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Optimizing Synthetic Microbial Communities for Sustainable Agriculture: Design, Functionality, and Field Performance ![](https://microbescipublisher.com/templates/images/OA.gif)
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Molecular Microbiology Research, 2024, Vol. 14, No. 1 doi: 10.5376/mmr.2024.14.0004
Received: 16 Dec., 2023 Accepted: 18 Jan., 2024 Published: 02 Feb., 2024
Han L.Z., 2024, Optimizing synthetic microbial communities for sustainable agriculture: design, functionality, and field performance, Molecular Microbiology Research, 14(1): 31-38 (doi: 10.5376/mmr.2024.14.0004)
The application of synthetic microbial communities (SynComs) in sustainable agriculture has emerged as a promising strategy to enhance crop performance and resilience. This systematic review explores the design, functionality, and field performance of SynComs, focusing on their potential to optimize plant-microbe interactions for improved agricultural outcomes. SynComs are engineered consortia of microorganisms selected for their beneficial traits, such as nutrient acquisition, disease suppression, and stress tolerance. Recent advances in microbial ecology, machine learning, and high-throughput phenotyping have facilitated the identification and assembly of effective SynComs tailored to specific crops and environmental conditions. Studies have demonstrated that SynComs can significantly improve plant health, nutrient efficiency, and yield under various stressors, including nutrient deficiencies and pathogen attacks. However, challenges remain in ensuring the stability and reproducibility of SynComs in field conditions. This review synthesizes current knowledge on SynCom design and application, highlighting successful case studies and identifying gaps for future research. By leveraging the synergistic interactions within SynComs, sustainable agriculture can achieve more consistent and resilient crop production.
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. Lizhen Han
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. Synthetic microbial communities
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. Plant-microbe interactions
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. Crop resilience
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. Nutrient acquisition
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. stress tolerance
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. Microbial ecology
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. Machine learning
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. High-throughput phenotyping
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