An Environmental Benefit-Cost Analysis Case Study of Nutrient Management in an Agricultural Watershed
Bibliographic record
Abstract
This paper illustrates the importance of valuing environmental goods and services associated with water quality improvements when assessing implementation of on-farm nutrient management plans as required by the Quebec Regulation Respecting Agricultural Operations (RRAO). Based on a case study in the Chaudiere River watershed, two scenarios were considered using the integrated, economic-hydrological, modelling framework provided by GIBSI: (i) a base-case scenario assuming application of all available manure; and (ii) an on-farm nutrient management scenario based on meeting phosphorus crop requirements with manure and treating any manure surpluses. Two types of management units were selected to evaluate these scenarios: (i) contiguous municipalities (administrative units of agricultural development); and (ii) subwatersheds. Results showed that management at subwatershed levels had larger benefit/cost (B/C) ratios when compared to contiguous municipalities. This illustrates that the watershed is a more meaningful management unit than the municipality, which is not a hydrological unit. For one of those subwatersheds, the B/C ratio was close to one although only various recreational benefits were accounted for in the evaluation. In all likelihood, if a more holistic set of benefits were accounted for, a B/C ratio greater than or equal to one would have resulted. A sensitivity analysis revealed that variations of 37.5%, -22.5%, and -20% for monetary benefits, on-farm manure treatment costs, and average probabilities of exceeding the targeted water quality standard (prevention of eutrophication of rivers), respectively, were necessary to obtain a B/C ratio greater than one.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".