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<p>Soils can sequester carbon and contribute to climate change mitigation, but credible soil carbon offset markets require robust Monitoring, Reporting, and Verification (MRV) programs. Current approaches rely on models with soil sampling or on repeated probability sampling. We propose a digital soil mapping (DSM) framework to estimate soil organic carbon (SOC) stock change and uncertainty. Using physical samples from a hypothetical project across seven fields in two U.S. states, we calibrate, predict, validate, and spatially aggregate SOC estimates. We assess the effect of calibration sample size on field- and project-level means and variances and extend the framework to quantify change. Estimated uncertainty of averaged SOC stocks was &#xa0;&lt;1 Mg ha<sup>−1</sup>, yielding a 12% uncertainty deduction for a 5-year, 430-acre project when using a probability of exceedance method. Assuming the sequestration rate is 0.6% of the stock size, simulations suggest deductions &#xa0;&lt;10% for most projects &#xa0;&gt;5 years and &#xa0;&lt;5% for large (&#xa0;&gt;50,000 acres), long-term projects. This DSM approach offers a cost-effective and scalable alternative to other existing methods.</p>

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A digital soil mapping approach to soil carbon monitoring, reporting and verification (MRV)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月6日

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  • Alexandre M.J-C. Wadoux,
  • Mitchell Donovan,
  • Peng Fu,
  • Nicholas R. Leach,
  • Julia Maddalena,
  • Rose Rustowicz,
  • David Schurman,
  • James R. Kellner

摘要

Soils can sequester carbon and contribute to climate change mitigation, but credible soil carbon offset markets require robust Monitoring, Reporting, and Verification (MRV) programs. Current approaches rely on models with soil sampling or on repeated probability sampling. We propose a digital soil mapping (DSM) framework to estimate soil organic carbon (SOC) stock change and uncertainty. Using physical samples from a hypothetical project across seven fields in two U.S. states, we calibrate, predict, validate, and spatially aggregate SOC estimates. We assess the effect of calibration sample size on field- and project-level means and variances and extend the framework to quantify change. Estimated uncertainty of averaged SOC stocks was  <1 Mg ha−1, yielding a 12% uncertainty deduction for a 5-year, 430-acre project when using a probability of exceedance method. Assuming the sequestration rate is 0.6% of the stock size, simulations suggest deductions  <10% for most projects  >5 years and  <5% for large ( >50,000 acres), long-term projects. This DSM approach offers a cost-effective and scalable alternative to other existing methods.