Effects of chemical fertilization on bacterial community in rhizosphere soil of sugarcane
Notice bibliographique
Résumé
Most sugarcane growing areas in China have undergone over 30 years of continuous monocropping, and long-term chemical fertilizer application has led to severe soil degradation. In order to provide a theoretical basis for the sustainable development of sugarcane production and scientific and reasonable fertilization, the effects of different fertilization levels on agronomic traits and rhizosphere soil bacterial community in plant crop of sugarcane were investigated. Four fertilization levels were set in the experiment conducted in the field of Guangxi Academy of Agricultural Sciences: no fertilization (0%); low fertilization level (25%); half-reduced fertilization level (50%); and full fertilization level (100%). Two sugarcane varieties GT29 and GT58 were used, and agronomic traits in sugarcane and soil physicochemical properties in the rhizosphere were measured. Illumina high-throughput sequencing technology was employed to analyze the bacterial community structure in soil. The results indicated that the cane yield showed the order of half-reduced fertilization level (50%) > full fertilization level (100%) > low fertilization level (25%) > no fertilization (0%) in both varieties, and that in GT58 showed significant differences in different treatments except for that between half-reduced fertilization level (50%) and full fertilization level (100%). The dominant bacterial phyla in the rhizosphere soil included Proteobacteria, Actinobacteria, Chloroflexi, and Acidobacteria (> 10%). Variance partitioning analysis (VPA) results indicated that soil physicochemical properties had the most significant impact on the bacterial community, followed by chemical fertilization, and then sugarcane variety. Notably, NO3-, pH, organic matter (OM), available P, Ca, Mg, Fe, Cu and Zn contents in soil all had a significant impact on the bacterial community composition (P < 0.05). Differential species analysis revealed that, under high chemical fertilization, three bacterial genera, Chujaibacter, Sporolactobacillus, and Ammoniphilus, were significantly enriched. In contrast, under no fertilization treatment, the bacterial genera such as Flavisolibacter, Aquicella, Ramlibacter, Anaeromyxobacter, Candidatus Solibacter, Polycyclovorans, and Candidatus Koribacter were significantly enriched. Co-occurrence network analysis showed that the half-reduced fertilization level (50%) treatment increased both the number of nodes and connections in the co-occurrence network, promoting positive correlations among the bacterial species in the rhizosphere of sugarcane. In summary, under the conditions of this study, compared to the full fertilization treatment, reducing fertilization by half did not decrease cane yield and did not significantly alter the community diversity of rhizosphere soil bacteria. Therefore, it was recommended as a viable alternative for fertilization in sugarcane.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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 ».