# Towards a modular, multi-ecosystem monitoring, reporting and verification (MRV) framework for soil organic carbon stock change assessment

Niels H. Batjes, Eric Ceschia, Gerard B.M. Heuvelink, Julien Demenois, Guerric le Maire, Rémi Cardinael, Cristina Arias-Navarro & Fenny van Egmond

**To cite this article:** Niels H. Batjes, Eric Ceschia, Gerard B.M. Heuvelink, Julien Demenois, Guerric le Maire, Rémi Cardinael, Cristina Arias-Navarro & Fenny van Egmond (2024) Towards a modular, multi-ecosystem monitoring, reporting and verification (MRV) framework for soil organic carbon stock change assessment, Carbon Management, 15:1, 2410812, DOI: 10.1080/17583004.2024.2410812

**To link to this article:** https://doi.org/10.1080/17583004.2024.2410812

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# Towards a modular, multi-ecosystem monitoring, reporting and verification (MRV) framework for soil organic carbon stock change assessment

Niels H. Batjesª, Eric Cheschiaᵇ, Gerard B.M. Heuvelinkª, Julien Demenoisᶜ,ᵈ, Guerric le Maireᵉ,*f, Rémi Cardinaelᶜ,*d,g, Cristina Arias-Navarroʰ and Fenny van Egmondª

ªISRIC – World Soil Information, Wageningen, The Netherlands; ᵇCESBIO, Université de Toulouse, CNES/CNRS/INRAE/IRD/UT3, Toulouse, France; ᶜCIRAD, UPR AIDA, Montpellier, France; ᵈAIDA, Univ Montpellier, CIRAD, Montpellier, France; ᵉCIRAD, UMR Eco&Sols, Montpellier, France; ṁEco&Sols, Université de Montpellier, CIRAD, INRAe, Institut Agro, IRD, Montpellier, France; ṊPlant Production Sciences and Technology, University of Zimbabwe, Harare, Zimbabwe; ᵉJoint Research Centre (JRC), European Commission, Ispra, Italy

## ABSTRACT

Soils are the largest terrestrial reservoir of organic carbon, yet they are easily degraded. Consistent and accurate monitoring of changes in soil organic carbon stocks and net greenhouse gas emissions, reporting, and their verification is key to facilitate investment in sustainable land use practices that maintain or increase soil organic carbon stocks, as well as to incorporate soil organic carbon sequestration in national greenhouse gas emission reduction targets. Building up on an initial review of monitoring, reporting and verification (MRV) schemes with a focus on croplands, grasslands, and forestlands we develop a framework for a modular, scalable MRV system. We then provide an inventory and classification of selected MRV methodologies and subsequently "score" them against a list of key characteristics. It appears that the main challenge in developing a unified MRV system concerns the monitoring component. Finally, we present a conceptual workflow that shows how a prototype for an operational, modular multi-ecosystem MRV tool could be systematically built.

## ARTICLE HISTORY

Received 28 March 2024
Accepted 24 September 2024

## KEYWORDS

Carbon sequestration;
climate change mitigation;
sustainable land
management; carbon
accounting; monitoring
framework

## Introduction

Soils, the largest terrestrial reservoir of organic carbon [1,2], are easily degraded when disturbed [3]. There is growing recognition of the importance of monitoring changes in soil organic carbon (SOC) stocks in the broader context of climate change mitigation ([4], Sustainable Development Goal (SDG) 13 [5]), halting and reversing land degradation (SDG 15), ensuring human livelihood/health (SDG 1,2,3) and reversing biodiversity loss ([6]; SDG 14, 15). Being able to reliably quantify the amount of organic carbon that is stored in soils and to accurately measure and model how these amounts change with management practices and land use change forms the first step towards making informed decisions about how SOC stocks can be preserved or increased and ecosystem services improved [7–11]. In this context, it is important to carefully distinguish the “sequestration of SOC in stable pools from the mere transient increases in

SOC storage that follow the incorporation of manure and plant residues into soils" [12–16].

SOC refers only to the carbon component of soil organic matter (SOM) [17–19]. SOM itself is an important determinant of the quantity and quality of many ecosystem services [9, 20] and soil functioning [21,22]. It should be noted that drivers of change in SOM concentration are not exactly the same as drivers of change in SOC stock [12, 15]. For example, interventions to build up SOM quantity and quality may only lead to a reduction in carbon losses (i.e. carbon loss mitigation) rather than result in real carbon sequestration in stable pools and negative emissions [12, 16].

SOC stocks and greenhouse gas (GHG) fluxes vary with environmental conditions such as soil type and terrain (e.g. drainage, exposition), climate, and land use (e.g. agriculture, forestry, peatlands, and urban land) and management [23,24]. The overarching policy setting, such as the EU

CONTACT Niels H. Batjes niels.batjes@isric.org ISRIC – World Soil Information, Wageningen, The Netherlands.

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Supplemental data for this article can be accessed online at https://doi.org/10.1080/17583004.2024.2410812.

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Common Agricultural Policy (CAP) and GreenDeal [25–28], create conditions aimed at maintaining current carbon stocks in carbon-rich ecosystems (e.g. peatlands, mangroves) respectively increasing SOC stocks in, and reducing GHG emissions from, degraded ecosystems.

The European Commission [29] proposed a Framework for Carbon Removals Certification that aims to incentivise carbon removals. Alongside other removal options, this includes a specific focus on promoting “carbon farming”, a category that includes nature-based solutions. The Framework establishes rules to certify and govern removals with the objective of ensuring high quality carbon removals within Europe and thereby elicit upscaling of carbon removals. The framework aims at allowing financing through various sources, for example public funding (CAP, state aid, and EU funds), food and biomass value chain (insetting), carbon credits outside the value chain (offsetting), and could entail a significant shift towards market-based incentives for mitigation in the land sector. Voluntary carbon markets are increasingly offering market-based incentives to landowners [30–32], but until now European policymakers have relied on action-based and regulatory approaches to manage the land sector, as exemplified by the CAP [29].

In data scarce countries, global default values for reference SOC stocks and emission factors are commonly used to infer changes in SOC stocks over time and variation over space, subject to defined land use and management interventions, using empirical models, i.e. Tier 1 level approaches [33,34]. The use of such default values, however, is prone to low accuracy and high uncertainty, especially when applied to estimate SOC stock change in local/landscape scale projects [35,36]. Through physical (in-situ) soil sampling combined, or not combined, with modeling researchers, project managers, and agricultural practitioners can improve estimation of current SOC stocks and changes under different land management practices. For instance, repeated measurements of SOC concentration, bulk density and proportion of coarse fragments can show how land management impacts SOC stocks over time and space, provided they are based on a strategic research/experimental design [37]. When paired with sustainable soil management and agricultural practices, the information can be used in financing frameworks to promote carbon sequestration while supporting livelihoods through increased soil health and

possibly agricultural yields, as well as addressing climate change. In practice, however, the cost of taking sufficient soil samples to reliably monitor changes in carbon farming projects can be prohibitive, hence the need for developing novel approaches (e.g. hybrid modeling). For such practices to be rewarded, the reported SOC gains need to be verified by a third party. Importantly, the experts or companies that are in charge of carrying out monitoring and reporting should not also carry out the verification, due to a possible conflict of interest; see for example the independent review of Australian carbon credit units (ACCUs) [38].

