Evaluating the effects of liming and wood-ash treatment on forest ecosystems through systematic meta-analysis
Bibliographic record
Abstract
Liming and wood-ash addition have long been used to attenuate the effects of acidic deposition on forest soils with the goal of promoting tree growth. We performed quantitative meta-analyses of treatment studies from managed forest ecosystems to assess general tendencies of effects of treatment on seven selected measures of performance thought to reasonably reflect the effects of Ca-addition treatment. We retrieved over 350 independent trials from 110 peer-reviewed liming and wood-ash addition studies that were integrated to determine soil pH, base saturation (BS), tree foliar Ca concentration, tree growth, ectomychorrhizae root colonization, soil C-to-N ratio, and microbial indices. The results were quantified through three separate meta-analysis effect size metrics: unweighted relative values and two weighted metrics, Hedges’ d and ln R. A surprising number of treatment trials (22%–85%) reported no significant effect, and soil pH and foliar Ca appeared more responsive to liming than to wood-ash addition, whereas BS and tree growth appeared more responsive to wood-ash addition. For six of the seven parameters, estimated mean effect sizes were similar in magnitude and positive in direction for all three meta-analysis metrics. Regression tree optimal models explained 38% of the variation in pH, 47% of the variation in BS, 51% of the variation in foliar Ca concentration, and 26% of the variation in tree growth. The largest predictors of effect size, within our selected group, were as follows: soil type for pH; soil type, trial duration in years, and species (hardwood or softwood) for BS; treatment dose and type for foliar Ca concentration; and trial duration, initial soil pH, and tree species for tree growth. This analysis shows that Ca additions are not universally beneficial and provides insight into when Ca additions to forest soils are likely to be most effective.
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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 | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.036 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".