Exploring the Role of Rhizobacteria in Sorghum bicolor Adaptation to Combined Drought and Heat Stress
Notice bibliographique
Résumé
Although rhizobacteria are known to improve plant adaptation to abiotic stressors, their possible contribution to the inherent resilience exhibited by crops such as Sorghum bicolor is still poorly quantified. Here, three sorghum pre-release lines and three check varieties were established and evaluated at two low-altitude sites of less than 600 masl. Treatments were laid out in a randomized complete block design, replicated two times. Twenty-four rhizospheric soil samples comprising six sorghum genotypes with two replications across two sites were collected, processed using Zymo Research DNA extraction protocols, and the 16S rRNA amplicon sequences were generated for bacterial diversity quantifications following the Divisive Amplicon Denoising Algorithm 2 (DADA2) workflow. Grain yield data were also recorded and expressed in tonnes per hectare. Rhizobacteria recruitment and GY performance significantly (p < 0.05) varied with sorghum genotypes. Bacterial abundance significantly (p < 0.05) associated with sorghum grain yield performance with Actinobacteriota and Firmicutes being identified to be of economic importance, explaining between 52.23 and 85.64% of the variation in grain yield performance. The modelled relationships between rhizobacteria and grain yield performance revealed R2 predicted values of up to 75.25% and a 10-fold R2 of 75.54%, implying no model overfitting. Sorghum genotypes did not consistently exhibit direct variation between genetic worth values and grain yield performance. Superior grain yield performers, namely ICSV111IN, CHITICHI, and SV4, consistently associated with high incidences of occurrence of the bacteria phyla Chloroflexi (class = Chloroflexia) and Firmicutes (class = Bacilli), whilst integrating the conventional selection method with microbial diversity data, changed the genotype performance ranking, in which all the three pre-release lines, namely, IESV91070DL, ASARECA12-3-1, and ICSV111IN, exhibited superiority over the check varieties. The results demonstrated that the inherent stress resilience exhibited by some sorghum genotypes under climate change-induced stresses such as CDHS may be influenced by specific bacterial taxa recruited in the rhizosphere environment of the plants. Hence, more effort should be made to further exploit these beneficial plant–microbe interactions for enhanced sorghum productivity under abiotic stress conditions.
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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 | 0,000 | 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 ».