Bioenergy feedstock supply from wheat straw: A farm level model incorporating trade‐offs in crop choices, disease risk, and soil fertility
Bibliographic record
Abstract
Abstract Second‐generation biofuel (e.g., ethanol, renewable diesel) can be made from crop residues. However, the availability of residues for biofuel production is uncertain, because farmers have the option to grow different crops and use the residues for alternative purposes, such as livestock bedding and feed, or leave them in the field to improve soil quality. Taking Canadian wheat straw supply as an example, we develop a dynamic programming model to investigate a farmer's wheat straw supply decision in response to different wheat straw and grain prices. Our model considers crop choices between wheat and canola in the context of disease risk, the trade‐off between the immediate payoffs a farmer may receive from bailing and selling wheat straw, and the long‐term adverse effects that removing wheat straw from the soil surface may have on wheat and canola yields. The results from this study provide insights into how farm‐level supply decisions, in response to wheat straw price changes, affect soil quality dynamics and scale up to regional wheat straw supply for biofuel production. This information also has implications for land use change and the sustainability of feedstock supply for biofuels.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".