This study integrates genome-inferred traits with a dynamic energy budget (DEB) model to predict life-history traits and trade-offs in soil bacteria, particularly in the rhizosphere. The model combines substrate uptake kinetics with trait-based approaches to simulate microbial growth strategies and resource acquisition. Key findings include: 1. **Trait-Based Modeling**: The model uses genome-inferred traits to predict microbial life-history strategies, such as growth rate and carbon use efficiency (CUE), which are influenced by interactions between microbial traits and root exudate chemistry. 2. **Resource Acquisition and Efficiency**: Bacteria exhibit distinct growth strategies, with slower-growing species showing enhanced CUE without sacrificing growth rate. This highlights the importance of resource specialization and trade-offs in microbial community composition. 3. **Rhizosphere Dynamics**: The model accurately predicts substrate-acquisition strategies in plant microbiome systems, aligning with observations. It reveals that root exudates, particularly organic acids, influence microbial growth rates and CUE. 4. **Implications for Carbon Stabilization**: The insights from the model have implications for understanding how microbial processes contribute to soil organic matter stabilization. The trade-offs between growth rate and efficiency are crucial for maintaining soil carbon pools. 5. **Methodological Approach**: The study employs a computational pipeline and a toolset ("microTrait") to infer microbial traits from genomic data, linking these traits to ecological strategies. This approach facilitates the development of large-scale predictive models of microbial communities. Overall, the research underscores the importance of integrating genomic and trait-based approaches to improve the representation of soil microbiomes in biogeochemical models, enhancing our understanding of microbial contributions to Earth's carbon cycle.This study integrates genome-inferred traits with a dynamic energy budget (DEB) model to predict life-history traits and trade-offs in soil bacteria, particularly in the rhizosphere. The model combines substrate uptake kinetics with trait-based approaches to simulate microbial growth strategies and resource acquisition. Key findings include: 1. **Trait-Based Modeling**: The model uses genome-inferred traits to predict microbial life-history strategies, such as growth rate and carbon use efficiency (CUE), which are influenced by interactions between microbial traits and root exudate chemistry. 2. **Resource Acquisition and Efficiency**: Bacteria exhibit distinct growth strategies, with slower-growing species showing enhanced CUE without sacrificing growth rate. This highlights the importance of resource specialization and trade-offs in microbial community composition. 3. **Rhizosphere Dynamics**: The model accurately predicts substrate-acquisition strategies in plant microbiome systems, aligning with observations. It reveals that root exudates, particularly organic acids, influence microbial growth rates and CUE. 4. **Implications for Carbon Stabilization**: The insights from the model have implications for understanding how microbial processes contribute to soil organic matter stabilization. The trade-offs between growth rate and efficiency are crucial for maintaining soil carbon pools. 5. **Methodological Approach**: The study employs a computational pipeline and a toolset ("microTrait") to infer microbial traits from genomic data, linking these traits to ecological strategies. This approach facilitates the development of large-scale predictive models of microbial communities. Overall, the research underscores the importance of integrating genomic and trait-based approaches to improve the representation of soil microbiomes in biogeochemical models, enhancing our understanding of microbial contributions to Earth's carbon cycle.