Consistent and accurate monitoring of changes in SOC stocks and net GHG emissions, reporting, and their verification, is key to facilitate investment in sustainable land use practices that maintain and increase SOC, as well as to incorporate SOC sequestration in GHG emission reduction targets at the international and national level (e.g. Nationally Determined Contributions, NDC) [39]. Yet, according to Wiese et al. [40], only 28 out of 184 countries in the Paris Climate Agreement referred to SOC, peatlands or wetlands in their NDCs: “to increase country commitments and attention to managing SOC, there is a need for improved SOC measurement and monitoring, for better evidence on the impacts of management practices on SOC, and for incentives for farmers to change practices and overcome barriers.”

The short- and longer-term socio-economic perspective of farmers versus the long-term perspective of SOC sequestration projects needs to be considered too [41–44]. Soil management interventions aimed at increasing organic matter (i.e. SOC) levels in soil and to decrease organic carbon loss in soils of different agro-ecological and urban systems, and their possible co-benefits, have been described elsewhere [24, 45–50].

While most Monitoring, Reporting and Verification (MRV) schemes focus on total SOC, it should be noted that the carbon in soils consists of different forms that are chemically varied and have specific turnover times [51]. The complex biological basis of SOC sequestration has recently been reviewed by Lavelle [52], while Doetterl et al. [53] focused on the effects of biotic and abiotic factors controlling SOC dynamics at continental to global scales. Potential, actual and attainable SOC sequestration rates are determined by defining factors, such as clay mineralogy, limiting factors (e.g. climate) and reducing factors (e.g. erosion, residue removal, soil fertility decrease, land mis-
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management) or increasing factors (e.g. improved land management, crop rotation, cover crops, additional C inputs) [54]. Further, there may be stoichiometric [55–58] and microbiological limitations [59–61], as well as often overruling social and economic limitations to attainable SOC gains [42, 62–65].

Although soils are a promising reservoir to store carbon, long time scales are required to sequester amounts of (stable) carbon of relevance to mitigate climate change [66–68]. Alternatively, particulate organic matter (POM) defined as the “0.053–2 mm size fraction” of SOM [69], can also play a role in climate change mitigation. Part of it can persist over longer time scales as it can be trapped within soil aggregates where it is not available for soil microbes to cycle [70,71]. For the fast-decomposing POM, the stock and carbon accrual can be high, but management needs to be maintained to be relevant for climate change mitigation [72] as there is a high risk of reversal (hence a percentage of credits is held in a buffer pool to mitigate this risk).

Possible gains in SOC are considered to be finite [73,74] and are reversible upon changes in land management practices [75,76]. The validity of the widely accepted assumption of “possible stable SOC gains being finite due to the limited mineral surface available” (i.e. saturation concept) has recently been questioned and remains an issue of scientific debate [77–79]. In this context, it is important to differentiate between the concepts of C-saturation versus C-equilibrium, which is based on inputs/outputs of C for a given system [16].

Importantly, interventions that are focused on SOC sequestration may not be as efficient for climate change mitigation as anticipated [80–83]. Also they might not always lead to increased crop productivity [13,14, 84,85], and often operate on longer time scales than many smallholder farmers can accept financially [42]. A recent study [86] found that the main incentives for smallholder farmers to participate in carbon payment schemes are non-monetary. These include improved yields, building soil resilience and limit erosion, increasing soil organic matter as a source of nitrogen (N) in “low soil fertility regions” [87–89] or, alternatively, to reduce the application of inorganic N fertilizers in parts of the world with N-related environmental problems [81, 90]. Further incentives include access to financial advisory services and credit, investments in local infrastructure, and the development of income-generating activities. Such co-

benefits play a central role in carbon payment projects as they can enhance the likelihood of permanence of practices to sustain SOC stocks, a central issue related to the credibility of SOC credits [86]. In this context, it is also important to be aware of the risk of land grabbing associated with some “carbon credit oriented” projects and large-scale investments in farmland [91–94].

The abbreviation MRV, as used in this review, stands for Monitoring, Reporting and Verification. The monitoring activities under consideration are related to national scale, landscape, plot and/or project scale inventories, and those focusing on the carbon markets (e.g. voluntary and compliance), as well as “insetting.” The economic considerations of carbon market-oriented MRV systems, i.e. underpinning business models, are intricate [95–97]. Payment models can focus on preserving or increasing forest biomass, conserving SOC, reducing net emissions from soil, increasing sequestration of carbon into soils or a combination thereof. Most voluntary carbon market schemes in agriculture work on the basis of “Net Abatement,” i.e. SOC stock increases plus soil derived GHG emission reductions (i.e. consider net GHG emission changes expressed as CO2eq). For some MRV systems, however, measuring SOC change is not required (i.e. “action-based” verification), but this precludes the scope for true verification (i.e. “result-based”). In this context, some groups prefer to use “Measurement, Monitoring, Reporting and Verification” (MMRV) to show the importance of field measurements, rather than MRV alone [98]. Many MRV guidelines and approved methodologies have been proposed, yet their differences remain unclear. In this study, we compared 17 of these guidelines using 26 criteria including the ecosystems covered, geographic scope, tier level and reporting frequency.

The WorldBank [7] identified three broad types of payment systems applicable to projects sequestering SOC in agricultural land. These were ranked according to cost of implementation, confidence of atmospheric impact, and degree of complexity: a) Payment for practice (input-based system); b) Payment for practice with performance dividend; and c) Payment for performance (output-based system, e.g. carbon-market, voluntary or compliance). For the first payment system, it is sufficient to implement an eligible practice to get paid. Alternatively, for the last payment system (c) an assessment of the impact is compulsory and the payment itself can be modulated based on the
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performance. The second payment system (b) is a blend of systems "a" and "c," with a fixed payment plus a bonus based on the performance.

Although much progress in national and sub-national level MRV systems for SOC has been achieved over the last two decades [11, 66, 99–103], a recent poll of staff working in environmental organizations, businesses, academic researchers and government entities identified MRV as "one of the largest challenges by entities developing carbon farming schemes" [104]. This has partly to do with the scale at which the MRV system is needed when referring to carbon farming, i.e. farm or plot scale, which may involve approaches that differ from those commonly used for national or sub-national inventories (i.e. Tier 1 and Tier 2) and make more systematic use of Tier 3 approaches, with exhaustive management and farm data collection for GHG emissions assessment. The most common challenges according to the poll were the lack of robust monitoring, reporting and verification systems as well as knowledge about the relevant costs. This is surprising considering that UNFCCC [105] principles indicate that MRV systems should be "transparent, complete, consistent, comparable and accurate" and also consider the common sense principles of being "pragmatic, cost-effective, scalable, timely, and operational." Importantly, safeguards against "greenwashing" [106,107], and the often associated "land grabbing" [92,93], through uncertainty quantification and solid verification by independent suitably experienced and qualified third parties will be essential [108–111]. Yet, verifiers may not have the right expertise, and many will not have the modeling experience, pointing at a need for training capacity [112].

