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Cropland Management
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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 counterfactualbaseline, 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:
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 biodiversity4,5,6,7. 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:
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
Additional principles that were considered in the development of this Module include:
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.
Project Proponents must enroll only land that is under active cropland management at the time of project initiation, or that has been under cropland management within the 5 years immediately prior to project initiation. Grazing land and pastoral systems are excluded from this Module.
[/R-5H5S-0]Project Proponents must not include any of the following land types within The Project Boundary:
[/R-S61E-0]Land that held native ecosystem cover at any point within the 10 years prior to project initiation including native grassland, native forest, wetlands (terrestrial or tidal, including peatlands, marshes, and mangroves), or other high-conservation-value habitat. Project Proponents must provide evidence that land-use change from native ecosystem cover did not occur within this lookback period, using remote sensing imagery, land cover classification data, or equivalent authoritative sources.
[/G-F7X4-0]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.
The Project must support the livelihoods of farmers and land users enrolled in or affected by project activities. Support must be documented in The Project Design Document. See Section 6.6.1.3 of the Improved Soil Management Protocol for requirements on revenue sharing.
[/R-NBDR-0]The Project must not result in net disturbance of existing soil carbon pools through tillage or soil inversion beyond that occurring in the baseline scenario.
[/R-909D-0]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 creditsCertificates 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 Project Commitment Period encompasses the Crediting Period and any Ongoing Monitoring Period commitments following the end of CreditingCertificate issuance.
The Project Commitment Period must be a minimum of 40 years and no longer than 100 years. The length of the Project Commitment Period must be set at project initiation.
[/R-NW67-0]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.
The Durability of Credits is set to one half the length of The Project Commitment Period.
[/G-1AFC-0]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.
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 CreditsCertificates 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 CreditsCertificates 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 creditsCertificates 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.
Project Proponents must identify and assess the environmental and social risks associated with all implemented land management practice changes in adherence with the requirements laid out within the Improved Soil Management Protocol. The following risks are specific to cropland management activities and must be addressed in The Project Design Document.
[/R-9H1W-0]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 creditcertificate 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.
The System Boundary for cropland management projects encompasses all GHG sources, sinks, and reservoirs (SSRs) associated with the implementation of SOC-enhancing land management practices on eligible croplands. The system boundary must be defined in accordance with Section 8.1 of the Improved Soil Management Protocol and the requirements below. GHG emissions and removals associated with The Project may be direct emissions from a process, or indirect emissions from combustion of fuels, electricity generation, or other sources.
A cradle-to-graveGHG 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 CreditsCertificates 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:
Activities or portions of activities that were already occurring in the baseline and would have continued to occur without the cropland management project may be omitted from the system boundary, subject to the conditions below. An activity, or portion of an activity, may only be excluded where The Project Proponent can demonstrate all of the following:
Evidence supporting these conditions must be provided in the the LCA. This must include either:
And the following:
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.
The following SSRs are particularly relevant to cropland management projects and must be assessed:
In accordance with the Improved Soil Management Protocol, the following are excluded from the system boundary for cropland management projects:
The baseline scenario for cropland management projects assumes that the SOC-enhancing management practices associated with The Project do not take place and that pre-project land management continues under business-as-usual conditions throughout the Crediting Period. The baseline must be defined in accordance with Section 8.2 of the Improved Soil Management Protocol and the requirements below.
[/The counterfactual represents the trajectory of SOC stocks that would have occurred in the absence of The Project, under continuation of pre-project management practices. Cropland management projects must assess the counterfactual using one of the approaches specified in Section 9.2 of this Module:
The counterfactual must be project-specific and reflect the land management practices, soil types, and climatic conditions of The Project area. It must be dynamically updated at each Reporting Period using the most recent available data, in accordance with the requirements set out in Section 9.2.
[/R-R4GG-0]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 CO2eEmissions,RP and are creditedaccounted for 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 module covers requirements for the assessment and quantification of emissions reductions associated with changes in agricultural practices.
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.
This Module covers requirements for GHG accounting for removals.
Leakage emissions, [math: 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:
All market-leakage discounts under this Module are applied to removal creditsCertificates. Where a Project concurrently generates emission reduction creditsCertificates 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-creditingissuance of removals.
Pre-Project Productivity ([math: PPP]) is defined as the annual productivity of a commodity type [math: c] on Project field [math: f] in relevant units (e.g., tonnes/ ha yr). [math: 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.
[math: PPP] must be calculated on a per-commodity basis using field-level yields indexed against regional benchmarks. Field-level yield data is mandatory for the productivity assessment. The Project Proponent must obtain it through farm records, grain elevator receipts, or crop insurance records. If it can be demonstrated that none of these is available then remote sensing-based crop yield estimation may be used. Pre-Project Productivity must be based on the 5 calendar years baseline period on field [math: f] at minimum (longer baseline periods are allowed). [math: PPP] must be calculated separately for each commodity within the field's crop rotation.
[/R-X5XH-0][math: PPP] for commodity [math: c] is calculated as:
[math: 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:
[math: 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 [math: PPP] is calculated from the corn rotations and soy [math: PPP] from the soy rotations within the 5-year baseline period.
Regional data must be sourced from official agricultural statistical publications at the county level (or equivalent sub-national jurisdiction). Acceptable sources include USDA NASS county-level yield data (for the United States), FAOSTAT national yield data (where sub-national data is unavailable), or equivalent national statistical services in other jurisdictions. The data source must be documented in the PDD.
[/G-J689-0]Project-Scenario Productivity ([math: PSP]) for commodity [math: c] on field [math: f] in Reporting Period [math: RP] is calculated as:
[math: 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:
Project-scenario productivity is assessed on a rolling basis beginning in the fifth project year ([math: 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 [math: RP]. Within each window, only years in which field [math: f] grew crop [math: 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 [math: \bar{Y}_{regional,c}] is fixed at project start and is identical to that used in [math: PPP_{f,c}] ensuring both metrics are directly comparable. Where crop c was not grown on field f in any year of the window, [math: PSP_{f,c,RP}] is undefined and no assessment is made for that field-crop pair.
The productivity shortfall for each commodity [math: c] in Reporting Period [math: RP] is:
[math: \Delta P_{c,f,RP} = \max(PPP_{c,f} - PSP_{c,f,RP}, 0)]
(Equation 3)
No leakage assessment is required for commodities where [math: \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:
[math: EP_{c,RP} = \sum_f PPP_{f,c} \times A_{f,c,RP}]
(Equation 4)
Where the sum is taken over all fields [math: f] planted with commodity [math: c] in the Reporting Period with a defined [math: PSP_{f,c,RP}], and [math: A_{f,c,RP}] is the area planted with commodity [math: c] on field [math: f] in the Reporting Period, in hectares.
Where [math: 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 [math: 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 [math: 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 creditingCertificate issuance suspension threshold is applied to the gross productivity shortfall for each commodity, before within-project leakage mitigation. The gross shortfall (GS) ratio for commodity [math: c] is:
[math: GS_{c,RP} = \frac{\sum_f \Delta P_{f,c,RP} \times A_{f,c,RP}}{EP_{c,RP}}]
(Equation 5)
Where [math: 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 creditingCertificate issuance for that Reporting Period unless a temporary exemption is granted under the exceptional circumstances provision detailed in Section 8.6. CreditsCertificates attributable to that Reporting Period are forfeited and may not be earned back at any point during the remainder of the Crediting Period.
The creditingCertificate issuance 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 [math: GS_{c,RP}] and [math: 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 creditingCertificate issuance 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 creditsCertificates issued in prior Reporting Periods.
Leakage is first assessed in the fifth project year ([math: 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, creditsCertificates 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, [math: 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 creditsCertificates 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 creditingCertificate issuance suspension threshold, however, does not apply retroactively. Where the first assessment identifies a productivity decline exceeding the creditingCertificate issuance suspension threshold for any commodity, ineligibility for creditingCertificate issuance applies to Reporting Period 5 only; creditsCertificates 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 creditsCertificates 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 [math: f] and each commodity [math: c] grown on that field, a final [math: 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 creditingCertificate issuance suspension threshold assessment is as set out in the CreditingCertificate Issuance 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 creditsCertificates available to The Project in the exit Reporting Period, the remainder must be deducted from subsequent Certificate issuances until fully settled; it may not be waived or earned back. The true-up does not reopen or retroactively void creditsCertificates 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.
[/G-B1SC-0]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 productivity surplus for commodity [math: c] on field [math: f] in Reporting Period [math: RP] is:
[math: \Delta P^{+}_{c,f,RP} = \max(PSP_{c,f,RP} - PPP_{c,f} \times 1.03, 0)]
(Equation 6)
Where:
The total project-level productivity surplus for commodity [math: c] is:
[math: 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 [math: f] within The Project that are eligible for positive leakage and are planted with commodity [math: c] in the Reporting Period, and [math: A_{c,f,RP}] is the area planted with commodity [math: c] on field [math: 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 [math: c] in each Reporting Period:
This sequence ensures the market-response model operates on the net supply impact after within-project mitigation, while the CreditingCertificate Issuance Suspension Threshold retains its function as a detector of systematic productivity failure.
Net Project Productivity for each commodity [math: c] is calculated as:
[math: 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:
The max ensures [math: 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, [math: ha_{LC}]. This estimate must be informed by:
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 [math: c] with [math: NPP_{c,RP} > 0], the hectares of induced land conversion are calculated as:
[math: ha_{LC,c,RP} = \frac{NPP_{c,RP} \times IS_c \times NL_c}{Y_{NL,c}}]
(Equation 9)
Where:
Where The Project falls into regions for which Isometric has provided default [math: IS] and [math: 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 ([math: 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:
[math: IS = \frac{\varepsilon_{s,c}}{\varepsilon_{s,c} + \lvert \varepsilon_{d,c} \rvert}]
(Equation 10)
Where:
Isometric has carried out a literature review of [math: \epsilon_{s}] and [math: \epsilon_{d}] values for certain regions. Values for [math: \epsilon_{s}] and [math: \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 [math: \epsilon_{s}] and [math: \epsilon_{d}] must be sourced from literature. The procedure and requirements for sourcing default values for [math: \epsilon_{s}] and [math: \epsilon_{d}] are set out in Appendix D.
[math: 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 [math: NL] values for certain regions. Values for [math: 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.
[math: Y_{NL,c}] considers the yield on new land brought into production for commodity [math: 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 ([math: \bar{Y}_{regional,c}]) used in Section 8.3.2.1 for assessing pre-project productivity must be assumed as the the value of [math: Y_{NL,c}].
[math: 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, [math: 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:
[math: CO_2e_{Leakage,RP} = \sum_c \left( ha_{LC,c,RP} \times EF_{Carbon Stock} \right)]
(Equation 11)
Where:
[math: CO_2e_{Leakage,RP}] is included as part of [math: CO_2e_{Emissions,RP}] as set out in the Improved Soil Management Protocol. The leakage discount is applied exclusively to removal creditsCertificates; no leakage discount is applied to emission reduction creditsCertificates issued under the Agricultural Practices Reductions Module.
[math: CO_2e_{Leakage}] is quantified for every Reporting Period.
The Project Proponent must document the baseline crop rotation for each enrolled field, including the sequence and frequency of each commodity planted over the 5-year baseline period. For each commodity in the baseline rotation, the number of plantings over the baseline period must be documented.
[/G-SFZK-0]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 [math: B] to commodity [math: A] is justified where the relative price of [math: A] has materially risen against its baseline relationship:
[math: PR = \frac{F_{A,t+1} / F_{B,t+1}}{\bar{P}_{A,bsl} / \bar{P}_{B,bsl}} > 1.2]
(Equation 12)
where [math: F_{A,t+1}]and [math: F_{B,t+1}] are harvest-time futures prices for the upcoming cultivation cycle, observed on the date of pre-registration, and [math: \bar{P}_{A,bsl}] , [math: \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 [math: c] in the most recent Reporting Period in which [math: c] was grown, and the field subsequently switches away from [math: c] under any provision of this subsection, the most recent leakage penalty assessed for [math: 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 [math: c'] not present in the baseline rotation, no direct [math: 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:
[math: PPP_{f,c'} = \bar{Y}_{\text{regional,c'}} \times \overline{YR}_{f}]
(Equation 13)
where [math: \overline{YR}_{f}] is the acre-year-weighted average of [math: YR_{f,c}] across all commodities [math: c] in the field's baseline rotation, and [math: \bar{Y}_{\text{regional,c'}}] is the long-run average regional yield for [math: c'], fixed per the existing multiplier convention. [math: PSP_{f,c',RP}] is then calculated per Equation 2 once yield observations for [math: 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 [math: 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 [math: 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 ([math: 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:
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 [math: PSP] shortfalls, Isometric may adjust the [math: 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:
Setup: A project with 100 hectares enrolled in a corn-soy rotation. Baseline [math: PPP] (regionally indexed): corn = 180 bu/ha, soy = 50 bu/ha. [math: IS] = 0.70 (global calories, from Appendix D), [math: NL] = 0.28 (US cropland, from Appendix D). [math: Y_{NL}] = 150 bu/ha (corn) and 45 bu/ha (soy). [math: EF_{Carbon Stock}] = 200 t CO₂e/ha.
