Enhanced Weathering in Agriculture
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This Protocol provides the requirements and procedures for the calculation of net carbon dioxide equivalent (CO2e)Removal from the atmosphere via Enhanced Weathering (EW) in agricultural settings. Agricultural EW is considered a subcategory of EW approaches.
Silicate weathering naturally sequesters approximately 0.1-0.3 Gt of CO2 per year1,2,3 and is thus a critical feedback mechanism in global climate regulation on geologic timescales. In this process, atmospheric CO2 dissolved in surface water reacts with silicate rocks and is converted to dissolved inorganic carbon or carbonate minerals, both of which constitute stable sinks for CO2 on geologic timescales4,5. Natural weathering can be accelerated by applying crushed silicate rock to agricultural land, where the increased reactive surface area of the rock and damp conditions in the soil lead to elevated reaction rates. Alkaline species produced through weathering reactions allow for the increased storage of CO2 in the aqueous phase, typically as dissolved bicarbonate. This dissolved inorganic carbon is eventually exported to the ocean where it can be stably stored for millennia6. This process is known as enhanced weathering and has been proposed as an effective natural Carbon Dioxide Removal (CDR) technique, in accordance with the Intergovernmental Panel on Climate Change (IPCC)7, to mitigate potential impacts of anthropogenic climate change8. Given that agricultural land covers approximately 38% of land surface on Earth, EW in agricultural settings has large scalability potential with relatively small changes in farm management. Scalability is aided by existing infrastructure of rock spreading as a soil amendment. Current estimates suggest that EW in cropland has the capacity to remove on the order of 1-2 Gt of CO2 per year8. In addition to CO2 removal, EW can potentially provide co-benefits to farmers in the form of increased crop yield and resiliency9,10, as well as replenishment of soil nutrients11,12 and reduction of nitrogen loss9,13.
This Protocol accounts for the quantification of the gross amount of CO2 removed via agricultural EW, as well as all cradle-to-grave, life-cycleGreenhouse Gas (GHG) emissions associated with the process.
The Protocol ensures:
In addition to CO2 removal, dissolution of silicate rocks in agricultural settings may provide co-benefits through the addition of nutrients, alkalinity and silica in soils. Potential co-benefits include:
Increased soil nutrients and crop resilience by EW may lead to increased yields. EW also has the potential to further mitigate emissions via reduction of nitrous oxide (N2O, NO) fluxes in cropland, which is currently thought to represent 50-80% of global anthropogenic N2O emissions13.
This Protocol mainly utilizes and is intended to be compliant with the following Standards and Protocols:
Additional reference standards that inform the requirements and overall practices incorporated in this Protocol include:
Additional standards, methodologies and Protocols that were reviewed, referenced or for which attempts were made to align with or leverage during development of this Protocol include:
This Protocol was developed based on the current state of the art, publicly available science regarding EW in agriculture. Because EW is a novel CDR approach, with limited published literature, this Protocol incorporates requirements that may be more stringent than some current relevant regulations or other Protocols related to EW for CDR.
Future versions of this Protocol may be altered, particularly regarding requirements for demonstrating Durability of EW, as the stability of CO2 captured by EW from Feedstock dissolution in agricultural soils is better demonstrated and documented and the overall body of knowledge and data regarding all processes, from feedstock supply to conversion and to permanent storage, is significantly increased.
This Protocol applies to Projects or processes which:
This Protocol applies to projects and associated operations that meet all of the following project conditions:
Projects that are explicitly ineligible include the following:
As previously stated, per the UN FAO definition of permanent and arable crop land, meadows and pastureland are eligible for EW projects. This Protocol will refer to monitoring requirements specific to row cropland, such as monitoring crop yield, health, and resiliency. Such measurements may not be applicable to projects taking place on pastureland and therefore may not be required in the Project Design Document (PDD). Justifications to omit measurements in such instances are allowable in the PDD.
The Project must consider environmental and social impacts at all Project locations, including the mine or quarry where feedstock is sourced, during feedstock transportation, and at the deployment site. Appropriate measures must be implemented to identify and eliminate potential risks to terrestrial and aquatic ecosystems and biodiversity. Where risks cannot be eliminated, the Project Proponent must identify measures to monitor ecosystem health and mitigate adverse effects through a project-specific mitigation plan. Refer to Environmental and Social Impacts Section of the Isometric Standard for further guidelines.
CreditsCertificatesissued under this Protocol are contingent on the implementation, transparent reporting and independent verification of comprehensive safeguards. These safeguards encompass a wide range of considerations, including environmental protection, social equity, community engagement and respect for cultural values. The process mandates that safeguard plans be incorporated into all major project phases, with detailed reports made accessible to stakeholders. Adherence to and verification of environmental and social safeguards, in accordance with the Environmental and Social Impacts Section of the Isometric Standard, is a condition for all crediting projectsCertificates.
Ongoing environmental assessment must be completed in accordance with the Isometric Standard to identify potential risks, followed by the development of tailored mitigation plans by subject matter experts where necessary. Project Proponents must first strive to avoid negative environmental impacts. In cases where adverse environmental effects take place, the Project Proponent must develop plans to minimize and rectify them. Effective implementation of these measures must also be accompanied by a robust monitoring plan to ensure efficacy. Project Proponents must demonstrate active stakeholder engagement throughout this process, in accordance with the Stakeholder Input Process outlined in the Isometric Standard. All mitigation strategies must align with local and international environmental laws and contribute to sustainable project outcomes.
Enhanced Weathering of rock or mineral feedstock may be associated with the release of Potentially Toxic Elements (PTEs) such as nickel (Ni) and chromium (Cr) or other harmful contaminants such as asbestiform minerals, which may pose an environmental risk. To prevent or mitigate such risks, the Project Proponent must take the following measures:
[/R-TNM1-1]Refer to the Rock and Mineral Feedstock Characterization Module for analysis to be conducted.
The Project Proponent must carefully consider and implement measures where necessary for the following potential impact areas before proceeding with an EW project. Appropriate measures must be implemented to identify and eliminate potential risks to human health and cultural rights at all project locations.
Maintaining agricultural productivity is critical to the environmental and social sustainability of EW projects. The Project Proponent must document how the project will monitor agricultural productivity and soil quality, including which productivity and soil characteristics will be tested and the frequency of testing. If justified, the Project Proponent may use proxy variables in lieu of direct testing or measurement.
[/R-EAFP-1]All environmental and social safeguards will be verified to be implemented at all locations in the EW process, including at the feedstock source, transportation, and distribution sites.
The Project Proponent must regularly assess the combined environmental impact of EW, which may include (but is not limited to) heavy metal concentrations in soil, crops, and groundwater. This may involve collecting data on soil and water quality, biodiversity indicators or agricultural productivity. The cadence of monitoring will vary based on the parameter. Accumulation of heavy metals in soils must be assessed at the end of the first Reporting Period, and should be assessed in subsequent Reporting Periods. Accumulation of heavy metals in crops is recommended in most cases, but required if soil or feedstock testing indicates potential for bio-accumulation. It is recommended that aqueous measurements be assessed for heavy metals.
The Project Proponent must use the collected data to inform ongoing management of EW practices. This data must be shared with the public through Isometric's platform, in accordance with Section 6.6 of this Protocol. The Project Proponent must be prepared to adjust the EW strategy based on monitoring results and feedback from environmental studies and community engagement.
The following topics are covered briefly in this Protocol due to their inclusion in the Isometric Standard, which governs all Isometric Protocols. See in-text references to the Isometric Standard for further guidance.
For each specific Project to be evaluated under this Protocol, the Project Proponent must document Project characteristics in a Project Design Document (PDD) as outlined in the Documentation Section of the Isometric Standard. The PDD will form the basis for project verification and evaluation in accordance with this Protocol, and must include consideration of processes unique to each project, such as:
detailed feedstock characterization (see Rock and Mineral Feedstock Characterization Module)
geographical designations of control plot(s) (see Section 10.1.1)
Refer to the Rock and Mineral Feedstock Characterization Module for requirements on feedstock characterization.
Projects must be validated and the Project GHG Statement (net CO2e removal) verified by an independent third party consistent with the requirements described in this Protocol as well as in the Isometric Standard.
The Validation and Verification Body (VVB) must consider following requisite components:
As EW Projects scale, a Project may expand its operations after initial validation by adding new feedstocks or Removal Areas. Project expansion does not generally require an entirely new validation, as only the new additions (e.g. feedstocks, Removal Areas) need to be validated by the VVB. A site visit is not always necessary for project expansion, but may be conducted if deemed necessary by the VVB, in consultation with Isometric. This decision is based on factors such as whether the expansion materially changes the project design, introduces new risks, affects additionality or LCA boundaries, or introduces new permitting requirements. In some cases, a remote site visit (e.g., remote interviews, photo or video evidence) may be sufficient.
The threshold for Materiality, considering the totality of all omissions, errors and mis-statements, is 5%, in accordance with the Materiality Threshold Section of the Isometric Standard.
Verifiers must also verify the documentation of uncertainty of the GHG Statement as required by the Uncertainty Section of the Isometric Standard. Qualitative Materiality issues may also be identified and documented, such as23:
Project Validation and Verification must incorporate site visits to project facilities, namely agricultural fields being used as control, treatment or deployment plots, in accordance with the requirements of ISO 14064-3:6.1.4.2. This is to include, at a minimum, site visits during the first Validation or Verification of a Project, to the project site(s). Validators should, whenever possible, observe project operation to ensure full documentation of process inputs and outputs through visual observation and validation of instrumentation, measurements, and required data quality measures.
All quarries typically need to be visited, but where multiple quarries are operated by the same company and located in a similar area, a representative subset may be sufficient. There is no fixed amount or percent of fields that need to be visited. Isometric and the VVB will determine the appropriate number of fields based on site specific characteristics such as cropping system and MRV approach.
A site visit must occur at least once during Project Validation. Additional site visits may be required if there are substantial changes to field operations over the course of a Project's Validation period, or if deemed necessary by Isometric or the VVB.
Verifiers and validators must comply with the requirements defined in the Validation and Verification Requirements Section of the Isometric Standard. In addition, teams must maintain and demonstrate expertise associated with the specific technologies of interest, including soil sampling, analysis and data processing.
CDR via EW in agriculture is a result of a multi-step process including quarrying, transporting and spreading rock, with activities in each step potentially managed and performed by a different operator, company or owner. When there are multiple parties involved in the process a single Project Proponent must be specified contractually as the sole owner of the CreditsCertificates to avoid double counting of CO2e removals. Contracts must comply with all requirements defined in the Ownership Section of the Isometric Standard.
The Project Proponent must be able to demonstrate additionality through compliance with the Additionality Section of the Isometric Standard. The Baseline scenarios and Counterfactual utilized to assess additionality must be project-specific, and are described in Sections 8 and 10 of this Protocol.
Additionality determinations must be reviewed and completed at the time of initial Verification as well as following significant changes to project operating, including but not limited to:
Any review and change in the determination of additionality will not affect the availability of Carbon Finance and Carbon CreditsCertificates for the current or past Crediting Periods, however, if the review indicates the Project has become non-additional, this will make the Project ineligible for future CreditsCertificates24.
The uncertainty in the overall estimate of the net CO2e removal as a result of the Project must be accounted for. The total net CO2e removed for a specific Reporting Period,[math: RP],[math: CO_2e_{Removal,\ RP}], must be conservatively determined in accordance with the requirements outlined in the Uncertainty Section the Isometric Standard. See Section 10.1.4 for more details on uncertainty requirements for this Protocol.
Projects must report a list of all input variables used in the net CO2e removal calculation and their uncertainties, including:
More detailed uncertainty information should be provided if available, as outlined in the Uncertainty Section of the Isometric Standard. Uncertainty must be quantified at the project scale, which may include multiple fields within a region.
In addition, a sensitivity analysis that demonstrates the impact of each input parameter's uncertainty on the overall net CO2e removal uncertainty must be provided. Details of the sensitivity analysis method must be provided so that the results are reproducible. Input variables may be omitted if they contribute to a [math: \leq] 1% change in the net CO2e removal.
In accordance with the Isometric Standard, all evidence and data related to the underlying quantification of CO₂e removal and environmental and social safeguards monitoring will be available to the public through Isometric's platform. That includes:
The Project Proponent can request certain information to be restricted (only available to authorized Buyers, the Registry and VVB) where it is subject to confidentiality. This includes emissions factors from licensed databases. However, all other numerical data produced or used as part of the quantification of net CO₂e removal will be made available.
The scope of this Protocol includes GHG sources, sinks and reservoirs (SSRs) associated with an EW CDR project.
A cradle-to-grave GHG Statement must be prepared encompassing the GHG emissions relating to the activities outlined within the system boundary.
GHG emissions associated with the Project may be as direct emissions from a process or storage system or as indirect emissions from combustion of fuels, electricity generation, or other sources. Emissions must include all GHG SSRs within the system boundary, from the construction or manufacturing of each physical site and associated equipment, closure and disposal of each site and associated equipment, and operation of each process, including embodied emissions of equipment and consumables used in the project. 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 activity that ultimately leads to the issuance of CreditsCertificates should be included in the system boundary.
The system boundary must include all SSRs controlled by and related to the Project, including but not limited to the SSRs in Figure 1 and Table 1. If any GHG SSRs within Table 1 are deemed not appropriate to include in the system boundary, they may be excluded provided that robust justification and appropriate evidence is provided in the PDD.
Figure 1. Process flow diagram showing system boundary for EW projects
[Image: Figure 1]
Table 1. Systems boundary and scope of activities to be included for EW projects
Activity | GHG source, sink or reservoir | GHG | Scope | Timescale of emissions and accounting allocation |
|---|---|---|---|---|
Establishment of project | Quarrying | All GHGs | Quarrying activities including the following emissions sources:
| Before Reporting Period - must be accounted for in the first Reporting Period or amortized in line with allocation rules (See Section 8.5.1) |
Transport to crushing site | All GHGs | Transporting the feedstock material from the excavation site to the crushing facility | ||
Crushing and grinding (including additional processing steps such as drying) | All GHGs | Crushing and grinding activities including the following emissions sources:
| ||
Feedstock transport to application site | All GHGs | Transporting the feedstock material from the quarrying site to the agricultural application site | ||
Feedstock characterization | All GHGs | Embodied, energy use and transport emissions associated with sampling the feedstock to measure the physical and geochemical characteristics necessary for weathering determinations | ||
Spreading on agricultural application site | All GHGs | Spreading activities including:
| ||
Misc. | All GHGs | Any GHG SSR not captured by categories above, for example related to field surveys | ||
Operation | Sampling and analysis | All GHGs | Sampling and analysis activities, including:
| Over each Reporting Period - must be accounted for in the relevant Reporting Period (See Section 8.5.2) |
CO2 stored | CO₂ | The gross amount of CO2 removed and durably stored from an EW project over a Reporting Period | ||
Misc. | All GHGs | Any GHG SSR not captured by categories above, for example related to refrigeration for storing soil cores | ||
End-of-Life | Misc. | All GHGs | Activities post-Reporting Period, for example end-of-life emissions associated with equipment or deconstructing infrastructure | After Reporting Period - must be accounted for in the first Reporting Period or amortized in line with allocation rules (See Section 8.5.3) |
Miscellaneous GHG emissions are those that cannot be categorized by the GHG SSR categories provided in Table 1.
The Project Proponent is responsible for identifying all sources of emissions directly or indirectly related to project activities and must report any outside of the SSR categories identified as miscellaneous emissions.
Emissions associated with a project's impact on activities that fall outside of the system boundary of a project must also be considered. This is covered under Leakage in Section 8.5.4.
In line with the GHG Accounting Module v1.1, The Project must:
Refer to the GHG Accounting Module v1.1.
In some instances, EW project activities may be integrated into existing activities. Activities or portions of activities that were already occurring in the baseline and would have continued to occur without the EW project may be omitted from the system boundary, subject to the conditions set below.
For the purpose of this provision, an "activity" may refer to an operational sub-unit, such as an individual trip leg, a single pass of field equipment, or a discrete processing step, where such sub-units can be cleanly delineated by equipment, timing, and physical scope. Where a pre-existing activity is partially modified by the project (for example, a transport trip whose route is extended to include project-specific stops, or a field operation whose pass is lengthened to include project inputs), the activity must be partitioned into:
An activity, or portion of an activity, may only be excluded where the Project Proponent can demonstrate all of the following:
Evidence supporting these conditions must be provided in the PDD. This must include either:
And the following:
Where these conditions are met, only the emissions associated with the activity as it would have occurred in the baseline may be excluded. Any incremental emissions attributable to the project must remain within the system boundary and be accounted for in the relevant emissions sub-section.
The baseline scenario for EW projects assumes the activities associated with the EW project do not take place, no new infrastructure is built and business as usual agricultural practices occur.
The counterfactual is the CO2 that would have been removed from the atmosphere and durably stored as a result of natural weathering or pre-existing land practices. This is determined through the use of a control plot, as described in Section 8.4, with detail on monitoring requirements described in Section 10.4.3.
EW in agriculture typically consists of an application of a characterized rock or mineral feedstock followed by discrete sampling periods to quantify CO2 removals. Rock or mineral feedstock must be sourced, which may include quarrying, processing (e.g., crushing) and transportation, before it is spread over agricultural land. A combination of soil, aqueous and other geochemical measurements are then used to quantify the total CO2 removals over a period of time.
The Reporting Period for EW represents an interval of time over which removals are calculated and reported for verification. Monitoring of CO2 removals for EW may include a combination of discrete sampling (e.g., soil sampling) and continuous sampling. The equations used to calculate net CO2e removals will pertain to all GHG emissions and CO2 removals occurring over a Reporting Period. In most cases, this Reporting Period will be an interval of time bounded by sampling events.
GHG emission calculations must include all emissions related to the project activities that occur within the Reporting Period. This includes:
Total net CO2e removal is calculated for each Reporting Period, and is written hereafter as [math: {CO}_{2}^{}e_{Removal,\ RP}^{}]. The final net CO2e removal quantification must be conservatively determined, giving high confidence that at a minimum, the estimated amount of CO2e was removed.
In line with the Isometric Standard, this Protocol requires that Removal CreditsCertificates are issued ex-post.
Net CO2e removal for EW in agriculture for each Reporting Period, [math: RP], can be calculated as follows. The final net CO2e removal quantification must be conservatively determined, giving high confidence that at a minimum, the estimated amount of CO2e was removed (refer to Section 6.5 for details). Net CO2e removal is calculated as:
[math: {CO}_{2}^{}e_{Removal,\ RP}^{}\ = \ {CO}_{2}^{}e_{Stored,\ RP}^{}\ –\ {CO}_{2}^{}e_{Counterfactual,\ RP}^{}–\ {CO}_{2}^{}e_{ Emissions,\ RP}^{}]
(Equation 1)
Where:
Note: Reversals that occur after CreditsCertificates have been issued are not included in this equation. See the Reversals and Buffer Pools Section of the Isometric Standard for further information.
The total amount of CO2 stored from an EW project conceptually includes the following terms:
[math: {CO}_{2}^{}e_{Stored,\ RP}^{}\ = \ \sum_{}^{}{CO}_{2}e_{Drawdown,\ RP} - \sum_{}^{}{CO}_{2}e_{Losses,\ RP}]
(Equation 2)
Where:
This quantification framework considers the terms given in Equation 2 relative to two distinct zones, referred to as the Near-Field Zone (NFZ) and the Far-Field Zone (FFZ). The NFZ represents the portion of the upper soil column in which the weathering reaction and subsequent processes (e.g. soil and biomass uptake, secondary mineral formation) must be directly tracked and, for practical purposes, is defined as the depth of deepest soil sampling. The FFZ is defined as the transport path for the weathering products from the boundary of the NFZ to the final storage reservoir (typically the ocean). This includes the deep soil column, groundwater network, riverine transport and ocean discharge.
In this mass/charge balance approach, we explicitly account for both: 1) permanent losses of base cations (and corresponding CO2 removals) that happen after base cations are released from a rock or mineral feedstock through weathering and 2) the possibility that base cations may be temporarily, but not permanently, rendered ineffective for removals.
Losses considered to be permanent include:
Losses considered to be temporary include:
One particularly consequential aspect of these CO2 removal loss terms is the potential time lag associated with cation sorption. A base cation may undergo many generations of sorption and desorption to cation exchange sites while migrating through the soil column, delaying the CO2 removal effect until that cation is sufficiently deep in the soil column or more permanently transitions to the aqueous phase. There is currently no widely held scientific consensus on the best practices for modeling these cation sorption dynamics.
The sorption of base cations to cation exchange sites will cause re-equilibration of dissolved inorganic carbon species to maintain charge balance, which will result in degassing of CO2 into soil pore space. If this degassing occurs sufficiently deep in the soil column, the resulting transient CO2 may be sufficiently isolated from the atmosphere as to be considered a removal while the cation is still migrating through the soil column.
We also note that while this Protocol treats non-carbonic acid neutralization as a loss, from a systems-level perspective, alkalinity consumed by non-carbonic acid neutralization in the NFZ may reduce CO₂ degassing in the far-field zone (FFZ). Robust quantification of this effect and its counterfactual remains an active area of research, and Isometric will consider incorporating FFZ-level accounting as the science and available tools mature.
This Protocol defines removal relative to the primary quantification medium (e.g. solid or aqueous measurements). Where soil is used for CDR quantification, this Protocol considers removal to have occurred when base cations are exported from the NFZ. This Protocol currently recommends the depth of the NFZ to be the deeper of
A shallower definition of the NFZ may be used in circumstances where meeting these criteria is not feasible (e.g., shallow water table which cannot be accessed through conventional sampling methods). Such deviations must be reported and justified in the PDD. Where aqueous phase measurements are used for quantification, base cations can be credited within the NFZ once they have entered the porewater.
[/G-3HE8-0]All Crediting Projects must design a sampling plan that directly measures the initial weathering of rock or mineral feedstock. Once a statistically significant amount of feedstock weathering has occurred, the Project Proponent may be eligible for CreditsCertificates. This Protocol currently allows for two primary quantification approaches for determining the carbon stored from an EW project: 1) soil-based quantification and 2) porewater-based quantification. The determination used for creditingissuances must be designated in the PDD. The details of quantification requirements are addressed in the following sections.
