Corn yield and nitrogen recovery following rye (<i>Secale cereale</i> L.) in monoculture and polyculture service crops
Bibliographic record
Abstract
Planting service crops (SCs) with late summer manure applications has been promoted as an agronomic practice to capture manure nitrogen (N) and release it to the following season’s cash crop, thereby reducing fertilizer N requirements. The present study explored this hypothesis using a cereal rye ( Secale cereale L.) monoculture SC, along with two polyculture SCs (4 species and 12 species) both containing rye, planted after winter wheat ( Triticum aestivum L.) harvest, in systems with and without liquid hog manure. The following spring, SC regrowth was chemically terminated 1 week prior to corn ( Zea mays L.) planting, and a sidedress N application was made at the 6–8 leaf stage to half of the plots. Corn N accumulation and final grain yield were reduced up to 20% following the rye monoculture in both years, even though SCs did not reduce soil mineral N nor partial plant-available N over the corn-growing season. Additionally, the sidedress N application could not overcome the yield loss associated with rye. Thus, this study did not observe N release by SCs to the following cash crop and demonstrates that yield loss can occur when corn follows rye SCs irrespective of changes in plant available N. This research reinforces the importance of selecting appropriate species and their proportions in polycultures, to mitigate negative impacts of SCs, especially those of rye on corn.
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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".