Forest‐derived liming by‐products: Potential benefits to remediate soil acidity and increase soil fertility
Bibliographic record
Abstract
Abstract Soil acidification is an important cause of declining crop yields in many countries, including Canada and the United States. Meanwhile, alkaline by‐products from forest resources are widely available but underused in agriculture despite their expected benefits on soil pH and fertility. The aim of this study was to determine the effects, throughout a 40‐wk laboratory incubation, of six different forest‐derived liming materials on soil pH and Mehlich‐3–extractable major nutrients in two acidic soils. Lime mud, two wood ashes (papermill biosolids and wood bark), two biochars (maple and pine), and a de‐inking paper sludge (DPS) were applied at calcium carbonate equivalence–based rates, according to the amount of lime required to achieve a target pH of 6.5 on each soil. A calcitic lime (CL) was used as a reference. All forest‐derived materials except pine biochar were equally effective as CL in increasing the pH of the two acidic soils after 40 wk of incubation. Lime mud quickly raised the pH after soil incorporation, and then the pH progressively declined. By contrast, DPS upon decomposition gradually increased soil pH over time. In terms of liming value based on dry mass of each material, lime mud was needed at the lowest amount (0.8 CL unit) to increase pH to the target value. Wood ash, particularly from wood combustion, was a significant direct source of P, K, and Mg, whereas maple biochar supplied large amounts of available K and Mg. This study demonstrated that forest‐derived alkaline by‐products can efficiently remediate soil acidity and improve soil fertility.
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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 | 0.000 | 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) | 0.000 | 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".