This Protocol allows for two approaches to quantification: direct measurement of weathering in all Removal Areas, or a combined model and measurement based approach. The measurement-only approach is described below in this Protocol, and the model and measurement approach is described in a separate Module.
Please see the Model and Measurement Based Quantification of Enhanced Weathering Module for requirements on the model and measurement approach.
The remainder of Section 8.3 describes the measurement-only approach. This Protocol includes a list of quantification approaches that can be used to determine the amount of carbon stored. There are a number of other technologies at various stages of development that may prove useful for quantifying enhanced weathering in an agriculture setting (e.g., reactive transport models, ion exchange resin). The Isometric science team will regularly assess the state of scientific consensus around new and emerging technologies for quantifying CDR via Enhanced Weathering. In addition to quantification approaches, this Protocol also includes a list of allowable optional validation approaches that may be used by Projects. The use of a validation approach is recommended.
Project Proponents must quantify the amount of carbon stored using one of the quantification approaches in List 1. The quantification approach must be conducted within an appropriate sampling framework, as discussed in Section 10.1 (e.g., 2-plot, 3-plot). Additionally, Project Proponents are recommended to select and implement at least one validation approach in List 2. This validation is meant to be a localized, independent sense check on the quantification approach. If the validation approach is conducted, the validation approach must be conducted within both a treatment and control area that meets the minimum percentages of the project area (see Equation 9). The same approach cannot be used for both quantification and validation.
[/R-829A-1]The quantification and validation approaches currently accepted for Crediting Projects are:
List 1: Quantification Approaches
[/G-BBY1-0]List 2: Validation Approaches
[/G-Z0SP-0]In the event that a Project Proponent does not select soil-based quantification or validation from List 1 or 2, the Project Proponent is still required to take soil agronomic measurements at the project scale for baseline sampling, and at 10% of porewater sampling locations in subsequent sampling events. Refer to Table 4 for recommended and required soil agronomic measurements.
[/G-JNNB-0]Projects are recommended to conduct an independent check on the primary quantification approach using a secondary quantification method from List 2. The quantification check is considered passed if the median of the secondary approach is greater than or equal to the 30th percentile of the primary approach, i.e.
[math: P_{50}(secondary) \geq P_{30}(primary)]
(Equation 3)
Where:
The optional validation check should assume the same NFZ depth as the quantification approach. In most cases this will be 20 cm. Projects unable to maintain parity between the quantification and validation approaches must justify this deviation in the PDD and describe how the difference in depth will be conservatively reconciled.
See Appendix 4 for the rationale behind making the validation check recommended instead of required.
To account for uncertainty in CDR quantification, CreditsCertificates are issued at a lower percentile of the distribution produced by the primary quantification approach (see Uncertainty), rather than at the median. The percentile depends on whether a validation approach has been implemented and passed:
See Section 10.1.4 for more details on CDR and uncertainty quantification.
It is anticipated that new technologies and methods for quantifying and validating Enhanced Weathering in agriculture will emerge in the coming years. These new technologies will be regularly assessed by the Isometric science team for inclusion in this Protocol. New technologies and methods that have some demonstrated efficacy when compared to conventional soil and porewater approaches will be included in List 2 and may be used as a validation approach. New technologies and methods that have demonstrated efficacy and have gathered wide scientific consensus will be included in both Lists 1 and 2 (may be used for either quantification or validation).
Particularly, we anticipate that reactive transport models or other suitable models will improve significantly in the coming years from large amounts of calibration data. Isometric will continue to evaluate reactive transport models for inclusion in Lists 1 and 2 in future iterations of this Protocol.
The exact requirements that must be met for a novel method to be used for either validation or quantification will depend on both the method itself and the parameter it is being used to quantify. In general, methods being used for quantification will have a higher burden of proof than those being used for validation.
At a minimum, all proposed novel methods for validation or quantification must submit the following for consideration:
In addition to the above, methods proposed for quantification must submit the following:
The total amount of carbon stored from an EW project can be determined from soil measurements according to:
[math: {CO}_{2}^{}e_{Stored,\ RP}^{} = ({CO}_{2}^{}e_{Weathering, RP}^{} - {CO}_{2}^{}e_{BiomassLoss,\ RP}^{} - {CO}_{2}^{}e_{NetNewCarbonate,\ RP}^{} \\ -\ {CO}_{2}^{}e_{NetNewSilicate,\ RP}^{}- {CO}_{2}^{}e_{NetSorption,\ RP}^{} - {CO}_{2}^{}e_{NonCarbonicNeut, RP}^{})*\eta_{RiverOcean}]
(Equation 4)
Where:
See Section 10 for specific requirements for the quantification of each term.
Note: [math: {CO}_{2}^{}e_{NetNewSilicate,\ RP}^{}] is the average net change in silicate mineral (e.g., clay mineral) content between the start and end of the Reporting Period. There is no widely accepted and operationally feasible quantitative method for determining modest changes in secondary silicate mineral content in soils; clay content of soils is typically expressed as a percentage of clay-sized particles, and mineralogy is determined by X-ray diffraction. Thus, detection of new secondary clays requires either significant enough clay formation to shift the percentage of clay in the soil column or quantitative formation of clay mineralogies that are distinct from the initial assemblage (unlikely for modest pH change in soils with similar moisture retention; see Wilson (1999)27 for an overview of the parameters controlling clay formation in soil). This term is included for completeness; Isometric will not explicitly require measurement of this parameter at this time.
Project Proponents pursuing soil-based quantification must consider all terms listed in Equation 4, but alternative methods may be appropriate for rigorous quantification of CDR. For example, some Project Proponents may choose to monitor the loss of alkalinity in a project area by performing full acid digests on soil samples with limited pre-processing (e.g. removal of carbonates, extraction of the soil exchangeable fraction). In this instance, weathering, cation sorption, carbonate formation and clay formation will all be integrated into a single measurement. Such deviations may be appropriate and will be considered on a project by project basis. In such cases, the Project Proponent must provide a detailed description of the methods and describe how the chosen analyses map onto the terms in Equation 4 in the PDD.
For aqueous phase quantification, we make the simplifying assumption that all of the major soil column processes, including the release of base cations from weathering and soil losses, are accounted for in the aqueous geochemistry of water that has infiltrated to some depth. At this time, we are recommending this depth coincide with the depth of the NFZ, typically 20 cm. Therefore, the total amount of carbon stored from an EW project can be determined from porewater measurements in the top 20 cm of soil. Alternatives to 20 cm may be justified for similar reasons described in the previous sections. Well-defined catchment waters may be used in place of porewaters where the project area is contained entirely within such well-defined catchments. In the context of this Protocol, a catchment is considered well-defined if:
In instances where catchment waters are used, the Project Proponent must provide supporting documents detailing site-specific hydrogeology including details on characterization, time accounting, attribution, measurement and sampling as well as loss accounting.
[math: CO_2e_{Stored, \ RP}] can be calculated from the aqueous phase via the following equation:
[math: {CO}_{2}^{}e_{Stored,\ RP}^{} = {CO}_{2}^{}e_{Aqueous,\ RP}^{} * \eta_{RiverOcean}]
(Equation 5)
Where:
See Section 10.5 for specific requirements for the quantification of each term. It is important to note that, although [math: {CO}_{2}^{}e_{BiomassLoss,\ RP}^{}] is not explicitly included in aqueous phase quantification, alkalinity may still be taken up by plants below the observation window. Project Proponents using aqueous phase quantification for CreditsCertificates must still consider and quantify plant uptake if roots extend below the depth of porewater sampling.
Project Proponents pursuing aqueous phase quantification must consider all terms listed in Equation 5, but alternative methods may be appropriate for rigorous quantification of CDR. Such deviations may be appropriate and will be considered on a project by project basis. In such cases, the Project Proponent must provide a detailed description of the methods and describe how the chosen analyses map onto the terms in Equation 5 in the PDD.
Synthetic ion exchangers, more commonly referred to as ion exchange resins, have been employed in soil science for many years to constrain nutrient fluxes in the soil column (e.g. Lehman et al., 200128, Lang & Kaupenjohann, 200429, Johnson et al., 200530, Predotova et al., 201131, Grahmann et al., 201832). Typically, monitoring will consist of a vessel filled with a mixture of ion exchange resin beads and quartz sand that is buried in the soil column. Soil water then flows through the device and ions sorb to the resin beads, accumulating within the device. At the end of a sampling period, the accumulated ions are analyzed.
Ion exchange resins are a novel technique for EW measurements, with limited data on their use in EW deployments. For this reason, ion exchange resins may be used as a validation medium (List 2 in Section 8.3.1) under this Protocol. As more data becomes available, the use of ion exchange resins as a quantification medium will be revisited. Calculation of [math: CO_2e_{Stored, \ RP}] can be calculated as a validation check using ion exchange resins following Equation 5. Project Proponents using ion exchange resins for validation must provide detailed information on:
[/R-2G3T-1][G-6AH0-0, Projects must state the ion classes, including base cations and non-carbonic acid anions, that the resin targets.]The resin(s) used, including documentation that demonstrates the resin(s) target one or more of the following ion classes relevant to the Project's quantification approach:[/G-6AH0-0]
[G-SZCN-0, Projects must report the total Ion Exchange Capacity (IEC) of each resin device deployed.]Total Ion Exchange Capacity (IEC) of each resin device deployed, expressed in milliequivalents (meq) or millimoles of charge (mmolc);[/G-SZCN-0]
[G-193Z-0, Projects must describe the ion exchange device installation procedure.]Description of the installation procedure, particularly focusing on:[/G-193Z-0]
[G-MXZM-0, Projects must describe the ion exchange device extraction and measurement procedure.]Description of the extraction and measurement procedures used, including:[/G-MXZM-0]
The use of selective ion resins (i.e., resins targeting a single ion class) is permitted, provided the Project Proponent demonstrates that weathering-relevant ions not captured by the resin are independently constrained. Mixed-bed or multi-resin configurations that capture multiple ion classes simultaneously are also permitted.
Project Proponents must evaluate whether ion exchange devices approached saturation during the deployment period. Capacity utilization should be calculated for each device by calculating the percentage of total IEC (ion exchange capacity) occupied by recovered ions using the following relationship:
[/G-8JV9-0][math: Capacity\ Utilization (\%) = \frac{\sum \text{recovered cations (meq)}}{\text{Total IEC (meq)}} \times 100]
(Equation 6)
If devices approach saturation, the Project Proponent should consider reducing deployment duration in subsequent monitoring periods.
Further, to ensure that non-target ions have not disproportionately occupied exchange sites at the expense of target weathering products, the molar ratio of target base cations (Ca2+ and Mg2+) to total recovered cations should be calculated. Ion exchange resin devices installed in the vadose zone are subject to localized flow divergence or convergence, where soil water may bypass the device or preferentially funnel through it. This can lead to under- or over-estimation of dissolved ion fluxes, respectively. Project Proponents should document the measures taken to ensure representative water contact with the resin (e.g., device design, backfill procedures, installation orientation). Where feasible, comparison of resin-derived fluxes against independent porewater or lysimeter measurements is recommended to assess the magnitude of any flow bias.
In addition, the fraction of total recovered charge attributable to Al3+ and Fe2+ should be reported, as elevated concentrations of these ions may indicate competitive displacement of target cations or preferential resin saturation.
Project Proponents should adjust deployment duration to ensure:
In regions subject to freeze/thaw cycles, Project Proponents should consider the potential for physical damage to resin devices (e.g., bead fracturing, loss of hydraulic contact with the surrounding soil) and for remobilization of sorbed ions. Deployment periods should be planned to avoid or account for freeze/thaw events, and any devices recovered after freeze/thaw exposure should be inspected for physical integrity before extraction and analysis.
As ion exchange resins quantitatively remove dissolved ions from porewaters, the derived estimates of removals should utilize Equation 5, and should otherwise follow the recommendations and requirements pertaining to aqueous phase sampling wherever applicable.
[math: {CO}_{2}^{}e_{Counterfactual,\ RP}^{}] describes the CO2 that would have been removed from the atmosphere and durably stored in the baseline scenario as a result of pre-existing land management practices, natural background soil weathering, or weathering of excess feedstock fines.
[math: {CO}_{2}^{}e_{Counterfactual,\ RP}^{} = {CO}_{2}^{}e_{Counterfactual,\ LMP}^{} + {CO}_{2}^{}e_{Counterfactual,\ Background}^{}+ {CO}_{2}^{}e_{Counterfactual,\ feedstock fines}^{}]
(Equation 7)
Where:
In practice, [math: {CO}_{2}^{}e_{Counterfactual,\ LMP}^{}] and [math: {CO}_{2}^{}e_{Counterfactual,\ Background}^{}] are not calculated distinctly and together are referred to as the control plot correction, [math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}], which monitors the decrease in base cations in the control plot. [math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}] and [math: {CO}_{2}^{}e_{Counterfactual,\ feedstock\ fines}^{}] are subtracted from [math: {CO}_{2}^{}e_{Stored,\ RP}^{}] in Equation 1. The control plot correction must be assessed for all projects, regardless of whether lime is applied in the business as usual scenario, as it is indistinguishable from background soil weathering.
[math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}] is calculated using a business as usual control plot. The control plot ensures that any removals associated with business as usual agricultural liming or natural weathering of the soil are not attributed to project activities. The control plot correction will only be applied if there has been a statistically significant change in alkalinity in control plots in a given Reporting Period (see [Section 10.1.3](Statistical Requirements for Crediting) for more details). The control plot will serve as the baseline in modeling with respect to measured soil and porewater parameters.
[/R-BBHE-0][math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}] must be determined with the same quantification method that is determinedused for determining [math: CO_2e_{Stored, RP}]. Projects using the Model and Measurement Based Quantification of Enhanced Weathering Module must simulate [math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}] using the same model. Projects using the direct measurement approach described in Section 8.3 will determine [math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}] using the same equations (Equations 4 and 5) as the previous section for [math: {CO}_{2}^{}e_{Stored,\ RP}^{}] in which all the encompassed terms are determined using a control plot of land as described in Section 10.1.1. [math: CO_2e_{Stored}] in the control plot will be calculated as the change in concentration of base cations in the NFZ within a Reporting Period. The calculation of [math: CO_2e_{Stored}] in the counterfactual term does not distinguish between weathering from BAU lime application and background soil weathering.
For instances where the Project Proponent has discretion as to which methods can be used to determine a particular weathering or loss term, the methods used must be the same for both the area over which feedstock is applied and the control plot.
Where a business as usual control plot is maintained and lime is applied between the beginning and end of the Reporting Period, but samples are not taken immediately post lime application, Project Proponents may estimate the cation addition attributed to lime application using the operational record of the lime application rate.
In some cases, the counterfactual land management practice may have been net emitting, for example if quicklime was used as the liming agent. In this scenario, [math: {CO}_{2}^{}e_{Counterfactual,\ RP}^{}] may be taken as zero on a case-by-case basis in consultation with Isometric and the VVB. To claim this scenario, Project Proponents must submit clear justification, including:
For further information on why the counterfactual may be set to zero in a net-emitting scenario, please see Appendix 3 of the Wastewater Alkalinity Enhancement Protocol v1.2.
Project Proponents that establish that the counterfactual land management practice is net emitting should not maintain that particular practice in a BAU control plot. Instead, these Project Proponents should maintain an unamended control plot to account for [math: {CO}_{2}^{}e_{Counterfactual,\ Background}^{}].
In addition to counterfactual CO2 related to business as usual farming practices, in some instances it may be appropriate to consider weathering of feedstock that would have occurred without project intervention. For example, if the feedstock used constitutes a waste product that was not mined or quarried specifically for project activities and was stored in open-air conditions, some degree of surficial weathering could be expected over timescales relevant to a project lifetime. Project Proponents using these feedstocks must account for counterfactual weathering if the feedstock does not undergo additional processing prior to deployment. Please see the Rock and Mineral Feedstock Characterization Module v1.2 for requirements related to quantifying counterfactual feedstock weathering.
[math: {CO}_{2}^{}e_{Emissions,\ RP}^{}] is the total quantity of GHG emissions from operations and allocated embodied emissions for each Reporting Period [math: RP]. This can be calculated as:
[math: {CO}_{2}^{}e_{Emissions,\ RP}^{}\ = \ {CO}_{2}^{}e_{Establishment,\ RP}^{}\ + \ {CO}_{2}^{}e_{Operations,\ RP}^{} + \ {CO}_{2}^{}e_{End-of-life,\ RP}^{}+ \ {CO}_{2}^{}e_{Leakage,\ RP}^{}]
(Equation 8)
Where:
The following sections set out specific quantification requirements for each variable. It is anticipated that most emissions associated with EW projects will occur during the Calculation of [math: {CO}_{2}^{}e_{Establishment,\ RP}^{}] phase.
GHG emissions associated with [math: CO_2e_{Establishment,\ RP}] should include all historic emissions incurred as a result of project establishment, including but not limited to the SSRs set out in Table 1.
Project establishment emissions occur from the point of project inception to after the spreading event has taken place. GHG emissions associated with project establishment may be allocated in one of the following ways, with the allocation method selected and justified by the Project Proponent in the PDD:
If a Project Proponent Creditsissues Certificates for a Removal Area once, ammortizes establishment emissions, but does not Creditissue in that Removal Area again, these amortized emissions must be accounted for within 2 years of when 50% of the feedstock weathering potential will be realized, by either Creditingissuing Certificates for that field again or by deducting the CreditsCertificates from other removals.
Requirements for monitoring, reporting, reviewing, and adjusting amortization schedules are outlined in Section 7 of the GHG Accounting Module v1.1.
See Section 7 of the GHG Accounting Module.
GHG emissions associated with [math: CO_2e_{Operations\, RP}] should include all emissions associated with operational activities including but not limited to the SSRs set out in Table 1. For EW projects, the Reporting Period begins after the spreading event on agricultural land has occurred and ends once the weathering potential has been realized and MRV activities have ceased.
[math: CO_2e_{Operations,\ RP}] emissions occur over the Reporting Period for the deployment being Creditedcredited and are applicable to the current deployment only. [math: CO_2e_{Operations,\ RP}] emissions must be attributed to the Reporting Period in which they occur. Allocation may be permitted on a case by case basis in agreement with Isometric.
[math: CO_2e_{End-of-life,\ RP}] includes all emissions associated with activities that are anticipated to occur after the Reporting Period, but are directly or indirectly related to the Reporting Period. For example this could include ongoing sampling activities for MRV for the specific deployment (directly related) if applicable, or end-of-life emissions for project facilities (indirectly related to all deployments).
GHG emissions associated with [math: CO_2e_{End-of-Life,\ RP}] may occur from the end of the Reporting Period onwards, and typically through to completion of project site deconstruction and any other end-of-life activities.
GHG emissions associated with activities that are directly related to each deployment must be quantified as part of that Reporting Period. GHG emissions associated with activities that are indirectly related to all deployments may be allocated in the same ways as set out in [math: CO_2e_{Establishment,\ RP}].
[math: CO_2e_{Leakage,\ RP}] includes emissions associated with a project's impact on activities that fall outside of the system boundary of a project.
It includes increases in GHG emissions as a result of the project displacing emissions or causing a knock on effect that increases emissions elsewhere. This includes emissions associated with activity-shifting, market leakage and ecological leakage.
It is the Project Proponent's responsibility to identify potential sources of leakage emissions. For an EW project, feedstock replacement must be considered as part of the leakage assessment, at a minimum.
[math: CO_2e_{Leakage,\ RP}] emissions must be attributed to the Reporting Period in which they occur. Allocation may be permitted in certain instances, on a case by case basis in consultation with Isometric.
GHG emissions accounting must be undertaken in alignment with the GHG Accounting Module v1.1, which ensures a consistently rigorous standard in how GHG emissions are quantified and reported between different CDR Projects and approaches. This includes:
Refer to the GHG Accounting Module for guidance on GHG accounting requirements.
The Energy Use Accounting Module 1.3 provides requirements on how energy-related emissions must be calculated for The Project so that they can be subtracted in the net CO2e removal calculation. It sets out the calculation approach to be followed for intensive facilities and non-intensive facilities and acceptable emission factors.
Energy emissions are those related to electricity or fuel usage. They may include, but are not limited to:
Refer to the Energy Use Accounting Module for guidance on fuel and energy emissions calculations.
The GHG Accounting Module v1.1 provides requirements on how transportation and embodied emissions must be calculated for the Project so that they can be subtracted in the net CO2e removal calculation.
Embodied emissions are those related to the life cycle impact of equipment and consumables. They may include, but are not limited to:
Transportation emissions are those related to transportation of products and equipment. They may include, but are not limited to:
Refer to Section 4.1 and Section 4.2 of the GHG Accounting Module for guidance on embodied and transportation emissions calculations.
The primary storage reservoir of the CO2 removed through Enhanced Weathering is Dissolved Inorganic Carbon (DIC) in the ocean. The durability and reversal risks of this storage reservoir are discussed in the Dissolved Inorganic Carbon Storage in Oceans Module.
Refer to DIC Storage in Oceans Module for storage requirements.
As outlined in the Reversals and Buffer Pools Section of the Isometric Standard, the Buffer Pool is a mechanism used to insure against Reversal risks that may be observable and attributable to a particular project through monitoring.
In Enhanced Weathering, carbon is stored as dissolved inorganic carbon (DIC) in the ocean, a vast and well-mixed reservoir where storage durability is expected to be over 10,000 years. As this constitutes an open system with no directly observable reversals, the Isometric Standard classifies Projects operating under this Protocol as “No observable risk” for the purposes of the Risk of Reversal assessment, and such Projects are not required to complete a Risk of Reversal questionnaire. This results in a 0% buffer pool for Projects using this Protocol. However, if a Project identifies project-specific factors which impact the Risk of Reversal, they are required to complete the Risk of Reversal Questionnaire. Notwithstanding this classification, Projects must maintain a monitoring plan that meets the requirements set out in Section 10 of this Protocol.
This section outlines the monitoring approach that Project Proponents must take in CreditingProjects Projectsusing the measurement-only quantification approach. Several of the monitoring requirements described below include measurements that will be used in the quantification of rock spread and determination of removals (see Section 8.2). Projects using the Model and Measurement Based Quantification of Enhanced Weathering Module must see Section 4 of the Module for monitoring requirements that supersede the requirements in this section of the Protocol.
