Cropland Management

This Module provides the requirements and procedures for the quantification of net carbon dioxide equivalent (CO₂e) removal removal from the atmosphere via improvements to soil organic carbon (SOC) stocks in cropland systems. This Module sits under the Improved Soil Management Protocol and must be read in conjunction with it.

Cropland soils represent one of the largest opportunities for terrestrial carbon sequestration¹. Globally, agricultural soils have lost an estimated 133 Pg of organic carbon since the onset of widespread cultivation, and a substantial fraction of this historical loss is recoverable through improved management practices². Recent estimates suggest that improved cropland management alone could sequester between 0.28 and 1.85 Pg CO₂ yr⁻¹ globally, placing cropland SOC enhancement among the most scalable nature-based climate solutions available³.

This Module is process-agnostic with respect to the specific management practices employed. Eligible project activities encompass any practice or combination of practices that results in a demonstrable net increase in SOC stocks against a counterfactual baseline, provided all eligibility and safeguarding requirements set out in this Module and the parent Protocol are met. This approach reflects the wide diversity of cropping systems, soil types, and climatic conditions encountered globally, and recognizes that effective SOC enhancement strategies will vary substantially by region and context. Using common terminology for SOC-enhancing practices, examples of such eligible activities include, but are not limited to:

• Cover cropping: Growing crops during fallow periods to increase organic matter inputs to the soil 
• Reduced or no-till: Minimizing soil disturbance to preserve existing SOC stocks and promote accumulation 
• Compost and organic matter application: Adding external organic inputs to increase soil carbon content 
• Biostimulant and biological inoculant application: Use of microbially-based or biochemical products that stimulate plant growth and root exudation, promoting SOC formation pathways

In addition to climate mitigation, improvements to SOC in cropland systems can provide environmental and social co-benefits, including enhanced soil water retention and drought resilience, improved crop yields and long-term agricultural productivity, reduced dependency on synthetic fertilizers, mitigation of erosion and nutrient runoff into waterways, and support for soil biodiversity ^4,5,6,7. Carbon finance presents a meaningful opportunity to overcome the adoption barriers, including implementation costs, agronomic support, and the absence of financial incentives, that have historically constrained farmer uptake of SOC-enhancing practices.

Project Proponents must meet all the requirements set out in the Improved Soil Management Protocol and relevant Modules, as well as the requirements set out in this Module. Where this Module contains requirements that duplicate or conflict with those in other Modules, this Module takes precedence.

Throughout this Module, the use of “must” indicates a requirement, “should” indicates a recommendation, and “may” indicates an option or permission.

This Module relies on and is intended to be compliant with the following Standards and Protocols:

• The Isometric Standard
• Improved Soil Management Protocol v1.0, Isometric
• ISO 14064-2: 2019 Greenhouse Gases Part 2: Specification with guidance at the project level for quantification, monitoring, and reporting of greenhouse gas emission reductions or removal enhancements

Additional reference standards that inform the requirements of this Module include:

ISO 14064-3: 2019 Greenhouse Gases Part 3: Specification with guidance for the verification and validation of greenhouse gas statements

• ISO 14040: 2006 Environmental Management, Lifecycle Assessment, Principles & Framework
• ISO 14044: 2006 Environmental Management, Lifecycle Assessment, Requirements & Guidelines

Additional principles that were considered in the development of this Module include:

• The Core Carbon Principles of The Integrity Council for the Voluntary Carbon Market, v1.1, ICVCM, 2024
• Criteria for High-Quality Carbon Dioxide Removal, Carbon Direct & Microsoft, 2025

This Module was developed based on the current state of the art and publicly available science regarding cropland soil organic carbon dynamics and land management interventions. This Module aims to be scientifically stringent and robust. We recognize that some requirements may exceed the status quo in the voluntary carbon market and that there are numerous opportunities to improve the rigor of this Module as new approaches and techniques emerge.

Additionally, this Module will be reviewed when there is an update to published scientific literature, government policies, or legal requirements which would affect net CO₂e removal quantification or the monitoring guidelines outlined in this Module, or at a minimum of every 2 years.

In addition to the requirements outlined in the Improved Soil Management Protocol, Projects are subject to the following applicability requirements, which must be demonstrated in The Project Design Document.

In the context of this Module, cropland is defined as land used for the cultivation of annual or perennial crops, including arable land under crop rotation, fallow land within an active rotation cycle, and land under permanent crops such as orchards, vineyards, or plantations of non-timber commodity crops. The unit of enrollment (see Section 4.2 of the Improved Soil Management Protocol) is a field.

• Native forest - A forest dominated by tree species indigenous to the region and that has been established and regenerated through natural processes. Generally with high biodiversity and intact ecological processes (nutrient cycling, natural disturbance regimes).
• Native grassland - A grassland dominated by grass and herbaceous species indigenous to the region and that has formed and persisted through natural processes, distinct from sown, "improved," or cultivated pasture made up of introduced forage species, and distinct from grassland created by clearing forest.
• Native wetland - A wetland whose hydrology, soils, and biological communities have formed and persisted through natural processes, and whose vegetation is dominated by water-adapted species indigenous to the region.

Following the requirements under the Protocol, Project Proponents should implement practices that provide ecological benefits in addition to increasing carbon sequestration. SOC-enhancing practices including cover cropping, reduced tillage, and organic amendment application are encouraged where they deliver co-benefits for soil health, water quality, and habitat connectivity alongside their primary carbon benefit.

Where cultivation is required as part of project establishment, soil inversion should be limited to 25 cm depth. This recommendation applies to areas where new management practices are being established as part of The Project and does not apply to any continuing agricultural activity that was occurring at the same depth prior to project initiation.

The Project's primary commitment is to maintain cumulative SOC stocks at or above the level corresponding to credits or certificates previously issued, quantified through the monitoring framework set out in Section 9. Because the Module is practice-agnostic, Project Proponents may adapt the specific practices used over time, provided the outcome-based commitment continues to hold.

The Project must provide a minimum of 40 years of SOC storage in The project area, as defined by the length of The Project Commitment Period (see Section 5.1)

• The primary commitment is to the maintenance of cumulative SOC stocks at or above the level corresponding to Credits previously issued, as quantified through the monitoring framework set out in Section 9.
• Project Proponents may adapt the specific land management practices implemented during The Project Commitment Period, provided that any such adaptation: (i) does not breach any other applicability requirement of this Module or the parent Protocol; (ii) is documented in advance in the Monitoring Report and approved at the next verification; and (iii) is accompanied by evidence, through the Section 9 framework, that the 30th-percentile estimate of cumulative SOC stocks (calculated in accordance with Section 9.1.1) remains at or above the level corresponding to Credits previously issued. Reversion to baseline land management practices, or any practice change for which the 30th-percentile cumulative SOC stock estimate falls below the level corresponding to Credits previously issued, will be treated as a reversal in accordance with the Isometric Standard.
• Independently of the outcome-based test set out above, Project Proponents must notify Isometric of any material change to the land management practices implemented within The Project Boundary as soon as practicable, and in any event no later than the next Monitoring Report.
  • A change is material where it alters the practices identified in The Project Design Document as responsible for generating the SOC increase, or where it could reasonably be expected to affect SOC stocks within The Project Area. Failure to notify a material practice change will be treated as a non-conformance and may result in the suspension of crediting until the change has been assessed and verified.

• Where The Project involves smallholder farmers or customary land users, land tenure arrangements must be consistent with the farmer and land user rights requirements of Section 6.6 of the Improved Soil Management Protocol.

• Planting trees and comparable land-use transformations are scoped under the Isometric Agroforestry and Reforestation Protocols.

The Project Commitment Period encompasses the Crediting Period and any Ongoing Monitoring Period commitments following the end of Crediting.

For grouped projects where new areas are added to The Project over time, The Project Timeline may be staggered across The Project areas to reflect different initiation times of Project activities.

• Duration. The total Crediting Period (including renewals) can be no longer than the Project Commitment Period set at project initiation.
• Issuance. Credit issuances occur throughout the Crediting Period. Credits are issued upon Verification of a Reporting Period. Credits issued at each verification event represent the cumulative net CO₂e removal from project initiation to the end of the current Reporting Period, less all credits previously issued under The Project.
• Renewal. The Crediting Period may be renewed up to the duration of The Project Commitment Period.
• Considerations for Grouped Projects. For grouped projects composed of multiple discrete areas, the individual Crediting Periods may be staggered across individual sites to reflect the different timings of project activity initiation within the sites.

• Duration. The minimum duration of a Reporting Period is one year. The maximum duration of a Reporting Period is five years. Project Proponents may request an alternative length of the Reporting Period provided they submit suitable justification for the deviation (e.g., evidence of carbon stock impacts of intervention anticipated to take longer during establishment; matching relevant cultivation cycles).
• Verification. Verification of project activities by a third-party Verification and Validation Body (VVB) is conducted for each Reporting Period (see Section 7.2 of the Improved Soil Management Protocol).
• Last Reporting Period. Project Proponents must indicate the last Reporting Period to be submitted for Verification. Failure to initiate a Verification within 5 years of the previous Reporting Period or request an extension will conclude the Crediting Period.

• Duration. If the Crediting Period is less than The Project Commitment Period, the Ongoing Monitoring Period must encompass the length of time from the end of the Crediting Period to the end of the Project Commitment Period.
• Monitoring. Monitoring for Reversals is the responsibility of The Project Proponent and must follow the same requirements for the measure re-measure approach used for quantification of $CO_2e_{stored}$ (Section 9.1.2). If monitoring is not possible (e.g., do not have access to land), the area is considered to experience a full Reversal.

The Project Commitment Period must be set at the time of PDD submission. If the initial Crediting Period is less than The Project Commitment Period, The Crediting Period may be extended up to the duration of The Project Commitment Period. If the initial Crediting Period is not extended, the remainder of The Project Commitment Period must consist of an Ongoing Monitoring Period. For grouped projects, The Project Commitment Period must be the same duration for all areas, but Crediting Period and Ongoing Monitoring Period duration may vary across individual areas to reflect differences in land tenure.

