The evolution of methods and tools used to assess soil health indicators − a scoping review

Soil health has become central to sustainable food production, conservation-oriented land management, and climate resilience. Yet methods used to assess soil health indicators remain conceptually fragmented and methodologically diverse. This review synthesises the evolution of tools and approaches used to assess soil health from 2000 to 2024, with emphasis on methodological trends, conceptual shifts, and implication for practice. Using the PRISMA methodology, peer-reviewed studies were analysed to identify dominant assessment approaches, temporal patterns, and geographic distribution. The results show a clear methodological progression from empirical statistical techniques and index-based frameworks towards data-driven and machine learning approaches. Early assessments relied heavily on principal component analysis, regression models, and composite soil quality indices, which provided structured but largely static representations of soil condition. More recent studies increasingly employ machine learning models to handle nonlinear interactions and high-dimensional datasets, reflecting advances in data availability and computational capacity. Despite these developments, key limitations persist, including limited spatiotemporal transferability, dependence on region-specific calibration, and insufficient linkage between indicator scores and soil functional processes. Geographic disparities remain evident, with strong research concentration in North America and Asia and comparatively limited representation from Africa and other data-scarce regions. Overall, the evolution of soil health assessment tools demonstrates increasing analytical sophistication but also reveals a need for more process-informed, dynamic, and regionally adaptable frameworks. Future research should prioritise integration of mechanistic understanding with predictive modelling to improve the robustness and applicability of soil health assessment across diverse agroecosystems.