europe, the United States and Canada. However, most researchers focus on data from the first harvest rotation only, whereas multiple rotations have been rarely investigated. The aim of this study was to evaluate the effect of cultivar (5), planting density (12,000–96,000 cuttings/ha) and harvest rotation (annual, biennial, triennial) on willow biomass yields during 12 consecutive years in northern Poland. Every experimental factor and the interactions between factors significantly impacted willow yields. Biomass yield was highest in the triennial harvest rotation (13.3 Mg ha−1 year−1), 15.9% lower in the biennial rotation and 26.9% lower in the annual rotation. The highest average yield (14.6 Mg ha−1 year−1) was noted at a planting density of 24,000 cuttings/ha, and yields were 9.3%–46.0% lower at the remaining densities. Cultivar UWM 095 had the highest average yield (13.0 Mg ha−1 year−1), whereas the yield of the remaining cultivars was 4.6%–32.4% lower. During the 12‐year period, yields were higher after the first harvest in annual, biennial and triennial harvest rotations. This above implies that high biomass yields can be obtained after the first harvest rotation if willows are cultivated on fertile soils at higher planting density, well managed and coppiced after the first year. However, yields are unlikely to be higher in successive harvest rotations, and they can even be lower, but more stable than in the first harvest rotation.
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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 | 0.001 | 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 | 0.000 |
| 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".