Rye cover crop improves vegetable crop nitrogen use efficiency and yield in a short season growing region
Bibliographic record
Abstract
Cover crops have the potential to immobilize nitrogen (N) that would otherwise be lost before or after the main crop production, leading to improved N management. However, information on how cover crops influence N management in intensive vegetable cropping systems is scarce. This study aimed to determine how an overwintering rye cover crop impacts crop yield and N cycling, for three common prairie vegetable crops. From 2017 to 2019, a broccoli – sweet corn – root crop sequence was tested (in which all crops of rotation were present each year), with each crop type receiving five N fertilizer treatments, ranging from 0 to 300 kg N·ha−1. After harvest each year, sub-plots were established with vs. without a rye cover crop, and the effect on vegetable yield, soil inorganic N, and N use efficiency (NUE) was followed into the subsequent growing season. In most cases, the cover crop increased vegetable crop productivity and N content in the subsequent growing season. The cover crop also lowered soil inorganic N levels at vegetable planting but increased levels at harvest. Vegetable crop NUE indices were frequently improved with vs. without the cover crop. As for the N fertilizer response, increasing N fertilizer rate did not continually increase vegetable crop productivity and N content. Higher N fertilizer rates increased soil inorganic N levels at vegetable planting and harvest, and often lowered vegetable crop NUE indices. These results demonstrate the importance of adjusting soil N levels to better align with crop needs — and that including a rye cover crop in the vegetable rotation is one method of doing so.
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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 | 0.000 |
| 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) | 0.001 | 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".