Simulating Timothy Growth and Nutritive Value with Observed and Synthetic Weather Data
Bibliographic record
Abstract
Stochastic weather generators and crop growth models are used to explore the impact of climate change on crop development and yield. Synthetic weather data from the stochastic weather generator AAFC‐WG have been evaluated with annual crop models but not with a perennial grass model. Our objective was to evaluate synthetic weather data from AAFC‐WG using a perennial crop model, the Canadian Timothy Model (CATIMO), at five sites representing different agro‐ecological regions of Canada. Synthetic 300‐yr weather data from AAFC‐WG and observed 30‐yr weather data, both for the period 1961 to 1990, were used to simulate dates of growth onset and harvests, and yield and nutritive value (neutral detergent fiber [NDF] concentration and in vitro digestibility of NDF [dNDF]) of timothy (Phleum pratense L.) grown in cycles of five consecutive years before reseeding and with two harvests per year. Dates of growth onset and harvests simulated from synthetic weather data were close to those simulated from observed weather data, with normalized root mean square errors (NRMSEs) of 4.2% for dates of growth onset and <0.6% for harvest dates. Simulated dry matter yield (NRMSE < 4.5%), NDF concentration (NRMSE < 1.1%), and dNDF (NRMSE < 0.4%) with synthetic and observed weather data were also very close. Synthetic weather data generated by AAFC‐WG accurately reproduced weather conditions of observed data for timothy development, yield, and nutritive value, confirming that they can be used with the CATIMO model to predict the impact of climate change at several sites in Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".