Residual Effects of Potassium Placement and Tillage Systems for Corn on Subsequent No‐Till Soybean
Bibliographic record
Abstract
Little is known about K fertility management for no‐till (NT) soybean [Glycine max (L.) Merr.]. This study was conducted to evaluate the residual effects of K application rate, timing, and placement for corn (Zea mays L.) in various tillage systems on subsequent NT soybean. Field experiments involving a corn–soybean rotation were conducted from 1998 to 2000 on long‐term NT fields with medium or high exchangeable soil K levels near Kirkton and Belmont, ON, Canada. In the corn year, treatments included the combinations of three fall K rates (0, 42, and 84 kg ha−1), spring K rates (two rates differing by 42 kg ha−1), and three tillage systems [NT, zone till (ZT), and moldboard plow (CT)]. Both CT and ZT (also known as intermittent tillage systems) reduced soil K stratification relative to continuous NT. Trifoliate leaf K concentrations increased with residual fall and spring K applications in most site‐years. Average soybean yield significantly increased by 8.3% with the application of 84 kg K ha−1 in fall plus 42 to 50 kg K ha−1 in spring to previous corn only on medium‐testing (K < 100 mg L−1) soils. Residual tillage had no effects on leaf K or yield of NT soybean. Application of fall and spring K fertilizers to corn was equally beneficial for subsequent soybean in either continuous or intermittent NT systems. Furthermore, soil K stratification and the residual effects of tillage and K placement method were not major production issues for narrow‐row NT soybean in these growing seasons.
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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) | 0.001 | 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".