Integrated Economic and Environmental Assessment of Nitrogenous Fertilizer Application in Canadian Prairies
Bibliographic record
Abstract
Adoption of mitigation measures to reduce greenhouse gas emissions may affect other members of the society, producing a situation of trade-offs. In this study, such a trade-off is has been analyzed using three aspects of the Canadian society: producers (farm level adopter), environment (through reduction in the GHG emissions),; and regional economy (including rest of the society through lost / gained economic activities). The nutrient management strategy involving the switching nitrogen fertilizer application from a combination of fall and spring application to a 100 percent spring application. Results suggest that the adoption of such a measure creates a 'win-win' situation, being both environmentally and economically desirable. Under the scenario, fertilizer expenditures decreased by $43 million (giving rise to an equivalent increase in farm income), GHG emissions (in CO2E) by 2.15 percent of the 2000 level of emissions, Canadian economy as a whole showed improvements, although on a regional basis the results were mixed.Key words: Canadian prairie agriculture; Greenhouse gases; Mitigation; Nitrogen fertilizer Use; Trade-off analysisThe Journal of AGRICULTURE AND ENVIRONMENT Vol. 11, 2010Page: 70-82Uploaded date: 15 Septembre, 2010
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".