Soil and Crop Response to Wood Ash and Lime Application in Acidic Soils
Bibliographic record
Abstract
Wood ash has the properties to be an effective liming material, and research is needed to compare its effectiveness relative to agricultural lime on acidic agricultural soils. Wood ash at a calcium carbonate rate of 6.72 t ha −1 was compared with an equivalent rate of agricultural lime on a clay loam soil with an initial pH of 4.9. Replicated plots were managed under a barley ( Hordeum vulgare L.)–canola ( Brassica rapa L.)–pea ( Pisum sativum L.) rotation for 4 yr (2002–2005). Soil pH increased in the order of: wood ash = lime > control (without lime or wood ash). Available soil P increased in the order of: wood ash > lime ≥ control. The effect of wood ash and lime application on pH and available P was greatest in the 0‐ to 5‐cm depth, less but still significant in the 5‐ to 10‐cm depth, and not significant below 10 cm. The effect on soil aggregation was: wood ash > lime > control. Averaged over 4 yr, application of wood ash increased grain yields of barley, canola, and pea by 49, 59, and 55%, respectively, compared to a corresponding increase of 38, 31, and 49% by agricultural lime. The increase in crop yield with wood ash compared with lime is attributed partly to increased P availability in wood ash‐amended plots. It is concluded that wood ash applied at rates equivalent to agricultural lime improved some soil chemical and physical properties and increased crop production relative to agricultural lime.
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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".