Climate change and crop production in southwestern Quebec : mitigation and adaptation
Bibliographic record
Abstract
Anthropogenic greenhouse gas emissions are the main cause of increasing global temperatures. Climate change will affect crop production in Canada and, in turn agriculture has the potential to mitigate climate change. Analysis of historical climate and corn yield data, and field and greenhouse experiments were carried out in order to study the effect of climate variability and changes on corn yield, the adaptability of cropping systems to climate change conditions, the effect of tillage on soil greenhouse gas emissions (CO2 and N2O) associated with corn and soybean production, and the potential of Nod factors to increase biomass as way to mitigate CO2 emissions. In southwestern Quebec, corn yield variability has been strongly associated to July temperature and May precipitation during the last three decades. Field studies showed that switchgrass and sorghum-sudangrass were best adapted to dry and warm climate events. No-till improved corn yield when spring temperatures were warmer than normal. Soil CO2 fluxes were associated with temperature, while soil N2O fluxes were associated with precipitation. Conventional tillage (CT) had greater CO2 emissions than no-till (NT), particularly after disking in the spring. Both tillage systems had large N2O emission peaks during the wettest part of the season. In corn, peaks of N 2O occurred after nitrogen fertilizer application. NT had greater N 2O emissions than CT in the corn production systems; however, CT had greater N2O fluxes than NT in the soybean production system. Nod factors sprayed on soybean increased photosynthesis and biomass under controlled conditions. In the field, yield was increased by Nod factors under CT, but not under NT, and drought reduced the soybean response to Nod factors.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".