Economic feasibility of biochar and agriculture coproduction from Canadian black spruce forest
Bibliographic record
Abstract
Abstract This study calculates the economic feasibility of converting biomass from black spruce forests into biochar and using it as soil amendment to grow potatoes (Solanum tuberosumL.) and beets (Beta vulgarisL.) to improve food availability in one of Canada's most consistently food insecure provinces. The trees were clear cut for the construction of the controversial Muskrat Falls hydroelectric dam and have been left to decay due to a lack of economically feasible processing options. A stochastic analysis conducted on a biochar production budget of a slow pyrolysis mobile biochar unit reveals fixed and variable cost estimates of $505.14 Mg−1and $499.13 Mg−1, respectively. Applying the biochar as a soil amendment for local beet or potato production makes the biochar venture profitable. Beet field trial data from the study region using 10 t C biochar application rates increases beet yield from 2.9 Mg/ha to 11.4 Mg/ha with a midline increase of 5.59 Mg/ha. A stochastic analysis with variable prices and yields shows a 0.99 probability of biochar production being profitable when applied to beets at the midline production rate, with an average annualized net return over variable costs of $4,953 ha−1, and maximum annualized net return of $11,288 ha−1, over variable costs. Potato production yields average annualized net returns of $965.48 ha−1over variable costs, but with much more downside risk, considering the minimum annualized net return of −$318.82 ha−1over variable costs. Biochar application covers average total costs for beets but not potatoes. Using biochar from forest biomass as a soil amendment presents an opportunity to create a local market for biochar in a remote area of Canada, where biochar may be used as an experimental soil amendment to improve food security.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".