The economics of controlled drainage with sub-irrigation and field drainage in Quebec
Bibliographic record
Abstract
Based on evidence from scientific studies, increases in Greenhouse Gas (GHG) emissions are a contributing factor to climate change that have global implications. Climate change has the potential of having detrimental effects on environmental quality and sustainability. Canada, as a member of the United Nation Framework Convention on Climate Change (UNFCCC) is committed to reducing its GHG emissions. As a result of this commitment, the Agricultural Greenhouse Gas Program (AGGP) was established to undertake research in order to develop and implement GHG mitigation strategies. The AGGP partnered with the Brace Centre for Water Resource Management to investigate water table management (WTM) systems that could have the potential for increasing yields and reducing GHG emissions. The purpose of this research was to evaluate the on-farm and off-farm costs and benefits of alternate drainage technologies. Benefit-cost analysis (BCA) was used to determine the Net Present Value, Internal Rate of Return and Benefit Costs Ratio of Controlled drainage with Sub-irrigation (CDSI) and Tile/Field Drainage (FD). The on-farm private benefit-cost analysis was based on data from a project site in St. Emmanuelle, Quebec. The historical yield data were based on data gathered from the site between 1993 and 2014 and projected for the next 20 years (useful life of the irrigation system) prices and costs were projected from 2015 to 2034. There was no statistically significant difference in the yields between the two irrigation systems. The Benefit Cost ratio for installing CDSI was 1.03 and 1.13 for FD. The net present value (NPV) at a 3.75% discount rate was C$713.12 for CDSI and C$1,501.5 for FD. An estimation of off-farm benefits was performed, by incorporating GHG emissions costs into the analysis and the results revealed a Benefit Cost Ratio of 1.01 and 1.12 for CDSI and FD respectively. Based on these results, it would be preferable for farmers to adopt FD, which is the status quo, situation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".