Potassium Placement and Tillage System Effects on Corn Response following Long‐Term No Till
Bibliographic record
Abstract
Stratification of immobile nutrients in long term no‐till (NT) fields may reduce corn (Zea mays L.) yield potential. Five field studies were conducted from 1995 to 1998 to evaluate corn response to different K placements and rates when fields with a NT cropping history were either fall zone‐tilled (ZT), fall moldboard‐plowed [conventional tillage (CT)], or continued in the NT system. The silt loam to silty clay loam soils had medium or high soil‐test K (0–15 cm) ratings with varying degrees of K stratification to the 30‐cm depth. Fall‐applied K at rates of 0, 42 and 84 kg ha−1 was surface‐broadcast in the NT system, deep‐banded to 15‐cm depth in the ZT system, and surface‐broadcast and incorporated in the CT system. Potassium was also shallow‐banded with the planter at rates of either 0 to 8 kg ha−1 (low) or 42 to 50 kg ha−1 (high). Average concentrations of corn ear‐leaf K near silking increased from 10.9 g kg−1 with no K to 15.2 g kg−1 with highest fall plus spring K rates on the three sites with soil‐test K levels of <100 mg kg−1. For these same sites, ear‐leaf K concentrations averaged 1.2 g kg−1 higher in CT compared with NT or ZT. On four of the five field sites, corn yields in the NT and ZT systems were maximized by applying the high rate of starter K, even when no K fertilizer was applied the previous fall. On long‐term NT soils with medium soil‐test K, corn producers may derive most K fertility benefit from shallow banding at planting.
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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 | 0.000 |
| 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) | 0.000 | 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".