A Removal Area is a collection of fields over which CDR quantification is conducted across consecutive Reporting Periods. A field is defined as a contiguous plot of land subject to the same field management practices, including crop rotation. A Project may consist of one or more Removal Areas, and Project Proponents may add new Removal Areas over time as feedstock is spread on new fields. The total Project area is the sum of all Removal Areas. All deployments and sampling events within a Removal Area must occur on similar time frames. For the purposes of CDR quantification, a Removal Area may be further subdivided into individual fields or groups of similar fields, referred to as Units collectively, or Control Units, Treatment Units and Deployment Units more specifically. Field similarity and time frame requirements are discussed in more detail in Section 10.1.2.
In all projects, the area of control and treatment plots (where applicable) should each total at least a minimum percentage of the total Project area. The minimum percentages, [math: p], are as follows:
[/G-Q69X-0][math: p = \begin{cases} 2.5\%, & A \le \text{1000 ha} \\ 2.5\% - 1.5\% \times \log_{10}\!\left(\frac{A}{1000}\right), & \text{1000 ha} \lt A \lt \text{10,000 ha} \\ 1\%, & A \ge \text{10,000 ha} \end{cases}]
(Equation 9)
Where:
There is no minimum or maximum Project area, however, projects must designate at least one control, treatment and (if using the 3-plot model) deployment plot per 2,000 hectares. Project areas exceeding 2,000 hectares but less than 4,000 hectares will thus need to designate a minimum of 2 control, treatment and/or deployment plots; Projects exceeding 4,000 hectares but less than 6,000 hectares will need to designate a minimum of 3 control, treatment and/or deployment plots; and so on. This lower limit is set primarily to avoid heterogeneity in factors that will influence weathering rate (e.g., precipitation).
[/G-AV5J-0]Project Proponents with Project areas distributed over large distances must maintain a set of plots for each region even if the area criteria listed above do not require it. This may include additional sets of units per climatic and/or geo-political region, in consultation with Isometric. Difference in field and climatic characteristics (e.g., soil type, cropping system, precipitation, temperature range, etc.) may also necessitate the maintenance of additional treatment and control sites.
Control, treatment and/or deployment plots will in many cases be contiguous, but this is not a requirement. Projects may contain non-contiguous fields, managed by different farmers or landowners, so long as all plots are representative of each other, in accordance with Section 10.1.2.3.
Where applicable, analytical methods must be cross-referenced with appropriate standards (e.g., ISO, EN, BSI, ASTM, EPA) or standard operating procedures (SOP). Where a project utilizes a non-standardized methodology or SOP for the determination of a listed parameter, the Project Proponent is required to outline the relevant method within the PDD submitted to the Validation and Verification Bodies (VVB).
Project Proponents have the choice between two monitoring frameworks in Crediting projects: the 2-plot and the 3-plot approach.
[/G-YG17-0]Figure 2
[Image: in field monitoring approaches]
Schematic of in-field monitoring approaches, illustrating the relative size of the control, treatment, and deployment plots in the 2- and 3-plot approaches, as well as CO2 drawdown from the atmosphere.
Table 2. Summary of Removal Area. [math: p] represents the minimum percentage given by Equation 9.
Area | 2-Plot | 3-Plot |
|---|---|---|
Control | p% of project area | p% of project area |
Treatment | (100 - p)% of project area | p% of project area |
Deployment | Not applicable | (100 - 2p)% of project area |
The 2-plot approach for quantifying removals from EW in agriculture calls for the designation of the Project area into one of two categories:
The Treatment area is used to directly observe the CO2 removal resulting from EW in agriculture, while the control is used to directly observe counterfactual (or business as usual) CO2 removal. The 2-plot approach should be generally thought of as a flat, per-area sampling scheme.
The 3-plot approach allows for intensive, high-resolution data collection and monitoring of EW projects and their counterfactuals at smaller scales, while monitoring the remainder of the Project at lower resolution.
In this construction, the project area must be divided into three sections that will be designated as the control, treatment and deployment areas defined as follows:
Unless otherwise specified, the measurements collected in the control, treatment or deployment plots will be used to quantify removal terms contained within that particular area (i.e. deployment samples are used to quantify removals in the deployment plot). There may be some cases where measurements collected in the treatment area may be extrapolated to the deployment area (e.g., quantification of biomass uptake of base cations), although measurements for quantifying gross weathering cannot be extrapolated. It is recommended to take the same number of samples from the control, treatment and deployment plots within a given Removal Area.
The purpose of a control plot is to quantify the removal that would have otherwise occurred without the application of rock or mineral feedstock (see Section 8.4.1). Most notably, this includes any CO2 removal effect of agricultural liming that would have occurred in the absence of project activities. The control area must represent a minimum of percentage of the total project area as given by Equation 9. The control area must be maintained using a continuation of historical agricultural practices to represent what would have occurred in the absence of the CO2 removal project. This includes, for example, liming if EW is replacing current liming practices. Note that all loss terms will also be accounted for in counterfactual removals. If the Project Proponent is unable to maintain a business as usual control plot concurrent with the project deployment, the Project Proponent may opt to do the following:
[/G-W7PY-0]In instances where the last 10 years of liming records cannot be obtained, the Project Proponent must work in consultation with Isometric to identify the most plausible counterfactual land management scenario. Examples of possible sources of information include signed affidavits from knowledgeable local individuals or aggregated county level records. The Project Proponent must detail the sources of this information in the PDD.
Project Proponents that establish that the counterfactual land management practice is net emitting, such as through the application of quicklime, should not maintain this practice in the control plot. Further guidance on establishing a net emitting counterfactual scenario may be found in Section 8.4.
The treatment (2-plot) encompasses the project area on which rock or mineral feedstock is applied. The treatment area will be subject to monitoring as prescribed by the sampling plan. Application of feedstock to the treatment area must be uniform unless otherwise stated (see Section 10.1.1.6.1: Variable Application Rates for further details).
The treatment (3-plot) must represent a minimum percentage of the total project area given by Equation 9. If a Project Proponent opts to maintain control and treatment areas that are larger than the minimum percentage, it is recommended that these two areas maintain parity in both size and sampling density. The treatment area will be subject to intensive monitoring as prescribed by the sampling plan. Application of feedstock to the treatment area must be at the same feedstock volume per unit area as the deployment area.
The deployment area encompasses the remaining project area that is not within the control or treatment plots. In most cases, this will represent (100-2p)% of the project area (Table 2).
In some cases, Project Proponents may vary the feedstock application rate within a single deployment due to project-specific constraints, such as heterogeneity in soil characteristics or regional agricultural practice, or for research purposes. These Projects are still be eligible for CreditingCertificate issuances under this Protocol.
As described in Section 10.4.4, this Protocol requires a soil-based determination of feedstock application to quantify the maximum Project CDR potential. Project Proponents utilizing variable application rates are required to submit application rate maps for the Project area. These maps can be generated from either recommended application rates supplied to the applicator (typically from farmer-supplied soil data) or directly from the applicator. This data must be used to design soil sampling plans such that the full range of application rate is captured.
[/G-J4WW-0]Projects seeking CreditsCertificates with variable application rates are required to use the 3-plot model and designate treatment areas that correlate with different application rates in the Project Area. For example, if application rate in a particular field varies from 1-4 tonnes/ha, the Project Proponent must subdivide this into at least two ranges of 1-2 tonnes/ha and 3-4 tonnes/ha, each with separate treatment plots. [math: CO_2e_{Stored,\ RP}] will be determined for areas with similar application rates, which will then be used to calculate a weighted average of [math: CO_2e_{Stored,\ RP}] for the full Project area.
The Project Proponent must designate one or more control areas that can be reasonably considered representative of the Removal Area. Project Proponents should consider a range of climatic, soil, environmental, topographic, agronomic and hydrological properties contained within the Removal Area when determining the representative plots. More details on how to assess representativeness can be found in Section 10.1.2.3. Project Proponents may use publicly available data sets to inform plot designation. All data and derived metrics used for designating control plots must be provided in the PDD. Where no data is available, the Project Proponent must undertake a soil survey to characterize relevant soil characteristics. Control plots based on the data provided in the PDD should capture both the central tendency and dynamic range of the listed control variables.
Some Project Proponents may choose to designate a contiguous area for the control area, however, this is not a requirement. The Project Proponent may designate two or more smaller areas that, in aggregate, represent the minimum percentage of the project area (see Equation 9) for either the control or treatment area. This might be done if field conditions do not readily allow for a single representative control and treatment areas.
In the 2-plot approach, the treatment area will consist of the remaining project area, which in many cases will be 97.5% of the total project area.
Project Proponents must report the boundaries of the 2-plot designations, including project area maps, soil pH maps and soil texture maps with clearly demarcated boundaries and the GPS coordinates for the boundaries. Where control plots are adjacent to treated plots, it is recommended that the Project Proponent designates "buffer zones" near the edges of the control plots wherein samples will not be collected due to an elevated risk of feedstock migration. Geographical details on such designations must be reported.
The criteria for designating the treatment and control in the the 3-plot approach are the same as those described for the control in the 2-plot approach. Project Proponents must report the boundaries of the 3-plot designations as described in the 2-plot approach above.
Projects exceeding 1,000 hectares are recommended to maintain research plots for the purpose of furthering scientific understanding of outstanding research question in EW projects. Research plots should include a control and treated plot, and may be located within the control, treatment or deployment plots established for Creditingissuances. Project Proponents may choose the location and design of the research plot pursuant to specific scientific questions. It is recommended that research plots are designated such that maximum variability is captured. Project Proponents should share results from the research plot publicly.
Individual treatment and deployment fields must be split into Removal Areas according to the feedstock applied and the season where spreading occurred, as determined by climatic conditions in the region and extreme precipitation events. Control fields must be assigned to Removal Areas. Control field baseline samples must be collected no more than six months before or one month after baseline samples in the treated Removal Areas. It is strongly recommended that control baseline samples be collected no more than one month before Removal Area baseline samples. If lime was applied to some but not all fields in the two years prior to baseline sample collection, this must also be considered when allocating fields to Removal Areas. Project Proponents must document and provide justification for the definition of each Removal Area, with reference to relevant climate data and farming records.
[/R-SZ02-0]Site conditions impacting the weathering rate can vary at the field level. Each field in a Removal Area must be characterized according to a baseline soil-geochemical fingerprint, which will be used to assess field similarity for stratification and control plot representativeness. The fingerprint should draw from the following pre-application soil properties:
[/G-MD75-0]If distinct application rates, irrigation rates or cropping systems apply to different fields, this should also be considered when constructing fingerprints.
Fingerprint similarity must be quantified using a multivariate distance metric. The Mahalanobis distance is recommended, as it accounts for correlations between fingerprint variables and differences in variable scale. The Mahalanobis distance between fields [math: i] and [math: j] is defined as:
[math: D_M(i,j) = \sqrt{(\mathbf{x}_i - \mathbf{x}_j)^T \mathbf{S}^{-1} (\mathbf{x}_i - \mathbf{x}_j)}]
(Equation 10)
where:
This distance metric requires stable estimation of the covariance matrix, which generally requires at least [math: n \geq 10p] observations across all fields, where [math: p] is the number of fingerprint variables. Where this condition is not met, Project Proponents should either reduce the number of fingerprint variables or justify an alternative distance metric in the PDD. The same distance metric must be used consistently for both stratification and the control plot representativeness test.
Project Proponents must document and provide justification in the PDD for:
To ensure that weathering is quantified over areas that are sufficiently homogeneous for signal resolvability while maintaining adequate statistical power for uncertainty quantification, Project Proponents must determine if it is appropriate to pool control, treatment and deployment fields (independently) into Units for CDR quantification using the fingerprint similarity distance metric defined above. Units may consist of the full set of fields of a given type in the Removal Area, a single contiguous field or a group of similar non-contiguous fields, depending on the sampling design and field characteristics.
[/G-0RBG-0]A power analysis should be used to determine if a Unit has a sufficient number of samples to detect the expected weathering signal. In some cases the Project Proponent may need to collect more than the recommended number of samples due to factors such as the in-field heterogeneity observed at the end of the Reporting Period. The minimum number of samples needed to detect the expected feedstock weathering can be calculated as follows assuming normally distributed data 35,36,37,38:
[/G-AAD3-0][math: N_x\ \geq \ \frac{2\sigma^{2}_{x}(z_{1-\frac{\alpha}{2}} + z_{1-\beta})^2}{{\Delta_{expected,\ x}}^{2}}]
(Equation 11)
where:
Project Proponents are advised to use a conservative estimate of weathering rate or a safety factor above the calculated [math: N_x] when using this equation to determine the number of samples needed to achieve statistical significance.
A rarefaction analysis should be conducted for each baseline variable that is an input to the bootstrap mean estimate of Unit-level CDR (see Section 10.1.4.3 on sampling uncertainty quantification below) to determine whether a Unit has sufficient statistical power for uncertainty quantification:
[/G-TYZ3-0]Where the Unit sample size cannot satisfy both criteria independently, fields should be pooled until both are satisfied using the distance metric to evaluate fingerprint similarity defined above. Where pooling cannot achieve both criteria even at the Removal Area scale, the parametric fallback described in Section 10.1.4.3.2 should be used.
This approach to stratification scales with the lifetime of the project. Over the course of the Crediting Period, Project Proponents may aggregate Control, Treatment, and Deployment Units within the same Removal Area to form new Units with sufficient sample size.
Project Proponents may propose alternative, reproducible approaches for pooling fields in consultation with Isometric.
Project Proponents must verify that at least one Control Unit is sufficiently representative of each Treatment Unit using the distance metric chosen for defining fingerprint similarity.
[/G-EKTR-0]The following are recommended approaches for determining control plot representativeness. Project Proponents may propose and justify alternative formulations.
Where a Treatment Unit consists of a single field, a Control Unit is considered sufficiently representative of a Treatment Unit if its distance from the Treatment Unit in fingerprint space, [math: D_{CT}], satisfies:
[math: D_{CT} \leq k \cdot \bar{D}_{within,\ T}]
(Equation 12)
where:
Where a Treatment Unit consists of multiple pooled fields, a Control Unit is considered sufficiently representative of a Treatment Unit if [math: D_{CT}] satisfies:
[math: D_{CT} \leq k \cdot \bar{D}_{T}]
(Equation 13)
where [math: \bar{D}_{T}] is the mean pairwise distance between fields within the Treatment Unit computed using the same metric, and [math: k] is defined as above.
Where no Control Unit satisfies the criterion for a given Treatment Unit, the Project Proponent must either designate an additional representative Control Unit, or work in consultation with Isometric to determine the most appropriate conservative approach, which may include using the nearest available Control Unit with an additional uncertainty penalty applied to the control correction distribution, [math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}].
[math: CO_2e_{Stored}] must be computed for each Treatment Unit. A control correction must be applied to each Treatment Unit as the weighted average of [math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}] in each representative Control Unit with a statistically significant change in alkalinity (see Section 10.1.3), where weights are derived from fingerprint similarity and sum to unity for each Treatment Unit.
Project Proponents using the 3-plot design should match each Deployment Unit to the most similar Treatment Unit using the same distance metric and demonstrate that the matched Treatment Units are sufficiently representative. Where representativeness is demonstrated and the Treatment Unit exhibits lower variance than the Deployment Unit, the statistical precision gained from denser Treatment Unit sampling can be used to reduce the downside adjustment applied to the Deployment Unit median for CreditingCertificate issuance, rewarding investment in more intensive monitoring where it is genuinely representative. This may be demonstrated through one of the following:
For this comparison, a two-sided Mann-Whitney U test should be used at a significance level of 0.05, as it is sensitive to differences in distributional shape beyond the central tendency and does not require assumptions of normality. Where the Project Proponent can demonstrate that the distributions in both the Treatment Unit and Deployment Unit are approximately normally distributed, a two-sample t-test may be used as an alternative. The statistical test used, input data, and results must be documented and reported.
Where similarity is demonstrated, the Credited [math: \rho_{CDR}] for the Deployment Unit may then be calculated as:
[math: \rho_{CDR,\ D,\ credit} = \rho_{CDR,\ D,\ p50} - \Delta_T]
(Equation 14)
Where:
In this case, the Deployment Unit should inherit the control correction computed for the matched Treatment Unit. A single Treatment Unit may be matched to multiple Deployment Units and its downside adjustment ([math: \Delta_T]) may be applied independently to each matched Deployment Unit.
If the statistical test fails to demonstrate similarity, or if [math: \rho_{CDR,\ T,\ credit}] is negative, the Treatment Unit and Deployment Unit must be treated independently for CreditingCertificate issuance purposes i.e., the Deployment Unit must be Creditedcredited based on its own distribution of [math: CO_2e_{stored}] per hectare and the same Control Unit representativeness tests apply independently.
The Project Proponent must demonstrate a statistically significant weathering signal in each Treatment and Deployment Unit. If a project is utilizing soils-based quantification methods, a statistically significant decrease in the concentration of base cations in the NFZ must be observed between the beginning and end of a Reporting Period. If a project is utilizing waters-based quantification methods, a statistically significant export of alkalinity must be observed. We recommend using a one-tailed t-test to demonstrate this statistical significance36. Alternative statistical tests may be appropriate and may be justified based on characteristics of the data distribution (e.g., use Mann-Whitney U-test if data is not normally distributed). The Project Proponent may alternatively use a two-dimensional interpolation framework to estimate the spatially integrated weathering over the project area (with uncertainty explicitly quantified). In such cases, the interpolation scheme, uncertainty quantification routine and an appropriate test for statistical significance must be described in detail in the PDD.
[/R-GHTQ-1]A significance level of 0.05 must be used for all statistical tests, where the the null hypothesis that there is no change in alkalinity at the end of the Reporting Period compared to the earliest time point. The statistical test used, the input data and the result of this statistical test must be reported.
For soils-based quantification, all statistical tests must be performed on base cation concentrations (e.g., mol/kg or g/kg) or base cation concentrations converted to units of equivalents of charge per kilogram of soil (e.g., eq/kg). In cases where Project Proponents opt not to measure the relative concentrations of cations in soil immediately after deployment, the mean and uncertainty distribution of cation abundance at the start of the corresponding Reporting Period may not be known. For the purposes of demonstrating statistical significance, the Project Proponent may estimate the uncertainty of cation abundance immediately after spreading to be the same as the uncertainty observed at the end of the Reporting Period. This estimation can only be done for the first Reporting Period after feedstock is applied.
Project Proponents must also apply the same statistical test to determine if there has been a statistically significant change in alkalinity in control plots. If a project is utilizing soil-based quantification methods, this test must be applied independently to the concentrations of each base cation in each Control Unit. Where a statistically significant change is detected in any base cation-Unit pairing, [math: {CO}_{2}^{}e_{Counterfactual,\ Control Plot}^{}] must be quantified and incorporated into weathering calculations (see Section 10.1.4 for more details).
Uncertainty in the calculation of [math: CO_2e_{stored}], including losses, must be quantified for each Removal Area using Monte Carlo methods. Use of analytic variance propagation will not be permitted for uncertainty quantification of this term.
[/R-6EDJ-0]The uncertainty requirements in this section apply to the direct measurement quantification approach. Projects using the Model and Measurement Based Quantification of Enhanced Weathering Module must quantify uncertainty in accordance with that Module. The analytical, sampling, and parameter uncertainty provisions of this section continue to apply to the measurements used as inputs to or validation of the model.
Uncertainty in weathering quantification originates from several distinct sources, each of which must be rigorously quantified and propagated through to the final net CDR calculation. These sources include analytical uncertainty, parameter uncertainty, sampling uncertainty, and model uncertainty. Analytical, parameter, and sampling uncertainties are propagated jointly via Monte Carlo simulation. Model uncertainty is evaluated through comparative model runs and incorporated via conservative selection or ensemble aggregation.
Analytical uncertainty is the uncertainty arising from the precision and accuracy of laboratory instruments and analytical methods, such as ICP-MS/OES for measuring cation and tracer concentrations. For each measured variable used in weathering calculations, the uncertainty must reflect the full analytical method, including sample preparation and instrument precision, and must separate random error from any identified systematic bias. Systematic bias must be corrected for, and the residual uncertainty documented.
After correction for bias, random analytical error must be characterized using the greater of: (i) the relative standard deviation of laboratory duplicate analyses, or (ii) the error derived from certified reference material (CRM) recovery. The resulting distribution must be documented and used to parameterize a mean-zero perturbation around the measured value in Monte Carlo simulations, unless uncertainty is propagated by resampling independently measured values (e.g. bootstrapping following the approach in Section 10.1.4.3.2), in which case random analytical error is already represented in the observed spread.
[/G-R5WB-0]Parameter uncertainty is the uncertainty in any non-measured inputs used in weathering calculations, including constants, conversion factors, or parameters derived from literature values rather than direct measurement. Some examples include the inputs to water flux calculations and carbonic acid system calculations in aqueous-phase quantification (see Section 10.5).
Similar to analytical uncertainty, a plausible error distribution must be constructed, documented and justified for each input parameter to sample from in Monte Carlo simulations. In all cases, the chosen distribution must demonstrably not underestimate uncertainty. Distributions should be informed by published literature wherever possible, such as the use of an error envelope of [math: \pm] 20% around the crop coefficient [math: K_c] used to compute evapotranspiration losses in Equation 33. Where no suitable literature exists, Project Proponents must derive the distribution from first principles or empirical data collected as part of the project, and provide a clear justification for why the chosen range is conservative. Where parameters are correlated, Project Proponents must preserve the correlation structure in sampling (e.g., through joint sampling), or justify assuming independence.
[/G-BTR0-0]Sampling uncertainty is the uncertainty arising from the finite number and spatial distribution of samples collected within each Unit and sampling event. Project Proponents must document and justify their approach to sampling uncertainty quantification in the PDD. Isometric may require an additional conservative uncertainty penalty in cases where sampling uncertainty cannot be reliably estimated.
[/G-ZFKN-0]Project Proponents must determine if there is significant spatial autocorrelation between observations at the field- or Unit-level. If significant spatial autocorrelation is present, this must be accounted for when quantifying sampling uncertainty.
