Biochar application on commercial field crops using farm-scale equipmen
Bibliographic record
Abstract
Commercial growers who wish to apply biochar to their field crops will need to use conventional agricultural machinery to amend large field areas. Biochar produced by fast pyrolysis of hardwood was applied at a target rate of 5.6 t ha-1 to a single swath (10 m x 100 m) in an agricultural field in Quebec, Canada, using a commercial lime spreader. Windborne losses of up to 30% biochar occurred during handling, transportation, and application. We recommend covering and moistening the biochar before spreading, avoiding surface application on windy days, or mixing it with other materials (e.g., compost, manure) to reduce biochar loss. The biochar-amended swath and an adjacent equally sized swath that received no biochar were harrowed. The entire field was seeded with soybean in the first season, followed by an oat-forage mixture in the second season, and forage in the third season. Soybean and oat yields increased by up to 20% with biochar. In the third season, forage in the biochar-amended swath had greater nutrient concentration and higher projected milk production when used as feed for dairy cattle, based on near-infrared spectroscopy analysis. The variable cost of applying biochar was an estimated CA$2,285 ha-1, indicating the need for a complete cost-benefit analysis of farm-scale biochar applications.
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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 | 0.001 | 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".