Genetic variation for physiological traits affecting drought tolerance among Ontario-adapted commercial soybean [Glycine max (L.) Merr.] varieties grown in 1-m rooting columns
Notice bibliographique
Résumé
Drought stress significantly limits soybean production in Ontario. Identification of physiological traits to improve drought tolerance in soybean would benefit from controlled-environment phenotyping methods. It was hypothesized that elite Ontario-adapted soybean varieties would differ for drought tolerance. A greenhouse culture system was developed employing 1-m rooting columns filled with amended field soil, that presents field-like volumetric soil water content and rooting profiles by depth. Different levels of drought stress were simulated in this system by restoring soil water to 100% (control), 75% (mild stress) or 50% (drought stress) of the maximum soil water holding capacity by daily weighing and watering from first flower (R1) until maturity. The effect of applying fertilizer throughout the 1-m soil profile instead of confining it to the upper 30 cm was tested. Distributing fertilizer over the entire 1-m rooting depth resulted in deeper rooting and more soil water extraction at depth at the R1 stage; however, these effects did not persist until maturity and so did not affect drought tolerance. Fifteen Ontario-adapted commercial soybean varieties were compared for their drought tolerance, defined as the ratio of their seed yield under drought conditions compared to control conditions (seed yield ratio; SYR). Similar to the effects of drought in the field, pod number was by far the yield component most affected, with effects on seeds per pod and single-seed weight being relatively minor. Based on their SYR, two drought-sensitive varieties (Saska and OAC Drayton) and three drought-tolerant varieties (OAC Lakeview, OAC Champion, and PRO 2715R) were identified. Principal components analysis showed that drought-tolerant varieties were those that maintained relatively high water use, shoot dry matter, and pod number under stress. Varieties differed for root biomass distribution by depth, but not for soil water extraction profiles, and there was no evidence that differences in drought tolerance were associated with rooting traits among these fifteen varieties. This study helps define the physiological basis of soybean variety differences in drought tolerance, and provides novel phenotyping tools for soybean breeders to select for root function and yield formation traits that could improve soybean yield under drought stress.
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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 | 0,000 |
| Études des sciences et des technologies | 0,001 | 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 ».