Simulating the Influence of Vernalization, Photoperiod and Optimum Temperature on Wheat Developmental Rates
Bibliographic record
Abstract
BACKGROUND AND AIMS: Accurately representing development is essential for applying crop simulations to investigate the effects of climate, genotypes or crop management. Development in wheat (Triticum aestivum, T. durum) is primarily driven by temperature, but affected by vernalization and photoperiod, and is often simulated by reducing thermal-time accumulation using vernalization or photoperiod factors or limiting accumulation when a lower optimum temperature (T(optl)) is exceeded. In this study T(optl) and methods for representing effects of vernalization and photoperiod on anthesis were examined using a range of planting dates and genotypes. METHODS: An examination was made of T(optl) values of 15, 20, 25 and 50 degrees C, and either the most limiting or the multiplicative value of the vernalization and photoperiod development rate factors for simulating anthesis. Field data were from replicated trials at Ludhiana, Punjab, India with July through to December planting dates and seven cultivars varying in vernalization response. KEY RESULTS: Simulations of anthesis were similar for T(optl) values of 20, 25 and 50 degrees C, but a T(optl) of 15 degrees C resulted in a consistent bias towards predicting anthesis late for early planting dates. Results for T(optl) above 15 degrees C may have occurred because mean temperatures rarely exceeded 20 degrees C before anthesis for many planting dates. For cultivars having a strong vernalization response, anthesis was more accurately simulated when vernalization and photoperiod factors were multiplied rather than using the most limiting of the two factors. CONCLUSIONS: Setting T(optl) to a high value (30 degrees C) and multiplying the vernalization and photoperiod factors resulted in accurately simulating anthesis for a wide range of planting dates and genotypes. However, for environments where average temperatures exceed 20 degrees C for much of the pre-anthesis period, a lower T(optl) (23 degrees C) might be appropriate. These results highlight the value of testing a model over a wide range of environments.
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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 | 0.001 |
| 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".