Assessing corn seedbed conditions for emergence
Bibliographic record
Abstract
Seedbed temperature and moisture conditions affect crop emergence rate and stand establishment. A 4 × 4 factorial experiment arranged in a split plot design with four replications was conducted for 3 yr at four sites to measure corn emergence rate and stand establishment while monitoring seedbed temperature and soil water content in situ. Four planting dates, beginning as early as the soil could be worked and every 10 d thereafter, were the main plots and four corn (Zea mays L.) hybrids the subplots. Volumetric soil water content in the top 0.10 m was measured a minimum of two times per week using time domain reflectometry (TDR) and estimated daily using a budget model. Measured and estimated soil water contents were similar (R2 = 0.73) and daily estimated values were used in the analysis. Stand establishment for most planting date-site-years ranged from 80 to 99%. Less than 15% of planting date-site-years had stands below 80%, and they were characterized by soil temperatures at or below 12.5°C combined with high soil water contents (>90% available water). Rate of emergence was not associated with stand establishment (P > 0.10) and could not be predicted from soil temperatures below 12.5°C. Results suggest that reduction in stand establishment under conditions of low (12.5°C) soil temperature and high (>field capacity) soil water content may be a factor in corn yield reductions associated with reduced or no tillage. Key words: Time domain reflectometry, soil water content, soil temperature, maize
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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".