The Supply of Switchgrass and Miscanthus in Ontario
Bibliographic record
Abstract
The province of Ontario created a potential demand for biomass with the Green Energy Act (2009), but the supply of agricultural biomass has not been established. The purpose of this dissertation is to assess the potential supply of agricultural biomass from two perennial grasses, switchgrass and miscanthus, using an integrated economic, bio-physical and GIS model. It assesses the yields of biomass, the farm-gate supply of biomass given changing agricultural opportunity costs and the effects of supply chain structure on biomass supply. Miscanthus has a mean peak yield that is 88.5% higher (29.6 t/ha versus 15.7 t/ha) and a mean farm gate break-even price that is 25.9% lower ($58.20/t versus $73.29/t) than switchgrass. The impact of climate change on the yields and break-even prices is dependent upon the climate model. Both crops show promise as biomass sources for bio-energy production. Miscanthus has lower costs and higher yields than switchgrass, making it relatively more economically attractive to Ontario farmers under current conditions. Miscanthus biomass production becomes attractive compared to traditional corn-soybean-wheat rotations at around $46/t of biomass in the northern regions of the province. All areas become attractive for conversion at around $130/t. Increasing the price of corn by $1 per bushel increases the biomass price required to produce 20 Mt of miscanthus by approximately $10/t. Switchgrass becomes more attractive at lower prices if yields are increased by 50%, but yields need to nearly double for switchgrass to become the preferred crop in this model. In a local, domestic supply chain, about 4 million tonnes of baled biomass production per year becomes attractive for transport to the Nanticoke Generation Station at $69/t. For a larger scale international export supply chain to Rotterdam, 20 million tons of production becomes attractive at approximately $198/t with on-farm aggregation and at $137/t with aggregation at major ports. Higher bale transportation costs involved in aggregation at major ports cause agricultural lands closer to ports, with smaller distances, but lower yields to be preferred compared to the on-farm aggregation scenario. While local supply chains to Nanticoke could emerge, the export scenarios are unlikely given price and infrastructure concerns.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".