Agroclimatic indices across the Canadian Prairies under a changing climate and their implications for agriculture
Bibliographic record
Abstract
Abstract With the objective of trying to understand the adaptability of agriculture across the Canadian Prairies under climate change, simple‐to‐use agroclimatic indices were calculated for the base climate period of 1981 to 2010 and for both the medium (RCP4.5) and high (RCP8.5) emission projections extending to the distant future (2071–2100). The agroclimatic indices included the Effective Growing Degree Days (EGDDs), Growing Season Length (GSL), the Climate Moisture Index (CMI), and the Temperature Humidity Index (THI). For climate change in 30‐year periods, these indices were calculated as multi‐model ensembles of six Global Climate Models recommended under the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the study area. We found that the GSL, EGDDs, CMI, and THI were amplified above the values of the base climate period in the order of 40–50 days, 600–1200 heat units, −100 to −120 mm and 3–4 THI units by the close of the distant future (2071–2100) under the RCP4.5 and RCP8.5, respectively. This amplification has implications on where crop and livestock production could become more suitable or riskier in future. Opportunities include expanding crop and livestock production to more northerly regions which currently have insufficient heat units, a short growing season and unfavourable temperature humidity thresholds for livestock production. Moisture deficits will continue to be the greatest risk during the growing season under climate change scenarios but options exist to implement adaptive measures such as staggering seeding times to take advantage of moisture availability in the spring and autumn seasons and crop substitution. This study has relevance for policy and program formulation and implementation in Canada's agricultural regions and potentially, other areas of the world with similar climate change outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".