The Economics of Erosion and Sustainable Practices: The Case of the Saint‐Esprit Watershed
Bibliographic record
Abstract
This paper examines the economics of the adoption of sustainable production practices for soil erosion control. The research was conducted on three case farms within the Saint‐Esprit watershed in Quebec using a two‐stage process. The first stage involved the use of GIS (Geographical Information Systems) to record erosion characteristics (slope, etc.) for these farmers'fields. This erosion information was then included as input information in the second stage of the process. Mixed integer linear programming (MILP) was used to model both individual farms and the watershed. Increasing erosion constraints were applied to these models to investigate changes in crop production mixes for farms and the watershed. A comparison of the results (farms versus watershed) was used to investigate policy questions concerning an optimal erosion constraint for society. Results generated indicate that farms with higher net incomes would be advantaged by erosion constraints set at the watershed level, whereas farms with lower net revenues would be disadvantaged. Thus, trading of pollution permits could be encouraged. Cet article examine les aspects économiques de l'adoption de pratiques de production durables visant a réduire l'érosion du sol. La recherche fut effectuée sur trois fermes situées dans le has sin du Saint‐Esprit au Québec, et impliqua un processus à deux étapes. Le premier étape consiste en l'utilisation du systéme d'informations géographiques “ SIG ” afin de noter les caractéristiques de l'érosion (pente, etc.) dans ces champs agricoles. Ces renseignements servirent de données au sein du deuxième étape. La méthode de programmation linéaire à nombres entiers mixtes fut employée afin de modéliser les fermes individuelles, ainsi que le bassin. Ensuite, les contraintes d‘érosion furent appliquées sur ces modèles de manière croissante, et ce afin d'étudier les changements dans le melange des productions de cultures pour les fermes et le bassin. Une comparaison des résultats (fermes vs. bassin) fut accomplie pour examiner les questions de politiques pouvant mener à une contrainte d‘érosion optimale pour la société. Les résultats obtenus démontrent que les fermes ayant des revenus nets élevés seraient avantagées par des contraintes d'érosion établies au niveau du bassin, tandis que les fermes aux revenus nets plus bas en seraient désavantagées. En conséquence, l'échange de permis de pollution est recommandé.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".