Contents
Introduction
This Module provides the requirements and procedures for the quantification of net carbon dioxide equivalent (CO2e) removal from the atmosphere via improvements to soil organic carbon (SOC) stocks in grazing lands. This Module sits under the Improved Soil Management Protocol and must be read in conjunction with it.
Grasslands encompass nearly 2 billion hectares of global grazing land and sequester approximately 20% of the world’s terrestrial carbon stocks1. Recent FAO assessments indicate a global storage potential of 0.3 tonnes of Carbon (C) per hectare annually, though this varies significantly by soil type and climatic conditions1. Grazing lands are characterized by high below-ground biomass; root-derived carbon is incorporated into SOC more efficiently than above-ground litter, as it is deposited directly into the soil matrix where it is better protected from oxidation2. While intensive management, specifically heavy grazing, is a documented driver of soil organic carbon (SOC) depletion3, regenerative strategies offer a pathway for restoration although these must be balanced alongside other emissions from livestock systems including methane and nitrous oxide4,5.
This Module is process-agnostic with respect to the specific management practices employed. Eligible project activities encompass any practice or combination of practices that results in a demonstrable net increase in SOC stocks against a counterfactual baseline, provided all eligibility and safeguarding requirements set out in this Module and the parent Protocol are met. This approach reflects the wide diversity of grazing 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:
- Rotational or Adaptive Grazing: The movement of livestock between two or more areas of grazing lands following a schedule or based on observations of vegetation, allowing periods of vegetation recovery and growth between grazing periods.
- Improved forage species: The seeding of new forage species such as N-fixing legumes or deep seeding grasses to increase biomass production.
- Water management: The use of irrigation to establish vegetation on degraded soils or extend the growing season to reduce periods of bare soil.
- Brush burning or mulching: The strategic management of woody encroachment through prescribed fire or mechanical means to restore grassland health, stimulate root biomass, and maintain high-quality forage for livestock.
In addition to climate mitigation, improvements to SOC in grazing lands can provide environmental and social co-benefits, including enhanced soil water retention and drought resilience, reduced dependency on synthetic fertilizers, mitigation of erosion and nutrient runoff into waterways, and support for soil biodiversity 6,7,8,9. 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 the 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, whereas “should” indicates a recommendation.
Sources and Reference Standards & Methodologies
This Module relies on and is intended to be compliant with the following Standards and Protocols:
- The Isometric Standard
- Improved Soil Management Protocol v1.0, Isometric
- ISO 14064-2: 2019 — Greenhouse Gases — Part 2: Specification with guidance at the project level for quantification, monitoring, and reporting of greenhouse gas emission reductions or removal enhancements
Additional reference standards that inform the requirements of this Module include:
- ISO 14064-3: 2019 — Greenhouse Gases — Part 3: Specification with guidance for the verification and validation of greenhouse gas statements
- ISO 14040: 2006 — Environmental Management — Lifecycle Assessment — Principles & Framework
- ISO 14044: 2006 — Environmental Management — Lifecycle Assessment — Requirements & Guidelines
Additional principles that were considered in the development of this Module include:
- The Core Carbon Principles of The Integrity Council for the Voluntary Carbon Market, v1.1, ICVCM, 2024
- Criteria for High-Quality Carbon Dioxide Removal, Carbon Direct & Microsoft, 2025
Future Versions
This Module was developed based on the current state of the art and publicly available science regarding grazing lands 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.
Applicability
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.
Eligible Land
This Module applies to land that is managed under a grazing land management framework at the time of project initiation, or that has been managed under such a framework within the 5 years immediately prior to project initiation. Land that is grazed for the first time at project initiation, with no prior grazing land use, is not eligible; the Module targets improved management of established grazing systems.
Land that is an established grazing system currently destocked or under reduced stocking, for example due to drought, remains eligible beyond the 5-year window, provided the historical grazing framework is evidenced under this Section, the land has not been converted to a non-grazing land use, and it has not transitioned to a native ecosystem type excluded under Section 4.1.2. Reversion toward natural grassland or open rangeland does not affect eligibility, consistent with Section 4.1.2.
A grazing land management framework is land that is used for grazing livestock on a permanent or rotational basis, together with associated grasslands used for feed production. For the purposes of this Module, livestock means herbivores managed under a grazing land management framework.
Livestock may include traditional domesticated species (e.g., cattle, sheep, goats) and non-traditional herbivores, including non-native or semi-wild species , provided the Project Proponent demonstrates, in accordance with the safeguarding requirements of Section 6.1, that their grazing has no negative consequences for native habitat. Grazing by genuinely wild, unmanaged wildlife does not, by itself, constitute a grazing land management framework.
Grazing on natural (non-converted) grasslands, and open rangeland systems is within the scope of this Module. The native-ecosystem provisions of Section 4.1.2 concern the conversion of native ecosystems, not the presence of natural vegetation under grazing; land that is, and has remained, natural rangeland under an established grazing framework is eligible provided no conversion has occurred.
Project Proponents must provide evidence of a grazing land management framework in the Project Design Document. Acceptable evidence, in order of preference, includes: stocking, livestock-sale, and grazing-management records; property grazing management plans; and attestations or affidavits from landowners, managers, or knowledgeable third parties (e.g., neighboring operators or local agricultural authorities). Attestations and affidavits must meet the Tier 3 requirements of Section 4.3.1, including corroboration by at least one independent source, such as remote sensing imagery or derived products, regional land-use records or statistics, or Tier 2 records. Uncorroborated attestations are not sufficient to evidence a grazing land management framework.
A recent change of ownership does not remove the requirement to evidence the prior grazing framework. Where, following a change of ownership or management, the Project Proponent demonstrates that none of the forms of evidence listed above (including prior-operator and third-party records under Section 4.3.1) can reasonably be obtained, the prior grazing framework may be evidenced by remote sensing alone, provided that all of the following are met:
- the Project Proponent documents the efforts made to obtain records, plans, and attestations (e.g., requests to prior owners or operators, neighboring operators, and relevant agencies) and why they were unsuccessful;
- the remote sensing evidence covers at least three growing seasons within the period for which the grazing framework is claimed, including the most recent growing season in which grazing is claimed;
- the evidence comprises at least two of the following independent lines of evidence, interpreted together: (a) high-resolution imagery (≤ 3 m ground sample distance, e.g., aerial orthoimagery or commercial satellite imagery) showing livestock, or grazing infrastructure such as fencing, water points, corrals, and stock trails in functional condition; (b) a multi-temporal vegetation-index or fractional-cover time series (e.g., derived from Landsat or Sentinel-2 imagery, or from established rangeland monitoring products) showing defoliation and regrowth patterns consistent with grazing and distinguishable from ungrazed reference areas and from haying or cropping; and (c) land-cover or land-use classification products classifying the land as grassland, pasture, rangeland, or shrubland, and not as arable cropland, in each year of the evidence period;
- the data sources, methods, and interpretation are documented in sufficient detail for the VVB to reproduce them, and are cross-checked against regional land-use records or statistics where available; and
- the evidence is reviewed by the VVB at Validation.
Where a Project Proponent cannot establish the prior grazing framework by any of these means, the land is not eligible.
Fodder Cropping
Arable croplands, including those in which fodder crops are grown in rotation, are excluded from this Module.
For the purposes of this Module, arable cropland is land that is cultivated for the production of annual crops (including annual fodder crops such as maize for silage or small grains), or on which perennial forage is grown in rotation with annual crops. A perennial forage stand that meets all of the conditions for perennial forage stands set out below is not arable cropland, and a break crop permitted under the Stand life and break crops condition does not place the stand in an annual crop rotation. Grassland is land whose vegetation is dominated by perennial grasses, legumes, or forbs, whether native or sown, that is managed for grazing or forage production and is not part of an annual crop rotation.
Perennial forage stands. Perennial forage stands, such as alfalfa or grass–legume hay stands, that form part of the grazing operation and whose forage is grazed or harvested for livestock within the Project are associated grasslands used for feed production. They may be included in the Project Area, including where they are irrigated or where the stand is re-established by tillage, provided that all of the following conditions are met:
- Baseline land use. The land has been maintained as grassland, or as a perennial forage stand meeting these conditions, throughout the five years preceding project initiation or, for an area added after Validation, the date it is added.
- Stand life and break crops. The stand is not part of an annual crop rotation (a break meeting this condition does not constitute one; see the definition of arable cropland above). Each stand is maintained in perennial forage for a minimum of four growing seasons after the establishment year and is re-established as perennial forage. Any break between stands is limited to a single growing season of a small-grain, annual grass, or cover crop used as livestock feed within the Project (for example, to manage alfalfa autotoxicity). Maize, sorghum, and other row crops are not permitted as break crops.
- Management records. Stand establishment and re-establishment, including any tillage, and any break crop are recorded in the GHG Statement as management events, with their dates and locations.
- Quantification. The stands are delineated as a separate stratum or quantification unit for SOC quantification under Section 9, and are sampled in accordance with the stand-cycle timing requirement in Section 9.1.2.1.
- Forage sold off the operation. Where forage from the stands was sold outside the Project during the baseline period, forage output is assessed as an additional commodity class under Section 8.3, in accordance with Section 8.3.2.1.1.
- No double enrollment. The stands are not enrolled under any other Module of the Improved Soil Management Protocol or under another GHG program.
System boundary treatment. Where perennial forage stands are included in the Project Area, the GHG sources associated with forage production on those stands are within the system boundary. They must be assessed in accordance with Section 9.5 of the Improved Soil Management Protocol and the "Perennial forage stand production" SSR in Table 1. These sources include: stand re-establishment and any break crop; the manufacture, transport and application of fertilizers, lime and other inputs, including direct CO₂ from liming and urea; direct and indirect N₂O from nitrogen inputs, nitrogen-fixing species and crop residues; irrigation energy; and harvesting, baling, ensiling, and the on-farm transport and storage of forage.
In accordance with Section 8.1.1, emissions from forage-production activities that were occurring in the baseline and are not materially altered by the Project may be excluded, so that only emissions above the baseline are quantified, provided all of the conditions in Section 8.1.1 are demonstrated. Where those conditions are not demonstrated, the full emissions must be quantified. Any increase relative to the baseline in the area of perennial forage, the frequency of re-establishment or harvest, the irrigated area, or input rates is a material alteration, and the resulting incremental emissions must be quantified. Consistent with Section 8.1.3, a reduction in one source must not be offset against an increase in another. Forage produced on these stands and fed to livestock within the Project is not imported fodder under Section 8.4.
Forage from land outside the Project Area. Forage produced on land outside the Project Area is imported fodder and must be accounted for under Section 8.4. This includes perennial forage stands or arable land on the same operation that are not included in the Project Area, or that do not meet the conditions above.
Project Proponents must classify each forage-producing area of the operation as inside or outside the Project Area at Validation, and record the classification in the Project Design Document and the boundary shapefile. The classification may change during the Project Commitment Period, for example where a perennial forage stand is added to or removed from the Project Area. Each change must meet all of the following:
- the change takes effect from the start of a Reporting Period and applies to the whole of that Reporting Period;
- the change, the reason for it, and the areas affected are described in the GHG Statement for that Reporting Period, with an updated boundary shapefile, for review by the VVB at the next Verification;
- pre-project and project-scenario feed imports are calculated on the same classification in every Reporting Period. Where the classification changes, Pre-Project Feed Imports () under Section 8.4.3 must be recalculated on the revised basis: forage from an area now inside the Project Area is excluded from both and , and forage from an area now outside it is included in both. Where the baseline forage supplied by the affected area cannot be quantified from Tier 1 or Tier 2 records (Section 4.3.1), the value adverse to The Project must be used: the upper credible value when an area is added, and the lower credible value when an area is removed;
- where the change alters the area supporting livestock, the Pre-Project Headage Rate, Stocking Rates and Pre-Project Productivity under Section 8.3 are recalculated on the revised area;
- an area added to the Project Area meets the conditions for perennial forage stands above, with the five-year baseline land-use condition assessed against the date it is added. It must be sampled at t₀ before it contributes to quantification under Section 9, and its forage-production emissions are quantified within the system boundary from the Reporting Period in which it is added; and
- an area removed from the Project Area is treated as unenrolled under Section 5.1.3 for any Certificates attributable to it (Section 10.4.2.1).
Recalculated values apply from the Reporting Period in which the change takes effect. Deductions for earlier Reporting Periods are not restated.
Projects must provide a shapefile of the Project boundaries and describe the rationale for the selection of the Project site, including any areas excluded from within the boundaries.
Land Use Exclusions
Project Proponents must not include any of the following land types within the Project Boundary:
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R-60Z0-0Land that was converted from native ecosystem cover, including native grassland, native forest, wetlands (terrestrial or tidal, including peatlands, marshes, and mangroves), or other high-conservation-value habitat, at any point within the 10 years prior to project initiation. Land that holds, and has retained, native ecosystem cover under an established grazing land management framework is not excluded by this paragraph (see Section 4.1). 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.
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G-02PS-0Land that was converted from forest, woodland, or shrubland to a grazing system within the 10 years prior to project initiation, unless the Project Proponent can demonstrate that the conversion was the result of a documented natural disaster or is consistent with non-industrial common practice in the region unrelated to carbon market activities or incentives. Evidence must include historical land ownership records, remote sensing data, land manager attestations, or traditional ecological knowledge documentation (e.g., documented oral histories or elder testimony, participatory community mapping of historical land use, or records of customary or seasonal land-management practices, provided it is gathered through structured methods and is verifiable and attributable to identified knowledge holders).
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G-KQJE-0Land classified as wetland, peatland, or organic soil (histosol), as evidenced by national or regional soil maps, land cover classification, or site-specific soil survey data. This exclusion applies regardless of whether the land is currently under grazing land management.
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G-7CE3-0Land under active litigation or dispute regarding ownership, tenure, or use rights, unless the dispute has been resolved prior to project validation, or unless all of the following are demonstrated to Isometric's satisfaction and verified by the VVB: (i) all parties to the dispute consent in writing to the land's enrolment; (ii) the Project Proponent demonstrates that the Project will not affect the resolution of the dispute and that the dispute will not affect the project's activities, carbon outcomes, or its ability to maintain the durability commitment for the Project Commitment Period, regardless of the dispute's outcome; and (iii) sufficient evidence supports both conditions. Where a dispute arising during the Project Commitment Period cannot satisfy these conditions and materially threatens the durability commitment or the rights of affected parties, the affected area is treated in accordance with the withdrawal and reversal provisions of this Module.
- Fractionated or undivided ownership interests (for example, heirship interests in allotted land in the United States, or undivided family interests under local law) and pending probate or estate administration do not, of themselves, constitute active litigation or dispute regarding ownership, tenure, or use rights for the purposes of this exclusion or of the corresponding land-dispute question in the Appendix A Risk Assessment, provided that the consents and approvals required under applicable law for the land's enrollment have been obtained and the applicable consent pathway in Section 5.1.1 of the Improved Soil Management Protocol (including the multiple-owner pathway) is satisfied. Where the ownership, partition, or use of the enrolled land is itself contested in a pending legal or administrative proceeding (e.g., a contested probate or a partition action), the land remains subject to this exclusion and the conditions above.
Definitions for Native Ecosystems and High Conservation Value Land
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G-EP5D-0Native forest — Land that meets both of the following criteria and is dominated by tree species indigenous to the region that have established and regenerated through natural processes:
- (i) Structure: the land meets the FAO forest definition (land spanning more than 0.5 ha with trees higher than 5 m and canopy cover of more than 10%, or trees able to reach these thresholds in situ, excluding land predominantly under agricultural or urban use). For the purposes of this definition, use of land for livestock grazing does not by itself constitute agricultural use; and
- (ii) Classification: the land is classified as a forest (tree-dominated) vegetation type under an established vegetation classification system, namely LANDFIRE Existing Vegetation Type in the United States or, elsewhere, an equivalent national vegetation or land-cover classification or the FAO Land Cover Classification System. Where the Project Proponent demonstrates that the classification product is inaccurate for the land in question, a documented site-level vegetation assessment applying the same classification system may be used instead, subject to review by the VVB.
- Native forest includes primary and naturally regenerating forest and excludes planted or plantation forest. Savanna, wooded rangeland, and other tree-scattered systems with a grass-dominated understory that are not classified as a forest vegetation type under criterion (ii) are not native forest for the purposes of this Module, irrespective of canopy cover; where such land meets the definition of native grassland below, it is treated as native grassland. In such systems, clearing of native woody vegetation within the lookback period is treated as conversion of native ecosystem cover under Section 4.1.2, except where it is the control of documented woody encroachment (i.e., expansion of woody species into areas previously dominated by grassland or savanna, evidenced by historical imagery, ecological site descriptions, or equivalent sources) or brush or fire management consistent with the historical management regime of the land. Delineation must be supported by remote sensing imagery and/or land-cover classification against these criteria.
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Native grassland — A grassland dominated by grass and herbaceous species indigenous to the region that has formed and persisted through natural processes, distinct from sown, "improved," or cultivated pasture composed of introduced forage species, and distinct from grassland created by clearing forest. For delineation, native grassland is characterised by a dominance (≥ 50%) of indigenous, non-sown species cover and the absence of documented cultivation or sowing, evidenced by land-cover classification and a remote-sensing time series.
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Native wetland — A wetland whose hydrology, soils, and biological communities have formed and persisted through natural processes, and whose vegetation is dominated by water-adapted species indigenous to the region.
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High-conservation-value habitat — Habitat holding one or more of the six High Conservation Values as defined by the HCV framework: HCV1 (concentrations of biological diversity, including endemic, rare, threatened or endangered species); HCV2 (large landscape-level ecosystems and mosaics); HCV3 (rare, threatened or endangered ecosystems, habitats or refugia); HCV4 (critical ecosystem services, including watershed protection and erosion control); HCV5 (sites and resources fundamental to the basic needs of local communities or Indigenous peoples); and HCV6 (sites and resources of cultural, archaeological or historical significance). For the exclusions above, conversion of an HCV3 ecosystem is treated as conversion of native ecosystem cover; the presence of any HCV1–6 triggers the no-harm and enhancement requirement rather than exclusion.
High Conservation Values present without conversion. Where high-conservation-value habitat is present within the Project Area but no conversion of native ecosystem cover has occurred within the lookback period, the land is not excluded under this Section by reason of that habitat alone. In such cases the Project Proponent must demonstrate, in accordance with the safeguarding requirements of Section 6.1, that project activities do not harm the High Conservation Values present and, where feasible, enhance them.
Project-induced conversion of native grassland, native forest and HCV habitat is prohibited.
Support for Biodiversity and Community Livelihoods
Following requirements under the Protocol, Project Proponents should implement practices which can provide additional ecological benefits in addition to increasing carbon sequestration. SOC-enhancing land management practices, including forage diversification, rotational grazing, and brush management are encouraged where they deliver co-benefits for soil health, water quality, and habitat connectivity in addition to 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 6.6.3 of the Improved Soil Management Protocol for requirements on engagement with enrolled landowners and land users, and Section 6.6.3.6 of the Improved Soil Management Protocol for requirements on revenue sharing.
Where a revenue-sharing arrangement with enrolled landowners is expressed as a percentage, the revenue-sharing percentages made public under Section 6.6.3.6 of the Improved Soil Management Protocol, and the revenue-sharing arrangement disclosed to enrolled parties under Section 6.6.3.5 of the Improved Soil Management Protocol, must state the base on which the percentage is calculated (for example, gross revenue from the sale of Certificates, or revenue or profit net of specified costs). Where the base is not gross Certificate revenue, the disclosure must also identify the categories of cost deducted and state the equivalent percentage of gross Certificate revenue projected in the financial model submitted with the Project Design Document (PDD). Absolute revenue figures, individual transaction values, and Buyer-specific pricing are not required to be made public.
Enrolled parties must be categorized in accordance with Section 6.6.3.2 of the Improved Soil Management Protocol. Owners of commercial (non-subsistence) grazing operations fall within the Landowner category and, where they manage the enrolled land, the Operator category; they do not fall within the Subsistence smallholder category unless they meet its description. This Module does not set a different revenue-share threshold from that in Section 6.6.3.6 of the Improved Soil Management Protocol.
Other Requirements
The Project must not result in net disturbance of existing soil carbon pools beyond that occurring in the baseline scenario.
The Project's primary commitment is to maintain cumulative SOC stocks at or above the level corresponding to Certificates 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 Proponent must maintain SOC stocks in The Project Area, monitor for Reversals, and compensate for any Reversal for the full length of the Project Commitment Period, which must be a minimum of 40 years (see Section 5.1). This 40-year minimum is the duration of the Project Proponent's storage obligation. It is distinct from the Durability assigned to Certificates issued under this Module, which is set in Section 5.3 at one half of the length of the Project Commitment Period (a minimum of 20 years).
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The primary commitment is to the maintenance of cumulative SOC stocks at or above the level corresponding to Certificates previously issued, as quantified through the monitoring framework set out in Section 9.
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Project Proponents may adapt the specific land management practices implemented during the Project Commitment Period, provided that any such adaptation:
does not breach any other applicability requirement of this Module or the parent Protocol;
is documented is in the next verification and unforeseen changes are notified within 90 days of implementation; and
is accompanied by evidence, through the Section 9 framework, that the 30th-percentile estimate of cumulative SOC stocks (calculated in accordance with Section 9.1.1) remains at or above the level corresponding to Certificates previously issued. Reversion to baseline land management practices, or any practice change for which the 30th-percentile cumulative SOC stock estimate falls below the level corresponding to Certificates previously issued, will be treated as a reversal in accordance with the Isometric Standard.
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Independently of the outcome-based test set out above, Project Proponents must notify Isometric in writing of any material change to the land management practices implemented within the Project Boundary. A planned material change must be notified before it is implemented. A material change made in response to circumstances that could not reasonably have been foreseen (e.g., drought, wildfire, flood, disease, or a regulatory order) must be notified within 90 days of its implementation. In all cases, notification must be made no later than the submission of the GHG Statement for the Reporting Period in which the change occurs. The notification must describe the change, the enrolled areas affected, the date or planned date of implementation, and the reasons for the change.
- A change is material where it alters, replaces, or discontinues a practice identified in the Project Design Document as responsible for generating the SOC increase, or where it could otherwise reasonably be expected to affect SOC stocks within the Project Area. Operational variation within the project grazing regime described in the Project Design Document, including its stocking ranges and adaptive-management decision rules, is not a material change. the Project Design Document must describe the project grazing regime with sufficient specificity (e.g., target stocking ranges, planned rest periods, and adaptive-management decision rules) to allow material changes to be distinguished from operational variation. For example:
- Material changes include: reverting from planned rotational or adaptive grazing to continuous or set-stocked grazing; discontinuing planned rest or recovery periods; stocking rates or grazing intensity outside the ranges set out in the project grazing regime for more than one grazing season; and introducing tillage-based pasture renovation, reseeding, cropping, irrigation, fertilizer or other soil amendments, prescribed burning, or mechanical brush management not described in the Project Design Document.
- Routine operational variation, which is not a material change, includes: adjusting paddock move dates, grazing durations, rotation sequence, or herd numbers within the ranges and decision rules of the project grazing regime; temporary destocking or deferral in response to drought, fire, or other exogenous events in accordance with the drought or contingency provisions of the project grazing regime; repair or like-for-like replacement of existing fencing and water infrastructure; subdividing paddocks to implement the same grazing system; and changes in livestock class or breed that keep stocking within the ranges of the project grazing regime.
- Where a Project Proponent is uncertain whether a change is material, it should notify Isometric; notifying a change that is subsequently determined not to be material carries no adverse consequence. All practice changes, whether or not material, must be described in the GHG Statement for the Reporting Period in which they occur. Failure to notify a material practice change will be treated as a non-conformance and may result in the suspension of crediting until the change has been assessed and verified."
- A change is material where it alters, replaces, or discontinues a practice identified in the Project Design Document as responsible for generating the SOC increase, or where it could otherwise reasonably be expected to affect SOC stocks within the Project Area. Operational variation within the project grazing regime described in the Project Design Document, including its stocking ranges and adaptive-management decision rules, is not a material change. the Project Design Document must describe the project grazing regime with sufficient specificity (e.g., target stocking ranges, planned rest periods, and adaptive-management decision rules) to allow material changes to be distinguished from operational variation. For example:
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The Project Proponent must provide evidence that the land management practices required to maintain SOC stocks can be sustained throughout the Project Commitment Period. This includes evidence that, for each enrolled area, the landowner or operator holds legal, documented land tenure or rights of use for the duration of the Crediting Period applicable to that area, in accordance with Section 5.1.1 of the Improved Soil Management Protocol. Consistent with Section 5.1 of the Improved Soil Management Protocol, the Project Commitment Period is a project-level obligation held by the Project Proponent. Individual tenure arrangements and contracts with enrolled landowners or operators are not required to run for the full Project Commitment Period, and land held under shorter or renewable tenure (e.g., fixed-term or renewable grazing leases or permits, including on public or state trust land) may be enrolled, provided that:
the Project Proponent demonstrates how monitoring and reversal liability for that land will be sustained for the remainder of the Project Commitment Period, including through any Ongoing Monitoring Period (Sections 5.1.3 and 10.4);
the Contract Coverage Buffer contribution set out in Section 10.4.1 of the Improved Soil Management Protocol is applied for each Reporting Period in which contracts do not cover the full remaining Project Commitment Period; and
for land held by a government or in commons, or held in trust, the requirements of the applicable pathway in Section 5.1.1 of the Improved Soil Management Protocol are met.
- Loss of access to such land during the Project Commitment Period is treated in accordance with Sections 5.1.3 and 10.4.
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Where the Project involves smallholder farmers or customary land users, land tenure arrangements must be consistent with the farmer and land user rights requirements of Section 6.6 of the Improved Soil Management Protocol.
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R-DAPA-0The Project must not transform the land use of the Project Area such that it ceases to be managed as a working grazing system.
- This includes:
- converting grazing land or pasture historically used for livestock production to a different land use (e.g., cropland, plantation forestry, or built development);
- abandoning grazing, or ceasing grazing management such that the land is no longer managed as a working grazing system;
- excluding livestock from all or part of the Project Area where the exclusion does not form part of a working grazing regime described in the Project Design Document (e.g., permanently retiring land from grazing); and
- planting trees.
- The following are permitted project activities, and do not constitute land-use transformation under this Section, where they are implemented as part of the project grazing regime described in the Project Design Document:
- planned rest, deferral, and rotational-recovery periods within a working grazing regime, including where such periods were not part of the baseline management system;
- reductions in stocking below historical stocking rates implemented as planned recovery from documented historic overstocking, where the Project Design Document documents the evidence of overstocking (e.g., against a carrying-capacity or rangeland-condition assessment) and the intended stocking trajectory;
- temporary destocking or deferral in response to drought, fire, flood, disease, regulatory orders, or other exogenous events (see Section 8.3.4); and
- exclusion of livestock from riparian zones, wetlands, or other sensitive areas within an otherwise working grazing system, as contemplated in Section 6.1.
- The productivity effects of these activities are addressed quantitatively through the productivity and leakage assessment of Section 8.3, not through this Section. Their inclusion in the project grazing regime does not exempt the Project from Section 8.3: leakage deductions apply to any productivity shortfall in accordance with Section 8.3, the crediting-suspension threshold of Section 8.3.3 applies to any project-attributable decline in net productivity, and the relief for exogenous events in Section 8.3.4 applies where its conditions are met. These activities also remain subject to the food security and pastoral productivity safeguards of Section 6.1.
- The Project must seek to maintain or enhance agricultural productivity, including livestock and forage productivity, on project sites over the medium to long term.
- G-9MRC-0Short-term reductions in productivity during the transition to the project grazing regime are not inconsistent with this requirement where the Project Design Document describes the expected productivity trajectory, supported by the grazing plan and by peer-reviewed literature or other documented evidence; any such reductions remain subject to Section 8.3.
- This includes:
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Planting trees and comparable land-use transformations are scoped under the Isometric Agroforestry and Reforestation Protocols.
Evidence Where Records Are Limited
This Section applies wherever this Module requires a Project Proponent to evidence historical, pre-project, or other site-specific facts or parameters (for example, prior land use and management, the prior grazing land management framework, livestock numbers and stocking, feed imports, and practice history) and the preferred evidence is incomplete or unavailable, including where records were never kept or have been lost, or where ownership, management, or customary rights over the land have changed. Where another Section of this Module sets specific evidence requirements, gap-filling rules, or default values for a particular fact or parameter, those requirements take precedence over this Section to the extent of any inconsistency, and this Section applies to matters they do not address.
Evidence must be drawn from the highest of the following tiers that is reasonably available for the fact or parameter in question:
- Tier 1: Contemporaneous project-specific records. Records created at or near the time of the events they record by the landowner, operator, or manager of the enrolled land, for example grazing and stocking records, livestock inventories and movement logs, livestock sale and purchase records, feed and input invoices, financial accounts, grazing management plans, and GPS or collar data.
- Tier 2: Prior-operator and third-party records. Records relating to the enrolled land that are held by a prior owner or operator or were created by an independent third party, for example livestock sale receipts and auction or saleyard records, brand inspection or animal identification and traceability records, tax schedules and agricultural-use property tax classifications, veterinary records, grazing leases, government agency permits and grazing allotment or permit records, and agricultural program or disaster-assistance records.
- Tier 3: Corroborated attestations. Signed and dated attestations or affidavits from landowners, managers, prior operators, or knowledgeable third parties (for example neighboring operators, extension officers, local agricultural authorities, or customary authorities), including traditional ecological knowledge documented in accordance with Section 4.1.2. Each attestation must identify the attester, their relationship to the enrolled land, the basis of their knowledge, and the period to which it relates, and must be corroborated by at least one independent source, such as remote sensing imagery or derived products, regional statistics, or Tier 2 records. Uncorroborated attestations are not sufficient.
- Tier 4: Regional or default values. Values derived from government statistics at the most local level available that is representative of the enrolled land's production system and agro-ecological context (by default the county or equivalent sub-national unit), or from peer-reviewed literature, or, where neither is available, from national statistics, FAOSTAT, or IPCC default values. Tier 4 values may be used for quantitative parameters but must not, on their own, be used to establish a site-specific fact such as the existence of a prior grazing land management framework or the absence of land-use conversion, unless another Section of this Module expressly permits it.
Where evidence below Tier 1 is relied upon, the Project Proponent must:
document, in the Project Design Document or the relevant GHG Statement, the fact or parameter to which the evidence applies, the efforts made to obtain higher-tier evidence and why it is unavailable, and why the evidence relied upon is credible and representative of the enrolled land;
where more than one source is available, cross-check the sources and explain any material discrepancy;
where the evidence is used to set a quantitative value, apply the value conservatively by selecting, from the range supported by the evidence, the value that is adverse to the Project for each use to which the value is put. Where the same parameter is used in more than one calculation, the conservative value must be determined separately for each use (for example, the higher credible value of pre-project stocking where it determines Pre-Project Productivity under Section 8.3, and the lower credible value where it determines the Methane Neutrality condition under Section 8.5.1); and
submit the evidence and justification for review by the VVB at Validation or Verification, as applicable. Isometric may require additional corroboration, or may reject evidence it considers insufficient.
This Section applies principally to historical and pre-project information. From the Project Enrollment Date, Project Proponents must maintain Tier 1 records for project-scenario activities and parameters. Lower-tier evidence may be used for a project-scenario parameter only to fill an isolated, documented gap, subject to the requirements above; repeated or systematic reliance on lower-tier evidence for project-scenario data is not permitted."
Project Timelines
Project Commitment Period
The Project Commitment Period encompasses the Crediting Period and any Ongoing Monitoring Period commitments following the end of this period.
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 in the PDD submission.
For grouped projects where new areas are added to the Project over time, the Project Timeline may be staggered across Project areas to reflect different initiation times of Project activities.
Crediting Period
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Definition. The full Crediting Period is the interval between project initiation (the date on which the first project-activity land management change, for example the first change in grazing management or the first application of another eligible practice such as brush management or forage seeding, is implemented on an individual site associated with the Project) and the end of the last Reporting Period. Where the Project uses the optional Enablement Window defined in Section 5.1 of the Improved Soil Management Protocol, installation of project infrastructure and the baseline (t₀) soil-carbon campaign during that window, subject to the conditions set out there, do not of themselves constitute project initiation. The Enablement Window defers the start of the Crediting Period and the claiming of Certificates only; it does not exclude any emissions from accounting. All GHG emissions arising from activities carried out for the Project during the Enablement Window must be quantified and accounted for as project establishment emissions (), in accordance with Section 9.5.1 of the Improved Soil Management Protocol and the SSR table in Section 8.2.1 of this Module. This includes the embodied, transport and installation emissions of project infrastructure (e.g., fencing, virtual fencing, water points and reticulation) and the emissions associated with the baseline () soil-carbon campaign (e.g., field sampling, travel and laboratory analysis). These emissions must be included in the first Reporting Period, or amortized in accordance with Section 7 of the GHG Accounting Module, and are subject only to the materiality provision for excluded SSRs referenced in Section 8.2.1.
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Duration. The total Crediting Period (including renewals) must be no longer than the Project Commitment Period set at project initiation.
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Issuance. Certificate issuances occur throughout the Crediting Period. Credits are issued upon Verification of a Reporting Period. Credits issued at each verification event represent the cumulative net CO₂e removal from project initiation to the end of the current Reporting Period, less all credits previously issued under the Project.
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Renewal. The Crediting Period may be renewed up to the duration of the Project Commitment Period.
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Considerations for Grouped Projects. For grouped projects composed of multiple discrete areas, the individual Crediting Periods may be staggered across individual sites to reflect the different timings of project activity initiation within the sites.
Reporting Period
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Definition. The Reporting Period is the interval of time over which removals are assessed and crediting calculations are updated. The first Reporting Period starts at the beginning of the Crediting Period. Subsequent Reporting Periods begin at the end of the previous Reporting Period.
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Duration. The minimum duration of a Reporting Period is one year. The maximum duration of a Reporting Period is five years. Project Proponents may request an alternative length of the Reporting Period provided they submit suitable justification for the deviation (e.g., evidence of carbon stock impacts of intervention anticipated to take longer during establishment; matching relevant cultivation cycles).
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Verification. Verification of project activities by a third-party Verification and Validation Body (VVB) is conducted for each Reporting Period (see Section 7.2 of the Improved Soil Management Protocol).
