Do cover crops on the Canadian prairies affect soil nitrogen cycling?
Bibliographic record
Abstract
For one of Canada’s most important regions of crop production—the prairies—it’s uncertain if cover crops can be successfully integrated into rotations; if so, will soil nitrogen (N) cycling be influenced to benefit main crops? To address these gaps, we compared a crop rotation with cover crops (CC) vs. without cover crops (LR) from 2018 to 2021 in Saskatoon, SK. The main crops were grown in sequence of wheat–canola–potato–pea; the cover crops included red clover, berseem clover/oat mix, fall rye, and a brassica cover. Yield and aboveground biomass were collected each year and analyzed to determine crop yield and N use efficiency (NUE). Soil N availability was monitored in various ways, that is, by assessing pre-plant soil nitrate, soil inorganic N (SIN) supply rate, and potentially mineralizable N (PMN). We found that the influence on soil N dynamics was restricted to the non-growing season where cover crops reduced SIN supply rate and nitrate content compared to the conventional practice without cover crops. Yet, rotations with vs. without cover crop did not differ in crop NUEs, yields, or in-season N dynamics. We found some evidence that diversifying rotations with cover crops may help the system to function more like perennial systems in terms of regulating N in the long run; but had limited impact during the three years studied. To ensure that cover crops are effective and functional on the prairies, innovative design approaches are needed to adapt cover crops to reach soil health goals under prairie conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".