FERTILIZER MANAGEMENT AND TILLAGE Soybean Responses to Potassium Placement and Tillage Alternatives following No-Till
Bibliographic record
Abstract
ABSTRACT growing seasons when drought occurs, since soil K avail-ability and root growth and activity in the surface layerMore information is needed about optimum potassium (K) fertil-are more vulnerable to drought than those in subsurfaceizer placement for soybean [Glycine max (L.) Merr.] production in layers. In addition, crop residue deposit at the soil sur-no-till fields. This study was conducted at two locations in Ontario, Canada, from 1998 to 2000 to examine soybean responses to K place- face in no-till usually results in higher soil moisture and ment methods and tillage systems on soils with a 5- to 7-yr no-till lower soil temperature in the surface layer, which may history and medium to high soil-test K levels. Fertilizer K treatments reduce soil K availability and restrict root growth early (15-cm deep banding in fall, 7.5-cm shallow banding in spring, surface in the season (Barber, 1971; Fortin, 1993). The risks of broadcast in fall, and a zero K control) were compared in three reduction in plant K uptake by drought or low tempera-conservation tillage systems (fall zone-till, fall disk, and no-till). The ture become severe when soil K concentrations in sub-K fertilizer rate was 100 kg ha1 for all but the control treatment. surface layers are too low to optimize plant K uptake.Soybean row widths (76 or 38 cm) varied with tillage systems, and Subsurface placement of K fertilizer, therefore, maysoybean rows were positioned above K fertilizer bands if applicable. improve applied K availability and reduce soil K stratifi-Yield responses to K application occurred in the fall zone-till and cation in no-till systems. Because the land area of no-tillno-till systems on some medium- to high-testing soils. There was no significant leaf K or seed yield advantage to band placement compared soybean in North America has increased rapidly since
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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".