Wood Ash Improved Soil Properties and Crop Yield for Nine Years and Saved Fertilizer
Bibliographic record
Abstract
Wood ash may be used to mitigate soil acidity and improve crop production. We compared effects of wood ash and recommended fertilizers on soil properties of a Gray Luivsol, crop yields and contribution margins in southeast Peace, Alberta, Canada. The CHK (no fertilizer, inoculation or wood ash), FRT (recommended fertilizers or inoculation), ASH (wood ash rate to supply amounts of phosphorus equivalent to the FRT treatment); and ASH+N (same as ASH + N fertilizer or inoculation) treatments were applied in 2006 and 2007. Their effects were studied from 2006 to 2014. Wood ash had all the essential plant nutrients, except nitrogen. Soil samples collected in 2007, 2008 and 2013 had or tended to have higher pH, P, K, Ca, Ca:Mg ratio, S, Cu, Zn and B levels for the ASH and ASH+N treatments than the CHK and FRT treatments. In the 2006 and 2007, the seed yields were ASH+N > FRT > ASH > CHK. The seed yields in 2008, 2010, 2012, 2013 and 2014 were greater from both the wood ash treatments than other treatments. Extra contribution margin from the ASH+N over the FRT treatment was $751/ha, i.e. $97 Mg-1 of applied wood ash. Overall, wood ash reduced fertilizer expenditure and improved seed yield, contribution margin and soil properties, with residual effects observed up to seven years and likely for few more years.
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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) | 0.002 | 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".