Income Risk Analysis of Alternative Tillage Systems ofr Corn and Soybean Production on Clay Soils
Bibliographic record
Abstract
Conservation tillage systems have not been widely adopted on clay soils. There are few empirical studies on the production potential and economic feasibility of conservation tillage systems for corn (Zea mays L.) and soybean (Glycine max L.) production on clay soils. On some soils in some regions, crop yields and possibly profitability can be increased, and yield and net farm returns risks may be reduced through the use of conservation tillage systems. Stochastic dominance efficiency criteria are used to rank net return distributions for one conventional tillage (CT) and seven conservation tillage (including five reduced tillage and two no-till) systems, conducted for corn and soybean cropping systems on two clay soils located in the 3050 to 3100 Corn Heat Unit areas of Ontario. Average yields are similar under con-ventional tillage and reduced tillage systems, although actual corn and soybean yield response to tillage treatment is affected by drought (year). Average net returns differ among tillage treatments due to two factors. First, actual corn and soybean yields vary among tillage systems for each soil type, depending on weather (i.e., year) effects. In addition, machinery costs that are crop-specific increase costs of pro-duction and therefore reduce net returns. In general, CT systems dominate both reduced tillage and no-till systems for almost all risk intervals for both clay soils, except for slightly high-risk-preferring intervals. L’adoption des régimes de travail de conservation du sol a été plutôt restreinte sur les terres argileuses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".