• Definition. The indefinite period of time after The Project Commitment Period has ended.

Project A is a smallholder cropland management project. It sets a Project Commitment Period of 40 years, with an initial Crediting Period of 20 years for all enrolled properties. At the end of the initial Crediting Period, most of the properties renew their enrollment for another 20 years and continue to accumulate Credits for the remainder of The Project Commitment Period. The remaining properties which did not renew their enrollment enter an Ongoing Monitoring Period to monitor for Reversals for the remaining 20 years of The Project Commitment Period. The Project Proponent is responsible for quantification and monitoring through all of The Project Area for the full duration of The Project Commitment Period, and the reported activities are verified by a Validation and Verification Body (VVB). All Credits from Project A have a durability of 20 years, equivalent to half of the 40 year Project Commitment Period.

Project B is a cropland project which sets a Project Commitment Period of 60 years, which fully consists of the Crediting Period. All Credits from Project B have a durability of 30 years, equivalent to half the 60 year Project Commitment Period.

Project C is a grouped cropland management project which sets a Project Commitment Period of 40 years, entirely composed of crediting periods. It is composed of two cohorts of enrolled properties - Group C1 which started in 2025 and Group C2 which started in 2026. The Project Commitment Period for Group C1 would run from 2025 to 2065, while The Project Commitment Period for Group C2 would run from 2026 to 2066. All credits would have a 20 year durability.

Project interventions involve deliberate changes to cropland management practices that may affect local ecosystems, communities, and land users beyond the direct project benefit.

SOC-enhancing land management practices frequently require upfront investment in new inputs, equipment, and agronomic expertise, while delivering climate benefits that are not directly captured by commodity markets.

Project Proponents must demonstrate that the adoption of project activities is contingent on Carbon Finance, i.e., that the practices would not be economically viable without the revenue generated from credit issuance.

Where project activities generate revenue from commodity production or other non-carbon sources within The Project area, Project Proponents must demonstrate that this revenue alone is insufficient to make The Project financially viable, in accordance with the financial additionality requirements of the Improved Soil Management Protocol and the Isometric Standard.

Projects must not occur in regions where implemented SOC-enhancing practices are already being driven to adoption by market demand, agricultural policy, or regulatory requirements that would lead to equivalent practice changes without Carbon Finance.

A cradle-to-grave GHG Statement must be prepared encompassing the GHG emissions and removals relating to all activities within the system boundary. The Project Proponent is responsible for identifying all sources of emissions directly or indirectly related to project activities.

Any emissions from sub-processes or process changes that would not have taken place without the CDR Project must be fully considered in the system boundary. Any additional activity that ultimately leads to the issuance of Credits must be included in the system boundary.

The system boundary must include all relevant GHG SSRs controlled and related to The Project, as set out in Table 4 of the Improved Soil Management Protocol. Any emissions from sub-processes or process changes that would not have taken place without the CDR Project must be fully considered in the system boundary.

Cropland management projects are implemented on land under active agricultural production, meaning that certain operational activities (e.g., planting, harvesting, fertilizer application, tillage passes) were occurring prior to, and may continue alongside, project activities. For the purpose of this provision, an "activity" may refer to an operational sub-unit, such as an individual equipment pass, a single fertilizer application event, or a discrete field management step, where such sub-units can be cleanly delineated by equipment, timing, and physical scope. Where a pre-existing activity is partially modified by The Project (for example, a field operation whose frequency is altered, or an application event whose inputs are changed), the activity must be partitioned into:

• Portions that are materially unchanged by The Project, which may be excluded from the system boundary under this provision; and
• Portions that are new, extended, or altered as a result of The Project, which must remain within the system boundary and be quantified in accordance with Section 9.5 of the Improved Soil Management Protocol.

Evidence supporting these conditions must be provided in the the LCA. This must include either:

• Historic farm records documenting the activity prior to project start. Acceptable records include management logs, operational records, equipment usage records, invoices, or equivalent documentation; or
• A signed affidavit from the relevant operator (e.g., farmer, land manager, or equivalent party) confirming the activity was part of routine operations prior to The Project.

And the following:

• A signed affidavit from the relevant operator confirming:
  • The equipment, timing, cadence, and intensity of the activity are not materially changed as a result of The Project; and
  • The activity would have continued at a comparable level absent The Project.

Where these conditions are met, only the emissions associated with the activity as it would have occurred in the baseline may be excluded. Any incremental emissions attributable to The Project must remain within the system boundary and be accounted for in the relevant emissions section.

In accordance with the Improved Soil Management Protocol, the following are excluded from the system boundary for cropland management projects:

• Aboveground woody biomass and belowground woody biomass carbon pools (these terms must be set to zero in Equation 7 of the Improved Soil Management Protocol);
• Deadwood, litter, and non-woody herbaceous biomass carbon pools, as these are considered transient

In accordance with Section 8.2.1 of the Improved Soil Management Protocol, improved cropland management interventions may involve reductions in activities that result in emissions, such as reduced CO₂ from diesel use in tractors corresponding to fewer passes in a no-till intervention, or avoided N₂O emissions from reduced fertilizer inputs to soils.

Emissions reductions do not inflate removals by counting against CO₂e_Emissions,RP and are credited separately in the Agricultural Practices Reductions Module. Emissions outlined in Table 1 of the Improved Soil Management Protocol and reported in accordance with Section 9.5 of the Improved Soil Management Protocol must be strictly positive.

This is in keeping with principles of conservativeness and maintaining consistency with Isometric's approach to GHG Accounting for removals as outlined in the GHG Accounting Module v1.1.

Leakage emissions, $CO_2e_{Leakage}$, occur when project activities lead to emissions that occur outside the system boundary of cropland management projects. For cropland management projects, the primary leakage risk is market-mediated leakage. This risk stems from project-induced reductions in agricultural production that may lead to land conversion and associated GHG emissions elsewhere to meet the supply shortfall. As a principle, Projects should seek to maintain or enhance yields from baseline levels.

Three key types of leakage can theoretically occur for cropland management projects, although, as noted above, the primary focus of the leakage assessment is market-mediated leakage:

• Activity-shifting leakage. Cropland management projects may displace activities in their project areas, leading to an increase in those activities outside of The Project area, which may result in potential land conversion. Examples are where the local community can no longer use the whole project site for subsistence, or where part of a farmer’s productivity is displaced as a result of project activities. This type of leakage is known as “Direct” leakage as the relevant stakeholders can be identified and the activity-shifting is traceable.
• Market leakage. Cropland management projects may displace activities, which results in a reduction in supply of a commodity. Changes to the supply and demand equilibrium causes other market actors to shift their activities, leading to potential land conversion. This type of leakage is known as “Indirect” leakage because its effects cannot be isolated and measured directly. Quantifying the likelihood and potential magnitude of market leakage is complex and relies heavily on modeling and available literature.
• Ecological leakage. Project activities may lead to emissions in areas outside of The Project site as a result of ecological interactions, for example unintended hydrological impacts, introduction of disease, or secondary impacts of faunal influx.

All market-leakage discounts under this Module are applied to removal credits. Where a Project concurrently generates emission reduction credits under the Agricultural Practices Reductions Module, no leakage discount is applied to those reduction credits. This allocation reflects the principle that market-mediated leakage from yield displacement is conservative to avoid the risk that emissions associated with removals are undercounted leading to over-crediting of removals.

Pre-Project Productivity ($PPP$) is defined as the annual productivity of a commodity type $c$ on Project field $f$ in relevant units (e.g., tonnes/ ha yr). $PPP$ must be calculated on a per-commodity basis using field-level yields indexed against regional benchmarks. This approach normalizes field-level performance against county-level (or equivalent sub-national jurisdiction) yields, controlling for weather, pest, and commodity-price variation that affects all fields in the region equally.

$PPP$ for commodity $c$ is calculated as:

$$PPP_{f,c} = \bar{Y}_{\text{regional,c}} \times \frac{\sum_{t \in T_{f,c}} Y_{f,c,t}}{\sum_{t \in T_{f,c}} Y_{\text{regional,c,t}}}$$

(Equation 1)

Where:

• $PPP_{f,c}$ is the Pre-Project Productivity for commodity $c$ on field $f$, in production units per hectare per year
• $\bar{Y}_{regional,c}$ is the mean regional yield for commodity $c$ in production units per hectare over the baseline period (minimum 5 years, may be longer)
• $Y_{f,c,t}$ is the field-level yield for commodity $c$ on field $f$ in year $t$ of the baseline period
• $Y_{regional,c,t}$ is the regional yield for commodity $c$ in year $t$ over the baseline period
• $T_{f,c}$ is the set of years in which field $f$ was planted with commodity $c$ during the baseline period (minimum 5 years, may be longer)

$PPP$ must be calculated separately for each commodity within the field's crop rotation. For example, in a corn-soy rotation with a 5-year baseline, corn $PPP$ is calculated from the corn rotations and soy $PPP$ from the soy rotations within the 5-year baseline period.

