Barley Biomass and Grain Yield and Canola Seed Yield Response to Land Application of Wood Ash
Bibliographic record
Abstract
Wood ash is considered a waste product that accumulates from the burning of wood waste for energy production. Field studies were conducted on acidic Boralf and Eutrochrept soils and in the greenhouse using material from the surface of these soils in randomized complete block designs to evaluate the effectiveness of wood ash as a liming material for improving crop production. For the greenhouse study, soil was treated with the equivalent of 0 to 200 t ha −1 (w/w) wood ash. Barley ( Hordeum vulgare L.) yielded up to 50% more dry matter in this study. Based on these findings, a 3‐yr field study was done to determine the effect of single applications of 6, 12.5, and 25 t ha −1 wood ash to Boralf soils in central Alberta. Significant increases in barley dry matter and grain yield and oil seed yields of canola ( Brassica rapa L.) were observed when soil was supplemented with 12.5 or 25 t ha −1 along with N fertilizer. Increases of 72 and 50% in barley dry matter and grain yield were observed while canola oilseed yield increased 124% due to wood ash application. Applications up to 25 t ha −1 did not have a deleterious effect on biomass or seed production in barley or canola crops. Results show that land application of wood ash increased pH and nutrient content of acid soils while having a beneficial effect on crop production. Land application of wood ash can provide timber companies with a viable alternative to landfill disposal.
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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".