Profitability and risk analysis of alternative tillage systems in southern Ontario
Bibliographic record
Abstract
The study determined the optimal tillage system by crop and soil type for a hypothetical risk neutral and risk-averse Ontario farmer growing corn, soybeans and wheat. The study used yield distribution data for 27 tillage-crop-soil texture combinations from field experiments adjusted for apparent discrepancies. Generally, medium-textured soils produce higher yields than alternative soil types with crop yields responding positively (negatively) to tillage intensity on heavy (light) textured soils. Rotational, minimum and no-till systems were the most profitable on heavy, medium and light-textured soils respectively. Crop yield of rotational (no-till) can decrease by 2 (5) percent on heavy (light) textured soils and still be competitive with conventional tillage. Using both expected utility and value-at-risk decision criteria, conservation tillage was risk-efficient particularly on medium and light-textured soils. The results suggest that static risk is not a reason for the perceived low adoption rate of conservation tillage and that the rates may be associated with the spatial heterogeneity in soil type.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.001 | 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".