Policy options for multiple environmental benefits in agricultural watersheds
Bibliographic record
Abstract
Intensive agriculture can generate excessive levels of multiple pollutants, thereby complicating design of agri-environmental policies. The main purpose of the study was to provide policy analysts and farmers with missing information that will assist them to understand the interrelationships among cropping practices, sediment loading and nitrate leaching, and the cost-effectiveness of controlling sediment and nitrate from an agricultural watershed. The study extended the theoretical model of the firm under production externalities to include multiple pollutants and showed that the optimal level of input use depends on the nature of the relationship among the pollutants. Therefore, when a production process generates multiple pollutants, control policies must take into account possible trade-offs among multiple environmental objectives. The Soil and Water Assessment Tool (SWAT), which has not previously been used to simulate actual multi-crop rotations for PEI agriculture, was calibrated and used to predict the effects of alternative cropping systems On sediment loading and nitrate leaching for the Wilmot River watershed. Simulation results showed that different sub-basins of the watershed are susceptible to different degrees of sediment loading and nitrate leaching and that sediment loading and nitrate leaching are influenced by different spatial factors. Spatial management of cropping systems can reduce sediment loading and nitrate leaching, but conflicts could arise due to trade-offs between sediment loading and nitrate leaching. Control policies need to take land- and soil-specific characteristics into account, and also consider how a given strategy for controlling one pollutant affects other pollutants. Thus, it is important to understand the effects of alternative farm management practices on both sediment loading and nitrate leaching before designing policies to effectively control both pollutants. Also, empirical analysis of cost-effectiveness Of agri-environmental policies for sediment abatement in Maine (US), the Netherlands, thud Prince Edward Island (Canada) showed that, although uniform, the policy for Maine was superior to the policy for the Netherlands, which in turn was superior to the policy for PEI. Therefore, design of policy for controlling pollution from agriculture is a fine balancing act, requiring the prioritization of policy objectives, analyzing strategies for their likely outcomes and making the right compromises.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".