Models and Methods for Evaluating the Soil-Based Ecosystem Services of Agricultural Soils—A Global Systematic Review

Soil-based ecosystem services (SESs) are various benefits provided by soils to society or the environment, and their assessment supports sustainable agricultural soil management aimed at preventing further soil degradation. Individual SES evaluation procedures need a set of adequate indicators to support countries in monitoring the status of soil health. Among them, soil organic carbon (SOC) is indicated as one of the main, widely accepted and practicable attributes reflecting the proper functioning of soils. This paper aimed to present the recent state-of-the-art on SOC’s role in modelling and mapping individual SESs. Therefore, the PRISMA method was applied to select 138 research articles. The results showed that SOC data has been applied in evaluations of provisioning, regulating and supporting SESs. Based on our findings, we recommend paying special attention to SOC monitoring systems at different scales and database preparations following the primary modelling rule, garbage in—garbage out, enhancing the reliability of various models and their applicability across different scales. The proper selection of input data and assessment methods is crucial for accurately evaluating ecosystem services while minimising the risk of misinterpretation or ineffective policy and management decisions. Despite the existence of many types of models for the evaluation of SESs, we want to highlight that for the preservation of consistency and harmonisation, the proper modelling framework should be kept. In our studies, we highlighted that a comprehensive modelling workflow that integrates DSM-derived SOC data, scale-aware validation and uncertainty propagation into SESs is crucial to achieve reproducible, high-quality modelling approaches. Future research should focus on a systematic review of the usability of SES indicators, refining methodologies and expanding their use in national-scale assessments to support sustainable decision making.