[/G-5QT1-0]Spatial autocorrelation should be assessed using Global Moran's I computed directly on the change in alkalinity between the baseline and the end of the Reporting Period (e.g. changes in base cation concentrations for soils-based quantification) using distance-based weights:
[math: I = \frac{n}{W} \times \frac{\sum_{i=1}^{n} \sum_{j=1}^{n} w_{ij}(\Delta_i - \bar{\Delta})(\Delta_j - \bar{\Delta})}{\sum_{i=1}^{n}(\Delta_i - \bar{\Delta})^2}]
(Equation 15)
Where:
Weights must be row-normalized such that [math: \sum_j w_{ij} = 1] for each location [math: i]. If different numbers of samples were collected at the baseline and the end of the Reporting Period, Project Proponents should use the locations from the sampling event with the lower sample count to compute [math: I]. [math: \Delta_i] should be computed using the values at these locations for that sampling event and the values from the closest location (calculated using the Euclidean distance) for the sampling event with the higher sample count.
Statistical significance should be assessed via a permutation test with at least 1,000 permutations, which will assess whether the observed [math: I] differs from that expected under spatial randomness. This should be done by repeatedly randomizing measured values across fixed sampling locations and recalculating [math: I] to generate the distribution of [math: I] expected under spatial randomness, [math: I_{\pi}]. To control the false discovery rate, p-values should be adjusted using the Benjamini-Hochberg procedure. Spatial autocorrelation is considered significant if:
[math: z_I = \frac{I - \mathbb{E}[I_{\pi}]}{sd(I_{\pi})}]
(Equation 16)
If significant spatial autocorrelation is detected, Project Proponents should calculate the effective number of independent observations, [math: n_{eff}], as follows:
[math: n_{eff} = \frac{n(1-I)}{1+I}]
(Equation 17)
If different numbers of samples were collected at the baseline and the end of the Reporting Period, [math: n] is the lower sample count. [math: I] can only be computed reliably if [math: n] is sufficiently large that it provides stable estimates. This should be determined using a rarefaction analysis. If this criteria is not met, Project Proponents should take [math: n_{eff} = n] and make a note in the GHG Statement acknowledging that spatial autocorrelation is unaccounted for and that sampling uncertainty may consequently be underestimated.
It is recommended to propagate sampling uncertainty through Monte Carlo simulation using the hierarchical bootstrap described below, since this approach is designed to capture both within-field and between-field variability, and makes the fewest assumptions about the shape of the underlying data.
Step 1 (field resampling): Resample fields with replacement [math: n_f] times, where [math: n_f] is the number of fields in the Unit. This can only be applied if [math: n_f] is demonstrated to result in stable estimates of the relevant quantity using rarefaction analysis. Where this cannot be justified, Project Proponents should move onto step 2 and resample observations from a single pool, making a note in the GHG Statement acknowledging that between-field correlation is not preserved and that sampling uncertainty may consequently be underestimated.
Step 2 (observation resampling): This step applies to either (i) the resampled fields from step 1, or (ii) Units composed of either multiple fields with small numbers of samples or a large contiguous field. Resample individual observations with replacement in order to generate a distribution of the quantity being calculated. Observations should be resampled as complete rows of co-located measurements across all variables at either (i) all sampling events simultaneously where observations are taken at the same locations at each sampling event, or (ii) each sampling event where they are not, preserving the correlation structure between variables. If the same measurement is resampled multiple times within a bootstrap replicate, the same perturbation sampled from the analytical error distribution should be applied to all repeated measurements. This ensures that sampling uncertainty and analytical uncertainty are propagated consistently without double counting measurement noise.
If observations are independent and identically distributed (i.i.d.), [math: n_{x,t}] observations should be drawn from the empirical distribution in each bootstrap replicate, where [math: n_{x,t}] is the number of samples collected in field or Unit [math: x] at sampling event [math: t]. This should be determined using the spatial autocorrelation test above. If significant spatial autocorrelation is detected, [math: n_{eff}] observations (calculated using Equation 17 above) should be drawn in each bootstrap replicate instead.
Step 3 (area-weighted average): If field resampling is performed, compute field-level means from the resampled observations within each field. Then compute the Unit-level mean as the area-weighted average of the resampled field-level means, adding area weights for a given field for each time it is resampled.
Steps 1 and 3 only apply to Units composed of multiple fields where [math: I] in Equation 15 can be computed reliably in each field, otherwise step 2 is sufficient for calculating the bootstrap mean distribution of the relevant quantity.
Table 3. Summary of hierarchical bootstrap routine under different conditions.
Step | Condition | Approach |
|---|---|---|
Step 1 — Field resampling | Unit composed of multiple fields, [math: n_f] demonstrated stable by rarefaction | Resample [math: n_f] fields with replacement |
Step 1 — Field resampling | Unit composed of multiple fields, [math: n_f] not demonstrated stable by rarefaction | Skip to Step 2, resampling observations from a single pool. Note in GHG Statement that between-field correlation is not preserved and sampling uncertainty may be underestimated. |
Step 1 — Field resampling | Unit composed of a single field | Skip to Step 2 |
Step 2 — Observation resampling | Observations collected at same locations at each sampling event | Resample [math: n_{x,t}] complete observation rows simultaneously across all sampling events. Where significant within-field autocorrelation detected, resample [math: n_{eff}] rows instead |
Step 2 — Observation resampling | Observations not collected at same locations at each sampling event | Resample [math: n_{x,t}] complete observation rows at each sampling event independently. Where significant within-field autocorrelation detected, resample [math: n_{eff}] rows instead |
Step 2 — Observation resampling | [math: n] not demonstrated stable by rarefaction | Apply parametric fallback as described above |
Step 3 — Area-weighted average | Field resampling performed in Step 1 | Compute field-level means from resampled observations. Compute Unit-level mean as area-weighted average, adding area weights for each time a field is resampled |
Step 3 — Area-weighted average | Field resampling not performed | Not applicable — Unit-level mean computed directly from Step 2 |
Where sample sizes within a Unit are insufficient for bootstrapping, whether for the primary weathering calculation or for individual loss terms, Project Proponents should use a conservative parametric distribution in place of bootstrapping for the affected term. This may arise in small-scale projects where only a small number of control, biomass or porewater samples have been taken.
Before applying the parametric fallback, Project Proponents should first determine whether the available sample size can nonetheless be justified as sufficient for bootstrapping using the rarefaction analysis described in Section 10.1.2.2. Where this justification can be made and accepted by Isometric, bootstrapping may proceed with the reduced sample size. The parametric fallback applies only where this justification cannot be made.
Where the parametric fallback is needed, the parametric distribution should be informed by all available data, including measurements collected as part of the project and relevant published literature. Where a normal distribution is assumed, it should be parameterized using the sample mean and standard deviation of the available measurements. Where fewer than 5 measurements are available, the standard deviation should be inflated by the appropriate [math: t]-distribution coverage factor for the given sample size and a 95% confidence level, to account for the additional uncertainty arising from estimating variance from a small sample. Where a normal distribution cannot be justified, Project Proponents should adopt an alternative distribution and provide justification in the PDD.
The parametric distribution must be sampled within the same Monte Carlo framework as the bootstrap distributions for other terms, ensuring consistent propagation through to the final [math: CO_2e_{stored}] distribution.
Project Proponents must document the algorithm they intend to use to quantify uncertainty in [math: CO_2e_{stored}] and demonstrate how it propagates analytical, parameter and sampling uncertainties jointly within each Monte Carlo replicate. The number of Monte Carlo replicates [math: N] must be computed such that the target percentile used for CreditingCertificate issuance changes by less than 1% under different random seeds, in order to meet the Materiality threshold outlined in the Isometric Standard. For soils-based quantification, [math: CO_2e_{stored}] and the associated uncertainty must be quantified for each base cation independently, as cations weather at very different rates.
The following algorithm should be applied to compute [math: CO_2e_{stored}] and the associated uncertainty for each Removal Area:
a. For soils-based quantification, this must be in the form of the difference in base cation concentration between the beginning and end of the Reporting Period. This must be calculated separately for each base cation used in quantification.
b. For waters-based quantification, this must be in the form of a [math: CO_2e_{stored, x_c}^{control}] distribution (pre-riverine and marine losses). 2. Compute a [math: CO_2e_{stored,\ x_t}^{treatment}] distribution for each Treatment Unit [math: x_t]:
a. Compute the control plot correction distribution for each Treatment Unit, [math: CO_2e_{stored,\ x_t}^{control}] (see Section 10.1.2 for details).
b. Generate bootstrap mean distributions of all input parameters to the weathering formula, covering all terms in the [math: CO_2e_{stored}] calculation including drawdown and all applicable loss terms, using the hierarchical bootstrap described above.
c. For soils-based quantification, compute a weathered fraction distribution for each cation and convert to [math: CO_2e_{stored,\ x_t}^{treatment}] based on the alkalinity added (see Section 10.4.3.3), applying the control plot correction from step 2(a).
d. For waters-based quantification, compute a [math: CO_2e_{stored, x_t}^{treatment}] distribution based on the Treatment Unit data, and subtract the control plot correction [math: CO_2e_{stored,\ x_t}^{control}]. 3. If using the 3-plot design, compute a [math: CO_2e_{stored, x_d}^{deployment}] distribution for each Deployment Unit, by using the control plot correction from the matched Treatment Unit, and performing steps 2(b)-(c). 4. Compute overall [math: CO_2e_{stored}], by summing the [math: CO_2e_{stored, x}] distributions for each Treatment Unit and Deployment Unit and taking the relevant percentile value (depending on the outcome of the validation check, as outlined in Section 8.3.1.1) of the summed distribution.
Project Proponents may propose alternative approaches to the quantification of [math: CO_2e_{stored}] and uncertainty in consultation with Isometric. The following information must be submitted for Isometric to review alternative approaches:
Isometric will review the above materials to assess:
If a model is used for part of the quantification workflow (e.g. geochemical speciation modeling for aqueous-phase quantification), there is some uncertainty associated with the model choice. Project Proponents must select a model that is appropriate for the soil and porewater geochemistry of each Removal Area and provide justification for this choice. If there are multiple applicable models, the selected uncertainty quantification algorithm should be run for each model and the final value of [math: CO_2e_{stored}] should be taken as the chosen CreditingCertificate issuance percentile of either:
One example of model choice relates to the selection of the thermodynamic database (e.g., phreeqc.dat, minteq.v4.dat, wateq4f.dat) to use in PHREEQC calculations for aqueous-phase quantification. Different thermodynamic databases represent alternative thermodynamic formulations and activity model parameterizations and may yield varying results for carbonic acid system calculations, particularly in solutions with elevated ionic strength or non-standard compositions. The choice of database must be justified based on local chemistry (e.g., ionic strength, temperature range, and relevant mineral phases).
Field management practices affect CO2 removal both directly and indirectly 39,40,41. For example, irrigation can significantly impact both moisture and pH, therefore acting as a strong control on weathering rate41. Some irrigation sources might contain significant amounts of alkalinity, influencing some quantification approaches. Furthermore, soil tilling can drive increased carbon flux in the upper soil column39,40,42, which can complicate the calculation of stored carbon. Thus, projects are required to provide detailed information on field management in the PDD. Field management information includes:
[/R-AAKK-1]Cropping system
Productivity levels
Fertilizer use and composition, if being used to account for non-carbonic acid weathering
Project Proponents should provide information on tillage practices.
In the event that a field is irrigated with a non-rainwater source and maintaining control plots that account for spatial variation in irrigation is operationally infeasible, Project Proponents must provide information on irrigation schedule and the irrigation source, including the following geochemical parameters:
[/G-822T-0]Project Proponents may use publicly available or farmer-supplied datasets where sufficient information on all of the above parameters is available.
If, at any point in the project duration, changes are implemented to any of the above field management strategies, the changes must be documented and reported. This includes a description of the change, the date the change was implemented and any required analyses associated with that field management strategy. For example, if the irrigation source changes, the fluid must be characterized according to the measurements listed above. Communication with agricultural partners should be established prior to deployment to facilitate data sharing. In extraordinary circumstances in which field management data cannot be provided, Project Proponents may still be eligible for CreditsCertificates. In such cases, Project Proponents must justify why they are unable to provide this data in the PDD.
Climatic variables, such as temperature and precipitation, are strong controls of both feedstock reaction kinetics and the rate of alkalinity export 41,43. Thus, Project Proponents are required to report rainfall data from the project area.
[/R-HMAX-1]Project Proponents are recommended to report temperature, humidity, wind speed, and solar radiation.
[/G-VPYF-0]The reported data must encompass the full time period for which Creditingissuances will take place. Acceptable data sources include sensors deployed directly in the project site or obtained from the geographically nearest weather station.
Inter-regional variability in rainfall can be significant even at the kilometer scale 44,35. Because of the direct impact of rainfall on CO2 sequestration in agricultural EW, the project therefore must take microclimatic variation into account in collecting and reporting rainfall at the project site by deploying a rain gauge with data logging. This Protocol recommends that one rain gauge is installed per 2,000 hectares. Project Proponents may propose alternative methods to account for rainfall at the project site if installation of a rain gauge is infeasible; for example, data from a weather station within a 10 km distance from the project site.
This Protocol considers CO2 removal in terms of the alkalinity flux beyond the depth of the NFZ. Given the complexity of determining the depth of the NFZ, as well as the operational difficulty of deep soil sampling, this Protocol sets a recommended sampling depth of 20 cm (or tillage depth with additional buffer), with the expectation that this recommendation will be updated as more information becomes available. CO2 removal based on soil measurements will be calculated based on the alkalinity that reaches the depth of the NFZ. However, we recognize the importance of accounting for temporal lags in alkalinity export from the soil column associated with cation sorption/desorption and formation/dissolution of secondary carbonates. To this end, Project Proponents are required to provide some constraints on alkalinity export from the chosen sampling depth to the watershed (see Section 10.4.5.4).
[/R-RY27-1]To account for in-field heterogeneity, soil samples should consist of multiple soil cores or subsamples. It is strongly recommended that soil samples be composed of 10-20 composited soil cores or subsamples randomly or arbitrarily distributed about a sample coordinate45,46,47. While this is a recommendation, Project Proponents are encouraged to consider increasing the number of composited cores per sample, especially in fields that have been characterized with a high level of spatial heterogeneity. The overall compositing procedure must be reported and justified in the PDD.
[/G-30NS-0]Sampling technique, including sampling design and methodology, type of sampling materials or machinery, must be stated in the PDD. Significant changes to sampling procedures throughout the lifetime of the project must be reported to Isometric.
[/G-PNHE-0]To minimize sampling bias, it is recommended that Project Proponents collect soil samples in the NFZ (typically 20 cm) at a high spatial sampling density25 (e.g. 1 sample per hectare) in accordance with the quantification and validation approaches used for CreditingCertificate issuance, as outlined in Section 8.2. Sampling guidance for each quantification approach are given in Appendix 3. If a Project Proponent is utilizing a 3-plot approach, it is recommended that the total number of samples be evenly divided between the three areas (i.e., one-third of the total number of samples collected in each plot). While random sampling routines are generally preferred, the Project Proponent may use alternative sampling routines as long as they are documented and justified in the PDD. Project Proponents employing grid sampling must composite a minimum of 5 soil cores per grid sample to incorporate spatial variation in the structure imposed by the grid. After sampling, the soil cores may be divided into separate soil increments or horizons and homogenized within each increment or horizon for analysis.
Establishing baseline (i.e., before feedstock application) soil conditions is critical to both verifying CO2 removal through EW activities and to monitor potential environmental impacts. Project Proponents are therefore required to collect baseline soil samples prior to spreading rock or mineral feedstock. Baseline soil sampling should be conducted within the 2-plot or 3-plot framework described above. If pre-existing data on soil characteristics is unavailable, it may be appropriate to conduct additional baseline sampling prior to determining the boundaries of the control, treatment and deployment plots.
[/R-EV85-1]Baseline samples are targeted to quantify heterogeneity in the soil characteristics most relevant to EW, including pH, soil texture, soil permeability, cation exchange capacity (CEC) and base saturation. Additionally, baseline sampling will allow the Project Proponent to determine the inherent heterogeneity of any tracer that may be used to determine rock or mineral feedstock addition. A full list of required analyses for baseline samples is given in Table 4.
[/G-XZ8R-0]Project Proponents using soil based quantification or validation from List 1 or 2 must quantify the baseline soil abundances of all elements, isotopes and isotope ratios that may be used for quantification. Project Proponents that do not use soil based quantification or validation from List 1 or 2 are required to take soil samples for baseline establishment and complete all analyses required in Table 4 except elemental and isotopic abundance.
The Project Proponent is advised that the above baseline establishment sampling guidance represents the minimum sampling recommendation. We encourage the Project Proponent to consider the impact of increasing the number of samples on the statistical significance threshold required for CreditingCertificate issuances.
This section details the determination of the total amount of feedstock-based alkalinity applied to a field during a deployment. This is a crucial part of the overall removal quantification because it sets the maximum potential CO2 removal. Given the importance of accurately determining the total amount of feedstock-based alkalinity added, this Protocol requires that the calculation of the average feedstock application rate from operational logs is cross-validated against direct soil measurements.
The following two subsections detail options for determining alkalinity application rate from operational logs and direct soil measurements. Project Proponents must select one method to determine alkalinity application rate from each of these subsections (10.4.3.1 and 10.4.3.2). Section 10.4.3.3 describes the alkalinity application rate check, which details how the application rate calculated from operational logs and soil measurements must be reconciled.
Determination of alkalinity added for a project is conducted by multiplying the average application rate (kg/m2) by the concentration of base cation alkalinity in the rock (eq/kg). Project Proponents must select one of the following options to determine average application rate from operational logs. The average application rate as determined from operational logs using Method A or B must then be multiplied by the moles of alkalinity per kilogram of rock, as outlined in the Rock and Mineral Feedstock Characterization Module v1.2.
[/R-YV4P-0]Method A
"As applied" data from the feedstock applicator, where available, in the form of .geojson files or similar maps showing application area and rate.
[/R-JG0B-0]Method B
The total mass of rock delivered to the site for this project divided by the area over which it was applied.
[/R-64W0-0]Total mass of rock must be supported by bills of lading or similar documentation from the feedstock supplier and/or transportation company.
[/G-25CG-0]Where this option is chosen, Project Proponents must also provide documentation, such as photographic evidence, of the spreading event.
[/G-NT85-0]Feedstock-based alkalinity added must also be determined from direct measurement of soils from one of the following methods outlined below.
[/R-SSQN-0]Project Proponents quantifying [math: CO_2e_{stored, RP}] using total cation accounting (i.e., without an immobile tracer) are required to select Method B.
[/G-ZC9R-0]Method A
A pan audit, where pans of a known dimension are placed in the field on the soil surface prior to feedstock spreading and retrieved after feedstock is spread. The mass of feedstock collected in the pan is recorded along with the area of the pan to determine feedstock application rate.
[/R-DEKA-0]Project Proponents pursuing a pan audit must state the sampling density and justify that it is sufficiently high to determine the feedstock application rate.
[/G-9F3T-0]Project Proponents should place pans about the project area using a random sampling approach.
[/G-X3ZF-0]Project Proponents must state the percentage of the project area that will be monitored through the pan audit.
[/G-GRV4-0]Project Proponents must describe how the application rate at the project area boundary, over driving tracks, and at the turn around point at the end of a row is accounted for, as the application rate in these areas is likely to be highly variable and may not be well represented by pan audits from the center of the applicator's reach.
[/G-4DPS-0]Project Proponents using Method A must perform a statistical check, such as a rarefaction analysis, to confirm that the sampling density over the project area is sufficient to account for in field heterogeneity
[/G-R2JE-0]Project Proponents selecting Method A are not required to collect bulk density samples at the baseline sampling event. These Project Proponents are still required to collect bulk density samples at the end of each Reporting Period to confirm if changes in tracer depletion or enrichment in the control plot over the Reporting Period are consistent with changes in bulk density.
Method B
Post-application sampling with direct measurement of mobile cations and immobile tracers, if applicable, that will be used to determine weathering.
[/R-13EZ-0]This sampling event must be conducted at all sampling points immediately after feedstock application. There is no maximum time period between spreading and post application sampling, but if enough feedstock weathering occurs between these two monitoring period so that the alkalinity application rate check does not pass, the Project Proponent must take the lower rate, as discussed in Section 10.4.3.3.
[/G-BQE3-0]Method C
Determination of the total amount of feedstock-based alkalinity or base cations added to a site as determined by measuring the soil concentration of a particular immobile element, isotope or isotope ratio before feedstock application and at the end of the Reporting Period, and using the ratio between that analyte and feedstock alkalinity to determine the true feedstock application rate. This method is discussed in detail below.
[/R-3H1N-0]Prior to application, the project area will be separated into two or three plots as described in Section 10.1.1. Baseline soil samples will be collected prior to the spreading of feedstock. Soil samples must be partitioned and analyzed as previously described. Following feedstock application, soil samples will be collected and analyzed in the same manner.
A mass balance approach is then used to determine the total mass of rock added in each field area (i.e., 2-plot or 3-plot) separately:
[/G-RXVQ-0][math: {\lbrack ITE\rbrack}_{PA,\ x}{mass}_{soil - PA,\ x} = {\lbrack ITE\rbrack}_{BL,\ x}{mass}_{soil - BL,\ x} + {\lbrack ITE\rbrack}_{FS}{mass}_{FS,\ x}]
(Equation 18)
Where:
The Project Proponent may, in consultation with Isometric, provide an alternate form of the mass balance equation that is more appropriate for the specific tracers being used, which must be included in the PDD.
Substituting [math: mass_{soil-PA,\ x} = mass_{soil-BL,\ x} + mass_{FS,\ x}] into Equation 18 and rearranging to calculate the mass of rock added yields
[math: {mass}_{FS,\ x} = \ \frac{(\lbrack{ITE\rbrack}_{PA,\ x} - \lbrack{ITE\rbrack}_{BL,\ x}){mass}_{soil-BL,\ x}}{\lbrack{ITE\rbrack}_{FS,\ x} - {\lbrack ITE\rbrack}_{PA,\ x}}]
(Equation 19)
The mass of soil can be estimated using the product of soil density, depth of the NFZ (typically 20 cm) and the area of interest.
