Impacts of potential climate change on selected agroclimatic indices in Atlantic Canada
Bibliographic record
Abstract
Agroclimatic indices (heat units and water deficits) were determined for the Atlantic region of Canada for a baseline climate (1961 to 1990 period) and for two future time periods (2010 to 2039 and 2040 to 2069). Climate scenarios for the future periods were primarily based on outputs from the Canadian General Circulation Model (GCM) that included the effects of aerosols (CGCMI-A), but variability introduced by multiple GCM experiments was also examined. Climatic data for all three periods were interpolated to a grid of about 10 to 15 km. Agroclimatic indices were computed and mapped based on the gridded data. Based on CGCMI-A scenarios interpolated to the fine grid, average crop heat units (CHU) would increase by 300 to 500 CHU for the 2010 to 2039 period and by 500 to 700 CHU for the 2040 to 2069 period in the main agricultural areas of the Atlantic region. However, increases in CHU for the 2040 to 2069 period typically varied from 450 to 1650 units in these regions when variability among GCM experiments was considered, resulting in a projected range of 2650 to 4000 available CHU. Effective growing degree-days above 5°C (EGDD) typically increased by about 400 units for the 2040 to 2069 period in the main agricultural areas, resulting in available EGDD from 1800 to over 2000 units. Uncertainty introduced by multiple GCMs increased the range from 1700 to 2700 EGDD. A decrease in heat units (cooling) is anticipated along part of the coast of Labrador. Anticipated changes in water deficits (DEFICIT), defined as the amount by which potential evapotranspiration exceeded precipitation over the growing season, typically ranged from +50 to −50 mm for both periods, but this range widened from +50 to −100 mm when variability among GCM experiments was considered. The greatest increases in deficits were expected in the central region of New Brunswick for the 2040 to 2069 period. Our interpolation procedures estimated mean winter and summer temperature changes that were 1.4°C on average lower than a statistical downscaling procedure (SDSM) for four locations. Increases in precipitation during summer and autumn averaged 20% less than SDSM. During periods when SDSM estimated relatively small changes in temperature or precipitation, our interpolation procedure tended to produce changes that were larger than SDSM. Additional investigations would be beneficial that explore the impact of a range of scenarios from other GCM models, other downscaling methods and the potential effects of change in climate variability on these agroclimatic indices. Potential impacts of these changes on crop yields and production in the region also need to be explored. Key words: Crop heat units, effective growing degree-days, water deficits, climate change scenarios, statistical downscaling, spatial interpolation
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".