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Last Reporting Period. Project Proponents must indicate the last Reporting Period to be submitted for Verification. Failure to initiate a Verification within 5 years of the previous Reporting Period or request an extension will conclude the Crediting Period.
Ongoing Monitoring Period
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Definition. The Ongoing Monitoring Period is an optional interval between the end of The Crediting Period through the end of The Project Commitment Period. An Ongoing Monitoring Period is only required if The Crediting Period is less than the length of the Project Commitment Period set at project initiation (minimum 40 years). This may be the case if an enrolled property chooses not to renew their contract to the full Project Commitment Period Length. An enrolled area that is unenrolled before the end of its Crediting Period, or for which the Project Proponent loses the access required to conduct sampling under Section 9.1.2 before the end of its Crediting Period, ceases to generate Certificates. It enters its Ongoing Monitoring Period from the date of unenrollment or loss of access.
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Duration. If the Crediting Period is less than the Project Commitment Period, the Ongoing Monitoring Period must encompass the length of time from the end of the Crediting Period to the end of the Project Commitment Period.
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Monitoring. Monitoring for Reversals is the responsibility of the Project Proponent and must follow the same requirements for the measure re-measure approach used for quantification of (Section 9.1.2). If monitoring is not possible for an enrolled area (e.g., the Project Proponent no longer has access to the land), that enrolled area is considered to have experienced a full Reversal of the cumulative Certificates attributable to it (Section 10.4.2). This does not apply where the Project Proponent elects, and is eligible for, the Remote Monitoring of Reversals procedure in Section 10.4.3. This treatment applies only to the enrolled area for which monitoring is not possible, and not to the remainder of the Project Area. Monitoring obligations for an enrolled area end at the end of that area's Project Commitment Period.
Post-Project Commitment Period
- Definition. The indefinite period of time after the Project Commitment Period has ended.
- Project Proponents must provide details of how the Project design and activities will encourage long-term maintenance and sustainability of soil carbon stocks after the Project Commitment Period to prevent Reversals after the Project ends.
Example Project Timelines
Project A is a grazing lands management project consisting of multiple properties. 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 Certificates 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 Certificates from Project A have a durability of 20 years, equivalent to half of the 40 year Project Commitment Period.
Project B is a soil carbon project which sets a Project Commitment Period of 60 years, which fully consists of the Crediting Period. All Certificates from Project B have a durability of 30 years, equivalent to half the 60 year Project Commitment Period.
Tracking Enrolled Areas
Where new areas are added to a grouped Project over time, the Project Proponent must maintain a complete and current record of all enrolled areas sufficient for Isometric and the VVB to track the project area and each area's Project Timeline over the life of the Project. At minimum, the Proponent must:
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Maintain a spatial enrollment register
- Georeferenced boundaries for every enrolled area, each assigned a unique and persistent identifier, uploaded to Certify at the point of enrollment and maintained thereafter;
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Record initiation dates and timelines per area
- The initiation date of each enrolled area, together with its resulting Project Commitment Period, Crediting Period, and Reporting Period schedule, so that staggered timelines are tracked individually rather than at the Project level only;
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Establish an area-specific baseline
- A baseline determined for each new area as at its own initiation, in accordance with the baseline requirements of this Module, so that removals are credited against the correct counterfactual;
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Prevent overlap and double counting
- A demonstration at each Verification that enrolled areas do not overlap spatially and are not enrolled in any other project, with spatial changes to the register version-controlled and auditable; and
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Evidence tenure per area
- Evidence of legal, documented land tenure or rights of use for each enrolled area for the duration of the Crediting Period applicable to that area, in accordance with Section 5.1.1 of the Improved Soil Management Protocol, together with the remaining contract term for each enrolled area and, where that term is shorter than the remainder of that area's Project Commitment Period, the arrangements for sustaining monitoring and reversal liability for the remainder of that period (see Section 4.3).
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Record Certificates attributable per area
- The cumulative Certificates attributable to each enrolled area, calculated in accordance with Section 10.4.2 and updated at each Verification.
Enrollment data must be made available to the VVB at each Verification, and any additions or changes since the previous Reporting Period must be clearly identified.
Durability
The Durability of Certificates is set to one half the length of the Project Commitment Period.
The Durability of Certificates is distinct from the length of the Project Commitment Period. the Project Commitment Period (a minimum of 40 years; Section 5.1) is the period over which the Project Proponent must maintain SOC stocks, monitor for Reversals, and compensate for any Reversal (Section 4.3). The Durability is the storage duration represented for each Credit issued under this Module. For example, a Project with a 40-year Project Commitment Period issues Credits with a Durability of 20 years (see Section 5.1.5).
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.
Demonstrating Adequate Management of Risks
Every risk listed in Section 6.1 must be screened for materiality, and the screening must be documented in the Project Design Document; it is not sufficient for the Project Proponent to assert that a risk has been considered. A risk is material where, in the context of the Project Area and the practice changes implemented, it is reasonably likely to occur and could, if it occurred, cause harm to the environment, livestock, or affected people that is more than minor or short-lived. The screening must consider:
whether the relevant activity, pathway, and receptor are present (for example whether any watercourses, soil amendments, changes in veterinary treatment, mobile pastoralists, or customary land users are present in or affected by the Project);
the likelihood of harm given the practice change and site conditions (for example slope, soil erodibility, climate, and the direction and size of changes in stocking density);
the severity and reversibility of potential harm; and
input received through stakeholder engagement under Section 6.6.1 of the Improved Soil Management Protocol.
A risk may be screened as not material only with supporting evidence, and where materiality is uncertain the risk must be treated as material. The screening is subject to VVB review at Validation and must be revisited where practice changes or new information could alter it. the Project Design Document must, for every risk screened as material:
- Quantify a baseline
- Establish a measurable pre-project baseline for the relevant indicator(s), using site data or, where unavailable, regionally representative reference values, in accordance with the evidence hierarchy in Section 4.3. Where the baseline relies on lower-tier evidence (for example operator attestations corroborated by remote sensing, or regional reference values), the basis must be documented and the value applied must be one that does not mask deterioration relative to the baseline;
- Define measurable indicators
- Specify the objective, monitorable indicator(s) by which the risk will be tracked, rather than qualitative description alone;
- State a performance threshold
- Commit to a defined threshold or performance standard that constitutes an acceptable outcome, referencing an applicable national or internationally recognized standard where one exists;
- Commit to monitoring
- Set out the monitoring method and frequency by which the indicator will be measured against the threshold over the Project lifetime. Monitoring must be proportionate to the materiality of the risk and may rely on periodic rather than continuous measurement, including operational and infrastructure records, telemetry, spot checks, photo points, and remote-sensing indicators, provided the method and frequency are sufficient to detect a breach of the threshold in time for the corrective action to be effective. Indicators may be evidenced from data the Project already generates (for example soil sampling campaigns under Section 9, remote sensing, and grazing and herd records) where those data are fit for purpose; and
- Define a corrective-action trigger
- Specify the remedial action, and the point at which it is triggered, should an indicator breach its threshold.
Where a threshold is breached and not remediated within the timeframe set out in the Project Monitoring Plan, the affected practice change may not be relied upon for crediting until the indicator is restored to its threshold. All indicators, thresholds, and monitoring results are subject to verification by the VVB. Certain risks in Section 6.1, including pastoralist and land user rights, and the treatment of enteric emissions, are governed by absolute requirements rather than thresholds, and those requirements apply in full irrespective of this subsection.
Illustrative indicators and thresholds. The following is indicative of the bar expected; Project Proponents must set ecoregion-appropriate values and justify them at Validation.
Risk | Example indicator | Example performance threshold |
|---|---|---|
Food security & pastoral productivity | Liveweight gain, milk yield, off-take per head; or the regionally indexed productivity metric of Section 8.3 | No material decline relative to the rolling baseline mean, absent documented justification; where the Section 8.3 metric is relied upon, the materiality and suspension thresholds of Section 8.3.3 |
Inputs, amendments & supplements | Contaminant / residue loading of applied inputs | Within all applicable regulatory limits; no substances prohibited under relevant national or international regulation |
Soil health & land degradation | Bare-ground extent; compaction / erosion indicators | No net increase relative to baseline |
Erosion & riparian protection | Permanent vegetative ground cover; livestock access to watercourses | Ground cover maintained at or above the ecoregion-appropriate minimum; exclusion enforced where erosion risk is identified |
Water use & access | Community and ecological water availability | No reduction in established access; abstraction within applicable regulatory limits |
Animal welfare | Compliance with recognized welfare standard | Full compliance with the WOAH Terrestrial Animal Health Code or equivalent |
Biodiversity & wildlife | Habitat connectivity; predator–livestock conflict | No net loss of connectivity; documented non-lethal mitigation in place |
Wildlife population dynamics | Habitat-condition proxies for priority species (e.g., vegetation structure, residual cover); presence/absence or occupancy of indicator species; existing regional monitoring data | No project-attributable decline in habitat condition for priority species and no project-attributable loss of indicator-species occupancy, assessed against regional trends |
Relation to Isometric Standard
Additionality
The Additionality requirements of the Improved Soil Management Protocol (Section 7.4) and the Additionality Section of the Isometric Standard apply in full to Projects under this Module. This Section sets out grazing-specific supplements and modifications that are necessary because the inherited provisions are, in places, calibrated to discrete, individually-datable land-management practices and to public datasets that do not record grazing management. Where a provision in this Section conflicts with the corresponding provision in the Protocol, this Module takes precedence.
As under the Protocol, additionality is assessed against a project-specific Baseline scenario and Counterfactual each Reporting Period, and Projects must not occur in regions where significant rates of the Project interventions are driven by market demand, local and/or national incentives, or policies that would lead to similar grazing management practices without Carbon Finance. Government subsidies or civil contractual obligations for specific grazing practices are assessed under the Financial additionality criteria of the Isometric Standard.
Prior Adoption
The prior-adoption requirement of the Protocol applies to Projects under this Module, with the following grazing-specific interpretations. The default 5 consecutive years of non-adoption, the extended 7–10 year window for a single year of adoption, and the reduced 3-year lookback following documented arm's-length management change are inherited unchanged.
Because grazing management regimes are not adopted on a single, discrete date in the manner of a tillage change, non-adoption of the Project intervention must be evidenced over the period beginning five years before the Enrollment Date (as required by Section 7.4.1 of the Protocol) and ending at project initiation. This period includes the five years immediately preceding project initiation used to determine the Pre-Project Stocking Rate (Equations 2–3). The optional Enablement Window defined in Section 5.1 of the Protocol applies to Projects under this Module: installation of project infrastructure (for example fencing, virtual fencing, water points and reticulation, and laneways) and the baseline (t₀) soil-carbon campaign during the Enablement Window do not, of themselves, constitute project initiation, which is the date on which the first project-activity land management change (for example the first change in grazing management) is implemented. Infrastructure installed during the Enablement Window falls within the lookback for the purposes of the installation-date provision below. The "project intervention" for this purpose is the specific soil-carbon-material grazing regime as defined in the Common Practice activity definition below, not the generic practice label.
Stocking and headcount records evidence livestock density but not the grazing regime itself. Non-adoption of the regime must therefore be evidenced using records that speak to management, which may include: grazing charts, paddock-move logs, or herd-movement records; paddock and fencing maps showing subdivision of the grazing area; installation, purchase, or lease dates for the fencing, water infrastructure, or stock-handling equipment that enable the regime; documented management plans or agronomic advice; and a signed attestation from the operator. Where the infrastructure required to operate the regime was installed within the lookback, its documented installation date may be taken as evidence that the regime was not practiced before that date.
Prior adoption is assessed against the regime as defined by its material parameters under Section 7.4.1, not against the generic practice label. A prior grazing regime that differs materially from the Project regime in those parameters (for example a simple rotation with materially longer grazing periods, materially shorter or unplanned recovery periods, or materially lower stock density) does not constitute prior adoption of the Project regime, provided the Project Proponent documents the prior regime against each material parameter and demonstrates that the difference is material. Infrastructure installed before the lookback, or installed for purposes other than operating the Project regime (for example boundary fencing, cross-fencing for stock separation, or water infrastructure for stock supply), does not by itself evidence adoption of the regime. In such cases the Project Proponent must evidence how the land was grazed during the lookback using the management records listed above; a signed attestation relied on for this purpose must be corroborated by independent evidence (for example paddock-use records, remote sensing of grazing patterns, or third-party records), in accordance with the evidence hierarchy in Section 4.3.
For episodic practices whose natural implementation cadence is longer than one year, in particular prescribed fire and mechanical brush management, non-adoption must be assessed against the practice's documented return interval rather than on a strict annual basis. A unit is treated as having prior adoption only where the episodic practice was applied within the lookback at a frequency and intensity comparable to that proposed by the Project; a single historical occurrence at an interval consistent with the practice's ordinary return period does not, by itself, constitute prior adoption.
Financial Additionality
The financial additionality requirements of the Protocol, including the USD $5.00 per-acre-per-year Per-Acre Materiality Threshold, the NPV test and its conventions, the incentive-payment definition, and the reassessment timeline, apply in full to Projects under this Module. The Per-Acre Materiality Threshold is a screening criterion rather than a bar: a practice that receives practice-linked incentive payments above the threshold may still demonstrate financial additionality under the NPV criterion (criterion 2 of Table 1 of the Protocol), which compares the incentive payments received, together with reduced input costs, against the installation and ongoing costs of the practice. The following additions are made to reflect that grazing incentives are commonly denominated per head of livestock or per unit of grazing capacity rather than per unit of area, that grazing practices often receive one-time cost-share payments for infrastructure, and that grazing Projects are commonly structured as cohorts of enrolled operations.
SOC-enhancing grazing practices frequently require upfront investment in fencing, water infrastructure, forage establishment, and agronomic expertise, while delivering climate benefits not captured by commodity (livestock) markets. Project Proponents must demonstrate that the adoption of project activities is contingent on Carbon Finance, in accordance with the financial additionality requirements of the Protocol and the Isometric Standard.
Where a practice-linked incentive is paid on a per-head, per-animal-unit, per-Livestock-Unit (LSU), or per-Animal-Unit-Month (AUM) basis, the Project Proponent must convert the payment to a per-acre basis before comparing it to the Per-Acre Materiality Threshold. The conversion must use the area of the enrolled operation on which the incentivized practice is implemented and, for the livestock category to which the incentive applies, the mean of the annual headage or stocking rates over the five-year baseline used to determine the Pre-Project Stocking Rate (Section 8.3.2.1, Equations 1–3), expressed in the unit in which the incentive is paid (head, animal unit, LSU, or AUM) using documented conversion factors applied consistently with Equation 2a. Where project-specific stocking data are unavailable, the Project Proponent must apply the regional (sub-national administrative unit or equivalent) average stocking density published by the relevant agricultural statistics agency, with documented justification. For Project Areas outside the United States, the threshold is applied at the equivalent value per local unit of area.
Where a practice-linked incentive is paid as a one-time or lump-sum payment, including a cost-share payment for fencing, water infrastructure, or forage establishment calculated per unit installed (for example per foot of fence), the Project Proponent must, for comparison against the Per-Acre Materiality Threshold, allocate the payment in equal annual amounts over five years beginning in the year of receipt, consistent with the five-year NPV horizon of the Protocol, and divide each annual amount by the area of the enrolled operation served by the funded infrastructure or practice. Each annual amount counts towards the threshold in any year of the Project to which it is allocated, together with any other practice-linked incentive payments for the same practice in that year. For the NPV criterion, one-time payments are placed in the year in which they are received, in accordance with Equation 2 of the Protocol.
Consistent with Table 1 of the Protocol, under which the NPV analysis is developed for each practice, cropping system, and region on the basis of typical expected costs and payments, the NPV criterion may be assessed for a cohort of enrolled operations (for example a group of ranches) rather than for each operation separately, provided that the operations in the cohort implement a materially equivalent anchor practice as defined under Section 7.4.1 and fall within the same production system, land type, and region as defined under Section 7.4.2. The Per-Acre Materiality Threshold criterion must continue to be applied to each enrolled operation individually, using that operation's own practice-linked incentive payments and stocking-rate conversion. Where the cohort NPV is relied upon for operations that exceed the threshold, the incentive term must reflect the per-acre practice-linked incentive payments of those operations, either individually or using the highest per-acre value among them, and must not be averaged with operations that receive lower or no incentive payments. The cohort composition, the basis for its homogeneity, and the cost and payment assumptions must be documented in the Project Design Document. Operations added to the Project after the assessment must either be shown to meet the cohort's homogeneity conditions or be assessed separately.
For the NPV criterion, fixed or start-up costs include the full cost of infrastructure such as fencing, water pipelines, storage tanks, pumps, and stock-handling facilities, whether or not partly funded by cost-share; any cost-share or other practice-linked payment is treated as an incentive under Equation 2 of the Protocol. Ongoing annual benefits include reductions in purchased-input costs attributable to the practice, such as purchased feed and hay, mineral and protein supplements, and veterinary costs; these must be included wherever the practice is expected to reduce them and must be estimated on the same typical-cost basis as the other terms of the analysis. Changes in revenue from livestock output (for example liveweight sold or calf crop) are yield effects and are excluded from both costs and benefits, consistent with the Protocol.
Common Practice
The Common Practice demonstration of the Protocol applies to Projects under this Module, adapted as set out below. The adaptations are necessary because (i) the public dataset the Protocol mandates where available — the USDA Census of Agriculture — does not record the eligible grazing activities, so the Census-anchored trend bands in ISM Table 3 cannot be evaluated for grazing; and (ii) coarse public statistics that do exist for grazing (for example, headline "rotational grazing" adoption rates) aggregate management sub-types with materially different soil-carbon outcomes, and so do not measure the specific regime a Project implements. Where this Section conflicts with Section 7.4.3 of the Improved Soil Management Protocol, this Module takes precedence.
The five demonstration steps of the Protocol are retained. Their grazing-specific application is as follows.
Step 1 — Define the Project Activity (Anchor Practice)
The Project Proponent must define the anchor practice by the management parameters that are material to soil carbon dynamics, and as a material change relative to the baseline grazing regime on the enrolled unit — not by the name of the grazing subtype. Grazing subtypes (for example continuous, deferred, simple rotational, strip, mob or high-density, leader-follower, or multi-species grazing) are recorded as descriptors of the regime but do not, by themselves, determine eligibility. The scientific evidence base does not support a fixed ranking of grazing subtypes by soil carbon outcome — comparisons of rotational and continuous systems are mixed and the response is strongly dependent on climate, soil, and baseline condition — and the soil carbon effect of a regime is established for crediting through measurement under Section 9, not asserted from its subtype.
The material parameters used to define the regime must be stated and justified, and must be the parameters through which the regime is expected to affect soil carbon. On the current evidence base these are, in particular: grazing intensity (stocking rate or density, and the resulting forage utilisation, residual biomass, or ground cover); the length and timing of rest and recovery periods; and the rotation and timing of grazing across the grazing area. The Project Proponent must reference the best available scientific literature linking the chosen parameters to soil carbon outcomes in a comparable production system, land type, and ecological context, and must acknowledge where that evidence is limited or contested.
Eligibility as an anchor practice does not depend on the regime bearing any particular label or matching a proprietary system; it depends on the regime representing a material change in these parameters from the baseline regime, expected to increase soil carbon. The regime as defined here is the basis for the like-for-like comparison used in the prior-adoption assessment above and in the remainder of this Common Practice assessment: adoption and non-adoption must be assessed against operations running a materially equivalent regime, not against the broad subtype label.
Step 2 — Geographic Area and Comparable Class of Adopters
For grazing, the geographic area and comparable class of adopters required by the Protocol must be stratified by production system (for example cow-calf, stocker, dairy, or sheep), by land type (rangeland versus improved or sown pasture), and by ecological context (Major Land Resource Area, ecological site, or equivalent). The comparison set must comprise grazing operations of a like system, land type, and ecological context that are not receiving Carbon Finance.
Where available data do not support stratification on one or more of these dimensions (for example where regional statistics do not distinguish cow-calf from stocker operations, or rangeland from improved pasture), the Project Proponent may use the finest stratification the available data support, with documented justification of why the resulting comparison set is representative of comparable operations. Stratification choices must be made on the basis of data availability and comparability, not to reduce the measured adoption rate, and are subject to review at Validation. Where the Project Proponent collects primary data (for example a survey under Step 3), the comparison set should be stratified as set out above, subject to the pooling provision in Step 3.
Step 3 — Assess the Market Penetration Rate (Grazing Data Hierarchy)
The Protocol's requirement to use the USDA Census of Agriculture where available does not apply to the eligible activities of this Module, as the Census does not record them. In its place, the Project Proponent must assess the market penetration rate of the anchor practice using the most granular source available from the following hierarchy that resolves the specific regime defined in Step 1, using the most recent available estimate:
- A representative survey of comparable operators conducted in accordance with the survey-based approach of the Protocol (documented sampling design; a minimum of 30 responses recommended), or first-party adoption data for a proprietary program;
- Sub-national or regional government or agency data (for example NRCS National Resources Inventory, or regional cow-calf statistics from USDA ERS/ARMS);
- National government or agency data, used only where regional data are unavailable and applied with the subcategory caution in Step 5;
- Peer-reviewed literature or independent research with full, transparent methods.
Remote-sensing-derived adoption estimates (for example the Rangeland Analysis Platform) may supplement the above with documented justification. The trend-based 20–35% and 35–50% adoption bands of ISM Table 3 are not available under this Module, as no grazing dataset provides a comparable multi-period time series.
A representative survey may be pooled across comparable strata defined under Step 2 where the Project Proponent justifies that the regime and the barriers to its adoption are materially similar across the pooled strata. The sampling design must draw respondents from each pooled stratum in proportion to its share of the comparable area, or apply equivalent weighting; the recommended minimum of 30 responses applies to the pooled survey; and results must be reported by stratum where sample sizes allow. Where stratum-level results indicate that adoption in a pooled stratum is materially higher than the pooled estimate, pooling is not justified for that stratum. First-party adoption data for a proprietary program may be used across all strata it covers, including at portfolio scale, provided the Project Proponent documents its coverage of each stratum, restricts the adoption estimate to operations not receiving Carbon Finance, and justifies its representativeness of the comparable class of adopters.
Step 4 — Anchor and Secondary Practices
Grazing Projects typically deploy an integrated regime in which one practice is the primary driver of the soil carbon increase and others are supporting. Accordingly, where a Project implements more than one eligible practice within a geographic area:
- the anchor practice — the practice defined in Step 1 as primarily responsible for the soil carbon increase — must carry the full Common Practice demonstration (Steps 1–3 and Step 5); and
- each co-implemented secondary practice (for example improved forage species, water management, or brush burning or mulching where these are not the anchor) requires only a qualitative demonstration that it is not, among the comparable class of adopters in the relevant region, a common practice already driven to adoption by market demand, agricultural policy, or regulatory requirement absent Carbon Finance. A separate quantitative penetration test is not required for secondary practices.
Where the Project implements a grazing-management regime (for example rotational or adaptive grazing) alongside supporting practices, that regime is the anchor practice; where no grazing-management regime is implemented, the anchor practice is the practice requiring the greatest capital investment. In all cases the Project Proponent must state and justify the anchor designation.
This modification is justified on the grounds that the anchor practice is the binding common-practice constraint and the principal SOC driver; secondary practices are supporting and are rarely the dominant regional practice; and disaggregated penetration data for secondary grazing practices generally do not exist. It does not relax the netting requirement below or the financial additionality and prior-adoption requirements, which continue to apply to the Project as a whole.
Step 5 — Adoption Assessment and Subcategory Caution for the Anchor Practice
Where the available data resolve the specific regime defined in Step 1 at the stratified regional level, the Common Practice threshold applies: the anchor practice must be adopted on less than 20% of the comparable, non-Carbon-Finance area (net of the adjustments below).
For the purposes of this Section and the Qualitative Common Practice demonstration, data resolve the specific regime where they are disaggregated on the material parameters identified under Step 1 (for example grazing intensity, rest and recovery period, and rotation timing), or on a category whose definition in the data source corresponds unambiguously to the regime as defined by those parameters, such that operations running a materially equivalent regime can be distinguished from those that are not. A subtype label that is not defined by reference to those parameters (for example a headline "rotational grazing" category) does not resolve the regime. The Project Proponent must document in the Project Design Document the basis on which each data source is judged to resolve or not resolve the regime, for review at Validation.
Where the available data cannot resolve the specific regime, for example, where the only source is a headline "rotational grazing" figure that aggregates simple, deferred, and management-intensive or adaptive systems, that coarse statistic must not be applied at face value to disqualify the Project, because it materially overstates adoption of the narrow soil-carbon-material regime. In such cases the Project Proponent must instead demonstrate that the anchor practice is not common practice through one of the following, consistent with the granular-evidence alternative already permitted in ISM Table 3 (Band 1):
- a comprehensive evidence package using data disaggregated to the regime (survey, first-party, or regional sources under the Step 3 hierarchy) showing adoption below the 20% threshold; or
- where such disaggregated data are not feasible to obtain, a structured qualitative demonstration in accordance with the Qualitative Common Practice demonstration provision below.
Disaggregated data are not feasible to obtain where the Project Proponent documents in the Project Design Document (i) a search of each level of the Step 3 hierarchy, identifying the sources examined and why each does not resolve the regime; and (ii) a costed feasibility assessment of commissioning a representative survey, including a pooled survey under Step 3, setting out the estimated cost and expected sample and demonstrating that the cost or practicability of the survey is disproportionate to the evidentiary value it would add, having regard to the scale of the Project. The search and the feasibility assessment must be submitted at Validation.
Where the anchor practice's Common Practice status is established qualitatively under this provision, financial additionality and prior adoption serve as the binding quantitative demonstrations of additionality for the Project.
Where the numeric threshold is used, the adoption rate assessed for the anchor practice must be net of adoption funded or motivated by other incentives or programes. Where common practice is instead established qualitatively, incentive and program prevalence must be addressed within the prevailing-practice and barriers elements of the Qualitative Common Practice demonstration below. Consistent with the Protocol, the following must be subtracted, expressed in the area or capacity unit used for the assessment, where quantifiable:
- area or capacity enrolled in any private-market carbon program for the relevant practice;
- area or capacity under any incentive program paying more than the Materiality Threshold (converted per head or AUM to a per-acre basis as under the Financial Additionality Section above) for the relevant practice; and
- area or capacity under USDA EQIP or CSP contracts, or equivalent government conservation programmes, that specifically name the relevant practice — including, as applicable, Prescribed Grazing (528) and its enhancement (E528R), Brush Management (314), Prescribed Burning (338), Forage and Biomass Planting (512), and Range Planting (550).
Because brush management and prescribed burning are heavily funded through conservation programes in some jurisdictions, gross and net adoption may diverge substantially for those activities; the Project Proponent must document the netting for each relevant program.
Qualitative Common Practice Demonstration
Where the Common Practice status of the anchor practice is established qualitatively under Step 5, the Project Proponent must document all of the following elements. The completed demonstration must be submitted for VVB review at validation.
- Regime definition and materiality. The material parameters of the regime (rest and recovery period, paddock count or stock density, planned rotation) and why each is material to soil carbon. Evidence: the management plan and supporting scientific literature.
- Comparable class of adopters and geographic area. The production system, land type, and ecological context defined under Step 2, and the operator population against which the regime is compared. Evidence: a description of the comparison set and its basis.
- Prevailing practice. A positive statement of the grazing management prevailing among that class absent Carbon Finance, and how it differs materially from the regime, including the prevalence of any incentive- or programme-driven adoption of the regime. Evidence: regional literature, agency or extension reports, expert statements, or first-party/survey observations.
- Barriers to adoption. The barriers that have prevented widespread adoption of the regime absent Carbon Finance, addressing at least: investment or capital barriers (for example fencing and water capital expenditure); knowledge or technical barriers (agronomic expertise, monitoring capability); and institutional or behavioural barriers (tenure, labour, risk aversion). Evidence: cost data, operator attestations, or literature.
- Conclusion. A reasoned conclusion, following from elements 1–4, that the specific regime is not common practice among comparable operators absent Carbon Finance.
The qualitative route may only be used where data cannot resolve the specific regime. Where resolving data are available and place the regime's net adoption at or above the 20% threshold, that numeric result prevails and the qualitative route must not be used to override it. The narrowness of the regime definition must reflect genuine soil-carbon materiality under element 1, not the avoidance of a numeric result.
Application Outside the United States
The datasets, conservation-practice standards, and stratification frameworks referenced in this Section — for example USDA and ARMS data, NRCS Conservation Practice Standards, EQIP and CSP, and Major Land Resource Areas — are specific to the United States. For Project Areas in other jurisdictions, the Project Proponent must apply the nearest available equivalent, with documented justification of its equivalence, drawing on: the national or sub-national agricultural statistics agency; the relevant government conservation-program registry; a recognized rangeland, pasture, or grazing-land classification; and national or regional soil and ecological mapping. Where no dataset resolves the specific regime in the jurisdiction, the Qualitative Common Practice demonstration above is the default pathway for the anchor practice. The financial-additionality Materiality Threshold is applied outside the United States.
System Boundary, Project Baseline and Leakage
System Boundary
The System Boundary for grazing land management projects encompasses all GHG sources, sinks, and reservoirs (SSRs) associated with the implementation of SOC-enhancing land management practices on eligible grazing lands. 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-grave GHG Statement must be prepared encompassing the GHG emissions and removals relating to all activities within the system boundary. the Project Proponent is responsible for identifying all sources of emissions directly or indirectly related to project activities.
Any emissions from sub-processes or process changes that would not have taken place without the CDR Project must be fully considered in the system boundary. Any additional activity that ultimately leads to the issuance of Certificates 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.
Activities Integrated into Existing Practices
Grazing lands management projects are implemented on land under active livestock production, meaning that certain operational activities (e.g., grazing management, supplementary feeding, pasture maintenance, animal husbandry) were occurring prior to, and may continue alongside, project activities. Activities or portions of activities that were already occurring in the baseline and would have continued to occur without the grazing lands management project may be omitted from the system boundary, subject to the conditions below.
For the purpose of this provision, an “activity” may refer to an operational sub-unit, such as a discrete supplementary feeding event, a rotational grazing movement, or a paddock maintenance operation, 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 feeding regime whose composition is altered, or a grazing rotation whose rest periods are changed), the activity must be partitioned into:
- Portions that are materially unchanged by the Project, which may be excluded from the system boundary under this provision; and
- Portions that are new, extended, or altered as a result of the Project, which must remain within the system boundary and be quantified in accordance with Section 9.5 of the Improved Soil Management Protocol.
Activities or portions of activities that were already occurring in the baseline and would have continued to occur without the grazing land 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:
- It was occurring as part of routine grazing land operations prior to project activities;
- It will continue to be represented in the counterfactual assessment;
- It would have continued to occur in the absence of the grazing lands management project; and
- Its scope, frequency, equipment, inputs, and intensity are not materially altered as a result of project activities.
Evidence supporting these conditions must be provided in the LCA. This must include either:
- Historic farm or station records documenting the activity prior to project start. Acceptable records include management logs, operational records, equipment usage records, invoices, livestock movement records, or equivalent documentation; or
- A signed affidavit from the relevant operator (e.g., grazier, station manager, pastoralist, or equivalent party) confirming the activity was part of routine operations prior to the Project.
And the following:
- A signed affidavit from the relevant operator confirming:
- The equipment, timing, cadence, and intensity of the activity are not materially changed as a result of the Project; and
- The activity would have continued at a comparable level absent the Project.
Where these conditions are met, only the emissions associated with the activity as it would have occurred in the baseline may be excluded. Any incremental emissions attributable to the Project must remain within the system boundary and be accounted for in the relevant emissions section.
Module-Specific SSR Considerations
The following SSRs are particularly relevant to grazing lands management projects and must be assessed:
- Enteric methane (CH₄): Where project activities alter the total animal-days, stocking rate, herd composition, or diet quality (as measured by NDF or equivalent forage quality parameters), the enteric methane emissions associated with the change must be quantified in accordance with Section 8.5 of this Module.
- Manure and urea nitrogen (N₂O): Where project activities alter stocking rate, forage crude protein content, or supplementary feeding regimes, the resulting change in nitrogen deposition via urine and dung must be quantified in accordance with Section 8.5.2 of this Module.
- Imported fodder and feed concentrate: Where project activities result in a net increase in fodder or concentrate feed imported from outside the project boundary, the embodied emissions, enteric emissions, and manure emissions associated with the additional imports must be quantified in accordance with Sections 8.4 of this Module.
- Fencing and water infrastructure: Where new fencing, watering points, or stock handling infrastructure is installed as part of the Project (e.g., for rotational grazing, riparian exclusion, or water-point relocation), the embodied emissions associated with materials manufacture, transport, and installation must be included.
- Pasture renovation and forage establishment: Where improved pasture species are sown, or pasture renovation is undertaken as a project activity, the embodied emissions associated with seed production, transport, and establishment operations (including any tillage or direct-drilling) must be included.
- Soil amendments: Where compost, manure, lime, biochar, or other soil amendments are applied as part of the Project, embodied emissions associated with their production, processing, and transport to site must be included.
- Brush management and prescribed fire: Where project activities include prescribed burning or mechanical brush clearing, CH₄ and N₂O emissions from biomass burning, and fuel emissions from mechanical operations, must be included.
Excluded Pools and Sources
In accordance with the Improved Soil Management Protocol, the following are excluded from the system boundary for grazing lands management projects:
- Aboveground woody biomass and belowground woody biomass carbon pools (these terms must be set to zero in Equation 3 of the Improved Soil Management Protocol, as specified in Section 9 of this Module);
- Deadwood, litter, and non-woody herbaceous biomass carbon pools, as these are considered transient;
- Emissions reductions: Any emissions reductions (e.g., reduced enteric methane from lower stocking rates, or reduced N₂O from reduced fertiliser application) will not count to offset positive emissions for purposes of calculating CO₂e Removals. All emissions sources within the system boundary must be reported as strictly positive values. Emissions reductions are not credited through this Module and emissions reductions scoped in the Agricultural Practices Reductions Module are not applicable.