Reporting Period 1 (Corn Year):
Reporting Period 2 (Soy Year):
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 ([math: 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):
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 ([math: 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 creditCertificate issuance.
The Project-level total carbon stock at any time [math: t] is expressed as total carbon mass of soil organic carbon across all strata within the primary quantification unit:
[math: CO_2e_{stored,t} = \sum_{j=1}^{J} \left( SOC_{j,t} \times A_j \right)\times \frac{44}{12}]
(Equation 14)
Where:
[math: SOC_{j,t_n}] is substituted as follows depending on the quantification approach and creditingCertificate issuance 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.
The values of [math: 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 ([math: CO_2e_{stored,RP}]), which feeds Equations 3 and 4 of the Protocol to calculate net removals ([math: CO_2e_{Removal,RP}]).
This module provides two options for quantification; measure and remeasure or measure and model with true-up. Project Proponents should document their indented quantification technique in the PDD.
[/R-GA0Q-0]Uncertainty in the net CO2e removal estimate arises from multiple sources under both quantification approaches. All uncertainty must be propagated through the net CO2e removal calculation using Monte Carlo simulation, in accordance with the requirements below and the corresponding section of the Isometric Standard.
Monte Carlo simulation is required under this Module for the following reasons:
[/R-K33F-0]Monte Carlo simulation must be implemented as follows:
The following uncertain inputs must be characterized and included in the Monte Carlo simulation:
Both approaches: field sampling variance (derived from the observed standard deviation of within-location stock changes across paired measurements within each stratum), with appropriate adjustment for spatial autocorrelation among sampling locations such that the effective sample size used in standard-error calculations reflects the empirical SOC residual variogram (see Section 9.1.1.1 Hierarchical Bootstrap, Step 3); laboratory analytical uncertainty characterized in accordance with Section 9.1.2.7; ESM measurement uncertainty characterized separately and propagated jointly with SOC concentration uncertainty; and baseline measurement uncertainty at [math: t_0], which propagates through all subsequent cumulative delta calculations.
[/G-VR9W-0]The conservative estimate used for creditCertificate 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 creditsCertificates 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.
The uncertainty information reported at each verification must include the items specified in Section 7.5 of the Improved Soil Management Protocol, and must document which input distributions were used, the correlation structure assumed, and evidence of convergence.
[/G-HNFT-0]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 [math: j], resample sampling locations with replacement, drawing [math: n_j] locations where [math: n_j] is the number of sampling locations in stratum [math: j]. Resampling is performed on locations (not on individual cores or depth increments), so that the paired structure ([math: t_0] and [math: t_n] measurements at the same location) is preserved within each bootstrap iteration. Location resampling at stratum [math: j] may only be applied where rarefaction analysis (see Step 4 below) demonstrates that [math: 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 [math: n_{eff}] replaces [math: 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 [math: I] or the empirical SOC residual variogram. Where significant autocorrelation is detected at the typical inter-location distance, compute an effective sample size [math: n_{eff}] for that stratum using a documented adjustment (e.g., Cressie's variance-inflation formula based on the fitted variogram), and substitute [math: n_{eff}] for [math: 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 ([math: 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 must document their soil sampling plan in the PDD.
[/R-TXK8-0]Projects must apply QA/QC procedures for soil inventory covering all stages of field data collection and data management, document them in The Project Design Document, and apply them consistently across all Reporting Periods.
[/G-9V01-0]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).
For all directly sampled parameters, The Project Design Document must:
The following requirements apply to all sampling and re-sampling campaigns:
Sampling must be designed to produce an unbiased, statistically defensible estimate of SOC stocks and stock changes at The Project level, with a transparent and reproducible link between sampling locations and the population they represent. The choice of design is determined by The Project Proponent and justified at validation against The Project's quantification approach, the heterogeneity of The Project area, and the expected precision of The Project-level estimate. The Module specifies a default design but does not mandate sub-field stratification or any particular set of stratification factors where a simpler design can be justified to deliver equivalent or better project-level precision.
[/R-6QZ4-0]The Project Proponent must define a hierarchy of quantification units in The Project Monitoring Plan. At minimum, this must specify:
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:
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
Strata, where used, must be delineated using the best available data on factors that influence SOC stock distribution and the response of SOC to project activities. The factors set out in this paragraph are illustrative of those typically relevant at field (10–100 ha) and landscape (100–1,000 ha) scales including climate, topography, historical land use and vegetation, parent material, soil texture, soil type, and where available, remote-sensed indicators such as bare-soil reflectance composites or vegetation indices, although not mandatory.
Project Proponents must select stratification factors based on their relevance to the specific project's heterogeneity and quantification approach, recognizing that adding additional less relevant factors yields diminishing precision returns and increases the risk that strata fall below the three-samples-per-stratum minimum. Soil maps and databases including the FAO Soils Portal, SoilGrids, or locally available digital soil maps may be used to inform stratum delineation. Field boundaries should be considered where management history aligns with them.
[/G-Y0M5-0]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:
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:
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 creditsCertificates, 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 ([math: 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:
Where any stratum fails the rarefaction criterion (no [math: 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 [math: n_j] for the next Reporting Period to meet the rarefaction criterion.
SOC stocks and stock changes must be reported to a common depth across all sampling locations, of a minimum of 30 cm, or to bedrock, hardpan, or other physical barriers where soils are shallower than 30 cm. A greater common depth may be selected by The Project Proponent (for example, 40 cm), in which case all sampling locations (with exceptions for locations where physical barriers are documented) must be sampled to that depth and the credited profile extends accordingly. The elected sampling depth must be documented and justified at validation.
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 for Certificate issuance must be calculated on this basis.
Soil sampling must follow established best practices for field collection and laboratory processing.
[/R-TCTS-0]The following requirements apply:
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.
Fine soil mass per unit area. For each depth increment [math: d] in stratum [math: j] at sampling event [math: t_n], the oven-dry fine soil mass per unit area is:
[math: 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:
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 ([math: \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:
[math: 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:
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 isare 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 [math: t_n] and at [math: 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 ([math: t_0] vs [math: 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.
The selected analytical laboratory must be listed as an approved analytical service provider for SOC measurements in accordance with national or international accreditation standards. The laboratory must hold ISO/IEC 17025 accreditation or operate under a documented equivalent quality assurance framework. Where an equivalent framework is relied upon, The Project Proponent must demonstrate equivalence to ISO/IEC 17025, addressing, at a minimum, method validation, measurement traceability, internal quality control, personnel competence, and proficiency testing, and this demonstration will be reviewed and accepted at project validation.
[/G-CM2S-0]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 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:
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:
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 partparty 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:
Project Proponents must include their sample analysis plan in the PDD.
[/G-RZ74-0]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.
In-situ proximal sensing techniques may be used as alternatives or complements to laboratory dry combustion analysis for the quantification of SOC. The following techniques are permitted under this Module, subject to the requirements below:
*
*
*
Prior to use, Project Proponents must demonstrate, in agreement with Isometric, that the selected technique is equivalent in accuracy and reliability to dry combustion analysis for the soil types, moisture conditions, and SOC content ranges present in The Project area.
This demonstration must be grounded in published peer-reviewed scientific literature and must be submitted for review and approval by Isometric prior to project validation. Techniques not yet supported by sufficient published evidence of equivalence to approved measurement methods will not be permitted.
All proximal sensing instruments must be calibrated against reference samples from The Project area with SOC content determined by dry combustion prior to deployment and at regular intervals throughout The Project lifetime. Calibration procedures must follow methods described in published peer-reviewed literature and must be conducted in consultation with Isometric. Prior to use for creditCertificate-relevant measurements, instruments and calibration models must be validated against independent reference samples not used in model development, with validation procedures and acceptance thresholds consistent with those reported in peer-reviewed literature and approved by Isometric.
Uncertainty associated with proximal sensing measurements must be quantified and propagated through the entire removal calculation in accordance with Section 7.5 of the Improved Soil Management Protocol. Where proximal sensing uncertainty exceeds that achievable through laboratory dry combustion, the more conservative estimate must be used for creditCertificate issuance. A subset of samples measured by proximal sensing must be independently verified by laboratory dry combustion at each Reporting Period.
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 creditingCertificate issuance step (the 30th-percentile estimate under Section 9.1.1).
Increment quantities. For each depth increment dd d in stratum [math: j] at sampling event [math: t_n], and at each sampling location, compute the soil mineral mass per unit area [math: M_{min,j,d,t_n}] (Equation 16) and the SOC mass per unit area:
[math: M_{C,j,d,t_n} = C_{j,d,t_n} \times M_{fine,j,d,t_n}]
(Equation 17)
where [math: C_{j,d,t_n}] is the measured SOC concentration (g C kg⁻¹ fine soil), [math: M_{fine,j,d,t_n}] is the fine soil mass per unit area (Equation 15, kg m⁻²), and [math: 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 [math: d]:
[math: \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 [math: j] as the minimum, across all sampling events, of the total cumulative soil mineral mass of the stratum-mean profile:
[math: M_{\min,\mathrm{ref},j} = \min_{t_n} \left( \sum_{k=1}^{D} \bar{M}_{\min,j,k,t_n} \right)]
(Equation 18)
where [math: D] is the total number of depth increments sampled (common to all sampling locations and all sampling events; see Section 9.1.2.4) and [math: \bar{M}_{min,j,k,t_n}] is the mean, across all sampling locations in stratum [math: j], of the increment-[math: k] mineral mass at event [math: t_n]. Taking the across-event minimum guarantees that the reference mass lies within the sampled stratum-mean column at every event, so no extrapolation beyond the measured profile is required11. At the sampling event that yields the minimum, the reference mass equals the stratum-mean profile total and the ESM correction ratio for the stratum-mean profile is unity.
ESM-corrected stock density. Let aa a be the deepest increment whose cumulative mineral mass does not exceed the reference mass, and let [math: b = a + 1] be the increment within which the reference mass falls:
[math: a = \max \left\{ d : \sum_{k=1}^{d} M_{\min,j,k,t_n} \leq M_{\min,\mathrm{ref},j} \right\}, \quad b = a + 1]
The ESM-corrected SOC stock density at a sampling location in stratum [math: j] at sampling event [math: t_n] is then:
[math: \mathrm{SOC}_{j,t_n} = \left[ \sum_{k=1}^{a} M_{C,j,k,t_n} + \left( M_{\min,\mathrm{ref},j} - \sum_{k=1}^{a} M_{\min,j,k,t_n} \right) \frac{M_{C,j,b,t_n}}{M_{\min,j,b,t_n}} \right] \times \frac{1}{100}]
(Equation 19)
Where:
When [math: a = D] the reference mass equals or exceeds the location's full profile mineral mass, increment bb b does not exist, and the interpolation term is set to zero; Equation 19 reduces to the location's full-profile SOC mass divided by 100.
Where the profile is sampled as a single depth increment ([math: D=1]), set [math: a=0] with both cumulative sums equal to zero; Equation 19 then reduces to the single-layer ESM correction:
[math: \mathrm{SOC}_{j,t_n} = M_{\min,\mathrm{ref},j} \times \frac{M_{C,j,1,t_n}}{M_{\min,j,1,t_n}} \times \frac{1}{100}]
(Equation 20)
The same equation therefore serves both single-layer and multiple-layer assessments. (Under Section 9.1.2.4 a minimum of two depth increments is required at project sampling events; the [math: D=1] reduction is retained for completeness and for the baseline case described below.)
Estimation and aggregation across locations. The reference soil mineral mass [math: M_{min,ref,j}] (Equation 18) is a stratum-level constant defined on the stratum-mean mineral profile. Equation 19 must be evaluated at each sampling location, using that location's own cumulative SOC and mineral masses against the common stratum reference [math: M_{min,ref,j}]. The stratum-level stock [math: SOC_{j,t_n}] carried into Equation 14 is the mean of the location-level values across all sampling locations in the stratum. Evaluating Equation 19 at the location level is required so that the location-level paired stock differences [math: \Delta SOC_{loc} = SOC_{loc,t_n} - SOC_{loc,t_0}] used in the rarefaction analysis (Section 9.1.2.3) and the hierarchical bootstrap (Section 9.1.1) are defined consistently with the creditingCertificate issuance point estimate.
Where an individual location's total profile mineral mass at an event is less than [math: M_{min,ref,j}] (which can arise because the reference is defined on the stratum mean) that location's cumulative SOC mass must be evaluated at its own profile total (that is, a is set to the location's deepest increment and the interpolation term is zero) rather than extrapolated beyond the measured column. This truncation omits any SOC that would lie between the location's profile total and the reference mass and is therefore conservative for creditingCertificate issuance. Each occurrence must be documented at Verification, and where truncation recurs at the same location across consecutive sampling events that location must be sampled to greater depth at the next campaign so that the measured column reaches the reference mass.
