Effects of temperature, photoperiod, and vernalization on the growth, development, and predictions by the CERES-wheat model, for spring wheat cultivars
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».