Testing the suitability of thermal time models for forecasting spring wheat phenological development in western Canada
Why this work is in the frame
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Bibliographic record
Abstract
Predicting crop development stages is fundamental to many aspects of agronomy (e.g., pesticides and fertilizer applications). Temperature is the main factor affecting plant development and its impact on crop development is often measured using thermal-time. We compared different thermal-time models to identify the best model for simulating spring wheat development in western Canada. Models compared include (i) North-Dakota growing-degree-day (NDGDD), (ii) growing-degree-day base-temperature zero (GDD 0 ), (iii) growing-degree-day base-temperature five (GDD 5 ), (iv) beta-function (BF), and (v) modified-beta-function (MBF). We utilised agro-meteorological data collected across western Canada from 2009–2011. Results showed that accumulated heat units/daily growth rates from the different models correlated well with spring wheat phenology with R 2 ≥ 0.91 and P < 0.001. However, when the developed models were used to predict time (calendar-days) from planting to anthesis for cultivar AC-Barrie, the BF and MBF models performed poorly. Average predicted times from planting to anthesis by NDGDD, GDD 0 , GDD 5 , BF, and MBF models were 63, 63, 62, 65, and 64 d, respectively; while the actual observed time was 60 d. Root-mean-square error (RMSE) for NDGDD was 4 d, 5 d for GDD 0 and GDD 5 , and 6 d for BF and MBF. These findings suggest that simple GDD-based models performed better than more complex BF-based models.
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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 | 0.001 |
| 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 | 0.001 | 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 it