Ash fertilization increases long-term timber production in drained nitrogen-poor Scots pine peatlands
Bibliographic record
Abstract
Wood ash fertilization remarkably increases tree growth and hence, carbon sequestration in drained boreal peatland forests, particularly in nitrogen (N)-rich Scots pine sites with limited phosphorus (P) and potassium (K). Because ash lacks N, N-deficient ombrotrophic and poor oligotrophic sites are generally considered unsuitable for ash fertilization. In this study, timber production was investigated in six field experiments in N-poor, drained Scots pine dominated peatlands in central Finland, where ash fertilization was applied 15–85 years earlier. Ash significantly increased tree growth in all the study sites. Unfertilized plots showed long-term average mean annual increment (MAI) of 2.01 m3·ha−1·a−1, whereas in fertilized plots MAI was 4.46 m3·ha−1·a−1. An analysis with nonlinear mixed effects model revealed a faster volume yield development and higher asymptote of the mean curve in fertilized plots. Higher amount of K in the ash significantly increased the response. Fertilizations were financially lucrative: on average, the break-even cost surpassed the ash fertilization cost (390 € ha−1) more than two-fold at 5% interest rate. The current nutrient status of fertilized trees was rather balanced. The results proved that the long-term growth response to ash fertilization in poor drained peatland sites is comparable to N-rich sites, but the response time is distinctively longer.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".