Notice bibliographique
Résumé
Goal. To assess rice cultivars for cold tolerance; to select the most valuable in terms of economic characteristics sources for their further use in breeding; to create a trait collection on their basis.Result and Discussion. The article presents the results of studying a rice collection. The collection material is represented by 51 accessions of different eco-geographical origin (European, Eastern, Mid-Asian, Latin American). The accessions belong to two subspecies: japonica and indica. Most of the accessions (22) are from Ukraine and account for 43.1%. Temperature is one of the most important environmental factors affecting the crop establishment and development. In Ukraine, rice is cultivated in the most northern zone of rice growing and, therefore, it is liable to negative effects of cold weather during the seed germination, sprouting, anthesis and grain ripening. Consequently, it is essential for breeders to create cultivars that will be tolerant to low temperatures during the sprouting period, with high field germinability and increased germinative power. Seed germination is particularly affected by temperature. Fifty one rice cultivars were investigated for the seedling development, seed germination rate and seedling growth intensity at 14°C; possible states of embryonic roots were estimated; lengths of these stages were determined. Selection of material with a set of parameters allowed forming a trait collection with increased levels of resistance to abiotic environmental factors. In breeding, it is very important to identify sources of valuable traits and to assess extent of their influence on the plant performance. The research distinguished rice accessions for breeding by levels of cold tolerance, performance and its components. We selected six donors of several valuable traits, the use of which will enhance the breeding efficiency (Kuban 3, Vikont, UkrNDS 8839, Ontario, VNIIR 546, Kaz ER – 6). Thus, the comprehensive assessment of the accessions allowed us not only to replenish the rice gene pool, but also, basing on this research, to choose starting material to involve it in further breeding for creating new cold-tolerant varieties. While creating such cultivars, we revealed morphological traits of rice, which are used as test parameters characterizing the stress tolerance of accessions.Conclusions. The study resulted in formation of the trait collection representing the genetic diversity of cultivars and breeding lines. Each trait and gradation has corresponding standards. Cultivars-standards reflect more specific and stable expression of a trait. The collection was created by 4 basic traits and 3 gradations and contains accessions from 9 countries (Ukraine, Russia, Kazakhstan, Uzbekistan, Kyrgyzstan, USA, Bulgaria, China, and Hungary). The standard accessions selected can serve as sources of economically valuable features to create starting material for breeding.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,000 | 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 tête enseignante, 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 ».