Project-Scenario Productivity ($PSP$) for commodity $c$ on field $f$ in Reporting Period $RP$ is calculated as:

$$PSP_{f,c,RP} = \bar{Y}_{\text{regional,c}} \times \frac{\sum_{t \in W_{f,c,RP}} Y_{f,c,t}}{\sum_{t \in W_{f,c,RP}} Y_{\text{regional,c,t}}}, \qquad RP \geq t_0 + 4$$

(Equation 2)

Where:

• $PSP_{f,c,RP}$ is The Project-Scenario Productivity for commodity $c$ on field $f$ in Reporting Period $RP$, in production units per hectare per year
• $\bar{Y}_{regional,c}$ is the mean regional yield for commodity $c$ in production units per hectare over the baseline period (minimum 5 years, may be longer) as defined in Equation 1
• $W_{f,c,RP}$ is the set of years t satisfying $RP$ - $4 ≤ t ≤ RP$ in which field f grew crop c
• $t_{0}$ is the first project year
• $Y_{f,c,t}$ is the Yield of field f for crop c in year t in production units per hectare
• $Y_{regional,c,t}$ is the regional average yield for commodity $c$ in production units per hectare, drawn from the same regional yield source used for $\bar{Y}_{regional,c}$

Project-scenario productivity is assessed on a rolling basis beginning in the fifth project year ($RP = t_{0} + 4$) and in each Reporting Period thereafter. The first assessment applies a retroactive lookback covering project years 1 through 5; subsequent assessments use the trailing five calendar years ending in Reporting Period $RP$. Within each window, only years in which field $f$ grew crop $c$ contribute to the calculation. No productivity assessment is performed in project years 1 through 4, though yield data for these years must be collected and reported from project year 1 for use in the year-5 assessment and subsequent rolling windows. Field yields are indexed to regional average yields over the same years, so that regional weather and market-driven variation does not distort the productivity estimate. The multiplier $\bar{Y}_{regional,c}$ is fixed at project start and is identical to that used in $PPP_{f,c}$ ensuring both metrics are directly comparable. Where crop c was not grown on field f in any year of the window, $PSP_{f,c,RP}$ is undefined and no assessment is made for that field-crop pair.

The productivity shortfall for each commodity $c$ in Reporting Period $RP$ is:

$$\Delta P_{c,f,RP} = \max(PPP_{c,f} - PSP_{c,f,RP}, 0)$$

(Equation 3)

No leakage assessment is required for commodities where $\Delta P_{c,f,RP} = 0$ (i.e., project-scenario productivity meets or exceeds the baseline).

The de minimis test is applied to the net productivity shortfall for each commodity, after within-project leakage mitigation has been applied per Equation 6. The Project-level expected production (EP) for commodity c is:

$$EP_{c,RP} = \sum_f PPP_{f,c} \times A_{f,c,RP}$$

(Equation 4)

Where the sum is taken over all fields $f$ planted with commodity $c$ in the Reporting Period with a defined $PSP_{f,c,RP}$, and $A_{f,c,RP}$ is the area planted with commodity $c$ on field $f$ in the Reporting Period, in hectares.

Where $NPP_{c,RP} / EP_{c,RP} \leq 0.03$ (i.e., the net productivity decline is 3% or less relative to expected baseline production for commodity $c$ across The Project), the decline is within the de minimis materiality threshold and no leakage discount applies for that commodity in that Reporting Period. The de minimis threshold is a Materiality gate, not a deductible: where the net productivity decline exceeds 3%, leakage must be assessed on the full value of $NPP_{c,RP}$, not only the portion exceeding the threshold.

The de minimis test operates on the net shortfall because the market-mediated leakage risk arises from The Project's net impact on commodity supply; production surpluses on fields within The Project relieve the same market pressure that shortfalls create.

The crediting suspension threshold is applied to the gross productivity shortfall for each commodity, before within-project leakage mitigation. The gross shortfall (GS) ratio for commodity $c$ is:

$$GS_{c,RP} = \frac{\sum_f \Delta P_{f,c,RP} \times A_{f,c,RP}}{EP_{c,RP}}$$

(Equation 5)

Where $GS_{c,RP} > 0.15$ (i.e., the gross productivity decline exceeds 15% for any single commodity across The Project), The Project is ineligible for crediting for that Reporting Period unless a temporary exemption is granted under the exceptional circumstances provision detailed in Section 8.6. Credits attributable to that Reporting Period are forfeited and may not be earned back at any point during the remainder of the Crediting Period.

The crediting suspension threshold tests the gross shortfall, without offset from within-project surpluses, because the threshold serves a different function from the leakage quantification: it is a detector of systematic, practice-driven productivity failure. Permitting surpluses on some fields to mask severe declines on others would defeat this function, even where the net market impact is modest.

For fields that unenroll from The Project, whether due to productivity declines coinciding with The Project intervention or for any other reason, the field's productivity in its final enrolled year must be included in the calculation of $GS_{c,RP}$ and $EP_{c,RP}$ for the Reporting Period in which unenrollment occurs, in addition to the unenrollment true-up requirements of Section 8.3.7. Unenrollment does not remove a field's contribution to The Project-wide crediting suspension threshold assessment for that Reporting Period.

The productivity assessment is performed independently for each Reporting Period against the original pre-project baseline. Each Reporting Period's leakage is calculated without reference to the leakage assessed in any prior Reporting Period. There are two exceptions to this rule: (i) leakage in years 1 through 4, as detailed in Section 8.3.6.1; and (ii) the unenrollment true-up under Section 8.3.7, which may apply an exit assessment against credits issued in prior Reporting Periods.

Leakage is first assessed in the fifth project year ($RP = t_{0} + 4$), coinciding with the first assessment of Project-Scenario Productivity under Equation 2. No leakage assessment is performed in project years 1 through 4; however, credits issued for Reporting Periods 1 through 4 are provisional with respect to leakage and remain subject to the retroactive assessment described here.

At the first assessment, $PSP_{f,c,RP}$ is calculated using the full retroactive lookback covering project years 1 through 5, and any resulting leakage discount is applied to the cumulative credits issued or issuable for Reporting Periods 1 through 5. The de minimis threshold and within-project netting provisions apply to the full retroactive period. The crediting suspension threshold, however, does not apply retroactively. Where the first assessment identifies a productivity decline exceeding the crediting suspension threshold for any commodity, ineligibility for crediting applies to Reporting Period 5 only; credits issued for Reporting Periods 1 through 4 are not voided, though they remain subject to the standard leakage discount calculated at the first assessment. From the sixth project year onward, leakage is assessed independently in each Reporting Period using the trailing five-year window per Equation 2, without reference to leakage assessed in any prior Reporting Period, and applies only to credits for that Reporting Period.

Fields that unenroll from The Project, or are removed from The Project for any reason, remain subject to a final productivity assessment ("true-up") in the Reporting Period in which unenrollment takes effect. The true-up ensures that a field's productivity shortfall history is settled before the field exits the leakage assessment, and that unenrollment cannot be used to remove underperforming fields from the dataset before their leakage impact is quantified.

For each unenrolling field $f$ and each commodity $c$ grown on that field, a final $PSP_{f,c,RP}$ must be calculated per Equation 2 using the trailing five-year window ending in the field's final enrolled year. Where a field unenrolls before the first productivity assessment (project years 1 through 4), the exit assessment must be calculated using all available project years, and the field's yield data must additionally be included in the retroactive first assessment at year 5 as if the field remained enrolled through its final enrolled year.

The field's resulting shortfall (Equation 3) and, where the eligibility conditions of the Within-Project Leakage Mitigation provisions are met, surplus (Equation 6) are included in The Project-level aggregations (Equations 4 through 8) for the Reporting Period in which unenrollment takes effect, using the field's area in its final enrolled year. The field's contribution to the crediting suspension threshold assessment is as set out in the Crediting Suspension Threshold section (Section 8.3.5).

Leakage attributable to an unenrolled field is quantified and applied within the exit Reporting Period's leakage calculation. Where the resulting leakage discount exceeds the credits available to The Project in the exit Reporting Period, the remainder must be deducted from subsequent issuances until fully settled; it may not be waived or earned back. The true-up does not reopen or retroactively void credits issued in prior Reporting Periods, except as provided for Reporting Periods 1 through 5 under the Leakage Assessment in Years 1 through 4 provisions.

The Project Proponent must retain field-level yield records for each enrolled field through its final enrolled year, including for fields that signal intent to unenroll. Where yield data for any year in the exit assessment window are unavailable, the field's yield for those years must be deemed zero for purposes of the exit assessment, unless a temporary exemption is granted under the exceptional circumstances provision in Section 8.6. Documented crop failures on unenrolling fields are treated per the Crop Failure provisions.

Where some fields within The Project experience yield increases as a result of SOC-enhancing interventions, the additional production may be used to offset productivity shortfalls on other fields within the same project. This within-project netting reflects the economic reality that additional supply from fields with increased yields fields relieves the market pressure that fields from decreased yields create.

A field may generate leakage mitigation (a productivity surplus that can offset negative leakage elsewhere in The Project) only where all of the following conditions are met:

• The field is enrolled in The Project and implementing SOC-enhancing interventions under the Improved Soil Management Protocol and this Module. This requirement is to provide confidence that the yield increases are a result of The Project intervention(s), not incidental.
• The yield increase exceeds 3% above the Pre-Project Productivity for the commodity. This gate is deliberatively stricter than the treatment of shortfalls: only the surplus portion exceeding 103% of $PPP_{f,c}$​ is counted (Equation 4), whereas shortfalls are counted in full from the first unit. This asymmetry is conservative and ensures that only meaningful, above-trend increases generate mitigation, not normal year-to-year variability or background yield growth.
• The commodity is not a switched commodity within its first five years on the field, per the Section 8.5.5 Legitimate Reasons for Cropping Change provisions.

The productivity surplus for commodity $c$ on field $f$ in Reporting Period $RP$ is:

$$\Delta P^{+}_{c,f,RP} = \max(PSP_{c,f,RP} - PPP_{c,f} \times 1.03, 0)$$

(Equation 6)

Where:

• $\Delta P^{+}_{c,f,RP}$ is the productivity surplus for commodity $c$ on field $f$, in production units per hectare.
• $PSP_{c,f,RP}$ is The Project-Scenario Productivity for commodity $c$ on field $f$ (Equation 2).
• $PPP_{c,f}$ is the Pre-Project Productivity for commodity $c$ on field $f$ (Equation 1).
• The factor of 1.03 applies the 3% positive-leakage gate.

