Allocating Land for an Ecosystem Service: A Simple Model of Nutrient Retention with an Application to the Chesapeake Bay Watershed
Bibliographic record
Abstract
There has been great interest in recent decades in “ecosystem services.” One of the services most often mentioned is the retention of nutrients. I construct a simple model of agricultural land use under a regulatory requirement that nutrient loading cannot exceed a fixed ceiling. Farmers decide the quantity of residual nutrients they will generate, how much land they will allocate to production, and, consequently, how much land that could have been used for production they will instead preserve for the retention of nutrients. I develop three propositions. First, when the regulatory constraint is relatively weak, there will be a corner solution in which no land is set aside to provide the service of nutrient retention. Second, for any given regulatory constraint, there is a maximum amount of land that would be set aside to provide ecosystem services, regardless of the efficiency with which preserved land performs the nutrient retention function. Third, when it would prove very valuable to set some land aside for nutrient retention, less land in total may optimally be preserved for this purpose than when the service is less valuable. I illustrate the implications of this model with an application to the Chesapeake Bay watershed. Depuis les dernières décennies, les services écosystémiques suscitent beaucoup d’intérêt. La rétention des éléments nutritifs figure parmi les services les plus mentionnés. Dans le présent article, j’ai élaboré un modèle simple d’utilisation des terres agricoles qui tient compte d’une exigence réglementaire selon laquelle les charges en éléments nutritifs ne peuvent excéder une quantité maximale établie. Les agriculteurs décident de la quantité d’éléments nutritifs résiduels qu’ils généreront, des superficies qu’ils consacreront à la production et, par conséquent, des superficies qui, au lieu d’être consacrées à la production, seront réservées à la rétention des éléments nutritifs. J’ai formulé trois propositions. Premièrement, lorsque la limite réglementaire n’est pas fermement imposée, une solution spéciale (corner solution) fera en sorte qu’aucune superficie ne sera réservée pour la rétention des éléments nutritifs. Deuxièmement, pour toute limite réglementaire établie, des superficies maximales seront réservées pour offrir des services écosystémiques, peu importe l’efficacité de rétention des éléments nutritifs de ces superficies. Troisièmement, lorsqu’il aura été prouvé que réserver des superficies pour la rétention des éléments nutritifs est d’une grande valeur, il se peut que les superficies réservées à cette fin soient inférieures à celles qui l’avaient été lorsque le service avait une moindre valeur. J’ai illustré les incidences de ce modèle en l’appliquant au bassin versant de la baie de Chesapeake.
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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 | 0.000 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".