Comparison of Five Wheat Models Simulating Phenology under Different Sowing Dates and Varieties
Bibliographic record
Abstract
Core Ideas We compared the mechanism and capacity of five wheat phenology models by varied phases. Models reproduced growing phases well by suitable sowing dates and local varieties. Simulations were unsatisfactory under late sowing dates and colder conditions. Crop phenology is closely related to yield formation and crop management. Phenology module is one of the essential components that affect the call of model parameters and performance of whole model. We analyzed different algorithms of five widely used wheat models (WOFOST, CERES‐Wheat, APSIM‐Wheat, SPASS, and WheatSM) and studied the simulation accuracy by field experimental data with three varietal types under varied sowing dates in four sites in the North China plain. Simulation results were in good agreement with the observations in terms of jointing, flowering, and maturity stage. The absolute root mean square error (RMSEa) value for emergence to jointing (E‐J) phase was 3.7 d for WheatSM and >5 d for CERES, APSIM, and SPASS. The RMSEa for the sowing to emergence (S‐E) phase was approximately 4 d, but the normalized root mean square error was >30% for APSIM and CERES. The RMSEa was larger (4.8 d) for SPASS from jointing to flowering (J‐F) compared with other models (2.8–3.7 d). It ranged from 3 to 4 d from flowering to maturity (F‐M) for all models. The five models yielded better predictions in warmer growing conditions than in colder conditions. The RMSEa increased with delayed sowing dates for five models, with average values of 2.7, 3.7, and 5.2 for suitable sowing, sowing delayed by 10 and 20 d, respectively. The five models could well reproduce different growing phases under the conditions of suitable sowing dates and with local varieties, but the performances of models was unsatisfactory, especially under late sowing dates.
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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 | 0.001 | 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".