Linear interpolation yields an approximately unbiased point estimate of the ESM-corrected stock provided increments are thin enough that the SOC–mineral-mass relationship is close to linear within each increment; the point estimate must be incorporated into the Monte Carlo simulation (Section 9.1.1) to account for uncertainty.
Linear interpolation assumes SOC concentration is uniform within each increment. Where increments are thick and the SOC–mineral-mass relationship is strongly curved (for example across buried or spodic horizons), this introduces interpolation error. That error propagates as increased uncertainty in the Section 9.1.1 Monte Carlo simulation and, through the conservative lower bound, into the creditedCertificates quantityissued. Project Proponents expecting a non-linear SOC distribution with depth should sample thinner increments across the affected depth intervals to reduce it.
Baseline event. At the baseline event, before any resampling event exists, the minimization in Equation 18 is taken over a single event and therefore returns that event's own stratum-mean profile total; the ESM correction ratio is unity at baseline by construction. The correction takes full effect from the first resampling event [math: t_1] onwards, once the cross-event minimum is defined over more than one event.
The variance terms used in Section 9.1.1 (Uncertainty Propagation) must reflect the sample size at each depth increment across all sampling locations.
Aggregation to total carbon stock. Equation 19 produces an SOC stock density for a single stratum at a single event. These densities are aggregated to The Project level, area-weighted across strata and converted to CO₂e, by Equation 14; no separate aggregation is defined here. Under this Module the stored-carbon pool is SOC only (the aboveground and belowground woody biomass terms are set to zero), so The Project total carbon stock is the SOC stock delivered by Equation 14.
Model validation must be conducted in accordance with the requirements set out in Section 9.1.3.2.3 and all outcomes and data used for validation purposes must be clearly documented.
Under this approach, an approved biogeochemical model (see Section 9.1.3.2.1) is used to estimate SOC stock changes between resampling campaigns based on measured initial SOC stocks, implemented land management practice changes, soil characteristics, and climatic conditions within each quantification unit.
Direct measurement of SOC stocks is required at a minimum of every five years. Remeasurement data must be used to re-estimate model prediction error and recalibrate the model against observed conditions at each Reporting Period (true-up procedure, see Section 9.1.3.2.4).
[/R-JA4V-0]Any model used to contribute to the quantification of net CO2e removal under this Module must be demonstrated to be well-validated and skillful for the purpose for which it is used, including the relevant soil types, land management practices, climatic conditions, and geographic context of The Project area. Recommended biogeochemical models include established process-based models such as DayCent, RothC, and CENTURY, as well as other models that meet the following eligibility criteria. However, even recommended models must be demonstrated to be fit for purpose in the context of The Project according to the below criteria.
Model eligibility must be demonstrated through one of the following two pathways, submitted in the PDD and approved by Isometric prior to Validation:
[/R-8R38-0]Model eligibility is context-specific. A model that is eligible for one project area is not automatically eligible for a project in a different ecoregion, soil type, or management context. Where the same model is to be used both to predict The Project scenario and to estimate the counterfactual under Section 9.2.2, eligibility must be demonstrated separately for The Project management practices and the baseline management practices.
[/G-DRXM-0]The selected model must be capable of simulating SOC dynamics across the sampled depth profile required under Section 9.1.2.4.
[/G-1DS9-0]Model calibration and parameterization must be fully documented and reproducible. Project Proponents must provide sufficient information in the PDD for an independent third party to replicate the model setup and obtain equivalent outputs from the same inputs. At a minimum, the following must be documented:
All data sources used in parameterization must be available to Isometric and the VVB. Where proprietary data sources are used, Project Proponents must demonstrate that equivalent publicly available data were not available and must provide sufficient metadata to allow independent assessment of data quality.
[/G-N2MY-0]Digital soil maps may be used for model initialization with approval from Isometric.
[/G-8QGK-0]Prior to use for creditCertificate-relevant quantification, the selected biogeochemical model must be validated for the specific conditions of The Project area. Validation results and supporting data must be submitted in the PDD and are subject to review and approval by Isometric at project validation.
Prior
Model quantification, the selected biogeochemical modelvalidation must be validatedconducted in accordance with the requirements set out in Section 9.1.3.2.3 and all outcomes and data used for validation purposes must be clearly documented.
Data points used for model parameterization or calibration must not be used for validation. This prohibition applies to all data sources used in model development, including literature-derived values used at initial parameterization and any project-collected data subsequently used for recalibration under the true-up procedure. A minimum of 20% of all available representative data points must be withheld from parameterization and reserved exclusively for validation. Representative means that the withheld data points must span the full range of SOC stock values, soil types, climatic conditions, and land management practices present in The Project area. A validation subset that satisfies the 20% threshold but clusters at one end of the observed distribution does not meet this requirement.
[/G-G17X-0]The validation dataset must:
Where the validation dataset is used to support a modeled counterfactual under Section 9.2.2, model performance must be reported separately for project-like and baseline-like management contexts and for project-area-relevant and project-area-non-relevant climatic contexts. The [math: R^2] performance threshold and bias requirements set out in this section apply independently to each project-like × climatic-coverage subset and to each baseline-like × climatic-coverage subset; where they are not met for the baseline-like × project-area-relevant climatic-coverage subset specifically, the model is not eligible to be used for counterfactual estimation under Section 9.2.2, regardless of its performance under other subsets.
[/G-3P3J-0]Climatic coverage of the validation dataset must be reassessed at each Reporting Period against the climatic conditions actually experienced by The Project area in that period. Where the validation dataset does not include observations spanning the climatic conditions actually experienced (defined as fewer than three validation points within the climatic envelope of the Reporting Period under each of project-like and baseline-like management) the climatic-coverage gap must be documented and triggers the additional consequences set out in Section 9.1.3.2.3.
Based on assessment against the validation dataset, the model must demonstrate the following:
The following statistics must be calculated and reported as documentation requirements at validation, but do not constitute enforceable performance thresholds: root mean square error (RMSE), reported in t C ha[math: ^{-1}]; and mean bias error (MBE), reported in t C ha[math: ^{-1}], as the primary diagnostic for systematic overprediction or underprediction; and the skewness of the residual distribution, reported alongside a quantile-quantile plot of residuals against a normal distribution. Significant skewness in the marginal residual distribution does not by itself indicate model misspecification and does not constitute grounds for rejection under the residual structure requirements below; however, where significant skewness is detected (|skewness| > 0.5, or where the Q-Q plot indicates material asymmetry in the tails), the asymmetric error distribution must be characterisedcharacterized and used in Monte Carlo propagation as set out in Section 9.1.1 rather than a symmetric RMSE-based distribution.
Residual prediction errors must show no statistically significant structure with respect to soil type, management practice, or time, tested using an appropriate statistical test (e.g., ANOVA/Kruskal-Wallis for soil type and management practice; a trend or autocorrelation test for time) at a significance threshold of p < 0.10. Where stratum-level sample sizes are insufficient to support a determinative test, residuals may instead be assessed over the generalized parameter space described later in this Section, subject to the same consultation and approval requirements set out there. Where significant structure is detected, the model must be investigated and recalibrated, and may not be approved for use until the structure is resolved.
[/G-SK9G-0]Where a model does not meet the [math: R^2] performance threshold or exhibits systematic bias that cannot be corrected, it must be reparameterized or recalibrated before resubmission for Isometric approval. Data points previously used for validation may not be reused for parameterization or recalibration.
Where The Project Proponent can demonstrate that representative in-situ data are not reasonably available for the specific combination of soil, climatic, and management conditions of The Project area, model performance over a generalized parameter space encapsulating The Project conditions may be considered for validation, subject to consultation with and approval by Isometric. The Project Proponent must document the data scarcity, characterize the parameter space against which generalized validation is conducted, and demonstrate that this space is sufficiently broad and representative to provide reasonable confidence in model performance under project conditions. The performance thresholds set out in this Section apply unchanged to the generalized validation.
[/G-5YDV-0]The validation dataset is constructed by withholding a minimum of 20% of representative data points from a broader corpus of available evidence. The integrity of this withholding process depends on the corpus itself being complete, since selective construction of the corpus before the 20% withholding is applied is functionally equivalent to selective construction of the validation set. Project Proponents must therefore construct the validation data corpus to the following completeness standard.
The corpus must include all peer-reviewed studies that satisfy each of the following criteria:
The Project Proponent must document the literature search procedure used to identify candidate studies, including the databases queried (at minimum: Web of Science, Scopus, AGRIS, and Google Scholar), the search terms used, the date of the search, and the total number of studies identified before any exclusion is applied.
[/G-Studies satisfying the mandatory inclusion criteria may be excluded from the corpus only on the basis of one or more of the following grounds, with each exclusion documented individually at the study level and subject to scrutiny at validation:
Exclusion on grounds outside this list is not permitted. In particular, a study may not be excluded on the basis that its inclusion would widen the Monte Carlo input distributions or increase the uncertainty discount.
[/G-MHWF-0]Changes to the corpus or to the validation subset after pre-registration require explicit Isometric approval and constitute a fresh validation event for which model performance metrics calculated prior to the change may not be relied upon.
[/G-86R4-0]Calibration and validation data points must be spatially independent. Spatial mixing of calibration and validation locations within the same continuous soil or bioclimatic neighborhood produces residual correlation between the two sets that systematically inflates reported model performance statistics relative to the model's true generalization skill. The data-separation rule set out above (no point used for both parameterization and validation) is necessary but not sufficient for this purpose; spatial separation is additionally required and must be implemented through one of the following two designs:
[/R-B0DH-0]Project Proponents must report at validation, for each bias-test subset (project-like, baseline-like, and the climate-coverage portions of each):
Where The Project area is too compact to support distance-based separation beyond the autocorrelation range, or where the available validation dataset is too small to populate block-level hold-outs at the required statistical-power thresholds, within-project spatial blocking is not feasible and The Project Proponent must instead use regional validation data drawn from beyond The Project boundary, with the regional dataset itself satisfying the management-coverage, climate-coverage, distributional-distance, and spatial-blocking requirements of this Section. The regional dataset must be drawn from the same ecoregion, must satisfy the comparability criteria set out elsewhere in this Section, and must include sufficient spatial extent for distance-based separation to be operationalized within it. Use of the fallback must be documented at validation, including a quantitative demonstration that within-project blocking is infeasible and a justification of the regional dataset's representativeness of The Project area.
[/G-AF2D-0]Where post-recalibration validation is conducted under Section 9.1.3.2.3 following a triggered bias test, the spatial-blocking requirements of this sub-section apply equally to the post-recalibration validation. New calibration points used in recalibration must be spatially separated from validation points under the same design as was used at initial validation. Where new calibration data are spatially correlated with existing validation data such that this requirement cannot be satisfied without restructuring the validation set, The Project Proponent must construct a fresh validation set under the spatial-blocking requirements before the recalibrated model may be used for creditCertificates-relevant quantification.
At a maximum interval of 5 years, the biogeochemical model must be updated using new SOC stock measurements collected during the resampling campaign in accordance with Section 9.1.2. A true-up event is additionally required, before the scheduled maximum interval, for any Reporting Period in which the climatic-coverage reassessment under Section 9.1.3.2.3 shows that a climatic driver used by the model falls below (validation minimum − 0.2 × validation range) or above (validation maximum + 0.2 × validation range) of that driver across the validation dataset. In such a Reporting Period the creditingCertificate issuance event is a true-up event rather than an interim creditingCertificate issuance event, and the measured result is subject to divergence testing and reconciliation under Section 9.1.3.2.3.
The true-up procedure serves three purposes: integrating new observational evidence into the model's cumulative data record; reassessing model error and uncertainty; and detecting and correcting systematic bias before it propagates into subsequent creditCertificate issuance estimates.
New field measurement data must be added to the full cumulative dataset comprising all prior literature-derived values and project-collected data used in parameterization and validation. New data points must first be used for model error reassessment before any portion is considered for recalibration, the same data points may not be used simultaneously for both purposes within the same Reporting Period.
At each true-up event, the sampling collected under Section 9.1.2.3 must be used to update model error statistics and test for systematic bias in the modeled profile. The MBE-based bias test (10% of mean observed SOC stock change) applies to the modeled profile. Where systematic overprediction is detected, the consequences set out in this section apply.
Updated error statistics must be calculated against the new observations and reported in the Monitoring Report at verification. At minimum the following must be reported, calculated separately for The Project-like and baseline-like subsets of the cumulative validation dataset (as defined in Section 9.1.3.2.3) and for each subset's project-area-relevant climatic-coverage portion: [math: R^2] calculated as [math: 1 - \frac{RSS}{TSS}], with a 90% confidence interval that must exclude 0; RMSE in t C ha[math: ^{-1}]; residual skewness with a Q-Q plot; and MBE in t C ha[math: ^{-1}], calculated as the mean of (predicted − observed) across all validation points in each subset, reported alongside its standard error and a one-sided 90% upper confidence bound on [math: |MBE|]. The pooled statistics for the cumulative validation dataset as a whole must additionally be reported. Updated error statistics must be used to revise the Monte Carlo simulation input probability distributions in accordance with Section 9.1.1, including, where residual skewness has changed materially, revisions of the error distributions. Where the true-up reveals greater model uncertainty than was previously characterized, the higher uncertainty estimate must be applied.