The primary objective of this study, carried out in the framework of the EU ORCaSA project [113], is to propose an approach for a modular, integrative, and multi-ecosystem MRV framework for SOC stock changes. First, we carried out an in-depth literature search using the Web of Science platform to retrieve studies that examined SOC and MRV systems focusing on croplands, grasslands and forest lands. Additional articles were identified from personal research libraries (For URLs see Supplementary information S4). Based on this, in Section "Components for a modular MRV framework", we propose a conceptual, modular, and scalable MRV framework. Subsequently, we provide an inventory and classification of current MRV methodologies and subsequently "score" them using a list of key

characteristics (Section "Inventory and classification of current MRV approaches"). Thereafter, in Section "Towards an operational, integrative, and multi-ecosystem MRV approach for SOC stock changes", we build on this and provide an outlook on how an operational, modular multi-ecosystem MRV system could be systematically built in the next phase of the ORCaSA project.

## Components for a modular MRV framework

Smith et al. [114] discussed a conceptual MRV framework for cropland dedicated to NDCs. They described how different "building blocks" (e.g. field measurements, datasets, models) could contribute to the three components of an MRV system for SOC changes. The study also provided a methodological basis for the ground monitoring, modeling, and verification of SOC stock changes. It requires to combine different datasets (e.g. input for models, calibration and validation data), together with models (e.g. empirical, soil process-based models, coupled soil-crop process-based models, carbon balance models), embedded in a spatial data infrastructure (SDI) allowing for handling of databases, intensive computing, decision support systems, and verification and distribution of results/reporting. It is important to note that when process-based models simulate only the soil compartment they require external information on biomass inputs to the soil, while coupled soil-vegetation models directly estimate biomass inputs. Therefore, the choice of modeling approach will have important consequences when the calculation of net SOC stock changes is operationalized.

A "building block," is one of the separate parts that are combined to make an operational MRV system. Examples are spatial data layers (e.g. maps of soil properties, land management and activity data), modeling approaches (e.g. process-based soil, vegetation or coupled soil-vegetation models, or data-driven models), or the combination of Earth Observation (EO) data and radiative transfer models that can produce biophysical data from EO to be assimilated in process-based models. The building blocks themselves, many elements of which already exist as loose modules [115–117], need to be assembled in an operational processing chain to be applied in one or several contexts of applications (e.g. CAP, Carbon market, or NDC) [118–120]. Note that the same building blocks (and their constituting parts) can be used in one or several components of an MRV system. For
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example, both the Comet Farm Tool [121] and the DayCent model [122,123] are Tier 3 approaches (and even include the same model), but their use and role in MRV systems is completely different.

A different conceptual MRV framework has been presented by Paustian et al. [124]. It includes components similar those proposed by Smith et al. [114], as well as a scalable quantification platform (which is not detailed in the paper itself), and further considers the different communities that should be served by an MRV system (e.g. national policies, carbon finance market and supply chains).

Depending on the size of the area to be monitored, the availability/accuracy of the input data, protocols for sampling/measurement, monitoring frequency, scale of interest and purpose, different MRV approaches and associated methodologies (e.g. Tiers as in Bockstaller et al. [119] for the CAP), will be needed.

Based on the above, and discussions during two international stakeholder workshops, we pictured a scalable, modular MRV framework (Figure 1):

a. Monitoring (M), which includes experiments or observatories (e.g. long-term soil observations, flux tower networks), direct (soil) measurements, activity data, spatial data layers, Earth Observation (see M1 to M5 in Figure 1) aimed at developing and/or applying models (M6 to M8). The gear wheel in the green monitoring box (M) serves to illustrate that these activities are performed within the context of a scalable quantification platform.

b. Reporting (R), which includes rules and procedures (R1 and R2).

c. Verification (V), which includes rules and procedures, verification itself, proof of adoption of practice, and data (soil and/or EO) for verification (V1 to V4).

The three components and their building blocks, as well as their practical application, have been discussed in detail in Batjes et al. [125]. Landscape-scale assessment of SOC stock changes in agriculture and forestry, for example, can present a number of practical problems. Data are needed from heterogeneous areas, often for multiple points in time, and the collection and laboratory analyses of these samples can be expensive and time consuming [126]. However, time and costs can be reduced by taking composite samples and using proximal sensing techniques, such as MIR/NIR spectroscopy, and developing soil spectral calibration libraries and estimation services [100, 127–131]. Overall, field measurements (even when considering the associated uncertainties [132,133]) are still considered the best option to quantify and verify SOC content and SOC changes. They are also needed for model development and calibration as well as verification. The use of field measurements, modeling and remote sensing for MRV purposes is often complementary [134,135].

Broadly speaking, three types of models are used to predict SOC stocks and SOC stock changes: a) process-based (M6 in Figure 1) or mechanistic models [114, 124, 136,137]; b) data-driven (M7, or

<figure><img src="image_18.png" /><figcaption>**Figure 1.** Schematic representation of components, building blocks, and information flow for a modular, scalable MRV system. (the dotted lines illustrate that Earth Observation can provide activity and spatial data for monitoring and reporting, respectively independent input for verification).</figcaption></figure>
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empirical) models [138,139]; and c) hybrid models (M8, [140–142]). The second type of model is based on statistical relationships derived directly from field (experiment) observations, while process-based models consider algorithms that are founded on more general scientific understanding. The latter is derived from laboratory- and field-based experiments, as well as a variety of field-based observations of SOC distribution along climatic, vegetation, topographic and geological gradients. Data-driven (M7) and process-based models (M6) can be combined in hybrid models (M8).

General specifications for the different model categories mentioned earlier, and their main characteristics, are presented in Table 1. A critical discussion of the differences between the applied methods, such as quality/differences of measuring protocols, remote sensing approaches (e.g. what is measured and how (SOC, biomass, Net Primary Production), model structures and data requirements, local or off-site calibration, use of measured or map-based (aggregated) data for driving modeling, and accounting for the associated uncertainty) is considered beyond the scope of this review as these have been addressed elsewhere [37, 99, 101, 118, 125, 141, 143–147]. However, such methodological differences are crucial and will have a major impact on the quality of the actual MRV system.

A fairly recent development has been the development of “hybrid models” that combine multiple data sources and modeling techniques [148]. For example, these can comprise a combination of “field data – remote sensing – machine learning” [149,150], “field data – remote sensing – ecosystem models” [151–157], or “field data – remote sensing – machine learning – ecosystem models” [120]. The combination of field measurements, remote sensing and ecosystem carbon models can be

used for upscaling plot data for carbon accounting to larger areas (e.g. regional, country or global scale). However, the approach can also be used for downscaling: in that case, large scale data or model output (e.g. from a Dynamic Global Vegetation Model) are combined with remote sensing data to refine ecosystem or data-driven model outputs at smaller spatial scales, thus capturing local variations in carbon dynamics [158]. Another recent development has been the use of geostatistical approaches for assessing space-time changes in SOC stocks using machine learning that draws on large soil databases and environmental covariates [138, 159]. Le Noë et al. [160] provided a comprehensive review of ~250 SOC models, spanning 90 years of model development history, and concluded that combining independent validation based on observed time series and improved information flow between predictive and conceptual models is needed to increase reliability in predictions. Different sources of uncertainty associated with MRV systems, and steps towards their quantification, are discussed elsewhere (see Section “Reporting and verification”).

