Reliability of approaches for measuring soil organic carbon and implications for results-based payments for...

Soil organic carbon (SOC) measurement is critical for result-based payments for carbon sequestration in agriculture. This study examined (i) the consistency of SOC measurements from a portable near-infrared (NIR) spectroscopy soil scanner and laboratory analyses (Walkley-Black method) using samples from 151 plots across Western Kenya, and (ii) explored the implications of measurement error for SOC stock change estimates in group-based carbon farming schemes. Scanner predictions showed a weak correlation with laboratory results (R² = 0.10-0.11; RMSE = 5.6-7.1 g/kg). Comparing results between the two laboratories showed only moderate agreement (R² = 0.34) with a systematic bias of 7.5 g/kg. There was low repeatability (R² = 0.19; CV = 21.9 %) for samples within the same laboratory. Repeated scans covered an average range of 8.8 g/kg between their highest and lowest readings. Simulations showed that an unbiased measurement error (mean 0, SD 4 g/kg) caused a 22 % deviation in stock change estimates for a group of 50 farmers. However, with increased farmer group size (>1000), the average values converged to the true value (<2 % deviation). A systematic bias of 1 g/kg resulted in 18 % error that persisted regardless of group size. These findings demonstrated a significant variability in the output of different techniques, which could severely undermine the viability of result-based payments, especially for smallholders. Reducing the within-approach measurement error to less than 1 g/kg will be key to enabling effective carbon farming group sizes of up to 30 farmers. To achieve this, there is a need for targeted capacity-building efforts to standardize SOC measurements across laboratories, calibrate soil scanners for the local context of each country and integrate error margins into the design of carbon farming interventions.