The economic impact of climate change on cash crop farms in Québec and Ontario
Bibliographic record
Abstract
This study estimated the economic impact of climate change on representative cash crop farms at selected sites in Québec and Ontario over the period 2010 to 2039 using a Mixed Integer Dynamic Linear Programming Model. Five climate scenarios (Hot & Dry, Hot & Humid, Median, Cold & Dry and Cold & Humid) and four weather conditions (the combination of with and without Carbon Dioxide (CO2) enhancement and water limitation) were selected and combined to form 20 different scenarios. Four major cash crops, i.e. corn, soybean, wheat, and barley, were considered using both reference and improved cultivars. Historical data on crop yields were used to validate the Decision Support System for Agro-Technology Transfer (DSSAT) model which was used to project future yields. Economic variables, such as cost of production and crop prices were projected using Monte Carlo simulation with Crystal Ball Predictor. The results indicate that the optimal resource allocation, outputs, net returns, economic vulnerability, and adaptation strategies were dependent on the climate scenarios, weather conditions, types of crop and variety, as well as site. Water accessibility plays an essential role in farm profitability, especially coupled with atmospheric CO2 enhancement. Producers at all sites and scenarios were worse off under unfavorable weather condition when water was limited and CO2 enhancement was absent, especially in Ste-Martine where producers were predicted to have a number of years with successive financial losses. Different climate scenarios also had different impacts on farm management. The representative farm in Ste-Martine performs best under the Hot & Dry scenario if water was adequate, while in North Dundas, the Median or Cold scenarios were preferred. Technological development decreased farm financial vulnerability for all sites and scenarios. Institutional development, in terms of insurance programs and risk management tools, were also used to improve resilience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".