Impact of Climate Change on Productivity and Technical Efficiency in Canadian Crop Production
Bibliographic record
Abstract
There is a wide consensus that throughout the 20th century climate has changed globally, with many parts of the world facing increases in average temperatures as well as an increased frequency and intensity of extreme weather events. While the existing climate models can predict future changes in climate with a high degree of confidence, the potential impacts of climate change on agricultural production and food security are still not well understood. In this work, we investigate the link between climate change, output, and inefficiency in Canadian crop production using provincial data for the period of 1972–2016. This study has built a unique climate dataset from station-level weather data and uses a panel stochastic frontier model to explore the effect of climatic conditions on crop production and inefficiency. The results reveal that climatic variables are significant predictors of both the maximum potential output (frontier) and technical inefficiency. The combined effect of higher temperatures and lower precipitation, as reflected in a lower Oury index, is a downward shift of the crop production frontier. While greater variability of daily temperatures during the growing season is found to have no statistically significant effect in the frontier equation, greater variation in rainfall results in a downward frontier shift. The results also show that weather shocks measured as a deviation from historical weather normals are significant predictors of technical inefficiency.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".