Decision support tools provide information on the quantification of SOC stock changes, GHG emissions, or both. They mainly use IPCC Tier 1 (i.e. consider default or country-specific emission factors) and Tier 2 (i.e. consider more detailed and region-specific data and models) approaches but can also include a module for Tier 3 (most comprehensive and site-specific methodologies) [156, 161,162]. Tier 3 type approaches, the most demanding ones, are run using spatially explicit inputs and farm/region-specific model parameters. Tier 3 is considered to be the most accurate approach [33]. Operational tools (in particular Tier 3 type) depend on several of the building blocks described in Figure 1 (e.g. spatial data for climate

<table><caption>Table 1. Examples of different model categories and current decision support tools and their characteristics<sup>a</sup>.</caption><thead><tr><th rowspan="2">Characteristics</th><th colspan="3">Models</th><th rowspan="2">Decision support tools</th></tr><tr><th>Data-driven</th><th>Process-based</th><th>Hybrid</th></tr></thead><tbody><tr><td>Data requirement</td><td>Low</td><td>High (environmental data)</td><td>High (environmental data)</td><td>High (farm specific data)</td></tr><tr><td>Calibration requirement</td><td>Low</td><td>High</td><td>High</td><td>Low</td></tr><tr><td>Required expertise</td><td>Low</td><td>Medium-high</td><td>High</td><td>Medium</td></tr><tr><td>Management options</td><td>Medium (categories)</td><td>No-high</td><td>High</td><td>Medium-high</td></tr><tr><td>Targeted scale</td><td>Country and larger</td><td>Point, country and larger</td><td>Point, country and larger</td><td>Field-farm</td></tr><tr><td>Uncertainty/expected error<br>for field scale</td><td>High</td><td>Medium-low</td><td>Medium-low</td><td>Medium-high</td></tr><tr><td>Examples</td><td>IPCC and UNFCC (Tier 1<br>and Tier 2); Machine<br>learning<sup>b</sup></td><td>Roth C (Tier 3),<br>EPIC, CENTURY,<br>DAYCENT, DNDC (Tier 3)</td><td>AgriCarbon-EO<sup>b</sup></td><td>Cool Farm Tool (Tier 1),<br>Comet Farm (Tier 1 and<br>2), CPB tools (Tier 1 and<br>2), SIMEOS-AMG (Tier 3)</td></tr></tbody></table>

<footnote><sup>a</sup>Adapted from Kuhnert et al. [94]. Note that some process-based models only consider C (e.g. RothC); whereas others consider C, N, P etc dynamics (e.g. DayCent) and these are often termed ‘biochemical models’. Some process-based models have no plant/crop component (e.g. RothC), while others have (e.g. DayCent). All have biochemical pathways related to the cycling of incoming C using defined conceptual pools with varying decay rates.</footnote>

<footnote><sup>b</sup>Machine-learning based models and hybrid models seldom have clear abbreviations; for examples see text.</footnote>
or activity data as input, in-situ soil data for
verification).

Inventory and classification of current MRV approaches

Inventory

National scale

Different MRV systems will be needed depending on their projected applications. National MRV frameworks under the UN Framework Convention on Climate Change (UNFCC) [28] focus on “what is measured, what is reported, and what is verified.” The adopted IPCC methodologies are intended to yield national GHG inventories that are transparent, complete, accurate, consistent over time and comparable across countries (i.e. compliance oriented).

Smith et al. [114] reviewed MRV methods in use
in countries participating in the Global Research
Alliance on Agricultural Greenhouse Gases (GRA).
All countries that are parties to the UNFCCC need
to deliver national inventories of emissions and
removals of GHG associated with human activities.
Since different countries have different capacities
to produce inventories, the IPCC guidelines lay out
tiers of methods for each emission source. They
[114] reported that countries listed as “non-annex
1” (i.e. mostly developing countries that have rati-
fied or accepted the UNFCCC and are not included
in Annex 1 of the Kyoto Protocol) face major chal-
lenges due to a paucity of data respectively limited
technical capacity to collect the data necessary for
the inventories. For instance, in sub-Saharan Africa,
countries often lack activity data in addition to
specific emission factors [163]. As a result, most
GRA countries use a Tier 1 approach to report SOC
changes associated with areas defined as cropland,
while industrialized (Annex I) countries such as
Australia, Canada and Denmark use a Tier 3
approach, respectively based on FullCAM, Century
and C-Tool models. Further, specificities on meth-
odologies and models used in selected GRA coun-
tries are provided in Smith et al. [114].

Subnational to project scale

More recently, Oldfield et al. [164] prepared an
overview of SOC estimation and sampling meth-
ods, listing main issues and approaches to be con-
sidered in an MRV framework. Their study
considered twelve published MRV “protocols” for
SOC credits generated on cropland and rangeland.
They assessed over forty characteristics for each

protocol. Unsurprisingly, these protocols take dif-
ferent approaches to quantifying SOC and net
GHG removals, often building upon national con-
ventions. While some use soil sampling only,
others combine sampling with process-based mod-
eling, or use only modeling and remote sensing.
These differences as well as the way issues such as
permanence (i.e. consider the sustained climate
mitigation effect in the long-term) and additional-
ity (i.e. consider if the reported emission reduc-
tions and/or carbon removals associated with the
adoption of new land management practices (i.e.
project scenario) would be greater than under the
"business-as-usual" scenario (i.e. without the car-
bon finance or incentive)) are accounted for may
create the risk of generating credits that are not
equal or comparable [94, 103, 165]. Furthermore, it
should be noted that some of the protocols
reviewed in Oldfield et al. [164] have since been
retracted by the certifying agencies as some of the
claims for carbon offsets made could not be sub-
stantiated due to "greenwashing" [106,107].

According to Arcusa and Sprenkle-Hyppolite [115], based on an analysis of the carbon dioxide removal (CDR) certification and standards ecosystem for the year 2021–2022, there are at least thirty standard developing organizations. These propose at least 125 standard methodologies for carbon removal from 23 different CDR activities. Further, they identified 27 different versions of certification instruments in voluntary and compliance markets. In practice, again, this diversity makes it cumbersome to determine whether net climate benefits have been achieved or not. This shows the importance of developing an operational unified, modular, multi-ecosystem MRV “tool” for SOC and ecosystem carbon stocks (see Section “Towards an operational, integrative, and multi-ecosystem MRV approach for SOC stock changes”).

Black et al. [103] presented an innovative global comparative analysis of farmland SOC “programmes or standards”, abbreviated below as “codes”, providing novel insights into the range of approaches governing this global marketplace. For this, they elaborated an analytical framework for the systematic comparison of “codes.” They used this to identify commonalities and differences in approaches, methods, administration, commercialization, and operations for twelve publicly available “codes” from around the world. These “codes” used a range of mechanisms to manage additivity, uncertainty and risks, baselines, measurement, reporting and verification, auditing, resale of
<figure><img src="image_20.png" /></figure>

carbon units, bundling and stacking, stakeholder engagement, and market integrity. They concluded that adapting or translating existing “codes,” or developing new approaches, to a workable farm level carbon “code” in a new country or region is not trivial, since these must address local economic, environmental, and social factors, including farming systems, soil and climatic conditions, regulations, social norms and values.

