International Journal of Horticulture, 2024, Vol.14, No.3, 195-206 http://hortherbpublisher.com/index.php/ijh 198 2.3 Comparative analysis of SynComs versus traditional microbial inoculants When comparing SynComs to traditional microbial inoculants, several advantages become apparent. Traditional inoculants typically consist of single or a few microbial strains selected for specific beneficial traits. However, these inoculants often fail to establish stable and effective microbial communities in the plant rhizosphere, leading to inconsistent results (Souza et al., 2020; Martins et al., 2023). In contrast, SynComs are designed using a more holistic approach that considers the complex interactions between multiple microbial species, plants, and the environment. This results in more stable and resilient microbial communities that can better withstand environmental stressors and provide consistent benefits to plants (Souza et al., 2020; Marín et al., 2021; Martins et al., 2023). Additionally, SynComs can be tailored to target specific plant phenotypes and functions, making them more versatile and effective than traditional inoculants (Souza et al., 2020; Martins et al., 2023). For instance, SynComs have been shown to regulate nutrient signaling networks at the transcriptional level, leading to enhanced growth pathways and improved plant performance (Wang et al., 2021). In summary, SynComs offer a promising alternative to traditional microbial inoculants in CEA by providing more stable, resilient, and effective microbial communities that enhance plant growth and health. Their applications in hydroponics, aquaponics, and vertical farming demonstrate their versatility and potential to revolutionize plant production in controlled environments. 3 Optimization Strategies for SynComs in CEA 3.1 Selection and engineering of microbial strains for specific agricultural goals The selection and engineering of microbial strains are critical for optimizing synthetic microbial communities (SynComs) in controlled environment agriculture (CEA). The primary goal is to identify and utilize microbial strains that can enhance plant health, growth, and resilience. This involves selecting microbes with beneficial traits such as biofilm formation, production of secondary metabolites, and the ability to induce plant resistance (Martins et al., 2023). Additionally, computational methods, including machine learning and artificial intelligence, can be employed to screen and identify beneficial microbes, ensuring the best combination of strains for desired plant phenotypes (Souza et al., 2020). The use of functional screening to construct SynComs has shown promising results in improving nutrient acquisition and crop yield, as demonstrated in soybean plants (Wang et al., 2021). 3.2 Optimization of SynCom composition and diversity Optimizing the composition and diversity of SynComs is essential for their stability and effectiveness in CEA systems. Studies have shown that well-structured microbial assemblages are more likely to thrive under environmental stressors compared to single microbial activities or taxonomies (Martins et al., 2023). The diversity of SynComs can be maintained by adjusting the starting composition ratios and using low-nutrient media to support the growth of individual organisms (Coker et al., 2022). Additionally, the use of model synthetic communities, such as those developed for the rhizosphere, can provide reproducible and stable systems for research and application in CEA (Coker et al., 2022). The functional assembly of root-associated microbial consortia has also been shown to improve nutrient efficiency and yield in crops like soybean (Wang et al., 2021). 3.3 Techniques for monitoring and managing SynComs in CEA systems Effective monitoring and management of SynComs in CEA systems are crucial for their success. Proximal sensors and non-destructive technologies can be used to monitor plant growth, yield, and water consumption, providing real-time data for managing SynComs (Amitrano et al., 2020). Deep learning (DL) methods have also been applied to CEA for crop monitoring, detecting biotic and abiotic stresses, and predicting crop growth (Ojo and Zahid, 2022). Additionally, digital twin (DT) architectures can optimize productivity by simulating and controlling crop microclimates and management strategies (Chaux et al., 2021). Proper water management is another critical factor, as it influences the availability of nutrients, plant physiological processes, and microbial communities within the rhizosphere (Tan et al., 2021). By integrating these advanced monitoring and management techniques, CEA systems can achieve better yields and quality crops while maintaining the stability and effectiveness of SynComs.
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