The total project-level productivity surplus for commodity $c$ is:

$$Total Surplus_{c,RP} = \sum_f \Delta P^{+}_{c,f,RP} \times A_{c,f,RP}$$

(Equation 7)

Where the sum is taken by over all fields $f$ within The Project that are eligible for positive leakage and are planted with commodity $c$ in the Reporting Period, and $A_{c,f,RP}$ is the area planted with commodity $c$ on field $f$ in the Reporting Period, in hectares.

Positive leakage may only offset negative leakage within the same commodity. A corn yield surplus cannot offset a soy yield shortfall as these are different markets with different supply chains and different land conversion intensities. Netting across commodities would conflate distinct market responses and risk underestimating actual leakage. A consequence of this rule is that shortfalls on a commodity grown nowhere else in The Project, including switched commodities, cannot be offset at all; this outcome is intentional and conservative.

Positive leakage may only offset negative leakage within the same Reporting Period. Positive leakage cannot be carried over to offset negative leakage in a subsequent (or prior) Reporting Period. For the purposes of this rule, the first assessment's retroactive lookback covering project years 1 through 5 constitutes a single assessment event, and netting operates across the full lookback.

The threshold tests and netting are applied in the following sequence for each commodity $c$ in each Reporting Period:

1. Calculate field-level shortfalls (Equation 3) and eligible field-level surpluses (Equation 6).
2. Aggregate to The Project level: gross shortfall (numerator of Equation 5), Total Surplus (Equation 7), and expected production (Equation 4).
3. Apply the Crediting Suspension Threshold to the gross shortfall ratio (Equation 5). Within-project surpluses do not offset the threshold test.
4. Apply within-project netting to obtain Net Project Productivity, $NPP_{c,RP}$ (Equation 8).
5. Apply the de minimis test to $NPP_{c,RP} / EP_{c,RP}$.
6. Where the de minimis threshold is exceeded, carry $NPP_{c,RP}$ forward to the induced land conversion calculation (Equation 9).

This sequence ensures the market-response model operates on the net supply impact after within-project mitigation, while the Crediting Suspension Threshold retains its function as a detector of systematic productivity failure.

Net Project Productivity for each commodity $c$ is calculated as:

$$NPP_{c,RP} = \max\left( \sum_f \left( \Delta P_{f,c,RP} \times A_{f,c,RP} \right) - Total\ Surplus_{c,RP},\ 0 \right)$$

(Equation 8)

Where:

• $\Delta P_{c,f,RP}$ is the productivity shortfall for commodity $c$ on field $f$ (Equation 3), in production units per hectare per year.
• $A_{c,f,RP}$ is the area planted with commodity c on field f in the Reporting Period, in hectares.
• $TotalSurplus_{c,RP}$ is the within-project positive-leakage surplus for commodity $c$ (Equation 7), in production units per year. The sum is taken over all fields $f$ within The Project planted with commodity $c$ in the Reporting Period.

The max ensures $NPP_{c,RP}$ is non-negative even when within-project positive leakage exceeds the gross shortfall.

Project Proponents are required to estimate the amount of new land brought into production, $ha_{LC}$. This estimate must be informed by:

• Pre-Project Productivity of a commodity type at The Project site (after accounting for system productivity);
• The estimated proportion of this productivity that would be replaced with new production via an increase in supply of the commodity type;
• The increase in supply that would result in new land being brought into production; and
• The yield of new land being brought into production.

The new land brought into production must be calculated separately for each commodity type being displaced as a result of The Project.

For each commodity $c$ with $NPP_{c,RP} > 0$, the hectares of induced land conversion are calculated as:

$$ha_{LC,c,RP} = \frac{NPP_{c,RP} \times IS_c \times NL_c}{Y_{NL,c}}$$

(Equation 9)

Where:

• $ha_{LC,c,RP}$ is the induced land conversion for commodity $c$ in the Reporting Period, in hectares.
• $NPP_{c,RP}$ is the Net Project Productivity, in appropriate units (e.g., tonnes per year).
• $IS_c$ is the Increased Supply fraction for commodity $c$, the proportion of the foregone deficit that will be replaced by increased supply elsewhere, calculated from supply and demand elasticities in accordance with Section 8.4.2.1 of this Module and Appendix D.
• $NL_c$ is the proportion of increased supply for commodity $c$ that will result in new land being brought into production, sourced in accordance with Section 8.4.2.2 of this Module and Appendix D.
• $Y_{NL,c}$ is the yield on new land brought into production for commodity $c$, in production units per hectare per year, determined in accordance with Section 8.4.2.3 of this Module.
• $c$ is commodity type.

Where The Project falls into regions for which Isometric has provided default $IS$ and $NL$ values in Appendix D, those default values must be used. For all other regions, values must be sourced from literature following the procedures set out in Appendix D.

Increased Supply ($IS$) is the proportion of foregone productivity that will be replaced by increased supply elsewhere. This is underpinned by the premise that foregone production will not necessarily be replaced in totality by increased supply elsewhere as a result of elasticities of supply and demand. Global markets for commodities have been assumed for the purposes of the leakage assessment.

Estimates for IS are determined using the following equation:

$$IS = \frac{\varepsilon_{s,c}}{\varepsilon_{s,c} + \lvert \varepsilon_{d,c} \rvert}$$

(Equation 10)

Where:

• $IS$ is increased supply.
• $\epsilon_{s}$ is elasticity in supply, as a ratio.
• $\epsilon_{d}$ is elasticity in demand, as a ratio.
• $c$ is commodity type.

Isometric has carried out a literature review of $\epsilon_{s}$ and $\epsilon_{d}$ values for certain regions. Values for $\epsilon_{s}$ and $\epsilon_{d}$ for these regions are provided in Appendix D. Where The Project falls into these regions, the default values provided must be used. This is because understanding which values to use from literature is challenging as academic papers are typically not written with this purpose or audience in mind. Isometric has completed this work for certain regions to lessen this complexity and provide consistency across projects.

The default values also serve as an example of appropriate values to select from the literature for other regions; however, it should be noted that the quality of research differs across regions. For all other regions, values for $\epsilon_{s}$ and $\epsilon_{d}$ must be sourced from literature. The procedure and requirements for sourcing default values for $\epsilon_{s}$ and $\epsilon_{d}$ are set out in Appendix D.

$NL$ considers the percentage of increased supply that will result in new land brought into production for the commodity type. This is underpinned by the premise that not all increased supply will result in new lands being brought into production. Some increased supply may be made up of intensification of activities and increased yields on existing production lands.

Isometric have carried out a literature review of $NL$ values for certain regions. Values for $NL$ for these regions are provided in Appendix D; their use is required where The Project falls into those regions, per Leakage Parameter Sourcing. The procedure and requirements for sourcing default values for NL are set out in Appendix D

The default values also serve as an example of appropriate values to select, however it should be noted that the quality of research differs across regions.

$Y_{NL,c}$ considers the yield on new land brought into production for commodity $c$. This is assessed based on the observed productivity in the region in the pre-project period. Here, the value of the regional mean yield ($\bar{Y}_{regional,c}$) used in Section 8.3.2.1 for assessing pre-project productivity must be assumed as the the value of $Y_{NL,c}$.

$EF_{Carbon Stock}$ must be derived from the IPCC average national aboveground biomass content of the land cover for the relevant ecosystem. Mean carbon stocks should be derived from aboveground biomass estimates in Table 3A.1.4 of the IPCC Good Practice Guidance for Land Use, Land Use Change and Forestry⁸.

Carbon stocks should be determined using the ratio of mass of CO₂ to mass of C, and carbon fraction, $CF$, specified for the relevant ecosystem/vegetation type by the IPCC.

Total leakage emissions for the Reporting Period are the sum of leakage across all commodities with a productivity shortfall:

$$CO_2e_{Leakage,RP} = \sum_c \left( ha_{LC,c,RP} \times EF_{Carbon Stock} \right)$$

(Equation 11)

Where:

• $CO_2e_{Leakage,RP}$ is the total leakage emission for the Reporting Period, in tonnes CO₂e.
• The sum is taken over all commodities $c$ for which $NPP_{c,RP} > 0$.

$CO_2e_{Leakage,RP}$ is included as part of $CO_2e_{Emissions,RP}$ as set out in the Improved Soil Management Protocol. The leakage discount is applied exclusively to removal credits; no leakage discount is applied to emission reduction credits issued under the Agricultural Practices Reductions Module.

$CO_2e_{Leakage}$ is quantified for every Reporting Period.

The Project Proponent must maintain the same set of commodities as the baseline rotation within a ±1 year tolerance over each 5-year window. For example, a baseline rotation of corn-corn-soy-corn-soy permits any combination that includes 2–4 years of corn and 1–3 years of soy within the next 5 years.

For fallow rotations, The Project can maintain the use of fallow rotations at the same historical rate. If the number of fallow rotations within The Project exceeds the historical rate (e.g., a third fallow rotation within a 5-year period when the baseline featured two), the forgone production for the additional fallow rotation is assumed to equal the average production of the most productive commodity grown on the field during the baseline period.

Crop changes beyond the ±1 year tolerance are permitted if pre-registered at least 1 month before planting with supporting evidence that the change is driven by regional market trends, agronomic factors, or farm-level economic conditions unrelated to The Project. Pre-registered crop changes that meet these criteria do not incur a leakage penalty.

Any unregistered crop change that results in a shift to a lower-value commodity or an increase in fallow rotations beyond the baseline rate must be treated as a productivity shortfall for leakage purposes. The forgone production is assumed to equal the average production of the most productive commodity grown on the field during the baseline period.

Crop rotation changes beyond the ±1 year tolerance may be accommodated without leakage penalty where The Project Proponent demonstrates that the change is driven by market, agronomic, or environmental factors unrelated to project enrollment. All changes under this provision must be pre-registered per the Pre-Registered Crop Changes requirements and must satisfy one of the following objective tests.