Project Boundary
The Project Boundary defines the spatial and temporal extent of the project within which all GHG SSRs are quantified. The Project Boundary is set at the time of project initiation in accordance with Section 8 of the Improved Soil Management Protocol and may be modified during the Crediting Period in accordance with the provisions therein. The Project Boundary must encompass:
- All areas of grazing land on which SOC-enhancing management practices are implemented as part of the Project, other than areas excluded at delineation under this Section or Section 4.1;
- All areas used for the production of fodder or feed that is consumed by livestock within the Project (where these areas are enrolled in the Project);
- All control plot areas established for counterfactual assessment under Section 9.2 of this Module; and
- Any areas affected by project-related infrastructure (e.g., fencing, water points, stock handling facilities) that lie within the Project footprint.
- G-J591-0Forest stands, woodlots, and patches of trees or dense woody vegetation within an enrolled property may be excluded from the Project Area at delineation, and must be excluded where they meet the definition of native forest in Section 4.1.1.1 or are otherwise excluded under Section 4.1.1. Excluded woody areas must be included in the boundary shapefile in the PDD. No soil, biomass, or remote-sensing monitoring obligation attaches to excluded woody areas. Where such an area is grazed as part of the operation, the requirements for grazed but ineligible in-holding areas in Section 8.3.5 apply (documentation of historical use and confirmation of no project-induced intensification); where it is not grazed, no further requirement applies. Where small tree patches that do not meet the native forest definition are retained within the Project Area, aboveground and belowground woody biomass remain excluded under Section 8.1.3.
SSR Table for Grazing Lands Management Projects
The system boundary must include all relevant GHG SSRs controlled and related to the Project, including but not limited to the SSRs set out in Table 4 of the Improved Soil Management Protocol and the module-specific SSRs set out in Table 1 below. Values for all SSRs must be strictly positive. Any emissions reductions will not count to offset positive emissions for purposes of calculating CO₂eRemovals.
If any GHG SSR within Table 1 is deemed not appropriate to include in the system boundary, it may be excluded provided that robust justification and appropriate evidence is provided in the PDD. Furthermore, if any SSR is expected to be negligible, it may be excluded in line with Section 5 (Materiality) of the GHG Accounting Module, which permits exclusion where the estimated emissions of the SSR are less than 1% of estimated net CO₂e removals for the Reporting Period, both individually and collectively with all other excluded SSRs. SSRs relating to monitoring (soil sampling campaigns, laboratory analysis, remote sensing data acquisition, site visits and associated travel, whether during the Crediting Period or the Ongoing Monitoring Period) and to fencing and water infrastructure may in many Projects fall below this threshold. The Materiality Assessment for these SSRs may use the high-level, conservative screening estimates permitted by the GHG Accounting Module (readily available project data, or expenditure data combined with an environmentally-extended input-output emission-factor model or physical benchmarks from life cycle inventory databases or literature). Where these SSRs are material and must be quantified, published default emission factors from Reputable Sources may be used, with the data-quality justification required by the GHG Accounting Module.
Table 1 — Module-Specific SSRs for Grazing Lands Management Projects
Activity Phase | GHG Source, Sink or Reservoir | GHG | Scope | Timescale |
Project Establishment | Fencing and water infrastructure | All GHGs | Embodied emissions associated with manufacture and transport of fencing materials, water troughs, pipes, tanks, and stock handling equipment installed for project activities (lifecycle modules A1-4). | Before project operations start. Must be accounted for in the first Reporting Period or amortised in line with allocation rules. |
Project Establishment | Pasture renovation and forage establishment | All GHGs | Emissions associated with seed production, transport, and establishment operations including any tillage, direct-drilling, or aerial seeding undertaken as part of project activities (lifecycle modules A1-5). | Before project operations start. Must be accounted for in the first Reporting Period or amortised in line with allocation rules. |
Operations | Enteric fermentation (CH₄) | CH₄ | Methane emissions from enteric fermentation by livestock on the project area. Must be assessed where project activities alter total animal-days, stocking rate, herd composition, or diet quality. Quantified in accordance with Section 8.5.1 of this Module. | Over each Reporting Period. |
Operations | Manure and urine nitrogen deposition (N₂O) | N₂O | Direct and indirect N₂O emissions from nitrogen deposited to soil via livestock urine and dung on the project area. Must be assessed where project activities alter stocking rate, forage crude protein, or supplementary feeding. Quantified in accordance with Section 8.5.2 of this Module. | Over each Reporting Period. |
Operations | Imported fodder emissions | All GHGs | Enteric CH₄, manure N₂O, and upstream transport CO₂ emissions associated with any net increase in fodder imported from outside the project boundary. Quantified in accordance with Section 8.4.7 of this Module. | Over each Reporting Period. |
Operations | Imported feed concentrate emissions | All GHGs | Embodied emissions of manufacture, processing, and transport of concentrates, supplements, mineral blocks, and other feed concentrate imported from outside the project boundary where there is a net increase relative to pre-project levels. Quantified in accordance with Section 8.4.7 of this Module. | Over each Reporting Period. |
Operations | Perennial forage stand production | All GHGs | Emissions from forage production on perennial forage stands included in the Project Area under Section 4.1: stand re-establishment and break crops; manufacture, transport and application of fertilizer, lime and other inputs, including direct CO₂ from liming and urea; direct and indirect N₂O from nitrogen inputs, nitrogen-fixing species and crop residues; irrigation energy; and harvesting, baling, ensiling, and on-farm transport and storage of forage. Only emissions that are new, extended or altered relative to the baseline are quantified, in accordance with Section 8.1.1. | Over each Reporting Period. Re-establishment events are attributed to the Reporting Period in which they occur. |
Operations | Soil amendment application | All GHGs | Embodied emissions associated with the manufacture and application of additional soil amendments including lime, biochar, or other organic and mineral amendments applied as part of project activities. Direct CO2 from liming/urea application. | Over each Reporting Period. |
Operations | Prescribed fire and brush management | CH₄, N₂O | CH₄ and N₂O emissions from prescribed burning of vegetation. Fuel combustion emissions from mechanical brush clearing operations. CO₂ from burning of biogenic vegetation is excluded. | Over each Reporting Period. |
Operations | CO₂ stored | CO₂ | The gross amount of CO₂ removed and durably stored as soil organic carbon (see Section 9). Quantification must be based on measured changes in SOC stocks within the system boundary. | Over each Reporting Period. |
End-of-Life | Ongoing Monitoring | All GHGs | Emissions relating to monitoring activities over the Ongoing Monitoring Period, including soil sampling campaigns, remote sensing data acquisition, site visits, laboratory analysis, and associated travel. | After Crediting Period. Must be estimated and accounted for in the first Reporting Period or amortised in line with allocation rules (see Section 9.5 of the Improved Soil Management Protocol). |
End-of-Life | Ongoing Grazing Management | All GHGs | Emissions relating to ongoing management activities required to maintain SOC stocks over the Project Commitment Period, including maintenance and repair of project infrastructure (fencing, water points, stock handling facilities), and continued implementation of grazing management practices. | After Crediting Period. Must be estimated and accounted for in the first Reporting Period or amortised in line with allocation rules. |
End-of-Life | Infrastructure Decommissioning | All GHGs | Anticipated end-of-life emissions (lifecycle modules C1–4) associated with decommissioning and disposal of project infrastructure, including fencing, water infrastructure, stock handling facilities, and any monitoring equipment installed as part of The Project. | After Crediting Period. Must be estimated and accounted for in the first Reporting Period or amortised in line with allocation rules. |
End-of-Life | Misc. | All GHGs | Any SSRs not captured by categories above (e.g., ongoing staff travel, administrative activities related to project maintenance during the Ongoing Monitoring Period). | After Crediting Period. Must be estimated and accounted for in the first Reporting Period or amortised in line with allocation rules. |
Leakage
Overview of Leakage Assessment
Leakage emissions, , occur when project activities lead to emissions that occur outside the system boundary of grazing land management projects. They include increases in GHG emissions as a result of grazing land management projects displacing emissions or causing a secondary effect that increases emissions elsewhere. Three key types of leakage can theoretically occur for grazing land management projects:
- Activity-shifting leakage. Grazing land management projects may displace activities in their project areas, leading to an increase in those activities outside of the project area, which may result in potential land conversion. Examples are where the local community can no longer use the whole project site for subsistence, or where part of a farmer’s productivity is displaced as a result of project activities. This type of leakage is known as “Direct” leakage as the relevant stakeholders can be identified and the activity-shifting is traceable.
- Market leakage. Grazing land management projects may displace activities, which results in a reduction in supply of a commodity. Changes to the supply and demand equilibrium causes other market actors to shift their activities, leading to potential land conversion. This type of leakage is known as “Indirect” leakage because its effects cannot be isolated and measured directly. Quantifying the likelihood and potential magnitude of market leakage is complex and relies heavily on modeling and available literature.
- Ecological leakage. Project activities may lead to emissions in areas outside of the project site as a result of ecological interactions, for example unintended hydrological impacts, introduction of disease, or secondary impacts of faunal influx.
Regarding activity-shifting leakage, as an eligibility criteria Projects must not systematically or permanently reduce the productive capacity of the enrolled land (as calculated in Section 8.3.2), and should seek to maintain or enhance production on Project Sites over the medium to long-term relative to historical productivity levels. Requirements for activity-shifting leakage are set out in Section 8.3.5.
Regarding market leakage, the Project should seek to maintain or enhance productivity from pre-project levels, so any reductions in productivity should be incidental. An approach for assessing productivity is outlined in Sections 8.3.2–8.3.4, and market leakage is quantified in accordance with Section 8.3.6.
Regarding ecological leakage, Project activities that adversely alter the water table, harming ecological integrity within the project area and surrounding landscape and watershed, are not permitted under this Module. Assessing wider ecological leakage impacts is complex. For this version of the Module ecological leakage is assumed to be zero. This will be revisited in future updates to the Protocol.
Regionally-Indexed Productivity Metric
Market-leakage risk arises where a project reduces the supply of a livestock commodity, inducing production and potential land conversion elsewhere. Project productivity is therefore indexed against a regional benchmark so that only project-attributable changes are assessed.
Headage Rate, Scaling Factor, and Stocking Rate
The Pre-Project Headage Rate (PPHR) is the annual average density of livestock supported by the project area, calculated for each livestock category by summing total animal-days for each herd and dividing by the days in the year and the project area or the allocated grazing area. PPHR must reflect an average of the five years prior to project activities. Headcounts and days on the project area (N and T in Equation 1) must be determined from farm/land-management records (production records, financial logs, activity logs, or GPS tags). Where such records are unavailable or incomplete for any year of the five-year baseline (for example, following a recent change of ownership), the gap-filling provisions below apply, consistent with the evidence hierarchy for limited records in Section 4.3.
Gap-filling where baseline records are incomplete. For each baseline year for which contemporaneous records are unavailable or incomplete, N and T must be established from the highest available of the following sources, and the source used for each year must be documented in the PDD:
- Prior-operator or third-party records, including livestock sale receipts, brand-inspection or animal-movement records, tax schedules, grazing lease, permit or allotment records issued by an agency or landowner, veterinary or animal-health records, and lender or insurance records. Where these records directly establish headcount and days on the project area, they may be used as if they were contemporaneous records.
- A signed attestation from the prior or current operator or another knowledgeable party (e.g., the landowner, a lessee, or a local agricultural authority) stating headcount and grazing periods by livestock category, corroborated by at least one independent source (e.g., records under item 1 that do not by themselves establish N and T, remote sensing, or regional statistics).
- Where neither of the above is available, an estimate based on the regional average stocking density for the same years from the benchmark source used under Section 8.3.2.2, with documented justification.
Conservative application. Where N or T for a year is established under item 2 or 3, or is derived from item 1 records only by assumption (for example, herd size inferred from sale volumes), the Project Proponent must document a plausible range for PPHR(l,y) for that year and must apply the end of that range that is adverse to The Project in each use of the parameter:
the upper end in Equations 3 and 4 (Pre-Project Productivity for the leakage assessment) and in the conversion of per-head or per-AUM incentive payments under Section 7.3, where a higher pre-project stocking rate increases the leakage deduction or the likelihood of exceeding the Materiality Threshold; and
the lower end in the livestock-emissions calculations of Section 8.5.1 (Equations 18a and 18c) and Section 8.5.2 (Equation 20), and in the counterfactual herd under Section 8.3.4(b), where a lower pre-project stocking rate increases the livestock-emissions deduction.
For any other use, the end of the range adverse to the Project for that use must be applied. The upper end of the range must be no lower than the highest, and the lower end no higher than the lowest, of the attested or estimated value and the values indicated by the corroborating evidence.
Remote-sensing cap. For any year gap-filled under item 2 or 3, the value applied under (b) must not exceed the stocking rate that the project area could have supported in that year, estimated from remote-sensing estimates of forage production at a documented utilization rate (e.g., a proper-use factor from regional grazing guidance), together with any documented imported feed, using the dry matter intake values of Section 8.5.2. Where an attested value materially exceeds this carrying capacity, the discrepancy must be explained.
The sources, ranges, carrying-capacity estimate, and their justification must be documented in the PDD and reviewed by the VVB at Validation.
For each livestock category l, the Headage Rate is the animal-day-weighted mean number of head carried per hectare over a year. It is calculated for the pre-project baseline (the Pre-Project Headage Rate, PPHR) and, identically, for each project year (the Project-Scenario Headage Rate, PSHR):
(Equation 1)
Where:
-
indexes livestock category;
-
is a distinct herd of category l present on the project area during the year;
-
is the headcount of herd h in year y (heads);
-
is the number of days herd h was supported by the project area in year y, including days housed off-pasture but fed on forage produced on the project area (days);
-
is the number of days in year y (365 or 366);
-
is the relevant grazing area: the project area, or the commodity's allocated area under Equation 9 where productivity is assessed at commodity level (ha);
-
is the Pre-Project Headage Rate (heads·ha⁻¹). The Project-Scenario Headage Rate is computed identically over project years .
Stocking Rate (PPSR and PSSR)
The Headage Rate is converted to the Stocking Rate in livestock units per hectare (LSU·ha⁻¹) using a category scaling factor, giving the Pre-Project Stocking Rate (PPSR) and Project-Scenario Stocking Rate (PSSR):
(Equation 2a)
(Equation 2b)
Where:
- is the scaling factor for category l (LSU·head⁻¹), converting one head of category l to a common livestock-unit basis by forage intake / liveweight equivalence. SF is sourced from standard coefficients (e.g., FAO 2011; IPCC 2019 Refinement, Vol. 4, Ch. 10) or from project-specific liveweight measurements, and must be applied consistently to the pre-project and project-scenario calculations;
- is the Pre-Project Stocking Rate (LSU·ha⁻¹);
- is the Project-Scenario Stocking Rate (LSU·ha⁻¹).
The stocking rate (LSU·ha⁻¹) is the productivity metric used for leakage estimation: it normalises headage across livestock categories and age classes and, where the scaling factor is derived from liveweight, reflects differences in animal size.
Productivity is assessed by livestock-commodity class c (e.g., beef cattle, dairy, sheep/goat meat, wool). The category stocking rates are aggregated to the commodity class they supply (Sections 8.3.2.3–8.3.2.4). Where Appendix D provides a single livestock default (as at present), all livestock commodities share that increased-supply and new-land value; commodity-specific defaults may be adopted as they become available.
A Project Proponent may instead use a direct output-based productivity metric (e.g., liveweight sold or milk produced, per hectare) where reliable saleable-output records and a corresponding regional output benchmark exist. The same metric and source must be used consistently for the pre-project and project-scenario calculations for a given commodity class.
Where perennial forage stands included in the Project Area under Section 4.1 supplied forage sold outside the Project during the baseline period, forage must be assessed as an additional commodity class. The assessment uses an output-based metric (t dry matter ha⁻¹ yr⁻¹ of forage sold outside the Project), indexed to a regional forage-yield benchmark (e.g., county-level hay yields published by the relevant agricultural statistics agency) in place of the regional stocking-rate benchmark. The increased-supply and new-land parameters applied to forage under Section 8.3.6 must be justified in the Project Design Document from peer-reviewed sources meeting the Appendix D criteria and approved by Isometric at Validation. Where no such parameters can be justified, the stands are not eligible for inclusion in the Project Area.
Regional Benchmark and Data Sources
The regional benchmark is the mean stocking rate of the same commodity class across the relevant region, in the same units as the project metric (LSU ha⁻¹ by default). It is derived from official statistics at the county or equivalent sub-national level — for example, USDA NASS county livestock inventory converted to livestock units using standard coefficients and divided by regional pasture/rangeland area; USDA NASS/ERS regional data; or, where sub-national data are unavailable, national statistics or FAOSTAT. Where a direct output-based metric is used (Section 8.3.2.1), the benchmark must be the corresponding regional output density. The benchmark region must be the smallest jurisdiction for which reliable data exist that is representative of the project's production system and agro-ecological context (by default the county or Major Land Resource Area). The chosen region, source, and vintage must be documented in the PDD and held constant for the baseline-mean calculation.
Regional indexing is the mechanism by which region-wide shocks are neutralised: when a drought depresses both project and regional productivity proportionally, the indexed ratio is preserved and no leakage is registered (see Section 8.3.4).
Pre-Project Productivity (PPP)
The project-level pre-project stocking rate for a commodity class aggregates the Pre-Project Stocking Rates of the livestock categories that supply that commodity:
(Equation 3)
Where:
- is the project-level pre-project stocking rate for commodity c in baseline year y (LSU·ha⁻¹);
- is the Pre-Project Stocking Rate of category (Equation 2a); ranges over the categories supplying commodity .
Pre-Project Productivity for commodity class is then calculated over a baseline of at least five calendar years, indexed to the regional benchmark:
(Equation 4)
Where:
- is the Pre-Project Productivity for commodity (LSU·ha⁻¹);
- is the regional benchmark stocking rate for commodity in baseline year (LSU·ha⁻¹);
- is the mean regional benchmark stocking rate for commodity c over the baseline period (≥5 years);
- indexes the ≥5 baseline years (y = −5 … −1 relative to project initiation).
Project-Scenario Productivity (PSP)
The project-level project-scenario stocking rate for a commodity class aggregates the Project-Scenario Stocking Rates of its supplying categories:
(Equation 5)
Where:
- is the project-level project-scenario stocking rate for commodity in project year (LSU·ha⁻¹);
- is the Project-Scenario Stocking Rate of category (Equation 2b).
From the fifth project year onward, Project-Scenario Productivity is evaluated on a rolling five-year window ending in the Reporting Period RP, using the same regional source:
(Equation 6)
Where:
- is the Project-Scenario Productivity for commodity in Reporting Period (LSU·ha⁻¹);
- is the trailing five-year window (RP−4 ≤ t ≤ RP);
- is the first project year; is the same baseline-period constant as in Equation 4.
For Reporting Periods ending before the fifth project year, comprises all project years from to the end of the Reporting Period, and Equation 6 is applied over those years.
Productivity Shortfall
The productivity shortfall for commodity c is the indexed decline relative to the pre-project baseline, floored at zero:
(Equation 7)
Where ≥ , the shortfall is zero and no leakage assessment is required for that commodity. Productivity surpluses are not carried over between Reporting Periods and are not used to offset shortfalls in other commodity classes.
Net Project Productivity, Materiality and Suspension Thresholds
Project-level expected baseline production for commodity , expressed in livestock units, is:
(Equation 8)
Where:
- is the expected baseline production for commodity in the Reporting Period (LSU);
- is the project area supporting commodity in the Reporting Period (ha), determined under Equation 9.
Area allocation for mixed-species paddocks. Where a paddock or management unit is grazed by more than one livestock-commodity class including at different times under a rotation its area is allocated among those commodities in proportion to each commodity's share of the paddock's total livestock units over the Reporting Period:
(Equation 9)
Where:
- indexes paddocks or management units within the project area;
- is the area of paddock (ha);
- is the animal-day-weighted livestock units of commodity c carried on paddock g over the Reporting Period, computed from the headage and scaling-factor terms of Equations 1–2b, so that a commodity's time-share of a rotationally grazed paddock is captured;
- the denominator sums over all commodities grazing paddock .
The same allocation is applied consistently to the pre-project baseline and the project scenario, and the allocated areas sum to the total grazed project area with no double-counting. The commodity stocking rates and (Equations 3 and 5) are expressed per hectare of the area so allocated; where a paddock is grazed by a single commodity, is simply that paddock's area.
The Net Project Productivity shortfall for commodity c is the indexed shortfall applied over that area:
(Equation 10)
Where:
- is the Net Project Productivity shortfall for commodity in the Reporting Period (LSU).
The shortfall ratio for commodity is / . Two thresholds apply to this ratio:
- De minimis threshold (materiality gate). Where / ≤ 0.03, the decline is within the de minimis materiality threshold and no leakage deduction applies for that commodity in that Reporting Period. The threshold is a materiality gate, not a deductible: where the ratio exceeds 3%, leakage is assessed on the full value of , not only the portion above 3%.
- Crediting-suspension threshold. Where / > 0.15 for any commodity class, the project is ineligible for crediting for that Reporting Period unless a temporary exemption is granted under Section 8.3.4. Because the decline is regionally indexed, this gate is triggered only by project-attributable (idiosyncratic) collapse in output, not by region-wide events.
- Planned reductions in stocking. Reductions in stocking that are permitted project activities under Section 4.3, including planned rest, deferral and rotational-recovery periods within a working grazing regime and planned recovery from documented historic overstocking, are assessed under this Section in the same way as any other project-attributable change in productivity. Where the resulting shortfall ratio exceeds the de minimis threshold, leakage is quantified under Section 8.3.6; where it exceeds the crediting-suspension threshold, the suspension applies. That a reduction is planned or documented in the PDD does not exempt it from either threshold. Relief under Section 8.3.4 applies only to the extent that a shortfall is attributable to exogenous causes, and the counterfactual-herd treatment of livestock emissions in Section 8.3.4 applies only to exogenous destocking; for planned reductions, livestock emissions are calculated on actual stocking under Section 8.5.
Exogenous Destocking and the Livestock-Emissions Counterfactual
Grazing systems, particularly on North American rangelands, are subject to large weather-driven swings in stocking. Multi-year drought, regulatory destocking orders, and disease or biosecurity events routinely force destocking well beyond a few percent, and destocking in these circumstances is the agronomically correct response; it protects baseline soil carbon, forage recovery, and animal welfare, consistent with the safeguarding requirements of Section 6.1. The approach handles these events in three complementary ways.
(a) Regional indexing neutralizes region-wide events. Because Project-Scenario Productivity is indexed to the regional benchmark (Equation 6), a decline that is shared across the region depresses both the numerator and denominator proportionally, leaving the indexed ratio, and therefore ΔP, largely unchanged. A project that destocks no more than its regional peers during a drought therefore registers little or no shortfall, incurs no leakage deduction, and does not approach the suspension gate. No application or exemption is required for this to operate.
(b) The soil is modeled as actually managed; livestock emissions are held at the normal-herd counterfactual. SOC is modeled and measured as activities actually occur (including reduced or removed livestock), while the enteric CH₄ (Section 8.5.1) and manure/urine N₂O (Section 8.5.2) terms are held at the emissions the regionally-indexed pre-project herd (the PPSR herd, Equation 2a) would have produced absent the event, so the project cannot bank reductions from cattle it intends to return.
(c) Localized events. Where an exogenous cause contributes to a PSP shortfall that is not reflected in regional data, Isometric may adjust PSP by a counterfactual estimate reflecting the localized impact. The Project must submit evidence that the shortfall is unrelated to project design, management, or land-use change, and that comparable systems in the surrounding region did not experience similar impacts (e.g., county-level statistics, meteorological or disaster records, government or insurance reports). Adjustments under this provision are applied to the annual project-scenario stocking rates of the affected years and are carried with those years in every window (Equation 6) that includes them.
Documentation and eligibility. Relief persists for as long as the exogenous condition is independently verified to be ongoing, consistent with the drought treatment recognized for reversals under the ISM Protocol.
Qualifying drought evidence. A drought classification of D2 (Severe Drought) or worse on the United States Drought Monitor or, outside the United States, an equivalent classification from a national or sub-national drought monitoring index whose equivalence is justified in the PDD, covering at least 50% of the Project Area for at least eight consecutive weeks during the grazing season of a project year, is accepted as independent verification that an exogenous drought condition existed in that year. For such a year, the Project Proponent is not required to demonstrate separately that the drought was unrelated to project design, management, or land-use change, but must still either provide the counterfactual estimate required under (c) or apply for a temporary exemption under the following paragraph. Qualifying drought evidence does not by itself reduce leakage deductions. It may also be relied upon as evidence of an exogenous natural cause under Section 8.4.5.
Temporary exemption from the crediting-suspension threshold. Where the shortfall ratio for a commodity class exceeds the crediting-suspension threshold in Section 8.3.3, and one or more years in the window are subject to an exogenous condition verified under this Section (including by qualifying drought evidence), Isometric may, on application by the Project Proponent, grant a temporary exemption from the crediting-suspension threshold for that Reporting Period, where the Project Proponent demonstrates that its destocking was a response to that condition consistent with its grazing management plan and the safeguarding requirements of Section 6.1. A temporary exemption does not reduce the leakage deduction, which is quantified on the full value of after any adjustment under (c). The exemption lapses once no year in the window is subject to a verified exogenous condition.
Order of operations. For each commodity class and Reporting Period: (1) Project-Scenario Productivity is calculated with regional indexing under Equation 6, which neutralizes region-wide events under (a) without application; (2) any adjustment under (c) is applied to the affected years; (3) the productivity shortfall is calculated under Equation 7; (4) Net Project Productivity and the shortfall ratio are calculated under Equations 8–10; (5) the de minimis and crediting-suspension thresholds of Section 8.3.3 are applied, taking account of any temporary exemption under this Section; and (6) where the shortfall ratio exceeds the de minimis threshold, leakage is quantified on the full value of under Section 8.3.6.
Activity-shifting leakage
Activity-shifting leakage occurs where project activities displace grazing or production to identifiable land outside the project boundary. Projects must not systematically or permanently reduce the productive capacity of enrolled land, and must seek to maintain or enhance production relative to historical levels. Where livestock must be relocated in the early stages of a project, the Project Proponent must either:
- include the land to which livestock are moved within the project boundary (in which case no deduction applies, provided the receiving land is not overgrazed to the detriment of its own soil carbon); or
- move livestock to alternative land or house and feed them, in which case the associated fodder-import emissions must be quantified under Section 8.4, and the associated enteric and manure emissions under Section 8.5.
Because productivity is assessed under Sections 8.3.2–8.3.4 and market leakage is quantified under Section 8.3.6, any genuine displacement that manifests as a project-attributable reduction in project-area output is captured through that assessment. Residual traceable activity-shifting leakage is treated as immaterial for this version of the Module.
Grazed but ineligible in-holding areas. Areas within a ranch that are grazed but not eligible for crediting (for example dense forest, crop-residue on adjacent row crops, or high-slope non-sampleable terrain) and that were grazed historically do not, by themselves, constitute activity shifting and do not trigger a leakage penalty, provided grazing on them is not increased beyond historical levels as a result of the project. Proponents must document historical use of such areas and confirm no project-induced intensification.
Market Leakage Quantification
For each commodity class with a net shortfall above the de minimis threshold, foregone project output expressed as foregone stocking, in livestock units is assumed to be partially replaced by production elsewhere, a portion of which is met by bringing new land into production. Default parameters for the quantification are provided in Appendix D.
Induced land conversion
The area of induced land conversion for commodity is:
(Equation 11)
Where:
- is the induced land conversion for commodity in the Reporting Period (ha);
- is the increased-supply fraction (Section 8.3.6.2);
- is the proportion of increased supply met by new land (Section 8.3.6.3);
- is the stocking rate on new land (Section 8.3.6.4).
Increased supply ()
IS is the proportion of the foregone output that will be replaced by increased supply elsewhere, derived from the price elasticities of supply and demand for the commodity:
(Equation 12)
Where:
- and are the price elasticities of supply and demand for commodity .
Where Isometric has provided default elasticities for the project's region and commodity in Appendix D, those defaults must be used unless a more specific value is substituted in accordance with Appendix D; Appendix D provides livestock elasticity defaults for North America and South America. For regions or commodities not covered, elasticities must be sourced from academic literature published in the last 15 years in reputable journals or reports, using dynamic-panel or instrumental-variable techniques based on planting/production-season time-series prices, following the procedure in Appendix D.
Proportion of increased supply met by new land ()
NL is the share of increased supply that is met by bringing new land into production (extensification) rather than by intensification on existing land. Where Isometric has provided a default NL for the project's region and land use in Appendix D, that default must be used unless a more specific value is substituted in accordance with Appendix D; Appendix D provides livestock NL defaults for the United States and Brazil. For other regions, NL is sourced by the standard approach (Method B), analysing land-use change relative to production change from a policy or price shock:
(Equation 13)
Where:
- is a one-tonne reduction in supply (or a one-unit price increase);
- and are the area-expansion and yield-intensification responses to .
Method A (the ratio of land deforested to total new agricultural land during large, demand-driven expansions in markets disconnected from international trade) may be used only in the special cases described in Appendix D.
Yield on new land ()
The stocking rate of new land brought into production for commodity is set equal to the pre-project regional mean stocking rate, , used in Equation 4.
Carbon-stock emission factor (EF)
is derived from the IPCC average national aboveground biomass content of the land cover for the ecosystem displaced by commodity c, using Table 3A.1.4 of the IPCC Good Practice Guidance for Land Use, Land-Use Change and Forestry (2003). Biomass carbon is converted to CO₂e using the carbon fraction (CF) specified by the IPCC for the relevant vegetation type and the CO₂/C mass ratio (44/12).
Leakage-emission calculation
Total leakage emissions for the Reporting Period are the sum across all commodity classes with a net shortfall:
(Equation 14)
Where:
- the sum is taken over all commodity classes c for which > 0.
is included in project emissions and the leakage discount is applied exclusively to removal Certificates, in accordance with the ISM Protocol. Leakage is quantified for every Reporting Period.
Monitoring and Quantification of Imported Fodder
Scope and Definitions
Imported feed refers to any feed material brought into the project boundary from an external source for consumption by livestock within the project area. Imported feed comprises two categories: fodder and feed concentrate, as defined in Table G3.
Table G3. Categories of imported feed.
Feed Category | Definition | Examples |
|---|---|---|
Fodder | Forage-based feed produced outside the Project Area, including on land of the same operation that is not within the Project Area (Section 4.1), and fed to livestock within The Project. Includes conserved forages and fresh-cut material. | Hay, silage (grass, maize, whole-crop), haylage, fresh-cut forage, straw (where fed), green chop. |
Feed concentrate | Manufactured, processed, or supplementary feed products imported into the project boundary that are not forage-based. Includes energy and protein concentrates, compound feeds, mineral and vitamin supplements, and feed additives. | Grain-based rations, oilseed meals (soybean, canola, cottonseed), distillers grains, compound pellets, mineral blocks, salt licks, urea licks, methane-reducing feed additives (e.g., 3-NOP, seaweed-derived compounds), veterinary supplements administered through feed or water. |
Ideally, all lands utilised for the production of fodder should be included within the project boundary. Incorporating these lands ensures a closed-loop system where all carbon cycles, from atmospheric sequestration in plant biomass to soil deposition via manure, are fully accounted for without the need for deductions. Where fodder or feed concentrate is imported from production areas outside of the project boundary, the project is subject to the monitoring and quantification requirements set out in this Section.
Forage produced on perennial forage stands included in the Project Area under Section 4.1 is not imported fodder; emissions from its production are quantified within the system boundary in accordance with Section 4.1 and Table 1. Forage produced on land of the same operation that is outside the Project Area is imported fodder, and the classification and recalculation requirements of Section 4.1.1 apply.
The requirements below apply separately to each feed category (fodder and feed concentrate). A net increase in one category cannot be offset against a net decrease in the other.
Net Increase Approach
Given that feed imports are already a part of many livestock systems, a net-increase approach is applied. Deductions are only required where there is a net increase in imported feed relative to the pre-project baseline. The net increase is calculated separately for each feed category:
(Equation 15)
Where:
- indexes feed category, with feed bases:
- : quantities are in (tonnes of dry matter);
- : quantities are in ;
- is the annual Pre-Project Feed Imports of category , defined as the 5-year historical mean plus one standard deviation of annual imports, in the units of , or, where records are unavailable for part of the five-year baseline, as determined under Section 8.4.3.3 (Equation 15a);
- is the Project-Scenario Feed Imports of category for the Reporting Period, calculated as the sum over the Reporting Period of annual imports of in the units of ;
- is the number of years in the Reporting Period.
- is the Net Feed Imports for category , in the units of . triggers a deduction; requires no deduction.
(Equation 16)
Where:
- is enteric CH₄ emissions associated with in the Reporting Period, in , calculated per Section 8.5.1 (applied incrementally to );
- is manure-management N₂O emissions associated with , in , calculated per Section 8.5.2;
- is embodied (upstream production + processing + packaging) emissions associated with , in , calculated using published LCA data or IPCC Vol. 4 Ch. 10 / 11 factors;
- is transport CO₂ emissions for imports in ;
- for , may be set to zero where the fodder is unprocessed grass/hay; for , must always be quantified.
Determining Pre-Project Feed Imports
Fodder
The Pre-Project Fodder Imports () into the project area must be quantified from farm or land management records, including financial receipts and invoices, delivery slips, or daily feed logs. The import rate must be reflective of an average of the five years prior to the project activities; where records are incomplete for part of that period, Section 8.4.3.3 applies.
In subsistence or smallholder contexts, if formal receipts and invoices are not available, the quantification of imported fodder should use a combination of standardized surveys and participatory rural appraisal (PRA). Project Proponents must establish a verifiable baseline by documenting business-as-usual feeding practices such as scavenging roadside grasses or utilizing communal lands through recall-based interviews and the weighing of local units (e.g., head-loads or carts).
For purposes of evaluating whether there are differences between PPFI and PSFI, and in recognition of the variability of feed usage in a given year for a range of factors many outside of the Project's control (e.g., weather, commodity prices), shall reflect one standard deviation above the mean of the historical fodder imports. While this presents the potential for a small amount of unaccounted emissions from additional fodder usage, this flexibility is considered reasonable given the natural incentive for farmers to reduce fodder imports to reduce costs.
Feed Concentrate
The Pre-Project Feed Concentrate Imports () into the project area must be quantified from farm or land management records, including financial receipts and invoices, delivery slips, feed mill records, or daily feed logs. The import rate must be reflective of an average of the five years prior to the project activities.