[math: {mass}_{soil,\ x} = \rho_{x} \times d_{x} \times A_{x}]
(Equation 20)
Where:
The alkalinity application rate can then be calculated as follows:
[math: Alkalinity\ application\ rate = \frac{{(mass}_{feedstock,\ Treatment} + {mass}_{feedstock,\ Deployment})\ \times \lbrack Alk\rbrack_{feedstock}}{(A_{Treatment} + A_{Deployment})\ }]
(Equation 21)
The soil based alkalinity application rate must be reported in the initial Reporting Period (i.e. Reporting Period that includes the first measurements following deployment).
The alkalinity application rate check serves two purposes: (1) to confirm that feedstock was applied to each Unit at the reported rate, and (2) to demonstrate that the soil or porewater sampling density is sufficient to detect the weathering signal above in-field noise.
First, to confirm the application rate, Project Proponents must assess consistency between the determination of alkalinity added from operational logs and direct measurement (as discussed in the two sections above) in each Treatment and Deployment Unit as follows:
[/R-T397-0]If this check passes, Project Proponents must use the application rate from operational logs and multiply this by the weathered fraction to quantify [math: {CO}_{2}^{}e_{Drawdown}]. If this check fails, Project Proponents must use the lower of the application rate from operational logs and the bootstrap mean application rate determined from direct measurement.
Second, Project Proponents must perform a statistical analysis to demonstrate that the number of samples collected is sufficient for resolving the weathering signal above in-field noise. It is recommended to carry out a power analysis as follows:
[/R-DR4W-0][math: \Delta_{min, x} = [ITE]_{PA,\ x} - [ITE]_{BL,\ x} = \frac{r_x ([ITE]_{FS, x} - [ITE]_{BL, x})}{(1 + r_x)}]
(Equation 22)
Where
[math: n_{BL} \geq \frac{(z_{1-\frac{\alpha}{2}} + z_{1-\beta})^2 \times (\sigma^2_{BL} + \frac{\sigma^2_{PA}}{r})}{\Delta^2_{min,\ x}}]
(Equation 23)
Where:
The statistical analysis must show that enough samples have been collected for a Unit to be eligible for CreditingCertificate issuance. If the required sample size exceeds the number of samples collected, Project Proponents may try pooling similar Treatment or Deployment Units or explore alternative quantification approaches, such as use of a different immobile tracer or accounting approach like Total Cation Accounting (TCA), and re-run this check. For Projects using TCA, this check must be applied separately to each base cation being considered for Creditingissuances. Only the base cation-Unit combinations that pass this check will be eligible for Creditingissuances. If no stratification or quantification approach can be found where this this check passes for a given Treatment or Deployment Unit, it must be excluded from CDR quantification.
Randomized sampling is recommended to minimize potential sampling bias in quantification of soil characteristics 49,50 . Alternative sampling frameworks may be appropriate. If a non-random sampling framework is used, the Project Proponent must describe the sampling approach and how it addresses in-field heterogeneity. The number of samples taken is dependent on the soil heterogeneity, as determined by baseline measurements, and the propagated analytical uncertainty of relevant measurements. It is recommended that Project Proponents seeking CreditsCertificates through soil-based quantification analyze (on average) a minimum of 1 sample per hectare of project area (Appendix 3); however, in cases of extreme heterogeneity, a higher average sampling density may be needed to achieve statistical significance.
Soil sampling must be conducted at a cadence that is appropriate for the alkalinity fluxes being observed and the particular crops being grown. At a minimum, Project Proponents are required to conduct soil sampling prior to feedstock application (baseline establishment) and once a Reporting Period. It is recommended that Project Proponents maintain an annual sampling cadence at minimum. It is further recommended that soil sampling is conducted shortly after feedstock application to confirm the feedstock application rate. Sampling of water and biomass must be conducted at intervals that are appropriate for capturing those alkalinity fluxes. The cadence of sampling and justification must be described in the PDD. It is recommended that the same number of soil samples be taken from the control, treatment, and deployment plots if using 3-plot approach. An additional sampling period may be appropriate following any events that could significantly impact soil characteristics, such as fertilizer application, irrigation, or significant precipitation (e.g., major storm). This will be determined on a project- and event-specific basis. Sampling at a higher frequency than originally described in the PDD is always permitted.
In addition to regular sampling of the NFZ, annual sampling of deep soil (e.g., 60 cm to 1 m depth) is strongly recommended, as this will help Project Proponents to constrain the time scales of alkalinity transport through the soil column. A suite of recommended analyses for these samples is given in Table 4.
This Protocol requires robust characterization of all soil parameters related to quantification of [math: CO_2e_{Stored}] in both baseline and post-application samples. Some measurements are required for all soil sampling events, some for baseline characterization, and some are recommended (see Table 4). Measurement procedures must be cross-referenced against an applicable standard. The following list details measurements which are recommended or required and a recommended analytical standard:
Details on requirements for soil characterization are given in Table 4.
This section describes the requirements for the calculation of [math: CO_2e_{Weathering, RP}] in Equation 4.
[math: {CO}_{2}^{}e_{Weathering,\ RP}^{}] must be determined from direct geochemical observation of the project area. Many emerging soil-based methods for determining in-field [math: {CO}_{2}^{}e_{Weathering,\ RP}^{}] rely on measuring the abundance of some insoluble tracer and its ratio to soluble base cations in the feedstock to determine weathering. This includes measuring the abundance of both insoluble tracer(s) and soluble base cations before and after feedstock application to determine the weathering potential, and measuring the evolution of these two groups of analytes at later time points to determine the gross CO2 removal before accounting for the various losses. The Project must clearly specify all of the following in the PDD:
[/R-FC0M-1][math: {CO}_{2}^{}e_{Weathering,\ RP}^{}] should be determined by calculating the total CO2 removal in the treatment and deployment areas separately before adding them together. In soils, this will take the following form:
[math: {CO}_{2}^{}e_{Weathering,\ RP,\ x}^{} = \ - \Delta{\lbrack ALK\rbrack}_{x} \times \rho_{x} \times d_{x} \times A_{x} \times \frac{CDR}{ALK}]
(Equation 24)
Where:
The extent of feedstock weathering can be estimated in soil samples by monitoring the flux of base cations through the soil column to the depth of the NFZ and calculating dissolution using a mass balance-based approach. In this method, an immobile element or tracer is measured against the flux of mobile base cations, commonly Ca2+ and Mg2+, and the extent of in-situ feedstock dissolution is calculated by mass balance:
[math: {\lbrack ALK\rbrack}_{add\ } = \ \lbrack ITE\rbrack_{add}\times\ \frac{{\lbrack ALK\rbrack}_{FS}\ - \ \lbrack{ALK\rbrack}_{BL}}{{\lbrack ITE\rbrack}_{FS} - {\lbrack ITE\rbrack}_{BL}}]
(Equation 25)
Where:
ALKadd - the concentration of the added mobile cations (e.g., Ca2+, Mg2+)
ITEadd - the concentration of added immobile tracer
ALKFS - the concentration of mobile cations in the feedstock
ALKBL - the concentration of mobile cations in baseline soil samples
ITEFS - the concentration of immobile tracer in the feedstock
ITEBL - the concentration of immobile tracer in baseline soil samples
Project Proponents using the Total Cation Accounting (TCA) approach rather than TiCAT should substitute the concentration of immobile tracer for the concentration of each base cation considered in the calculation of CDR.
The mobile cation flux can then be determined by subtracting the cation concentration in post-application soil samples from the sum of the added cations and the baseline soil cation concentration:
[math: \Delta\lbrack ALK\rbrack\ = \ \lbrack{ALK\rbrack}_{add}\ + {\lbrack ALK\rbrack}_{BL}\ - \ \lbrack{ALK\rbrack}_{PAS}]
(Equation 26)
Where:
ΔALK - the change in alkalinity (base cations) in the soil column between the beginning and end of the Reporting Period, in eq/kg
ALKadd - the concentration of the added mobile cations (e.g., Ca2+, Mg2+)
ALKBL - the concentration of mobile cations in baseline soil samples
ALKPAS - the concentration of the major mobile cations in post-application sample
The fraction of feedstock dissolved is calculated as the cation flux divided by the added cations. Note that the set of equations given above (adapted from Reershemius et al. 2023) represents one possible example of mass balance calculations and other calculation methods can be accepted51. Project Proponents using this mass balance approach must select an immobile tracer based on feedstock and soil composition; this may include, for example, Ti, Zr, Th, Nb, Ta or rare earth elements (REEs). This approach requires that the concentration of the selected immobile element is sufficiently different between the soil (including amendments such as fertilizers) and the chosen feedstock. Project Proponents should consider baseline variance of the chosen tracer in the project area and how this may impact the ability to resolve a weathering signal. Project Proponents are required to detail their mass balance calculation method and provide detailed justification for the selected immobile tracer in the PDD, including literature references.
Chemical weathering results in significant mass changes in the soil column, up to 50% or more52. When calculating the extent of feedstock weathering, this mass loss needs to be corrected for in order to evaluate the element distribution among samples from different parts of the weathering profile. This correction can be performed in two ways:
Approaches to correcting for mass change in soil during the weathering reaction have been discussed in Reershiumius et al., 202351 and Surhoff et al., 202553. Project Proponents may propose their own approach to correct for mass loss or follow one of the above approaches.
Cation concentrations in soil samples must be measured using a total digestion method coupled with either inductively coupled plasma mass spectrometry (ICP-MS) or inductively coupled plasma optical emission spectroscopy (ICP-OES). Project Proponents should consider the analytical precision required to detect trace elements when selecting their measurement method. For example, sample analysis via ICP-OES may be appropriate for characterization of major elements, but lacks the analytical precision to accurately account for trace elements that may be used as immobile tracers. Project Proponents are required to describe their total digestion and measurement methods in the PDD. This should include consideration of cation sorption and secondary carbonate mineral formation where appropriate, such as extraction method for the soil exchangeable fraction (Section 10.4.5.3) or the method for removal of secondary carbonates before soil digestion. If Project Proponents are using a method that does not require explicitly accounting for SIC or cation sorption, this must be justified in the PDD. When reporting data from ICP-MS/OES, Project Proponents must include information on calibration standards, blanks, and certified reference materials (CRMs); requirements for data reports are described in Section 11.
Project Proponents should cross-reference their measurement procedures with the following standards or equivalent standards:
As applied feedstock weathers, a portion of the dissolved cation load will be transiently bound on soil exchange sites (the soil exchangeable fraction). Though uptake of dissolved cations by soil sites is temporary, it can affect short-term mass balance calculations for determining CO2 removed. Project Proponents are required to quantify the amount of base cations sorbed by soil for complete carbon accounting, which is [math: {CO}_{2}^{}e_{NetSorption,\ RP}^{}] in Equation 4.
[/R-596J-1][math: {CO}_{2}^{}e_{NetSorption,\ RP}^{}] is determined by measuring any change in adsorbed base cations in the NFZ over the Reporting Period. Typically, this is determined using the difference in the product of cation exchange capacity and base cation saturation between the start and end of the Reporting Period. A positive value corresponds to a net increase in base cations sorbed to cation exchange sites, leading to outgassing of dissolved CO2. Conversely, a negative value corresponds to a net decrease in base cations sorbed, leading to CO2 removal.
There are several potential methods that can be used to isolate the exchangeable fraction. Refer to Appendix 2: Monitoring Plan Requirements for further guidance.
Some agronomic soil testing facilities may use regionally specific methodologies that deviate from the standards listed above. Such methodologies are generally permissible, but require approval by Isometric and justification in the PDD. We note that some methods for measuring enhanced weathering in soils (e.g., total acid digestion followed by bulk cation quantification) implicitly include net sorption monitoring; this is generally acceptable under this Protocol.
Project Proponents are required to outline their chosen extraction procedure for both CEC and base saturation, including post-extraction analysis, in the PDD, as well as justification of their choice. If Project Proponents are using an analytical method that does not require explicit accounting of sorbed cations, this must be justified in the PDD.
Because of temporal lags in alkalinity export associated with cation sorption/desorption, Project Proponents are required to outline their approach to constraining the time between when weathering occurs and when bicarbonate is exitsexported to the NFZwatershed. This may include:
Though these time estimates will not be used for CreditCertificate generation, Project Proponents are required to submit a report containing the estimated time series between mineral application and alkalinity export to the ocean as derived by the above methods.
Project Proponents must consider the impact of secondary mineral formation in soils as a result of EW activities. Secondary minerals can include both carbonates and silicate clays. Carbonate formation can be constrained as the difference in soil inorganic carbon (SIC) in the NFZ between baseline and post-application samples. This section describes the requirements for the calculation of [math: {CO}_{2}^{}e_{NetNewCarbonate,\ RP}^{}] in Equation 4.
[/R-ZQ12-1][math: {CO}_{2}^{}e_{NetNewCarbonate,\ RP}^{}] represents the average net change in soil inorganic carbon (SIC) between the start and the end of the Reporting Period. This measurement pertains to all soils collected in the NFZ, typically 0 to 20 cm, but should also proportionally represent sample fractions if samples are partitioned for determination of initial weathering (e.g., total SIC is determined from a weighted average of 0-10 cm samples and 10-20 cm samples).
This value of [math: {CO}_{2}^{}e_{NetNewCarbonate,\ RP}^{}] may be positive, zero or negative. A positive value corresponds to a net increase in soil inorganic carbon, which will result in less CO2 being stored. A negative value corresponds to net dissolution of carbonate minerals, and may lead to net CO2e removal if dissolution is the result of reaction with carbonic acid.
[/G-TPRY-0]Soil inorganic carbon is typically determined in weight percent (e.g., kg calcium carbonate per kg soil). The corresponding amount of CO2 stored in net new carbonate is determined as follows:
[math: {CO}_{2}^{}e_{CaCO_{3}, t}^{}\ = (wt\%\ {CaCO}_{3}) \times (\frac{1}{100}) \times (0.44) \times \rho_{x} \times d_{x} \times A_{x} \times (\frac{1\ ton}{1000\ kg})]
(Equation 27)
[math: {CO}_{2}^{}e_{NetNewCarbonate,\ RP}^{} = \ {CO}_{2}^{}e_{CaCO_{3},\ \ t = 2}^{} - {CO}_{2}^{}e_{CaCO_{3},\ \ t = 1}^{}]
(Equation 28)
Where:
Some project areas may have soil conditions where carbonate precipitation is not likely and not observed above analytical detection limits (e.g., low pH soils). Project Proponents operating in such areas may omit large scale soil inorganic carbonate measurements from routine monitoring with adequate justification in the PDD. In such instances, Project Proponents must conduct smaller scale measurements of soil inorganic carbon with each major sampling event to demonstrate that these assumptions still hold. If local geochemical conditions change over the course of a project and lead to measurable soil carbonate mineral accumulation, soil inorganic carbonate measurements must be reinstated at the same density as other primary soil measurements.
This Protocol requires that SIC is measured via either calcimetry or thermo-gravimetric analysis, though alternative analytical methods, including ramped combustion coupled with infrared spectroscopy and powder x-ray diffraction, may be appropriate. We note that some methods for measuring enhanced weathering in soils (e.g., total acid digestion followed by bulk cation quantification) implicitly include SIC monitoring; this is generally acceptable under this Protocol. Project Proponents utilizing other carbonate quantification methods, in consultation with Isometric, must outline and justify these alternative analyses in the PDD. If Project Proponents are using an analytical method that does not require explicit accounting of SIC, this must be justified in the PDD.
Secondary clay formation can be investigated through x-ray diffraction analysis. Further mineralogical analysis, such as scanning electron microscopy coupled with energy dispersive spectroscopy (SEM-EDS), can be used as an additional check. Project Proponents are recommended to perform x-ray diffraction analysis on beginning and end of Reporting Period soil samples to monitor secondary clay formation, but this is not required because secondary mineral formation is difficult to quantify using widely accessible techniques and so will not be counted towards CDR quantification.
[/G-KD1M-0]Project Proponents should cross-check their methods with the following standards or equivalent standards:
If alternative methods are used, relevant standards must be referenced in the PDD.
Some cations released during weathering reactions will be taken up by crops, thus creating a pool of reaction products outside of the soil. This Protocol requires that cations taken up by biomass are quantified (Equation 4). This section describes the requirements for calculating [math: {CO}_{2}^{}e_{BiomassLoss,\ RP}^{}] in Equation 4.
[/R-PNQY-1][math: {CO}_{2}^{}e_{BiomassLoss,\ RP}^{}] is determined from direct, representative sampling of plant tissues of harvested biomass (annual crops) or new plant growth (perennial crops) in an area in which feedstock is applied (treatment area if using 3-plot approach) and the control plot. This does not need to be directly measured in the deployment plot if using the 3-plot approach. Sampling routines must be identical between areas with and without feedstock.
Plant samples must be analyzed for Na, K, Mg and Ca concentrations (base cations not used for CreditingCertificate issuance may be omitted from this measurement).
Project Proponents are required to outline and justify their method for digestion (dissolving sample into liquid phase) of plant material in the PDD.
Project Proponents are required to cross reference their measurement procedures with an applicable standard. Examples include:
Sampling plans targeting plant uptake must consider the total amount of biomass produced over the Reporting Period and the crop type. The Project Proponent must describe in the PDD how measurements of base cations removed by plant uptake are conservatively extrapolated over the project area for the determination of [math: {CO}_{2}^{}e_{BiomassLoss,\ RP}^{}]. This may be done by multiplying the base cation concentration in plant tissue by biomass yield to calculate an export number that may be converted to tCO2e. [math: {CO}_{2}^{}e_{BiomassLoss,\ RP}^{}] will be taken as the difference between plant uptake in the treatment and control plots.
[/G-1SWE-0]Project Proponents are required to include all data and calculations in submitted reports, including information on standards and calibration curves. This data will likely include total shoot mass, total plant mass and cation concentrations.
[/G-NER3-0]In non-crop settings like pastureland, Project Proponents must similarly quantify the difference in total plant biomass and associated cation uptake between treated and control plots in above ground biomass, using a conservative estimate of biomass removed from the field via grazing as the mass exported term. This estimate must be derived from empirical data or literature sources specific to the forage and livestock, where available. This estimate must account for the grazing duration and number of grazing rotations, if applicable.
Project Proponents are required to account for any non-carbonic acid neutralization that may occur in the upper soil column.
[/R-5SBY-0]We note that, where soil pH is less than 5.2 as measured in a soil-water slurry, non-carbonic acid neutralization may dominate and result in significant losses.55 Project Proponents are encouraged to consider the suitability of a given field's pH prior to feedstock deployment. Soil pH will be screened during the validation process to determine the suitability of soil conditions for aCertificate Crediting outcomeissuance.
We also note that non-carbonic acid neutralization in the NFZ may offset degassing losses downstream; however, the tools to robustly quantify this effect and establish an appropriate counterfactual are not yet available. Isometric intends to revisit this accounting boundary in future Protocol versions as the science evolves.
[math: {CO}_{2}^{}e_{NonCarbonicNeut,\ RP}^{}] from Equation 4 may be determined from one of three methods:
Each method and their associated data reporting requirements are described below. Project Proponents that directly measure porewaters anions, either through time integrated porewater samples or ion exchange resins, must use Method A to quantify [math: {CO}_{2}^{}e_{NonCarbonicNeut,\ RP}^{}]. Project Proponents that did not directly measure porewater anions, either because they were started prior to October 31, 2024 (see section 12.0) or because they pursue aqueous phase quantification or validation with carbonic acid system parameters, may elect Method B or Method C.
Method A: direct measurement of porewater anions
If non-carbonic acid weathering is being determined from direct measurement of porewater cations, major anions should be measured in porewater samples by ion chromatography. All Project Proponents are required to explicitly outline how their approach accounts for non-carbonic acid neutralization in the PDD.
[/R-SE7K-0]If direct measurement of anions is used, samples must be collected at or below the depth of the NFZ (typically 20 cm). Major anions should include NO3-, PO43-, Cl-, SO42- and any others that may be relevant to local land management practices and the feedstock being used. Measurement methods must comply with ISO 10304-1:2007 or an equivalent standard. Unlike some soil based measurements in this Protocol, aqueous concentration of anions in porewaters will likely be dynamic and vary significantly over a Reporting Period. The Project Proponent must provide details of their sampling plan and describe how the chosen sampling frequency is appropriate for capturing any significant deviation in concentration at the project location.
[/G-P4RZ-0]The Project Proponent must describe in the PDD how site-based observations (e.g., precipitation, irrigation, other climatic variables, etc.) are used to determine the total volume of water infiltrated into the soil, and how discrete anion concentrations are combined with this data to determine the total amount of non-carbonic acid neutralization that occurred over the Reporting Period. Given a known volume of water infiltrated into the soil, non-carbonic acid neutralization can be calculated from anion measurements as:
[/G-9SMM-0][math: {CO}_{2}e_{NonCarbonicNeut\ a_i,\ RP} = \sum_{i = 1}^{T} \Delta{t_i} \times P_i \times \rho_{F, i} \times Q_i \times \frac{mol CO_2}{mol a_i} \times MW_{CO_2}]
(Equation 29)
Where:
Method B: soil-based assessment of non-carbonic acidity
This Method follows the approach outlined in Dietzen & Rosing (2023)55 in which an efficiency factor is calculated to determine weathering attributable to non-carbonic sources of acidity using soil pH and pCO2 as inputs. We note that this method represents a simple geochemical calculation assuming ideal conditions and equilibrium with porewater export, but is nonetheless a conservative estimate based on available literature and research.
[/R-D338-0]Project Proponents using Method B must use pH values from two time points as the pH input: [math: RP_n] and [math: RP_{n-1}], where [math: n] is the Reporting Period being Creditedcredited. In the first Reporting Period, baseline soil pH values should be used as [math: RP_{n-1}]. Two time points are required because this method assumes pH remains constant. Measured pH values from the beginning and end of the Reporting Period provide a simplified approach to approximate a constant pH over the length of the Reporting Period.
Project Proponents using Method B must identify an appropriate pCO2 input. Project Proponents taking direct measurements of soil gas flux must use that data to inform the pCO2 input. pCO2 may vary widely seasonally, thus Project Proponents taking direct pCO2 measurements should identify an appropriate way to account for temporal variation, such as through a seasonally weighted average, in the PDD. Project Proponents that do not take direct measurements of soil gas flux must use one of two literature derived pCO2 inputs. Projects operating in unsaturated cropping systems must use a soil pCO2 of 4,000 [math: \mu]atm which is a conservative but realistic estimate of pCO2 in unsaturated row cropping systems56. Projects operating in saturated cropping systems, such as rice paddies, must use a soil pCO2 of 50,000 [math: \mu]atm which is a conservative but realistic estimate of pCO2 in unsaturated row cropping systems57. There may be some regions which cannot be classified neatly as saturated or unsaturated, or some regions that are well documented to exhibit pCO2 values other than what is documented here. Isometric will review alternative proposals for an appropriate pCO2 value on a case by case basis. Any alternative must be well evidenced using data and/or literature values from the project region.
