Footprints of corn nitrogen management on the following soybean crop
Bibliographic record
Abstract
Abstract Corn ( Zea mays L.)–soybean [ Glycine max (L.) Merr.] is among the most typical crop rotations in the U.S. Corn Belt, and N is the most limiting nutrient for both crops. This study aims to assess the effects of N management for corn on the following soybean crop. Two corn–soybean rotation N fertilizer rate studies—a long‐term study (1983–2020, Case Study I) and a two‐season study (2019–2020, Case Study II)—were conducted in Kansas (United States). Case Study I focused on soybean seed yield as the response variable, whereas Case Study II included a detailed seasonal characterization of soil N, symbiotic N fixation (SNF), and plant N uptake for soybean considering N fertilizer rates on the previous corn crop. Apparent N budgets from corn (N fertilizer minus grain N removal) ranged from approximately −100 to approximately +50 kg N ha −1 , and soybean yields were slightly or not affected by corn N management. Case Study I showed that long‐term N budgets in corn crops did not affect the following soybean crop yields. In Case Study II, the previous corn N management produced negative or small N surplus that influenced neither soil residual N nor SNF, without compromising soybean productivity. Farmers applying close to economic optimum N rates on corn will likely not generate scenarios of N surplus to compromise SNF or soybean yields. Forthcoming research should further address how long‐term and large soil N mining or surplus in corn may enhance or inhibit N fixation for the next soybean crop.
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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 | 0.001 |
| 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".