Effective Monitoring, Reporting, and Verification (MRV) is the cornerstone of high-integrity carbon farming projects. A well-known approach to quantify the effect of a carbon farming project is to measure sequestered soil organic carbon (SOC) in the project area and compare it to sequestered SOC in designated baseline control sites (“Measure & Re-Measure” in Verra’s VM0042). The required number of soil samples in the project area and in baseline control sites is a key driver of MRV costs and determines the uncertainty deductions enforced by carbon methodologies.

This presentation addresses a critical discrepancy between expected and actual sample size requirements when following the “Measure & Re-Measure” approach in a classical design-based estimation (DBE) framework. Sample size equations commonly found in the literature and in methodologies focus on temporal change rather than effect size (against a baseline). This leads to significantly lower sample sizes than required to achieve the desired statistical power. Apart from deriving more suitable equations, we analyze the role of sample allocation between project area and baseline control sites. Our research shows that the optimal sample allocation ratio can rarely be attained, as it demands practically infeasible sampling densities in the baseline control sites. At the same time, deviations from optimal allocation can inflate total sample sizes by a factor of three or more, directly threatening project economics.

We propose a two-fold remedy to this inefficiency. First, instead of applying a purely statistical criterion for sample size determination, we propose using the Economic Optimum Number of Samples (EONS). EONS is a holistic approach that integrates sampling costs, uncertainty deductions, and carbon credit prices. The economic optimum is attained where the cost of taking an additional soil sample equals the marginal revenue gained from lower uncertainty deductions. Second, we demonstrate how shifting from classical DBE to a Digital Soil Mapping (DSM) approach drastically improves project economics by lowering required sample sizes. We provide a method to determine EONS for DBE and DSM-based approaches, and argue that DSMs should be the preferred option wherever applicable.

The main methodologies governing the voluntary carbon market (VCM) and Scope 3 accounting now allow the use of Digital Soil Mapping (DSM) for soil organic carbon (SOC) stock estimation. Verra’s VT0014 has quickly become the industry standard for DSM-based SOC stock estimation and provides step-by-step guidance on uncertainty estimation. This guidance is largely based on Wadoux & Heuvelink (2023) and is applicable for a large class of models. However, the generality of the approach has the cost of suboptimality: uncertainty of the resulting SOC stock estimates is larger than necessary, which directly influences project economics.

In this work, we empirically study the uncertainty estimator from Wadoux & Heuvelink (2023) and compare it to a Random Forest Residual Kriging (RFRK) approach (Hengl et al., 2015; Szatmári et al., 2024). We also include the classical Horvitz-Thompson estimator (HTE) calculated from soil samples only. For a fair comparison, we apply the Wadoux & Heuvelink (2023) estimator on top of a Quantile Regression Forest (QRF) model that uses the same calibration data, covariates and hyperparameters as the Random Forest model from the RFRK approach. We benchmark all estimators across three agricultural projects. Special attention is given to the uncertainty of the estimators across differing project sizes and sampling intensities.

Both DSM approaches are compliant with VT0014 and passed the model validation requirements with comparable validation metrics. RFRK consistently showed slightly higher R² scores, with empirical coverage probabilities closer to the nominal coverage. Throughout the benchmark, RFRK was consistently more precise than HTE, reducing uncertainty of the SOC stock estimate substantially across all scenarios. In contrast, QRF improved over HTE only in the largest-sample project, while inflating the uncertainty in the other two. DSM uncertainty estimates showed diminishing returns as sample sizes increased, and dropped sharply as project area increased.

These findings confirm that a DSM-based SOC stock estimate can be substantially more precise than a classical soil sampling approach: RFRK roughly halved the uncertainty of the estimate, translating to reduced uncertainty deductions. However, a DSM-based estimate can also inflate uncertainties, despite being validated with state-of-the-art performance metrics, as demonstrated by the QRF-Wadoux approach. The exact choice of the DSM model appears to govern precision more than the model accuracy in terms of R² score. Therefore, project proponents should carefully select the most suitable modelling approach for their project.