A switch from commodity $B$ to commodity $A$ is justified where the relative price of $A$ has materially risen against its baseline relationship:

$$PR = \frac{F_{A,t+1} / F_{B,t+1}}{\bar{P}_{A,bsl} / \bar{P}_{B,bsl}} > 1.2$$

(Equation 12)

where $F_{A,t+1}$and $F_{B,t+1}$ are harvest-time futures prices for the upcoming cultivation cycle, observed on the date of pre-registration, and $\bar{P}_{A,bsl}$ ​, $\bar{P}_{B,bsl}$ are average marketing-year prices over the baseline period (per USDA NASS or equivalent). This threshold is grounded in the empirical acreage-price response literature (Hendricks et al. 2014; Miao et al. 2016), which finds corn acreage responses of 7–9% to a 20% relative price increase.

A switch to a lower-nitrogen-requiring commodity is justified where regional nitrogen fertilizer prices exceed the baseline-period average by more than 40%, as measured by a published reference series such as the USDA AMS Illinois Production Cost Report (anhydrous ammonia) or an equivalent regional index, observed on the date of pre-registration. Switches to higher-nitrogen-requiring commodities are not eligible under this test regardless of nitrogen price movements, given the risk that improved soil condition attributable to The Project is enabling intensification.

A switch is justified where the Proponent documents (i) the pest, disease, or water constraint via USDA or state extension reporting, and (ii) a corresponding regional acreage response, demonstrated by a shift of at least 5% in USDA intended or observed planted acreage for the affected commodity in the relevant region.

A switch into a higher-value commodity (e.g., vegetables, cotton) is justified where the Proponent provides evidence of a secured offtake contract, a documented major shift in local demand, or comparative crop budgets demonstrating the economic case. Such transitions are presumed to increase the quality-adjusted quantity of agricultural output and therefore do not incur a leakage penalty, subject to the productivity assessment below.

No flexibility is available under this subsection for switches from higher-value commodities (grains, oilseeds, cotton, vegetables) to lower-value or lower-intensity uses (hay, alfalfa, grazing, fallow). Such changes are treated under the Unregistered Crop Changes and fallow provisions regardless of registration or supporting evidence.

Where a field has been assessed a leakage penalty for commodity $c$ in the most recent Reporting Period in which $c$ was grown, and the field subsequently switches away from $c$ under any provision of this subsection, the most recent leakage penalty assessed for $c$ must be carried over and applied to that field for the first year following the switch, calculated per Section 8.4. This prevents a productivity-shortfall history from being extinguished through crop switching.

Where a field transitions to a commodity $c'$ not present in the baseline rotation, no direct $PPP_{f,c'}$​ exists. The Pre-Project Productivity for the new commodity shall be constructed by transferring the field's demonstrated relative productivity to the new crop:

$$PPP_{f,c'} = \bar{Y}_{\text{regional,c'}} \times \overline{YR}_{f}$$

(Equation 13)

where $\overline{YR}_{f}$ is the acre-year-weighted average of $YR_{f,c}$ across all commodities $c$ in the field's baseline rotation, and $\bar{Y}_{\text{regional,c'}}$ is the long-run average regional yield for $c'$, fixed per the existing multiplier convention. $PSP_{f,c',RP}$​ is then calculated per Equation 2 once yield observations for $c'$ accrue, and the productivity shortfall per Equation 3.

Fields may not generate positive leakage (productivity surplus) for a switched commodity during its first five years on the field. Additionally, because within-project netting requires commodity matching, shortfalls on a switched commodity that is not grown elsewhere in The Project cannot be offset by surpluses on other commodities.

Where $PSP$ is significantly lower than projected due to exogenous natural causes beyond The Project's reasonable control (extreme weather, drought, flooding, region-wide pest or disease outbreaks), The Project Proponent may request that $PSP$ be evaluated in a broader regional context.

The regional indexing approach (Equations 1–2) already controls for most region-wide shocks; if the field and the region both experience a drought, the yield ratio ($Y_{field}/Y_{regional}$) is largely unaffected. The exceptional circumstances provision therefore applies to localised events that affect The Project field but not the broader region.

Evidence must demonstrate that:

1. The cause of the $PSP$ shortfall is unrelated to project design, management decisions, or land-use change; and
2. Comparable agricultural systems in the surrounding region did not experience similar yield impacts during the same period.

Evidence may include regional or county-level yield statistics, meteorological or disaster records, government or insurance reports, and peer-reviewed or authoritative third-party data.

Where Isometric determines that the exogenous cause contributed to $PSP$ shortfalls, Isometric may adjust the $PSP$ by applying a counterfactual estimate that reflects the localized nature of the impact for the purposes of leakage assessment.

If a crop failure occurs in a Reporting Period, The Project Proponent must provide evidence that the failure was caused by factors outside The Project's control (e.g., extreme weather, pest outbreak). If accepted as a genuine crop failure:

• For commodities present in the baseline rotation, the field's yield for that Reporting Period is imputed as $\bar{Y}_{regional,c,t}×YR_{f,c}$ the regional yield for that year scaled by the field's pre-project yield ratio. This prevents a crop failure from inflating the productivity shortfall while remaining consistent with the regional indexing applied throughout the productivity assessment.
• For switched commodities with no baseline observations (i.e., those assessed under the Section 8.5.5 Legitimate Reasons for Cropping Change provisions), the field's yield is imputed as $\bar{Y}_{regional,c,t}×\overline{YR}_{f}$ consistent with the transferred-ratio approach used to construct $PPP_{f,c′}$​.

Setup: A project with 100 hectares enrolled in a corn-soy rotation. Baseline $PPP$ (regionally indexed): corn = 180 bu/ha, soy = 50 bu/ha. $IS$ = 0.70 (global calories, from Appendix D), $NL$ = 0.28 (US cropland, from Appendix D). $Y_{NL}$ = 150 bu/ha (corn) and 45 bu/ha (soy). $EF_{Carbon Stock}$ = 200 t CO₂e/ha.

Reporting Period 1 (Corn Year):

• PSP (regionally indexed = 170 bu/ha. Decline = (180 − 170) / 180 = 5.6%.
• Exceeds 3% de minimis gate. Leakage assessed on the full shortfall: $\Delta P$ = 180 − 170 = 10 bu/ha.
• $NPP$ = 10 × 100 ha = 1,000 bu/yr.
• $ha_{LC}$ = (1,000 × 0.70 × 0.28) / 150 = 1.307 ha.
• $CO_2e_{Leakage,1}$ = 1.307 × 200 = 261.3 t CO₂e.

Reporting Period 2 (Soy Year):

• PSP (regionally indexed) = 46 bu/ha. Decline = (50 − 46) / 50 = 8%.
• Exceeds 3% de minimis gate. Leakage assessed on the full shortfall: $\Delta P$ = 50 − 46 = 4 bu/ha.
• $NPP$ = 4 × 100 ha = 400 bu/yr.
• $ha_{LC}$ = (400 × 0.70 × 0.28) / 45 = 1.742 ha.
• $CO_2e_{Leakage,2}$ = 1.742 × 200 = 348.4 t CO₂e.

Note: the soy-year leakage is larger than the corn-year leakage despite a smaller absolute shortfall in bushels (4 vs 10), because soy has a lower yield on new land ($Y_{NL}$ = 45 vs 150), meaning more land must be converted per unit of displaced soy production. Each Reporting Period is assessed independently against the baseline.

Reporting Period 3 (Corn Year):

• PSP (regionally indexed) = 176 bu/ha. Decline = (180 − 176) / 180 = 2.2%.
• Below 3% de minimis gate. No leakage assessed.
• $CO_2e_{Leakage,3}$ = 0.

Note: the 2.2% corn yield decline in RP3 is within the 3% de minimis threshold. The decline is treated as natural year-to-year variability rather than a project-induced productivity loss. Because the de minimis threshold is a gate (not a deduction), no portion of the 2.2% decline is subject to leakage; the entire Reporting Period is leakage-free for corn.

The net removals are calculated following the requirements within the Improved Soil Management Protocol. Under this Module, the carbon storage ($CO_2e_{stored}$) is considered to consist of soil organic carbon. Terms for aboveground and belowground woody biomass must be set to 0.

The following calculations apply equally to both Quantification Approach 1 (direct measurement and remeasurement, Section 9.1.2) and Quantification Approach 2 (biogeochemical modeling with remeasurement, Section 9.1.3). They consume the stratum level SOC stock outputs from either approach and produce The Project-level total stock and cumulative stock change used for credit issuance.

The Project-level total carbon stock at any time $t$ is expressed as total carbon mass of soil organic carbon across all strata within the primary quantification unit:

$$CO_2e_{stored,t} = \sum_{j=1}^{J} \left( SOC_{j,t} \times A_j \right)\times \frac{44}{12}$$

(Equation 14)

Where:

• $CO_2e_{stored,t}$ is The Project-level carbon stock at time $t$ in tonnes CO₂e
• $SOC_{j,t}$ is the SOC stock in stratum $j$ at time $t$ in tonnes C ha^-1
• $A_j$ is the area of stratum $j$ in ha
• $J$ is the total number of strata
• $\frac{44}{12}$ is the mass ratio for converting carbon to CO₂e

$SOC_{j,t_n}$ is substituted as follows depending on the quantification approach and crediting event type. All substitution targets are calculated on an equivalent soil mineral mass (ESM) basis using the reduced cumulative-mass formulation across the depth increments required under Section 9.1.2.4, with a single stratum-level reference mineral mass and linear interpolation between increments as defined in Equations 18–19, and with parameters estimated from the sampled depth increments set out in that Section.

• Approach 1, all events: $SOC_{j,t_n}$ from Equation 19 (ESM-corrected direct measurement on a mineral-mass basis).
• Approach 2, interim crediting events: $SOC^{model}_{j,t_n}$ from Equation 21 (mineral-mass model output using baseline reference mineral mass).
• Approach 2, true-up events: $SOC^{model,ESM}_{j,t_n}$ from Equation 22 (ESM-corrected mineral-mass model output using true-up-updated reference mineral mass; ).

