Planting Date by Maturity Group in Kansas: 2016 Season and Three-Year Summary
Bibliographic record
Abstract
Optimal planting should be timed to capture a favorable environment (e.g., fall rains and cooler temperatures during grain filling). Five field studies were conducted during the 2014 growing season (Manhattan, Topeka, Ottawa, Parsons, and Hutchinson); five in 2015 (Manhattan, Rossville, Ottawa, Parsons, and Hutchinson); and three in 2016 (Manhattan, Topeka, and Ottawa). This study explores the impact of planting date (early-, mid-, and late-planted) on yield for soybean cultivars from a range of maturity groups (early, medium, and late groups). For 2016, the overall main factor impacting yield across sites was planting date, which increased yields with early-planted soybeans. Based on all 13 sites (2014, 2015, and 2016), maximum soybean yield potential decreased by 0.5 bushels per day of delay on planting date when soybean is planted after April 15. Comparable yield penalties have been documented for other main production regions. In summary, weather patterns dictate soybean yields, especially under dryland conditions. There is no guarantee that any certain planting date will always work out the best when it comes to soybean yields in Kansas.
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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 | 0.001 |
| 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) | 0.002 | 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".