Three parallel bias tests apply at each true-up. Each test combines a statistical-significance criterion (to control false positives and false negatives at small or noisy validation sample sizes) with a residual minimum effect-size criterion (to prevent triggering on statistically detectable but practically immaterial bias). Any single test, if triggered, requires the mandatory consequences set out below.
Pooled bias test. The pooled-MBE test fires where both of the following are satisfied:
Where the test fires with a positive MBE point estimate (the model over-predicts), this constitutes systematic pooled overprediction. A negative MBE point estimate, or failure of either criterion, does not trigger the mandatory consequences but must be documented and investigated where statistically significant.
Differential bias test (management-conditional). The differential test fires where both of the following are satisfied:
Where the test fires, the model is biased toward over-estimating The Project effect (over-crediting). A negative point estimate, or failure of either criterion, does not trigger the mandatory consequences but must be documented and investigated where statistically significant.
Differential bias test (climate-conditional). The climate-conditional differential test fires where both of the following are satisfied:
Where either subset under the climate-conditional test contains fewer than three validation points within the relevant climatic-coverage portion, the test is not statistically defined, and the climatic-coverage gap consequences set out below apply in lieu of the test result.
Counterfactual bias assessment and correction. The management-conditional and climate-conditional differential tests assess whether the model systematically over-estimates The Project effect by mis-estimating the counterfactual, evaluated against the baseline-like subset of the validation dataset. This assessment must be performed at initial model approval and re-performed upon any recalibration or other change to the model parameterization, and upon any update to the validation dataset. It is not triggered by true-up events in themselves, as a true-up provides no new observation of the counterfactual.
Where a differential test fires, the modeled counterfactual SOC stock change must be adjusted by subtracting [math: MBE_{base}] (or [math: MBE_{base,C}] where the climate-conditional test fires) from the modeled counterfactual for each Reporting Period to which the affected parameterization applies. The adjustment must be capped at the lesser of (i) 25% of the unadjusted modeled counterfactual stock change for the period, or (ii) the absolute value of the bias point estimate from the relevant subset, with the cap and any application of it documented in the Monitoring Report. Where the climate-conditional test cannot be statistically defined, the climatic-coverage gap consequences below apply. Where a counterfactual bias correction is applied, the combined project-effect estimate for each affected Reporting Period is calculated as the difference between The Project SOC stock change (measured or modeled, according to the Reporting Period type) and the bias-corrected modeled counterfactual.
[/R-5Z0G-0]Consequences where a bias test fires. The bias tests diagnose the presence, source, and location of model bias. The consequences of the pooled and management-conditional bias tests are as follows.
Where a bias test fires, the source of the bias must be investigated, documented, and reported to Isometric. The Project Proponent must respond in accordance with Section 9.1.3.2.3, by either recalibrating the model or applying an increased model prediction uncertainty in the Section 9.1.1 propagation. Where the source cannot be identified or corrected, continued use of the model must be approved by Isometric before the next Reporting Period commences. Reconciliation of project-side creditingCertificate issuance against field measurement is governed by Section 9.1.3.2.3; the counterfactual bias correction is governed by the counterfactual bias assessment above.
Climatic-coverage gap consequences. Where the climate-conditional differential test cannot be statistically defined for a Reporting Period because the validation dataset contains fewer than three validation points within one or both subsets restricted to the climatic conditions actually experienced, the consequences are as follows:
Project Proponents may elect to recalibrate model parameters at any true-up event using the updated cumulative dataset, subject to the following requirements:
Data points previously used for validation may not be reused for parameterization or recalibration at any point in The Project lifetime.
Remeasurement serves model calibration and true-up purposes rather than primary quantification. The following requirements apply to all resampling campaigns conducted under this approach:
The requirements of Section 9.1.2 and all subsections apply equally to remeasurement under Approach 2. In-situ proximal sensing (Section 9.1.2.9) may also be used for remeasurement, subject to the same requirements.
The model output can be used directly in Equation 19. Project Proponents electing this approach must i) clearly document this as being the case in the model's output within the model description in the PDD; ii) demonstrate the model's internal reference mass is consistent with, or more conservative than, the reference mineral mass defined in Section 9.1.2.10; and iii) ensure that model validation is conducted against observations on the same mass basis sof the the model output.
At interim events no field measurement is available, so the mineral-mass profile and the reference mass are held at their baseline ([math: t_0]) values. The cumulative-mass estimator (Equation 19) evaluated at the baseline reference mass therefore has correction ratio unity ([math: a = D], interpolation term zero) and reduces to the modelled SOC mass over the baseline profile:
[math: \mathrm{SOC}_{j,t_n}^{\mathrm{model}} = \left( \sum_{d=1}^{D} \hat{C}_{j,d,t_n} \times M_{\mathrm{fine},j,d,t_0} \right) \times \frac{1}{100}]
(Equation 21)
Where:
This equation evaluates the cumulative-mass estimator at the baseline reference mineral mass [math: M_{\min,\mathrm{ref},j}(t_0) = \sum_{k=1}^{D} \bar{M}_{\min,j,k,t_0}]; because no post-baseline mineral-mass measurement is available at an interim event, the cross-event correction ratio is unity and the interpolation term vanishes. Modelled SOC mass is formed on the fine soil mass basis, consistent with Equation 17.
Estimation of [math: M_{min,ref,j,d}^{(t_0)}]: the baseline reference soil mineral mass for each sampled increment is estimated using baseline ([math: t_0]) measurements from all sampling locations within stratum [math: j].
At true-up events, field measurements from the resampling campaign are available, so the full cumulative-mass ESM correction (Equation 19) is applied to the model output, with measured SOC mass replaced by modeled SOC mass:
[math: \mathrm{SOC}_{j,t_n}^{\mathrm{model,ESM}} = \left[ \sum_{k=1}^{a} \hat{M}_{C,j,k,t_n} + \left( M_{\min,\mathrm{ref},j} - \sum_{k=1}^{a} M_{\min,j,k,t_n} \right) \frac{\hat{M}_{C,j,b,t_n}}{M_{\min,j,b,t_n}} \right] \times \frac{1}{100}]
(Equation 22)
Where:
As in Approach 1, Equation 22 is evaluated at each sampling location against the stratum reference [math: M_{min,ref,j}], and the stratum-level value is the mean across locations. The cross-event minimization is applied consistently across all sampled increments using all sampling locations.
The cross-event minimization under the ESM correction is applied consistently across all sampled increments using all sampling locations.
[math: SOC^{model,ESM}*{j,t_n}] is substituted for [math: SOC*{j,t_n}] in Equation 14 for the purposes of calculating The Project-level total SOC stock at true-up events.
With the true-up events under the model and remeasurement approach, the values across the two methods must be reconciled, and any model overprediction must be detected and corrected. These requirements have implications for all creditCertificate issuances under this approach, at both interim crediting events and true-up events.
Governing principle. CreditingCertificate Issuance under this Module and the Improved Soil Management Protocol is cumulative. At all times, the total creditsCertificates issued to The Project must correspond to the cumulative increase in SOC stock established at the most recent true-up event, less creditsCertificates issued in respect of periods before that event. Each true-up event re-establishes the cumulative SOC stock from field measurement and reconciles total creditsCertificates issued to it, consistent with the cumulative creditingCertificate issuance basis and the derivation of net removals under Equation 10 of the Improved Soil Management Protocol.
Divergence test. At each true-up event, the measured cumulative change in SOC stock since the most recent true-up event (or since t0 for the first true-up event) must be compared against the modeled cumulative change over the same interval. The test statistic is the difference between the measured and modeled cumulative change, standardized by the combined standard error of the two quantities. The comparison must be conducted as two one-sided tests at the 90% confidence level, one for overprediction and one for underprediction, comparing the standardized difference against the corresponding one-sided critical value. The distributional basis for the test must be documented, and a small-sample correction (use of the t-distribution in place of the normal approximation) must be applied where sample sizes are low.
The combined standard error must be formed from the sampling uncertainty of the measurement and the model prediction uncertainty, treated as independent (combined in quadrature) unless a correlation between them is demonstrated:
The outcome of the divergence test must be reported at every true-up event, whether or not a significant difference is found.
Establishing the corrected SOC stock. On the basis of the divergence test, the corrected cumulative SOC stock at the true-up event ([math: SOC^{corrected}_{j,t_n}]) is established as follows, and used in place of the modeled stock in Equation 14 for the derivation of net removals:
In each case the corrected stock is established at the module level; the derivation of the corresponding cumulative change and net removals from [math: t=t0] is performed under Equations 3-5 of the Improved Soil Management Protocol.
Reconciliation of creditsCertificates. Total creditsCertificates issued to The Project must be reconciled to the corrected cumulative SOC stock (as given effect through the derivation of cumulative net removals under Equations 3 and 4 of the Improved Soil Management Protocol):
Where creditsCertificates conservatively forgone at a true-up event (through reconciliation to a conservative lower bound) are confirmed by a subsequent true-up event that establishes a higher corrected cumulative SOC stock, the difference is issued at that subsequent event through the reconciliation above. No separate tracking of forgone amounts is required, as the cumulative reconciliation gives effect to this automatically.
Detection roles. The divergence test is the sole trigger for reconciliation under this Section. The bias tests under Section 9.1.3.2.3 serve to diagnose the source and location of a divergence and to guide the forward response; they are recommended at each true-up event and become required where the divergence test identifies a significant difference. Bias in the counterfactual is addressed through model validation against baseline-like observations under Section 9.1.3.2.3 rather than through reconciliation under this Section, as The Project counterfactual is not directly observable.
Forward response. Where the divergence test identifies significant overprediction, reconciliation of the elapsed period under this Section is mandatory. For subsequent periods, The ProjectProponentProject Proponent must either recalibrate the model in accordance with Section 9.1.3.2.3 or apply updateupdated the model prediction uncertainty to correspond to the latest true up data and use this increased uncertainty value for all Certificate issuances until the next true-up event. In either case, modeled increments for subsequent periods must be applied to the corrected cumulative SOC stock established at the true-up event, and not to the model's uncorrected cumulative trajectory.
Escalation. The model loses eligibility for interim model-based creditingCertificate issuance, and creditingCertificate issuance reverts to the measure-and-remeasure procedure under Section 9.1.2 for the remainder of The Project, where either: (i) significant overprediction is identified at two consecutive true-up events; or (ii) the overcreditingover Certificate issuance excess at a single true-up event exceeds 20% of the creditsCertificates issued in respect of the periods since the most recent true-up event. Eligibility for interim model-based creditingCertificate issuance may be restored where the model is re-validated against the full requirements of Section 9.1.3.2.1 and 9.1.3.2.3 and re-approved by Isometric, including demonstration of performance under the conditions associated with the divergence. Following restoration, a single subsequent identification of significant overprediction re-suspends eligibility.
Projects using a measure-remeasure quantification approach for carbon storage within The Project Area will determine the counterfactual carbon storage by applying the same measurement techniques within control plots. The control plots must be areas where project interventions are not taking place, but are otherwise representative of The Project Area. As such, these areas represent “business as usual” management practices that would have continued in absence of The Project intervention.
Control plots must be selected from within The Project Area at the time of project initiation, following a two-step procedure:
[/G-FXY0-0]At minimum, the total area of the control plots must be equivalent to 2.5% of The Project Area, with a minimum of three control plots for each stratum within The Project Area. Management practices within the control plots must continue to follow "business as usual" practices, and Project Proponents must provide evidence and details on the implementation of these practices at each Reporting Period. At project initiation, the management practices must be evidenced with records from the enrolled properties over a minimum of three years or full crop rotation (whichever is longer) prior to project implementation. In order of preference, this evidence can include:
At least once every five years, The Project Proponent must demonstrate that the implemented practices within the control plots continue to represent regional management trends. This must be evidenced by regional data records from government bodies, academic or research institutions, international organizations, and/or peer-reviewed literature. In the event that there is a shift in these business-as-usual management practices, the management of the control plots must be updated to reflect these practices for the subsequent Reporting Period.
Where a control plot becomes unavailable during The Project Commitment Period for reasons outside The Project Proponent's reasonable control (e.g., land sale, change of operator, withdrawal of the participating landowner), a replacement control plot must be drawn from the Control Plot Candidate Set under the same stratified random sampling procedure, within the same stratum, and re-baselined at the next sampling event. Discontinuity in the control-plot time series introduced by such a replacement must be documented and propagated through the uncertainty framework under Section 9.1.1. Replacement of control plots for reasons within The Project Proponent's control (e.g., agronomic preference) is not permitted.