The values of $CO_2e_{stored,t}$ are then used in Equation 6 of the Improved Soil Management Protocol to derive the Reporting-Period change in stored carbon ($CO_2e_{stored,RP}$), which feeds Equations 3 and 4 of the Protocol to calculate net removals ($CO_2e_{Removal,RP}$).

• It does not require the assumption that measurement or model errors are uncorrelated beyond the field level, making it more appropriate for the structured spatial and temporal error patterns present in both direct measurement campaigns and biogeochemical SOC models;
• It can directly estimate the correlation of errors across years, avoiding underestimation of uncertainty on shorter time horizons where annual errors may be positively correlated;
• Where implemented as a hierarchical bootstrap (see below), it requires minimal assumptions about the shape of the underlying data distributions, which is particularly important for paired SOC stock differences that are typically non-normal and exhibit short-range spatial autocorrelation.

Monte Carlo simulation must be implemented as follows:

• G-5V3Y-0An appropriate number of iterations must be performed. Project Proponents must demonstrate convergence by showing that the uncertainty estimate does not materially change (> 1%) with additional iterations.
• G-0SPX-0Each iteration of the Monte Carlo simulation must be independent of other iterations. Within a given iteration, the sources of uncertainty contributing to the credit estimate must be sampled jointly, with correlations between sources preserved in accordance with the correlation requirements set out below; the requirement of inter-iteration independence does not imply that sources sampled within an iteration are independent of one another. The materially uncertain sources sampled at each iteration may include
  • (i) input feature uncertainty, sampled from the probability distributions of materially uncertain inputs;
  • (ii) model parameter or weight uncertainty, sampled from the posterior distribution for Bayesian models or via bootstrap resampling for frequentist methods;
  • (iii) ensemble-member sampling, where the ensemble represents diversity in training data, hyperparameters, or architecture;
  • or (iv) any combination of these, depending on which sources are material to the model in use. The relevant sources differ by approach as specified below.
• G-D4V8-0Input probability distributions must be justified with reference to empirical data or published literature and must not be assumed normal without justification. Where distributions are non-normal, appropriate alternative distributions must be used.
• G-MEJJ-0Correlations between uncertain inputs must be explicitly assessed and, where material, accounted for through appropriate joint sampling procedures rather than assuming independence.
• G-83E7-0The correlation structure of errors across time periods must be characterized and incorporated into the simulation, to avoid underestimation of cumulative uncertainty over multi-year Reporting Periods.

The following uncertain inputs must be characterized and included in the Monte Carlo simulation:

• In the event that any sampling locations are substituted during a given Reporting Period (see Section 9.1.2.2.4), the unpaired component of variance for these locations must be propagated separately from the paired component, with the two components using sample-count weighting. The procedure for this must be clearly documented.
• Survey uncertainty arising from estimating stratum-level mean SOC stocks from a finite sample of locations or modeled units within each stratum must also be characterized and propagated, distinct from per-location measurement and analytical uncertainty.
• Model-remeasure only: interim crediting events: model prediction error (characterized by RMSE from the validation dataset); where residuals are approximately symmetric (|skewness| ≤ 0.5), RMSE may be used as the basis for a symmetric error distribution; where significant skewness is detected under Section 9.1.3.2.3, model prediction error must instead be sampled from a fitted asymmetric distribution (e.g., empirical bootstrap of validation residuals or a parametric skew-normal distribution) that preserves the observed tail behavior parameterization uncertainty; and temporal error correlation across modeled time steps between resampling campaigns. Note that at interim events there is no measured ESM, so the reference ESM carries uncertainty from the original baseline measurement.
• Model-remeasure only: true-up events: model prediction error, characterized as set out above for interim crediting events and updated through the true-up procedure in Section 9.1.3.1.6, and parameterization uncertainty as similarly updated through the true-up procedure in Section 9.1.3.1.6. Measured ESM from the resampling campaign replaces the reference value, reducing uncertainty relative to interim events.

The conservative estimate used for credit issuance must correspond to the 30th percentile of the distribution of net CO₂e removal estimates across all simulation iterations, in line with the Isometric standard. Where the 30th percentile estimate is negative, no credits may be issued for that Reporting Period.

Where model-based error propagation is used under Measure-Model, the coverage of model predictions must be evaluated at each true-up event. Where coverage is materially below the nominal confidence level, the input uncertainty distributions must be revised before the simulation is rerun. This requirement does not apply under Measure-Remeasure.

For propagating sampling and analytical uncertainty under both Approach 1 and Approach 2, Project Proponents are recommended to implement a hierarchical bootstrap Monte Carlo procedure, since it makes minimal assumptions about the shape of the underlying data distributions and naturally captures within-stratum and between-stratum variability. The procedure has three steps:

Step 1: Location resampling within strata. Within each stratum $j$, resample sampling locations with replacement, drawing $n_j$ locations where $n_j$ is the number of sampling locations in stratum $j$. Resampling is performed on locations (not on individual cores or depth increments), so that the paired structure ($t_0$ and $t_n$ measurements at the same location) is preserved within each bootstrap iteration. Location resampling at stratum $j$ may only be applied where rarefaction analysis (see Step 4 below) demonstrates that $n_j$ supports stable estimates; otherwise The Project must fall back to a parametric Monte Carlo at the stratum level with explicit assumptions documented and reported at each Verification.

Step 2: Observation perturbation. For each resampled location, perturb the measured SOC concentration and soil mineral mass per depth increment by drawing from the analytical uncertainty distribution. Where a location is drawn more than once in a single bootstrap iteration, the same analytical perturbation must be applied to all repeated draws of that location, so that analytical noise is not double-counted as sampling variance. Where significant spatial autocorrelation is detected (see Step 3), the effective sample size $n_{eff}$ replaces $n_j$ in the resampling count for that stratum.

Step 3: Spatial autocorrelation test. Within each stratum, test for spatial autocorrelation in the location-level paired SOC stock differences using Moran's $I$ or the empirical SOC residual variogram. Where significant autocorrelation is detected at the typical inter-location distance, compute an effective sample size $n_{eff}$ for that stratum using a documented adjustment (e.g., Cressie's variance-inflation formula based on the fitted variogram), and substitute $n_{eff}$ for $n_j$ in Step 1.

Step 4: Stratum and project aggregation. Compute the stratum-level mean SOC stock change from the resampled locations. Compute The Project-level mean as the area-weighted average of stratum-level means. Repeat steps 1–4 across iterations until the 30th-percentile estimate (see below) has converged within the 1% threshold of Section 9.1.1.

Parametric fallback. Where the number of sampling locations in a stratum is too small to support stable bootstrap estimates, or where the rarefaction analysis fails (see below), Project Proponents may fall back to a parametric Monte Carlo simulation under Section 9.1.1, drawing from a stratum-level distribution justified against empirical data or literature. The fallback must be flagged in the Monitoring Report and a conservative additional uncertainty penalty applied at Isometric's direction.

Direct measurement is used to quantify changes in SOC stocks under this approach. This approach is applicable where predictive models are unavailable, have not been validated for the relevant soil type or land management context, or have not been sufficiently parameterized for The Project area. Project Proponents may also elect to use direct measurement where they prefer not to rely on modeled outputs for SOC stock change quantification.

Under this approach, baseline SOC stocks within The Project Area are established through direct field measurement at project initiation ($t_0$) and re-measured at each subsequent Reporting Period. The counterfactual SOC trajectory is established through paired measurement of business-as-usual control plots, as set out in Section 9.2.1. This approach is applicable to the quantification of SOC stock changes and is not used for other GHG sources or sinks within the system boundary, which are quantified separately under Section 9.5 of the Improved Soil Management Protocol.

Project Proponents are encouraged to directly adopt or adapt QA/QC procedures from established published frameworks, including those produced by the Food and Agriculture Organization of the United Nations (FAO) and available via the FAO Soils Portal, the ISO soil sampling standards (including ISO 18400-104: Soil Quality - Sampling - Part 104: Strategies), or the IPCC Good Practice Guidance for Land Use, Land-Use Change and Forestry (2003).

The following requirements apply to all sampling and re-sampling campaigns:

• Sample locations must be georeferenced to a horizontal accuracy of 3 m (or better) to enable re-sampling at the sampling locations set at project initiation during subsequent Reporting Periods;
  • At each re-sampling, samples must be collected within a 5 m radius of the initial location set at project initiation, making sure to avoid the exact location of any prior sampling disturbance, with the coordinates of the new sample collected and recorded in the Monitoring Report;
• Intra-annual variability must be considered in the sampling design, and all sampling and re-sampling campaigns must be conducted during the same crop phase (e.g., pre-planting, post-harvest) and within +/- 30 days across Reporting Periods to ensure comparability;
• Where organic amendments have been applied, Project Proponents must delay sampling or re-sampling to the latest practicable point after the previous application and the earliest practicable point before the next application, in order to minimize confounding effects on measured SOC stocks.
• Sample locations should avoid areas subject to localized edge effects that would render them unrepresentative of the stratum as a whole, such as field margins, tramlines, or gateways. Any such exclusions must be clearly disclosed, justified, and spatially documented.

The Project Proponent must define a hierarchy of quantification units in The Project Monitoring Plan. At minimum, this must specify:

• The primary quantification unit: the unit at which credits are issued and uncertainty is reported. This is typically The Project Area or, for grouped projects, an enrolled property or farm.
• The sampling unit: the unit at which sampling locations are selected and at which a single SOC stock estimate is generated. Sampling units must nest within primary quantification units.
• Where applicable, intermediate units (e.g., field, management zone) used to organize the design.

The choice of units must reflect the management, soil, and climatic heterogeneity of The Project area and the practical constraints of sampling, including for projects involving smallholders. Sub-field stratification is not required where a coarser unit can be shown to deliver equivalent or better project-level precision.