For France, Yogo et al. [166] proposed three possible options for carbon balance evaluation and monitoring with different methodologies, tools and data that can be mobilized, as well as recommendations for the specific case of croplands, and pointed at the advantage of moving towards methods that include remote sensing for a territorial deployment. Their comprehensive assessment included a review of twenty different methodologies, and tools, to assess at least one of the three main GHGs (CO₂, N₂O and CH₄) and/or carbon sequestration in soil and above-ground biomass. The underlying calculations include IPCC Tier 1 or Tier 2 emission factors, but also a range of models and use of satellite data.

For the Netherlands, Lesschen et al. [117] developed an elaborate “rating” system. It considers criteria and describes characteristics for twelve selected models and tools, to identify their suitability for application by farmers in the Netherlands. Criteria for selection include public availability, licensing, validation, accessibility of input data, applicability to cropland and grassland under climatic conditions similar to those in the Netherlands, as well as other characteristics, such as whether models are maintained, the number of C-pools, temporal scale and temporal resolution, spatial resolution, soil depth and number of layers, consideration of water balance and nitrogen interactions. On the basis of their inventory, Lesschen et al. [117] selected four potentially suitable soil C-models. Subsequently, they identified the data requirements of these models in terms of soil parameters, weather data, kind, and type of organic materials (manure) applied, soil management, information on crop type, etc. After a qualitative comparison, the four models were compared quantitatively using datasets for two long-term experiments in the Netherlands. It followed that there are substantial differences between the models – this made the comparison of SOC changes uncertain. While some models simulated the same trends, changes in SOC levels varied substantially between models. Several

studies [167–169] indicated that a multi-model analysis reduced the uncertainty in simulated SOC stocks, which would suggest that MRV systems should not rely on one model only. All of this will have implications for the verifiability of modelled SOC stock changes, or net GHG emissions, at an accepted confidence level (e.g. 90%). In this context, it should be noted that carbon markets do not look for change in SOC stocks over time, but rather at additionality or the difference in estimated GHG emissions under the “project” scenario versus GHG emissions that would have occurred under the “business-as-usual” scenario (i.e. net abatement).

Similarly, different MRV approaches and methodologies are used in the forest sector [170–173]. Differences in statistical sampling design, for example, as well as field sampling techniques and subsequent laboratory analyses will impact on the predictive quality of different monitoring networks [99, 174,175], making inter-comparison of results derived from various monitoring systems problematic [147, 176]. According to Olsson [177], unmeasurable uncertainties, such as political issues and economic rebound effects potentially leading to carbon “leakages,” tend to be neglected in inventories. Importantly, different certification schemes can result in different prices being paid per net tonne of CO₂eq sequestered. These prices, in turn, will among others influence land use and crop management decisions [178–180] hence achievable carbon sequestration.

## Reviewed guidelines and approved methodologies

From the above it follows that many different guidelines and methodologies relating to MRV exist and that the terms used are not always clear-cut with a diverse range of associated certification schemes. In this context, Demenois et al. [94] referred to a “jungle of certification schemes”. For this review, as indicated earlier, we considered a selection of guidelines recognized as being most relevant based on the expertise of the writing team, and subsequent feedback during two international stakeholder workshops (Table 2). Succinct descriptions thereof are provided in Supplementary Information S2.

Typically, each approved methodology is based on one, or several, standards. These are often documented in a central registry which lists whether methodologies are accepted, in (scientific
<figure><img src="image_21.png" /><figcaption>**Table 2.** List of reviewed guidelines and approved methodologies (listed in alphabetical order of their abbreviation).</figcaption></figure>

<table><thead><tr><td>Abbreviation</td><td>Name</td></tr></thead><tbody><tr><td>AU-CFIDV</td><td>Carbon Farming Initiative—Estimating Sequestration of Carbon in Soil Using Default Values<br>https://www.dcceew.gov.au/climate-change/emissions-reduction/emissions-reduction-fund/methods/estimating-sequestration-of-carbon-in-soil-using-default-values</td></tr><tr><td>AU-CFMM</td><td>Carbon Farming Initiative—Estimating soil organic carbon sequestration using measurement and models method<br>https://www cleanenergyregulator.gov.au/ERF/Choosing-a-project-type/Opportunities-for-the-land-sector/Agricultural-methods/estimating-soil-organic-carbon-sequestration-using-measurement-and-models-method</td></tr><tr><td>BC-SCM</td><td>BCarbon Soil Carbon Protocol<br>https://static1.squarespace.com/static/611691387b74c566a67f385d/t/63483a986a24ac421c4f4414/1665677979013/2022-10-13-BCarbon-Soil-Carbon-Protocol-V2.pdf</td></tr><tr><td>CARSSE</td><td>Climate Action Reserve Soil Enrichment Protocol v 1.0<br>https://www.climateactionreserve.org/wp-content/uploads/2020/10/Soil-Enrichment-Protocol-V1.0.pdf</td></tr><tr><td>DE-MOOR</td><td>Moor Futures<br>https://www.moorfutures.de/downloads/</td></tr><tr><td>FR-LBC</td><td>Label Bas Carbone (There are six approved methodologies for SOC, see below for details).<br>https://label-bas-carbone.ecologie.gouv.fr/quest-ce-que-le-label-bas-carbone</td></tr><tr><td>Gold Standard</td><td>Soil Organic Carbon Framework Methodology<br>https://globalgoals.goldstandard.org/</td></tr><tr><td>GSOC-MRV</td><td>FAO GSOC MRV Protocol<br>https://www.fao.org/documents/card/en/c/cb0509en</td></tr><tr><td>IPCC</td><td>IPCC guidelines for national greenhouse gas inventories<br>https://www.ipcc.ch/site/assets/uploads/2019/12/19R_V0_01_Overview.pdf</td></tr><tr><td>NL-SNK</td><td>Stichting Nationale Koolstofmarkt<br>https://nationaleco2markt.nl/<br>https://nationaleco2markt.nl/methoden/</td></tr><tr><td>Nori</td><td>Nori Croplands Methodology, v 1.3<br>https://nori.com/resources/croplands-methodology</td></tr><tr><td>Plan Vivo</td><td>Plan Vivo standard methodology<br>https://www.planvivo.org/standard-documents</td></tr><tr><td>Regen</td><td>Regen Network Methodology for GHG and Co-Benefits in Grazing Systems<br>https://registry.regen.network/v/methodology-library/published-methodologies/carbonplus-methodology-for-grazing-systems-v1.0-and-credit-class</td></tr><tr><td>UK-PC</td><td>IUCN-UK Peatland Code<br>https://www.iucn-uk-peatlandprogramme.org/peatland-code-0</td></tr><tr><td>US-ACR</td><td>American Carbon Registry (There are four methodologies for SOC, see 3.2.2)<br>https://americancarbonregistry.org/carbon-accounting/standards-methodologies</td></tr><tr><td>VM0006</td><td>Methodology for Carbon Accounting for Mosaic and Landscape-scale REDD Projects, v2.2<br>https://verra.org/methodology/vm0006-methodology-for-carbon-accounting-for-mosaic-and-landscape-scale-redd-projects-v2-2/</td></tr><tr><td>VM0042</td><td>VM0042 Methodology for Improved Agricultural Land Management, v2.0<br>https://verra.org/methodologies/vm0042-methodology-for-improved-agricultural-land-management-v2-0/</td></tr></tbody></table>

peer review or open for public comment. Further, registries list inactive (or repealed) methodologies and their version. Major registries in the voluntary carbon offset market include the American Carbon Registry (ACR) [181], Verified Carbon Standard (VERRA) [182], Climate Action Reserve (https://www.climateactionreserve.org/) and Gold Standard Impact Registry (https://www.goldstandard.org/).

