Economic analysis of beneficial water management practices in Quebec and Ontario
Bibliographic record
Abstract
Concerns are increasing with regard to global warming, suggested to be caused by the increasing greenhouse gas (GHG) emissions. Scientific research emphasizes that high concentrations of GHGs in the atmosphere causes increased temperatures, changes in precipitation patterns, and the occurrence of extreme events, such as floods or droughts. All these phenomena constitute climate change that has become a main concern in the agriculture community around the world (excess moisture or water stress). However, the agricultural sector is also a source of GHGs. According to the most recent estimation reported in 2017, the Canadian agricultural sector accounted for 10% of the total national emissions.\nAs Canada has committed, through the Paris Agreement, to reduce its GHG emissions by 30% from the level of 2005 by 2030, federal and provincial governments are taking action to reduce agricultural GHG emissions but, at the same time, developing programs to adapt to climate change. Under the Agricultural Greenhouse Gases Program (AGGP) project, responsible agricultural activities are being promoted by supporting, among other areas, the development of technologies in irrigation, drainage and water table management, beneficial management practices (BMPs), which promote efficiency in the use and improvement in the quality of water (environmental performance), and ensure optimal agricultural production to protect the interests of Canadian producers (profitability performance).\nAdoption of these BMPs by producers, however, depends entirely on their perception of the new technology and its effect on their farm business. Producers are more likely to adopt innovative practices that translate into an increase in their profits, relative to the pre-investment situation. To investigate this issue, this thesis evaluates the economic effects of adopting BMPs on the farm-level, deriving an incremental cash flow as a result of comparing net flows with and without the practice, in each of the four research farms located in Quebec and Ontario. The economic performance was estimated through the use of several indicators, but mainly the net present value. This thesis has also undertaken simulations on the economic desirability outcomes under changes in various factors that would affect the economics of the BMPs and as possible measures that influence its adoption. Crop yield was considered among these factors, whose variation results from projections into climate change.\nThe study findings show that the selected BMP technology for each of the four case studies is the most desirable alternative when compared to the base technology. This would imply that producers, may be interested in adopting innovative water management practices. However, depending on whether they are grain or vegetable producers, their interest may change as the results on economic desirability are very sensitive to variations in certain parameters due to uncertainties associated with economic and non-economic events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".