Economic Analysis of Beneficial Management Practices in Corn and Soybean Production under Different Climate Change Scenarios in Quebec and Ontario
Bibliographic record
Abstract
In Canada, the Agricultural Greenhouse Gases Program (AGGP) project is driving the promotion of responsible agricultural practices. This encompasses support for the advancement of technologies in irrigation, drainage, and water table management, along with the promotion of beneficial (or best) management practices (BMPs) that enhance water efficiency. These initiatives not only ensure sustainable agricultural production but also protect the interests of Canadian producers in terms of profitability, especially in the context of climate change. Generally, farmers are more likely to adopt innovative practices that leads to an increase in their profits, relative to the pre-investment situation. Under different climate change scenarios, this thesis conducted an economic analysis to explore the economic attractiveness of the BMPs and potentially influence their adoption on the farm-level. Using the Decision Support System for Agrotechnology Transfer (DSSAT) model and financial indicators such as Net Present Value (NPV), Benefit-Cost Ratio (BCR), and Internal Rate of Return (IRR), the financial performance of the BMP technology in comparison to the base technology was assessed. The research examined different climate scenarios, past and future, and conducted sensitivity analyses related to variation in crop prices and discount rates. At St. Emmanuel research site, the BMP technology outperformed the base technology in both past and future climate scenarios. Positive NPV, BCR values greater than one, and higher IRR for the BMP technology demonstrated its economic attractiveness. Similar findings were observed at Harrow research site, with the BMP technology consistently surpassing the base technology in terms of NPV, BCR, and IRR values. The results of the study at both research sites revealed that the selected BMP technology stands out as the preferable choice when contrasted with the conventional base technology under climate change. This suggests that producers might be inclined to consider the adoption of BMP. Nevertheless, the degree of their interest could vary since the economic attractiveness of these options is highly sensitive to changes in specific factors, owing to the uncertainties linked to both economic and non-economic factors.
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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) | 0.002 | 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".