Double Crop Soybean After Wheat
Bibliographic record
Abstract
Two double crop (DC) soybean studies were conducted at Ottawa, KS, during the 2016 growing season. Soybean cultivar Asgrow 4232 (MG 4.2) was planted immediately after two different wheat harvest timings (Study 1: early-wheat harvest 18-20% seed moisture content, and Study 2: conventional-harvest, 13-14% seed moisture content). Seven treatments were evaluated in each of the soybean planting dates: 1) common practice, 2) no seed treatment (without seed fungicide + insecticide treatment), 3) non-stay green (without foliar fungicide + insecticide application), 4) high seeding rate (180,000 seeds per acre), 5) wide rows (30-inch row spacing), 6) nitrogen (N) fixation (without late fertilizer N application), and 7) kitchen sink (includes all management practices). Aboveground biomass, seed harvest index (HI) and yield were evaluated. For the early-planted study, a trade-off was documented between biomass and seed HI, presenting maximum yield also for values with lower HIs. Yield was greatest when planting in wide rows (64.5 bu/a) for the late-planted timing, and for the Nfixation treatment (64.0 bu/a) for the early-planted study. For the early-planted, yield gap (calculated as maximum minus minimum yield) was 6 bu/a, while for late-planted, yield gap was 7.5 bu/a. Best management practices for DC soybean can improve overall productivity via increasing yield with modifications in biomass and HI, and overall yield efficiency. Further testing on the effect of multiple management practices on DC soybean will be performed during the upcoming growing season.
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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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".