Evaluating the impact of climate change on Canadian Prairie agriculture
Bibliographic record
Abstract
Climate change is a long-term shift in the average weather conditions that threatens settlements, societies, and industries. Agriculture is one of the most climate-sensitive industries since the production in this sector is highly dependent on various weather factors. The main objective of this study is to evaluate the economic impact of climate change on Canadian Prairie agriculture using the well-known Ricardian model. The Ricardian model is best described as a hedonic regression of farmland value on an assortment of climatic and non-climatic variables. This model is widely used in economic analysis because it captures farmers’ adaptation strategies to climate change. To estimate the parameters of the Ricardian model, three methods are utilized: pooled weighted least squared (WLS), random effects, and spatial random effects. The estimated coefficients are used to predict the impact of three potential climate and price change scenarios on farmland value in the Canadian Prairies. The main contributions of this study relative to existing studies are: the use of updated data, the inclusion of expected prices rather than actual prices in the model, and the utilization of spatial econometrics methods to estimate the model. The estimated marginal impacts of climate demonstrate that an increase in rainfall and winter, spring, and fall temperatures will increase farmland value; the effect is opposite for July temperature. The signs of the marginal impacts of rainfall and July temperature reveal that water availability plays a very important role in crop production on the Canadian Prairies. Overall, climate change is predicted to increase the value of farm land on the Canadian Prairies by an average of 0.9% to 3.87% annually. However, the northern part of Saskatchewan and the north-eastern part of Alberta are forecasted to experience a decrease in farmland value under a medium climate change scenario. The current analysis predicts that farm welfare in the Prairies will increase by about $1.14 - $4.1 billion annually as a result of climate change. This suggests that with proper adaptations, climate change can be beneficial for Prairie agriculture.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".