## Classification characteristics

We defined a list of main characteristics that should be considered when comparing the guidelines and approved methodologies considered in Table 2. This list (Table 3) considers characteristics such as purpose of the MRV system, ecosystem(s) covered, Tier level, geographic scope, scope of monitoring as well as verification requirements such as “additionality” and “permanence.”

For each characteristic, either one or several answers are possible. For example, for the classification characteristic “Ecosystem(s) covered,” there

are nine options, and one could answer “Croplands,” “Grasslands” or “Wetlands/peatlands.” Alternatively, for the characteristic “Leakage requirement” only two answers are possible (Yes or No). During the “scoring” it proved cumbersome to unmistakably assign a class for some characteristics in view of the overall diversity/complexity of the considered MRV guidelines/methodologies, such as consideration of multiple Tiers. In such instances, pragmatically, “best appraisals” were provided considering the available “multi-faceted” information. This level of “uncertainty” has been expressed under “Confidence in ratings,” which was assessed as: High (5 times), Medium (8 times) and Low (4 times). Results of the assessments were stored in a spreadsheet with eighteen rows and twenty-seven columns (see Supplementary Information S3) of which only the first eight columns and rows are shown in Table 4 in view of space. As indicated, in some cases, we only assessed one specific methodology whereas there can be more (e.g. six for Label Bas Carbone (FR-

<footnote><sup>a</sup>Listed in alphabetical order of abbreviations (all URLs last accessed 5 July 2024). Short descriptions are provided as Supplementary Information S1.</footnote>

<footnote><sup>b</sup>These guidelines include several approved methodologies, see Supplementary Information S1 for additional information.</footnote>
<figure><img src="image_22.png" /><figcaption>Table 3. List of characteristics for classification of guidelines and approved methodologies.</figcaption></figure>

<table><thead><tr><th>Classification characteristic</th><th>Answer 1</th><th>Answer 2</th><th>Answer 3</th><th>Answer 4</th><th>Answer 5</th><th>Answer 6</th><th>Answer 7</th><th>Answer 8</th><th>Answer 9</th></tr></thead><tbody><tr><td>Purpose of MRV system</td><td>Compliance market (National inventories, CAP)</td><td>Corporate Supply Chain (insetting)</td><td>Voluntary carbon market (offsetting)</td><td>Woodland/ Shrubland</td><td>Wetlands/ peatlands</td><td>Urban land</td><td>Agric. land and agro-forestry</td><td>Agric. land and woody vegetation</td><td>Forestland, grassland & cropland<sup>b</sup></td></tr><tr><td>Ecosystem(s) covered</td><td>Croplands</td><td>Grasslands</td><td>Forestlands<sup>a</sup></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Geographic scope</td><td>Specific country</td><td>Multiple countries</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Aggregation (bundling) of farms</td><td>Allowed</td><td>Not allowed</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tier level</td><td>1</td><td>2</td><td>3</td><td>All</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Scope of monitoring</td><td>SOC stock change</td><td>GHG accounting</td><td>All</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GHGs targeted</td><td>CO<sub>2</sub></td><td>CH<sub>4</sub></td><td>N<sub>2</sub>O</td><td>All</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Baseline setting</td><td>Soil measurements</td><td>Historic land management data</td><td>Modelled</td><td>All</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dependence on Earth Observation data</td><td>No</td><td>Partly</td><td>Fully</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Requires ground truth SOC observations in reporting phase</td><td>No</td><td>Yes, at start date</td><td>Yes, at final date</td><td>Yes, at start and final date</td><td></td><td></td><td></td><td></td><td>High frequency</td></tr><tr><td>Probability-based (soil) sampling</td><td>No<sup>c</sup></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Target depth interval</td><td>Topsoil</td><td>Topsoil and subsoil</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Method of soil analysis</td><td>Wet/dry chemistry</td><td>Proximal-sensing derived</td><td>NA</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Quality assurance during successive stages of measurement / monitoring</td><td>No</td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Modelling in reporting stage</td><td>Not applied</td><td>Data-driven models</td><td>During monitoring round</td><td>Process-based models</td><td>Final reporting</td><td>Hybrid models</td><td>All</td><td></td><td></td></tr><tr><td>Reporting periods</td><td>Pre-implementation &lt; 5 years</td><td>5 - 10 year</td><td>Result-based: convenience sampling<sup>c</sup></td><td>10-15 year</td><td>Result-based: probability sampling</td><td>&gt; 15 years</td><td></td><td></td><td></td></tr><tr><td>Frequency of reporting</td><td>Action-based: proof of adoption of practice</td><td></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Verification approach</td><td>No</td><td></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Uncertainty quantified in reporting stage</td><td>No</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Defines acceptable level of uncertainty in verification stage</td><td>No</td><td></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Transparency and reproducibility requirements</td><td>Low</td><td>Moderate</td><td></td><td>High</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Leakage requirement</td><td>No</td><td></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Additionality requirement</td><td>No</td><td></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Permanence requirement</td><td>No</td><td></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Reversal requirement</td><td>No</td><td></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Data retention/ sharing policy</td><td>No</td><td></td><td>Yes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

<footnote><sup>a</sup>No differentiation is made here for Forestland, which may include Afforestation and Reforestation.</footnote>

<footnote><sup>b</sup>These ecosystems and not listed in any particular order.</footnote>

<footnote><sup>c</sup>Non-probabilistic sampling.</footnote>
<figure><img src="image_23.png" /><figcaption>**Table 4.** Scoring of MRV guidelines and approved methodologies<sup>a</sup>.</figcaption></figure>

<table><thead><tr><th>Abbreviation</th><th>Purpose of MRV</th><th>Ecosystem(s) covered</th><th>Geographic scope</th><th>Aggregation (bundling) of farms</th><th>Tier level</th><th>Scope of monitoring</th><th>GHGs targeted</th></tr></thead><tbody><tr><td>AU-CFIDV</td><td>Voluntary carbon market</td><td>Agricultural land and woody vegetation</td><td>Specific country</td><td>Allowed</td><td>1</td><td>GHG accounting</td><td>All</td></tr><tr><td>AU-CFMM</td><td>Voluntary carbon market</td><td>Agricultural land and woody vegetation</td><td>Specific country</td><td>Allowed</td><td>3</td><td>SOC stock change</td><td>CO<sub>2</sub></td></tr><tr><td>BC-SCM</td><td>Voluntary carbon market</td><td>Croplands</td><td>Specific country</td><td>Allowed</td><td>3</td><td>SOC stock change</td><td>CO<sub>2</sub></td></tr><tr><td>CARSSE</td><td>Voluntary carbon market</td><td>Croplands</td><td>Specific country</td><td>Allowed</td><td>All</td><td>All</td><td>All</td></tr><tr><td>DE-MOOR</td><td>Voluntary carbon market</td><td>Wetlands/ peatlands</td><td>Specific country</td><td>Not allowed</td><td>1</td><td>GHG accounting</td><td>N<sub>2</sub>O</td></tr><tr><td>FR-LBC<sup>b</sup></td><td>Voluntary carbon market</td><td>Croplands</td><td>Specific country</td><td>Allowed</td><td>All</td><td>All</td><td>All</td></tr><tr><td>Gold Standard</td><td>Voluntary carbon market</td><td>Agricultural land and agro-forestry</td><td>Multiple countries</td><td>Allowed</td><td>All</td><td>SOC stock change</td><td>All</td></tr><tr><td>GSOC-MRV</td><td>Voluntary carbon market</td><td>Agricultural land and agro-forestry</td><td>Multiple countries</td><td>Allowed</td><td>All</td><td>All</td><td>All</td></tr></tbody></table>

LBC), see Supplementary Information S2 and footnote to Table 4).

