Soil change and broadleaf tree growth 10 years after wood ash and brash co-application to a clearfelled lowland conifer site in Britain
Bibliographic record
Abstract
Abstract Wood ash use, to raise pH and replace nutrients lost through forest harvesting, is well regulated in Scandinavia and Canada, but not yet in the UK. This experiment applied granulated wood ash from mixed tree thinnings to a lowland clay mineral soil at ~2.3 t ha−1 dose rate, after clearfell of western hemlock (Tsuga heterophylla (Raf.) Sarg.). With brash (~56 t ha−1 dry weight) as N resource, a 4 × 4 matrix of ash only, ash + brash, brash only and control treatments were planted with oak seedlings (Quercus robur L.). Soil survey was undertaken before ash application and followed over 10 years. By year 2, self-sown grass was dominant in the control and ash plots, and birch (Betula pendula, Roth.) was present across all treatments by year 4. In year 10, oak height was 25–29 per cent greater in brash and ash + brash plots, with DBH increased >20 per cent over the controls. Birch DBH was greater by 30 per cent in brash plots but was over 50 per cent in the ash + brash plots. Foliar concentrations of Ca, K and P significantly increased with ash addition in both oak and birch, as Al, Mn, Fe and Cr decreased. Soil pH (CaCl2) was reduced in all treatments in year 2 likely due to nitrification, during organic matter and needle breakdown, but recovered 8 years later to be significantly highest in ash and ash + brash plots. Mg, K, Ca, Na, Ba and S concentrations were higher in ash plot soils, but soil organic matter, total carbon and total nitrogen declined due to mineralization and uptake by grass and trees. Wood ash addition did not significantly increase heavy metal concentrations in either soil or foliage. In the long term, wood ash aided soil recovery and promoted tree growth in combination with the brash – it could be beneficial for tree growth after thinning/coppicing on heavy mineral soils, with the prerequisite of an existing ground cover. Aim To quantify the effects of wood ash and brash addition over time to soils and tree growth after conifer clearfelling on a lowland clay soil site.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".