The default sampling design is stratified random sampling, in which each sampling unit is divided into strata more homogeneous in expected SOC than the unit as a whole. Project Proponents may instead propose:

• Conditioned Latin hypercube sampling (cLHS) or other covariate-informed probability sampling designs, where ancillary variables (e.g., elevation, slope, remote-sensed indices, digital soil maps) are available and shown to correlate with SOC stocks; or
• Model-based sampling designs (e.g., spatial coverage sampling, geostatistical designs) where The Project intends to use a model-based estimator at The Project level.

Any design must be documented in The Project Design Document and justified with reference to peer-reviewed literature. Grid sampling and unstratified simple random sampling are not permitted. Project Proponents should consider, as part of the design choice, the robustness of the design to anticipated point-level data loss arising from withdrawal, sampling-access loss, or laboratory error. A design with a large number of fine-grained strata can be more sensitive to such losses than a coarser design

Where stratified random sampling is used in a paired-difference design (i.e., the same locations are resampled across Reporting Periods), stratification factors should be selected for their expected influence on the rate of SOC change under project management, not on absolute baseline SOC levels. Within-location baseline absolute SOC largely cancels out in the paired difference; residual within-stratum variance is dominated by heterogeneity in management practice intensity, climate, soil texture, and initial SOC saturation. Where The Project area is homogeneous in these factors, a coarser sampling unit may deliver equivalent project-level precision to a finely stratified design.

Project Proponents must use the sample design set at project initiation for sampling during subsequent Reporting Periods. Sampling locations may be substituted over the course of The Project Commitment Period only when there are documented barriers, including:

• Loss of contractual access (e.g., disenrollment of a property)
• Active hazards that prevent access to sampling site (e.g., natural disaster)

In such a scenario, the sampling location must be replaced with another location in the same stratum selected using the same probability sampling rule that was applied at project initiation. The substitution must be clearly documented in the Monitoring Report for review as part of the subsequent Verification. Uncertainty associated with the substitution also must be accounted for following the requirements in Section 9.1.1. This substitute sampling point must then be used for the remainder of the Reporting Period.

Project Proponents must track all substitutions over the entire Project Commitment Period. If the number of substitutions within a single stratum exceeds 15% of sampling points within that stratum within a single Reporting Period, or cumulatively exceeds 25% of the sampling points within that stratum over The Project Commitment Period, the stratum must be re-evaluated in accordance with Section 9.1.2.2.5.

If the sampling location is only temporarily inaccessible (e.g., temporary flooding) or data collection was missed because of a documented operational issue (e.g., unforeseen capacity issues that did not allow sampling in the temporal window), the sampling location will be considered missing for the given Reporting Period. These locations may be excluded from the stratum-level paired-difference calculation for that Reporting Period and uncertainty appropriately handled. These missing sampling points must be clearly documented at the relevant Verification. If data are missing for a sampling location during a second consecutive Reporting Period, the sampling point must be substituted.

Strata must be defined to support direct comparison of SOC stocks across Reporting Periods. Project Proponents may re-aggregate, split, or otherwise revise strata at a Reporting Period where this improves project-level precision, where field divergence under management has rendered the original stratification non-homogeneous, or where additional data has become available. Any change must:

• Preserve a documented mapping between the previous and revised stratification, so that prior measurements can be reconciled to the new design;
• Demonstrate that the revised design does not selectively exclude areas of expected SOC loss; and
• Be reported in the Monitoring Report and approved at verification.

Project-specific strata, their areas, the sampling locations within each, and any revisions across Reporting Periods must be reported as an annex to project documentation at every verification.

The requirements of this Section govern the design of the sampling campaign and, specifically, whether the campaign supports reliable estimation of the change in SOC stocks within each stratum. They do not determine the size of the uncertainty discount applied to issued credits, which is set separately under Section 9.1.1. Project Proponents are recommended to conduct a formal power analysis to set the number of samples at design stage; a recommended minimum-detectable-difference (MDD) power-analysis procedure is set out in Appendix E. This procedure can help ensure a well-powered design that can decrease the associated uncertainty.

A minimum of three composite samples per stratum is required as an absolute floor to support variance estimation, since a within-stratum standard deviation cannot be computed from fewer than three observations. This minimum enables estimation only; it is not a representation of the sample size needed for a well-powered design, which is determined by the rarefaction analysis below (and, as a design aid, by the recommended power analysis in Appendix E) and is typically substantially larger. Strata containing fewer than three samples must be pooled with the most similar neighboring stratum for estimation, following a documented and reproducible pooling rule.

Beginning at the first re-sampling event ($t_1$), Project Proponents must conduct a rarefaction analysis to demonstrate empirically that each stratum's sample count supports stable estimates of the mean SOC stock change. The rarefaction procedure is:

1. For each stratum, compute the bootstrap mean of the location-level paired SOC stock difference $\Delta SOC_{loc} = SOC_{loc,t_n} - SOC_{loc,t_0}$ using progressively larger random subsets of the available sampling locations, starting from a minimum subset of 5 locations and increasing in increments of 1 (or 5, where the stratum contains > 50 locations) up to the full sample set.
2. Repeat each subsample size at least 200 times to generate a distribution of bootstrap means at each subsample size.
3. Identify the smallest sample size $n_j$ at which the width of the 70% confidence interval of the bootstrap mean changes by less than 5% of the overall observed range of $\Delta SOC_{loc}$ in the stratum, for all subsequent increments. The stratum's design sample size $n_j$ must equal or exceed $n_j$.

Where any stratum fails the rarefaction criterion (no $n^*_j \leq n_j$ exists), location resampling under Section 9.1.1 Step 1 is not permitted for that stratum at that event, and the parametric fallback under Section 9.1.1 applies. The Project Proponent must increase $n_j$ for the next Reporting Period to meet the rarefaction criterion.

Soils must be sampled using a minimum of two depth increments across the sampled profile at all sampling locations. The number of increments and the depths at which the profile is subdivided may be selected by The Project Proponent, provided at least two increments are used and the choice is documented and justified at validation (for example, with reference to expected vertical distribution of SOC change, soil horizons, rooting depth, or tillage depth). A default subdivision of 0–15 cm and 15–30 cm is recommended where The Project Proponent has no project-specific basis for an alternative. Where soils at a location are shallower than the sampled depth, the deepest increment must be reported to the sampled depth and documented. SOC content analysis may be performed on a single composited sample per increment provided soil mass data are recorded separately for each increment.

Projects must apply an ESM correction. The ESM correction must be applied across all sampled depth increments using soil mass data from all sampling locations.

For sampling events post-project initiation, this may require extension (up to 10 cm) to a further depth in order to capture the mass established at project initiation. The actual depth of collection must be recorded and reported, and the additional mineral mass below the nominal increment boundary must be included in the ESM correction calculation for the deepest increment. Where a 10 cm additional buffer does not capture an equivalent mass of soil compared to project initiation, the deepest increment must be reported on a fixed-depth basis and documented as such. Note that baseline measurements at project initiation do not require multiple depth increments for ESM purposes within a given depth band.

The credited cumulative SOC stock change must be calculated on this basis.

The following requirements apply:

• Both the intended and actual sampling point locations must be recorded and georeferenced.
• SOC content, oven-dry fine soil mass, and sample volume must be obtained from the same sample or from adjacent samples taken during the same sampling event, if the sample size is insufficient. Where multiple cores are combined into a single sample, all cores must be taken from the same depth and fully homogenized prior to subsampling for the different measurements and this must be disclosed in the documentation of the sampling procedures.
• Soil cores must be collected using equipment of documented cross-sectional area. The auger or core diameter and cross-sectional area used at each sampling event must be recorded and reported at each Verification event. Core dimensions must be held constant across all sampling events within a project, including between baseline and all subsequent remeasurement campaigns. Where a change in core dimensions is unavoidable, The Project Proponent must document the change, demonstrate that sampled volumes remain comparable, and propagate any resulting volume uncertainty through the ESM correction and the Section 9.1.1 Monte Carlo simulation.

All organic material (e.g., living plants, crop residue) must be cleared from the soil surface prior to sampling. This must be documented via a photograph taken after sample removal.

• SOC stocks within each stratum must be calculated on an equivalent soil mineral mass (ESM) basis. Use of soil mineral mass, rather than fine soil mass, ensures that the reference quantity against which SOC stocks are normalized is itself insensitive to changes in soil organic matter content driven by project activities. Sampling events are denoted $t_n$, where $t_0$ is the baseline measurement at project initiation and $t_1, t_2,$... are subsequent resampling events.

Fine soil mass per unit area. For each depth increment $d$ in stratum $j$ at sampling event $t_n$​, the oven-dry fine soil mass per unit area is:

$$M_{fine,j,d,t_n} = \frac{m_{fine,j,d,t_n}}{\pi \left(\dfrac{\phi}{2}\right)^{2} \times N} \times 1000$$

(Equation 15)

Where:

• $M_{fine,j,d,t_n}$​​ is the oven-dry fine soil mass per unit area in depth increment $D$ of stratum $j$ at sampling event $t_n$​ (kg m⁻²);
• $m_{fine,j,d,t_n}$ is the oven-dry mass of the fine (< 2 mm) soil recovered for that increment ($g$), after exclusion of coarse material retained on the 2 mm sieve;
• $\phi$ is the internal diameter of the auger or corer (mm);
• $N$ is the number of cores composited into the sample, such that $π(ϕ/2)2×N$ is the total cross-sectional area sampled (mm²);
• the factor 1000 converts g m⁻² to kg m⁻².

Coarse material must be prevented from passing through the 2 mm sieve. Deriving the fine soil mass per unit area directly from sample mass and corer cross-sectional area in this way eliminates the need for independent bulk-density sampling and the imprecision associated with it (Wendt & Hauser 2013). This is the reason core dimensions ($\phi$) and the compositing design (N) must be documented and held constant across sampling events.

