Photoperiod and Vernalization Effect on Anthesis Date in Winter‐Sown Spring Wheat Regions
Bibliographic record
Abstract
Accurate prediction of phenology is required to guide crop management decisions and to predict crop growth and yield. However, the relative importance of photoperiod and vernalization in predicting anthesis dates for spring bread and durum wheat ( Triticum aestivum L. and T. durum Desf.) sown in the winter has not been reported. The purpose of this research is to determine the improvement in predicting anthesis dates of spring wheat sown in the winter when photoperiod and vernalization are considered. Observed dates of anthesis were obtained from University of Arizona wheat variety trials conducted at Maricopa, Wellton, and Yuma, AZ. The Cropping Systems Model CROPSIM‐CERES as released in DSSAT 4.5 was used to simulate days to anthesis based on temperature, daylength, and vernalization. For 12 bread and durum wheat cultivars, the model predicted days to anthesis with a root mean square error (RMSE) of 7.6 d if all cultivar differences were ignored, 6.4 d considering only differences in thermal time (TT), 6.1 d with differences in TT and daylength response, 6.4 d with TT and vernalization, and 6.2 d with TT, daylength, and vernalization. Consideration of cultivar differences in TT and photoperiod response improved the prediction of days to anthesis for winter‐sown spring wheat, but there was no benefit from considering effects of vernalization in CROPSIM‐CERES.
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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 | 0.000 | 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) | 0.000 | 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".