Effects of temperature, photoperiod, and vernalization on the growth, development, and predictions by the CERES-wheat model, for spring wheat cultivars
Bibliographic record
Abstract
Organizations such as the Canadian Wheat Board rely on yield forecasts to plan grain handling and marketing strategies. Therefore, a model that accurately predicts yield would be useful. In plant breeding programmes involving advanced line evaluations at different locations, lack of adequate resources may be a limitation. Differences among genotypes at different locations are due to genetic as well as environmental effects. These studies investigated the effects of temperature, photoperiod, and vernalization on the growth and development of spring wheat cultivars, and assessed the ability of the CERES-wheat model to predict yield in yield trials conducted in western Canada. Field experiments were conducted at Winnipeg and Carman, Manitoba, using three seeding dates at each location, to provide data for model calibration and validation. Controlled environment studies elucidated environmental effects which may be difficult to discern under field conditions. Da a on phenology, yield-related components, weather, and model-required soil properties were collected for all trials. High temperatures accelerated the growth of vernalization-insensitive cultivars by decreasing time to anthesis and time to maturity, and reduced the number of main stem leaves and yield-related components. High temperatures decelerated the growth of vernalizafion-sensitive cultivars and prolonged the length of the vegetative growth period. Differential cultivar phyllochron responses to temperature increases were evident. Therefore, the use of modified thermal time calculations in the CERES-wheat model may not be appropriate for all genotypes. To reduce errors in phyllochron interval calculations, crop modellers may need new equations to address temperature sensitivity of cultivars. Cultivar differences in time to heading, anthesis, and maturity, were attributable to differences in the time to terminal spikelet initiation. The CERES-wheat model was sensitive to changes in seeding date and locations, and was capable of deciphering cultivar differences. Cultivar genetic coefficients determined under an early seeding environment at one location could be used at another location in the same region. The CERES-wheat model however, showed several weaknesses which included a general tendency to underestimate grain yield, phyllochron interval, and dry matter production. Also, its predictive power declined with delays in seeding date. The extensive data requirement of the CERES-wheat model are deterrents to its use. These concerns need to be addressed by the model builders if researchers are to find the CERES-wheat model less demanding and user-friendly.
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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) | 0.000 | 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 | 0.000 | 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".