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Soils have the potential to sequester and store significant amounts of carbon, contributing towards climate change mitigation. Soil carbon markets are emerging to pay farmers for management changes that absorb atmospheric carbon, governed by codes that ensure eligibility, additionality and permanence whilst protecting against leakage and reversals. This paper presents the first global comparative analysis of farmland soil carbon codes, providing new insights into the range of approaches governing this global marketplace. To do this, the paper developed an analytical framework for the systematic comparison of codes which was used to identify commonalities and differences in approaches, methods, administration, commercialisation and operations for 12 publicly available codes from around the world. Codes used a range of mechanisms to manage additionality, uncertainty and risks, baselines, measurement, reporting and verification, auditing, resale of carbon units, bundling and stacking, stakeholder engagement and market integrity. The paper concludes by discussing existing approaches and codes that could be adapted for use in the UK and evaluates the need for an over-arching standard for soil carbon codes in the UK and internationally, to which existing codes and other schemes already generating soil carbon credits could be assessed and benchmarked. [ABSTRACT FROM AUTHOR]

Using the International Centre for Research in Agroforestry–International Soil Reference and Information Centre (ICRAF-ISRIC) global soil spectroscopy database, models were developed to estimate a number of soil variables using different input data types. These input types included: (1) site data only; (2) visible–near-infrared (Vis-NIR) diffuse reflectance spectroscopy only; (3) combined site and Vis-NIR data; (4) red–green–blue (RGB) color data only; and (5) combined site and RGB color data. The models produced variable estimation accuracy, with RGB only being generally worst and spectroscopy plus site being best. However, we showed that for certain variables, estimation accuracy levels achieved with the “site plus RGB input data” were sufficiently good to provide useful estimates (r2 > 0.7). These included major elements (Ca, Si, Al, Fe), organic carbon, and cation exchange capacity. Estimates for bulk density, contrast-to-noise (C/N), and P were moderately good, but K was not well estimated using this model type. For the “spectra plus site” model, many more variables were well estimated, including many that are important indicators for agricultural productivity and soil health. Sum of cation, electrical conductivity, Si, Ca, and Al oxides, and C/N ratio were estimated using this approach with r2 values > 0.9. This work provides a mechanism for identifying the cost-effectiveness of using different model input data, with associated costs, for estimating soil variables to required levels of accuracy. [ABSTRACT FROM AUTHOR]