A pragmatic approach for national-scale resampling of soil organic carbon monitoring sites
Monitoring soil organic carbon (SOC) stocks is critical for understanding changes in soil health, supporting land management, and informing climate change mitigation policies. This study presents a pragmatic and scientifically robust approach for national-scale resampling of SOC monitoring sites in Australia. Using legacy data from the Soil Carbon Research Program (SCaRP), which surveyed over 4000 agricultural sites between 2009 and 2012, we developed a hybrid resampling framework that combines model-based and design-based sampling principles. A subset of 308 sites comprising 207 ‘standard’ (composited) and 101 ‘detailed’ (replicated) sites (sampling support is 25 m × 25 m) was selected using a two-stage procedure involving k-means clustering, fuzzy allocation, and conditioned latin hypercube sampling. Covariates representing SOC drivers, including climatic, biophysical, and management-related factors, were processed using discounted temporal weights to capture antecedent conditions. The final set of 308 resampled sites (7.57% of SCaRP) reflects the diversity of Australian agroecological conditions and maintains statistical representativeness across key SOC drivers. This was demonstrated through Jensen–Shannon divergence analysis, which yielded values close to zero, indicating high efficiency in the resampling campaign. The resampling approach outlined here demonstrates how legacy soil datasets can be efficiently leveraged through resampling designs to meet practical constraints while preserving scientific rigour. These findings have broad implications for long-term SOC monitoring, particularly in contexts where site access, budget, and temporal sensitivity pose challenges.