Project Proponents must follow the same requirements for sampling design, sample collection, sample analysis, and reporting as for quantification of carbon storage within The project area as described in Section 9.1.2 and all subsections therein. If control plots are directly adjacent to project intervention plots, sampling should occur at least 10 meters from the boundary. Project Proponents must follow the same sampling and analysis procedures for both Project Area and control samples. The total counterfactual storage at any time ([math: t]) is then calculated as:
[math: CO_2e_{counterfactual,t} = \frac{44}{12} \sum_{i=1}^{n} SOC_{i,t} A_i]
(Equation 23)
Where:
[math: CO_2e_{counterfactual,t}] is the total storage of carbon in the counterfactual scenario at time [math: t], in tonnes CO2e
[math: SOC_{i,t}] is the mean soil carbon density at time [math: t] in in the control plots corresponding to strata [math: i] of The Project Area of [math: n] total strata in the control plots calculated following the procedures in Section 9.1.2, in tonnes C ha-1
[math: A_i] is the area of strata [math: i] in The Project Area, in ha
Assessment of uncertainty in the value of [math: CO_2e_{counterfactual}] estimated via measure remeasure techniques must follow the same requirements and procedure for assessing uncertainty in the measure remeasure approach for Project Area carbon quantification.
Projects using a measure and model approach for quantification of carbon stocks within The Project Area must use the same modeling approach for assessment of the [math: CO_2e_{counterfactual}]. For modeling [math: CO_2e_{counterfactual}] the model must follow the same implementation and parameterization as is used for The Project Area and only reflect differences in management practices. Project Proponents must follow all the requirements for model application and reporting for the counterfactual assessment as for the assessment of project area carbon storage.
[/G-YHCM-0]For each parameter within the model related to management practices, Project Proponents must provide evidence of the historical parameter values based on records from the enrolled properties over a minimum of three years or full crop rotation (whichever is longer) prior to project implementation. In order of preference, this evidence can include:
In addition to property-specific data, The Project Proponent must also indicate for each variable where regional data records from government bodies, academic/research institutions, international organizations, and/or peer-reviewed literature exist.
For each Reporting Period, The Project Proponent must report any newly available data based on the regional data sources, as well as report availability of any new regional data sources. Where there is a difference between the recent regional parameter values and the historical farm parameter values, the more conservative (i.e. the one yielding a higher counterfactual discount) must be used for the prediction of [math: CO_2e_{counterfactual}]. If there is a lag between the collection and reporting of regional data, the historical data may be used for years with as of yet unreported data, but the values must be updated in subsequent Reporting Periods to reflect the most recent data availability.
In lieu of the above data sources, Project Proponents may elect to take a probabilistic modeled approach for assessing counterfactual management practices following the procedure and requirements in Section 9.2.2.1. The model used to estimate [math: CO_2e_{counterfactual}] must satisfy the eligibility and validation requirements of Sections 9.1.3.1 with respect to the baseline management practices being modeled, in addition to The Project management practices.
Projects may elect to develop a statistical model for describing management practices in absence of The Project interventions within The Project area. This model must be developed and validated based on property- and region-specific data. The model must predict the probabilistic distribution of management practices within The Project area based on variables which are dynamically updated over the course of The Project. The Project Proponent should consider all potentially relevant factors to farm activity and management, including but not limited to:
The model must be approved by Isometric prior to use, with this section outlining criteria which will form the basis of the assessment. If approved, the distributions of management practices generated by the model can then be used as input parameters for the biogeochemical model for the prediction of a distribution of [math: CO_2e_{counterfactual}] values. Following initial approval, the continued performance of the model at each Reporting Period based on recent data must be demonstrated for continued use. If the model still falls below the performance thresholds, The Project must revert to the default procedure for modeled counterfactual determination.
The model development must be documented and shared with sufficient detail for replicability. At minimum, this should include details of model type, model structure, all input variables and sources, calibration procedure for selection and tuning of parameters and hyperparameters, details of data pre-processing/quality controls, and model code. The calibration data must be demonstrated to cover the range of historical scenarios within The Project area.
[/G-Q7NA-0]To be eligible for use, the performance of the model must be demonstrated at The Project and regional level using data that is withheld from the training process and both spatially and temporally blocked from the training data. The validation data must cover at least 80% of the range of each management parameter which is fed into the biogeochemical model. To be approved for use, the model skill for predicting the validation data must be demonstrated via:
The input parameters for the model are also subject to the sensitivity analysis requirements under the Protocol as part of the broader uncertainty assessment.
For each Reporting Period, the continued model performance must be demonstrated against all newly available regional data since the prior Reporting Period. The model may be recalibrated, but all details must be reported and the recalibration must apply across the full modeled project time period, including retroactively
The relative uncertainty of the modeled counterfactual carbon storage should use the same uncertainty assessed for the model using the validation against collected measurements.. Where Project Proponents elect to use the method described in Section 9.2.2.1 to generate probabilistic descriptions of management practice parameter values, the distributions of [math: CO_2e_{counterfactual}] generated as part of the procedure should be propagated through the broader assessment of uncertainty for The Project.
This is a hybrid approach to quantification pairing direct measurement and remeasurement of SOC stock change within The Project Area (Approach 1, Section 9.1.2), with a modeled counterfactual SOC trajectory (Section 9.2.2) in place of measured control plots. This approach may only be used where The Project Proponent demonstrates that control plots representative of The Project Area cannot be established, with the demonstration documented in the PDD and approved at validation. Project Proponents must also justify why a model is not capable of accurately representing The Project scenario.
Where this approach is used, The Project-scenario SOC stock change must be quantified by direct measurement under Section 9.1.2 and all its subsections, and the counterfactual SOC stock change must be modeled under Section 9.2.2, subject to the additional requirements of this Section. The model used for the counterfactual must satisfy the eligibility, validation, spatial-blocking, true-up, and (where applicable) multi-model-ensemble requirements of Section 9.2.2.3.4 with respect to the baseline management practices being modeled.
The quantification pairings derive the net project effect as the difference between two estimates established on a common basis. Measurement, seasonal, laboratory, and model-structural biases common to both estimates therefore substantially offset one another in the difference. This pairing does not achieve such offsetting: any systematic bias in the modeled counterfactual propagates without offset into the credited net effect. The requirements of this Section must be applied to ensure that residual bias in the modeled counterfactual acts only in the direction that reduces creditCertificate issuance.
The counterfactual term must be calculated from the same measured baseline SOC stock at project initiation (t₀), the same stratification, and the same reference mineral mass, so that the difference reflects only the divergence in SOC trajectory between The Project and counterfactual scenarios. This section should be applied in addition to Section 9.2.
The modeled counterfactual must be corrected for systematic bias in the conservative direction only. Where the bias tests in Section 9.1.3.1.6 detect that the model systematically predicts the counterfactual to lose more, or gain less, SOC than the baseline-like validation evidence supports, the modeled counterfactual stock change must be adjusted upward to remove the detected bias. Where the model predicts the counterfactual to gain more SOC than the validation evidence supports, no adjustment may be made; bias in this direction is conservative for creditingCertificate issuance and must be retained.
The uncertainty discount framework of Section 9.1.3.1 applies to the measured project-scenario component as for Approach 1. The modeled counterfactual component must receive the 1.0 SD true-up discount. Counterfactual model uncertainty must be propagated through the Section 9.1.1 Monte Carlo simulation.
The conservative estimate used for creditCertificate issuance must correspond to the 30th percentile of the distribution of [math: \Delta \mathrm{SOC}_{\mathrm{net,RP}}] across all simulation iterations, in line with Section 9.1.1. Where the 30th-percentile estimate is negative, no creditsCertificates may be issued for that Reporting Period.
A multi-model ensemble (MME), combining the outputs of two or more constituent biogeochemical models, may be used for SOC quantification or counterfactual estimation, subject to the requirements of Section 9.1.3 and the additional requirements of this Section. An MME comprises the constituent models and a combination procedure (the "ensemble layer") that weights, selects, or otherwise aggregates constituent outputs into a single prediction. Models must meet the following requirements:
Projects must complete Isometric’s Cropland Soil Carbon Risk Assessment in Appendix A and provide supporting evidence, where required.
[/R-1PJ3-0]The Cropland Soil Carbon Risk Assessment is independently evaluated by a third-party VVB. The Cropland Soil Carbon Risk Assessment is used to determine the risk profile of The Project, including risks to CreditCertificate delivery and storage. Aspects of The Project which have higher risk exposure must be accompanied by an appropriate risk mitigation plan. To safeguard against high risk projects, The Project must score below the indicated thresholds to be eligible for creditingCertificate issuance under this Module. The Cropland Soil Carbon Risk Assessment must be updated each Reporting Period by The Project Proponent and increased risk scores will result in additional mitigation activities.
Projects creditingissuing Certificates under this Module may determine their Buffer Pool contribution via either:
For any portion of The Project Area which is in an Ongoing Monitoring Period, The Project Proponent is responsible for continuing quantification of soil carbon stocks to monitor for reversals for the full duration of the ongoing monitoring period. Monitoring must follow the procedures and frequency used for the quantification of [math: CO_2e_{stored}] via the measure and remeasure approach (Section 9.1.2).
[/R-3YWC-0]If monitoring reveals a loss event representing a reduction of carbon stored in soil carbon stocks greater than 1% of the cumulative tonnes of CO2e removed by The Project (based on total number of CreditsCertificates issued), The Project Proponent must follow the requirements for reporting, investigating, and compensating for the Reversal set out in Section 10.4.3 of the Improved Soil Management Protocol (Buffer Pool Compensation Process).
If The Project Proponent is unable to conduct the required sampling within a property (e.g., no longer has requisite access to property), the property is considered to have experienced a full Reversal.
Quantification of Reversals must be calculated via the same methods and procedures as are used for the quantification of carbon storage via the measure and re-measure approach (Section 9.1.2). Declines in the measured soil carbon stocks within The Project Area are conservatively assumed to represent carbon that was immediately released to the atmosphere. In the scenario that measurements are not able to be carried out in the relevant portion of The Project Area, the area will be considered to have experienced a full reversal of the cumulative carbon removed.
Where a portion of The Project Area is within an Ongoing Monitoring Period and The Project Proponent has lost contractual access to that portion such that direct field sampling under Section 9.1.2 cannot be conducted, the default treatment is full Reversal of the cumulative tonnes creditedcertified to the affected portion, drawn from the Buffer Pool.
As an alternative to that default, a Project Proponent may elect to apply the Remote Monitoring of Reversals procedure set out in this sub-section, subject to the eligibility conditions, monitoring requirements, and conservatism rules below. Election must be made in the Monitoring Report covering the Reporting Period in which the loss of access first occurred and is irrevocable for that portion of The Project Area until the procedure either succeeds through to the end of the Ongoing Monitoring Period or fails into the default treatment.
Each Remote Monitoring Report must be independently reviewed by The Project's VVB at the same frequency as The Project's primary verification cycle.
Remote Monitoring of Reversals is available only where all of the following conditions are met for the affected portion of The Project Area:
Remote Monitoring under this sub-section must satisfy each of the following requirements:
The following events, detected through the Remote Monitoring procedure, trigger the immediate consequences set out below:
Isometric would like to thank the following external contributors to this Module:
The Cropland Soil Carbon Risk Assessment is used to assess the overall delivery and storage risk associated with the cropland management activities and may inform the Buffer Pool contribution during CreditCertificate delivery (see Section 10.2). The assessment must first be filled in by The Project Proponent with corresponding evidence supplied and must then be validated by a VVB. During project Validation, discrepancies between The Project Proponent’s self reported score and VVB may result in monitoring or risk mitigation activities, or project ineligibility. Eligible projects must have an initial risk score ≤ 20 and initial risk category scores at or below the following thresholds:
* Project Proponent Capacity Risk ≤ 7
* Financial Viability Risk ≤ 8
* Social Governance Risk ≤ 11
* Disturbance Risk ≤ 13
All risk categories have a minimum score of 0, regardless of the outcome of the Cropland Soil Carbon Risk Assessment.
For projects with discrete planting areas, the risk assessment must include all geographic areas relevant to The Project. For risk indicators that are geographically explicit (e.g., disturbance risks), the score may be calculated via an area-weighted average and rounded up to the nearest whole number.
If Project Proponents choose to forgo a flat 20% Buffer Pool contribution (see Section 10.2), the Cropland Soil Carbon Risk Assessment will inform Buffer Pool contributions for The Project according to the process outlined in Appendix A for each Reporting Period and in accordance with the requirements in Section 10.2.
After each new Cropland Soil Carbon Risk Assessment evaluation, Isometric will update the required percentage of newly issued CreditsCertificates that must be contributed to the Buffer Pool by The Project. We encourage Project Proponents to continuously monitor, mitigate, and reduce risks.
Table A1. Cropland Soil Carbon Risk Assessment, with the score to be filled out for each question.