[/G-H3BY-0]Project Proponents using Method B must perform this calculation on all pH values and use the average result as the efficiency factor. Project Proponents must not average pH values before input into the calculator.
Project Proponents using non-silicate feedstocks are not eligible for Method B.
Method C: empirical estimate of acid production
For projects using multiple fertilizer types, non-carbonic acid sources should be distinguished between atmospheric, fertilizer, and soil organic matter origins. Where necessary, isotopic analysis (e.g., δ¹⁵N of nitrate) should be used to differentiate these sources. Projects must provide detailed accounting of each non-carbonic acid source, including:
Where data is available, Project Proponents may choose to use fertilizer application rates as a proxy for non-carbonic acid weathering. Quantification of non-carbonic acid weathering from fertilizer records may come from:
Project Proponents must provide data on fertilizer application rates and calculations of the fraction of feedstock weathering resulting from non-carbonic acid weathering in the PDD.
[/G-RD38-0]If non-carbonic acid neutralization is accounted for using fertilizer application rates, Project Proponents must provide evidence to justify the assumption that nitric acid is the only significant non-carbonic acid source. This includes measurements of total sulfur in the feedstock and in the soil. Projects with significant sulfur concentration in baseline soil or the feedstock may not be eligible for Method C.
[/G-4R7K-0]Project Proponents are encouraged to monitor the flux of major GHGs (CO2, CH4, N2O) in control, treatment, and deployment (if using 3-plot) plots to monitor potential changes in short-term soil carbon cycling as a result of EW. This measurement can be accomplished with either gas flux chambers or eddy covariance towers and can be fully automated after installation. When using gas flux chambers, soil collars should be installed to a minimum depth of 5 cm. This Protocol recommends that flux chambers remain in the location of installation for a full growing season to appropriately monitor changes occurring during that time as a result of the project. Project Proponents are encouraged to install 1 gas analyzer per plot (control, treatment, and deployment) with a minimum of 5 gas flux chambers per analyzer. The gas flux chambers should be spaced to capture maximum field area and variability. When using eddy covariance towers, 1 tower per deployment is sufficient.
For both baseline sampling and the first year of project activities, gas samples should be collected continuously at equally spaced intervals. This can be automated using the appropriate software. Sampling rates after one year of deployment will be determined on a project basis.
Table 4. Summary of soil-based measurement requirements.
Parameter | Rationale | Determination Method | Baseline and Deployment Requirements |
|---|---|---|---|
Concentration of immobile tracers and mobile cations that will be used for weathering determinations | Calculation of CO2 removal | Total soil digestion coupled with ICP-MS or ICP-OES | Required for all soil sampling events |
Cation exchange capacity (CEC) | Assessment of soil quality Determination of exchangeable cations | Cation extraction coupled with analysis via ICP-MS/OES or AAS | Required for baseline soil samples Required if used for calculation of CO2 removal |
Base cation saturation | Assessment of soil quality Determination of exchangeable cations | Cation extraction coupled with analysis via ICP-MS/OES | Required for baseline soil samples Required if used for calculation of CO2 removal |
Soil inorganic carbon (SIC) | Determination of secondary carbonate formation | Calcimetry, Thermo-gravimetric analysis, Ramped combustion coupled with infrared gas analysis | Required for 10% of baseline soil samples Required for 10% of samples if used for calculation of CO2 removal |
Soil moisture | Assessment of weathering potential | Oven drying | Recommended for all soil sampling events |
Soil pH | Assessment of weathering potential Assessment of weathering progression | pH measurement in soil slurry | Required for all soil sampling events |
Soil texture | Assessment of soil heterogeneity | Oven drying coupled with gravimetric sieving, Laser diffraction or x-ray scattering | Required for baseline soil samples Recommended for subsequent soil sampling events |
Soil permeability | Assessment of soil heterogeneity | Water flow test | Recommended for all soil sampling events |
Soil organic carbon (SOC) | Assessment of soil quality Calculation of CO2 removal Carbon cycle monitoring | Dry combustion, Walkley-Black method | Required within a representative parcel at baseline and one time point within the second half of the Crediting Period |
Total carbon content | Assessment of soil quality | Dry combustion | Recommended for all soil sampling events |
Total sulfur content | Assessment of soil quality | Dry combustion | Required for baseline samples if using fertilizer records to account for non-carbonic acid weathering; Recommended for all sampling events |
Soil bulk density | Assessment of soil quality | Drying and weighing | Required for all soil sampling events on the lesser of 10% of total samples or 30 samples per removal area. Project Proponents selecting Method A for Determination of Alkalinity Application Rate from Direct Measurement are not required to collect bulk density samples at baseline, but this requirement holds for all end of Reporting Period samples |
Secondary clay formation | Determination of secondary mineral formation | X-ray diffraction | Recommended for all soil sampling events |
Soil CO2 flux | Short-term carbon cycle monitoring | Gas flux chamber, Eddy covariance tower | Recommended for all soil sampling events |
Carbon isotopes | Calculation of CO2 removal | Isotope ratio mass spectrometer (IRMS) | Optional for all soil sampling events |
Project Proponents may substitute required analyses with suitable alternatives in consultation with Isometric.
After feedstock application, porewaters must be sampled at a frequency that is appropriate for the local water budget and temporal evolution of dissolved ions. This must include sufficient samples to calculate a time-integrated cation flux (see Equation 30). The porewater sampling plan must be described and justified in the PDD. The sampling plan should additionally consider events that significantly impact soil moisture, such as irrigation and heavy rainfall; this will be determined on a project- and event-specific basis.
[/R-V6CG-1]It is recommended that Project Proponents pursuing a porewater-based quantification install an average of 1 porewater collection device (e.g., lysimeter, rhizon) per 25 hectares of total project area to facilitate fluid sampling. Project Proponents pursuing porewater-based validation are recommended to install an average of 1 porewater collection device per 10 hectares in the observation plots (e.g., treatment and control). Project Proponents should endeavor to constrain local changes in porewater chemistry on research sites.
[/G-C1YX-0]The type of porewater collection device and sampling methodology must be stated in the PDD. Porewater sampling devices must be installed to the depth of the NFZ for CreditingCertificate issuance (typically 20 cm).
To facilitate calculation of alkalinity release, described below, it is recommended that Project Proponents install a weighing lysimeter in addition to an in-situ drainage lysimeter, as weighing lysimeters can measure precipitation and evaporation rates of the weathering zone. Where this is infeasible, due to cost or accessibility, Project Proponents must account for spatial heterogeneity of evapotranspiration (ET) across the deployment area through an alternative method. This may include one or more of the following approaches:
The ET estimation method, including the spatial coverage approach, must be described in the PDD.
[/G-KBP7-0]As part of the baseline establishment, Project Proponents are required to report local topographic and geologic data relevant to soil drainage patterns (e.g. water table depth, hydrologic maps, watershed boundaries, etc). Project Proponents may use publicly available data records to fulfill this requirement.
[/G-70HW-0]Calculating the interval-average alkalinity [math: \bar{Alk_i}] as the arithmetic mean of [math: Alk_i] at adjacent sampling events assumes first-order temporal smoothness of alkalinity between measurements. The sampling frequency, including event-triggered sampling during periods of high water flux, should be designed to ensure that this assumption is reasonable and that discretization error is small relative to analytical uncertainty. Where hydrological data indicates that water flux within an interval is strongly temporally skewed (e.g., dominated by a short-duration rainfall or irrigation event), the Project Proponent may apply event-aligned sampling or compute a flow-weighted interval alkalinity where sufficient sub-interval information is available, to ensure that exported alkalinity is not systematically over- or under-estimated.
[/G-EA96-0][math: {CO}_{2}^{}e_{Aqueous,\ RP}^{}] from Equation 5 is the integrated amount of CDR as determined from measurements of aqueous phase base cation abundance from water that has infiltrated to the depth of the NFZ, typically 20 cm. [math: {CO}_{2}^{}e_{Aqueous,\ RP}^{}] can be calculated as:
[/R-81P7-0][math: {CO}_2e_{Aqueous,\ RP} = \sum\limits_{i}^{n} (\bar{Alk_i} \times Q_i) \times \frac{mol CO_2}{mol Alk} \times M_{CO_2}]
(Equation 30)
Where:
There are two generally accepted approaches for determining aqueous phase alkalinity at each sampling event, [math: Alk_i]:
In both cases, Project Proponents must also produce a water budget for the project area encompassing the Reporting Period as detailed in Section 10.5.1.1.1. Carbonic acid system measurements may include carbonate alkalinity titration to the CO2 equivalence point, pH, DIC and/or pCO2, followed by calculating the concentration of bicarbonate using the 2-for-6 method. Further guidance is given in Section 10.5.2.1.
Isometric has developed a tool for porewater CDR quantification. The tool calculates CDR potential by integrating water balance modeling with porewater chemistry measurements. It utilizes meteorological data from the NASA POWER API and applies the FAO-56 Penman-Monteith equation to calculate reference evapotranspiration ([math: \text{ET}_o]), which is then adjusted using crop coefficients (Kc) to determine actual crop evapotranspiration (ETc). The water flux [math: Q_i] represents the volume of water moving through the soil profile and transporting dissolved weathering products. The tool quantifies CDR from measurements taken at the beginning and end of the Reporting Period using either two carbonic acid system variables, dissolved cation and anion concentrations, or both. Both methods adopt Equation 5 to quantify CO2 removal in tonnes.
Quantification of drainage ([math: Q_i]) is required to convert localized porewater concentration measurements to total alkalinity export mass per unit area. The Project Proponent must calculate [math: Q_i] for each time interval over which porewater measurements are taken using one of the following approved methods. The same method must be applied consistently to both treatment and control plots throughout the Reporting Period.
[/R-VPHW-0]Method A: Direct Measurement
Drainage may be measured directly using devices such as a drainage lysimeter or drainage flux meters installed at or below the NFZ depth. Project Proponents should specify the device type used in the context of dominant water transport pathways at the site. Tension lysimeters should be treated carefully, as they extract samples under applied suction that does not correspond to natural flow gradients and do not integrate flow over a defined collection area.
Project Proponents using zero-tension, wick, or pan-type lysimeters must report the collection efficiency of the device, as these device types can recover anywhere from approximately 10% to 90% of actual drainage flux depending on design, soil conditions, and installation. Collection efficiency should be established through calibration tests, comparison with a weighing lysimeter or equivalent reference, or literature values for comparable installations, and must be documented in the PDD.
The flow volume is integrated over the measurement interval and divided by the collection area to determine [math: Q_i]. Project Proponents using this method must document lysimeter installation depth, collection area, and maintenance procedures.
[/G-1Z4V-0]Method B: Water Balance Equation
Drainage can be calculated using a mass balance approach if the following parameters and data are available.
[/G-NM0G-0][math: Q_i] can be calculated via the following equation:
[math: Q_i = V_{precip,\ i} - V_{evapotranspiration,\ i}]
(Equation 31)
Where:
[math: Q_i = V_{precip,\ i} + V_{irrigation,\ i} - V_{evapotranspiration,\ i} - \Delta V_{S,\ i} - V_{R,\ i}]
(Equation 32)
Where:
[math: V_{\text{evapotranspiration},\ i} = K_c \times \text{ET}_o \times A]
(Equation 33)
Method C: Unsaturated Flow Modeling
Drainage flux may be estimated using a physics-based unsaturated flow model that solves the Richards equation (or equivalent formulation) for the vadose zone. Acceptable models include HYDRUS, SWAP, MACRO, or other peer-reviewed codes appropriate for simulating water flow in unsaturated agricultural soils. Project Proponents selecting this method should:
A steady-state Darcy-type approximation may be used where where the Project Proponent can demonstrate that soil moisture conditions remain near saturation and approximately steady over the interval of interest.
[/G-98W5-0][math: Q = -K \times A \times \frac{\partial h}{\partial z} \times \Delta t]
(Equation 34)
Where:
Hydraulic conductivity may be derived from laboratory measurements (e.g., constant or falling head permeameter), field infiltrometer tests, or published literature values for similar soil types and textures
When using literature values, the Project Proponent must document the source and demonstrate similarity between the reference soil conditions and site conditions (soil texture, structure, and bulk density).
Project Proponents pursuing quantification of carbon removal via aqueous measurements may choose to monitor the carbonic acid system in porewater to calculate stored bicarbonate. To adequately constrain the carbonic acid equilibrium considerations in the weathering zone, Project Proponents selecting this option are required to measure at least two of the following parameters:
[/R-FBDH-0]To reduce uncertainty in carbonic acid system calculations, it is highly recommended to measure all four of these parameters where possible. Bicarbonate (HCO3-) and carbonate (CO32-) can be subsequently calculated using the two-for-six method58, which is commonly accomplished using geochemical speciation modeling. Project Proponents must use a validated geochemical speciation program (e.g., PHREEQC, Geochemist's Workbench, note that CO2SYS 59 is not recommended for calculations in the weathering zone) to solve the carbonic acid system for each porewater sample. When performing speciation calculations, Project Proponents must:
If measured, Project Proponents should input major ion concentrations to the speciation model and charge balance error (CBE) should be calculated for each sample according to Section 10.5.2.4.
Samples should be flagged if the geochemical speciation model indicates supersaturation (SIc > 0) with respect to carbonate minerals (calcite, aragonite, dolomite). Because aqueous-phase quantification (Equation 5) creditsquantifies alkalinity or DIC that has already infiltrated to the depth of the NFZ, carbonate precipitation occurring upstream of the sampling point is implicitly excluded from the measured signal and is not double-counted as a separate loss term. Flagged samples nevertheless warrant investigation to confirm that the measured aqueous signal is not biased by active precipitation at or near the sampling point. Where precipitation is confirmed, Project Proponents should justify the chosen precipitation rate model.
Project Proponents are required to submit data for all parameters of the carbonic acid system, both measured and calculated; where calculations were performed using methods other than PHREEQC or Geochemist's Workbench, Project Proponents are required to submit detailed description of the method / model used and the script or spreadsheet where the calculations were made.
Project Proponents pursuing quantification of carbon removal via aqueous measurements may choose to measure the concentration of base cations in porewater within the NFZ.
[/R-CJAS-0]This Protocol recommends a full suite of elemental analyses in baseline pore water samples (see Rock and Mineral Feedstock Characterization Module v1.2). Porewater sample analyses taken post-application must include the elements utilized for removal quantification and those with potential health and environmental impact implications. This will be determined on a project basis. Porewater samples are required to be analyzed by either inductively coupled plasma mass spectrometry (ICP-MS; e.g., ISO 17294-1:2024) or inductively coupled plasma optical emission spectroscopy (ICP-OES; e.g., ISO 11885:2007) as the primary determination method. Project Proponents should take analytical precision and detection limits into account when determining their measurement method.
When reporting data from ICP-MS/OES, Project Proponents must include information on calibration standards, blanks, and CRMs; requirements for data reports are described in Section 11).
Projects using aqueous-phase quantification should apply the following chemistry consistency checks appropriate to the selected aqueous quantification approach.
[/G-0XKX-0]Recommended Suitability Checks
Charge Balance Errors
Projects pursuing aqueous phase quantification using direct measurement of major and trace element concentrations should assess the Charge Balance Error (CBE) of each Treatment and Deployment Unit. CBE provides a fundamental check on the completeness and accuracy of water chemistry analyses, and can be calculated as follows:
[math: CBE\ (\%) = \frac{\Sigma\ cations - \Sigma\ anions}{\Sigma\ cations + \Sigma\ anions} × 100]
(Equation 35)
Where
PHREEQC automatically calculates and reports charge balance error (CBE) when solution chemistry is inputted. Project Proponents should input all measured major cation and anion concentrations, temperature, and/or carbonic acid system parameters (i.e., pH, alkalinity, or dissolved inorganic carbon (DIC). PHREEQC will then output a charge balance error that indicates whether the measured ion concentrations are internally consistent.
All cations and anions present in the system should be included in the test. In agricultural settings, the following cations and anions are the major ions present in the system:
Major Cations:
Major Anions:
Additional ions may be included depending on site-specific water chemistry:
Units with |CBE| ≤ 5% are considered research-grade and do not warrant additional investigation. Units with 5% < |CBE| ≤ 10%, should be investigated further by the Project Proponent to determine potential causes (e.g., unmeasured ionic species, analytical uncertainty in low-ionic-strength waters) and to demonstrate that the error does not systematically bias CDR calculations 60. |CBE| > 10% could indicate potential measurement errors, missing organic anions, or unmeasured acidity/alkalinity contributors. Project Proponents are recommended to investigate the data quality of Units with |CBE| > 10% prior calculating [math: CO_2e_{stored,\ RP}].
Table 5 Data quality classification given |CBE|
CBE Range | Classification | Action Recommended |
|---|---|---|
|CBE| ≤ 5% | Research-grade | None |
5% |CBE| ≤ 10% | Acceptable | Justification required |
|CBE| 10% | Unacceptable | Sample excluded from |
Carbonic acid system consistency
Project Proponents pursuing aqueous phase quantification using carbonic acid system measurements are required to measure two carbonic acid system parameters, but encouraged to measure three or more. Measuring three or more parameters enables internal consistency checks through comparison between measured and calculated values. When three or more parameters are measured from a representative subset of samples (e.g., 10-20% of sampling density at each sampling event, or regular periodic checks), system consistency may be demonstrated to validate the two-parameter approach across different conditions and time periods. When three or more carbonic acid system parameters are measured, the system is well determined, enabling comparison between measured and calculated values.
To perform internal consistency checks, calculations should be performed using established geochemical software (e.g., PHREEQC, Geochemical Workbench) with appropriate equilibrium constants for freshwater systems 61. The thermodynamic database, equilibrium constants, and calculation methodology must be documented in the monitoring report62.
Project Proponents should conduct a statistical comparison between measured and calculated values across all Treatment and Deployment Units. The recommended approach includes:
The carbonic acid system consistency check is considered satisfactory if:
If statistical tests indicate systematic deviations between measured and calculated values, Project Proponents should provide an explanation of the possible sources of the discrepancy, which may include:
Projects that anticipate significant organic alkalinity contributions should consider alternative speciation approaches that account for organic matter. Such approaches should be documented and justified in the monitoring plan, in consultation with Isometric.
Cation Ratio Consistency
The cation ratio consistency check is a recommended suitability check and is similar to the application rate check for soil based quantification. The ratio of base cations released during weathering is expected to reflect feedstock mineralogy. As a result, the ratio of base cation release may be predicted if the feedstock mineral composition and stoichiometry are well characterized.
This check encompasses two distinct assessments. First, it verifies whether base cations are being released at expected rates in relation to each other (e.g., the ratio of Ca2+/Mg2+). Second, it evaluates whether the base cation release rate is consistent with the expected feedstock weathering rate. While deviations from the expected base cation release ratio may occur due to natural processes (incongruent dissolution, cation exchange, preferential weathering of specific mineral phases, secondary silicate formation) or analytical factors (measurement uncertainty, contamination), systematic or large discrepancies may warrant further investigation.
Project Proponents are encouraged to compute the bootstrap mean distribution of the implied feedstock weathering rate for each Treatment and Deployment Unit based on aqueous data (time-series porewater or ion exchange resin). The bootstrapping approach should account for temporal autocorrelation and spatial distribution of sensors (i.e., lysimeters) to properly capture hydrological variability.
Project Proponents should evaluate whether:
If either criterion shows significant deviation (e.g., negative alkalinity flux in >5% of replicates, or implied weathering rates exceeding theoretical maxima by more than a factor of 2), Project Proponents should:
If criterion (1) is met but signals are highly variable, a power analysis may help determine whether insufficient sensor density or sampling frequency is limiting detection capability relative to hydrological variability:
[math: n ≥ 2 × [(z_α + z_β) × σ / δ]²]
(Equation 36)
Where:
Project Proponents utilizing fluid measurements to quantify removals must complete fluid characterization measurements in both baseline and post-deployment samples, as outlined in Table 6.
Table 6. Summary of required and recommended measurements for fluid samples.
Parameter | Rationale | Determination Method | Requirements |
|---|---|---|---|
pH | Determination of weathering potential Determination of weathering progress Fluid characterization | pH meter | Required for all porewater sampling events |
Temperature | Fluid characterization | Thermometer | Required if used for calculation of CO2 removal |
Total alkalinity | Calculation of CO2 removal | Titration | Required if CO2 removal is determined using carbonic acid system parameters, and alkalinity is chosen as one of the two monitored parameters |
Concentration of any tracers and mobile cations that will be used for weathering determinations | Calculation of CO2 removal | Inductively coupled plasma mass spectrometry (ICP-MS) | Required if using major and trace element concentration for calculation of CO2 removal |
Non-carbonic acid acidity (anions) | Determination of non-carbonic acid neutralization Calculation of CO2 removal | Ion chromatography | Required if used for calculation of CO2 removal |
Dissolved inorganic carbon (DIC) | Calculation of CO2 removal Characterization of carbon pools | Acid titration | Required if CO2 removal is determined using carbonic acid system parameters, and DIC is chosen as one of the two monitored parameters |
Electrical conductivity | Fluid characterization | Conductivity probe | Recommended for porewater samples |
pCO2 | Calculation of CO2 removal | Gas equilibration coupled with gas chromatography, infrared detection | Required if CO2 removal is determined using carbonic acid system parameters, and pCO2 is chosen as one of the two monitored parameters |
Stable isotopes | Calculation of CO2 removal | Method will vary | Optional for porewater samples |
As dissolved weathering products are exported from the NFZ, the generated alkalinity is transported through the FFZ, with eventual durable CO2 storage in the oceans. The downstream transport of captured CO2 to the oceans may result in carbon losses. These losses must be quantified following the River and Ocean Losses Module.
Refer to Quantification of Losses Section of the River and Ocean Losses Module.
