Soybean Responses to Potassium Placement and Tillage Alternatives following No‐Till
Bibliographic record
Abstract
More information is needed about optimum potassium (K) fertilizer placement for soybean [ Glycine max (L.) Merr.] production in no‐till fields. This study was conducted at two locations in Ontario, Canada, from 1998 to 2000 to examine soybean responses to K placement methods and tillage systems on soils with a 5‐ to 7‐yr no‐till history and medium to high soil‐test K levels. Fertilizer K treatments (15‐cm deep banding in fall, 7.5‐cm shallow banding in spring, surface broadcast in fall, and a zero K control) were compared in three conservation tillage systems (fall zone‐till, fall disk, and no‐till). The K fertilizer rate was 100 kg ha −1 for all but the control treatment. Soybean row widths (76 or 38 cm) varied with tillage systems, and soybean rows were positioned above K fertilizer bands if applicable. Yield responses to K application occurred in the fall zone‐till and no‐till systems on some medium‐ to high‐testing soils. There was no significant leaf K or seed yield advantage to band placement compared to surface broadcasting, and to fall zone‐till or fall disk systems relative to no‐till, for soybean of similar row width. Neither leaf K nor seed yield was negatively affected by degree of soil K stratification. Despite vertical soil K stratification after continuous no‐till, there was no significant leaf K or yield benefit to replacing narrow‐row, no‐till soybean systems (involving surface K fertilizer application) with wide‐row zone‐till or no‐till systems (involving deep banding of K), or with narrow‐row, fall disk systems (involving surface‐applied, but tillage‐incorporated K).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".