## Characterization of reviewed MRV systems and methodologies

In view of the large number of characteristics involved, it would be helpful to reduce the multi-dimensional space in which the MRV approaches are scored to only two dimensions. As a result, each MRV approach would be situated in a two-dimensional plane, where MRV approaches that have similar characteristics are close to each other, while those exhibiting distinct features are positioned farther apart. This dimension reduction can be achieved with a statistical technique known as multidimensional scaling [MDS, see 183,184]. Here we applied MDS using the "cmdscale" function of the "cluster" package of the R software for statistical computing [185]. This approach differs from the one adopted by Demenois et al. [94], who used multiple correspondence analysis (MCA) to assess main differences between SOC standards. An important difference between MDS and MCA is that a characteristic that is systematically isolated from all the other characteristics (and hence considered "very particular") will always appear on the first dimension of the MCA plot, but not necessarily on the MDS plot [186].

Multidimensional scaling requires a dissimilarity matrix as input. This is a square matrix that has as many rows and columns as there are MRV approaches, and whose value at row *i* and column *j* stores the dissimilarity between the *i*-th and *j*-th MRV approach.

The dissimilarity between two MRV approaches is derived from the characteristics of the two MRV approaches. Since the characteristics of MRV approaches listed in Table 2 are measured on a nominal scale, common Euclidean distances cannot be computed. We therefore used the Gower metric [187]. This simply assigns distance 0 if the two MRV approaches have the same value for the characteristic, and distance 1 if they are not the same. This was done for all characteristics and the average of all distances was taken to define the dissimilarity between two MRV approaches. It is possible to assign weights to the characteristics and thus allow some characteristics to have more influence on the final dissimilarity metric than others. We did not do this here and assumed that all characteristics are equally important, which is a simplification. Furthermore, we treated all characteristics as nominal variables, even if the reported classes are on an ordinal scale. This means that for "Transparency and reproducibility of requirements," for example, the distance between two MRV approaches that score "low" and "high" is the same as that between two MRV approaches that score "low" and "moderate" or "moderate" and "high".

Results of the multidimensional scaling are shown in Figure 2. The MRV approaches are fairly uniformly distributed in the two-dimensional space and there are no clear clusters or extremes, although some patterns can be observed. MRV approaches in the lower right (LR) quadrant, for

<footnote><sup>a</sup>Only the first eight columns are shown here in view of the length of the full table (see Supporting Information S2 for the full set of ratings).</footnote>

<footnote><sup>b</sup>For abbreviations see Table 2. Note that several guidelines, such as FR-LBC, consider different methodologies and only one or two of these are assessed here (in *casu*, 'Field crops' for FR-LBC). Details are provided in Supporting Information S1.</footnote>
<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /><figcaption>**Figure 2.** Position of considered MRV guidelines and approved methodologies in a two-dimensional space after application of multidimensional scaling to MRV characteristics.</figcaption></figure>

example, all relate to a specific country and most follow a Tier 1 approach. While they focus on different ecosystems, they mainly have “moderate” or “high” transparency requirements, and most score yes for “additionality”, “permanence,” “leakage,” “reversibility,” and “data retention” requirements. Verification for all is action-based and transparency requirements are mostly assessed as “moderate.” All methodologies are aimed at the compliance market in the LR quadrant. In the lower left quadrant (LL), Gold Standard and GSOC-MRV are quite close indicating that they share key characteristics. Indeed, both MRV approaches aim at the voluntary carbon market, are applied in multiple countries, also consider Tier 3 level approaches, predominantly are result-based and agree on many of the requirements, yet not in a systematic way. Alternatively, IPCC is focused on the compliance market, considers all ecosystems (e.g. forestlands, grasslands, and croplands), all three Tier levels, yet can follow different verification pathways. Other clusters of apparently similar MRV approaches are AU-CFMM, BC-SCM, NL-SNK and Regen; all four are developed for a specific country or countries, make use of process-based models except for Regen, consider soil measurements, have similar reporting frequency, are result-based, and have a

data retention/sharing policy. They all occur in the UL quadrant and mainly are for agricultural land, i.e. grassland and/or cropland. In the upper right quadrant (UR) all MRV approaches focus on the voluntary carbon market. VM0006, Nori and Plan Vivo all focus on SOC stock change and are applicable in multiple countries, use historic land management as baseline setting and do not require ground truth SOC observations (i.e. verification is action-based), but have different frequencies and periods of reporting. VM0042 is somewhat “isolated” in Figure 2 in the sense that it occurs at the top of the central vertical axis, but no clear explanation for this can be found. Filtering the considered MRV approaches by axis (i.e. “LL-LR” for Dimension 1 resp. “LL-UL” for Dimension 2) does not provide any clearcut “messages” for possible captions. Possibly, the main conclusion that can be drawn from Figure 2, considering the simplifications involved, again is that the landscape of MRV guidelines and applied methodologies is quite diverse.

It should be noted that five out of the twenty-six characteristics are related to a verification “requirement” (e.g. absence of leakage or reversal requirement). Since no weights were applied these characteristics together have a strong effect on the
<figure><img src="image_26.png" /></figure>

outcome of the multidimensional scaling, for example five times stronger than “Purpose of MRV” or “Ecosystem(s) covered.” There is much to say for reducing their influence by assigning weights. Likewise, many users might wish to assign a higher weight to characteristics such as “requires ground truth SOC observations” and “Target depth interval (for soil sampling),” or “Tier level” than to characteristics such as “Reporting periods” and “Frequency of reporting.” Assigning weights involves subjective choices but so does the a priori decision about which characteristics are included in the analysis. A sensitivity analysis on a few characteristics could show which characteristics are more important and how the results would be affected. Alternatively, it could be worthwhile to organize stakeholder workshops to jointly define and refine key characteristics of MRV guidelines and methodologies, assign associated weights, and evaluate the sensitivity of the multidimensional scaling results to choices made. Finally, it should be noted that the reduction to a two-dimensional space caused a significant loss of information, indicating that Figure 2 should only be used in an indicative way.