Risk Category | Risk Indicator | Evidence | Scoring Guidelines | Score |
|---|---|---|---|---|
Project Proponent Capacity Risk | Does the Project Proponent maintain staff with domain expertise relevant for soil organic carbon projects? (e.g., sustainable agriculture, agroecology, soil science, carbon accounting) | Project's team structure | If no, describe how gaps in relevant expertise will be filled, +1. | |
Does the Project Proponent maintain a staff presence in the local vicinity (within one day of travel) of all portions of the project area? | Project's team structure | If no, +2. | ||
Was the Project Proponent established more than 12 months ago? | Project Proponent declaration | If no, +1. | ||
Does the Project Proponent have prior experience in agricultural carbon projects? | Review of Project Proponent provided evidence and independent research | If yes, -1. | ||
Has the Project Proponent abandoned or failed previous projects? | Review of projects on other registries | If yes, +3. | ||
What proportion of the project area requires active enforcement against external threats (e.g., illegal logging, other agricultural encroachment, unauthorized grazing) to protect carbon stocks? | Peer-reviewed publications, local or national government databases, NGO reports and assessments, site security assessment, satellite data, data on enforcement from other projects in the same region, local or national reports on environmental crimes or violations | If > 50% of project area, +2. If 25 to 50% of project area, +1. If no active enforcement required, -1. | ||
Financial Viability Risk | Has The Project secured funding to cover all activities required before carbon/commodity revenue accrues? | Project financial plan | If > 90%, -2. If > 80%, -1. If < 50%, +1. If < 30%, +2. If < 10%, fail. | |
What is the projected time to reach financial breakeven? | Project financial plan | If > 20 years, fail. If 15 to 20 years, +3. If 10 to 15 years, +2. If 5 to 10 years, +1. | ||
Is the budget reasonable given the proposed project activities and ex-ante estimates for carbon sequestration? Budget should at minimum include: personnel, equipment and supplies, infrastructure, travel and certification fees. | Project financial plan | If no, +2. | ||
Does the project financial plan demonstrate sufficient cash flow throughout the full Project Commitment Period to maintain project carbon stocks? | Project financial plan | If continued financial incentive is low compared to likely opportunity cost of harvest, +2. | ||
Does the project financial plan rely on future increases in market price for | Project financial plan | If yes, +1. | ||
Social Governance Risk | Are there currently or have there been disputes over land ownership over the last 20 years? | Jurisdictional history | If yes, +2. | |
Does the government have a history of revoking legal agreements regarding land ownership, access, and usage? | Jurisdictional history | If yes, +2. | ||
Does the project host country score below the 40th percentile on 3+ of the Worldwide Governance Indicators over the last 10 years? | If yes, +2. | |||
Does the government have an NDC in place that addresses corresponding adjustments/prevents double-counting of project | National registries | If no, +1. | ||
Does The Project have a detailed benefit-sharing plan that includes: clear distribution mechanisms, transparent criteria for beneficiary selection, a grievance resolution process, monitoring and reporting procedures? | Project financial plan | If no, +2. If missing elements, +1. If legally binding with all elements, -1. If audited by 3rd party with all elements, -1 | ||
Does the Project Proponent have a presence on human rights, environmental or labor infraction lists? | National registries | If yes, fail. | ||
Does the Project Proponent have ongoing legal disputes? | National registries | If yes, +1. | ||
Does the Project Proponent have a presence in negative press content? | Online search | If yes, +1. | ||
Have projects on Indigenous or Community Lands been identified? | Cross reference project documentation with Global Forest Watch | If no, fail. | ||
Are baseline activities primarily subsistence-driven? | Land use documentation, Socioeconomic surveys | If yes, proceed to (a) If no, proceed to (b) | ||
(a) Are there anticipated or demonstrated net positive community impacts? | Community impact assessment, project financial plan, socio-economic surveys | If no, +2. | ||
(b) What is the net present value (NPV) of alternative land use/management compared to project NPV? | NPV analysis comparing alternative uses to project activities over Crediting Period, price forecasts, discount rate justification | If > 150%, fail. If 100 to 150%, +3. If 50 to 100%, +2. If 20 to 50%, +1. If -20 to -50%, -1. If -50 to -100%, -2. If -100% or more, -3. | ||
Are opportunity cost risk mitigations in place? | Legal agreements protecting carbon stocks, Non-profit status documentation, grant/funding agreements | Legally protected for Crediting Period, -1. Legally protected for ≥ 100 years, -2. Non-profit status or secured additional funding, -1. | ||
Disturbance Risk | Fire risk | If > 10, +1. If > 30, +2. If > 50, +3. If > 75, fail. | ||
Pest and disease outbreak risk | Regional third-party maps, if available. | If high, +2. If medium, +1. If low, 0. | ||
Extreme weather (temperature - heat and cold) | IPCC AR612 - See Appendix B for scoring | If high, +2. If medium, +1. If low, 0. | ||
Extreme weather (hydrologic - flood and drought) | IPCC AR612 - See Appendix B for scoring | If high, +2. If medium, +1. If low, 0. | ||
Coastal risks (sea level rise, storm surge, tropical cyclones, salinity intrusion) | Regional third-party maps, if available. | If high, +2. If medium, +1. If low, 0. | ||
Geologic risks (earthquakes, tsunami, volcanoes) | If historical hazards in area, +1. | |||
Surrounding anthropogenic activities pose environmental risk (e.g., toxic pollution, new developments etc.) | Satellite imagery, site visit | If yes, +1. | ||
Ecological Resilience | Project Design Document | If > 80% of vegetation planted heat/drought tolerant, -1. | ||
Flood plain hazards | Project area overlap with identified floodplain based on Nardi et al., 201913 or regional/local equivalent | If > 25% of project area located in flood plain, +1. If > 50% of project area located in flood plain, +2. | ||
Inherent erosion risk | Assess mean inherent erosion risk potential for the project area (R x K x LS) capturing rainfall erosivity, soil erodibility, and slope using Borrelli et al. (2022) or equivalent localized datasets | If > 20 t/ha/yr, +1. If > 50 t/ha/yr, +2. If implementing management practices which reduce erosion risk, -1. |
The following section outlines how Isometric calculates the indicator scores for climate-related extreme weather disturbance risks to carbon permanence within a project’s region (Figure B1). Project Proponents should use Table B1 to lookup the Isometric-calculated values for their project's region and include those scores in their Risk Assessment (see Appendix A).
Extreme weather risks are assessed via two indicators: a temperature indicator (extreme heat and/or cold) and a hydrologic indicator (flooding and/or drought). Each indicator includes both historical data (1961-2015) and projected future extreme events. Historical data indicate the likelihood of extreme events based on past climate patterns, e.g., projects in regions with extended dry periods are expected to experience increased water stress as part of their typical climate. Climate model projections describe how changing climate conditions, relative to historical patterns, might present an increased risk of disturbances. Areas where there is a larger shift towards extreme conditions under future climate relative to their historical baseline have a greater disturbance risk (e.g., drier conditions relative to historical averages increases risk for drought-driven mortality).
To calculate the scores in Table B1, Isometric uses values from the Intergovernmental Panel on Climate Change AR6 report14. The temperature indicator is calculated using data describing the annual number of frost days (FD, minimum temperature below 0°C) and annual number of days with a maximum temperature above 40°C ([math: TX_{40}]) to capture extreme cold and extreme heat risks, respectively. The hydrologic indicator is calculated using data describing the maximum 5-day precipitation ([math: RX_{5Day}]) and annual maximum number of consecutive dry days (CDD) to capture risks of flooding and drought, respectively. All values come from the CMIP6 climate models. Future projections use the SSP2-4.5 medium term (2041-2060) scenario and are assessed as the change in value relative to a historical baseline (1961-1990).
For each indicator subcomponent, the region’s terrestrial median value is compared with the global terrestrial distribution of the same variable (Table B2). To convert the regional value into a subscore, regional values below the global 50th percentile are considered Low Risk, regional values equal to or greater than the global 50th percentile but below the 75th percentile are Medium Risk, and any regional values equal to or greater than the global 75th percentile are High Risk. Low Risks are given a subscore of 0, Medium Risks are 0.25, and High Risks are 0.5. The overall score for each of the indicators is calculated by summing the corresponding subscores, as described below:
[math: Indicator_{Temperature} = Historical_{TX_{40}} + Future_{TX_{40}} + Historical_{FD}]
(Equation B1)
[math: Indicator_{Hydrological} = Historical_{CDD} + Future_{CDD} + Historical_{RX_{5Day}} + Future_{RX_{5Day}}]
(Equation B2)
Projected change in the number of frost days is not included as a subcomponent since it is projected that they will decline under future climate across the globe, representing a low risk of future extreme cold events.
Figure B1. Map and lookup table for IPCC regional codes
[Image: image]
Table B1. Regional Lookup Table of Disturbance Risk
Region | Indicator | Variable | Time | Value | Risk | Score | Total |
|---|---|---|---|---|---|---|---|
NW North America (NWN) | Temperature | Frost Days | Historical | 224.2 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 23.5 | Low | 0 | 0.5 | |
Change (Days) | -1.4 | Low | 0 | ||||
5-Day Precip | Historical | 54.4 | Low | 0 | |||
Change (%) | 11.9 | High | 0.5 | ||||
NE North America (NEN) | Temperature | Frost Days | Historical | 242.9 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 25.1 | Low | 0 | 0.5 | |
Change (Days) | -2.7 | Low | 0 | ||||
5-Day Precip | Historical | 50.6 | Low | 0 | |||
Change (%) | 11.8 | High | 0.5 | ||||
Western North America (WNA) | Temperature | Frost Days | Historical | 128.4 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0.9 | Low | 0 | |||
Change (Days) | 2 | Low | 0 | ||||
Hydrological | CDD | Historical | 40.6 | Low | 0 | 0 | |
Change (Days) | -0.2 | Low | 0 | ||||
5-Day Precip | Historical | 67.3 | Low | 0 | |||
Change (%) | 5.5 | Low | 0 | ||||
Central North America (CNA) | Temperature | Frost Days | Historical | 104.9 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 4.7 | Low | 0 | |||
Change (Days) | 8.9 | Low | 0 | ||||
Hydrological | CDD | Historical | 23.7 | Low | 0 | 0.25 | |
Change (Days) | -0.4 | Low | 0 | ||||
5-Day Precip | Historical | 84.3 | Medium | 0.25 | |||
Change (%) | 6.7 | Low | 0 | ||||
Eastern North America (ENA) | Temperature | Frost Days | Historical | 116 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0.1 | Low | 0 | |||
Change (Days) | 0.6 | Low | 0 | ||||
Hydrological | CDD | Historical | 15.5 | Low | 0 | 0.75 | |
Change (Days) | -0.3 | Low | 0 | ||||
5-Day Precip | Historical | 89.4 | High | 0.5 | |||
Change (%) | 9 | Medium | 0.25 | ||||
Northern Central America (NCA) | Temperature | Frost Days | Historical | 15.9 | Low | 0 | 0 |
Days > 40°C | Historical | 4.7 | Low | 0 | |||
Change (Days) | 8.7 | Low | 0 | ||||
Hydrological | CDD | Historical | 51.6 | Low | 0 | 0.5 | |
Change (Days) | -0.2 | Low | 0 | ||||
5-Day Precip | Historical | 92.9 | High | 0.5 | |||
Change (%) | 6 | Low | 0 | ||||
Southern Central America (SCA) | Temperature | Frost Days | Historical | 0.1 | Low | 0 | 0 |
