A Bioeconomic Analysis of Climate Change Impacts on Grape Growers in the Niagara Peninsula
Bibliographic record
Abstract
This study evaluates the economic impacts of future climate change (2015-2044) on representative vineyards in the Niagara Peninsula, Ontario. An integrated Bio-Economic Approach was used to evaluate the Net Present Value (NPV) of grape growers. First, three future climate scenarios (Median, Warm/Dry, Cold/Humid) and two conditions (with and without atmospheric carbon dioxide (CO2) enhancement) were selected. Climate scenario data were obtained from the Regional Climate Model system RegCM v.4. Second, yield data were obtained through simulation with the Cropping Systems Simulation Model (CropSyst). Finally, the Net Present Value (NPV) was estimated. Land allocation was determined exogenously. Six wine-grape varieties (Chardonnay, Riesling, Cabernet Sauvignon, Cabernet Franc, Merlot, and Pinot Noir) were selected for the study. The results indicate that net present value, output, economic vulnerability and financial management tools varied with each climate scenario and condition. The direction and magnitude of the impacts changed as CO2 enhancement conditions varied by crop. Even under the worst climate condition of Cold/Humid with the absence of CO2 fertilization effects, the only crops that had a relative economic advantage were Cabernet Franc and Cabernet Sauvignon (red varieties). The returns on the white grape varieties were significantly lower for all scenarios. The results indicate that the Agristability program helped absorb the variability in NPV and stabilized income, but did not ensure producersâ financial stability from the risks of climate change. Financial risk management tools would help growers to increase their financial strength, have flexibility in their choice of adaptation options, and reduce their economic vulnerability. These management tools, and should remain profitable, and has to as well as including e an environmental decisions such as increasing soil fertility.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".