Models of growing season weather impacts on breadmaking quality of spring wheat from producer fields in western Canada
Bibliographic record
Abstract
Abstract BACKGROUND: The purpose of this study was to quantify growing season weather and wheat quality at individual producer fields to understand weather impacts and develop weather‐based models of spring wheat quality. RESULTS: Ninety‐six hard red spring wheat samples of two genotypes supplied by western Canadian producers in 2003 and 2004 underwent comprehensive analysis for breadmaking quality. For each individual field, daily rainfall, maximum and minimum air temperature were observed and used to calculate several measures of heat. Modeled water use, demand and stress were also calculated and all weather variables were accumulated over different phenological stages. Relationships between weather variables and wheat quality variables were determined by multivariate regression analysis separated into four steps, each adding more complex information into the models. There was substantial variation in weather conditions and wheat quality between years with generally higher quality and stronger dough in the warmer and drier year, 2003. The two genotypes displayed many differences in response to the environment. Prediction of wheat quality improved with increasing complexity of weather models and close to 50% of the variation in quality could be explained by multivariate models. CONCLUSIONS: Results showed that even for top‐grade milling wheat of similar protein content, significant differences exist in breadmaking quality of wheat from different farms. The improvement in r2 when using modeled environmental variables indicates that crop water use and water stress are important for wheat quality. The development of Canada Western Red Spring quality prediction models for western Canada based on growing season weather shows promise. Copyright © 2008 Society of Chemical Industry
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".