In subsistence or smallholder contexts, if formal receipts and invoices are not available, the quantification of imported feed concentrate should use a combination of standardized surveys and participatory rural appraisal (PRA), consistent with the approach for fodder in Section 8.4.3.1.
As with fodder, shall reflect one standard deviation above the mean of the historical feed concentrate imports, for the same rationale as set out in Section 8.4.3.1.
Incomplete Pre-Project Records
Where the records required under Sections 8.4.3.1 and 8.4.3.2 are unavailable for part of the five-year baseline (for example, following a recent change of ownership), the following applies separately to each feed category, consistent with the evidence hierarchy for limited records in Section 4.3.
- Documented years. A baseline year is a documented year where annual imports are established from farm or land management records, or from prior-operator or third-party records such as supplier or feed-store statements, invoices obtained from suppliers, or farm tax schedules. Where records state expenditure only, quantities may be derived using a published regional price series for the relevant feed type and year, applying a price at the upper end of the published range so that the derived quantity is not overstated.
- Gap-filled years. Up to two of the five baseline years may be gap-filled using a signed attestation from the current or prior operator, supplemented by whatever records exist. Each gap-filled year must be entered at the lower of the attested value and the lowest annual import among the documented years.
- Calculation. Where at least three baseline years are documented, is calculated as:
(Equation 15a)
Where:
- is the set of documented baseline years and is the set of gap-filled baseline years, with and ;
- is the documented annual import of category in year , in the units of ;
- is the attested annual import of category in gap-filled year , in the units of ;
- is the sample standard deviation of calculated over documented years only, in the units of .
- Fewer than three documented years. Where fewer than three baseline years can be documented, or where any baseline year is neither documented nor gap-filled under item 2, must be set to the lowest documented annual import, with no standard-deviation uplift, or to zero where no baseline year can be documented.
The sources, attestations, and derivations used must be documented in the PDD and reviewed by the VVB at Validation. The subsistence and smallholder provisions of Sections 8.4.3.1 and 8.4.3.2 are unaffected.
Determining Project-Scenario Feed Imports
The Project-Scenario import of feed into the project area must be documented in a Feed Imports Log and quantified for each Reporting Period, separately for each feed category. Imports must be quantified from farm or land management records, including financial receipts and invoices, delivery slips, feed mill records, or daily feed logs.
For subsistence or smallholder projects where formal receipts may be unavailable, the project must utilize Digital Proxy Logs (e.g., time-stamped photos of feed bags, fodder bundles, or containers with standardized weight estimations) or a Community Ledger verified by a project coordinator.
Exceptional Circumstances Affecting Project-Scenario Feed Imports
Where for either feed category is significantly larger than the corresponding , and the increase is primarily due to exogenous natural causes beyond the reasonable control of the Project (such as extreme weather events, drought, flooding, hail, or region-wide pest or disease outbreaks), PSFI may be evaluated in a broader regional context.
In such circumstances, the Project Proponent must provide evidence demonstrating that:
- The cause of the feed requirement is unrelated to Project design, management decisions, or land-use change; and
- Comparable agricultural systems in the surrounding region experienced similar impacts during the same period.
Evidence may include, but is not limited to: 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 natural causes materially contribute to the increase in , Isometric may adjust the for that Reporting Period by applying a counterfactual estimate that reflects regional impacts, for the purposes of a difference-in-differences analysis.
Feed Import Units
Fodder
All imported fodder must be converted to Metric Tonnes of Dry Matter to ensure standardized carbon and nitrogen accounting. If records only provide bale counts, the project must periodically weigh representative samples to establish an average mass per unit. In the absence of lab-tested moisture data, the project shall apply conservative default values (e.g., 15% moisture for hay, 60% moisture for silage) to determine the dry weight. The assumptions made for unit conversion must be clearly documented.
Feed Concentrate
All imported feed concentrate must be converted to Metric Tonnes (as-fed basis) to ensure standardized accounting. Where records provide unit counts (e.g., bags, blocks, tubs), the average mass per unit may be taken from the net mass stated on sale receipts, invoices, delivery slips, product labels, or manufacturer specifications. Where no stated mass is available, the project must establish an average mass per unit through periodic weighing of representative samples. Where feed concentrate is supplied in compound or mixed form, the total mass of the compound product must be recorded. The assumptions made for unit conversion must be clearly documented.
Emissions Associated with Increased Feed Imports
Emissions must be calculated for any net increase in feed imports ( > 0). The emission sources differ by feed category, as set out below.
Fodder
The following emission sources must be quantified for any net increase in imported fodder, using the latest IPCC Guidelines for National Greenhouse Gas Inventories:
- Enteric Fermentation (CH₄): Emissions resulting from the digestion of the additional fodder by the livestock, following the approach in Section 8.5.1 of this Module.
- Manure Management (N₂O): Emissions resulting from the nitrogen content of the fodder once excreted as manure on the project land, following the approach in Section 8.5.2 of this Module.
- Upstream/Transport Emissions (CO₂): If the fodder is sourced from further than 50 km one-way delivery distance, CO₂ emissions from transport must be included.
Feed Concentrate
The following emission sources must be quantified for any net increase in imported feed concentrate:
- Embodied Emissions (CO₂e): Emissions associated with the manufacture, processing, and packaging of the feed product, using published lifecycle assessment data or emission factors from the latest IPCC Guidelines for National Greenhouse Gas Inventories.
- Enteric Fermentation (CH₄): Where the additional feed concentrate is consumed by livestock on the project area, the enteric methane emissions resulting from digestion of the additional feed must be calculated, following the approach in Section 8.5.1 of this Module.
- Manure Management (N₂O): Emissions resulting from the nitrogen content of the feed once excreted as manure on the project land, following the approach in Section 8.5.2 of this Module.
- Upstream/Transport Emissions (CO₂): If the feed concentrate is sourced from further than 50 km one-way delivery distance, CO₂ emissions from transport must be included.
Deduction for Increases in Feed Imports
The total deduction for increased feed imports in a given Reporting Period is the sum of all quantified emissions across both feed categories:
(Equation 17)
Where:
- is the total feed-import deduction for the Reporting Period, in .
This deduction must be subtracted from the net carbon benefit of the project for that Reporting Period. If the total deduction exceeds the sequestration gains, the project shall yield zero Certificates issued for that period.
Emissions Associated with Livestock
As all grazing lands include livestock the cradle-to-grave GHG Statement must consider CH₄ emissions due to enteric fermentation as well as CH₄ and N₂O emissions due to manure and urea deposition.
Methane Emissions Due to Livestock
The quantification of enteric methane (CH₄) emissions is a requirement of this methodology to ensure a comprehensive net carbon balance. Given that enteric fermentation is the primary source of anthropogenic greenhouse gas emissions in livestock-based land use, it represents a significant potential source of emissions that could offset gains achieved through soil organic carbon (SOC) sequestration.
Project proponents shall be exempt from enteric methane deductions provided they demonstrate Methane Neutrality, defined as a project-scenario methane load that does not exceed the baseline. This condition is satisfied if the following condition is met:
(Equation 18a)
Where:
- is the project-level Pre-Project Stocking Rate, in . It is the mean, over the pre-project baseline years, of the Pre-Project Stocking Rates (Equation 2a) summed across all livestock categories present on The Project Area: . Here y indexes the baseline years used to determine the Pre-Project Headage Rate (Section 8.3.2.1), and is the number of those years (a minimum of five);
- is the project-level Reporting-Period mean Project-Scenario Stocking Rate, in . It is the mean, over the project years of the Reporting Period, of the Project-Scenario Stocking Rates (Equation 2b) summed across all livestock categories present on The Project Area: . Here is the number of years in the Reporting Period (as in Equation 18c);
- for both and , the grazing area in Equation 1 must be taken as The Project Area (not a commodity-allocated area ), so that both are on the same area basis as the Project Area in Equation 18c. The same livestock categories and scaling factors (Section 8.3.2.1.1) must be applied to both;
- is the pre-project Methane Conversion Factor (dimensionless), produced by Equation 18b applied to the 5-year pre-project NDF time series or, under the Conservative Default Pathway, set to (Equation 18d);
- is the Reporting-Period weighted-average project-scenario Methane Conversion Factor (dimensionless), produced by Equation 18b applied to the Reporting-Period NDF time series or, under the Conservative Default Pathway, set to (Equation 18d).
(Equation 18b)
Where:
- is the weighted-functional-average Neutral Detergent Fibre concentration (g/kg DM), per Equation 19;
- , are the analogous weighted-functional averages of Water-Soluble Carbohydrate and Starch (g/kg DM);
- are the regression coefficients from Niu et al. (2018), which must be cited verbatim with their reported standard errors and propagated through the Section 9.1.1 Monte Carlo;
- is the IPCC tabular function (step function over diet-quality classes);
- is any alternative peer-reviewed model approved at Validation.
(Equation 18c)
Where:
-
is the Project Area, in ;
-
is the gross energy intake per LSU per year, in corresponding to the pre-project stocking rate (IPCC 2019 Refinement Vol.4 Ch.10 Eq. 10.16, per livestock category, LSU-weighted);
-
is the gross energy intake per LSU per year, in corresponding to the project-scenario stocking rate (IPCC 2019 Refinement Vol.4 Ch.10 Eq. 10.16, per livestock category, LSU-weighted);
-
(IPCC default);
-
is the 100-year Global Warming Potential of methane in , with an implicit conversion () folded into 's declared units;
-
is the number of years in the Reporting Period;
-
is the Reporting-Period methane deduction, in . It is applied only where the Methane Neutrality condition in Equation 18a is not met for the Reporting Period. Where the applicable condition is met, is zero for that Reporting Period.
Conservative Default Pathway (screening test). A Project Proponent may demonstrate Methane Neutrality, or quantify the methane deduction, without monitoring NDF, by applying IPCC default Methane Conversion Factors bounded in the direction adverse to The Project. Under this pathway, Methane Neutrality is demonstrated where:
(Equation 18d)
Where:
- is the Reporting-Period mean Project-Scenario Stocking Rate of livestock category (Equation 2b), in ;
- is the five-year baseline mean Pre-Project Stocking Rate of livestock category (Equation 2a), in ;
- is the highest Methane Conversion Factor given for livestock category in IPCC 2019 Refinement Vol. 4 Ch. 10 Table 10.12 or, for livestock categories not covered by Table 10.12, in the corresponding IPCC 2019 Refinement default table, representing a low-quality diet (dimensionless);
- is the lowest Methane Conversion Factor given for livestock category in the same table, excluding feedlot diet classes (diets with less than 15% forage), representing a high-quality diet (dimensionless).
A Project satisfying Equation 18d satisfies Equation 18a for any diet quality within the IPCC range, and no NDF monitoring under Sections 8.5.1.1–8.5.1.3 is required for that Reporting Period. Where Equation 18d is not satisfied, the Project Proponent must either:
(i) quantify the deduction under Equation 18c, applied to each livestock category and summed, with and , and with and calculated using the digestibility associated in the IPCC table with the diet classes from which and are respectively taken; no NDF monitoring is required; or (ii) determine from monitored NDF under Sections 8.5.1.1–8.5.1.3, to test Equation 18a or to quantify a smaller deduction under Equation 18c.
The pathway used must be stated in each GHG Statement. Within a Reporting Period, the pre-project and project-scenario Methane Conversion Factors must be determined under the same pathway.
Where normalization to regional conditions is elected under Section 8.5.1, in Equations 20 and 20a is replaced by (Equation 18e) for each project year .
Normalization of stocking rates to regional conditions (optional). To separate the effect of The Project from year-to-year regional variation in stocking driven by weather and markets, the Project Proponent may elect at Validation to evaluate Equations 18a, 18c, and 18d, and Equations 20 and 20a in Section 8.5.2, against a pre-project comparator indexed to regional conditions in each project year, consistent with the regionally-indexed counterfactual herd in Section 8.3.4:
(Equation 18e)
Where:
- is the normalized Pre-Project Stocking Rate of livestock category in project year , in ;
- is the five-year baseline mean Pre-Project Stocking Rate of livestock category (Equation 2a), in ;
- is the regional benchmark stocking rate in project year for the commodity class supplied by category , and is its baseline-period mean, both as used in Equations 4 and 6, in ;
- is the highest annual Pre-Project Stocking Rate of category in the baseline period, which caps the normalized comparator.
Where normalization is elected, replaces , and its Reporting-Period mean summed over livestock categories replaces , in the equations listed above, subject to the following conditions:
(a) normalization must be applied in every project year and Reporting Period of the Crediting Period, to both the methane and nitrous oxide calculations, and may not be elected or withdrawn for individual years or Reporting Periods; (b) the regional benchmark must be the benchmark, region, source, and vintage fixed in the PDD under Section 8.3.2.2, and must be at county, Major Land Resource Area, or equivalent sub-national level; normalization is not available where the benchmark relies on national statistics or FAOSTAT; (c) only stocking rates are normalized; Methane Conversion Factors, gross energy intake, and nitrogen excretion rates are not; (d) for any year in which relief under Section 8.3.4(b) applies, the comparator for that year is the non-normalized , consistent with the normal-herd counterfactual in that Section; and (e) the normalized series, the regional benchmark values, and the calculation must be reported in each GHG Statement.
Where Methane Neutrality status is achieved, the following applies:
- The proponent is not required to perform additional herd-level methane emission modeling or deductions for the reporting period, although these could be conducted for reporting purposes following the IPCC Tier 1 and Tier 2 methodologies.
- Methane emissions shall be considered "managed and stable" within the project baseline, thus no methane emissions deductions are imposed.
- To maintain this exemption, proponents must provide, for each Reporting Period, the headage and animal-day records underlying (Equations 1–2b). Where Methane Neutrality is demonstrated using monitored NDF, proponents must also provide the weighted-functional-average NDF (Equation 19) together with the herd-movement records used to derive in Equation 19 (for example, paddock-move logs, grazing charts, or GPS collar data). Where Methane Neutrality is demonstrated under the Conservative Default Pathway (Equation 18d), no NDF data or herd-movement records beyond those underlying are required.
Where Methane Neutrality status is not achieved:
-
Methane emissions must be explicitly quantified and compared against the baseline using Equation 18c, either under the Conservative Default Pathway (option (i) above) or using a higher-tier approach following IPCC Tier 2/3 protocols, the Niu et al.5 predictive equations, or another peer-reviewed and justified alternative model (option (ii) above).
-
Any calculated increase in methane emissions relative to the baseline must be subtracted as a deduction from the total Certificates generated by the project for that reporting period.
Estimation of Methane Conversion Factor
The primary metric for determining methane intensity is the concentration of Neutral Detergent Fiber (NDF). NDF is a measure of the structural components in the forage, which tends to increase with forage age. High concentration of NDF lead to longer fermentation duration increasing the methane emitted. Additional measures such as the Water Soluble Carbon (WSC) could be detected.
Where the Project determines from monitored NDF (that is, where it does not use the Conservative Default Pathway in Section 8.5.1), NDF must be estimated using RS-derived data, and the following minimum standards must be met:
-
Imagery must be sourced from multispectral or hyperspectral sensors with a spatial resolution of 30 meters (e.g., Sentinel-2, Landsat 8/9, or commercial equivalents).
-
G-FC69-0Any algorithm or model used to translate spectral reflectance into NDF values must demonstrate a correlation () on independent validation data (not used for calibration) against reference NDF values from local laboratory "wet chemistry" analysis or from NIR (Near-Infrared) analysis using standardized, regional, or portfolio-level NIR spectral calibration libraries. A regional or portfolio-level library or remote-sensing calibration may be used across Projects, and project-by-project wet-chemistry calibration is not required, provided the library or calibration is validated for the forage types present in the Project Area and the independent validation data include samples that span the range observed in the project fields.
-
Data must be processed at a frequency that captures significant phenological shifts in forage quality, at a minimum of once per month during the growing season or aligned with harvest/grazing rotations.
-
The remote-sensing workflow must be documented in the PDD in sufficient detail for the VVB to reproduce it, including: the sensors and spectral bands or indices used; preprocessing, atmospheric correction, and cloud masking; temporal compositing; the model form mapping reflectance to NDF and, under the Standard Pathway, to diet-quality categories; and the calibration and independent validation datasets and statistics. A workflow validated for one Project may be applied to other Projects where its validation data are representative of their forage types and conditions.
Calculation of Spatial and Temporal Averages
Because livestock consume forage across diverse management units, a single project-wide average is insufficient. Instead, the project must calculate a Weighted Functional Average based on the following:
- Zonal Mean: Forage quality parameters (NDF, WSC, CP) must be calculated as the mean value of pixels within a defined management unit (paddock or field).
- Time-Weighting: The final value used for methane modeling must be a weighted average reflecting the proportion of fodder/silage consumed from each fields. This should be calculated using a weighted average reflecting the livestock stocking units and time spent in each field.
As an example for NDF, which could also be used for other measures, the weighted functional average is calculated as follows:
(Equation 19)
Where:
- indexes time bins within the averaging window, ;
- is the number of time bins (e.g. months) within the relevant averaging window (5 pre-project years for ; the Reporting Period for );
- indexes herd, ;
- is the number of herds present in the relevant period;
- indexes field/grazing area, ;
- is the total number of fields/grazing areas in the Project Area;
- is the field-level zonal-mean NDF in field at time bin , in ;
- is the count of Livestock Stocking Units in herd , in (a count, not a density);
- is the number of days herd spent grazing in (or fed from) field during time bin , in ;
- is the weighted-functional-average NDF, in .
Determination of the Methane Conversion Factor
Where the Project does not use the Conservative Default Pathway in Section 8.5.1, it must use the monitored NDF data to determine through one of the following pathways:
- Standard Pathway (IPCC): Proponents may adopt the updated default values from the IPCC 2019 Refinement, utilizing the RS-derived NDF to justify the selection of "High Quality" vs. "Low Quality" diet categories. A Project Proponent that does not monitor NDF must instead use the Conservative Default Pathway in Section 8.5.1, which applies the IPCC default values without diet-quality classification and requires no RS-derived NDF.
- Advanced Pathway (Niu et al.5): Proponents may utilize the predictive equation developed by Niu et al.5 to calculate a dynamic based on NDF and fermentable carbohydrates (WSC/Starch).
- G-311Y-0Alternative Models: The use of alternative Tier 3 enteric models is permitted provided the proponent provides a written justification demonstrating that the alternative model is better suited to the specific geography, livestock breed, or forage species of the project area, and is supported by peer-reviewed literature.
Nitrous Oxide Emissions Due to Livestock
The quantification of nitrous oxide (N2O) emissions is a requirement of this methodology to ensure a comprehensive net carbon balance. Nitrous oxide is a highly potent greenhouse gas. Project activities that increase Livestock Units (LSU) or improve forage quality (measured via Crude Protein) inevitably increase the volume of nitrogen (N) entering the soil via animal excreta (urine and dung). In grazing systems, this deposited nitrogen is the primary precursor for N2O production through the microbial processes of nitrification and denitrification.
The CO2e of nitrous oxide emissions due to the project activities shall be calculated for each reporting period following:
(Equation 20)
Where:
- — explicit annual integration step, so dimensions reconcile;
- in (per Equation 2b);
- and in ;
- is the combined direct + indirect N₂O emission factor for nitrogen deposited on pasture, range, and paddock, in (IPCC 2019 Refinement Vol.4 Ch.11 + + );
- is the Project Area, in ;
- is the molar mass ratio converting to ;
- is the 100-year GWP of N₂O consistent with the parent Protocol's GWP register, in , with an implicit conversion () folded into 's declared units;
- is the Reporting-Period N₂O deduction, in .
The nitrogen intake of grazing livestock is directly linked to the crude protein (CP) content of the forage they consume. Where The Project determines CP from monitored data rather than under the Conservative Default Pathway below, CP must be estimated using RS-derived data, and the following minimum standards must be met:
-
Imagery must be sourced from multispectral or hyperspectral sensors with a spatial resolution of 30 meters (e.g., Sentinel-2, Landsat 8/9, or commercial equivalents).
-
G-PNXK-0Any algorithm or model used to translate spectral reflectance into CP values must demonstrate a correlation () on independent validation data (not used for calibration) against reference CP values from local laboratory "wet chemistry" analysis or from NIR (Near-Infrared) analysis using standardized, regional, or portfolio-level NIR spectral calibration libraries. A regional or portfolio-level library or remote-sensing calibration may be used across Projects, and project-by-project wet-chemistry calibration is not required, provided the library or calibration is validated for the forage types present in the Project Area and the independent validation data include samples that span the range observed in the project fields.
-
Data must be processed at a frequency that captures significant phenological shifts in forage quality, at a minimum of once per month during the growing season or aligned with harvest/grazing rotations.
(Equation 21a)
(Equation 21b)
Where:
- denotes scenario (pre-project or project-scenario); each equation is applied separately under each scenario using the appropriate , , values for that scenario;
- indexes livestock category;
- indexes year (a calendar year in scenario );
- is the Dry Matter Intake per Livestock Unit for category in year , in (IPCC 2019 Refinement Vol.4 Ch.10 Table 10A.1 scaled to LSU, or measured);
- is the average Crude Protein content of the diet consumed by category in year , as a dimensionless mass fraction of dry matter (e.g. for 16 % CP). RS-derived where applicable, with the spatial average via the Weighted Functional Average (Equation 19);;
- is the protein → nitrogen conversion factor (g protein per g N), dimensionless;
- is the fraction of nitrogen intake retained by livestock of category in year , dimensionless;
- , are in (consistent with the units required by Equation 20).
Dry Matter Intake (DMI) shall be determined using standardized animal physiological tables (e.g., IPCC 2019). These values shall be cross-referenced with remote-sensed biomass estimates to ensure that the total DMI does not exceed the available herbage mass within the project boundary.
Where category-specific cannot be evidenced, for the affected category must be set to (i.e. the highest IPCC default across categories present) and this conservative substitution must be documented in the GHG Statement.
Conservative Default Pathway (screening test). A Project Proponent may demonstrate that no N₂O deduction arises, or quantify the deduction, without monitoring CP, by applying crude protein values bounded in the direction adverse to the Project. No N₂O deduction arises for year where:
(Equation 20a)
Where:
- and are calculated for livestock category using Equations 21a and 21b with and respectively, and with the same and values (IPCC defaults for category ) in both, in ;
- and are the upper and lower bounds of the annual diet-average crude protein content (dimensionless mass fraction of dry matter) for livestock category and the forage types present in the Project Area, taken from published regional forage-quality data or national feed composition tables and justified in the PDD.
Where Equation 20a is satisfied for every year of the Reporting Period, and no CP monitoring is required. Where it is not satisfied, the Project Proponent must either (i) quantify Equation 20 with and , with no CP monitoring required; or (ii) determine CP from RS-derived data meeting the standards of this Section.
Net Carbon Quantification
The net removals are calculated following the requirements within the Improved Soil Management Protocol. Under this Module, the carbon storage () is considered to consist of soil organic carbon. Terms for aboveground and belowground woody biomass must be set to 0.
Calculation of CO2estored
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 Certificates issuance.
The Project-level total carbon stock at any time is expressed as total carbon mass of soil organic carbon across all strata within the primary quantification unit:
(Equation 22)
Where:
- is the Project-level carbon stock at time in tonnes CO2e
- is the SOC stock in stratum at time in tonnes C ha-1
- is the area of stratum in ha
- is the total number of strata
- is the mass ratio for converting carbon to CO2e
is substituted as follows depending on the quantification approach and crediting event type. All substitution targets are calculated on an equivalent soil mineral mass (ESM) basis using the reduced cumulative-mass formulation across the depth increments required under Section 9.1.2.4, with a single stratum-level reference mineral mass and linear interpolation between increments as defined in Equations 26–27, and with parameters estimated from the sampled depth increments set out in that Section.
- Approach 1, all events: from Equation 27(ESM-corrected direct measurement on a mineral-mass basis).
- Approach 2, interim crediting events: from Equation 29 (mineral-mass model output using the baseline reference mineral mass) before the first true-up event and, thereafter, from Equation 30 with the mineral-mass profile and reference mineral mass held at their values from the most recent true-up event (Section 9.1.3.2.1).
- Approach 2, true-up events: from Equation 30 (ESM-corrected mineral-mass model output using true-up-updated reference mineral mass; ).
The values of are then used in Equation 6 of the Improved Soil Management Protocol to derive the Reporting-Period change in stored carbon (), which feeds Equations 3 and 4 of the Protocol to calculate net removals ().
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.
Uncertainty Propagation
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:
- It does not require the assumption that measurement or model errors are uncorrelated beyond the field level, making it more appropriate for the structured spatial and temporal error patterns present in both direct measurement campaigns and biogeochemical SOC models;
- It can directly estimate the correlation of errors across years, avoiding underestimation of uncertainty on shorter time horizons where annual errors may be positively correlated;
- Where implemented as a hierarchical bootstrap (see below), it requires minimal assumptions about the shape of the underlying data distributions, which is particularly important for paired SOC stock differences that are typically non-normal and exhibit short-range spatial autocorrelation.
Monte Carlo simulation must be implemented as follows:
- An appropriate number of iterations must be performed. Project Proponents must demonstrate convergence by showing that the uncertainty estimate does not materially change (> 1%) with additional iterations.
- Each iteration of the Monte Carlo simulation must be independent of other iterations. Within a given iteration, the sources of uncertainty contributing to the Certificate estimate must be sampled jointly, with correlations between sources preserved in accordance with the correlation requirements set out below; the requirement of inter-iteration independence does not imply that sources sampled within an iteration are independent of one another. The materially uncertain sources sampled at each iteration may include
- (i) input feature uncertainty, sampled from the probability distributions of materially uncertain inputs;
- (ii) model parameter or weight uncertainty, sampled from the posterior distribution for Bayesian models or via bootstrap resampling for frequentist methods;
- (iii) ensemble-member sampling, where the ensemble represents diversity in training data, hyperparameters, or architecture;
- or (iv) any combination of these, depending on which sources are material to the model in use. The relevant sources differ by approach as specified below.
- Input probability distributions must be justified with reference to empirical data or published literature and must not be assumed normal without justification. Where distributions are non-normal, appropriate alternative distributions must be used.
- Correlations between uncertain inputs must be explicitly assessed and, where material, accounted for through appropriate joint sampling procedures rather than assuming independence.
- The correlation structure of errors across time periods must be characterized and incorporated into the simulation, to avoid underestimation of cumulative uncertainty over multi-year Reporting Periods.
The following uncertain inputs must be characterized and included in the Monte Carlo simulation:
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 , which propagates through all subsequent cumulative delta calculations.
- In the event that any sampling locations are substituted during a given Reporting Period (see Section 9.1.2.2.4), the unpaired component of variance for these locations must be propagated separately from the paired component, with the two components using sample-count weighting. The procedure for this must be clearly documented.
- Survey uncertainty arising from estimating stratum-level mean SOC stocks from a finite sample of locations or modeled units within each stratum must also be characterized and propagated, distinct from per-location measurement and analytical uncertainty.
- Model-remeasure only: interim crediting events: model prediction error (characterized by RMSE from the validation dataset); where residuals are approximately symmetric (|skewness| ≤ 0.5), RMSE may be used as the basis for a symmetric error distribution; where significant skewness is detected under Section 9.1.3.1.3, model prediction error must instead be sampled from a fitted asymmetric distribution (e.g., empirical bootstrap of validation residuals or a parametric skew-normal distribution) that preserves the observed tail behavior parameterization uncertainty; and temporal error correlation across modeled time steps between resampling campaigns. Note that at interim events there is no measured ESM, so the reference ESM carries the uncertainty of the most recent measurement event: the baseline () measurement before the first true-up event and, thereafter, the most recent true-up measurement until it is replaced by the next true-up event (see Section 9.1.3.2.1).
- Model-remeasure only: true-up events: model prediction error, characterized as set out above for interim crediting events and updated through the true-up procedure in Section 9.1.3.1.6, and parameterization uncertainty as similarly updated through the true-up procedure in Section 9.1.3.1.6. Measured ESM from the resampling campaign replaces the reference value, reducing uncertainty relative to interim events.
The conservative estimate used for Certificates 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 Certificates 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.
Recommended Approach: Hierarchical Bootstrap
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 four steps:
Step 1: Location resampling within strata. Within each stratum , resample sampling locations with replacement, drawing locations where is the number of sampling locations in stratum . Resampling is performed on locations (not on individual cores or depth increments), so that the paired structure ( and measurements at the same location) is preserved within each bootstrap iteration. Location resampling at stratum may only be applied where the rarefaction analysis in Section 9.1.2.3 demonstrates that supports stable estimates; otherwise the parametric fallback below applies.
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 replaces 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 or the empirical SOC residual variogram. Where significant autocorrelation is detected at the typical inter-location distance, compute an effective sample size for that stratum using a documented adjustment (e.g., Cressie's variance-inflation formula based on the fitted variogram), and substitute for 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 a stratum has fewer than six sampling locations with paired measurements, or fails the rarefaction analysis (Section 9.1.2.3), and is not pooled under the pooling rule in Section 9.1.2.3, a parametric Monte Carlo simulation must be used for that stratum. In each iteration, the stratum-level mean paired change is drawn from a Student-t distribution with degrees of freedom, centered on the observed stratum mean, with scale , where is the observed standard deviation of location-level paired differences, is the effective sample size under Step 3, and is the conservative uncertainty penalty:
where is the 30th percentile of the chi-squared distribution with degrees of freedom. is the one-sided 70% upper confidence bound on the ratio of the true to the observed standard deviation, consistent with the 30th-percentile issuance criterion. It depends only on and can be calculated at design stage (for example, = 1.67, 1.45, 1.35, 1.29, 1.19 and 1.11 for = 3, 4, 5, 6, 10 and 20). Where the observed distribution of paired differences is materially skewed or heavy-tailed, a distribution preserving that shape, scaled by the same , must be used in place of the Student-t distribution, with justification. Analytical and ESM measurement uncertainty are propagated for the stratum as under Step 2. The use of the fallback and the value of must be reported in the GHG Statement; no other additional uncertainty penalty applies.
Combination with bootstrap strata. In each iteration, the Project-level mean is the area-weighted average (Step 4) of the resampled means of strata that pass the rarefaction analysis and the parametric draws for fallback strata. The penalty applies only to the sampling variance of fallback stratum . Its effect on the Project-level variance is therefore bounded by that stratum's squared area weight (a contribution of ), and it does not alter the treatment of any other stratum.
Approach 1 — Direct Measurement and Remeasurement
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 () 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.
Soil Sampling Requirements
Project Proponents must document their soil sampling plan in the PDD.
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.
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:
- Clearly delineate the spatial extent of the sample population;
- Specify sampling intensities, quantification unit selection criteria, and sampling stages where a multi-stage design is applied;
- Identify unbiased estimators of population parameters for use in all calculations; and
- Include a statistical analysis plan, submitted as part of the sampling plan at project validation.
The following requirements apply to all sampling and re-sampling campaigns:
- Sample locations must be georeferenced to a horizontal accuracy of 15 m (or better) to enable re-sampling at the sampling locations set at project initiation during subsequent Reporting Periods;
- At each re-sampling, samples must be collected within a 15 m radius of the initial location set at project initiation, making sure to avoid the exact location of any prior sampling disturbance, with the coordinates of the new sample collected and recorded in the GHG Statement;
- G-AAS8-0Intra-annual variability must be considered in the sampling design. All sampling and re-sampling campaigns must be conducted during the same phenological/seasonal stage (e.g., the same point in the growing season, such as peak biomass or dormancy), and each sampling location must be re-sampled within ±30 days of the calendar date on which it was sampled at project initiation (the anchor date), to ensure comparability. The window applies to the date of field collection; the timing of laboratory analysis is governed by Section 9.1.2.6. Where a sampling location cannot be sampled within the ±30-day window for documented operational reasons (e.g., weather or access, livestock or grazing-plan constraints, or field contractor capacity), the sample may be collected outside the window and used for quantification, rather than treated as missing under Section 9.1.2.2.4, provided that: (a) the reason is documented; (b) the sample is collected at the same phenological/seasonal stage and within ±60 days of the anchor date, or later only with Isometric approval; (c) evidence of comparability is provided, including the phenological stage at sampling (e.g., a remote-sensed vegetation index compared with that at the anchor date) and soil moisture conditions at sampling; and (d) out-of-window samples are identified in the GHG Statement, together with the stratum-level estimates with and without them, for review at Verification. Where comparability cannot be demonstrated, the sampling location must be treated as missing under Section 9.1.2.2.4;
- Where organic amendments (e.g., slurry, manure, compost) are applied to the grazing land, Project Proponents must delay sampling or re-sampling to the latest practicable point after the previous application and the earliest practicable point before the next application.
- In addition, sampling and re-sampling should be timed relative to grazing management to minimize confounding effects on measured SOC stocks. The Project Proponent must use best efforts, consistent with the grazing plan and animal welfare, to sample when livestock are not present in the sampling unit and not immediately after a grazing event; sampling while livestock are present, or soon after their removal, is permitted where best efforts do not allow otherwise. For each sampling location at each sampling event, the Project Proponent must record whether livestock were present in the sampling unit and the number of days since livestock were last present, and report this in the GHG Statement. Where the grazing system results in spatially concentrated dung and urine deposition (e.g., around water points, feeding areas, or stock camps), the sampling design must avoid these localized deposition zones, and sampling points must avoid fresh dung pats, urine patches and trampled ground at the time of sampling. The animal-management context of each campaign must be recorded and should be held as consistent as practicable across Reporting Periods.
- Sample locations should avoid areas subject to localized effects that would render them unrepresentative of the stratum as a whole. On grazing land these include zones of concentrated dung and urine deposition or trampling, such as stock camps and loafing areas, water points and troughs, supplementary feeding sites, mineral licks, livestock trails, gateways, and areas around shade trees and fence lines, as well as pasture margins. Because such hotspots have a stronger and more spatially clustered effect on SOC than the equivalent features in cropland, the sampling design must give particular attention to identifying and excluding them. Any such exclusions must be clearly disclosed, justified, and spatially documented.
- G-XZMX-0Perennial forage stands included in the Project Area under Section 4.1 must be sampled at the same phase of the stand cycle (years since establishment or re-establishment) at baseline and at each re-sampling event. Alternatively, the stratum's counterfactual (control plots under Section 9.2.1, or a modeled counterfactual under Section 9.2.2) must follow the documented baseline re-establishment schedule. Baseline (t₀) sampling of such stands must not be conducted within 24 months after a re-establishment tillage event unless the counterfactual condition in the preceding sentence is met.
