Selecting maize for rapid kernel drydown: timing of moisture measurement.
Bibliographic record
Abstract
Previous studies have shown that maize ear moisture measured using a modified Electrophics Moisture Meter model MT808 was highly correlated to kernel moisture and could be used as a selection tool in breeding maize genotypes with faster rates of kernel drydown. Such a tool would need to be standardized for practical and routine use in a breeding program with large numbers of plants. The objective of this study was to determine the opti¬mum time for measuring ear moisture using this meter. In a split-plot design with three replicates in 2007, 2008 and 2009, ear moisture of six inbred lines and eight F1 hybrids were measured weekly from one to eight weeks post-silking using a modified MT808 moisture meter. To determine if multiple ear moisture readings (EMRs) could be made on the same ear, an additional treatment was added so that all eight readings were made on the same ear. There was a positive correlation between weekly EMRs readings done on separate ears and those done on the same ear, indicating that repeated readings, if desired, could be made on the same ear. Significant genotypic differences in EMRs were found five to eight weeks post-silking. The EMRs at week one, five, and eight could be used to calculate a daily drydown rate (DDR). Maize genotypes (hybrids and inbreds) could be divided into four groups based on their DDRs during development as: high-high, high-low, low-high, and low-low DDRs from weeks one to five and five to eight, respectively. Genotypes with higher DDRs from weeks one to five tended to have overall higher DDRs by eight weeks post-silking. Inbred lines with higher DDRs at either stage expressed this trait in their hybrid crosses. This non-destructive method will improve selection for fast kernel drydown in maize breed¬ing programs, especially in short-season areas.
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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 | 0.000 | 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) | 0.000 | 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".