Influence of climate change on crop growth and soil microbial functional potential
Bibliographic record
Abstract
Climate change is expected to reshape agroecosystems by altering temperature and precipitation patterns, with cascading effects on soil processes, microbial communities, herbicide behavior, and crop performance. This thesis examined two field experiments conducted in a temperate agricultural system in Quebec, Canada: one investigating the effects of elevated soil temperature (+2.5 °C), and the other testing ±30% altered rainfall treatments (DART). The study assessed responses in soil physicochemical properties, microbial abundance, greenhouse gas emissions, herbicide degradation, and plant physiological traits.In the temperature experiment, elevated temperature led to modest reductions in soil moisture and a general trend toward increased CO₂ flux, although statistical significance was observed only mid-season. Soil pH remained stable throughout the season, and warming had no significant effect on nitrous oxide (N₂O) flux. Bacterial (16S rRNA) and fungal (28S rRNA) gene abundances showed no statistically significant changes under warming, although 16S rRNA abundance trended upward over time in heated plots. These results suggest moderate warming can influence microbial respiration and carbon cycling, but may not significantly impact microbial abundance or nitrogen gas fluxes within a single season.In the rainfall experiment, altered rainfall did not result in statistically significant differences in soil moisture, crop physiological performance, indicating strong physiological resilience of common bean under ±30% precipitation changes. Glyphosate degradation followed expected time-dependent declines, while AMPA concentrations remained relatively stable over time. However, neither glyphosate nor AMPA concentrations were significantly affected by rainfall treatments. Similarly, gene abundances of goxA, 16S rRNA, and 28S rRNA remained unaffected by rainfall, suggesting microbial degradation capacity and population size were stable under short-term rainfall manipulation.Collectively, the findings from both experimental systems suggest that short-term, moderate environmental changes did not lead to statistically significant shifts in soil chemical properties, microbial abundance, herbicide degradation, or plant traits. These results highlight the potential resilience of temperate agroecosystems to moderate climatic variability and underscore the importance of long-term studies to assess cumulative impacts under ongoing climate change
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".