Sampling Design and Stratification
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.
Quantification Units
The Project Proponent must define a hierarchy of quantification units in the Project Monitoring Plan. At minimum, this must specify:
- The primary quantification unit: the unit at which Certificates are issued and uncertainty is reported. This is typically the Project Area or, for grouped projects, an enrolled property or farm.
- The sampling unit: the unit at which sampling locations are selected and at which a single SOC stock estimate is generated. Sampling units must nest within primary quantification units.
- Where applicable, intermediate units (e.g., field, management zone) used to organize the design.
The choice of units must reflect the management, soil, and climatic heterogeneity of the Project area and the practical constraints of sampling, including for projects involving smallholders. Sub-field stratification is not required where a coarser unit can be shown to deliver equivalent or better project-level precision.
Sampling Design
The sampling design must be tailored to the specific Project and appropriate to its context, reflecting the soil, vegetation, climatic, and management heterogeneity of the project area, the intended estimator, and the practical constraints of sampling. The default sampling design is stratified random sampling, in which each sampling unit is divided into strata more homogeneous in expected SOC than the unit as a whole. Project Proponents may instead propose:
- Conditioned Latin hypercube sampling (cLHS) or other covariate-informed probability sampling designs, where ancillary variables (e.g., elevation, slope, remote-sensed indices, digital soil maps) are available and shown to correlate with SOC stocks; or
- Model-based sampling designs (e.g., spatial coverage sampling, geostatistical designs) where the Project intends to use a model-based estimator at the Project level.
- Other probability-based or model-based sampling designs, including spatially balanced probability designs such as Generalized Random Tessellation Stratified (GRTS) sampling, that satisfy the probability-sampling requirement, documented and justified with reference to peer-reviewed literature.
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.
Stratification Factors
Strata, 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 (100–1000 ha) scales including climate, topography, historical land use and vegetation, parent material, soil texture, and soil type. On grazing land, factors influencing the distribution of SOC and its response to project activities also include stocking rate and grazing intensity, grazing regime (e.g., continuous versus rotational or adaptive management), pasture or forage type and species composition, and whether the land is improved pasture or native rangeland. Where available, remote-sensed indicators such as bare-soil reflectance composites or vegetation indices may also be used, although these are 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 sample-size floors set out in Section 9.1.2.3. 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.
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 grazing 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.
Missed and Substituted Samples during Re-Sampling
Project Proponents must use the sample design set at project initiation for sampling during subsequent Reporting Periods. Sampling locations may be substituted over the course of the Project Commitment Period only when there are documented barriers, including:
- Loss of contractual access (e.g., disenrollment of a property)
- Active hazards that prevent access to sampling site (e.g., natural disaster)
In such a scenario, the sampling location must be replaced with another location in the same stratum selected using the same probability sampling rule that was applied at project initiation. The substitution must be clearly documented in the GHG Statement 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.
This mechanism applies equally where a timing miss affects a whole campaign or a large share of a stratum's sampling locations; in such cases, the out-of-window provision in Section 9.1.2.1 should be used in preference to exclusion. Where sampling locations treated as missing make up more than 25% of a stratum's sampling locations in a Reporting Period, the Project Proponent must demonstrate that the retained locations are representative of the stratum (e.g., by comparing baseline SOC stocks and stratification covariates between missing and retained locations). Where this cannot be demonstrated, the missing locations must be sampled (within or outside the window in accordance with Section 9.1.2.1) before the Reporting Period is submitted for Verification. Samples whose integrity is compromised after collection (e.g., through a failure to meet the sample handling and storage requirements of Section 9.1.2.6) should be re-collected, within or outside the window in accordance with Section 9.1.2.1, or otherwise treated as missing.
Redefining Strata
Strata must be defined to support direct comparison of SOC stocks across Reporting Periods. Project Proponents may re-aggregate, split, or otherwise revise strata at a Reporting Period where this improves project-level precision, where field divergence under management has rendered the original stratification non-homogeneous, or where additional data has become available. Any change must:
- Preserve a documented mapping between the previous and revised stratification, so that prior measurements can be reconciled to the new design;
- Where the revision changes the membership of any stratum, recalculate from the retained location-level data, for each revised stratum and for all sampling events from onwards, the stratum-mean mineral-mass profile and reference mineral mass (Equation 26), and re-evaluate all location-level ESM-corrected SOC stocks at the revised reference mineral mass (Section 9.1.2.10). Where a revised stratum contains sampling locations selected at different sampling intensities under the previous design, stratum means must weight each location in proportion to the area it represented under the previous design;
- Repeat the rarefaction analysis (Section 9.1.2.3) and update the power analysis (Appendix E) for the revised strata at the event at which the revision takes effect;
- Demonstrate that the revised design does not selectively exclude areas of expected SOC loss, including by reporting the cumulative SOC stock change at the event at which the revision takes effect under both the previous and the revised stratification; and
- Be reported in the GHG Statement and approved at verification.
Certificates issued for Reporting Periods before the revision are not recalculated or reissued. Consistent with the cumulative accounting approach of the Improved Soil Management Protocol (Section 9.1 and Equation 5), any difference arising from the revision is reflected in the quantity issued for the current Reporting Period, and a net decrease is addressed under the Buffer Pool provisions of the Improved Soil Management Protocol.
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.
Sample Size Determination
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 Certificates, 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.
Sample-size floors. The following floors apply to the number of sampling locations with paired measurements in each stratum (after any pooling under this Section). They are the only sample-size floors under this Module:
- Three sampling locations is the absolute floor for estimation. A within-stratum standard deviation estimated from fewer than three observations (fewer than two degrees of freedom) is too unstable to support variance estimation. Strata with fewer than three sampling locations must be pooled for estimation under the pooling rule below.
- Six sampling locations is the operative minimum design sample size per stratum. It is the smallest number at which the rarefaction analysis below can test at least one increment beyond its minimum subset of five locations. A stratum with three to five sampling locations may be used for estimation but cannot pass the rarefaction analysis, and the parametric fallback in Section 9.1.1.1 applies to it.
These floors enable estimation only. The sample size needed for a well-powered design is set by the power analysis in Appendix E, is confirmed empirically by the rarefaction analysis below, and is typically substantially larger.
Strata may be pooled for estimation only under a documented and reproducible pooling rule declared in the Project Monitoring Plan at Validation. The pooling rule must identify, for each stratum, the family of strata with which it may be pooled (strata sharing the same primary stratification factors, for example soil group and grazing regime, and differing only in secondary factors), and the objective triggers for pooling. Pooling is required for strata with fewer than three sampling locations, and may be applied to strata with fewer than six sampling locations or that fail the rarefaction criterion. Strata may be pooled only within their pre-declared family, and pooling must not be selected on the basis of observed SOC stock changes. Within a pooled stratum, each sampling location must be weighted in proportion to the area it represents ( for a location in original stratum ), so that each original stratum contributes to the pooled estimate in proportion to its area; the rarefaction analysis and location resampling under Section 9.1.1.1 are applied to the pooled set with draws weighted accordingly, and the pooled estimate is applied to the combined area of its member strata in Equation 22.
Beginning at the first re-sampling event (), 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:
- For each stratum, compute the bootstrap mean of the location-level paired SOC stock difference using progressively larger random subsets of the available sampling locations, starting from a minimum subset of 5 locations and increasing in increments of 1 (or 5, where the stratum contains > 50 locations) up to the full sample set.
- Repeat each subsample size at least 200 times to generate a distribution of bootstrap means at each subsample size.
- Identify the smallest sample size at which the width of the 70% confidence interval of the bootstrap mean changes by less than 5% of the overall observed range of in the stratum, for all subsequent increments and continues to do so for every larger subsample size tested (i.e. the confidence-interval width has plateaued). The stratum passes the rarefaction analysis where such an exists and . The stratum's design sample size for subsequent Reporting Periods must equal or exceed .
Where any stratum fails the rarefaction criterion (no exists, or ) and is not pooled under the pooling rule above, location resampling under Section 9.1.1.1 (Step 1) is not permitted for that stratum at that event, and the parametric fallback in Section 9.1.1.1 applies, including the uncertainty penalty set out there. the Project Proponent must increase the design sample size for the next Reporting Period to at least or, where could not be identified, to the number of sampling locations allocated to the stratum by power analysis under Appendix E, using the observed standard deviation of location-level paired differences.
Additional sampling locations required under this Section must be selected within the stratum using the same probability sampling rule that was applied at project initiation (Sections 9.1.2.2.2 and 9.1.2.2.4): for example, further random draws under the documented random-seed procedure for stratified random sampling, or the next points in the ordered reserve sample for a spatially balanced design. They must avoid the exclusion zones defined under Section 9.1.2.1, and are retained as permanent sampling locations for the remainder of the Project Commitment Period. Until an additional location has been measured at two sampling events, it contributes to the stratum estimate as an unpaired observation, with the unpaired component of variance propagated separately as set out in Section 9.1.1 for substituted locations. Project Proponents are recommended to generate and document, at project initiation, an ordered reserve list of candidate locations for each stratum, for use in both substitution and augmentation.
Sampling Depth and Equivalent Soil Mass
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.
At sampling events after project initiation, an individual sampling location may require an extension increment of up to 10 cm below the common reporting depth so that the location's measured column reaches the reference mineral mass defined in Section 9.1.2.10 (Equation 26). An extension is required at a sampling location in the circumstances set out in Section 9.1.2.10, and Project Proponents may elect to collect an extension increment at all sampling locations at every re-sampling event (recommended where soil mineral mass is expected to decline, for example following relief of compaction). The actual depth of collection must be recorded and reported. The extension increment is used only to evaluate the location's SOC stock at the reference mineral mass by interpolation under Equation 27 (as increment ); it does not extend the credited profile, and it is excluded from the stratum-mean profile totals used to define the reference mineral mass in Equation 26. The extension is limited to 10 cm. Where the extended column does not reach the reference mineral mass, the location's SOC stock is evaluated at its own measured profile total (a fixed-depth basis for that location) in accordance with Section 9.1.2.10 and documented as such; no further increase in sampling depth is required at subsequent sampling events. For ESM purposes, baseline () increments do not need to be subdivided into finer layers, and each increment may be measured as a single sample at baseline. Project Proponents may also collect an extension increment at baseline. For ESM purposes, baseline () increments do not need to be subdivided into finer layers, and each increment may be measured as a single sample at baseline. Project Proponents may also collect an extension increment at baseline.
The credited cumulative SOC stock change must be calculated on this basis.
Sample Collection and Processing
Soil sampling must follow established best practices for field collection and laboratory processing. Project Proponents must set out how they will meet the requirements of Sections 9.1.2.1 to 9.1.2.9 in a standard operating procedure (SOP) for soil sampling, sample handling and storage, and laboratory analysis. The SOP must be submitted with the PDD for review at validation. It must be applied at every sampling event: baseline () sampling, each re-sampling, true-up sampling under Section 9.1.3.1.8, and sampling of control plots under Section 9.2.1. The SOP may be combined with the soil sampling plan and QA/QC procedures required under Section 9.1.2.1 and the sample analysis plan required under Section 9.1.2.8, provided each element below can be identified in it.
The SOP must describe:
- Field procedure, including:
- how sampling points are located in the field, georeferenced and re-sampled to the accuracy required under Section 9.1.2.1, and how missed points are substituted under Section 9.1.2.2.4;
- how field staff identify and exclude the localized features listed in Section 9.1.2.1, such as stock camps, water points, supplementary feeding sites and livestock trails, and how exclusions are spatially documented;
- how sampling is timed to the seasonal stage and the ±30-day anchor-date window, and relative to grazing and organic amendment applications, and how livestock presence and the days since livestock were last present are recorded, under Section 9.1.2.1;
- the corer type and dimensions, the number of cores per composite and their layout around the sampling point, and the depth increments and any ESM extension increments under Section 9.1.2.4;
- surface clearing and the photographic record required under this Section; and
- the procedure where the corer meets refusal, soils are shallower than the sampled depth, or coarse fragments are present.
- Sample handling and storage: sample labelling and unique sample identifiers, chain-of-custody records from collection to analysis, and the conditions and time limits for on-site storage, shipping and storage before analysis under Section 9.1.2.6.
- Sample processing: drying, sieving to less than 2 mm, the treatment of coarse fragments and roots, the determination of oven-dry fine soil mass and the 105 °C moisture correction under this Section, homogenization of composites, subsampling, and grinding.
- Archiving: how archive subsamples are selected, packaged and stored, and how the inventory, storage log, annual inspection and co-stored reference material are maintained under Section 9.1.2.6.1.
- Laboratory analysis, including:
- each laboratory used, with evidence of its accreditation or demonstrated equivalence under Section 9.1.2.7;
- each parameter to be analyzed and its analytical method, citing the standard followed (for example, ISO 10694 for SOC by dry combustion), including carbonate screening and inorganic-carbon treatment under Section 9.1.2.8.1;
- how analytical batches and campaigns are defined, the certified reference materials and duplicates used, and the acceptance criteria applied under Section 9.1.2.7; and
- where Section 9.1.2.7.1 applies, the third-party validation procedure.
- Proximal sensing, where used: the technique and instrument, the calibration model, the pathway under which the technique is shown to be fit for purpose, the dry-combustion reference set, the measurement procedure, and the schedule for re-validation under Section 9.1.2.9.
- Data management: how field and laboratory data are recorded, checked and retained, including the retention of run-level analytical data under Section 9.1.2.7.
The SOP must be version-controlled. Any change made after validation must be documented and justified before the revised procedure is used, and reported at the next verification. Some changes affect the comparability of measurements between sampling events, including changes to core dimensions, compositing design, depth increments, laboratory or analytical method. These are permitted only as provided in the relevant Section of 9.1.2.
The following requirements apply:
- Both the intended and actual sampling point locations must be recorded and georeferenced.
- SOC content, oven-dry fine soil mass, and sample volume must be obtained from the same sample or from adjacent samples taken during the same sampling event, if the sample size is insufficient. Where multiple cores are combined into a single sample, all cores must be taken from the same depth and fully homogenized prior to subsampling for the different measurements and this must be disclosed in the documentation of the sampling procedures.
- Core dimensions and corer type must be held constant across all sampling events within a project, including between baseline and all subsequent remeasurement campaigns, except as set out below, because they affect the fine soil mass recovered per unit area independently of Equation 23 (e.g., through compaction during insertion, loss from the core base, edge effects, and the exclusion of coarse fragments and roots). A change in core dimensions or corer type is permitted where justified by soil conditions (e.g., stoniness, hardness or dryness that prevents full recovery with the original equipment) or by the unavailability of the original equipment, subject to the following: (a) within a sampling event, the same core dimensions and corer type must be used at all sampling locations within a stratum, except where conditions at a specific location require otherwise, in which case the exception must be documented for that location; (b) before or during the first sampling event at which the new equipment is used, the Project Proponent must conduct a paired comparison in which co-located cores are collected with both the previous and the new equipment at a representative subset of sampling locations (at least 10% of the sampling locations in each affected stratum, and not fewer than ten sampling locations in total), and test for a difference in fine soil mass per unit area (Equation 23) and SOC concentration; (c) where a mean difference is statistically significant at the 10% level (two-sided), a documented correction derived from the paired comparison must be applied to measurements made with the new equipment; and (d) the uncertainty of the comparison, and of any correction, must be propagated through the ESM correction and the Section 9.1.1 Monte Carlo simulation. The change, its justification and the paired comparison must be reported at the next Verification.
All organic material (e.g., living plants, litter/thatch, root mat and dung) must be cleared from the soil surface prior to sampling. This must be documented via a photograph taken after sample removal.
- SOC stocks within each stratum must be calculated on an equivalent soil mineral mass (ESM) basis. Use of soil mineral mass, rather than fine soil mass, ensures that the reference quantity against which SOC stocks are normalized is itself insensitive to changes in soil organic matter content driven by project activities. Sampling events are denoted , where is the baseline measurement at project initiation and ... are subsequent resampling events.
Fine soil mass per unit area. For each depth increment in stratum at sampling event , the oven-dry fine soil mass per unit area is:
(Equation 23)
Where:
- is the oven-dry fine soil mass per unit area in depth increment of stratum at sampling event (kg m⁻²);
- is the oven-dry mass of the fine (< 2 mm) soil recovered for that increment (), after exclusion of coarse material retained on the 2 mm sieve;
- is the internal diameter of the auger or corer (mm);
- is the number of cores composited into the sample, such that is the total cross-sectional area sampled (mm²);
- the factor 1000 converts g m⁻² to kg m⁻².
Coarse material must be prevented from passing through the 2 mm sieve. Deriving the fine soil mass per unit area directly from sample mass and corer cross-sectional area in this way eliminates the need for independent bulk-density sampling and the imprecision associated with it (Wendt & Hauser 2013). This is the reason core dimensions () and the compositing design (N) must be documented at every sampling event and held constant across sampling events, except as permitted under the core-dimension requirements above.
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:
(Equation 24)
Where:
- is the oven-dry fine soil mass per unit area as derived in Equation 23 (kg m⁻²);
- is the SOC concentration determined in accordance with Sections 9.1.2.8 and 9.1.2.8.1 (g C kg⁻¹ fine soil). Total carbon must not be used for samples that require treatment under Section 9.1.2.8.1; and
- 1.724 is the van Bemmelen conversion factor from organic carbon mass to organic matter mass. Where Project Proponents have project-area-specific data on the carbon fraction of soil organic matter, that value may be substituted for 1.724 with justification at validation. This protocol is only scoped to mineral soils.
Sampled volume, where required, is the product of the cross-sectional area sampled and the increment depth. Fine-earth bulk density, where reported, is a derived quantity calculated as the oven-dry fine soil mass divided by the sampled volume; it is used only for reporting and diagnostic purposes. Neither bulk density nor sampled volume is used as a direct input for the quantification calculations.
Samples for SOC analysis and for archiving (Section 9.1.2.6.1) must be air-dried, or oven-dried at a temperature not exceeding 40 °C, before sieving (consistent with ISO 11464). The oven-dry fine soil mass used in Equations 23 to 25 is the mass after drying at 105 °C to constant mass (consistent with ISO 11465). It may be determined by drying a separate subsample at 105 °C to derive a moisture-correction factor that is applied to the mass of the fine soil dried at no more than 40 °C, so that the analytical and archive subsamples are not heated above 40 °C. SOC concentrations must be expressed on the same 105 °C oven-dry basis.
"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 and at ) 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 ( vs ) 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).
Sample Handling and Storage
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.
Sample Archiving
Project Proponents must retain archived subsamples to enable future cross-calibration and audit. A dried, sieved (<2 mm) archive subsample, with sufficient mass for at least one full dry-combustion re-analysis, must be retained from: (a) at least 25% of baseline () sampling locations in each stratum, and not fewer than five sampling locations per stratum (or all sampling locations where a stratum has five or fewer), selected by stratified random selection documented before analytical results are available and spanning the soil types and SOC range of the Project Area; and (b) at least 5% of sampling locations in each subsequent campaign, including, where practicable, sampling locations archived at baseline. Project Proponents are encouraged to archive all baseline sampling locations where storage allows.
Archived samples must be stored air-dried or oven-dried at a drying temperature not exceeding 40 °C (Section 9.1.2.5; the 105 °C oven-dry determination is made on a separate subsample and does not apply to archived samples), in sealed, labelled, inert containers, under cool, dry, dark conditions protected from moisture, contamination, and pests, and retained for the duration of the Project Commitment Period.
Archived samples stored under these conditions are assumed to be stable, and periodic re-analysis of the archive is not required. The Project Proponent must maintain an inventory and a storage log recording storage conditions and an inspection of container integrity at least annually. A certified reference material or an in-house reference soil must be stored under the same conditions and analyzed alongside archived samples whenever they are re-analyzed. Where the co-stored reference indicates a material change, the affected archived results must not be used without a documented correction, with additional uncertainty propagated through the Monte Carlo simulation.
Where archived samples are unavailable, including for project years predating this requirement, cross-calibration on a laboratory transition must instead be performed using certified reference materials and shared independent reference soils spanning the project's soil types and SOC range, with a conservative additional analytical uncertainty applied and propagated through the Monte Carlo simulation.
Analytical Laboratory Requirements
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.
All samples collected throughout the Project lifetime should be analyzed by the same laboratory wherever practicable. A transition to a different laboratory is permitted, including in cases where the incumbent laboratory is no longer able or willing to process project samples, provided that:
- The change is justified in writing and documented for review at the subsequent verification;
- The replacement laboratory satisfies the accreditation requirements set out above; and
- A documented cross-calibration procedure is completed prior to the new laboratory's results being used for quantification. The cross-calibration must involve parallel analysis of a representative set of samples (including, where available, archived samples and certified reference materials) by both laboratories. The cross-calibration passes where, across the parallel-analyzed set, the mean relative bias between laboratories is within ±5% (or within the certification uncertainty of the certified reference materials used, whichever is greater) and the precision, expressed as the relative standard deviation of paired results, is within 10%. Where either threshold is exceeded, the systematic offset must be addressed through method alignment or a documented correction before the new laboratory's results are used, and any residual uncertainty propagated through the Monte Carlo simulation. The cross-calibration report must be submitted as part of project documentation.
- Where any project samples require treatment under Section 9.1.2.8.1, the parallel-analyzed set must include such samples, analyzed by both laboratories using the same treatment method.
The selected laboratory must quantify and report analytical error statistics to the Project Proponent on a regular basis, derived from repeated analyses of the same sample and from analyses of certified reference materials. The laboratory must provide documentation of its internal quality control program, including:
- Use of certified soil reference materials with known SOC content (e.g., NIST SRM 2710, BCR-129, or an equivalent traceable certified reference material appropriate for the SOC content range of the Project samples);
- Monitoring of variation in analysis against defined error thresholds; and
- Participation in external proficiency testing schemes (e.g., round-robin testing) or registration as a member of the Global Soil Laboratory Network (GLOSOLAN), or an equivalent nationally recognized program.
For the purposes of this Section, an analytical batch is the set of project samples analyzed together under a single instrument calibration and quality-control bracket, as defined in the laboratory's standard operating procedures. An analytical campaign is the set of all project samples analyzed under the same analytical method and calibration regime within a single Reporting Period. The batch and campaign membership of every project sample must be documented and auditable. Each analytical batch must include at least one certified reference material appropriate to the SOC content range of the project samples.
Quantification of analytical uncertainty for Monte Carlo propagation. For each analytical campaign, the analytical uncertainty distribution used to perturb SOC concentration measurements in the Monte Carlo simulation (Section 9.1.1) must be characterized as follows:
-
Systematic bias correction. For each analytical batch, identify any systematic bias in the analytical run against the certified reference material(s), expressed as the mean signed difference between measured and certified SOC values. Where systematic bias exceeds the CRM's certification uncertainty, project sample measurements must be corrected for the bias and the residual uncertainty (after correction) documented in the GHG Statement.
-
Random error characterization. The random analytical error must be characterized at the campaign level as the greater of:
- (i) the relative standard deviation (RSD) of certified reference material recovery across the campaign, i.e., the standard deviation of CRM measurements divided by the certified value; or
- (ii) the relative standard deviation of laboratory duplicate analyses across the campaign, expressed as a fraction of the measured value.
A minimum of 5% of project samples across each campaign must be analyzed as laboratory duplicates, with a minimum of three duplicates per campaign.
-
Monte Carlo input distribution. The random error from Step 2 must be used to parameterize a mean-zero perturbation around each measured SOC concentration value within the Monte Carlo simulation under Section 9.1.1. The distribution may be assumed Gaussian provided the laboratory's quality-control data support this assumption; where the duplicate/CRM distribution is materially non-Gaussian, the empirical distribution must be used directly via inverse-transform sampling or a documented alternative.
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.
Third Party Validation
Where a Project Proponent utilizes laboratory facilities within an academic institution or a non-accredited commercial laboratory, this Protocol requires that 10% of samples are sent to an accredited laboratory for validation. External laboratory validation is required for both quantification and validation samples in this scenario. The analyses conducted by the third party lab must be sufficient to calculate gross CDR and confirm the analytical results of the academic or non-accredited institution.
Prior to data submission, the Project Proponent is required to identify the sampling locations that will be sent to a third part laboratory to Isometric and the VVB for approval. The third party facility must be approved by Isometric. Data from the third party validation must be sent directly to Isometric from the accredited laboratory.
If the results of the third party validation show significant discrepancies with the overall dataset, an audit will be conducted by Isometric and the VVB. As part of this audit, Isometric or the VVB may request that additional samples are sent for third party validation. In this instance, Isometric or the VVB will select the samples for validation. Other materials that may be requested in an audit include:
- Run logs of analytical instruments within the academic or non-accredited facility;
- Information on sample preparation, such as laboratory notebooks and SOPs;
- Chain of custody of the samples analyzed;
- Raw, uncorrected data files for each sample analyzed.
Sample Measurement
Project Proponents must include their sample analysis plan in the PDD. The plan must set out the carbonate screening and inorganic-carbon methods required under Section 9.1.2.8.1.
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). Dry combustion measures total carbon. Its result may be used as SOC only for samples that do not require treatment under Section 9.1.2.8.1.
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.
Soil Inorganic Carbon
Soil inorganic carbon, and changes in it, are not credited under this Module.
Carbonate screening. Project Proponents must screen every analytical sample for carbonates before SOC analysis. This covers each sampling location and depth increment, including samples from control plots (Section 9.2.1). Screening must be carried out at baseline () and at each subsequent sampling event. It must include soil pH measured in water (e.g., ISO 10390) and an effervescence test with dilute (approximately 1 M) hydrochloric acid. A sample is carbonate-positive where its pH (H₂O) is 7.0 or above, or where it effervesces. A carbonate-positive sample may be treated as carbonate-free only where a quantitative inorganic-carbon determination on that sample, made under this Section, is below the method's limit of quantification.
When inorganic carbon must be removed or determined. Inorganic carbon must be removed or determined, using a method below, for every sample that:
- (a) is carbonate-positive;
- (b) comes from an area to which lime or another carbonate-bearing amendment was applied, whatever its screening result. This covers application in the five years before the Enrollment Date or at any time during the Project. Carbonate-bearing amendments include dolomite, marl, wood ash, and carbonate-containing biochar or industrial by-products; or
- (c) comes from an area that was irrigated in the five years before the Enrollment Date or at any time during the Project, whatever its screening result.
Project Proponents must record, for each field, every application of a carbonate-bearing amendment and every irrigation season over the same periods.
Once a sampling location and depth increment requires treatment, it must be treated at every later sampling event, including where a later screen is negative.
Where treatment is first required after , Project Proponents may re-analyze archived subsamples (Section 9.1.2.6.1) using the same method, subject to Section 9.1.2.7. Where they do:
- this must be done for every archived subsample in the stratum to which the requirement applies; and
- the re-analyzed values must replace the original results, whatever their direction.
Methods. Inorganic carbon must be addressed by either:
- (i) acid pre-treatment of the analytical subsample before dry combustion (e.g., acid fumigation or in-capsule acidification), with organic carbon measured directly; or
- (ii) a separate determination of inorganic carbon on a subsample of the same homogenized sample, with SOC calculated as total carbon minus inorganic carbon (consistent with ISO 10694). Suitable methods include volumetric calcimetry consistent with ISO 10693 and temperature-ramped combustion consistent with DIN 19539.
The method must remove or quantify dolomitic as well as calcitic carbonate. Results must be expressed per unit mass of untreated fine soil, on the 105 °C oven-dry basis of Section 9.1.2.5.
Each analytical batch must include a certified reference material or in-house reference soil of known organic and inorganic carbon content. It is used to verify complete carbonate removal under method (i), or inorganic-carbon recovery under method (ii).
Archived subsamples (Section 9.1.2.6.1) must not be acid-treated.
The method used for each sampling location must be documented in the sample analysis plan and held constant at every sampling event. A change of method must be treated as a laboratory transition and cross-calibrated in accordance with Section 9.1.2.7.
Uncertainty. The analytical uncertainty of SOC determined under this Section must be characterized in accordance with Section 9.1.2.7 and propagated through the Monte Carlo simulation (Section 9.1.1). Under method (i), duplicates and reference materials must be acid-treated in the same way as project samples. Under method (ii), the uncertainties of both the total-carbon and the inorganic-carbon determinations must be characterized and propagated.
Reporting. For each stratum and sampling event, the GHG Statement must state:
- the screening results;
- which samples were treated, and on which basis ((a), (b) or (c));
- the method used; and
- under method (ii), the measured inorganic-carbon concentrations
Proximal Sensing Techniques
In-situ proximal sensing techniques may be used as alternatives or complements to laboratory dry combustion analysis for the quantification of SOC. Dry combustion remains the reference method against which all proximal sensing measurements are anchored. The following techniques are permitted under this Module, subject to the requirements below:
- Infrared spectroscopy, including near infrared (NIR), visible near infrared (Vis-NIR), and mid-infrared spectroscopy (MIR);
- Laser-induced breakdown spectroscopy (LIBS); and
- Inelastic neutron scattering (INS, also known as neutron-stimulated gamma ray analysis or spectroscopy).
Prior to use for Certificates-relevant measurements, the Project Proponent must demonstrate, to Isometric's satisfaction and for review at Project Validation, that the selected technique is fit for purpose under one of the two pathways below. A technique that qualifies under either pathway is permitted.
Pathway A — Published equivalence
The Project Proponent demonstrates, through published, peer-reviewed evidence, that the technique achieves accuracy and reliability comparable to dry combustion for the soil types, moisture conditions, and SOC content ranges present in the project area. Under this pathway, instruments and calibration models must be calibrated against project-area reference samples measured by dry combustion, following methods described in peer-reviewed literature and repeated at defined intervals over the project lifetime, and validated against independent reference samples not used in model development, with acceptance thresholds consistent with those reported in peer-reviewed literature.
Pathway B — Project-specific validation
The Project Proponent demonstrates accuracy on the project's own soils, validated against a representative set of the project's samples measured by dry combustion, without reliance on published equivalence. Under this pathway:
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Commercial or library-based calibration models may be used; per-instrument recalibration against project samples is not required.
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A representative reference set of project samples, spanning the soil types, moisture conditions, and SOC content ranges of the project area, must be measured by dry combustion.
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Before use for crediting, the technique's predictions must be checked against this reference set and must meet accuracy and bias targets that are pre-registered for the project's conditions and reviewed by the VVB. The check rests on the project's own data and need not cite published studies.
- The reference set may be collected, and the validation analysis performed, by the Project Proponent, provided that the reference samples are measured by dry combustion at a laboratory meeting the requirements of Section 9.1.2.7, the accuracy and bias targets are pre-registered before the check is run, and the full validation dataset (including instrument outputs, model predictions and dry-combustion results) is available for audit by the VVB and Isometric. Data from outside the Project Area may be used to develop the calibration model but do not substitute for the project reference set.
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Any systematic offset identified must be corrected to the dry-combustion values before the predictions are used.
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Samples falling outside the range that the calibration model can reliably predict must be measured by dry combustion.
For Both Pathways:
- Uncertainty. The measurement uncertainty of the technique must be fully quantified and propagated through the entire removal calculation in accordance with Section 7.6 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 Certificate issuance; a less precise method is not excluded, but yields fewer Certificates.
- Ongoing verification and re-validation. A subset of proximal sensing measurements must be independently verified by laboratory dry combustion at each Reporting Period. The fit-for-purpose demonstration under Pathway A or Pathway B must be repeated at each measurement campaign and whenever the instrument, calibration model, or laboratory changes, with a portion of samples re-confirmed by dry combustion.
- Inorganic carbon. LIBS and INS measure elemental (total) carbon. They must not be used for samples that require treatment under Section 9.1.2.8.1 unless inorganic carbon is determined separately under that Section. For spectroscopic techniques, dry-combustion reference values must be SOC determined in accordance with Section 9.1.2.8.1. The calibration and validation sets must include carbonate-positive samples wherever such samples occur in the Project Area. Carbonate screening applies whether or not proximal sensing is used.
SOC Stock Calculation (Approach 1)
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 reference mineral mass common to all sampling events compared in a given calculation (Equation 26). This is the reduced cumulative-mass formulation common to the methods of Gifford & Roderick10 and Ellert & Bettany11 as set out in Wendt & Hauser12. A single method is prescribed and conservatism is applied at the crediting step (the 30th-percentile estimate under Section 9.1.1).
Increment quantities. For each depth increment in stratum at sampling event , and at each sampling location, compute the soil mineral mass per unit area (Equation 24) and the SOC mass per unit area:
(Equation 25)
where is the the SOC concentration determined in accordance with Sections 9.1.2.8 and 9.1.2.8.1 (g C kg⁻¹ fine soil). Total carbon must not be used for samples that require treatment under Section 9.1.2.8.1., is the fine soil mass per unit area (Equation 23, kg m⁻²), and 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 :
Reference mineral mass. Define the reference soil mineral mass for stratum as the minimum, across all sampling events, of the total cumulative soil mineral mass of the stratum-mean profile:
(Equation 26)
where is the total number of depth increments sampled to the common reporting depth (common to all sampling locations and all sampling events, and excluding any extension increment collected under Section 9.1.2.4) and is the mean, across all sampling locations in stratum , of the increment- mineral mass at event . 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 required12. 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.
The minimum in Equation 26 is taken over and all sampling events up to and including the current event, so the reference mineral mass can decrease at a later event. At each sampling event, the ESM-corrected SOC stocks at , and at any other earlier event used in the calculation, must be re-evaluated from the retained location-level data at the current reference mineral mass, so that the cumulative stock change from is always calculated on a common mineral-mass basis. This restatement applies only to the calculation of the current cumulative stock change. Consistent with the cumulative accounting approach of the Improved Soil Management Protocol (Section 9.1 and Equation 5), Certificates issued for prior Reporting Periods are not recalculated or reissued, and any difference arising from an updated reference mineral mass is reflected in the quantity issued for the current Reporting Period. Where the reference mineral mass changes, the GHG Statement must report the previous and updated values and the effect of the change on the cumulative stock change.
