Application of Wood Ash to Acidic Boralf Soils and its Effect on Oilseed Quality of Canola
Bibliographic record
Abstract
Acidic Typic Cryoboralf soils amended with wood ash can raise soil pH and can supplement plant growth by adding minerals and micronutrients. However, presence of other elements in soils such as Cd, S, and Zn can affect plant growth and seed quality. In an earlier paper, we have shown that wood ash applications on Typic Cryoboralf and Typic Cryocrept soils in Alberta, Canada, increased canola (Brassica rapa L.) yield by 72%. In this study, the effect of a single application of 0, 6, 12.5, and 25 t ha−1 (dry weight) wood ash on oilseed quality, based on oil, protein, chlorophyll, and glucosinolate content, was examined over three growing seasons from 1998 to 2000. Seed oil and protein content of ash‐treated plots either increased or remained the same as controls. In contrast, significant increases (p < 0.05) in tissue concentrations of S and seed oil glucosinolates were observed in ash‐amended plots. While these changes remained within acceptable limits for canola, seed oil and tissue quality were lower than the average level found in Canada no. 1 grade canola. During the 3‐yr period, average Zn content of the oilseed was not different from control plots (P > 0.05). Levels of B in ash‐treated soils were different from each other but not from the controls (P < 0.05). Cadmium levels were below detection limits for the instrumentation used (0.08 mg kg−1). These results indicate that use of wood ash on acidic soils has the potential to increase seed oil content but may adversely affect quality of the oilseed produced.
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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 | 0.001 | 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".