The impact of climate change on Canadian agriculture : a Ricardian approach
Bibliographic record
Abstract
Climate change may change the frequency and intensity of weather events which will likely challenge human and natural systems more than normal change. Agriculture is considered one of the most vulnerable systems to climate change. The main goal of this study is to estimate the economic impact of climate change on agriculture in the Canadian prairies and to capture the impact of weather conditions on the viability of production systems along with the impact of market price effects by predicting the economic impact of climate change. A two way fixed effects panel model with time and provinces group fixed effects is calibrated to simulate a set of potential climate change and global change in prices on the economics of prairie agriculture. The predicted impact of change in rainfall, increase in temperature and rise in future global market prices indicate that climate change will have complicated nonlinear effects on prairie agriculture. The results of this study also highlight the importance of precipitation for agriculture on the Canadian prairies. Marginal impacts of the evapo-transpiration proxy, rainfall, and July relative humidity indicate direct and positive relationship between agricultural land values and water related climate variables. It verifies that agriculture in the Prairies is very vulnerable to water scarcity and land use and land value strongly depends on the precipitation. The most important finding of this study is that climate change is beneficial for Canadian prairie agriculture except for some south east regions of Alberta. Comparing the results from direct impacts of climate and price changes on land value with the results from indirect impacts through area response estimation reveals that direct impacts of climate and price change increase farmland value by 31% while the indirect impacts from different scenarios increase simulated land value by up to 51%. The results from base and three scenarios in this study reveal that climate change may not be a big threat for prairie agricultural economics if farmers employ appropriate adaptation strategies such as switching between crops and introducing new crops. As a matter of fact, climate change may provide an opportunity for agricultural producers in the prairies to gain from future price and environmental change. To achieve this goal, policies to address climate change concerns need to put a greater emphasis on dealing with water deficit and scarcity. Policies that facilitate access to irrigation and crop choices will help farmers to adapt to climate change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".