ESM-corrected stock density. Let aa a be the deepest increment whose cumulative mineral mass does not exceed the reference mass, and let be the increment within which the reference mass falls:
The ESM-corrected SOC stock density at a sampling location in stratum at sampling event is then:
(Equation 27)
Where:
- is the ESM-corrected SOC stock density in stratum at sampling event (t C ha⁻¹);
- is the cumulative SOC mass to the bottom of increment (g C m⁻²);
- is the cumulative soil mineral mass to the bottom of increment (kg m⁻²);
- is the ratio of SOC mass to soil mineral mass within increment , used as the interpolation slope across that increment (g C kg⁻¹ mineral soil). This is distinct from the measured SOC concentration , which is expressed per fine soil;
- is the reference soil mineral mass (Equation 26, kg m⁻²);
- the factor converts g C m⁻² to t C ha⁻¹.
When 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 27 reduces to the location's full-profile SOC mass divided by 100.
Where the profile is sampled as a single depth increment (), set with both cumulative sums equal to zero; Equation 27 then reduces to the single-layer ESM correction:
(Equation 28)
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 reduction is retained for completeness and for the baseline case described below.)
Estimation and aggregation across locations. The reference soil mineral mass (Equation 26) is a stratum-level constant defined on the stratum-mean mineral profile. Equation 27 must be evaluated at each sampling location, using that location's own cumulative SOC and mineral masses against the common stratum reference (or against the location reference mineral mass where the baseline truncation rule below applies). The stratum-level stock carried into Equation 22 is the mean of the location-level values across all sampling locations in the stratum. Evaluating Equation 26 at the location level is required so that the location-level paired stock differences used in the rarefaction analysis (Section 9.1.2.3) and the hierarchical bootstrap (Section 9.1.1) are defined consistently with the crediting point estimate.
Because the reference mineral mass is defined on the stratum-mean profile, an individual sampling location's measured column may not reach it. The following rules apply:
- Truncation after baseline. Where a location's total measured mineral mass at a sampling event after (including any extension increment under Section 9.1.2.4) is less than , that location's cumulative SOC mass at that event must be evaluated at its own measured profile total (that is, is set to the location's deepest measured increment and the interpolation term is zero) rather than extrapolated beyond the measured column. This truncation omits SOC between the location's profile total and the reference mass and is conservative for crediting.
- Truncation at baseline. Where a location's total measured mineral mass at (including any extension increment) is less than , evaluating the location at its profile total and at the full reference mass at later events would overstate its paired stock change. For such a location, the location reference mineral mass is its profile total, and its SOC stock at every sampling event must be evaluated at that mass (or, where lower, at its measured profile total at that event under the previous rule), so that its paired difference is calculated on a common mineral mass.
- Extension where truncation recurs. Each occurrence of truncation must be documented at Verification. Where truncation after baseline occurs at the same location at two consecutive sampling events, that location must be sampled with an extension increment of up to 10 cm below the common reporting depth under Section 9.1.2.4 at the next and all subsequent sampling events. Where the extended column still does not reach the reference mineral mass, the location continues to be evaluated at its own measured profile total under the first rule, and no further increase in sampling depth is required.
Truncation is an expected consequence of a stratum-mean reference: at the sampling event that sets the reference, a substantial proportion of locations (up to about half) may fall below it. Project Proponents are therefore recommended to collect an extension increment at all sampling locations at every re-sampling event, which avoids truncation at nearly all locations and the associated conservative reduction in the credited stock change.
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 credited quantity. 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 26 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 onwards, once the cross-event minimum is defined over more than one event, and the reference mineral mass is re-evaluated at each subsequent event as set out under Reference mineral mass above.
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 27 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 22; 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 22.
Approach 2 — Biogeochemical Modeling with Remeasurement
Model validation must be conducted in accordance with the requirements set out in Section 9.1.3.1.3 and all outcomes and data used for validation purposes must be clearly documented.
Under this approach, an approved biogeochemical model (see Section 9.1.3.1.1) is used to estimate SOC stock changes between resampling campaigns based on measured initial SOC stocks (or initial SOC stocks back-modeled from measurements in accordance with Section 9.1.3.1.9), 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. At each true-up event, remeasurement data must be used to re-estimate model prediction error and test for systematic bias, and may be used to recalibrate the model against observed conditions (true-up procedure, see Sections 9.1.3.1.6 and 9.1.3.1.7).
Model Requirements
Model Eligibility
This Module is model-agnostic in principle: any biogeochemical model may be used provided it meets the eligibility and validation requirements of this Section. In practice, the validation requirements; spatial independence of calibration and validation data, representative management and climatic coverage, and the corpus-completeness standard, favor models with demonstrated broad regional or global applicability, validated across many sites and conditions, over models calibrated bespoke to a single project area. This reflects a deliberate trade-off: for a standard issuing Certificates across a portfolio of projects, assurance that a model is not overfit to locally favorable conditions and that its performance holds across contexts is prioritized over local-scale precision. Approach 2 is therefore best suited to models of demonstrated broad applicability; a locally parameterized model remains eligible only where it genuinely satisfies the validation requirements of this Section. Project Proponents should assess this fit before electing Approach 2.
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.
The two pathways below govern eligibility only. Every model, including an established model, must additionally be validated for the conditions of the Project area in accordance with Sections 9.1.3.1.3 to 9.1.3.1.5 before it is used for Certificate-relevant quantification. For all models, the data used for that validation must comprise quality-controlled in-situ SOC stock measurements and must be available to Isometric and the VVB in accordance with Section 9.1.3.1.2.
Model eligibility must be demonstrated through one of the following two pathways, submitted in the PDD and approved by Isometric prior to Validation:
- Established models
- The model has a track record of use in science, industry, or government applications, demonstrated through multiple peer-reviewed publications reporting its application to SOC stock change estimation under land management conditions comparable to those of the Project area.
- The model must be demonstrably relevant to the climate, soil types, vegetation types, and land management practices present in the Project.
- Newly developed models
- Where a model does not yet have an established peer-reviewed track record, it must be validated against reputable independent data sources prior to use.
- Validation data must comprise quality-controlled in-situ SOC measurements and publicly available datasets adhering to FAIR (Findable, Accessible, Interoperable, and Reusable) principles.
- Sufficient validation data and results must be submitted with the PDD and approved by Isometric prior to validation.
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.
Eligibility does not depend on a model's internal input structure or on its being process-based; empirical, statistical, and other model types are eligible on the same performance basis, with residual model limitations reflected in the conservative uncertainty treatment under Sections 9.1.1 and 9.1.3.1.3 rather than in prescribed inputs..
The selected model must be capable of simulating SOC dynamics across the sampled depth profile required under Section 9.1.2.4.
Model Calibration and Parameterization
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 parameter values used, including their source (e.g., published literature, national databases, site-specific measurements, or model defaults);
- Project Proponents must clearly specify which model parameters were subject to calibration
- The version of the model used, including any modifications made to the base model code or configuration;
- The procedure for model calibration reported in enough detail to enable reproducibility;
- All input data used to initialize and run the model, including measured initial SOC stocks, soil physical and chemical properties, climate data, and land management practice records; and
- Any assumptions made where site-specific data were unavailable, including justification for the values adopted.
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.
The practice change that generates the claimed removal must be represented in the model, and the data needed to distinguish the project and baseline scenarios must be recorded and reported.
Digital soil maps may be used to initialize model state variables (e.g., baseline SOC pools, texture, bulk density) where site-measured values are unavailable. Because the acceptability of such products varies by region and property, their use is assessed against the following criteria, and a product will be accepted where the Project Proponent demonstrates each of the following to Isometric's satisfaction:
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Provenance
- The product is an authoritative or peer-reviewed soil dataset (e.g., a national or regional soil survey, SoilGrids, or the FAO Soils Portal), with a documented and reproducible methodology;
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Resolution and coverage
- The spatial resolution and depth intervals of the product are appropriate to the project's quantification units and the model's initialization requirements, with any resolution mismatch documented;
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Quantified uncertainty
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The initialization uncertainty of each initialized variable is quantified and propagated through the removal calculation in the Section 9.1.1 Monte Carlo simulation, using either:
the prediction uncertainty reported by the product; or
where the product does not report a quantified prediction uncertainty for the variable (e.g., map-unit component properties from a soil survey), an empirical initialization uncertainty derived from the Local corroboration comparison below, calculated from the differences between initialized and measured values across the reference measurements (e.g., as the empirical distribution of those differences, or as their mean bias and RMSE) and propagated on the same basis as a reported prediction uncertainty.
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Under option (b): the reference measurements must be project-area measurements of the variable where these exist, distributed across the strata in which the product is used, with at least three reference locations per stratum (where only regionally representative field data are available, their representativeness of the Project area's soils must be justified); any systematic difference between initialized and measured values must be corrected, or carried as a bias term, rather than absorbed into the variance; and the derivation, the corroboration sample, and the resulting uncertainty term must be documented in the PDD and reviewed at Validation. Under either option, where the discrepancy observed in the Local corroboration comparison is larger than the reported prediction uncertainty, the larger must be used;
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Local corroboration
- The initialized values are corroborated against project-area reference measurements (or, where unavailable, regionally representative field data), with material discrepancies documented and reconciled; and
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Scope of use
- The product is used only for model initialization, not as a source of Certificates-relevant SOC stock change; modeled baselines remain subject to the model validation and true-up requirements of Sections 9.1.3.1.3 and 9.1.3.1.6.
The product used, the criteria assessment, and the basis for Isometric's approval are recorded in the Project Design Document.
Model Validation
Prior to use for Certificates-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.
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.
The validation dataset is a single cumulative dataset: the representative subset (a minimum of 20%) withheld from the corpus constructed under Section 9.1.3.1.4 at initial validation, which grows at each true-up event by the project-collected observations that are not used for recalibration under Section 9.1.3.1.7. The corpus comprises the peer-reviewed studies identified under Section 9.1.3.1.4, any regional validation data used under Section 9.1.3.1.5 and, from the first true-up event, project-collected data. References in this Section and in Sections 9.1.3.1.4 to 9.1.3.2.3 to the "validation dataset" or the "cumulative validation dataset" are to this dataset
The validation dataset must:
- Include representative coverage of SOC stock values across the sampled depth profile required under Section 9.1.2.4;
- Be drawn from the same ecoregion and encompass soil types, climatic conditions, and land management practices comparable to those of the Project area; and
- Include the range of management practice changes implemented under the Project, so that model performance under project-relevant conditions can be directly assessed.
- Where the model is to be used to estimate the counterfactual under Section 9.2.2, additionally include observations representative of the baseline management practices that will be input to the counterfactual simulation, so that model performance under baseline-relevant conditions can be directly assessed.
Observations are classified as project-like or baseline-like by reference to the management parameters material to soil carbon that define the Project regime and the baseline regime under Section 7.4.1 (grazing intensity; the length and timing of rest and recovery periods; and the rotation and timing of grazing), and not by the name of the grazing subtype. Where a study does not report one or more of these parameters, its observations may be classified using the regime descriptors it does report (e.g., multi-paddock rotational, short-duration, deferred, or continuous grazing), provided that the Project Proponent documents a mapping from each descriptor used to the expected range of each material parameter, supported by the information reported in the study and by peer-reviewed literature. The mapping must be applied consistently across the corpus, fixed at pre-registration, and reviewed by the VVB at Validation. Observations that cannot be classified by either route do not satisfy criterion 4 of Section 9.1.3.1.4. Representative management coverage under this Section requires coverage of the management regimes implemented under the Project (and, where applicable, the baseline) across the climatic, soil, and vegetation conditions of the Project area; it does not require dense coverage of every combination of management parameter values. Where observations classified by descriptor are used under the generalized-parameter-space pathway below, their position on each management driver is represented by the mapped range when characterizing the validated parameter space and applying the interpolation requirement.
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 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.
Where the Project Area spans more than one ecoregion, references in this Section and in Sections 9.1.3.1.4 and 9.1.3.1.5 to the Project's ecoregion apply to each ecoregion represented in the Project Area. The corpus must meet the completeness standard of Section 9.1.3.1.4 for each such ecoregion; the validation dataset must include representative observations for the strata in each ecoregion; performance statistics must be reported for each ecoregion; and residual structure with respect to ecoregion must be tested under this Section. Where the requirements of this Section cannot be met for the strata in an ecoregion, the generalized-parameter-space pathway may be used for those strata, subject to its conditions; otherwise, the model is not eligible for use in those strata.
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 9.1.3.1.6.
Based on assessment against the validation dataset, the model must demonstrate the following:
- , calculated as the coefficient of determination against validation dataset SOC stock values, must be greater than 0. A 90% confidence interval for must be calculated and must exclude 0, demonstrating statistically significant predictive skill. must not be calculated as the coefficient of determination of a linear regression of actual against predicted values, as this can produce misleadingly high values for biased models.
- Where model-based error propagation is used, the coverage of model predictions must be evaluated. The proportion of observed values falling within the model's predicted uncertainty intervals must be assessed and reported. This requirement does not apply where model-assisted or analytic error propagation is used.
- No systematic bias in predictions across the range of SOC stock values observed in the Project area. Where bias is present, it must be characterized, documented, and corrected prior to use.
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; and mean bias error (MBE), reported in t C ha, 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 characterised 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 be tested for structure with respect to soil type, management practice, and time (and, where the Project Area spans more than one ecoregion, ecoregion), using an appropriate statistical test (e.g., ANOVA/Kruskal-Wallis for soil type and management practice; a trend or autocorrelation test for time), for each subset for which performance is reported under this Section. Because these tests form a family applied to a single model, the p-values of all residual-structure tests conducted at a validation event must be adjusted for multiple comparisons using the Benjamini–Hochberg false-discovery-rate procedure at q = 0.10; structure is statistically significant where it remains significant after this adjustment. 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. Statistically significant structure is material where its magnitude, being the largest absolute difference between the mean residual for any level of the factor and the mean residual across all validation points in the subset (or, for time, the fitted residual trend extrapolated over the interval to the next scheduled true-up event), exceeds 5% of the mean observed SOC stock in the Project area, consistent with the minimum effect size of the bias tests in Section 9.1.3.1.6. Where significant and material structure is detected, the model must be investigated and recalibrated, and may not be approved for use until the structure is resolved. Where significant structure is detected that is not material, it must be characterized and documented and must be reflected in the Section 9.1.1 Monte Carlo simulation (e.g., by sampling model prediction error conditional on the affected factor). The unadjusted and adjusted p-values and the magnitude of structure must be reported for every test, whether or not structure is detected.
Where a model does not meet the 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.
Spatially blocked k-fold cross-validation. The fixed hold-out of a minimum of 20% of representative data points is the preferred validation design. Where the Project Proponent demonstrates that no fixed hold-out drawn from the available data can simultaneously satisfy the representativeness requirements, the validation dataset requirements, and the subset and climatic-coverage requirements of this Section, validation may instead be conducted by spatially blocked k-fold cross-validation, subject to the following:
- Fold design. Folds must be constructed from spatial blocks defined in accordance with the block-level hold-out design of Section 9.1.3.1.5. Each block must be held out in exactly one fold, and a minimum of five folds must be used (leave-one-block-out is permitted). The block definitions, the number of folds, and the rule for assigning blocks to folds (including blocks added at later true-up events) must be pre-registered.
- Separation within each fold. Within each fold, no data from a held-out block may be used in any step of model calibration, parameterization, or selection, including hyperparameter tuning, variable selection, and bias correction; each such step must be repeated within each fold using only that fold's calibration data. The spatial-blocking diagnostics required under Section 9.1.3.1.5 must be reported for each fold. The prohibition on reuse of validation data for parameterization or recalibration in this Section and in Section 9.1.3.1.7 applies within each fold; a data point held out in one fold may be used for calibration in other folds.
- Performance assessment. The performance thresholds, coverage evaluation, bias requirements, and residual-structure tests of this Section must be applied to the pooled out-of-fold predictions, in which each observation is predicted by a model calibrated without its block. The representativeness, subset, and climatic-coverage requirements of this Section are assessed on the full set of out-of-fold predictions.
- Final calibration. Once the performance and bias thresholds are met, the model may be calibrated on the complete dataset using the same pre-registered calibration procedure applied within each fold, without further tuning. The model prediction error used in the Section 9.1.1 Monte Carlo simulation must be derived from the out-of-fold residuals, not from the in-sample fit of the final calibration.
- Subsequent use. Where this design is used, references in this Section and in Sections 9.1.3.1.6 and 9.1.3.1.7 to the validation dataset are to the out-of-fold predictions from the most recent cross-validation, together with the project-collected observations first assessed against the model in use at subsequent true-up events. Recalibration under Section 9.1.3.1.7 must repeat the cross-validation over the updated cumulative dataset under the pre-registered design, in place of the 20% hold-out.
The regional validation data fallback of Section 9.1.3.1.5 and the generalized-parameter-space pathway below may be used only where validation by spatially blocked k-fold cross-validation is shown to be unable to satisfy the requirements of this Section.
Generalised-parameter-space validation (data-sparse pathway). Where representative in-situ data are not reasonably available for the specific combination of soil, vegetation, climatic, and management conditions of the project area, model performance may be validated over a generalised parameter space encapsulating the project conditions, in place of project-like observations. This pathway is available where all of the following are met:
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Data-scarcity trigger. A documented systematic corpus search conducted in accordance with Section 9.1.3.1.4 returns fewer project-like observations than are required to meet the statistical-power thresholds for the project-like × climatic-coverage subset under either the 20% representative validation subset or the spatially blocked k-fold cross-validation design permitted under this Section, and the regional validation dataset permitted under Section 9.1.3.1.5 is likewise insufficient. The search, its results, and the resulting data gap must be documented.
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Parameter-space characterisation. The generalised parameter space must be defined explicitly over the underlying drivers of SOC dynamics — the climatic envelope (temperature and moisture/aridity regime), soil properties (texture, type, SOC range, and depth profile), and the management drivers that characterise the project intervention (e.g. stocking density, rest and recovery periods, defoliation frequency) — rather than by named practice categories.
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Geographic scope of the corpus. Once this pathway is invoked, the requirements of this Section and of Sections 9.1.3.1.4 and 9.1.3.1.5 that observations be drawn from the Project's ecoregion do not apply to the observations used to validate the generalized parameter space, and observations from other ecoregions may be used where their conditions lie within the generalized parameter space. the Project Proponent must define the geographic scope of the extended corpus search before it is executed (e.g., the ecoregions whose climatic and soil conditions overlap the generalized parameter space), justify that scope by reference to the parameter-space characterization, and apply the completeness standard, the mandatory inclusion criteria other than criterion 2, and the closed exclusion grounds of Section 9.1.3.1.4 across that entire scope.
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Interpolation, not extrapolation. the project's conditions must lie within the interior of the validated parameter space on each key driver. Validation does not support crediting where project conditions fall outside, or only at the boundary of, the validated envelope.
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Performance. The R² and systematic-bias thresholds set out in this Section apply unchanged, and residual prediction errors must be shown to be randomly distributed with respect to the drivers defining the parameter space.
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Conservatism. While generalized-parameter-space validation is relied upon, a conservative additional model-prediction uncertainty must be applied and propagated through the Section 9.1.1 Monte Carlo simulation, reflecting the substitution of generalized for project-specific validation. The additional uncertainty is applied by scaling the dispersion of the model prediction error distribution (derived from validation over the generalized parameter space) by a multiplier :
where is the number of observations in the validation dataset that are project-like observations drawn from the Project area or the Project's ecoregion, and is the number of other observations in the validation dataset relied upon under this pathway. For a symmetric RMSE-based error distribution, the RMSE is multiplied by ; for an asymmetric or empirical error distribution, the deviation of each residual from the mean residual is multiplied by . , and hence must be recalculated at each true-up event as project-collected observations are added to the validation dataset, so that the additional uncertainty reduces as reliance on generalized validation reduces. Where the validation requirements of this Section can be met without reliance on this pathway, the pathway ceases to apply and . The values of , and must be reported in the PDD and in each GHG Statement.
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Convergence at true-up. As project remeasurement data accumulate, the model must be re-validated and, where required, recalibrated toward project-specific observations at each true-up event under Section 9.1.3.1.6, progressively reducing reliance on generalised validation.
Use of this pathway must be pre-registered, reviewed by the VVB, and approved by Isometric. Because cumulative credits under Approach 2 are in all cases reconciled to field measurement through the model divergence testing and reconciliation requirements — including compensation of any overcrediting from the Buffer Pool and loss of interim model-based crediting on persistent overprediction — generalised-parameter-space validation does not weaken the environmental-integrity controls applied at each true-up event.
Completeness of Validation Data
The validation dataset (see Section 9.1.3.1.3) is constructed by withholding a minimum of 20% of representative data points from a broader corpus of available evidence, the peer-reviewed component of which must meet the completeness standard below. 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 be constructed through a pre-registered, reproducible systematic review designed to identify all peer-reviewed studies that satisfy each of the following criteria:
- Published in the peer-reviewed literature within the 25 years preceding the date of the Project's initial validation, or at any time after that date. For this criterion, the date of initial validation remains fixed at subsequent validation events, so that studies admitted to the corpus continue to satisfy it;
- Drawn from the Project's ecoregion, defined consistently with the management-coverage and climate-coverage requirements set out elsewhere in this Section;
- Reporting SOC stocks (not solely SOC concentration) at depth profiles compatible with the Project's reported sampling depths under Section 9.1.2.4; and
- Reporting SOC stocks under management practices identifiable as project-like or baseline-like under the partition defined elsewhere in this Section.
Before the search is executed and before any model performance statistics are calculated, the Project Proponent must pre-register the systematic review protocol, either by submitting it to Isometric or by depositing it in a publicly accessible, time-stamped registry (for example, Open Science Framework Registries; an embargoed registration is acceptable), such that the registration date can be independently verified, and must include the registered protocol and its registration record in the Project Design Document. The protocol must specify the databases to be queried (at minimum: Web of Science, Scopus, AGRIS, and Google Scholar), the search strings, the date limits, the inclusion and exclusion screening criteria, and the screening procedure.
The Project Proponent must document the execution of the protocol, including the date of the search, and report its results in a PRISMA-style flow diagram giving the number of records identified, screened, assessed for eligibility, excluded (by exclusion ground), and included at each stage. Compliance with the completeness standard is assessed on the adequacy and reproducibility of the pre-registered protocol and its faithful execution, reviewed by the VVB at Validation, rather than on a demonstration that no qualifying study exists. As part of this review, the VVB must test the adequacy of the search, for example by checking that qualifying studies cited in recent reviews or meta-analyses relevant to the Project's ecoregion are captured; where a qualifying study that the protocol should reasonably have identified is missing, the protocol must be revised and re-executed.
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:
- Methodological incompatibility: the study uses an SOC measurement method (e.g., wet oxidation with no dry-combustion comparator, or a non-comparable depth-aggregation procedure) that cannot be reconciled with the Project's measurement framework;
- Documented data quality issues: the study has been subject to a published correction, retraction, or methodological criticism that materially undermines the reliability of its SOC stock measurements;
- Soil-type incompatibility: the study covers a soil type that is not present in the Project area, where soil type is the primary control on the model's predictions;
- Replacement by superseding study: the study has been superseded by a later peer-reviewed remeasurement of the same locations, in which case the later study replaces (rather than is added to) the earlier one in the corpus; or
- Inaccessibility: the underlying data are not available to the Project Proponent through reasonable efforts, including direct request to the corresponding author. Where this exclusion is invoked, the Project Proponent must document the specific data-request actions taken and the response (or non-response) received.
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.
Changes to the corpus or to the validation subset after pre-registration require explicit Isometric approval and, except as provided in the following paragraph, constitute a fresh validation event for which model performance metrics calculated prior to the change may not be relied upon.
Spatial Blocking of Calibration and Validation Data
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:
- Distance-based separation. Calibration and validation points are drawn from the same dataset, but each validation point must lie beyond the empirical spatial autocorrelation range of the SOC residual variogram from the nearest calibration point. The autocorrelation range must be estimated from the Project's available data (or, where insufficient project-area data are available, from regional comparable data) using a documented variogram fitting procedure, and is defined as the lag distance at which the empirical semivariogram reaches 95% of its sill value. Where the variogram is anisotropic, the longer of the two principal-axis ranges must be used.
- Block-level hold-out. Entire spatial blocks (defined as project strata, quantification units, sub-regions, or other contiguous spatial units of meaningful agronomic or pedogenic homogeneity) are reserved for validation rather than individual points within shared blocks. Where this design is used, the number of held-out blocks must be sufficient to satisfy the performance and bias requirements of Section 9.1.3.1.3 independently for each project-like × climatic-coverage subset and each baseline-like × climatic-coverage subset for which Section 9.1.3.1.3 requires performance to be demonstrated. The held-out blocks must collectively satisfy the representativeness, management-coverage, and climatic-coverage requirements of Section 9.1.3.1.3.
Project Proponents must report at validation, for each bias-test subset (project-like, baseline-like, and the climate-coverage portions of each):
- The empirical SOC residual variogram or equivalent autocorrelation diagnostic, including the fitted autocorrelation range and 90% confidence interval on the range estimate;
- The minimum, median, and 5th-percentile distances between validation locations and the nearest calibration location;
- Where the distance-based separation design is used, evidence that the 5th-percentile distance exceeds the autocorrelation range; and
- Where the block-level hold-out design is used, the spatial definition of each held-out block, the rationale for the choice of block size, and a demonstration that no calibration-point neighborhood (defined by the autocorrelation range) crosses a block boundary into a held-out block.
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, or to support spatially blocked k-fold cross-validation under Section 9.1.3.1.3, 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 Where permitted under Section 9.1.3.1.3, the block-level hold-out may be rotated across folds (spatially blocked k-fold cross-validation), in which case each block is held out in exactly one fold and the diagnostics below must be reported for each fold. 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.
Where post-recalibration validation is conducted under under Section 9.1.3.1.7 (including following a bias test triggered under Section 9.1.3.1.6), 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 Certificate-relevant quantification.
Model True-Up
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 9.1.3.1.3 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 crediting event is a true-up event rather than an interim crediting event, and the measured result is subject to divergence testing and reconciliation under Section 9.1.3.2.3.
Where a true-up event occurs before the scheduled maximum interval, whether triggered under this paragraph, required by Isometric, or elected by the Project Proponent, the maximum interval of 5 years for the next true-up event runs from that true-up event. The resampling campaign for a triggered true-up event must be conducted in the next sampling window consistent with the seasonal-timing requirements of Section 9.1.2.1, and issuance for the affected Reporting Period is deferred until the true-up event is completed.
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 Certificate 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. Assessment and recalibration may take place within the same Reporting Period, in the following sequence:
- Assessment. The new data points are used to reassess the error of the model in use (the incumbent model) against the performance metrics and tests of Section 9.1.3.1.3 and the bias tests of this Section, and the results are recorded as the independent performance assessment of the incumbent model. The divergence test and reconciliation of the elapsed interval under Section 9.1.3.2.3 must use the incumbent model's predictions.
- Recalibration. Where the assessment indicates that recalibration is warranted, or the Project Proponent elects to recalibrate, the model may be recalibrated under Section 9.1.3.1.7. Before recalibration, the new data points must be allocated, using a pre-registered and spatially blocked allocation rule, either to recalibration or to the cumulative validation dataset. The recalibrated model must be validated against the cumulative validation dataset in accordance with Section 9.1.3.1.7 and the spatial-blocking requirements of Section 9.1.3.1.5 and, once approved, may be used for the remaining quantification of that Reporting Period (including the modeled counterfactual) and for subsequent Reporting Periods.
Use of a new data point in the assessment of the incumbent model under step 1 does not bar its allocation to recalibration under step 2. Data points allocated to the cumulative validation dataset may not subsequently be used for parameterization or recalibration, and no data point may be used both to recalibrate a model and to validate that same model.
At each true-up event, the sampling collected under Section 9.1.3.1.8 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 GHG Statement 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 9.1.3.1.3) and for each subset's project-area-relevant climatic-coverage portion: calculated as , with a 90% confidence interval that must exclude 0; RMSE in t C ha; residual skewness with a Q-Q plot; and MBE in t C ha, 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 . 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:
- The lower bound of a one-sided 90% confidence interval on MBE on the cumulative validation dataset as a whole exceeds zero, i.e., the test rejects the null hypothesis of no overprediction at the 10% one-sided significance level; and
- The point estimate of MBE on the cumulative validation dataset exceeds 5% of the mean observed SOC stock in the Project area across the cumulative dataset.
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:
- The lower bound of a one-sided 90% confidence interval on exceeds zero, where and are the point estimates on the Project-like and baseline-like subsets of the cumulative validation dataset respectively, and the confidence interval is calculated using a Welch-style standard-error formulation that accommodates unequal sample sizes and unequal within-subset variances between the two subsets; and
- The point estimate exceeds 5% of the mean observed SOC stock in the Project area across the cumulative dataset.
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:
- The lower bound of a one-sided 90% confidence interval on exceeds zero, where and are the point estimates on the Project-like and baseline-like subsets of the cumulative validation dataset restricted to the climatic-coverage portion overlapping with the climatic conditions actually experienced by the Project area during the Reporting Period, calculated using the same Welch-style standard-error formulation as the management-conditional test; and
- The point estimate exceeds 5% of the mean observed SOC stock in the Project area across the cumulative dataset.
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 unless baseline-like observations are added under the following paragraph.
Sources of baseline-like observations. Baseline-like observations may be added to the validation dataset from: (i) peer-reviewed studies meeting the criteria of Section 9.1.3.1.4; (ii) regional datasets meeting the requirements of Section 9.1.3.1.5; and (iii) business-as-usual reference areas within the Project Area, including control plots established under Section 9.2.1 where present, that are maintained and evidenced under business-as-usual management and sampled in accordance with the requirements for control plots in Section 9.2.1. Observations from business-as-usual reference areas collected during the Crediting Period count toward the climatic-coverage requirements of Section 9.1.3.1.3, and toward the baseline-like subset for the climate-conditional differential test, for the Reporting Period in which they are collected, provided they satisfy the spatial-blocking requirements of Section 9.1.3.1.5 relative to the calibration data. Their addition constitutes an update to the validation dataset, upon which the counterfactual bias assessment must be re-performed.
Change-basis bias. For the counterfactual bias correction, model bias must be estimated on a change basis. For each subset (project-like and baseline-like, and their climatic-coverage portions), the change-basis mean bias error is:
(Equation 28a)
Where:
- is the number of paired change observations in subset . A paired change observation is two observations of SOC stock at the same location, at two dates. The location must have stayed in the same management class (project-like or baseline-like, as classified under Section 9.1.3.1.3) between the two dates. Both observations must be on the same mineral-mass basis as the model output (Section 9.1.3.2);
- and are the observed SOC stocks at the first and second dates (t C ha⁻¹);
- and are the model's predictions for that location and those dates (t C ha⁻¹);
- is the interval between the two dates (years);
- is in t C ha⁻¹ yr⁻¹. A positive value means the model over-predicts SOC gain (or under-predicts loss).
Some validation datasets contain project-like and baseline-like observations at the same site and date, from areas that shared a common starting condition. In that case, may instead be estimated directly. For each site , compute , where is the time since the two management regimes diverged. The estimate is the mean of these values across sites.
Counterfactual bias correction. In each stratum, the modeled counterfactual SOC stock change must be increased by the following amount. This applies to each Reporting Period to which the affected parameterization applies:
(Equation 28b)
Where:
- is the number of years in the Reporting Period;
- is the bias in the credited project effect (t C ha⁻¹ yr⁻¹). It depends on how the project-scenario term is quantified:
- where the project-scenario SOC stock change is modeled (interim crediting events): ;
- where the project-scenario SOC stock change is measured, or reconciled to measurement under Section 9.1.3.2.3 (true-up events, and projects under Section 9.2.2.3): .
Where the climate-conditional portions are statistically defined, must also be calculated from the climate-conditional change-basis estimates, and the larger of the two values of used.
is never negative and is added to the counterfactual, so the correction can only reduce the credited project effect. It applies in every Reporting Period, whether or not a bias test fires. The consequences of a bias test firing still apply as set out below. No cap applies to the correction.
At each true-up event, the adjustment for the whole interval since the most recent true-up event must be recalculated using the measured-project form of . The recalculated adjustment replaces the adjustments applied at interim crediting events in that interval, and is the one used in reconciling cumulative Certificates under Section 9.1.3.2.3. Adjustments in intervals already reconciled at an earlier true-up event remain in place.
Uncertainty. The uncertainty of must be propagated through the Monte Carlo simulation (Section 9.1.1). In each iteration, is drawn from its sampling distribution, using the Welch-style standard error for the modeled-project form, and the floor at zero is applied within that iteration.
Insufficient change data. The correction is not statistically defined where the subsets needed for the applicable form of contain fewer than three paired change observations, or, for the direct estimate, fewer than three paired sites. In that case consequence (1) of the climatic-coverage gap consequences applies. Before the next Reporting Period, the Project Proponent must add paired change observations to the validation dataset, or obtain Isometric's acceptance of a documented gap, as in consequence (2).
Combined project effect and reporting. For each affected Reporting Period, the combined project effect is the Project SOC stock change (measured or modeled, according to the Reporting Period type) minus the bias-corrected modeled counterfactual. The GHG Statement must report:
- for each subset, , its standard error and ;
- the form of applied and its value; and
- for each stratum.
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 crediting 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:
-
For the current Reporting Period, a default conservative adjustment must be applied to the modeled counterfactual, in place of the correction under Equation 28b. The modeled counterfactual SOC stock change in each stratum must be increased by the larger of:
- (i) from Equation 28b, calculated from the management-conditional (not climate-conditional) change-basis estimates, using the form of that applies to the Reporting Period type; or
- (ii) 5% of the mean observed SOC stock in the Project area across the cumulative dataset.
Where is not statistically defined, limb (ii) applies on its own. In the Monte Carlo simulation, the larger of the two limbs is taken within each iteration. The combined project effect is then the project SOC stock change (modeled or measured, according to the Reporting Period type) minus the adjusted modeled counterfactual.
-
The Project Proponent must, prior to the next Reporting Period, expand the validation dataset to remedy the climatic-coverage gap. Where the climatic conditions experienced are unprecedented in the available validation evidence base for the Project's ecoregion, this requirement may be satisfied by acceptance from Isometric of a documented gap, with the conservative adjustment under (1) continuing to apply at each subsequent Reporting Period until the gap is closed.
