A spatial model of climate change effects on yields and break‐even prices of switchgrass and miscanthus in Ontario, Canada
Bibliographic record
Abstract
Abstract Concerns over global climate change have led many jurisdictions to implement strategies aimed at reducing greenhouse gas levels. One example is the replacement of coal with dedicated energy crops, such as switchgrass and miscanthus. The yields and costs of these potentially valuable bio‐energy crops have been evaluated in only a few cases, and previous studies have not focused on climate change effects. This article assesses the potential yields and costs of growing switchgrass and miscanthus on the agricultural land base in Ontario, Canada, under different climate assumptions, using a GIS‐based integrated biophysical and economic simulation model. The model shows that miscanthus has a mean peak yield that is 88.5% (29.6 t ha−1 compared with 15.7 t ha−1) higher and a mean farm gate break‐even price that is 25.9% ($58.20 per tonne compared with $73.29 per tonne) lower than switchgrass. The impact of climate change on the yield and break‐even price of switchgrass and miscanthus is dependent upon the climate model. CGCM3.1 predicts that mean peak yields of switchgrass and miscanthus could drop by 17.8% and 14.9%, whereas CCSM3.0 predicts that mean yields could increase to 41.4% and 44.9%, from 2071 to 2100, in the A2 climate scenario respectively. Both crops show promise as biomass sources for bio‐energy production, but a changing global climate, along with cultivar and planting technology developments, could affect crop choices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".