Critical Leaf Potassium Concentrations for Yield and Seed Quality of Conservation‐Till Soybean
Bibliographic record
Abstract
Leaf K concentrations needed for optimum soybean [ Glycine max (L.) Merr.] production under conservation tillage systems may be different from those in conventional tillage (moldboard plow) because soil properties (such as soil‐test K distribution) and soybean root distribution within the soil profile under conservation tillage systems differ from those in conventional tillage. Little information is available about adequate leaf K concentrations for soybean on conservation‐tilled soils with significant vertical soil‐test K stratification. This study was conducted at three locations in Ontario, Canada from 1998 through 2000 to estimate the critical leaf K concentrations for conservation‐till soybean on K‐stratified soils with low to very high soil‐test K levels and a 5‐ to 7‐yr history of no‐till management. Three K fertilizer placement methods (band placement, surface broadcast, and zero K), two conservation tillage systems (no‐till and fall tandem disk), and two soybean row widths (19 and 38 cm) were used to create a wide spectrum of production environments. For maximum seed yield, the critical leaf K concentration at the initial flowering stage (R 1 ) of development was 24.3 g kg −1 This concentration is greater than the traditional critical leaf K values for soybean that are being used in Ontario and in many U.S. Corn Belt states. Critical leaf K values for the maximum concentrations of K, oil, and isoflavone in seed were 23.3, 24.1, and 23.5 g kg −1 , respectively. The extent of vertical soil‐test K stratification seems to be one of the factors contributing to apparently higher critical leaf K concentrations for conservation‐till soybean.
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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".