Soil mineral mass per unit area. The soil mineral mass per unit area for each depth increment is derived from the fine soil mass per unit area, adjusted to exclude the mass of soil organic matter:

$$M_{min,j,d,t_n} = M_{fine,j,d,t_n} \times \left(1 - 1.724 \times C_{j,d,t_n} \times 10^{-3}\right)$$

(Equation 16)

Where:

• $M_{fine,j,d,tn}$ is the oven-dry fine soil mass per unit area as derived in Equation 15 (kg m⁻²);
• $C_{j,d,t_n}$ is the measured SOC concentration in that increment (g C kg⁻¹ fine soil); and
• 1.724 is the van Bemmelen conversion factor from organic carbon mass to organic matter mass. Where Project Proponents have project-area-specific data on the carbon fraction of soil organic matter, that value may be substituted for 1.724 with justification at validation. This protocol is only scoped to mineral soils.

Sampled volume, where required, is the product of the cross-sectional area sampled and the increment depth. ESM, where reported, is a derived quantity calculated as the oven-dry fine soil mass divided by the sampled volume; it is used only for reporting and diagnostic purposes. Neither bulk density nor sampled volume is used as a direct input for the quantification calculations.

Drying and sieving procedures must follow laboratory-specific standard operating procedures (SOPs) and must be applied consistently across all samples throughout The Project lifetime, including where there is a change in analytical laboratory. Sample processing procedures must be reported in detail, explicitly describing sieving and grinding procedures.

"Composite" in this Section refers to the physical homogenization of multiple cores collected at a single sampling location, prior to laboratory analysis. The composite is the analytical sample; the sampling location is the statistical unit. Pairwise SOC stock differences computed at the sampling-location level (the difference between the composite measurement at $t_n$ and at $t_0$) are the basis for stratum-level variance estimation in Section 9.1.2.3. Project Proponents are not required to physically pool samples across sampling locations or across strata at any point.

Composite sample (composite). A single analytical sample formed by physically combining and fully homogenizing two or more soil cores collected at the same sampling location and from the same depth increment, prior to laboratory analysis or subsampling. The composite sample is the analytical unit, the specimen on which SOC concentration and soil mass are measured, whereas the sampling location is the statistical unit at which SOC stocks are estimated and at which paired ($t_0$ vs $t_n$​) stock differences are computed for variance estimation under Section 9.1.2.3.

Compositing is a physical operation and is distinct from statistical pooling. Cores must not be combined across different sampling locations, across different depth increments, or across strata. Where cores are composited, all constituent cores must derive from the same sampling location and the same depth increment and must be fully homogenized before any subsampling for separate measurements; soil mass data must be recorded separately for each depth increment even where SOC content is determined on a single composite per increment (Section 9.1.2.4).

Following sampling, samples may be temporarily stored on-site in a location protected from sunlight, humidity, and precipitation, with different soil materials kept separate. Soil samples must be shipped within five days, or stably stored (e.g., dried or refrigerated, but not frozen) in a way that will be maintained until analysis. Once shipped, samples must be stored under environmentally controlled conditions that minimize biological activity (e.g., dried or refrigerated, but not frozen) until analysis. The duration of refrigerated storage prior to analysis must not exceed three months.

All samples collected throughout The Project lifetime should be analyzed by the same laboratory wherever practicable. A transition to a different laboratory is permitted, including in cases where the incumbent laboratory is no longer able or willing to process project samples, provided that:

• The change is justified in writing and documented for review at the subsequent verification;
• The replacement laboratory satisfies the accreditation requirements set out above; and
• A documented cross-calibration procedure is completed prior to the new laboratory's results being used for quantification. The cross-calibration must involve parallel analysis of a representative set of samples (including, where available, archived samples and certified reference materials) by both laboratories, with results compared against pre-defined acceptance criteria for bias and precision. Any systematic offset identified must be addressed through method alignment or a documented correction, and the cross-calibration report must be submitted as part of project documentation.

The selected laboratory must quantify and report analytical error statistics to The Project Proponent on a regular basis, derived from repeated analyses of the same sample and from analyses of certified reference materials. The laboratory must provide documentation of its internal quality control program, including:

• Use of certified soil reference materials with known SOC content (e.g., NIST SRM 2710, BCR-129, or an equivalent traceable certified reference material appropriate for the SOC content range of The Project samples);
• Monitoring of variation in analysis against defined error thresholds; and
• Participation in external proficiency testing schemes (e.g., round-robin testing) or registration as a member of the Global Soil Laboratory Network (GLOSOLAN), or an equivalent nationally recognized program.

Quantification of analytical uncertainty for Monte Carlo propagation. For each batch of project samples, the analytical uncertainty distribution used to perturb SOC concentration measurements in the Monte Carlo simulation (Section 9.1.1) must be characterized as follows:

1. Systematic bias correction. Identify any systematic bias in the analytical run against the certified reference material(s), expressed as the mean signed difference between measured and certified SOC values. Where systematic bias exceeds the CRM's certification uncertainty, project sample measurements must be corrected for the bias and the residual uncertainty (after correction) documented in the Monitoring Report.
2. Random error characterization. The random analytical error must be characterized as the greater of:

• (i) the relative standard deviation (RSD) of laboratory duplicate analyses on project samples within the same analytical batch, expressed as a fraction of the measured value; or
• (ii) the relative error derived from CRM recovery, i.e., the standard deviation of CRM measurements within the batch divided by the certified value.

3. Monte Carlo input distribution. The random error from Step 2 must be used to parameterize a mean-zero perturbation around each measured SOC concentration value within the Monte Carlo simulation under Section 9.1.1. The distribution may be assumed Gaussian provided the laboratory's quality-control data support this assumption; where the duplicate/CRM distribution is materially non-Gaussian, the empirical distribution must be used directly via inverse-transform sampling or a documented alternative.

A minimum of 5% of project samples within each analytical batch must be analyzed as laboratory duplicates. Where the batch size is small (< 20 samples), a minimum of three duplicates must be analyzed regardless of percentage.

The Project Proponent must retain run-level analytical data for all project samples, including individual measurement results, replicate analyses, calibration records, reference material results, and associated QC flags, and ensure data is available for audit by Isometric or a VVB. Quality control documentation, together with any cross-calibration reports, must be submitted as an annex to project documentation at each verification.

Where a Project Proponent utilizes an accredited laboratory for all analyses, The Project Proponent must provide raw data files to Isometric and the VVB, if requested.

Where a Project Proponent utilizes laboratory facilities within an academic institution or a non-accredited commercial laboratory, this Protocol requires that 10% of samples are sent to an accredited laboratory for validation. External laboratory validation is required for both quantification and validation samples in this scenario. The analyses conducted by the third party lab must be sufficient to calculate gross CDR and confirm the analytical results of the academic or non-accredited institution.

Prior to data submission, The Project Proponent is required to identify the sampling locations that will be sent to a third part laboratory to Isometric and the VVB for approval. The third party facility must be approved by Isometric. Data from the third party validation must be sent directly to Isometric from the accredited laboratory.

If the results of the third party validation show significant discrepancies with the overall dataset, an audit will be conducted by Isometric and the VVB. As part of this audit, Isometric or the VVB may request that additional samples are sent for third party validation. In this instance, Isometric or the VVB will select the samples for validation. Other materials that may be requested in an audit include:

• Run logs of analytical instruments within the academic or non-accredited facility;
• Information on sample preparation, such as laboratory notebooks and SOPs;
• Chain of custody of the samples analyzed;
• Raw, uncorrected data files for each sample analyzed.

SOC content must be measured using dry combustion (Dumas method) with known and reported measurement uncertainty from the specific analytical run (i.e., default values not accepted).

Walkley-Black (wet) oxidation and loss on ignition (LOI) are not permitted except where no other analytical method is available, in which case their use must be justified in The Project Monitoring Plan and approved at validation. Approval may require a subset of samples to be sent for analysis using dry combustion for calibration and validation of these methods. Project Proponents must document the known limitations of these methods and apply appropriate uncertainty adjustments to the resulting SOC stock estimates.

The ESM-corrected SOC stock density is obtained by expressing cumulative SOC mass as a function of cumulative soil mineral mass and evaluating it, by linear interpolation between measured increments, at a fixed reference mineral mass common to all sampling events. This is the reduced cumulative-mass formulation common to the methods of Gifford & Roderick⁹ and Ellert & Bettany¹⁰ as set out in Wendt & Hauser¹¹. A single method is prescribed and conservatism is applied at the crediting step (the 30th-percentile estimate under Section 9.1.1).

Increment quantities. For each depth increment dd d in stratum $j$ at sampling event $t_n$​, and at each sampling location, compute the soil mineral mass per unit area $M_{min,j,d,t_n}$ (Equation 16) and the SOC mass per unit area:

$$M_{C,j,d,t_n} = C_{j,d,t_n} \times M_{fine,j,d,t_n}$$

(Equation 17)

where $C_{j,d,t_n}$ is the measured SOC concentration (g C kg⁻¹ fine soil), $M_{fine,j,d,t_n}$​​ is the fine soil mass per unit area (Equation 15, kg m⁻²), and $M_{C,j,d,t_n}$ is the SOC mass per unit area for the increment (g C m⁻²).

Cumulative profiles. At each sampling location, accumulate mineral mass and SOC mass from the soil surface to the bottom of each increment $d$:

$$\text{cumulative mineral mass to } d = \sum_{k=1}^{d} M_{min,j,k,t_n} \qquad \text{cumulative SOC mass to } d = \sum_{k=1}^{d} M_{C,j,k,t_n}$$

Reference mineral mass. Define the reference soil mineral mass for stratum $j$ as the minimum, across all sampling events, of the total cumulative soil mineral mass of the stratum-mean profile:

$$M_{\min,\mathrm{ref},j} = \min_{t_n} \left( \sum_{k=1}^{D} \bar{M}_{\min,j,k,t_n} \right)$$