The Future of Canadian Canola Production: Sustainability Under Climate Change
Bibliographic record
Abstract
In my dissertation, my concern is the impacts that climate change is imposing on Canadian agriculture, specifically on canola production. Canola is a significant crop in Canadian agriculture and the economy. However, Canada's temperature has rapidly risen, and precipitation has shifted, altering water availability for crops, posing significant challenges to crop productivity. Addressing these issues requires interdisciplinary research and sustainable adaptation strategies. Hence, this thesis aims to analyze the status of canola producers and adaptation strategies due to climate change and canola future production under four climate scenarios from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) in Canada with the Decision Support System for Agrotechnology Transfer (DSSAT) to find future projections to base strategic management decisions. More specifically, Chapter 2 aims to assess food security through mathematical models of major staple crops, focusing on food availability. This involves evaluating the applicability and performance of eight crop models for crop production and an intercomparison project. Chapter 3 aims to explore self-reported changes in climate and water resources, the adoption of adaptation strategies, and factors influencing decision-making in response to climate change. This chapter also identifies beneficial practices and policies for supporting canola production in the Canadian Prairies. Chapter 4 aims to determine the optimal rainfed canola production in the Canadian Prairie Region under various future climate scenarios using the DSSAT-Pythia model. Specific objectives include identifying the most resilient current canola hybrid cultivar, defining the optimal planting period, and determining the optimal nitrogen concentration in fertilization. Chapter 5 aims to demonstrate the impacts of air temperature and soil water content on spring canola production under future climate scenarios from 2025 to 2050. This includes exploring the joint effects of soil water content and temperature, analyzing future water stress, and projecting canola yield under changing climate conditions. Overall, this thesis evaluates the future availability of canola for food and energy security, quantifying and assessing temperature and soil water content effects on canola yield losses and characterizing the Prairie canola farmers' climate change risk perception and their implementation of adaptation strategies derived from climate change challenges. Overall, this thesis empowers shareholders, farmers, technicians, decision-makers, and policymakers to develop more effective adaptation strategies and policies. These strategies aim to enhance the mitigation of climate change impacts on canola production, thereby ensuring the availability of food and biofuel feedstock from Canadian agriculture.
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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 | 0.000 | 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 | 0.001 | 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".