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UID:pretalx-soil-health-now-2026-KJWDRR@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Athens:20261208T143000
DTEND;TZID=Europe/Athens:20261208T144500
DESCRIPTION:Effective Monitoring\, Reporting\, and Verification (MRV) is th
 e cornerstone of high-integrity carbon farming projects. A well-known appr
 oach to quantify the effect of a carbon farming project is to measure sequ
 estered soil organic carbon (SOC) in the project area and compare it to se
 questered SOC in designated baseline control sites (“Measure & Re-Measur
 e” in Verra’s VM0042). The required number of soil samples in the proj
 ect area and in baseline control sites is a key driver of MRV costs and de
 termines the uncertainty deductions enforced by carbon methodologies.\n\nT
 his presentation addresses a critical discrepancy between expected and act
 ual sample size requirements when following the “Measure & Re-Measure”
  approach in a classical design-based estimation (DBE) framework. Sample s
 ize equations commonly found in the literature and in methodologies focus 
 on temporal change rather than effect size (against a baseline). This lead
 s to significantly lower sample sizes than required to achieve the desired
  statistical power. Apart from deriving more suitable equations\, we analy
 ze the role of sample allocation between project area and baseline control
  sites. Our research shows that the optimal sample allocation ratio can ra
 rely 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.\n\nWe propose a two-fold remedy to
  this inefficiency. First\, instead of applying a purely statistical crite
 rion for sample size determination\, we propose using the Economic Optimum
  Number of Samples (EONS). EONS is a holistic approach that integrates sam
 pling costs\, uncertainty deductions\, and carbon credit prices. The econo
 mic optimum is attained where the cost of taking an additional soil sample
  equals the marginal revenue gained from lower uncertainty deductions. Sec
 ond\, we demonstrate how shifting from classical DBE to a Digital Soil Map
 ping (DSM) approach drastically improves project economics by lowering req
 uired sample sizes. We provide a method to determine EONS for DBE and DSM-
 based approaches\, and argue that DSMs should be the preferred option wher
 ever applicable.
DTSTAMP:20260825T193654Z
LOCATION:Amphitheater II
SUMMARY:Sample Size Requirements for Carbon Farming Projects with Baseline 
 Control Sites - Erik Scharwächter
URL:https://pretalx.earthmonitor.org/soil-health-now-2026/talk/KJWDRR/
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