On-Farm Composting Using Two Different Windrow Methods: A Stochastic Budgeting Analysis
Bibliographic record
Abstract
Two commercial-scale windrow composting methods were investigated for their relative costs of production and value for the horticulture industry, including: (i) aerobic (or thermophilic) composting; and (ii) fermentative (or static pile inoculated) composting. Economic costs, including opportunity costs, were estimated and analyzed using data from a case study on-site compost production for tree and shrub nursery production in British Columbia, Canada. Deterministic and stochastic budgeting models were used to determine breakeven prices and short-run shut-down prices (SRSDP), and testing for potential economies of scale. Monte Carlo simulations were used to assess the sensitivity of costs to uncertainty in key output variables. The composting methods used demonstrate that the composts produced are of satisfactory quality, with physical and chemical properties within typical recommended ranges for agricultural use. Total cost of producing a tonne of fermentative compost (CAD$23) was lower than for thermophilic compost (CAD$37). Short-term shutdown price was higher for thermophilic than for fermentative compost produced by CAD$11 tonne−1. Economies of scale were more apparent for thermophilic than the fermentative composting system. Conclusions from the stochastic analysis were consistent with results from the deterministic cost analysis. The empirical economic cost estimates are useful for a wide variety of audiences, including policy makers and decision makers interested in capital and operating costs of composting, and cost-based pricing strategy for compost produced. Breakeven prices fill an industry knowledge gap regarding profitability of compost production given prevailing market prices.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".