It may be appropriate in some cases to pool post-deployment samples for more resource-intensive analyses (e.g., analysis of trace metal abundance). Solid and liquid phase sample pooling is acceptable in Creditingissuing projects, with a maximum recommendation of 10 samples pooled per analysis. It is strongly recommended that Project Proponents do not pool samples during baseline sampling events to ensure baseline heterogeneity in soil properties and geochemistry are fully appreciated. Note that liquid samples may be pooled only for elemental analysis and not for analysis of carbonic acid system parameters. All sample pooling plans must be approved by Isometric and described in the PDD.
Project Proponents are responsible for the delivery of all required data to Isometric and the VVB. It is the responsibility of the Project Proponent to deliver data that is accurate and externally verifiable. Submitted data reports are required to include results of all standards to verify data quality. Project Proponents are required to maintain data records for a minimum of 5 years after the end of the monitoring period.
Project Proponents are required to report data such that the data analysis methods used are easily identified, verified, and replicated where appropriate. This Protocol requires that any data reports from accredited labs have Isometric listed as a direct recipient. From accredited labs, raw data is not required so long as the Project Proponent puts Isometric as a direct recipient of the data. A summary containing information on analytical uncertainty, number of samples taken, standards used and number of standard runs, standard deviation and percentage error on the standards must also be included. This may, for example, take the form of a spreadsheet containing four sheets:
[/G-GQVW-0]Project Proponents are required to pre-register the areas for which they will be claiming removals with Isometric prior to data submission. The removal areas must be clearly defined and mapped in the PDD.
In general, Units cannot be excluded from the CDR calculation. However, exclusion may be considered if there are extenuating circumstances outside the Project Proponent's control that result in missing usable Reporting Period data for a large portion of a removal area or Unit. This may be due to logistical or operational reasons or data quality issues. Examples of such circumstances may include:
In these scenarios, the Project Proponent may request to exclude the area lacking high quality data for CreditingCertificate issuance, subject to approval from Isometric. Where a Project Proponent seeks to exclude a Unit, direct evidence must be provided to Isometric and the VVB to demonstrate a logistical or operational burden or data quality issues outside the Project Proponents control. The evidence required will depend on the specific circumstance, but may include:
For example, Project Proponents seeking to exclude a portion of a Unit because the immobile tracer is no longer resolvable must demonstrate that the selected tracer was resolvable in the baseline conditions, and that they executed the MRV plan as outlined in the PDD. This information will be assessed by Isometric to determine that the data quality issues are external to project design.
If it is deemed permissible to exclude a portion of the project area no CreditsCertificates will be generated from the excluded area. In this scenario, a proportion of the GHG emissions equal to the relative size of the excluded area may be excluded from the overall GHG accounting so long as the Project Proponent can demonstrate that the expected Removals from the excluded areas exceed the associated LCA burden. Data from the observable portion of the Project Area may be used in this estimation. For example, if 10% of the total Project Area is unobservable, the Project Proponent may reduce their LCA burden by up to 10%, so long as they can demonstrate that the project is net negative in the observable portion of the Project Area.
Where a Project Proponent utilizes laboratory facilities within an academic institution or unaccredited commercial laboratory, this Protocol requires that a portion of samples are sent to an accredited laboratory for validation. The Project Proponent must list Isometric and the VVB as a recipient of the data report so that data quality may be investigated. The number of samples to be validated by an accredited laboratory is given below.
[/G-4CQH-0][math: m = \begin{cases} 10\%, & \text{n} \le \text{1,000} \\ 100, & \text{n} \gt \text{1,000} \end{cases}]
Equation 37
Where:
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.
Isometric will identify a random subset of the sampling locations to be sent to a third party laboratory. The third party facility must be accredited for the analyses being performed. 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. Other materials that may be requested in an audit include:
In some cases, extreme, localized field heterogeneity may result in measurements or data that are missing, incomplete or out of line with expectations given the project design or previous measurements. Similarly, disruptions to the project area and ongoing monitoring (e.g., extreme weather events or equipment failure) may result in missing data, outliers or unexplained results. For the purposes of this Protocol, outliers are defined as data that are more than three standard deviations from the mean (or equivalent percentiles for non-normal distributions). In such instances, the Project Proponent must seek clarification on how the data should be handled. When such instances occur, the details must be reported to the VVB and Isometric as quickly as possible after identification. In such situations Isometric will work to remedy the situation in consultation with the Project Proponent and VVB. Examples of possible remedies include omitting outliers that represent a highly improbable result, replacing missing data with a conservative estimator of a group of samples collected from a nearby and likely representative area, or incorporating validation data into removal quantification.
When unexpected results occur, submitted data may be subject to an audit process to ensure appropriate data quality and confidence before CreditsCertificates are issued. Examples of situations that may trigger an audit include (but are not limited to) the following:
The above list is not exhaustive and an audit may be triggered at the discretion of Isometric and the VVB. When an audit is triggered, Isometric must inform the Project Proponent in writing. This must include the reason for the audit, the planned steps that will be taken and any additional information that must be provided to Isometric and the VVB by the Project Proponent. If the Project Proponent fails to provide any requested information, validation and verification will be paused until the information is provided.
All records associated with the characterization, design, deployment and monitoring should be kept for a minimum of 10 years after the end of the monitoring period, and submitted to proper authorities as required by local permitting regulations.
Projects that were started prior to the certification of this Protocol (V1.0, certified in April 2024) and spread feedstock within 6 months of publication (October 2024) may be eligible for CreditingCertificate issuance under this Protocol on a case-by-case basis. Project Proponents seeking CreditsCertificates for pre-existing deployments must justify the approach taken in these Projects in the PDD, with special attention paid to:
Projects started prior to the publication of this Protocol are subject to the same statistical significance requirements throughout this Protocol. Where a deviation from this Protocol precludes a robust determination of CDR, that Project is not applicable under this clause. At a minimum, all Projects must:
Projects that areissue CreditedCertificates under this clause may be subject to additional tests and checks that are not explicitly required in this Protocol, as these Projects often use methods and techniques that differ from those given within this Protocol. The purpose of these checks is to evaluate the validity of such methods and ensure that there is sufficient confidence for CreditCertificate issuance. The specific checks required will depend on the Project and the deviations from this Protocol.
Isometric would like to thank following contributors to this Protocol and relevant Modules:
Isometric would like to thank following reviewers of this Protocol and relevant Modules:
Refer to Appendix 1 of the Alkaline Feedstock Characterization Module.
This appendix details how the Project Proponent must monitor, document and report all metrics identified within this Protocol. Following this guidance will ensure the Project Proponent measures and confirms CO2e removal and long-term storage compliance, and will enable quantification of the emissions removal resulting from the Project activity during the Project Crediting Period, prior to each Verification. This appendix includes some monitoring requirements that are required for all sampling events and some that are required for baseline samples only; this will be detailed in the notes section for each parameter.
This Protocol utilizes a comprehensive monitoring and documentation framework that captures the GHG impact in each stage of a Project. Monitoring and detailed accounting practices must be conducted throughout to ensure the integrity of the CO2e removals and Creditingissuances.
The Project Proponent must develop and apply a monitoring plan according to ISO 14064-2 principles of transparency and accuracy that allows the quantification and proof of the GHG assessment.
Parameter | Soil pH |
|---|---|
Unit | N/A |
Equation or Section(s) | Field Management, Designating Control and Treatment Areas, Baseline Establishment, Soil Characterization |
Description | Soil pH to be measured by slurry |
Example Measurement Method/Data Source | Slurry pH probe. Refer to ISO 10390:2021. |
Data Reporting Guidelines | Slurry probe must be calibrated regularly according to manufacturer instructions. Probe must be regularly tested against known standards. The results of standard measurements must be reported alongside data, including standard pH values, number of standards, number of replicates, and % error on standards. |
Additional Notes | Soil pH must be reported from soil samples from control, treatment, and deployment fields (in 3-plot approach) for all sampling events. |
Parameter | Soil texture |
|---|---|
Unit | % clay, % sand, % silt |
Equation or Section(s) | Designating Control and Treatment Areas, Baseline Establishment, Soil Characterization |
Description | Soil texture is based on the grain size distribution of dried soil. |
Example Measurement Method/Data Source | Soil texture can be obtained by oven drying and gravimetric sieving, as described in ISO 11277:2020 , or from publicly available soil data |
Data Reporting Guidelines | Soil texture must be reported according to USDA guidelines of soil type. This must include percentages of sand, silt, and clay-sized particles as well as the classification of each soil sample based on those percentages. In the event that this requirement is satisfied by publicly available soil data, Project Proponents must report the agency from which this data was obtained, as well as the determination method and year of measurement. For measurements of soil texture, the results of standard measurements must be reported alongside data, including standard values, number of standards, number of replicates, and % error on standards. |
Additional Notes | Soil texture must be reported from soil samples from control, treatment, and deployment fields (in 3-plot approach) for baseline samples. |
Parameter | Soil organic carbon (SOC) |
|---|---|
Unit | Mass fraction in soil (e.g., g/kg or equivalent) |
Equation or Section(s) | |
Description | Organic carbon content of soil samples |
Example Measurement Method/Data Source | In accordance with the Walkley-Black method, dry combustion with a correction applied for carbonate formation, following ISO 10694:1995. Project Proponents may choose to pre-treat soil samples to remove the carbonate component prior to combustion. |
Data Reporting Guidelines | The results of standard measurements must be reported alongside data, including standard values, number of standards, number of replicates, and % error on standards. |
Additional Notes | SOC measurement is required for baseline sampling and must be reported from control, treatment, and deployment (if using 3-plot approach) plots. Following rock application, SOC must be measured in the control plot and a subplot constituting the relevant minimum percentage, p, of the total project area (if using 2-plot) and control and treatment plots (if using 3-plot) at one time point in the second half of the Crediting period. |
Parameter | Cation exchange capacity (CEC) |
|---|---|
Unit | mEq/100 g |
Equation or Section(s) | Calculation of CO2estored, Baseline Establishment, Soil Characterization, Cation Exchange and Base Saturation |
Description | Capacity of soil to retain cations (base cations + acid cations) |
Example Measurement Method/Data Source | CEC is measured by cation extraction and subsequent measurement using ICP-MS (ISO 17294-1:2024)/OES (ISO 11885:2007) or AAS (standard). Appropriate extraction methods include ISO 11260:2018, ISO 23470:2018, the Chapman method, or an ammonium acetate extratction as described in Reershemius et al., 2023. |
Data Reporting Guidelines | The extraction method must be clearly described in the PDD. When reporting elemental data, Project Proponents must include data from calibration standards and CRMs as part of the data report. The results of standard measurements must be reported alongside data, including standard values, number of standards, number of replicates, and % error on standards. |
Additional Notes | CEC must be reported from control, treatment, and deployment (if using 3-plot approach) plots for baseline samples and at all sampling events if used to calculate [math: CO_2e_{stored}]. |
Parameter | Base saturation |
|---|---|
Unit | % |
Equation or Section(s) | Calculation of CO2estored, Baseline Establishment, Soil Characterization, Cation Exchange and Base Saturation |
Description | Percentage of soil active sites occupied by base cations (Ca2+, Mg2+, Na+, K+) in a soil sample; calculated as the sum of base cations divided by the CEC multiplied by 100. |
Example Measurement Method/Data Source | Base saturation is measured by BaCl2 cation extraction (ISO 11260:2018) and subsequent measurement using ICP-MS (ISO 17294-1:2024)/OES (ISO 11885:2007) or AAS (standard). |
Data Reporting Guidelines | The extraction method must be clearly described in the PDD. When reporting elemental data, Project Proponents must include data from calibration standards and CRMs as part of the data report. The results of standard measurements must be reported alongside data, including standard values, number of standards, number of replicates, and % error on standards. |
Additional Notes | Base saturation must be reported from control, treatment, and deployment (in 3-plot approach) fields. Required for baseline sampling. Required for subsequent sampling events if used to calculate CO2estored. |
Parameter | Total sulfur (soil) |
|---|---|
Unit | ppm (mg/kg) |
Equation or Section(s) | |
Description | Sulfur content of soil samples |
Example Measurement Method/Data Source | Total sulfur must be measured by dry combustion of soil samples, following ISO 15178:2000. |
Data Reporting Guidelines | Data reports must include standard data, including the standards used, number of standards analyzed, number of replicates, and percent error on the standard. |
Additional Notes | Required for baseline samples if using fertilizer records to calculate non-carbonic acid weathering. |
Parameter | Major and trace elements used for quantification |
|---|---|
Unit | ppm (mg/kg) |
Equation or Section(s) | Field Management, Calculation of CO2estored, Determination of Weathering |
Description | Major and trace elements that may be released into the soil as a result of project activities, with particular emphasis on the chosen immobile trace element (e.g. Ti, Zr, REEs) and mobile base cations (Ca2+, Mg2+, Na+, K+) |
Example Measurement Method/Data Source | This Protocol requires that major and trace elements be measured by a total soil digest, following EPA Method 3050B, coupled with elemental analysis via ICP-MS (ISO 17294-1:2024)/OES (ISO 11885:2007). |
Data Reporting Guidelines | The digestion method must be clearly described in the PDD. When reporting elemental data, Project Proponents must include data from calibration standards and CRMs as part of the data report. The results of standard measurements must be reported alongside data, including standard values, number of standards, number of replicates, and % error on standards. |
Additional Notes | Major and trace elements must be reported from control, treatment, and deployment (if using 3-plot approach) plots for all sampling events. |
Parameter | Soil inorganic carbon (SIC, soil) |
|---|---|
Unit | ppm (mg/kg) |
Equation or Section(s) | Calculation of CO2estored, Equation 4, Equation 27, Equation 28, Secondary Mineral Formation |
Description | Inorganic carbon content of soil samples |
Example Measurement Method/Data Source | Inorganic carbon must be measured using either calcimetry (ISO 23400:2021) or thermogravimetric analysis (ASTM D8474-22). |
Data Reporting Guidelines | Measurement methods must be described in the PDD.Data reports must include standard data, including the standards used, number of standards analyzed, number of replicates, and percent error on the standard. |
Additional Notes | Total inorganic carbon must be reported from control, treatment, and deployment (if using 3-plot approach) plots for 10% of soil samples and 10% of all sampling events if used to calculate CO2estored. |
Parameter | Biomass uptake of cations |
|---|---|
Unit | ppm (mg/kg) |
Equation | Calculation of CO2estored, Equation 2, Equation 4, In-field Monitoring Approach |
Description | Concentration of base cations (Ca2+, Mg2+, Na+, K+) taken up by crops |
Example Measurement Method/Data Source | Plant material digestion coupled with elemental analysis via ICP-MS (ISO 17294-1:2024 )/OES ISO 11885:2007). |
Data Reporting Guidelines | The digestion method must be clearly described in the PDD. When reporting elemental data, Project Proponents must include data from calibration standards and CRMs as part of the data report. The results of standard measurements must be reported alongside data, including standard values, number of standards, number of replicates, and % error on standards. |
Additional Notes | Biomass uptake is required for the control and treatment plots once per crop cycle at peak biomass. |
Parameter | Porewater pH |
|---|---|
Unit | N/A |
Equation or Section(s) | |
Description | pH of porewater samples; one component of the carbonic acid system |
Example Measurement Method/Data Source | pH is measured using a pH probe. |
Data Reporting Guidelines | pH probe must be calibrated regularly according to manufacturer instructions. Probe must be regularly tested against known standards. The results of standard measurements must be reported alongside data, including standard pH values, number of standards, number of replicates, and % error on standards. 2-for-6 calculations to constrain the carbonic acid system must be reported, whether this was completed in CO2SYS (or similar), PHREEQC (or similar), or in an Excel sheet. When using calculation scripts/programs other than CO2SYS or PHREEQC, the script/program must be provided in the data report. The final data report must include all components of the carbonic acid system, both measured and calculated. |
Additional Notes | Porewater pH must be reported from control, treatment, and deployment (if using 3-plot approach) plots. Required for all porewater sampling events. |
Parameter | Porewater alkalinity |
|---|---|
Unit | mg/L |
Equation or Section(s) | |
Description | Total alkalinity of porewater samples; one component of the carbonic acid system |
Example Measurement Method/Data Source | Alkalinity must be measured by titration, following ISO 9963-1:1994 . |
Data Reporting Guidelines | Data reports must include information on the titration process, including acid type and concentration, sample mass, and sample dilution. Standards of known alkalinity must be regularly run using the same method as porewater samples. Data reports must include the standards used, number of standards run, and percent error on the standards. Project Proponents must describe their calculation methods for alkalinity in the PDD and provide a copy of the calculations in the report. Where a calculation script is used, a copy of the script must be provided.2-for-6 calculations to constrain the carbonic acid system must be reported, whether this was completed in CO2SYS (or similar), PHREEQC (or similar), or in an Excel sheet. When using calculation scripts/programs other than CO2SYS or PHREEQC, the script/program must be provided in the data report. The final data report must include all components of the carbonic acid system, both measured and calculated. |
Additional Notes | Alkalinity must be reported from control, treatment, and deployment plots (in 3-plot approach). Required for all porewater analyses if used to calculate CO2estored. |
Parameter | Dissolved inorganic carbon (DIC) |
|---|---|
Unit | mol/kg |
Equation or Section(s) | |
Description | Total inorganic carbon dissolved in a porewater samples; one component of the carbonic acid system |
Example Measurement Method/Data Source | DIC is measured by coulometric titration, sometimes coupled with infrared spectroscopy. The setup and measurement method must be described in detail in the PDD. |
Data Reporting Guidelines | Standards of known DIC must be regularly run using the same method as porewater samples. Data reports must include the standards used, number of standards run, and percent error on the standards.2-for-6 calculations to constrain the carbonic acid system must be reported, whether this was completed in CO2SYS (or similar), PHREEQC (or similar), or in an Excel sheet. When using calculation scripts/programs other than CO2SYS or PHREEQC, the script/program must be provided in the data report. The final data report must include all components of the carbonic acid system, both measured and calculated. |
Additional Notes | DIC must be reported from control, treatment, and deployment (if using 3-plot approach) plots for all porewater analyses if used to calculate CO2estored. |
Parameter | pCO2 |
|---|---|
Unit | μatm |
Equation or Section(s) | |
Description | Partial pressure of CO2 in porewater; one component of the carbonic acid system |
Example Measurement Method/Data Source | pCO2 in fluids is measured by equilibration of the fluid sample with a set headspace volume, followed by extraction of the headspace and analysis on a gas analyzer. pCO2 may be analyzed in-situ by sensors where possible. Sensor details must be reported in the PDD. |
Data Reporting Guidelines | Where pCO2 is measured by equilibration, Project Proponents must report the volume of equilibrated fluid, the volume of headspace, the equilibration time/method, the analyzer used, and the time between sample collection and measurement. If samples are not immediately analyzed, Project Proponents must provide sufficient evidence that degassing has not occurred during sample storage. Where pCO is measured by sensor, Project Proponents must report the sensor brand/build, effective range and accuracy. Standards of known pCO must be regularly run using the same method as porewater samples. Data reports must include the standards used, number of standards run, and percent error on the standards. 2-for-6 calculations to constrain the carbonic acid system must be reported, whether this was completed in CO2SYS (or similar), PHREEQC (or similar), or in an Excel sheet. When using calculation scripts/programs other than CO2SYS or PHREEQC, the script/program must be provided in the data report. The final data report must include all components of the carbonic acid system, both measured and calculated. |
Additional Notes | Where pCO2 is measured by equilibration, Project Proponents must report from the control, treatment, and deployment (if using 3-plot approach) plots. Required for porewater analyses if used to calculate CO2estored. |
Parameter | Dissolved major and trace elements used for quantification |
|---|---|
Unit | mol/kg |
Equation or Section(s) | Calculation of CO2estored, Equation 4 Major and Trace Element Analysis |
Description | Major and trace elements that may be released into porewater as a result of project activities. These elements must include cations that are most likely to be added to the system as a result of project activities (i.e. Ca, Mg, Na, K, P, Fe, Mn, Ni, Co, Cr). |
Example Measurement Method/Data Source | This Protocol requires that major and trace elements be measured ICP-MS (ISO 17294-1:2024 )/OES (ISO 11885:2007). |
Data Reporting Guidelines | Sample preparation, including calibration standards and CRMs, must be described in detail in the PDD. When reporting elemental data, Project Proponents must include data from calibration standards and CRMs as part of the data report. The results of standard measurements must be reported alongside data, including standard pH values, number of standards, number of replicates, and % error on standards. |
Additional Notes | Major and trace elements must be reported from the control, treatment, and deployment (if using 3-plot approach) plots for all sampling events. Required for all porewater analyses if used to calculate CO2estored. |
Parameter | Dissolved anions |
|---|---|
Unit | mol/kg or eq/kg |
Equation or Section(s) | Calculation of CO2estored, Equation 4, Equation 5 |
Description | Non-carbonic acid acidity (NO3-, PO43-, SO42-, Cl-) |
Example Measurement Method/Data Source | Dissolved anions must be measured by ion chromatography. |
Data Reporting Guidelines | Sample preparation, including standards, must be described in detail in the PDD. This must also include the element used during measurement. Standards of known anion concentrations must be regularly run using the same method as porewater samples. Data reports must include the standards used, number of standards run, and percent error on the standards. |
Additional Notes | Dissolved anions must be reported from control, treatment, and deployment (if using 3-plot approach) plots. Required for all porewater analyses if necessary to calculate CO2estored. |
Parameter | Temperature |
|---|---|
Unit | ℃ |
Equation or Section(s) | |
Description | Temperature |
Example Measurement Method/Data Source | Temperature is measured via thermometer. This Protocol requires that temperature is measured in-situ. |
Data Reporting Guidelines | Project Proponents must report the thermometer used and locations/depths of their deployment. |
Additional Notes | Temperature must be reported from control, treatment, and deployment (if using 3-plot approach) plots. Required for all porewater analyses if necessary to calculate CO2estored. |
Soils | |||
|---|---|---|---|
Area | 2-plot | 3-plot | |
Quantification | Control | 1/1 ha | 1/0.075 ha |
Treatment | 1/1 ha | 1/0.075 ha | |
Deployment | N/A | 1/2.85 ha | |
Validation | Control | 1/1 ha | 1/1 ha |
Treatment | 1/1 ha | 1/1 ha | |
Deployment | N/A | N/A |
Note that all units in the above table are in soil samples per plot area. For example, a project utilizing a 3-plot model with soil-based quantification and a 1,000 hectare project area (25 ha control plot, 25 ha treatment plot, and 950 ha deployment plot) has a recommended minimum soil sampling 334 samples in the control, 334 samples in the treatment, 334 in the deployment.
