Adapting estimation methods of daily solar radiation for crop modelling applications in Canada
Bibliographic record
Abstract
Estimating daily solar radiation from common meteorological variables plays an important role in agricultural applications, such as driving crop growth models. Relatively simple and accurate estimation methods, which use only daily air temperature together with precipitation, are often required. Based on all available solar radiation data across Canada, the most common and representative solar radiation models were evaluated. All estimation models provided more accurate estimates, in terms of all performance statistics used in this study, than those extracted directly from a high-resolution global dataset of meteorological forcings for land surface modelling. The DS model adapted from one originally developed for the Canadian Prairies performed better than other representative models for all stations. The DS model was then adapted for regional use in southern Canada, mostly the major agricultural regions. We compared simulated crop yields using the CSM–CERES–wheat and CSM–CROPGRO–canola models driven by observed and estimated daily solar radiation data, and we found a difference of approximately 5% for spring wheat (Triticum aestivum L.) and 12% for canola (Brassica napus L.). Based on the results for two locations under different climate regimes with relatively long records (45 and 40 yr, respectively) of solar radiation data, the models using daily temperature range and precipitation were found to be robust for daily solar radiation estimation for the entire time periods of the data records.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".