Performance of Agricultural Systems under Contrasting Growing Season Conditions in South‐western Quebec
Bibliographic record
Abstract
Abstract Climate change will alter temperature and rainfall patterns over North American agricultural regions and there will be a need to adapt crop production systems to the altered conditions. A set of field experiments were conducted in south‐western Quebec, Canada, with soybean (Glycine max L.), corn (Zea mays L.), sorghum (Sorghum bicolor L.) × sudangrass (Sorghum sudanense Piper) hybrid and switchgrass (Panicum virgatum L.) under two tillage and three nitrogen fertility regimes, to study their performance in three successive growing seasons (2001–2003), two of them with unusually warm and dry conditions. The annual crops were established in two tillage systems: conventional and no‐till (NT). All crops except soybean were fertilized with three levels of nitrogen: corn – 0, 90 and 180 kg N ha−1, sorghum‐sudangrass – 0, 75 and 150 kg N ha−1, switchgrass – 0, 30 and 60 kg N ha−1. The 2001 and 2002 seasons were hotter and drier than the 2003 season, which was the most favourable for crop growth. The capacity of the crops to yield in dry seasons was as follow: switchgrass > sorghum‐sudangrass > corn > soybean. The corn and sorghum‐sudangrass responses to nitrogen fertilizer were low in 2001 due to the combined effect of dry growing season and coarse soil texture. Soybean did not perform well under NT. Corn yielded better at the highest nitrogen fertilizer rate under NT when the early season was warmer than the normal. Our results show that switchgrass and sorghum‐sudangrass could be an option in south‐western Quebec if the frequency of hot and dry seasons increase in the future, because of 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".