Canola productivity and carbon footprint under different cropping systems in eastern Canada
Bibliographic record
Abstract
Abstract Diversified crop rotation with an appropriate sequence may be a promising strategy for increasing crop productivity while reducing greenhouse gas emissions (GHGs) and lowering carbon (C) footprint for more sustainable agricultural systems. The objectives of this study were to (i) assess the agronomic performance and C footprint of canola ( Brassica napus L.) production in different cropping systems, and (ii) better understand how canola could be adapted to existing cropping systems in eastern Canada. A four-year canola-based phase rotation study, including maize ( Zea mays L.), wheat ( Triticum aestivum L.), and soybean ( Glycine max L.), started in 2011 and continued for two cycles in Ottawa, ON; Montreal, QC; and Canning, NS. It was found that, compared to continuous monoculture (canola, maize or wheat), diversified cropping systems increased crop yields by an average of 32% and reduced the C footprint of all rotations by 33%, except under severe heat and drought conditions. The effect of rotation on yield and C footprint of canola production varied significantly among site-years. At Ottawa, the canola following soybean (SC) had 12% higher canola yield than monoculture canola (CC), 5 and 8% higher canola yield than canola following wheat (WC) or maize (MC). At Montreal, canola yield ranked as MC > SC > WC > CC. At Canning, the highest canola yield was in WC (21%) and SC (13%). Overall, most SC rotations had the lowest C footprint, and CC cropping had the highest C footprint, with only a few exceptions. Regardless of the cropping system, canola required more N input and was high in oil and protein in the harvested product, and produced the highest C footprint, while soybean had the lowest C footprint at all three sites. Our findings indicate that a diversified cropping system with canola production following soybean significantly improved canola yield while lowering the C footprint. However, profitable and sustainable canola production in eastern Canada is threatened by climate change-induced drought and heat stress.
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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 | 0.000 | 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) | 0.000 | 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".