Aqueous | |||
|---|---|---|---|
Area | 2-plot | 3-plot | |
Quantification | Control | 1/25 ha | 1/1.875 ha |
Treatment | 1/25 ha | 1/1.875 ha | |
Deployment | N/A | 1/71.25 ha | |
Validation | Control | 1/10 ha | 1/10 ha |
Treatment | 1/10 ha | 1/10 ha | |
Deployment | N/A | N/A |
Note that all units in the above table are in aqueous phase sampling devices per plot area. For example, a project utilizing a 3-plot model with porewater-based quantification and a 1,000 hectare project area (25 ha control plot, 25 ha treatment plot, and 950 ha deployment plot) has a recommended minimum aqueous phase density of 14 sampling devices in the control, 14 sampling devices in the treatment, and 14 sampling devices in the deployment.
Notably, we recommend a higher aqueous phase sampling density for validation approaches than quantification approaches in the two plot model. This is to achieve a sufficient number of samples for statistical significance given validation measurements are collected from a minimum total of 2p% of the project area (Equation 9).
This document is a companion to the Isometric Enhanced Weathering in Agriculture Protocol v1.2, providing supporting information regarding the rationale for making the validation check optional. This document should be read in conjunction with the Protocol and is provided as guidance. Should there be any discrepancy or inconsistency between this companion document and the Protocol itself, the requirements of the Protocol will prevail.
When we first published the EW Protocol v1.0 in 2023, there was widespread consensus across the EW scientific and industry community around requiring a secondary validation approach alongside the primary quantification method. Pairing solid-phase soil measurements with aqueous-phase porewater measurements was broadly regarded as best practice. The rationale at the time was that given the nascent state of the field, an independent cross-check would provide additional confidence that the primary quantification was capturing the weathering signal correctly. We believe that was indeed the scientifically appropriate decision at the time, given the lack of sufficient real-world data indicating otherwise.
Over the past 3 years, we’ve seen a number of EW projects move through validation and verification on the Isometric Registry. From the data generated from these projects, we’ve seen that agreement between the solid-phase and aqueous-phase approaches has been poor across the board. After analyzing 10 EW project submissions, no project achieved secondary quantification agreement within two standard deviations of the primary quantification approach, and only one agreed within three standard deviations.
There are two factors that explain why the cation signal is evading current porewater sampling methods. First, weathering-derived cations tend to leave the soil at the leading edge of precipitation events. Unless sampling occurs at the onset of a rain event, which can be operationally challenging, the transient pulse of cations is missed. Second, negative-pressure porewater samplers (e.g. tension lysimeters) preferentially sample water from larger pore spaces rather than the capillary zones where much of the weathering reaction is occurring. The sampled water may therefore not reflect where the signal is strongest.
Thus in practice, porewater measurements are always biased low compared to soil-based measurements, which capture the cumulative weathering signal over the Reporting Period. The Protocol continues to allow aqueous-phase measurements as a primary quantification approach, as porewater methods that underestimate the weathering signal are conservative for CreditingCertificate issuance purposes. However, these two approaches are not directly comparable to each other.
These learnings from real-world EW deployments the past few years were echoed strongly in the EW 1.2 public consultation feedback. We received over 30 comments on the validation check, spanning themes such as the financial and operational burden of secondary validation, systemic differences between solid-phase and aqueous-phase methods, and challenges with the standard-deviation-based comparison framework.
After reviewing the community feedback and conducting our own analyses of past projects, we decided to stop mandating a validation check that was not functioning as intended. The validation check is now optional, and EW 1.2 introduces a tiered CreditingCertificate issuance framework to preserve incentives for Project Proponents to continue investing in validation:
This framework replaces the previous standard-deviation-based validation comparison, which had the unintended consequence of incentivizing higher uncertainty in the primary quantification approach to make it easier to pass the validation check.
We encourage Project developers to keep experimenting with porewater methods, watershed monitoring, ion exchange resins, and other emerging techniques. The Protocol’s Verification of Novel Methods section (Section 8.3.1.3) provides a pathway for new technologies and methods to be assessed for inclusion in the accepted validation and quantification lists as they mature.
Walker, J. C. G., Hays, P. B., & Kasting, J. F. (1981). A negative feedback mechanism for the long‐term stabilization of Earth's surface temperature. Journal of Geophysical Research, 86(C10), 9776–9782. https://doi.org/10.1029/JC086iC10p09776↩
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, R. B., Piao, S., & Thornton, P. (2014). Carbon and other biogeochemical cycles. In T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P. M. Midgley (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 465–570). Cambridge University Press. ↩
Moon, S., Chamberlain, C. P., & Hilley, G. E. (2014). New estimates of silicate weathering rates and their uncertainties in global rivers. Geochimica et Cosmochimica Acta, 134, 257–274. ↩
Gaillardet, J., Dupré, B., Louvat, P., & Allègre, C. J. (1999). Global silicate weathering and CO2 consumption rates deduced from the chemistry of large rivers. Chemical Geology, 159(1–4), 3–30. ↩
Dessert, C., Dupré, B., Gaillardet, J., François, L. M., & Allègre, C. J. (2003). Basalt weathering laws and the impact of basalt weathering on the global carbon cycle. Chemical Geology, 202(3), 257–273. https://doi.org/10.1016/j.chemgeo.2002.10.001↩
Renforth, P., & Henderson, G. (2017). Assessing ocean alkalinity for carbon sequestration. Reviews of Geophysics, 55(3), 636–674. https://doi.org/10.1002/2016RG000533↩
Shukla, P. R., Skea, J., Slade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R., Belkacemi, M., Hasija, A., Lisboa, G., Luz, S., & Malley, J. (Eds.). (2022). Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. https://doi.org/10.1017/9781009157926↩
Beerling, D. J., Leake, J. R., Long, S. P., Scholes, J. D., Ton, J., Nelson, P. N., Bird, M., Kantzas, E., Taylor, L. L., Sarkar, B., Kelland, M., DeLucia, E., Kantola, I., Müller, C., Rau, G., & Hansen, J. (2018). Farming with crops and rocks to address global climate, food and soil security. Nature Plants, 4(3), 138–147. https://doi.org/10.1038/s41477-018-0108-y↩↩2
Kantola, I. B., Masters, M. D., Beerling, D. J., Long, S. P., & DeLucia, E. H. (2017). Potential of global croplands and bioenergy crops for climate change mitigation through deployment for enhanced weathering. Biology Letters, 13(4), 20160714. https://doi.org/10.1098/rsbl.2016.0714↩↩2↩3
Haque, F., Santos, R. M., Dutta, A., Thimmanagari, M., & Chiang, Y. W. (2019). Co-Benefits of Wollastonite Weathering in Agriculture: CO2 Sequestration and Promoted Plant Growth. ACS Omega, 4(1), 1425–1433. https://doi.org/10.1021/acsomega.8b02477↩
Barak, P., Chen, Y., & Singer, A. (1983). Ground basalt and tuff as iron fertilizers for calcareous soils. Plant and Soil, 73(1), 155–158. https://doi.org/10.1007/BF02197765↩
Ma, J. F., Miyake, Y., & Takahashi, E. (2001). Chapter 2: Silicon as a beneficial element for crop plants. In L. E. Datnoff, G. H. Snyder, & G. H. Korndörfer (Eds.), Silicon in Agriculture. Studies in Plant Science, Vol. 8. Elsevier. https://doi.org/10.1016/S0928-3420(01)80006-9↩
Chiaravalloti, I., Theunissen, N., Zhang, S., Wang, J., Sun, F., Ahmed, A. A., Pihlap, E., Reinhard, C. T., & Planavsky, N. J. (2023). Mitigation of soil nitrous oxide emissions during maize production with basalt amendments. Frontiers in Climate, 5, 1203043. https://www.frontiersin.org/articles/10.3389/fclim.2023.1203043↩↩2↩3
Taylor, L. L., Quirk, J., Thorley, R. M. S., Kharecha, P. A., Hansen, J., Ridgwell, A., Lomas, M. R., Banwart, S. A., & Beerling, D. J. (2016). Enhanced weathering strategies for stabilizing climate and averting ocean acidification. Nature Climate Change, 6(4), 402–406. https://doi.org/10.1038/nclimate2882↩↩2
te Pas, E. E. E. M., Hagens, M., & Comans, R. N. J. (2023). Assessment of the enhanced weathering potential of different silicate minerals to improve soil quality and sequester CO2. Frontiers in Climate, 4, 954064. https://www.frontiersin.org/articles/10.3389/fclim.2022.954064↩↩2
Beerling, D. J., Epihov, D. Z., Kantola, I. B., Masters, M. D., Reershemius, T., Planavsky, N. J., Reinhard, C. T., Jordan, J. S., Thorne, S. J., Weber, J., Martin, M. V., Freckleton, R. P., Hartley, S. E., James, R. H., Pearce, C. R., DeLucia, E. H., & Banwart, S. A. (2023). Enhanced weathering in the U.S. Corn Belt delivers carbon removal with agronomic benefits (arXiv:2307.05343). arXiv. http://arxiv.org/abs/2307.05343↩
Ye, M., Song, Y., Long, J., Wang, R., Baerson, S. R., Pan, Z., Zhu-Salzman, K., Xie, J., Cai, K., Luo, S., & Zeng, R. (2013). Priming of jasmonate-mediated antiherbivore defense responses in rice by silicon. Proceedings of the National Academy of Sciences, 110(38), E3631–E3639. https://doi.org/10.1073/pnas.1305848110↩
Edwards, D. P., Lim, F., James, R. H., Pearce, C. R., Scholes, J., Freckleton, R. P., & Beerling, D. J. (2017). Climate change mitigation: Potential benefits and pitfalls of enhanced rock weathering in tropical agriculture. Biology Letters, 13(4), 20160715. https://doi.org/10.1098/rsbl.2016.0715↩↩2
Liang, Y., Nikolic, M., Bélanger, R., Gong, H., & Song, A. (2015). Silicon in Agriculture: From Theory to Practice. Springer. https://doi.org/10.1007/978-94-017-9978-2↩
Hartmann, J., West, A. J., Renforth, P., Köhler, P., De La Rocha, C. L., Wolf-Gladrow, D. A., Dürr, H. H., & Scheffran, J. (2013). Enhanced chemical weathering as a geoengineering strategy to reduce atmospheric carbon dioxide, supply nutrients, and mitigate ocean acidification. Reviews of Geophysics, 51(2), 113–149. https://doi.org/10.1002/rog.20004↩
Chen, Q., Goll, D., Abdalqadir, M., He, X., Li, G., Bi, B., Xu, T., Li, C., Chen, Y., Li, Z., Fang, Y., Hao, Z., & Yuan, Z. (2025). Divergent responses of carbon and nitrogen functional genes composition to enhanced rock weathering. Communications Earth & Environment, 6, 115. https://doi.org/10.1038/s43247-025-02455-2↩
Calogiuri, T., Hagens, M., Van Groenigen, J., Corbett, T., Hartmann, J., Hendriksen, R., Janssens, I., Dominguez, G., Loescher, G., Mortier, S., Neubeck, A., Niron, H., Poetra, R., Rieder, L., Struyf, E., Van Tendeloo, M., De Schepper, T., Verdonck, T., Vlaeminck, S., Vicca, S., & Vidal, A. (2023). Design and construction of an experimental setup to enhance mineral weathering through the activity of soil organisms. Journal of Visualized Experiments, 201, e65563. https://doi.org/10.3791/65563↩
International Organization for Standardization. (2019). ISO 14064-3:2019, Greenhouse gases — Part 3: Specification with guidance for the verification and validation of greenhouse gas statements (Section 5.1.7). ↩
Schneider, L., Schallert, B., & Kizzier, K. (2022). Methodology for assessing the quality of carbon credits. Carbon Credit Quality Initiative. https://carboncreditquality.org/download/Methodology/CCQI%20Methodology%20-%20Version%203.0.pdf↩
Tautges, N. E., Chiartas, J. L., Gaudin, A. C. M., O'Geen, A. T., Herrera, I., & Scow, K. M. (2019). Deep soil inventories reveal that impacts of cover crops and compost on soil carbon sequestration differ in surface and subsurface soils. Global Change Biology, 25(11), 3753–3766. https://doi.org/10.1111/gcb.14762↩↩2
Almaraz, M., Bingham, N. L., Holzer, I. O., Geoghegan, E. K., Goertzen, H., Sohng, J., & Houlton, B. Z. (2022). Methods for determining the CO2 removal capacity of enhanced weathering in agronomic settings. Frontiers in Climate, 4, 970429. https://doi.org/10.3389/fclim.2022.970429↩
Wilson, M. J. (1999). The origin and formation of clay minerals in soils: past, present and future perspectives. Clay Minerals, 34(1), 7–25. https://doi.org/10.1180/000985599545957↩
Lehmann, J., Kaiser, K., & Peter, I. (2001). Exchange resin cores for the estimation of nutrient fluxes in highly permeable tropical soil. Journal of Plant Nutrition and Soil Science, 164(1), 57–64. ↩
Lang, F., & Kaupenjohann, M. (2004). Trace element release from forest floor can be monitored by ion exchange resin tubes. Journal of Plant Nutrition and Soil Science, 167(2), 177–183. ↩
Johnson, D. W., Verburg, P. S. J., & Arnone, J. A. (2005). Soil extraction, ion exchange resin, and ion exchange membrane measures of soil mineral nitrogen during incubation of a tallgrass prairie soil. Soil Science Society of America Journal, 69(1), 260–265. ↩
Predotova, M., Bischoff, W. A., & Buerkert, A. (2011). Mineral‐nitrogen and phosphorus leaching from vegetable gardens in Niamey, Niger. Journal of Plant Nutrition and Soil Science, 174(1), 47–55. ↩
Grahmann, K., Verhulst, N., Palomino, L. M., Bischoff, W. A., Govaerts, B., & Buerkert, A. (2018). Ion exchange resin samplers to estimate nitrate leaching from a furrow irrigated wheat-maize cropping system under different tillage-straw systems. Soil and Tillage Research, 175, 91–100. ↩
Rayner, J. L., Lee, A., Corish, S., Leake, S., Bekele, E., & Davis, G. B. (2024). Advancing the Use of Suction Lysimeters to Inform Soil Leaching and Remediation of PFAS Source Zones. Groundwater Monitoring & Remediation, 44(1), 49–60. https://doi.org/10.1111/gwmr.12670↩
Qian, P., & Schoenau, J. J. (1996). Ion exchange resin membrane (IERM): A new approach for in situ measurement of nutrient availability in soil. Plant Nutrition and Fertilizer Sciences, 2, 322–330. ↩
Fiener, P., & Auerswald, K. (2009). Spatial variability of rainfall on a sub‐kilometre scale. Earth Surface Processes and Landforms, 34(6), 848–859. https://doi.org/10.1002/esp.1779↩↩2
MIT OpenCourseWare. (2014). 18.05 Introduction to Probability and Statistics, Spring 2014: Reading 18. Massachusetts Institute of Technology. https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/resources/mit18_05s14_reading18/↩↩2
MIT OpenCourseWare. (2014). 18.05 Introduction to Probability and Statistics, Spring 2014: Reading 22. Massachusetts Institute of Technology. https://ocw.mit.edu/courses/18-05-introduction-to-probability-and-statistics-spring-2014/resources/mit18_05s14_reading22/↩
Singh, A., & Masuku, M. (2014). Sampling Techniques and Determination of Sample Size in Applied Statistics Research: An Overview. International Journal of Commerce and Management, 2(11), 1–22. ↩
Sainju, U. M., Jabro, J. D., & Stevens, W. B. (2008). Soil carbon dioxide emission and carbon content as affected by irrigation, tillage, cropping system, and nitrogen fertilization. Journal of Environmental Quality, 37(1), 98–106. https://doi.org/10.2134/jeq2006.0392↩↩2
Dencső, M., Horel, Á., Bogunovic, I., & Tóth, E. (2021). Effects of Environmental Drivers and Agricultural Management on Soil CO2 and N2O Emissions. Agronomy, 11(1), 54. https://doi.org/10.3390/agronomy11010054↩↩2
Cipolla, G., Calabrese, S., Porporato, A., & Noto, L. V. (2022). Effects of precipitation seasonality, irrigation, vegetation cycle and soil type on enhanced weathering – modeling of cropland case studies across four sites. Biogeosciences, 19(16), 3877–3896. https://doi.org/10.5194/bg-19-3877-2022↩↩2↩3
Haddaway, N. R., Hedlund, K., Jackson, L. E., Kätterer, T., Lugato, E., Thomsen, I. K., Jørgensen, H. B., & Isberg, P.-E. (2017). How does tillage intensity affect soil organic carbon? A systematic review. Environmental Evidence, 6(1), 30. https://doi.org/10.1186/s13750-017-0108-9↩
Larkin, C. S., Andrews, M. G., Pearce, C. R., Yeong, K. L., Beerling, D. J., Bellamy, J., Benedick, S., Freckleton, R. P., Goring-Harford, H., Sadekar, S., & James, R. H. (2022). Quantification of CO2 removal in a large-scale enhanced weathering field trial on an oil palm plantation in Sabah, Malaysia. Frontiers in Climate, 4, 959229. https://doi.org/10.3389/fclim.2022.959229↩
Sivakumar, M. V. K., & Hatfield, J. L. (1990). Spatial variability of rainfall at an experimental station in Niger, West Africa. Theoretical and Applied Climatology, 42(1), 33–39. https://doi.org/10.1007/BF00865524↩
Ferguson, R. B., Hergert, G. W., Shapiro, C. A., & Wortmann, C. S. (2007). Guidelines for Soil Sampling. University of Nebraska–Lincoln Extension, Institute of Agriculture and Natural Resources. https://extensionpubs.unl.edu/publication/g1740/pdf/view/g1740-2007.pdf↩
Ackerson, J. (2018). Soil Sampling Guidelines. Purdue University Extension. https://www.extension.purdue.edu/extmedia/AY/AY-368-w.pdf↩
Austin, R., Gatiboni, L., & Havlin, J. (2020). Soil Sampling Strategies for Site-Specific Field Management. North Carolina State Extension. https://content.ces.ncsu.edu/soil-sampling-strategies-for-site-specific-field-management↩
Fleiss, J. L., Levin, B., & Paik, M. C. (2003). Statistical Methods for Rates and Proportions (3rd ed.). Wiley. ↩
Stolbovoy, V., Montanarella, L., Filippi, N., Selvaradjou, S.-K., Panagos, P., & Gallego Pinilla, F. J. (2005). Soil Sampling Protocol to Certify the Changes of Organic Carbon Stock in Mineral Soils of European Union. European Commission, Joint Research Centre. ↩
Chaney, D. (2017). Common Research Designs for Farmers. Sustainable Agriculture Research and Education (SARE). https://www.sare.org/publications/how-to-conduct-research-on-your-farm-or-ranch/basics-of-experimental-design/common-research-designs-for-farmers/↩
Reershemius, T., Kelland, M. E., Jordan, J. S., Davis, I. R., D'Ascanio, R., Kalderon-Asael, B., Asael, D., Suhrhoff, T. J., Epihov, D. Z., Beerling, D. J., Reinhard, C. T., & Planavsky, N. J. (2023). Initial validation of a soil-based mass-balance approach for empirical monitoring of enhanced rock weathering rates. https://doi.org/10.48550/ARXIV.2302.05004↩↩2
Chadwick, O. A., Brimhall, G. H., & Hendricks, D. M. (1990). From a black to a gray box—a mass balance interpretation of pedogenesis. Geomorphology, 3(3–4), 369–390. ↩
Suhrhoff, T. J., Reershemius, T., Jordan, J. S., Li, S., Zhang, S., Milliken, E., … & Planavsky, N. J. (2025). An Updated Framework and Signal-to-Noise Analysis of Soil Mass Balance Approaches for Quantifying Enhanced Weathering on Managed Lands. Environmental Science & Technology, 59(49), 26440–26453. ↩
Kemp, S. J., Lewis, A. L., & Rushton, J. C. (2022). Detection and quantification of low levels of carbonate mineral species using thermogravimetric-mass spectrometry to validate CO2 drawdown via enhanced rock weathering. Applied Geochemistry, 146, 105465. https://doi.org/10.1016/j.apgeochem.2022.105465↩
Dietzen, C., & Rosing, M. T. (2023). Quantification of CO2 uptake by enhanced weathering of silicate minerals applied to acidic soils. International Journal of Greenhouse Gas Control, 125, 103872. https://doi.org/10.1016/j.ijggc.2023.103872↩↩2
Lal, R. (2017). Encyclopedia of Soil Science (3rd ed.). CRC Press. ↩
Ponnamperuma, F. N. (1972). The chemistry of submerged soils. Advances in Agronomy, 24, 29–96. ↩
Zeebe, R. E., & Wolf-Gladrow, D. A. (2001). CO2 in Seawater: Equilibrium, Kinetics, Isotopes. Elsevier Oceanography Series, Vol. 65. Elsevier. ↩
Humphreys, M. P., Lewis, E. R., Sharp, J. D., & Pierrot, D. (2022). PyCO2SYS v1.8: marine carbonate system calculations in Python. Geoscientific Model Development, 15(1), 15–43. https://doi.org/10.5194/gmd-15-15-2022↩
Appelo, C. A. J., & Postma, D. (2005). Geochemistry, Groundwater and Pollution (2nd ed.). A. A. Balkema Publishers. ↩
Parkhurst, D. L., & Appelo, C. A. J. (2013). Description of Input and Examples for PHREEQC Version 3—A Computer Program for Speciation, Batch-Reaction, One-Dimensional Transport, and Inverse Geochemical Calculations (U.S. Geological Survey Techniques and Methods, Book 6, Chapter A43). U.S. Geological Survey. https://doi.org/10.3133/tm6A43↩
Dickson, A. G., Sabine, C. L., & Christian, J. R. (Eds.). (2007). Guide to Best Practices for Ocean CO2 Measurements (PICES Special Publication No. 3). North Pacific Marine Science Organization. ↩