From traits to field performance: A mechanistically informed framework for designing and evaluating synthetic microbial communities in sustainable agriculture

Sustainable agriculture requires microbial strategies that can perform reliably under heterogeneous soil, plant, and climatic conditions. Microbial bioresources can support nutrient mobilization, pathogen suppression, pollutant transformation, and carbon cycling, while advances in genetic engineering and synthetic biology (SynBio) provide new opportunities to modify these functions. However, a major knowledge gap persists between trait-level microbial design and demonstrated field performance: existing studies often examine microbial traits, community assembly, predictive modelling, or field validation separately, making it difficult to determine why systems that perform well under controlled conditions fail to establish or function consistently in agricultural environments. This review addresses this gap by integrating these components into a trait-to-field framework for microbiome intervention development. The framework links target agronomic functions and measurable microbial traits with interaction-aware assembly, environmental filtering, controlled validation, multi-site and multi-season field testing, performance/failure diagnosis, and iterative redesign. It distinguishes intervention types ranging from natural microbial communities and non-engineered inoculants to synthetic microbial communities (SynComs), genetically engineered microorganisms, engineered plants, and coordinated plant–microbe systems, recognizing their differing technical maturity, ecological risks, and regulatory requirements. Evidence is organized according to translational maturity, progressing from conceptual and laboratory studies through controlled plant experiments, single-site field validation, multi-environment validation, and authorized deployment. The available evidence supports strong mechanistic and proof-of-concept evidence for several microbial functions, but comparatively limited and heterogeneous evidence for reproducible performance across diverse field environments. Key limitations include context dependence, incomplete prediction of community interactions, variable establishment and persistence, biosafety and regulatory constraints, and limited long-term economic and ecological validation. The resulting framework provides a practical basis for identifying translational bottlenecks and prioritizing evidence needed to move microbiome and SynBio interventions toward robust agricultural applications.