Days > 40°C | Historical | 0.5 | Low | 0 | |||
Change (Days) | 1.7 | Low | 0 | ||||
Hydrological | CDD | Historical | 39.7 | Low | 0 | 0.5 | |
Change (Days) | -1.6 | Low | 0 | ||||
5-Day Precip | Historical | 134.3 | High | 0.5 | |||
Change (%) | 4.3 | Low | 0 | ||||
Caribbean (CAR) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 24.3 | Low | 0 | 0.5 | |
Change (Days) | 0.1 | Low | 0 | ||||
5-Day Precip | Historical | 99.9 | High | 0.5 | |||
Change (%) | 0.8 | Low | 0 | ||||
NW South America (NWS) | Temperature | Frost Days | Historical | 0.6 | Low | 0 | 0 |
Days > 40°C | Historical | 0.1 | Low | 0 | |||
Change (Days) | 0.8 | Low | 0 | ||||
Hydrological | CDD | Historical | 28.6 | Low | 0 | 0.5 | |
Change (Days) | -0.2 | Low | 0 | ||||
5-Day Precip | Historical | 133 | High | 0.5 | |||
Change (%) | 7.5 | Low | 0 | ||||
Northern South America (NSA) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
Days > 40°C | Historical | 0.5 | Low | 0 | |||
Change (Days) | 9.4 | Low | 0 | ||||
Hydrological | CDD | Historical | 46.7 | Low | 0 | 1 | |
Change (Days) | 9.7 | High | 0.5 | ||||
5-Day Precip | Historical | 111.6 | High | 0.5 | |||
Change (%) | 5.5 | Low | 0 | ||||
NE South America (NES) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
Days > 40°C | Historical | 0.3 | Low | 0 | |||
Change (Days) | 4.4 | Low | 0 | ||||
Hydrological | CDD | Historical | 95 | High | 0.5 | 1.5 | |
Change (Days) | 6.3 | High | 0.5 | ||||
5-Day Precip | Historical | 144.3 | High | 0.5 | |||
Change (%) | 5.7 | Low | 0 | ||||
South America-Monsoon (SAM) | Temperature | Frost Days | Historical | 7.5 | Low | 0 | 0.25 |
Days > 40°C | Historical | 2.2 | Low | 0 | |||
Change (Days) | 11.5 | Medium | 0.25 | ||||
Hydrological | CDD | Historical | 64.8 | Medium | 0.25 | 1.25 | |
Change (Days) | 13.7 | High | 0.5 | ||||
5-Day Precip | Historical | 133.7 | High | 0.5 | |||
Change (%) | 6 | Low | 0 | ||||
SW South America (SWS) | Temperature | Frost Days | Historical | 35.8 | Low | 0 | 0 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 65.9 | Medium | 0.25 | 0.5 | |
Change (Days) | -4.2 | Low | 0 | ||||
5-Day Precip | Historical | 83 | Medium | 0.25 | |||
Change (%) | -0.9 | Low | 0 | ||||
SE South America (SES) | Temperature | Frost Days | Historical | 16.6 | Low | 0 | 0 |
Days > 40°C | Historical | 2.8 | Low | 0 | |||
Change (Days) | 5.5 | Low | 0 | ||||
Hydrological | CDD | Historical | 36.3 | Low | 0 | 0.75 | |
Change (Days) | 0.4 | Medium | 0.25 | ||||
5-Day Precip | Historical | 105.1 | High | 0.5 | |||
Change (%) | 8.4 | Low | 0 | ||||
Southern South America (SSA) | Temperature | Frost Days | Historical | 75.4 | Low | 0 | 0 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0.1 | Low | 0 | ||||
Hydrological | CDD | Historical | 20.5 | Low | 0 | 0.5 | |
Change (Days) | 1.6 | High | 0.5 | ||||
5-Day Precip | Historical | 57.5 | Low | 0 | |||
Change (%) | 3.4 | Low | 0 | ||||
Northern Europe (NEU) | Temperature | Frost Days | Historical | 150.3 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 18.5 | Low | 0 | 0.25 | |
Change (Days) | 0 | Low | 0 | ||||
5-Day Precip | Historical | 52.8 | Low | 0 | |||
Change (%) | 10 | Medium | 0.25 | ||||
Western & Central Europe (WCE) | Temperature | Frost Days | Historical | 109.7 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0.1 | Low | 0 | |||
Change (Days) | 0.7 | Low | 0 | ||||
Hydrological | CDD | Historical | 22.8 | Low | 0 | 0.5 | |
Change (Days) | 1.3 | High | 0.5 | ||||
5-Day Precip | Historical | 55 | Low | 0 | |||
Change (%) | 8.5 | Low | 0 | ||||
Eastern Europe (EEU) | Temperature | Frost Days | Historical | 171.4 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 1 | Low | 0 | |||
Change (Days) | 2.7 | Low | 0 | ||||
Hydrological | CDD | Historical | 27.6 | Low | 0 | 0.5 | |
Change (Days) | 1.1 | Medium | 0.25 | ||||
5-Day Precip | Historical | 42.9 | Low | 0 | |||
Change (%) | 10 | Medium | 0.25 | ||||
Mediterranean (MED) | Temperature | Frost Days | Historical | 27.6 | Low | 0 | 0.25 |
Days > 40°C | Historical | 6 | Low | 0 | |||
Change (Days) | 11.8 | Medium | 0.25 | ||||
Hydrological | CDD | Historical | 75 | High | 0.5 | 1 | |
Change (Days) | 6.7 | High | 0.5 | ||||
5-Day Precip | Historical | 49.5 | Low | 0 | |||
Change (%) | 3.9 | Low | 0 | ||||
Western Africa (WAF) | Temperature | Frost Days | Historical | 0 | Low | 0 | 1 |
Days > 40°C | Historical | 24 | High | 0.5 | |||
Change (Days) | 26.4 | High | 0.5 | ||||
Hydrological | CDD | Historical | 83.5 | High | 0.5 | 1.25 | |
Change (Days) | -0.4 | Low | 0 | ||||
5-Day Precip | Historical | 85.2 | Medium | 0.25 | |||
Change (%) | 19.6 | High | 0.5 | ||||
Central Africa (CAF) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0.25 |
Days > 40°C | Historical | 11.1 | Medium | 0.25 | |||
Change (Days) | 9.2 | Low | 0 | ||||
Hydrological | CDD | Historical | 61.8 | Low | 0 | 0.75 | |
Change (Days) | -0.1 | Low | 0 | ||||
5-Day Precip | Historical | 84.4 | Medium | 0.25 | |||
Change (%) | 14.9 | High | 0.5 | ||||
North Eastern Africa (NEAF) | Temperature | Frost Days | Historical | 0 | Low | 0 | 1 |
Days > 40°C | Historical | 16.1 | High | 0.5 | |||
Change (Days) | 15.4 | High | 0.5 | ||||
Hydrological | CDD | Historical | 80.4 | High | 0.5 | 1 | |
Change (Days) | -2.1 | Low | 0 | ||||
5-Day Precip | Historical | 64.6 | Low | 0 | |||
Change (%) | 15.4 | High | 0.5 | ||||
South Eastern Africa (SEAF) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0.3 | Low | 0 | ||||
Hydrological | CDD | Historical | 78.5 | High | 0.5 | 1.5 | |
Change (Days) | 0.4 | Medium | 0.25 | ||||
5-Day Precip | Historical | 91.9 | High | 0.5 | |||
Change (%) | 9.7 | Medium | 0.25 | ||||
West Southern Africa (WSAF) | Temperature | Frost Days | Historical | 1.2 | Low | 0 | 0 |
Days > 40°C | Historical | 0.1 | Low | 0 | |||
Change (Days) | 3 | Low | 0 | ||||
Hydrological | CDD | Historical | 108.7 | High | 0.5 | 1.5 | |
Change (Days) | 10.5 | High | 0.5 | ||||
5-Day Precip | Historical | 87.6 | High | 0.5 | |||
Change (%) | 2 | Low | 0 | ||||
East Southern Africa (ESAF) | Temperature | Frost Days | Historical | 2.7 | Low | 0 | 0 |
Days > 40°C | Historical | 0.7 | Low | 0 | |||
Change (Days) | 2.8 | Low | 0 | ||||
Hydrological | CDD | Historical | 68.7 | Medium | 0.25 | 1.25 | |
Change (Days) | 4.3 | High | 0.5 | ||||
5-Day Precip | Historical | 127.5 | High | 0.5 | |||
Change (%) | 6 | Low | 0 | ||||
Madagascar (MDG) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0.2 | Low | 0 | ||||
Hydrological | CDD | Historical | 46.7 | Low | 0 | 0.5 | |
Change (Days) | -1.2 | Low | 0 | ||||
5-Day Precip | Historical | 175.7 | High | 0.5 | |||
Change (%) | 5.8 | Low | 0 | ||||
Russian-Arctic (RAR) | Temperature | Frost Days | Historical | 271.3 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 31.5 | Low | 0 | 0.5 | |
Change (Days) | -4.3 | Low | 0 | ||||
5-Day Precip | Historical | 39.7 | Low | 0 | |||
Change (%) | 16.7 | High | 0.5 | ||||
West Siberia (WSB) | Temperature | Frost Days | Historical | 203.1 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0.7 | Low | 0 | |||
Change (Days) | 2 | Low | 0 | ||||
Hydrological | CDD | Historical | 31.4 | Low | 0 | 0.25 | |
Change (Days) | -0.4 | Low | 0 | ||||
5-Day Precip | Historical | 37.5 | Low | 0 | |||
Change (%) | 10.8 | Medium | 0.25 | ||||
East Siberia (ESB) | Temperature | Frost Days | Historical | 233.9 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0.1 | Low | 0 | ||||
Hydrological | CDD | Historical | 34.1 | Low | 0 | 0.25 | |
Change (Days) | -3.7 | Low | 0 | ||||
5-Day Precip | Historical | 54.5 | Low | 0 | |||
Change (%) | 11.5 | Medium | 0.25 | ||||
Russian-Far-East (RFE) | Temperature | Frost Days | Historical | 238.1 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 28.8 | Low | 0 | 0.5 | |
Change (Days) | -3.2 | Low | 0 | ||||
5-Day Precip | Historical | 65.1 | Low | 0 | |||
Change (%) | 14.4 | High | 0.5 | ||||
West Central Asia (WCA) | Temperature | Frost Days | Historical | 95.8 | High | 0.5 | 1.5 |
Days > 40°C | Historical | 22 | High | 0.5 | |||
Change (Days) | 17.5 | High | 0.5 | ||||
Hydrological | CDD | Historical | 113.6 | High | 0.5 | 0.75 | |
Change (Days) | -0.3 | Low | 0 | ||||
5-Day Precip | Historical | 42.5 | Low | 0 | |||
Change (%) | 10 | Medium | 0.25 | ||||
East Central Asia (ECA) | Temperature | Frost Days | Historical | 195.6 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 0.5 | Low | 0 | |||
Change (Days) | 2.5 | Low | 0 | ||||
Hydrological | CDD | Historical | 75.1 | High | 0.5 | 1 | |
Change (Days) | -6.2 | Low | 0 | ||||
5-Day Precip | Historical | 30.5 | Low | 0 | |||
Change (%) | 12.9 | High | 0.5 | ||||
Tibetan-Plateau (TIB) | Temperature | Frost Days | Historical | 258.6 | High | 0.5 | 0.5 |
Days > 40°C | Historical | 1.6 | Low | 0 | |||
Change (Days) | 0.4 | Low | 0 | ||||
Hydrological | CDD | Historical | 42.3 | Low | 0 | 0.75 | |
Change (Days) | -2.6 | Low | 0 | ||||
5-Day Precip | Historical | 80.9 | Medium | 0.25 | |||
Change (%) | 11.6 | High | 0.5 | ||||
East Asia (EAS) | Temperature | Frost Days | Historical | 91.7 | Medium | 0.25 | 0.25 |
Days > 40°C | Historical | 0.3 | Low | 0 | |||
Change (Days) | 0.7 | Low | 0 | ||||
Hydrological | CDD | Historical | 29.1 | Low | 0 | 0.75 | |
Change (Days) | 0.1 | Low | 0 | ||||
5-Day Precip | Historical | 132 | High | 0.5 | |||
Change (%) | 9.6 | Medium | 0.25 | ||||
South Asia (SAS) | Temperature | Frost Days | Historical | 7.9 | Low | 0 | 1 |
Days > 40°C | Historical | 33.1 | High | 0.5 | |||
Change (Days) | 14.5 | High | 0.5 | ||||
Hydrological | CDD | Historical | 93.9 | High | 0.5 | 1.5 | |
Change (Days) | -3.3 | Low | 0 | ||||
5-Day Precip | Historical | 132.2 | High | 0.5 | |||
Change (%) | 12 | High | 0.5 | ||||
Southeast Asia (SEA) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
Days > 40°C | Historical | 0.3 | Low | 0 | |||
Change (Days) | 1.1 | Low | 0 | ||||
Hydrological | CDD | Historical | 26.8 | Low | 0 | 0.75 | |
Change (Days) | 0.8 | Medium | 0.25 | ||||
5-Day Precip | Historical | 168.4 | High | 0.5 | |||
Change (%) | 7.3 | Low | 0 | ||||
Northern Australia (NAU) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0.75 |
Days > 40°C | Historical | 11.8 | Medium | 0.25 | |||
Change (Days) | 20.4 | High | 0.5 | ||||
Hydrological | CDD | Historical | 95.7 | High | 0.5 | 1.25 | |
Change (Days) | 0.7 | Medium | 0.25 | ||||
5-Day Precip | Historical | 163.7 | High | 0.5 | |||
Change (%) | 7.7 | Low | 0 | ||||
Central Australia (CAU) | Temperature | Frost Days | Historical | 0.1 | Low | 0 | 1 |
Days > 40°C | Historical | 27.8 | High | 0.5 | |||
Change (Days) | 75.8 | High | 0.5 | ||||
Hydrological | CDD | Historical | 27.3 | Low | 0 | 1 | |
Change (Days) | 3.5 | High | 0.5 | ||||
5-Day Precip | Historical | 86.5 | High | 0.5 | |||
Change (%) | 4.7 | Low | 0 | ||||
Eastern Australia (AU) | Temperature | Frost Days | Historical | 1.4 | Low | 0 | 0 |
Days > 40°C | Historical | 2.7 | Low | 0 | |||
Change (Days) | 4.2 | Low | 0 | ||||
Hydrological | CDD | Historical | 35.8 | Low | 0 | 0.5 | |
Change (Days) | -0.5 | Low | 0 | ||||
5-Day Precip | Historical | 120.6 | High | 0.5 | |||
Change (%) | 5.6 | Low | 0 | ||||
Southern Australia (SAU) | Temperature | Frost Days | Historical | 1.3 | Low | 0 | 0 |
Days > 40°C | Historical | 7.1 | Low | 0 | |||
Change (Days) | 6.9 | Low | 0 | ||||
Hydrological | CDD | Historical | 40.2 | Low | 0 | 0.5 | |
Change (Days) | 2 | High | 0.5 | ||||
5-Day Precip | Historical | 60.3 | Low | 0 | |||
Change (%) | 2.8 | Low | 0 | ||||
New Zealand (NZ) | Temperature | Frost Days | Historical | 5.9 | Low | 0 | 0 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 13.8 | Low | 0 | 0.75 | |
Change (Days) | 0.5 | Medium | 0.25 | ||||
5-Day Precip | Historical | 92.3 | High | 0.5 | |||
Change (%) | 5.6 | Low | 0 | ||||
South Pacific Ocean (SPO) | Temperature | Frost Days | Historical | 0 | Low | 0 | 0 |
Days > 40°C | Historical | 0 | Low | 0 | |||
Change (Days) | 0 | Low | 0 | ||||
Hydrological | CDD | Historical | 19.4 | Low | 0 | 0.5 | |
Change (Days) | -0.5 | Low | 0 | ||||
5-Day Precip | Historical | 183.3 | High | 0.5 | |||
Change (%) | 3.8 | Low | 0 |
Table B2. Global Benchmark Values for Extreme Weather Risks.
