Testing the suitability of thermal time models for forecasting spring wheat phenological development in western Canada
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 (GDD0), (iii) growing-degree-day base-temperature five (GDD5), (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 R2 ≥ 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, GDD0, GDD5, 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 GDD0 and GDD5, 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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".