Optional Recalibration
Project Proponents may elect to recalibrate model parameters at any true-up event using the updated cumulative dataset, subject to the following requirements:
- Recalibration must use the full cumulative dataset of literature-derived and project-collected data, excluding all data points reserved for validation;
- A minimum of 20% of all available representative data points in the updated cumulative dataset must be withheld from recalibration and used for post-recalibration validation, consistent with the data separation requirements of Section 9.1.3.1.3. A validation subset that satisfies the 20% threshold but clusters at one end of the observed distribution does not meet this requirement;
- Post-recalibration model performance must satisfy the performance threshold of Section 9.1.3.1.3, that is, greater than 0 with a 90% confidence interval excluding 0, and must demonstrate no systematic bias before the recalibrated model is used for Certificate-relevant quantification. Where recalibration was undertaken in response to a bias test firing, it must specifically target the source(s) of the identified bias and must restore each fired test to non-firing status (both criteria failing on the post-recalibration validation dataset), evidenced through subset-by-subset performance reporting under Sections 9.1.3.1.3 and 9.1.3.1.6;
- All recalibration inputs, revised parameter values, updated validation results, evidence that the 20% representative validation split requirement has been satisfied, updated performance statistics, and revised Monte Carlo input probability distributions must be submitted to Isometric and approved prior to use of the recalibrated model for Certificate-relevant quantification, whether in the Reporting Period in which recalibration is undertaken (in accordance with the sequence set out in Section 9.1.3.1.6) or in a subsequent Reporting Period.
Data points in the cumulative validation dataset may not be reused for parameterization or recalibration at any point in the Project lifetime. The assessment of the incumbent model under step 1 of the sequence set out in Section 9.1.3.1.6 is not use for validation for this purpose.
Remeasurement (True-up) Soil Sampling Requirements
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 QA/QC, georeferencing, seasonal consistency, and organic amendment timing requirements set out in Section 9.1.2.1 apply equally to remeasurement campaigns.
- The sampling design and stratification requirements of Section 9.1.2.2 apply equally to remeasurement.
- Stratification for remeasurement must be consistent with the stratification used for initial baseline measurement and for model parameterization, to ensure that remeasurement data are directly comparable to modeled outputs at the stratum level.
- Under Approach 2, strata may be revised at a Reporting Period in accordance with Section 9.1.2.2.5, subject to the following. Where revision consists of aggregating existing strata, modelled stratum-level outputs are reconciled by area-weighted aggregation under the documented old-to-new mapping, and no re-run is required. Where revision splits or otherwise redraws strata such that modelled outputs cannot be reconciled by aggregation, the model must be re-run at the revised stratum resolution. In either case, the revised strata must remain within the model's validated domain under Sections 9.1.3.1.3–9.1.3.1.5; where they do not, the revision constitutes a fresh validation event and the affected strata revert to interim-crediting treatment until re-validation is completed.
- The power analysis requirements of Section 9.1.2.3 apply to remeasurement (true-up) campaigns. The must not exceed the smaller of the expected net SOC stock change at the true-up event and the model prediction error identified at initial validation.
- At true-up events, field measurements of ESM must be collected during the resampling campaign and used to apply an ESM correction to the model output, consistent with the ESM approach selected under Section 9.1.2.4. The treatment of ESM differs by event type:
- Interim crediting events: no ESM is measured; the model output is evaluated against the mineral-mass profile and reference mineral mass from the most recent measurement event (the baseline measurement before the first true-up event, and the most recent true-up event thereafter), as set out in Section 9.1.3.2.1.
- True-up events: measured ESM data from the resampling campaign must be used to apply a full ESM correction to the model output, consistent with the approach described in Section 9.1.2.4.
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.
Initial SOC Stocks Measured After Project Initiation
The baseline () soil sampling campaign should be completed before or at Project Initiation, including within the Enablement Window where one is used (Section 5.1 of the Improved Soil Management Protocol). Under Approach 2, where the baseline campaign for an enrolled area is completed after Project Initiation, initial SOC stocks at may be back-modeled from the baseline measurements, subject to all of the following requirements:
- Window. The baseline campaign must be completed within 24 months after Project Initiation of the enrolled area concerned. Back-modeling to a date before Project Initiation is not permitted.
- Model. The back-cast must use the same validated model, model version and parameterization applied to project quantification under Section 9.1.3.1, and the model's validation must cover the management practices implemented during the back-cast interval.
- Management records. Management between Project Initiation and the baseline sampling date must be documented to the standard required for project-scenario management data and used as model inputs. Any soil-disturbing activity in the interval (e.g., reseeding, mechanical brush management, prescribed fire or organic amendment application) must be represented in the model.
- Uncertainty. The model prediction error over the back-cast interval must be characterized as a cumulative prediction uncertainty, including temporal error correlation, and propagated through the Section 9.1.1 Monte Carlo simulation as part of the baseline measurement uncertainty at , jointly with the sampling, analytical and ESM uncertainty of the baseline campaign.
- Conservative treatment. The back-cast increment is the modeled SOC stock change between Project Initiation and the baseline sampling date. Where it indicates an SOC gain, the increment used to derive the stock must not exceed the central modeled increment less one standard deviation of its cumulative prediction uncertainty, and must not be less than zero. Where it indicates an SOC loss, the back-cast stock must be used without adjustment.
- True-up. The back-cast stock remains subject to divergence testing and reconciliation at each true-up event under Section 9.1.3.1.3. At the first true-up event, the divergence test is applied over the interval from the baseline sampling date to the true-up event. Where it identifies significant overprediction, the back-cast increment must be multiplied by the ratio of the measured to the modeled change over that interval (bounded between zero and one), and total Certificates must be reconciled accordingly.
The same back-cast stock, stratification and reference mineral mass must be used for the Project and for the modeled counterfactual (Section 9.2.2). This Section does not apply under Approach 1 or under the approach in Section 9.2.2.3; under those approaches, initial SOC stocks must be measured directly at . This Section does not modify: the prior-adoption and additionality requirements of Section 7; the requirement of Section 5.1 of the Improved Soil Management Protocol that, where the Enablement Window is used, the campaign is completed before any management change; or the requirement of Section 4.4.1 of the Improved Soil Management Protocol that pre-existing deployments have taken soil samples before the commencement of SOC-enhancing activities. Use of this Section, the back-cast method and its results must be documented in the PDD and reviewed at Validation.
Back-modeling is not permitted for an enrolled area where any soil-disturbing activity (including tillage, reseeding, trenching or pipeline installation, or mechanical brush management) occurred between Project Initiation and the baseline sampling date; for such areas the late-baseline treatment below applies.
SOC Stock Calculation (Approach 2)
The model output can be used directly in Equation 27. 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 s the model output.
Interim Crediting Events
At interim events no field measurement is available, so the mineral-mass profile and the reference mass are held at their values from the most recent measurement event: the baseline () before the first true-up event, and the most recent true-up event thereafter. Before the first true-up event, the calculation is as follows. The cumulative-mass estimator (Equation 27) evaluated at the baseline reference mass therefore has correction ratio unity (, interpolation term zero) and reduces to the modelled SOC mass over the baseline profile:
(Equation 29)
Where:
- is the modelled SOC stock density in stratum at modelled time step , on a mineral-mass basis (t C ha⁻¹);
- is the modelled SOC content in depth increment of stratum at time (g C kg⁻¹ fine soil);
- is the baseline fine soil mass per unit area for increment in stratum (Equation 23, kg m⁻²), estimated from all sampling locations within stratum ;
- is the total number of depth increments sampled under Section 9.1.2.4. Where soils within stratum are shallower than the sampled depth, is reduced accordingly and the deepest increment is reported to the sampled depth;
- the factor converts g C m⁻² to t C ha⁻¹.
This equation evaluates the cumulative-mass estimator at the baseline reference mineral mass ; 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 25.
Estimation of : the baseline reference soil mineral mass for each sampled increment is estimated using baseline () measurements from all sampling locations within stratum .
Interim events following a true-up event. At interim crediting events following a true-up event , must instead be calculated with Equation 30, using the modeled SOC content at the interim time step together with the measured fine soil mass, measured soil mineral mass and reference mineral mass established at , held fixed until the next true-up event. Modeled increments are applied to the corrected cumulative SOC stock established at in accordance with Section 9.1.3.2.3, and the uncertainty of the reference ESM is that of the measurement (Section 9.1.1).
True-Up Events
At true-up events, field measurements from the resampling campaign are available, so the full cumulative-mass ESM correction (Equation 27) is applied to the model output, with measured SOC mass replaced by modeled SOC mass:
(Equation 30)
Where:
- is the ESM-corrected modeled SOC stock density in stratum at true-up event , on a mineral-mass basis (t C ha⁻¹);
- is the modeled SOC mass per unit area for increment , formed from the modeled SOC content (g C kg⁻¹ fine soil, updated through the true-up procedure) and the measured fine soil mass at the true-up event (Equation 23, kg m⁻²);
- is the measured soil mineral mass per unit area for increment at the true-up event (Equation 24, kg m⁻²);
- is the reference soil mineral mass (Equation 26), the across-event minimum of the stratum-mean profile total over and all true-up events including the current one;
- and are defined as in Equation 27;
- the factor converts g C m⁻² to t C ha⁻¹.
As in Approach 1, Equation 30 is evaluated at each sampling location against the stratum reference , applying the truncation rules in Section 9.1.2.10, 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.
is substituted for in Equation 22 for the purposes of calculating the Project-level total SOC stock at true-up events.
Model Divergence Testing and Reconciliation
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 Certificate issuances under this approach, at both interim crediting events and true-up events.
Governing principle. Crediting under this Module and the Improved Soil Management Protocol is cumulative. At all times, the total Certificates issued to the Project must correspond to the cumulative increase in SOC stock established at the most recent true-up event, less Certificates issued in respect of periods before that event. Each true-up event re-establishes the cumulative SOC stock from field measurement and reconciles total credits issued to it, consistent with the cumulative crediting 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 for the first true-up event or, where initial SOC stocks were back-modeled under Section 9.1.3.1.9, since the baseline sampling date) 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 measurement sampling uncertainty must be the same within-stratum sampling uncertainty characterized for propagation through the Section 9.1.1 Monte Carlo simulation, so that the divergence test and Certificate issuance use a consistent uncertainty basis.
- The model prediction uncertainty must be the uncertainty in the model's cumulative prediction over the interval since the most recent true-up event, propagated consistently with Section 9.1.1, including any temporal correlation of model errors across periods. The per-period model prediction error must not be used in place of the cumulative interval uncertainty, as this would understate the uncertainty against which the divergence is tested.
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 () is established as follows, and used in place of the modeled stock in Equation 22 for the derivation of net removals:
- Overprediction (measurement significantly below model). The corrected stock is the lower of the modeled cumulative stock or the measured cumulative stock.
- Underprediction (measurement significantly above model). The corrected stock is the conservative lower bound of the measured cumulative stock, evaluated at the confidence level used for issuance under Section 9.1.1.
- No significant difference (conservative reconciliation applies). The corrected stock is the lower of the modeled cumulative stock or the conservative lower bound of the measured cumulative stock, evaluated at the confidence level used for issuance under Section 9.1.1.
In each case the corrected stock is established at the module level; the derivation of the corresponding cumulative change and net removals from is performed under Equations 3-5 of the Improved Soil Management Protocol.
Reconciliation of Certificates. Total Certificates 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 total Certificates issued exceed the corrected position, the excess constitutes overcrediting. No credits are issued for the current reporting period, and the excess is compensated from the Buffer Pool, subordinate to any reversal compensation claims on the Buffer Pool in the same period. Any amount so compensated constitutes a replenishment obligation on the Project Proponent, which is senior to any further issuance: no credits may be issued to the Project until the Buffer Pool has been fully replenished by the compensated amount.
- Where the corrected position exceeds total Certificates issued, the shortfall is issued as Certificates attributed to the current reporting period.
Where Certificates 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 9.1.3.1.6 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 under Sections 9.1.3.1.3 and 9.1.3.1.6 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 Project Proponent must either recalibrate the model in accordance with Section 9.1.3.2.3 or apply update the model prediction uncertainty to correspond to the latest true up data and use this increased uncertainty value for all 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 crediting, and crediting 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 overcrediting excess at a single true-up event exceeds 20% of the Certificates issued in respect of the periods since the most recent true-up event. Eligibility for interim model-based crediting may be restored where the model is re-validated against the full requirements of Sections 9.1.3.1.1 and 9.1.3.1.3 to 9.1.3.1.5 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.
Assessment of Counterfactual Carbon Storage
Counterfactual Assessment via Measurement of Control Plots
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:
- Candidate-set identification. At enrollment, participating landowners must identify the contiguous areas within their holdings that they are willing and able to maintain under business-as-usual management for the duration of the Project Commitment Period and to make available for sampling under the protocol. The aggregate candidate set across all participating landowners (the "Control Plot Candidate Set") must, in aggregate, span the soil, climate, and management strata represented in the Project Area. Where the Control Plot Candidate Set materially under-represents one or more strata of the Project Area, defined as fewer than three contiguous candidate areas within a stratum, or aggregate candidate area within a stratum less than 5% of the corresponding stratum in the Project Area, the Project Proponent must either (a) extend the candidate set to remedy the gap before validation, or (b) document the gap, justify why representativeness can nonetheless be reasonably assumed, and accept the consequences for uncertainty quantification under Section 9.1.1.
- Stratified random selection. Control plots must then be drawn from the Control Plot Candidate Set using a stratified random sampling approach, with stratification reflecting the soil, climate, and management variability of the Project Area. Project Proponents must provide details of how the stratification and selection process was done, including the random seed and procedure used, and must document the relationship between the Control Plot Candidate Set and the Project Area (i.e., how the candidate set was constructed and any areas that were considered for inclusion but excluded, with reasons).
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 or, where strata are pooled under the following paragraph, for each control-plot group.
For control-plot purposes only, the Project Proponent may pool two or more strata into a control-plot group, while those strata remain distinct for sampling within the Project Area, where: (a) the pooled strata are similar in the factors expected to govern the counterfactual SOC trajectory, in particular climate, soil type or texture, and baseline management, and do not differ in any stratification factor identified in the PDD as a primary control on SOC change; (b) control plots within the group are distributed across its constituent strata in proportion to their area as far as the Control Plot Candidate Set allows; (c) the pooling rule is documented, reproducible, and fixed at Validation, with any revision subject to Section 9.1.2.2.5; and (d) the candidate-set representativeness test in step 1 above is applied at the level of the control-plot group. For each stratum in a control-plot group, in Equation 31 is the mean for the control plots in that group, and the additional uncertainty arising from pooling, including between-stratum heterogeneity within the group, must be propagated under Section 9.1.1. The justification for each control-plot group must be documented in the PDD and reviewed at Validation.
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 prior to project implementation. In order of preference, this evidence can include:
- Farm management logs
- Farm management plans
- Affidavits from enrolled land owners
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 () is then calculated as:
(Equation 31)
Where:
is the total storage of carbon in the counterfactual scenario at time , in tonnes CO2e
is the mean soil carbon density at time in in the control plots corresponding to strata of the Project Area of total strata in the control plots calculated following the procedures in Section 9.1.2, in tonnes C ha-1
is the area of strata in the Project Area, in ha
Assessing counterfactual uncertainty
Assessment of uncertainty in the value of 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.
Counterfactual Assessment via Modeling
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 . For modeling 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.
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 prior to project implementation. In order of preference, this evidence can include:
- Farm management logs
- Farm management plans
- Affidavits from enrolled land owners
In addition to property-specific data, the Project Proponent must also identify for each variable the regional data records from government bodies, academic/research institutions, international organizations, and/or peer-reviewed literature that exist, and document the regional value or distribution for that variable.
The value of each management parameter used for the prediction of must be selected as follows:
- Where the parameter is evidenced by farm management logs or farm management plans covering the minimum three-year period, the property-specific value must be used. The regional data serve as an outlier check: where the property-specific value lies outside the 10th–90th percentile range of the regional distribution (or, where only a central regional value is available, differs from it by more than 25%), the divergence must be documented and explained with supporting evidence (e.g., ecological site, infrastructure, herd and sale records). Where the divergence cannot be explained to the satisfaction of the VVB, Isometric may require the regional value to be used.
- Where the parameter is evidenced only by affidavits from enrolled landowners, or by records covering less than the minimum three-year period, the more conservative of the property-specific value and the regional value (i.e., the one yielding a higher counterfactual discount) must be used.
- Where adequate property-specific evidence cannot be provided, the regional value must be used, applied conservatively where the regional data provide a range, consistent with the evidence hierarchy in Section 4.3.
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, and must repeat the outlier check under (1) and the comparison under (2) above against the most recent regional data. Where the most recent regional data show a shift in business-as-usual management since the pre-project period that would increase the counterfactual discount, the counterfactual management parameters must be updated to reflect that shift for subsequent Reporting Periods, analogous to the updating of control-plot management under Section 9.2.1; a shift that would decrease the counterfactual discount may be applied only with Isometric approval. 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 must satisfy the eligibility and validation requirements of Section 9.1.3.1 with respect to the baseline management practices being modeled, in addition to the Project management practices.
Development of Probabilistic Management Practice Model
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:
- Climate variables
- Fertilizer prices
- Commodity prices
- Recent practices/management decisions
- Livestock/cattle prices
- Stocking-rate decisions
- Forage/pasture availability
- Drought/feed conditions
- Destocking triggers
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 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.
Model Development and Reporting
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.
Model Validation and Updates Over Time
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:
- Root mean square error of less than 30% of the mean value for all continuous variables
- Classification accuracy of 75% for categorical variables
- Misclassifications of parameter values that would deflate the baseline (and thus decrease the counterfactual discount) must not exceed 15%
- Mean prediction error not significantly different from 0 at 90% confidence interval for the Project area overall and for each strata; If a bias is present it must be demonstrated that it results in a more conservative baseline assessment
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
Assessing Modeled Counterfactual Uncertainty
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 generated as part of the procedure should be propagated through the broader assessment of uncertainty for the Project.
Measure-Remeasure Project with Modeled Counterfactual
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, and true-up requirements of Section 9.1.3.1 and (where applicable) the multi-model-ensemble requirements of Section 9.2.2.3.4 with respect to the baseline management practices being modeled.
Loss of Error Cancellation
The other quantification pairings in this Module, a measured project scenario with measured control plots (Sections 9.1.2 and 9.2.1) and a modeled project scenario with a modeled counterfactual (Sections 9.1.3 and 9.2.2), 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. The pairing in this Section, a measured project scenario with a modeled counterfactual, 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 Certificate 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.
One-Sided Counterfactual Bias Correction
The modeled counterfactual must be corrected for systematic bias in the conservative direction only, using the counterfactual bias correction of Section 9.1.3.1.6 with at every crediting event. The model may predict the counterfactual to gain more SOC, or lose less, than the baseline-like validation evidence supports (). In that case no adjustment is made: bias in this direction is conservative for crediting and must be retained.
One-Sided Uncertainty Treatment of the Counterfactual
The uncertainty discount framework of Section 9.1.1 applies to the measured project-scenario component as for Approach 1. Counterfactual model uncertainty must be propagated through the Section 9.1.1 Monte Carlo simulation.
The conservative estimate used for Certificates issuance must correspond to the 30th percentile of the distribution of across all simulation iterations, in line with Section 9.1.1. Where the 30th-percentile estimate is negative, no Certificates may be issued for that Reporting Period.
Multi-Model Ensembles
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:
- Constituent model eligibility.
- Each constituent model must independently satisfy the eligibility requirements of Section 9.1.3.1 for the Project area. An ensemble must not include a constituent model that does not, in its own right, meet either the established-model or newly-developed-model pathway. A minimum of five eligible constituent models is required.
- Ensemble-level data separation.
- The data-separation requirements and the spatial-blocking requirements apply at the ensemble level. A validation data point must be withheld from the calibration of every constituent model and from the fitting of the ensemble layer. The minimum 20% representative validation hold-out must be calculated against the union of all data used in the development of any constituent model and the ensemble layer. Where the ensemble layer is fitted on data seen by any constituent model during its own calibration, reported ensemble performance is not admissible.
- Ensemble-level performance.
- Performance statistics under Sections 9.1.3.1.3 and 9.1.3.1.5 and the bias tests under Section 9.1.3.1.6 must be reported both for the ensemble and for each constituent model. The ensemble must satisfy the R² performance threshold and the no-systematic-bias requirement; constituent-level reporting must be sufficient to demonstrate that the ensemble result does not mask systematic bias in any constituent model.
- Frozen ensemble layer.
- The ensemble layer, including any weighting that varies by parcel, stratum, or covariate, must be documented and fixed at validation. Any change to the ensemble layer constitutes a fresh validation event and requires Isometric approval before use.
- Context-specific eligibility.
- Consistent with Section 9.1.3.1.1, an MME's eligibility is bounded by the validation evidence available for each project area. Validation of an MME over one ecoregion, soil set, or management context does not establish its eligibility for a project in a different context.
- Proprietary ensemble layers.
- Where the ensemble layer is proprietary, it is permitted only where the VVB has independent access to the underlying data and to a documented specification of the combination logic sufficient to reproduce the ensemble output from constituent outputs. Reproducibility under Section 9.1.3.1.2 applies to the ensemble layer as to any other model component.
Storage and Durability of CO2e Removals
Durability
The Durability of Certificates is distinct from the length of the Project Commitment Period. the Project Commitment Period (a minimum of 40 years; Section 5.1) is the period over which the Project Proponent must maintain SOC stocks, monitor for Reversals, and compensate for any Reversal (Section 4.3). The Durability is the storage duration represented by each Certificate issued under this Module. It refers to the maintenance of the credited net SOC stock increase, not to the turnover or residence time of carbon in the soil.
Certificates are issued progressively over the Crediting Period, and each Certificate's storage is monitored and guaranteed only until the end of the Project Commitment Period. Certificates issued later are therefore backed by a shorter remaining period of guaranteed storage.
The Durability is therefore set to one half of the Project Commitment Period. This equals the average remaining period of guaranteed storage across Certificates where issuance is spread evenly over the full Project Commitment Period. It is conservative where issuance is concentrated earlier, for example where the Crediting Period is shorter than the Project Commitment Period, or where SOC accrual slows over time. For example, a Project with a 40-year Project Commitment Period issues Certificates with a Durability of 20 years.
Project Risk Assessment and Management
Projects must complete Isometric's Grazing Lands Soil Carbon Risk Assessment in Appendix A and provide supporting evidence, where required. The Grazing Lands Soil Carbon Risk Assessment is independently evaluated by a third-party VVB. The Grazing Lands Soil Carbon Risk Assessment is used to determine the risk profile of the Project, including risks to Certificate 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 crediting under this Protocol. The Grazing Lands Soil Carbon Risk Assessment must be updated each Reporting Period by the Project Proponent and increased risk scores will result in additional mitigation activities.
Buffer Pool
Projects crediting under this Module may determine their Buffer Pool contribution via either:
- Taking a set 20% contribution to the Buffer Pool for each Reporting Period; or
- Opting-in to the method outlined in Appendix C which determines the Buffer Pool contribution from the Grazing Lands Soil Carbon Risk Assessment in Appendix A. This method requires the risk assessment and contribution to be re-assessed over the Project lifetime to capture changing risk profiles.
In addition to the contribution determined under either option above, a Contract Coverage Buffer contribution applies for any Reporting Period in which contractual agreements with enrolled landowners or operators do not cover the full remaining duration of the Project Commitment Period. It is determined in accordance with Section 10.4.1.1 of the Improved Soil Management Protocol, including the flat-rate election in Section 10.4.1.1.6 where the required data are unavailable. This Module does not apply any other contract-coverage contribution.
Ongoing Monitoring for Reversals
Reversal Detection
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 CO2estored via the measure-and-remeasure approach (Section 9.1.2).
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 Certificates issued), the Project Proponent must follow the requirements for reporting, investigating, and compensating for the Reversal set out in the Buffer Pool Compensation Process of the Improved Soil Management Protocol.
If the Project Proponent is unable to conduct the required sampling within an enrolled area (e.g., no longer has the requisite access to it), that enrolled area is considered to have experienced a full Reversal, subject to the alternative procedure set out in Section 10.4.3 of this Module. This treatment applies only to the enrolled area for which the required sampling cannot be conducted, and only to the cumulative Certificates attributable to that area (Section 10.4.2). It does not affect Certificates attributable to other enrolled areas. The procedure in Section 10.4.3 gives effect to the remote monitoring route in Section 5.1.1.1 of the Improved Soil Management Protocol, for interventions whose continuity is detectable by remote sensing. It is available only for enrolled areas whose project interventions meet the eligibility conditions of Section 10.4.3.1. Monitoring obligations under this Section end at the end of the enrolled area's Project Commitment Period, and a loss of access occurring after that date has no accounting consequence under this Module.
Reversal Quantification
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-remeasure 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. Where measurements cannot be carried out in an enrolled area, that area is considered to have experienced a full Reversal of the cumulative Certificates attributable to it, subject to the alternative procedure set out in Section 10.4.3 of this Module.
Where a full Reversal of the Certificates attributable to an enrolled area is applied under this Section, the cumulative removals and the Certificates attributable to that area are excluded from the calculation of Certificates issued to the Project in subsequent Reporting Periods (Section 5.1.1), so that the Reversal is not also netted against subsequent issuance.
Certificates attributable to an enrolled area
The Certificates issued to the Project in each Reporting Period are apportioned to each enrolled area in proportion to that area's contribution to the area-weighted SOC stock change of the Project in that Reporting Period. The contribution of an enrolled area is calculated as follows: for each stratum that intersects the enrolled area, multiply the stratum-level SOC stock change per unit area by the area of that stratum lying within the enrolled area, then sum across those strata. The cumulative Certificates attributable to an enrolled area are the sum of its apportioned Certificates over all Reporting Periods up to the loss of access or unenrollment. The apportionment method and its inputs must be documented in the GHG Statement.
Where a full Reversal of the Certificates attributable to an enrolled area is applied under this Section, the cumulative removals and the Certificates attributable to that area are excluded from the calculation of Certificates issued to the Project in subsequent Reporting Periods (Section 5.1.1), so that the Reversal is not also netted against subsequent issuance.
Remote Monitoring of Reversals (Alternative to Default Full-Reversal Treatment)
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 under Section 10.4.1 is full Reversal of the cumulative tonnes credited 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 GHG Statement 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.
The Remote Monitoring of Reversals procedure is materially more restrictive in this Module than in the Cropland Management Module. This is because the majority of grazing-management interventions — stocking-rate changes, grazing-system changes (e.g., continuous to rotational), rest-period management, supplementary feeding regimes, and herd composition changes — are not reliably detectable from publicly or commercially available remote sensing data. Projects whose intervention rests solely or principally on such interventions are not eligible for this procedure, and the default full-reversal treatment under Section 10.4.1 applies on loss of access. This procedure is the Module's implementation of the remote and modeled monitoring route in Section 5.1.1.1 of the Improved Soil Management Protocol and is available for the interventions listed in Section 10.4.3.1.
Each Remote GHG Statement must be independently reviewed by the Project's VVB at the same frequency as the Project's primary verification cycle.
Eligibility
Remote Monitoring of Reversals is available only where all of the following conditions are met for the affected portion of the Project Area:
-
The Project intervention(s) implemented at the property are limited to practices whose continuity or reversion can be reliably detected from publicly or commercially available remote sensing data. Eligible interventions are:
- Pasture renovation, oversowing, or sowing of improved forage species, where the establishment and persistence of the introduced species produces a detectable spectral or phenological signature distinct from the baseline pasture community;
- Riparian, wetland, or sensitive-area exclusion, where fencing or other physical exclusion structures are visible at the project's chosen spatial resolution and the resulting reduction of bare-ground extent or vegetation recovery is remotely detectable over time;
- Bare-ground reduction or ground-cover-improvement interventions, where the project intervention produces a measurable change in bare-ground extent or vegetation cover relative to the baseline, and where the baseline bare-ground extent and within-Reporting-Period variability are characterized at validation;
- Designated rest or set-aside of defined areas from active grazing, where the rested area is spatially delineated and the resulting vegetation recovery is remotely detectable.
Projects whose intervention at the property includes stocking-rate change, grazing-system change (e.g., continuous to rotational), rest-rotation cycle changes within continuously grazed paddocks, supplementary feeding regime change, herd composition change, mineral or feed-additive application, or any other intervention not surface-detectable are not eligible for this procedure for those interventions. Where a project at the property combines an eligible intervention with one or more ineligible interventions, the Remote Monitoring procedure applies only to the Certificate volume attributable to the eligible intervention. The Project Proponent must demonstrate the Certificate volume attribution at Validation; in the absence of an approved attribution method, the project is not eligible for this procedure at that property.
-
The Project Proponent has, at Validation, set out a documented Remote Monitoring Plan for the project that specifies the remote sensing data sources, indices, classification methods, spatial resolution, revisit frequency, accuracy thresholds, ground-truth validation approach, and the project-specific treatment of fire events, including the burn-severity index, burn-severity threshold, and baseline vegetation-cover range used for the fire reversal assessment (see Triggers below), in accordance with the Required Remote Monitoring requirements below. The Remote Monitoring Plan must be in place at the time of loss of access; it cannot be retroactively constructed.
-
The portion of the Project Area to which the procedure is applied does not, in aggregate over the Project Commitment Period, exceed 10% of the original Project Area. Where loss of access exceeds this threshold, the default full-reversal treatment under Section 10.4.1 applies to the surplus.
Required Remote Monitoring
Remote Monitoring under this sub-section must satisfy each of the following requirements:
- Spatial resolution. Sufficient to resolve sub-management-unit variability at the affected property. As a default, ≤ 10 m ground sample distance for optical / multispectral imagery; lower-resolution data may be permitted with documented justification. Where the Project intervention depends on detection of physical features such as fencing, ≤ 3 m ground sample distance may be required and must be specified in the Remote Monitoring Plan.
- Revisit frequency. Sufficient to reliably detect each eligible practice within its agronomic or phenological window. As a default, ≤ 16-day revisit or compositing interval during the active growing season for the Project's vegetation community, with longer revisit intervals permitted during dormant or non-growing periods where documented at Validation. Longer intervals during the active growing season may be permitted with documented justification, reviewed by the VVB at Validation, demonstrating that each eligible practice and each trigger under Section 10.4.3.3 remains reliably detectable within its window and that the detection-accuracy requirements of this Section are met. For projects in fire-prone regions, the Remote Monitoring Plan must additionally support reliable detection of fire events within seven days of occurrence.
- Detection accuracy. The classifier(s) used for each practice must be validated against ground-truth data — including, at minimum, the Project Proponent's own historical sampling campaign data and any auxiliary ground-truth data collected for the purpose — at an accuracy of ≥ 85% per practice, with documented confusion matrices and false-negative rates separately reported. Where the intervention's detectability varies seasonally (e.g., pasture establishment most detectable in early growing season; bare-ground extent most detectable in late dry season), accuracy must be reported separately by season and must meet the 85% threshold in each season relevant to the intervention.
- Independence and reproducibility. RS data sources, classification methods, and accuracy assessments must be documented in sufficient detail that an independent verifier can reproduce the classification outputs from the source data. Proprietary classifiers are permitted only where the verifier has independent access to the underlying data and to a documented specification of the classifier's logic.
- Reporting frequency. A Remote GHG Statement covering each affected portion of the Project Area must be submitted at every Reporting Period for the duration of the Ongoing Monitoring Period.
Triggers
The following events, detected through the Remote Monitoring procedure, trigger the immediate consequences set out below:
-
Practice reversion. Detection of reversion to baseline conditions, including (but not limited to):
- For pasture renovation, oversowing, or sown forage projects: loss of the introduced-species spectral or phenological signature for two or more consecutive growing seasons, or detection of the baseline pasture community returning to dominance;
- For exclusion projects (riparian, wetland, sensitive-area): visible damage or removal of exclusion fencing, or recurrence of bare-ground or trampled-vegetation signatures within the exclusion zone for two or more consecutive growing seasons;
- For bare-ground-reduction or ground-cover-improvement projects: bare-ground extent within the affected portion returning to baseline levels or exceeding baseline levels for two or more consecutive growing seasons;
- For rest or set-aside projects: detection of grazing pressure indicators within the rested area (e.g., reappearance of trail networks, bare-ground patches, vegetation removal signatures) for two or more consecutive growing seasons.
Practice reversion triggers full Reversal of the cumulative tonnes previously credited to the affected portion of the Project Area, drawn from the Buffer Pool, and termination of the Remote Monitoring procedure for that portion.
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Acute disturbance. Detection of land-use conversion (e.g., conversion to cropland, settlement, or other non-grazing land use), a fire event that triggers Reversal under sub-paragraph 3 below, or other acute disturbance events affecting the soil carbon pool triggers full Reversal of the cumulative tonnes previously credited to the affected portion of the Project Area, drawn from the Buffer Pool, and termination of the Remote Monitoring procedure for that portion.
-
Fire events. Treatment of fire events depends on the project context as set out in the Remote Monitoring Plan at Validation:
- Fire-prone systems with documented baseline fire regime. Where the project area lies within a vegetation type whose baseline management regime includes recurrent fire (e.g., savanna, tropical grassland, fire-dependent rangeland), and where the Remote Monitoring Plan documents the baseline fire frequency, severity distribution, and seasonal pattern at Validation, fire events consistent with the documented baseline regime do not trigger Reversal under this Section. Fire events outside the documented baseline regime — defined as fires of materially greater severity, longer duration, larger areal extent, or out-of-season relative to the baseline — are subject to the fire reversal assessment below.
- Non-fire-prone systems. Where the baseline regime does not include recurrent fire, any fire event affecting the affected portion of the Project Area is subject to the fire reversal assessment below.
- Fire reversal assessment. A fire event subject to this assessment does not trigger Reversal where all of the following are demonstrated from remote-sensed evidence reported in the GHG Statement:
- Burn severity in the burned area, assessed using the burn-severity index specified in the Remote Monitoring Plan (e.g., differenced Normalized Burn Ratio (dNBR) or relativized dNBR), is below the burn-severity threshold documented in the Remote Monitoring Plan at Validation. The threshold must be justified with reference to peer-reviewed evidence on the effects of fire on soil organic carbon in the Project's vegetation type, and must not exceed the upper bound of the low-severity class of the burn-severity classification applied. The Remote Monitoring Plan must specify how the threshold is applied across the burned area, including any allowance for classification noise, which must not exceed 5% of the burned area;
- No land-use conversion or other acute disturbance under sub-paragraph 2 above is detected in the burned area; and
- Vegetation cover in the burned area recovers to within the baseline range documented in the Remote Monitoring Plan at Validation within two consecutive growing seasons following the fire event or, where the fire event is followed by drought meeting the conditions of sub-paragraph 5 below, within two consecutive growing seasons following the return of typical precipitation conditions.
