Economic Targeting of Agricultural Beneficial Management Practices to Address Phosphorus Runoff in Manitoba
Bibliographic record
Abstract
Abstract The mechanisms used to deliver agricultural beneficial management practices (BMPs) can influence the performance of these policies. Research has suggested that agri‐environmental instruments targeted based on specific economic or environmental characteristics can improve policy performance. Using a case study approach, we evaluate the relative performance of different mechanisms to target subsidized water retention pond BMPs to reduce phosphorus (P) runoff in an agriculture dominated subwatershed within the Lake Winnipeg watershed in southern Manitoba. The water retention pond BMPs were targeted based on estimated establishment costs (cost targeting), total phosphorus removal from surface water (benefit targeting), and pond‐specific benefit–cost ratios. The targeting was simulated using predictions of retention pond‐specific P removal from an adapted hydrology model and site‐specific pond construction and land opportunity costs assembled in a geographic information system database. Targeting of water retention pond BMPs has an impact on the cost effectiveness of the policy delivery with benefit–cost targeting being the most cost‐effective approach. Water retention ponds providing higher P removal at lower cost were smaller in size and on land previously used for the production of lower value crops. Le ciblage économique des pratiques de gestion bénéfiques en agriculture pour remédier au ruissellement du phosphore au Manitoba Les mécanismes utilisés pour livrer des pratiques de gestion bénéfiques (PGB) peuvent influencer la performance de ces politiques. Des études suggèrent que le ciblage d'instruments agroenvironnementaux basé sur des caractéristiques économiques ou environnementales précises peut améliorer la performance des politiques. Au moyen d'études de cas, nous évaluons la performance relative de divers mécanismes pour cibler les PGB des bassins de rétention d'eau subventionnés pour réduire le ruissellement de phosphore (P) dans un sous‐bassin du bassin du Lac Winnipeg au sud du Manitoba. Les PGB du bassin de rétention des eaux ont été ciblées en fonction des coûts estimés d'établissement (ciblage des coûts), de l′élimination totale du phosphore de la surface de l'eau (ciblage des bénéfices), et des ratios avantages‐coûts liés au bassin. Le ciblage fut simulé au moyen de prédictions du taux de suppression de P spécifique à chaque bassin de rétention obtenues à partir d'une adaptation d'un modèle hydrologique et d'une base de données d'un système d'information géographique (SIG) contenant les sites de chaque bassin de rétention et le coût d'opportunité du terrain. Le ciblage des PGB des bassins de rétention d'eau a un impact sur la rentabilité de la mise en œuvre de politiques, le ciblage coût‐avantages étant l'approche la plus rentable. Les bassins de rétention d'eau ayant le plus haut taux d'élimination de P à moindre coût s'avéraient plus petits et sur des terrains ayant servi, auparavant, à la production de cultures de moindre valeur.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".