Crop rotations and canola yields: Evidence from field‐level data in Western Canada
Bibliographic record
Abstract
Abstract Canola ( Brassica napus ) acreage increased in Western Canada in recent years, leading to rotations with fewer break years between canola plantings. Field trials suggest that frequent plantings of canola reduce canola yields. However, there is considerable disagreement about the magnitude and persistence of these effects. We analyze the effect of rotational practices on canola yields in Saskatchewan using over 20 years of observational data, representing 61% of canola hectares in the province. We examine how the impact of rotations varies across time, soil zone, soil moisture conditions and the distribution of yields. We regress canola yields in Saskatchewan on the share of land that was planted with particular crops in previous years, using a battery of covariates and fixed effects to address potential bias in the model. After including these fixed effects, we cannot reject the hypothesis that there is no sample selection bias. We use an unconditional quantile estimator to investigate how rotations affect different deciles of the yield distribution. Our analysis confirms that crop rotations significantly influence canola yields, albeit more modest than field trials suggest. We find a 7.5% yield reduction when canola follows canola, compared to cereals, with this penalty persisting for 4 years but diminishing in magnitude with each additional year. The adverse effects of consecutive canola plantings are more pronounced in wetter regions and at lower yield deciles. Conversely, canola yields are higher when planted after pulse crops (as opposed to after cereal crops).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".