Time Frame | Variable | Median | 75th% | 90th% |
|---|---|---|---|---|
Historical | Frost Days | 89.8 | 94.1 | 97.5 |
Days Max Temp > 40°C | 9.9 | 15.1 | 21.9 | |
Consecutive Dry Days | 63.7 | 71 | 76.2 | |
Maximum 5-day Precipitation (mm) | 79.5 | 86 | 90.4 | |
Projected Future | Frost Days | |||
Days Max Temp > 40°C | 9.9 | 11.8 | 14.6 | |
Consecutive Dry Days | 0.3 | 1.1 | 1.8 | |
Maximum 5-day Precipitation (%) | 8.9 | 11.5 | 14.1 |
By default, Projects are subject to a flat 20% Buffer Pool contribution as outlined in Section 10.3. Project Proponents may opt to calculate a project-specific Buffer Pool contribution based on the outputs of their Cropland Soil Carbon Risk Assessment for each Reporting Period.
The following steps are used to convert the outputs of the Cropland Soil Carbon Risk Assessment into a Buffer Pool contribution:
Table C1. Risk score to Buffer Pool contribution conversion for each risk category.
Risk Category | Cumulative Risk Score | Buffer Pool Contribution |
|---|---|---|
Project Proponent Capacity Risk | 0 | 2.5% |
1 | 2.6% | |
2 | 3.1% | |
3 | 4.8% | |
4 | 7.7% | |
5 | 9.4% | |
6 | 9.9% | |
7 | 10.0% | |
Financial Viability Risk | 0 | 2.5% |
1 | 2.6% | |
2 | 2.9% | |
3 | 3.9% | |
4 | 6.3% | |
5 | 8.6% | |
6 | 9.6% | |
7 | 9.9% | |
8 | 10.0% | |
Social Governance Risk | 0 | 2.5% |
1 | 2.6% | |
2 | 2.7% | |
3 | 3.1% | |
4 | 3.9% | |
5 | 5.3% | |
6 | 7.2% | |
7 | 8.6% | |
8 | 9.4% | |
9 | 9.8% | |
10 | 9.9% | |
11 | 10.0% | |
Disturbance Risk | 0 | 2.5% |
0.25 | 2.5% | |
0.5 | 2.6% | |
0.75 | 2.6% | |
1 | 2.6% | |
1.25 | 2.6% | |
1.5 | 2.6% | |
1.75 | 2.6% | |
2 | 2.7% | |
2.25 | 2.7% | |
2.5 | 2.8% | |
2.75 | 2.8% | |
3 | 2.9% | |
3.25 | 3.0% | |
3.5 | 3.1% | |
3.75 | 3.2% | |
4 | 3.3% | |
4.25 | 3.5% | |
4.5 | 3.7% | |
4.75 | 3.9% | |
5 | 4.2% | |
5.25 | 4.5% | |
5.5 | 4.8% | |
5.75 | 5.1% | |
6 | 5.5% | |
6.25 | 5.9% | |
6.5 | 6.3% | |
6.75 | 6.6% | |
7 | 7.0% | |
7.25 | 7.4% | |
7.5 | 7.7% | |
7.75 | 8.0% | |
8 | 8.3% | |
8.25 | 8.6% | |
8.5 | 8.8% | |
8.75 | 9.0% | |
9 | 9.2% | |
9.25 | 9.3% | |
9.5 | 9.4% | |
9.75 | 9.5% | |
10 | 9.6% | |
10.25 | 9.7% | |
10.5 | 9.7% | |
10.75 | 9.8% | |
11 | 9.8% | |
11.25 | 9.9% | |
11.5 | 9.9% | |
11.75 | 9.9% | |
12 | 9.9% | |
12.25 | 9.9% | |
12.5 | 9.9% | |
12.75 | 10.0% | |
13 | 10.0% |
The Buffer Pool contribution for each risk category is determined using a sigmoid function described by Equation C1. The Buffer Pool contribution for each risk category ranges from 2.5% to 10%.
[math: BP_{risk} = \frac{L}{1 + e^{-k(x - x_0)}} + 2.5]
(Equation C1)
Where:
Regardless of whether The Project is taking the flat contribution or risk assessment-adjusted contribution, the additional contribution related to contract coverage (see Protocol Section 10.4.1) still applies in addition to the flat or risk-adjusted buffer pool contribution.
The Project has completed the Cropland Soil Carbon Risk Assessment and obtained the following risk scores in a Reporting Period:
Mapping these risk scores to Table C1, the total Buffer Pool contribution for The Project is:
3.1% + 6.3% + 3.1% + 2.9% = 15.4%
If The Project did not have contracts in place for all enrolled properties for the full duration of the Crediting Period, the total would be 20.4%.
Isometric has carried out a literature review of [math: ε_s] and [math: ε_d] values to inform [math: IS], as well as values for [math: NL] for certain regions. 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.
These 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.
The following sections set out the procedure to be followed to obtain [math: IS] and [math: NL] values and set out the default values to be used for the regions studied.
The regions considered in the literature review were:
These regions were selected following a review of projected project demand. Isometric will update this analysis with additional regions iteratively based on demand. Values for other regions will be reviewed by Isometric on a case by case basis.
[math: IS] represents the amount of production that is diverted to other locations. The [math: IS] value does not provide any information on where or in what manner that production is produced.
Procedure for determining [math: IS] values:
Where possible:
*
*
Table D1.[math: IS] default values.
Geography | Crop | εdc | εsc | [math: IS] | Key citation |
|---|---|---|---|---|---|
Global | Calories (rice, wheat, corn, soy) | -0.05 | 0.12 | 0.70 | Roberts and Schlenker15 |
Global | Coffee | -0.305 | 0.285 | 0.48 | Akiyama and Varangis16 |
Global | Cocoa | -0.075 | 0.075 | 0.50 | Askari and Cummings12 |
South America | Livestock | -0.40 | 0.4 | 0.5 | Fragoso et al.17 |
North America | Livestock | -0.40 | 1.6 | 0.80 | Mintert et al.18 |
Procedure for determining [math: NL] values:
In an ideal world, there would be estimates of the specific types of land use that were converted and their locations. However, this data is not available. Instead, The Project Proponent should focus on the most important elements of potential land use change from a carbon emissions perspective. [math: NL] values proposed aim to capture the net effect of a one unit removal of crop area on forestland conversion. These [math: NL] values will be smaller in magnitude than [math: NL] values that incorporate the possibility of conversion of grazing land or the conversion of lower-value crops to higher-value crops. Focusing on forests is more tractable and likely provides a large share of the relevant land use change emissions, since forest conversion is relatively permanent in a way that livestock to cropland conversion is not. In general, the [math: NL] values are more speculative than the [math: IS] values and often rely on assumptions about the yield-price elasticity that have not been empirically confirmed.
Two possible methodologies for obtaining [math: NL] values are set out here. Method B is in most cases the preferred approach. This is because the necessary conditions to implement Method A (limited trade/ disconnected markets and demand driven quantity increase) are rarely met in practice. Method A should only be used in special cases and justified appropriately. Both methods are set out below:
* Method A: In cases where a large increase in deforestation has accompanied a large increase in cropland, the ratio of land deforested for agriculture to total new agriculture is taken. Note, this procedure is only accurate for cases where (1) the deforestation followed a large demand-driven increase in production and (2) where the land is not well-connected to international markets. This approach is not reflected in the default values, as it is not an acceptable methodology for the majority of crop-regions.
* Method B: In most cases, such as the US, analyses of large changes in land use due to a policy shock is relied upon, and then the ratio of the percentage change in agricultural land to the percentage change in production is taken. This way of calculating [math: NL] is represented in the following definition:
[math: NL = \frac{{Gross\: new\: production\:}_x}{{Gross\: new\: production\:}_x +\ {\Delta \ Average\: yield \:}_x\: +\ Total\: land \:area}]
(Equation D1)
Where:
[math: x] is variable under the assumption that changes to supply are predominantly channeled through price changes19.
By dividing the numerator and denominator, the above equation can be reformulated as:
[math: NL = \frac{{\Delta \ Area\:}_x}{{\Delta \ Area\:}_x +\ {\Delta \ Yield \:}_x\:}]
(Equation D2)
Where:
The following default values have been gathered using Method B.
Table D2.[math: NL] default values.
Geography | Land use | Value | Key citation |
|---|---|---|---|
Brazil | cropland | 0.61 | Pendrill et al.20 |
US | cropland | 0.28 | Lark et al.21 |
Brazil | livestock | 0.83 | Bowman22 |
US | livestock | 0.20 | Wu23 |
Global | coffee | 0.60 | Report: "60% of land suitable for coffee is forested"24 |
The power analysis applies at the primary quantification unit level, not stratum-by-stratum. Where a stratified design is used, the total number of samples should be allocated across strata to meet The Project-level MDD using optimal (Neyman) allocation based on stratum area and within-stratum variance, or an equivalent allocation rule documented in the Project Monitoring Plan.
Where ancillary variables (e.g., remote-sensed indices, terrain attributes, digital soil maps) are used either to inform the design or to support a regression or model-assisted estimator at The Project level, the sample size calculation may incorporate the variance reduction expected from those covariates, provided the correlation has been quantified using project-area or comparable regional data and is reported transparently.
The MDD-based power analysis below uses the SD of location-level paired SOC stock changes (the differences between [math: t\_0] and the most recent sampling event at each location), because this directly reflects the noise in what The Project is trying to detect. Cross-sectional SD in absolute SOC stocks is not used and is generally a poor predictor of paired-difference variance.
The minimum number of samples needed to detect a given MDD at The Project level is calculated as:
[math: MDD = \frac{S}{\sqrt{n}} \times (t_{\alpha,\upsilon} + t_{\beta,\upsilon})]
(Equation E1)
[math: n \geq \left(\frac{S \times (t_{\alpha} + t_{\beta})}{MDD}\right)^2]
(Equation E2)
Where:
[math: MDD] is the minimum detectable difference in SOC stocks (t C ha[math: ^{-1}]);
[math: S] is the standard deviation of the SOC stock change at fixed sampling locations, i.e., the location-level differences [math: \Delta SOC_{loc} = SOC_{loc,t_n} - SOC_{loc,t_0}] computed at each composite sampling location, pooled across strata weighted by stratum area (t C ha[math: ^{-1}]). "Pooled" here refers to the statistical aggregation of within-stratum SDs into a project-level SD for the purposes of this power analysis, and does not imply any physical pooling of samples.
[math: n] is the minimum number of samples required;
[math: \upsilon = n - 1] is the degrees of freedom;
[math: t_{\alpha}] is the two-sided critical value of the t-distribution at significance level [math: \alpha]. [math: \alpha] should not exceed 0.05, meaning the sampling design should control the probability of falsely concluding a SOC change has occurred when none has, to no more than 5%.
[math: t_{\beta}] is the one-sided quantile of the t-distribution corresponding to the probability of a Type II error [math: \beta]. [math: \beta] should not exceed 0.10, meaning the sampling design should achieve at least 90% statistical power to detect a true SOC change of magnitude [math: MDD] when one is present.
The within-stratum standard deviation [math: S] used in the design-stage power analysis must be estimated using the most direct evidence reasonably available for The Project area, in the following order of preference:
Where database- or literature-derived variance is the only design-stage source available, The Project Proponent should conduct a pilot pre-sampling round of at least 10 sampling locations per stratum, drawn under the same probabilistic design as will be used for the full sampling campaign, prior to finalizing the sample size and committing to the first sampling campaign. The variance observed in the pilot replaces the database- or literature-derived estimate in the power analysis. The pilot samples are not creditCertificate-relevant and may be drawn at reduced analytical cost (e.g., proximal sensing where validated under Section 9.1.2.9, or single-increment composited samples).
The initial power analysis remains provisional and should be updated at each subsequent re-sampling event using observed within-stratum variance from the preceding Reporting Period. Where the observed variance is materially higher than was assumed at the design stage, the consequences for creditingCertificate issuance precision are absorbed through the uncertainty discount at creditCertificate issuance under Section 9.1.1; where the observed variance is materially higher than the design-stage power analysis can support at the proponent's chosen MDD, the sample size should be increased for the next Reporting Period.
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