Evaluations of Climate Change Impacts on Crop Yields in Saskatchewan and Ontario and of Opportunities for Agricultural Expansion in Northern Ontario
Bibliographic record
Abstract
In this work we use a mix of statistical analysis and process-based modeling with the decision support system for agrotechnology transfer (DSSAT) crop modelling platform to investigate the two issues of great concern for sustaining agricultural productivity in warming Canada. These are the changing crop response to environmental stresses as crop technology develops and the crop responses to changing weather in the current growth environment and at higher latitudes. The research has shown increased sensitivity of new oats, barley and spring wheat cultivars in Saskatchewan to mostly unchanged daytime temperatures, between 1961 and 2000. Increasing nighttime temperatures impacted oats, barley and spring wheat yields negatively between 1981 and 2000. In another aspect of the research, we selected cultivar and soil combinations to project yields into the climate changed future with DSSAT, without changing technology. Selection was based on the smallest root mean squared error (RMSE) in simulated yields compared with measurement, 1987 to 2016, provided that their correlations were significant at the 5% level. Planted areas prioritised the influence of county yields in the aggregations to representative subregional values for northern and southern Southwestern Ontario. The representative maize ecotypes and/or cultivars identified for various Southwestern Ontario counties were Pio-3563, GL482, 2600-2650-GDD, Dekalb-485, Pio-3192, Pio-3147 and Mokwa-87TZPB-SR, while for soybean these were M-Group-000, M-Group-00, Altona (00), and M-Group-0. Data from 4 regional climate models (RCM) were used in DSSAT runs to project soybean and maize yields into the climate changed future, to year 2069, under the RCP8.5 scenario. We found that soybean can withstand most of the high temperatures expected under RCP8.5, while maize cannot and hence maize is likely to have decreasing yields in Southwestern Ontario from the late 2030s. Nevertheless, in this enhanced warming scenario, soybean and maize planted in Cochrane District, Northern Ontario, in the mid 2020s and 2030s respectively, can yield at least 80% of their maximums attainable by the mid-to-late 2040s. Decreasing precipitation would be of greatest concern in relation to changes in soybean yield, while increasing temperatures would be of greatest concern in relation to changes in maize yield.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".