- Where the burn-severity threshold is exceeded, the fire event triggers Reversal under sub-paragraph 2 above. Where the threshold is not exceeded but recovery to within the baseline range is not observed within the applicable window, the fire event triggers Reversal under sub-paragraph 2 above at the end of that window. Where remote-sensing data of sufficient quality to assess burn severity or recovery cannot be obtained, the conditions above are treated as not met.
- Fire reversal assessment. A fire event subject to this assessment does not trigger Reversal where all of the following are demonstrated from remote-sensed evidence reported in the GHG Statement:
- In all cases, where a fire event is detected, the Project Proponent must report the event in the next GHG Statement, with supporting remote-sensed evidence of fire extent, severity (e.g., burn severity index), and post-fire vegetation recovery trajectory.
-
Loss of remote sensing coverage. Where remote sensing data of sufficient quality (per Required Remote Monitoring above) cannot be obtained for the affected portion for two consecutive Reporting Periods, the procedure terminates and full Reversal of the cumulative tonnes previously credited to the affected portion applies, drawn from the Buffer Pool.
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Drought-driven ground-cover loss in semi-arid and arid systems. In semi-arid and arid project areas, transient drought-driven reductions in vegetation cover and increases in bare-ground extent are part of the baseline interannual variability and do not, in themselves, constitute a Reversal trigger under sub-paragraph 1 above, provided that:
- The Remote Monitoring Plan documents the expected interannual variability in vegetation cover and bare-ground extent at the Project area at Validation, including the baseline distribution under documented historical drought events;
- The observed reduction is consistent with the documented variability; and
- Post-drought recovery to baseline cover levels is observable within two consecutive growing seasons following the return of typical precipitation conditions.
Where post-drought recovery does not occur within this window, the loss is treated as Practice Reversion under sub-paragraph 1 above and triggers Reversal accordingly.
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End of Ongoing Monitoring Period. At the end of the Ongoing Monitoring Period, where Remote Monitoring has not previously triggered a full Reversal, the procedure terminates without further accounting consequences.
Acknowledgments
Isometric would like to thank the following external contributors to this Protocol:
- Lindsay Todman PhD (Cyclops MRV Inc.)
Definitions and Acronyms
- ActivityAn activity or process or group of activities or processes that alter the condition of a Baseline and leads to Removals or Reductions.
- BaselineA set of data describing pre-intervention or control conditions to be used as a reference scenario for comparison.
- BiodiversityThe diversity of life across taxonomic and spatial scales. Biodiversity can be measured within species (i.e. genetic diversity and variations in allele frequencies across populations), between species (i.e. the total number and abundance of species within and across defined regions), within ecosystems (i.e. the variation in functional diversity, such as guilds, life-history traits, and food-webs), and between ecosystems (variation in the services of abiotic and biotic communities across large, landscape-level scales) that support ecoregions and biomes.
- Carbon Dioxide Equivalent Emissions (CO₂e)The amount of CO₂ emissions that would cause the same integrated radiative forcing or temperature change, over a given time horizon, as an emitted amount of GHG or a mixture of GHGs. One common metric of CO₂e is the 100-year Global Warming Potential.
- Carbon Dioxide Removal (CDR)Activities that remove carbon dioxide (CO₂) from the atmosphere and store it in products or geological, terrestrial, and oceanic Reservoirs. CDR includes the enhancement of biological or geochemical sinks and direct air capture (DAC) and storage, but excludes natural CO₂ uptake not directly caused by human intervention.
- Carbon FinanceResources provided to projects that are generating, or are expected to generate, greenhouse gas (GHG) Emission Reductions or Removals.
- CertificateA publicly visible, uniquely identifiable, Verified instrument Issued on the Isometric Registry. Isometric Issues three Certificate Types: Carbon Dioxide Removal Certificates, Emission Reduction Certificates and Environmental Attribute Certificates.
- ConservativePurposefully erring on the side of caution under conditions of Uncertainty by choosing input parameter values that will result in a lower net CO₂ Removal or GHG Reduction than if using the median input values. This is done to increase the likelihood that a given Removal or Reduction calculation is an underestimation rather than an overestimation.
- ConversionA retirement pathway in which an existing EAC is retired to enable the issuance of a new EAC with different specified characteristics.
- CounterfactualAn assessment of what would have happened in the absence of a particular intervention – i.e., assuming the Baseline scenario.
- Cradle-to-GraveConsidering impacts at each stage of a product's life cycle, from the time natural resources are extracted from the ground and processed through each subsequent stage of manufacturing, transportation, product use, and ultimately, disposal.
- Crediting PeriodThe period of time over which a Project Design Document is valid, and over which Removals, Reductions or Environmental Attributes may be Verified, resulting in Issued Certificates.
- Direct EmissionsEmissions that are produced by a specific CDR process and are directly controllable.
- Double CountingImproperly allocating the same Removal or Reduction from a Project Proponent more than once to multiple Buyers.
- DurabilityThe amount of time carbon removed from the atmosphere by an intervention – for example, a CDR project – is expected to reside in a given Reservoir, taking into account both physical risks and socioeconomic constructs (such as contracts) to protect the Reservoir in question.
- Ecological IntegrityThe ability of an ecosystem to support and maintain ecological processes and a diverse community of organisms. It is measured as the degree to which a diverse community of native organisms is maintained, and is used as a proxy for ecological resilience, intended as the capacity of an ecosystem to adapt in the face of stressors, while maintaining the functions of interest.
- Embodied EmissionsLife cycle GHG emissions associated with production of materials, transportation, and construction or other processes for goods or buildings.
- Emission ReductionsLowering future GHG releases from a specific entity.
- EmissionsThe term used to describe greenhouse gas emissions to the atmosphere as a result of Project activities.
- GHG StatementA document submitted alongside Claimed Removals and/or Reductions that details the calculations associated with a Removal or Reduction, including the Project's emissions, Removals, Reductions and Leakages, presented together in net metric tonnes of CO₂e per Removal or Reduction.
- Global Positioning System (GPS)A satellite-based navigation system.
- Greenhouse Gas (GHG)Those gaseous constituents of the atmosphere, both natural and anthropogenic (human-caused), that absorb and emit radiation at specific wavelengths within the spectrum of terrestrial radiation emitted by the Earth’s surface, by the atmosphere itself, and by clouds. This property causes the greenhouse effect, whereby heat is trapped in Earth’s atmosphere (CDR Primer, 2022).
- Issuance (of a Certificate)Certificates are issued to the Certificate Account of a Project Proponent with whom Isometric has a Validated Protocol after an Order for Verification and Certificate Issuance services from a Buyer and once a Verified Removal or Reduction has taken place.
- LeakageThe increase in GHG emissions outside the geographic or temporal boundary of a project that results from that project's activities.
- Life Cycle Analysis (LCA)An analysis of the balance of positive and negative emissions associated with a certain process, which includes all of the flows of CO₂ and other GHGs, along with other environmental or social impacts of concern.
- MaterialityAn acceptable difference between reported Removals/emissions or Reductions/emissions and what an auditor determines is the actual Removal/emissions or Reduction/emissions.
- ModuleIndependent components of Isometric Certified Protocols which are transferable between and applicable to different Protocols.
- Monte Carlo SimulationsA mathematical approach for estimating the possible outcomes of an uncertain event through repeated random sampling. It can also be referred to as a "multiple probability simulation".
- ProjectAn activity or process or group of activities or processes that alter the condition of a Baseline and leads to Removals or Reductions.
- Project Design DocumentThe document, written by a Project Proponent, which records key characteristics of a Project and which forms the basis for Project Validation and evaluation in accordance with the relevant Certified Protocol. (Also known as “PDD”).
- Project ProponentThe organization that develops and/or has overall legal ownership or control of a Removal or Reduction Project.
- Project boundaryThe defined temporal and geographical boundary of a Project.
- ProtocolA document that describes how to quantitatively assess the net amount of CO₂ removed by a process. To Isometric, a Protocol is specific to a Project Proponent's process and comprised of Modules representing the Carbon Fluxes involved in the CDR process. A Protocol measures the full carbon impact of a process against the Baseline of it not occurring.
- ProxyA measurement which correlates with but is not a direct measurement of the variable of interest.
- Remote SensingThe use of satellite, aircraft and terrestrial deployed sensors to detect and measure characteristics of the Earth's surface, as well as the spectral, spatial and temporal analysis of this data to estimate biomass and biomass change.
- RemovalThe term used to represent the CO₂ taken out of the atmosphere as a result of a CDR process.
- ReservoirA location where carbon is stored. This can be via physical barriers (such as geological formations) or through partitioning based on chemical or biological processes (such as mineralization or photosynthesis).
- ResidueA product that is not an economic driver of the process it is produced in.
- ReversalThe escape of CO₂ to the atmosphere after it has been stored, and after a Certificate has been Issued. A Reversal is classified as avoidable if a Project Proponent has influence or control over it and it likely could have been averted through application of reasonable risk mitigation measures. Any other Reversals will be classified as unavoidable.
- SOCSoil Organic Carbon
- SSRsSources, Sinks and Reservoirs
- Sensitivity AnalysisAn analysis of how much different components in a Model contribute to the overall Uncertainty.
- SinkAny process, activity, or mechanism that removes a greenhouse gas, a precursor to a greenhouse gas, or an aerosol from the atmosphere.
- SourceAny process or activity that releases a greenhouse gas, an aerosol, or a precursor of a greenhouse gas into the atmosphere.
- StakeholderAny person or entity who can potentially affect or be affected by Isometric or an individual Project activity.
- StorageDescribes the addition of carbon dioxide removed from the atmosphere to a reservoir, which serves as its ultimate destination. This is also referred to as “sequestration”.
- System BoundaryGHG sources, sinks and reservoirs (SSRs) associated with the project boundary and included in the GHG Statement.
- UncertaintyA lack of knowledge of the exact amount of CO₂ removed by a particular process, Uncertainty may be quantified using probability distributions, confidence intervals, or variance estimates.
- ValidationA systematic and independent process for evaluating the reasonableness of the assumptions, limitations and methods that support a Project and assessing whether the Project conforms to the criteria set forth in the Isometric Standard and the Protocol by which the Project is governed. Validation must be completed by an Isometric approved third-party (VVB).
- Validation and Verification Bodies (VVBs)Third-party auditing organizations that are experts in their sector and used to determine if a project conforms to the rules, regulations, and standards set out by a governing body. A VVB must be approved by Isometric prior to conducting validation and verification.
- VerificationA process for evaluating and confirming the net Removals and Reductions for a Project, using data and information collected from the Project and assessing conformity with the criteria set forth in the Isometric Standard and the Protocol by which it is governed. Verification must be completed by an Isometric approved third-party (VVB).
Appendix A: Risk Assessment
The Grazing Lands Soil Carbon Risk Assessment is used to assess the overall delivery and storage risk associated with the grazing lands management activities and may inform the Buffer Pool contribution during Certificate delivery (see Section 10.3). 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 Grazing Lands Soil Carbon Risk Assessment.
For projects with discrete management units, 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 (or, for Disturbance Risk indicators using 0.25-increment subcomponents, rounded up to the nearest 0.25).
If Project Proponents choose to forgo a flat 20% Buffer Pool contribution (see Section 10.3), the Grazing Lands Soil Carbon Risk Assessment will inform Buffer Pool contributions for the Project according to the process outlined in Appendix C for each Reporting Period and in accordance with the requirements in Section 10.3.
After each new Grazing Lands Soil Carbon Risk Assessment evaluation, Isometric will update the required percentage of newly issued Certificates that must be contributed to the Buffer Pool by the Project. We encourage Project Proponents to continuously monitor, mitigate, and reduce risks.
**Table A1.** Grazing Lands 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 grazing-lands soil organic carbon projects? (e.g., sustainable grazing or rangeland management, livestock management, rangeland ecology or 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 or grazing-lands 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., unauthorised grazing or encroachment by neighbouring graziers, cattle rustling, illegal land clearing, poaching of associated wildlife, illegal water extraction)? | 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 and livestock 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, livestock-management equipment, fencing and water infrastructure, monitoring and measurement (including soil sampling campaigns and any remote-sensing services), 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, considering the opportunity cost of alternative land-management strategies (e.g., reversion to higher-stocking grazing, conversion to cropping, or non-agricultural land-use change)? | Project financial plan | If continued financial incentive is low compared to likely opportunity cost of reversion or alternative land use, +2. | ||
Does the project financial plan rely on future increases in market price for Carbon Certificate? | Project financial plan | If yes, +1. | ||
Social Governance Risk | Are there currently or have there been disputes over land ownership, customary tenure, or grazing rights over the last 20 years? | Jurisdictional history | If yes, +2. | |
Does the government have a history of revoking legal or customary agreements regarding land ownership, access, usage, or grazing rights? | 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 Certificates and NDC contributions? | National registries | If no, +1. | ||
Does The Project have a detailed benefit-sharing plan that includes: clear distribution mechanisms among graziers, pastoralists, and any other affected land users; transparent criteria for beneficiary selection; a grievance resolution process; monitoring and reporting procedures? | Project financial plan, stakeholder engagement documentation | 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 the LandMark Global Platform of Indigenous and Community Lands, and with Global Forest Watch where forested portions of the Project Area exist | If no, fail. | ||
Are baseline activities primarily subsistence-driven (e.g., subsistence pastoralism, smallholder mixed crop-livestock systems)? | 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 for graziers, pastoralists, smallholder herders, and mobile herders affected by the project? | Community impact assessment, project financial plan, socio-economic surveys | If no, +2. | ||
(b) What is the net present value (NPV) of alternative land use or management compared to project NPV? Alternatives may include reversion to higher-intensity grazing, conversion to cropping, conversion to urban or industrial use, or sale and consolidation. | 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. | ||
Where the Project intersects with mobile pastoralist systems, customary grazing routes, or transhumance corridors, are these rights formally recognised in legal documentation, and have they been addressed in the PDD's stakeholder engagement and free, prior and informed consent (FPIC) procedures? | Documented land tenure arrangements, FPIC documentation, agreements with regional pastoralist organisations, customary land use mapping | If no mobile pastoralism or transhumance affects the Project Area, 0. If present and rights are formally recognised and addressed via FPIC, 0. If present and rights are not formally recognised but FPIC has been obtained from all affected groups, +1. If present and rights are neither formally recognised nor addressed via FPIC, fail. | ||
Disturbance Risk | Fire risk | If > 10, +1. If > 30, +2. If > 50, +3. If > 75, fail. Where the Project Area lies within a vegetation type whose baseline management regime includes recurrent fire (e.g., savanna, fire-dependent rangeland) and where this baseline regime is documented in the Project Monitoring Plan in accordance with Section 10.4.3, the scoring above applies to fires of severity, extent, or seasonality outside the documented baseline regime; baseline-consistent fire events do not contribute to the score. | ||
Forage pest and disease outbreak risk (e.g., locusts, armyworm, grass smut, leaf rust, invasive herbivorous insects) | Regional third-party maps, FAO Locust Watch (where applicable), if available | If high, +2. If medium, +1. If low, 0. | ||
Livestock disease outbreak risk (endemic or emerging livestock diseases, including foot-and-mouth disease (FMD), lumpy skin disease (LSD), anthrax, contagious bovine pleuropneumonia, peste des petits ruminants, bluetongue, and equivalent species-relevant diseases, where outbreak would force destocking or affect the Project's grazing management regime) | World Organisation for Animal Health (WOAH) regional reports, national veterinary services, project-region disease surveillance data | If high, +2. If medium, +1. If low, 0. | ||
Extreme weather (temperature — heat and cold) | IPCC AR6 — See Appendix B for scoring | Value from Appendix B, Table B1 | ||
Extreme weather (hydrologic — flood and drought; drought is particularly material for grazing systems, where multi-year drought can force destocking and reduce ground cover) | IPCC AR6 — See Appendix B for scoring | Value from Appendix B, Table B1 | ||
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, neighbouring mining, oil and gas operations, cropland conversion pressure, urban expansion, road or pipeline development) | Satellite imagery, site visit | If yes, +1. | ||
Ecological Resilience | Project Design Document; baseline vegetation assessment | If pasture or sward composition includes a substantial perennial component (≥ 50% perennial cover) and a diverse mix of native or well-adapted species (at least double the locally typical species count for the vegetation type), −1. If > 80% of forage species are documented as heat- or drought-tolerant for the project's climatic conditions, −1. | ||
Flood plain hazards | Project area overlap with identified floodplain based on Nardi et al., 2019 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 (considering grazing-specific drivers including stocking-density-induced bare ground, livestock-trail networks, and watering-point sacrifice areas, in addition to the underlying rainfall-erosivity × soil-erodibility × slope-length-and-steepness factors) | Assess mean inherent erosion risk potential for the project area (R × K × LS) using Borrelli et al. (2022) or equivalent localized datasets, supplemented by site-specific assessment of grazing-induced erosion drivers | If > 20 t/ha/yr, +1. If > 50 t/ha/yr, +2. If implementing management practices which reduce erosion risk (e.g., stocking-density management, rotational grazing, riparian exclusion, watering-point hardening), −1. | ||
Bush or woody encroachment risk (relevant in arid and semi-arid grazing systems where encroachment can suppress herbaceous SOC inputs, shift the system carbon balance, and undermine the Credited Practice; not applicable in mesic or temperate grazing systems where encroachment is not a documented risk) | Regional encroachment monitoring data, baseline vegetation cover assessment, peer-reviewed studies on encroachment dynamics in the project's vegetation type | If high encroachment risk under current and projected climate, +2. If moderate, +1. If low or not applicable to the project's vegetation type, 0. |
Appendix B: Calculating Extreme Weather Risk Scores
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 look up 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 increase risk for drought-driven destocking, reduced forage production, and bare-ground exposure in grazing systems).
To calculate the scores in Table B1, Isometric uses values from the Intergovernmental Panel on Climate Change AR6 report. 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 () to capture extreme cold and extreme heat risks, respectively. The hydrologic indicator is calculated using data describing the maximum 5-day precipitation () 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 75th percentile are considered Low Risk, regional values equal to or greater than the global 75th percentile but below the 90th percentile are Medium Risk, and any regional values equal to or greater than the global 90th 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:
Projected change in the number of frost days is not included as a subcomponent since it is projected that frost days 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
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 (EAU) | 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 |
Appendix C: Buffer Pool Calculations
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 Grazing Lands Soil Carbon Risk Assessment for each Reporting Period.
The following steps are used to convert the outputs of the Grazing Lands Soil Carbon Risk Assessment into a Buffer Pool contribution:
- Sum the total score for each risk category in Table A1.
- Map the risk score for each risk category into a Buffer Pool contribution using Table C1.
- Sum the Buffer Pool contribution for each risk category to obtain the total 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%.
(Equation C1)
Where:
Where:
- is the Buffer Pool contribution for a given risk category.
- is the range of Buffer Pool contributions within each risk category (2.5% to 10%).
- is the steepness parameter of the sigmoid curve and determines how quickly the function transitions between its minimum and maximum values.
- is the midpoint of the sigmoid curve.
- is the risk score for a given risk category.
- is the value above which the Project fails the Reforestation Risk Assessment, noted in Appendix A.
Regardless of whether the Project is taking the flat contribution or the risk-assessment-adjusted contribution, a Contract Coverage Buffer contribution applies in addition for any Reporting Period in which contractual agreements with enrolled landowners or operators are not in place for the full remaining duration of the Project Commitment Period across all portions of the Project Area. The Contract Coverage Buffer contribution is determined in accordance with Section 10.4.1.1 of the Improved Soil Management Protocol and is not modified by this Module.
Project-Specific Buffer Pool Contribution Example
The Project is a rotational-grazing intervention on semi-arid rangeland in a fire-prone savanna ecoregion. The Project Proponent is a recently-established entity with local staff presence; project financing is partially secured with a longer-than-typical time to breakeven; the Project Area sits on land with documented historical tenure complexity and a multi-stakeholder community structure; and the ecoregion carries moderate fire and drought disturbance risk under both historical and projected climate conditions.
The Project has completed the Grazing Lands Soil Carbon Risk Assessment and obtained the following risk scores in a Reporting Period:
- Project Proponent Capacity Risk = 3
- Financial Viability Risk = 5
- Social Governance Risk = 4
- Disturbance Risk = 6.5
Mapping these risk scores to Table C1, the total Buffer Pool contribution for The Project is:
4.8% + 8.6% + 3.9% + 6.3% = 23.6%
If The Project did not have contracts in place for all enrolled properties for the full remaining duration of the Project Commitment Period, a Contract Coverage Buffer contribution determined under Section 10.4.1.1 of the Improved Soil Management Protocol (at most 5%) would be added to this total.
In this example, the risk-assessment-adjusted contribution (23.6%) exceeds the default flat contribution (20%). Any Contract Coverage Buffer contribution applies equally under either method and does not affect this comparison. The Project Proponent would therefore typically opt for the flat contribution rather than the risk-based methodology. The risk-based methodology benefits Projects whose risk profile is materially lower than the implicit risk profile of the flat 20% default — typically Projects with experienced Project Proponents, secure financing, strong tenure and governance contexts, and low-disturbance ecoregions.
Appendix D: Market Leakage Parameters
Isometric has carried out a literature review of and values to inform , as well as values for for certain regions. Where The Project falls into these regions, the default values provided must be used, unless a more specific value is substituted in accordance with the substitution provision below. 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.
Documentation of defaults. For each default value, Tables D1 and D2 report the key citation, the year of publication, the geography and commodity definition of the underlying estimate, whether the estimate is short-run or long-run, and, for , the calculation from the supply and demand elasticities using Equation 12. Isometric reviews these defaults at each review of this Module and will adopt commodity- and region-specific defaults (for example, for U.S. beef cow-calf systems) as suitable estimates become available.
Substitution of more specific values. A Project Proponent may substitute supply and demand elasticities (for ) or an value specific to the project's region and livestock commodity in place of a default, provided that:
- the source is peer-reviewed or published by a reputable organization and meets the criteria for a valid estimate set out below (for ) or applies Method B (for );
- the region–commodity definition is more specific to The Project than that of the default, and its representativeness is justified in the PDD;
- supply and demand elasticities are taken from the same source where available;
- the substitution is approved by Isometric at Validation; and
- the substituted value is applied for all Reporting Periods of the Crediting Period, unless Isometric publishes an updated default, in which case the Project Proponent may adopt the updated default from the next Reporting Period.
The following sections set out the procedure to be followed to obtain and values and set out the default values to be used for the regions studied.
Regions Studied
The regions considered in the literature review were:
- Brazil
- Panama
- Mexico
- United States
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.
Values
represents the amount of production that is diverted to other locations. The value does not provide any information on where or in what manner that production is produced.
Procedure for determining values:
- Define a crop-region pair broadly enough to reasonably assume that the supply of all other crop and regions is zero. For example, for livestock, “beef in Mato Grasso” is too specific and “all meat globally” is too broad, “beef in South America” may come closer to a balance.
- Examine the existing academic literature for papers that estimate the supply/demand of the crop-region pair or some other similar pair. For example, we might use “meat in South America” in lieu of “beef in South America” if the former estimates are available.
- Ensure that the paper meets the criteria for a valid estimate:
- Analysis uses either a “dynamic panel” or “instrumental variables” technique.
- Based on time-series variation in prices, rather than cross-sectional (i.e., using prices that are varying over time, rather than spatial differences in transportation costs).
- Published in the last 15 years in a reputable economics or land use journal, or is published in a report for a reputable organization such as the European Union or California Air Resources Board.
- Analysis clearly notes whether the estimate is to be interpreted as a short-run or long-run estimate.
- Analysis uses planting season prices (rather than harvest season).
- Use the appropriate formula to calculate from the supply and demand elasticity
- In cases where no academic literature exists in the relevant context, select the most similar available default value (e.g., for cocoa, we might use the default value for coffee).
Where possible:
- Supply and demand elasticities used should be estimated within the same paper; and
- Paper should be cited by a reputable organization compiling a meta-analysis or parameterizing a partial equilibrium model for policy analysis (CARB, EU, FAO, etc.).
Table D1. default values.
Livestock rows (in bold) apply to grazing projects; other commodity rows apply only where a distinct crop commodity is displaced.
Geography | Crop | εdc | εsc | IS | Key citation |
|---|---|---|---|---|---|
Global | Calories (rice, wheat, corn, soy) | -0.05 | 0.12 | 0.70 | Roberts and Schlenker13 |
Global | Coffee | -0.305 | 0.285 | 0.48 | Akiyama and Varangis14 |
Global | Cocoa | -0.075 | 0.075 | 0.50 | Askari and Cummings15 |
South America | Livestock | -0.40 | 0.4 | 0.5 | Fragoso et al.16 |
North America | Livestock | -0.40 | 1.6 | 0.80 | Lawrence et al.17 |
Values
Procedure for determining 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. values proposed aim to capture the net effect of a one unit removal of crop area on forestland conversion. These values will be smaller in magnitude than 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 values are more speculative than the values and often rely on assumptions about the yield-price elasticity that have not been empirically confirmed.
Two possible methodologies for obtaining 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 is represented in the following definition:
(Equation D1)
Where:
- is Gross New Production From Extensification due to .
- is Change in regional average yield due to .
- is Total land area under study.
- is a 1-tonne reduction in supply, or a 1-unit price increase.
is variable under the assumption that changes to supply are predominantly channeled through price changes18.
By dividing the numerator and denominator, the above equation can be reformulated as:
(Equation D2)
Where:
-
is the change in area due to .
-
is the change in yield due to .
-
is a 1-tonne reduction in supply, or a 1-unit price increase.
The following default values have been gathered using Method B.
Table D2. default values.
Livestock rows (in bold) apply to grazing projects; other commodity rows apply only where a distinct crop commodity is displaced.
Appendix E: MDD Power Analysis for Sample Design
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 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:
(Equation E1)
(Equation E2)
Where:
is the minimum detectable difference in SOC stocks (t C ha);
is the standard deviation of the SOC stock change at fixed sampling locations, i.e., the location-level differences computed at each composite sampling location, pooled across strata weighted by stratum area (t C ha). "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.
is the minimum number of samples required;
is the degrees of freedom;
is the two-sided critical value of the t-distribution at significance level . 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%.
is the one-sided quantile of the t-distribution corresponding to the probability of a Type II error . should not exceed 0.10, meaning the sampling design should achieve at least 90% statistical power to detect a true SOC change of magnitude when one is present.
The within-stratum standard deviation 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:
- Project-area pre-sampling. A pre-sampling campaign within The Project area, designed to characterize within-stratum SOC variability at the spatial scale at which the full sampling campaign will be conducted. Pre-sampling is the preferred source under all conditions and is the default where database- or literature-derived variance is otherwise the only available source (see paragraph below).
- Empirical variance estimates from peer-reviewed literature. Within-stratum or within-field SOC variance values reported in peer-reviewed studies conducted in The Project's ecoregion, on comparable soil types, and at comparable spatial scales. Where a range of values is reported across studies, the upper end of the range applicable to The Project area should be used.
- Gridded predictive products. Variance estimates derived from gridded SOC prediction products (e.g., SoilGrids, SSURGO, or comparable national or regional digital soil maps) may be used only subject to the following conditions:
- The Project Proponent should explicitly document that the variance reported by the product reflects between-pixel prediction uncertainty conditional on the product's covariate structure, and does not, on its own, capture within-pixel local SOC heterogeneity at the spatial scale at which field samples will be drawn.
- The variance estimate used in the power analysis should be inflated to account for the within-pixel heterogeneity component. The inflation factor should be documented and justified using either (i) regional empirical studies of within-field or within-pixel SOC variability for comparable soil and management contexts, or (ii) a default inflation of the standard deviation by a factor of (i.e., a doubling of the variance) where (i) is unavailable. Any inflation factor below on the standard deviation should be justified at validation against published evidence specific to The Project's ecoregion.
- Where the gridded product reports prediction uncertainty at a coarser spatial resolution than The Project's quantification units, the variance estimate should be additionally inflated, or rejected as a source, to reflect the resolution mismatch. Where the product does not report any estimate of prediction uncertainty, it may not be used as a variance source.
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 Certificate-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 crediting precision are absorbed through the uncertainty discount at Certificate 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.
Relevant Works
Footnotes
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FAO (2023) Global assessment of soil carbon in grasslands: From current stock estimates to sequestration potential. FAO Animal Production and Health Paper No. 187. Rome: FAO. Available at: https://doi.org/10.4060/cc3981en (Accessed: 24 April 2026). ↩ ↩2
-
Bai, Y. and Cotrufo, M.F. (2022). Grassland soil carbon sequestration: Current understanding, challenges, and solutions. Science, 377(6606), pp.603-608. ↩
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Lai, L. and Kumar, S. (2020). A global meta-analysis of livestock grazing impacts on soil properties. PloS one, 15(8), p.e0236638. ↩
-
Jordon, M.W., Buffet, J.C., Dungait, J.A., Galdos, M.V., Garnett, T., Lee, M.R., Lynch, J., Röös, E., Searchinger, T.D., Smith, P. and Godfray, H.C.J. (2024). A restatement of the natural science evidence base concerning grassland management, grazing livestock and soil carbon storage. Proceedings of the Royal Society B, 291(2015), p.20232669. ↩
-
Niu, M., Kebreab, E., Hristov, A.N., Oh, J., Arndt, C., Bannink, A., Bayat, A.R., Brito, A.F., Boland, T., Casper, D. and Crompton, L.A., (2018). Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database. Global Change Biology, 24(8), pp.3368-3389. ↩ ↩2 ↩3 ↩4
-
Chlela, S., & Selosse, S. (2025). The co-benefits of integrating carbon dioxide removal in the energy system: A review from the prism of natural climate solutions. Science of The Total Environment, 976, 179271. ↩ ↩
-
Curt, C., Di Maiolo, P., Schleyer-Lindenmann, A., Tricot, A., Arnaud, A., Curt, T., Parès, N., & Taillandier, F. (2022). Assessing the environmental and social co-benefits and disbenefits of natural risk management measures. Heliyon, 8(12), e12465. ↩ ↩
-
McGuire, R., Williams, P. N., Smith, P., McGrath, S. P., Curry, D., Donnison, I., Emmet, B., & Scollan, N. (2022). Potential co-benefits and trade-offs between improved soil management, climate change mitigation and agri-food productivity. Food and Energy Security, 11(2), e352. ↩ ↩
-
Milne, E., Banwart, S. A., Noellemeyer, E., Abson, D. J., Ballabio, C., Bampa, F., Bationo, A., Batjes, N. H., Bernoux, M., Bhattacharyya, T., Black, H., Buschiazzo, D. E., Cai, Z., Cerri, C. E., Cheng, K., Compagnone, C., Conant, R., Coutinho, H. L. C., de Brogniez, D., … Zheng, J. (2015). Soil carbon, multiple benefits. Environmental Development, 13, 33–38. ↩ ↩
-
Gifford, R. M., & Roderick, M. L. (2003). Soil carbon stocks and bulk density: spatial or cumulative mass coordinates as a basis of expression? Global Change Biology, 9(11), 1507–1514. ↩
-
Ellert, B. H., & Bettany, J. R. (1995). Calculation of organic matter and nutrients stored in soils under contrasting management regimes. Canadian Journal of Soil Science, 75(4), 529–538. ↩
-
Wendt, J. W., & Hauser, S. (2013). An equivalent soil mass procedure for monitoring soil organic carbon in multiple soil layers. European Journal of Soil Science, 64(1), 58–65. ↩ ↩2
-
Roberts, M. J., & Schlenker, W. (2013). Identifying supply and demand elasticities of agricultural commodities: Implications for the US ethanol mandate. American Economic Review, 103(6), 2265–2295. https://doi.org/10.1257/aer.103.6.2265 ↩
-
Akiyama, T., & Varangis, P. N. (1990). The impact of the International Coffee Agreement on producing countries. The World Bank Economic Review, 4(2), 157–173. https://doi.org/10.1093/wber/4.2.157 ↩
-
Askari, H., & Cummings, J. T. (1977). Agricultural supply response: A survey of the econometric evidence. Praeger Publishers. ↩
-
Fragoso, R., Marques, C., Lucas, M. R., Martins, M. B., & Jorge, R. (2011). The economic effects of Common Agricultural Policy on Mediterranean montado/dehesa ecosystem. Journal of Policy Modeling, 33(2), 311–327. https://doi.org/10.1016/j.jpolmod.2010.12.007 ↩
-
Lawrence, J. D., Mintert, J., Anderson, J. D., & Anderson, D. P. (2008). Feed grains and livestock: Impacts on meat supplies and prices. Choices, 23(2), 11–15. ↩
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UN-REDD: Definition of market leakage [Accessed October 2024]. Available at: https://www.un-redd.org/glossary/market-leakage#:~:text=Definition,actors%20to%20shift%20their%20activities ↩
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Pendrill, F., Persson, U. M., Godar, J., & Kastner, T. (2019). Deforestation displaced: trade in forest-risk commodities and the prospects for a global forest transition. Environmental Research Letters, 14(5), 055003. https://doi.org/10.1088/1748-9326/ab0d41 ↩
-
Lark, T. J., Hendricks, N. P., Smith, A., Pates, N., Spawn-Lee, S. A., Bougie, M., Booth, E. G., Kucharik, C. J., & Gibbs, H. K. (2022). Environmental outcomes of the US Renewable Fuel Standard. Proceedings of the National Academy of Sciences, 119(9). https://doi.org/10.1073/pnas.2101084119 ↩
-
Bowman, M. S., Soares-Filho, B. S., Merry, F. D., Nepstad, D. C., Rodrigues, H., & Almeida, O. T. (2012). Persistence of cattle ranching in the Brazilian Amazon: A spatial analysis of the rationale for beef production. Land use policy, 29(3), 558-568. https://doi.org/10.1016/j.landusepol.2011.09.009. ↩
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Wu, Z., Satter, L., & Sojo, R. (2000). Milk production, reproductive performance, and fecal excretion of phosphorus by dairy cows fed three amounts of phosphorus. Journal of Dairy Science, 83(5), 1028–1041. https://doi.org/10.3168/jds.s0022-0302(00)74967-8 ↩
-
Coffee Barometer. In Coffee Barometer (pp. 1–36). https://hivos.org/assets/2018/06/Coffee-Barometer-2018.pdf ↩
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