Previous Corn Row Effects on Potassium Nutrition and Yield of Subsequent No‐Till Soybeans
Bibliographic record
Abstract
Narrow‐row soybean [Glycine max (L.) Merr.] production in corn [Zea mays L.]–soybean rotations results in various distances of soybean rows from previous corn rows, yet little is known about soybean responses to proximity to prior corn rows in no‐till systems. The objective of this study was to evaluate the impacts of preceding corn rows on potassium (K) nutrition and yield of subsequent no‐till soybeans. Four field experiments involving a corn–soybean rotation were conducted on long‐term no‐till fields with low to medium K levels from 1998 to 2000 near Paris and Kirkton, Ontario, Canada. In the corn year, treatments included K application rate and placement in conjunction with tillage systems or corn hybrids. Before soybean flowering, soil exchangeable K concentrations (0–20 cm depth) in previous corn rows were significantly higher than those between corn rows. At the initial flowering stage, trifoliate leaf K concentrations of soybeans in preceding corn rows were 2.0 to 5.3 g kg−1 higher than those from corresponding plants between corn rows. Yield of no‐till soybeans in previous corn rows increased 10 to 44% compared to those between previous corn rows. Positive impacts of prior corn rows on soil K fertility, soybean leaf K, and soybean yield occurred even when K fertilizer was not applied in the prior corn season. Deep banding of K fertilizer tended to accentuate row vs. between‐row effects on soybean leaf K concentrations in low‐testing soils. Corn row effects on soybeans were generally not affected by either tillage system or corn hybrid employed in the prior corn crop. Potassium management strategies for narrow‐row no‐till soybeans should take the potential preceding corn row impacts on soil K distribution into account; adjustments to current soil sampling protocols may be warranted when narrow‐row no‐till soybeans follow corn on soils with low to medium levels of exchangeable K.
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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".