Simulating Climate Change Impacts and Management Strategies on Crop Yield and Soil Organic Carbon Dynamics in Eastern Canada
Bibliographic record
Abstract
Highlights Warmer climate and enhanced CO2 levels are projected to generally increase crop yields and SOC levels in Eastern Canada. Average crop yields and SOC significantly increased under all future climate scenarios compared to the baseline. Managing the impacts of climate change on crop growth and SOC ensures long-term sustainability of the agriculture sector. Abstract. The agricultural sector in Eastern Canada is facing growing challenges from climate change, with regional warming occurring at twice the global average, necessitating the development of effective adaptation strategies. This study aimed to assess changes in crop growth and soil organic carbon (SOC) under current and projected future climate, exploring the potential implications of climate change on diverse crop rotations combined with long-term application of different manure and tillage types. The Denitrification-Decomposition (DNDC) model was calibrated and validated against historical data to simulate crop yields and SOC and was then used to project the impacts of climate change on these cropping systems from 1981 to 2100 with reporting for three future periods: Near-term (2030s), mid-term (2050s), and distant future (2070s). Simulations were conducted under three Shared Socio-economic Pathways (SSPs) scenarios (SSP 1-2.6, SSP 3-7.0 and SSP 5-8.5) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) for Eastern Canada. Three cropping systems were considered including cereal monoculture, cereal-perennial legume hay rotation (1989-2010) on a silty clay soil near Normandin, Quebec and an annual crop rotation (wheat-corn-soybean) from 2009-2016 on a sandy loam soil near St-Augustin-de-Desmaures, Quebec. The DNDC model demonstrated satisfactory performance in simulating yields at the experimental sites for annual crops, legume hay, and SOC stock. Compared to the 2000-2029 baseline scenarios, the average yields and SOC significantly increased under all future climate scenarios at Normandin due to a longer growing season, and the beneficial effects of elevated atmospheric CO2. However, SOC showed minimal change, as increased carbon inputs from crop residues were offset by higher SOC mineralization rates under elevated temperatures. In contrast, diversified rotations, such as barley-hay and corn-soybean-wheat, demonstrated further yield increases and SOC gains. These improvements were linked to reduced crop nutrient stress and enhanced soil water-holding capacity associated with higher simulated SOC levels. As expected, increasing the proportion of perennials in rotation, particularly leguminous forages, enhances both yields and long-term soil organic carbon (SOC) accumulation. However, diverse annual cropping systems also show potential for improving soil health and resilience, which could, in turn, boost economic returns for farmers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".