## Towards an operational, integrative, and multi-ecosystem MRV approach for SOC stock changes

### General considerations

MRV frameworks typically comprise several “building blocks” (see Figure 1) that consider various levels of complexity, as shown in Table 1 for modeling approaches. Building on the present review, and recommendations made by the International Consortium on Soil Carbon Sequestration in Agriculture [188], we highlight the need to develop a methodological framework and prototypes for operational multi-ecosystem monitoring tools for net SOC stock changes. Ideally, this modular tool (preferably web-based) would:

a. define the project's name, boundaries, duration, scope (e.g. NDC or insetting), and ecosystem(s) under consideration;

b. permit uploading of activity data and of other relevant spatial data layers, with preference for local data when available (e.g. soil data, climatic data, aboveground biomass data);

c. define the relevant method for designing the baseline (e.g. project specific, generic) and

test different scenarios (e.g. type of land use conversion, recommended management practices) to increase SOC stocks and reduce GHG emissions (decision tool);

d. produce biophysical products derived from EO data that will eventually be assimilated in Tier 3 modeling approaches or used in ML (machine learning) and AI (artificial intelligence) approaches.

e. utilize a decision tree to select and run the appropriate methodology for monitoring (e.g. Tier 1 to Tier 3 with or without assimilating EO data, AI or hybrid). One of the criteria for building the decision tree itself is the context of MRV (e.g. NDCs, voluntary carbon market, insetting) that defines the duration and frequency of monitoring (e.g. annually estimates for insetting prevent the use of in-situ soil sampling approaches), while other considerations such as data availability, technical expertise, costs and project scope will also be accounted for.

To guide the development of such operational tools, we propose a methodological framework presented in Figure 3. However, there are several limitations to this framework: it is not yet adapted to situations involving land use change, does not account for non-CO₂ greenhouse gas emissions and climatic (e.g. albedo changes) and environmental effects, and it does not consider the trade-off between cost and accuracy in monitoring approaches. Further, Figure 3 does not visualize the various levels of maturity, accuracy or scalability of the different methods available for monitoring or verifying SOC stock changes.

### Monitoring

It appears that the main challenges in developing a unified, operational MRV system concern the monitoring component. As shown in Figure 3, this component can rely on various approaches the choice of which may depend on the previously mentioned decision tree (their spatial arrangement in Figure 3 is arbitrary);

a. Soil MRV approaches that combine SOC stock change with consideration of field measurements,

b. Tier 1 or Tier 2 approaches, for instance when there is no process-based model calibrated and validated for the local context;
<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /><figcaption>**Figure 3.** Proposition for a multi-ecosystem methodological framework depicting a modular flowchart for building an operational MRV tool for assessing net SOC stock changes (abbreviations: A.I., artificial intelligence; M.L., machine learning; IR, infrared; FMIS, Farm Management Information system).</figcaption></figure>

c. Tier 3 approaches which may or may not: assimilate remote sensing, be based only on soil modelling, use independent soil and vegetation models (e.g. biomass estimates being used as input in the soil model), or use ecosystem models that assess the full ecosystem carbon budget, sometimes with limited field validation. Alternatively, emerging AI [189,190] and evolving ML-based SOC MRV approaches [138, 191] may be considered.

In the French LBC methodology for arable land, for example, a Tier 3 approach with a focus on soil was chosen. There are no clear guidelines for modelling or measuring crop or cover crop biomass in the field or by satellite, and no field-measured SOC changes over time are considered for local validation [192,193]. However, large uncertainties in SOC stock change estimates can result from rough biomass estimates. Wijmer et al. [120], for example, showed that assimilating averaged LAI (leaf area index) at plot level instead of using high-resolution LAI products (10 m) can result in a significant underestimation of the winter wheat biomass. Overall, very few guidelines, methodologies or tools rely on biomass quantification by remote sensing (or by hybrid modeling approaches such as remote sensing data assimilation) to map its

spatial variability in an attempt to provide the soil models with more accurate estimates of biomass carbon inputs.

Assimilation of remote sensing data in plant or ecosystem models can correct the model's trajectory [194]. Such models assimilate biophysical products (e.g. LAI, aboveground biomass) derived from radiative transfer models and remote sensing products (corrected for atmospheric effects with orthorectification) that may be built on sensors with different wavelengths [195–198] as shown in Figure 3. Such radiative transfer models can be integrated in the monitoring tool as in Wijmer et al. [119]. However, as shown for croplands [188] and forestlands [199,200], upscaling process-based models initially developed for local applications by assimilating remote sensing data is challenging as they need much data for model evaluation and calibration. Based on these considerations, Tier 3 type estimation methods for current carbon stocks may benefit from assimilation of remote sensing data. However, a dedicated new generation of models such as SAFYE-CO₂ [154,155], tailored to upscaling the carbon budget components, would have to be developed for this. Meanwhile, another approach may consist in correcting the model's output based on the analysis of the vegetation's spatial variability by using spectral indices such as
<figure><img src="image_29.png" /></figure>

the Normalized Difference Vegetation Index or LAI maps derived from remote sensing [201].

Alternatively, a combination of Tier 3 approaches could be used. For example, a single vegetation model, with or without remote sensing data assimilation, may provide biomass input data to one or several soil models (e.g. for an ensemble approach). Further, for Tier 3 methods EO assimilation may not always be possible (e.g. for very small plots, or optical EO data in very cloudy areas) or relevant (e.g. in hilly areas where the limited accuracy of biophysical products may result in a high uncertainty on the final estimates). It should be noted also that several biophysical products, such as LAI and superficial soil water content, could be assimilated in a specific process-based model and that several radiative transfer models could be used to derive a specific biophysical variable in an ensemble approach. As such, each methodology should be regularly evaluated and whenever possible improved based on *in situ* data collected at long-term experimental sites or from flux tower networks as shown in Figure 3.

Implementation of a Tier 3 approach, however, may not always be relevant or possible for example when process-based models are not calibrated or validated for the project's local context. We refer to the prototype quantification platform developed for croplands in France as an example. It considers the joint use of NIVA's [195] algorithms for Tiers 1 and 2 and the SAFYE-CO₂ model [154,155] for Tier 3. Alternatively, for Tier 3 a hybrid modeling approach could be used for an area defined by land use type, building for instance on Wijmer et al. [120]. Depending on the context and scale of monitoring, soil data to run the soil module may be obtained from local sources, respectively from appropriately scaled soil products (e.g. SoilGrids [196], LUCAS [197] or national soil databases) complemented with in-situ field measurements for validation.

Each ecosystem has its specificities, therefore some "building blocks" (or modules) will have to be designed, added or implemented in a context-specific way [198, 202-204]. For instance, monitoring SOC stock changes for peatlands (highly organic soils) would require adapted SOC models [205] and an adapted framework that considers seasonal fluctuations of the water Table [206,207], and may require coupling with a hydrological module [208]. Likewise, specific approaches would be needed to model net SOC stock changes in other ecosystems such as forestland [31, 170, 173, 199] or in urban environments [209,210].

## Reporting and verification

Reporting requirements are fully linked to the objectives of the applied methodologies (e.g. national GHG inventories or carbon farming), the stakeholder responsible for the reporting (e.g. a state, a farmer) and the nature of the payment systems (i.e. payment for practice, payment for performance, or payment for practice with performance dividend). Generally, the more complex the methodologies are, the more demanding the reporting will become. For example, reporting on the implementation of a practice might be straightforward for a farmer, but this will be far more challenging if a farmer aims for a payment for performance derived from model-based approaches. Therefore, it is essential to consider the heterogeneity of capacities (e.g. reliable access to Internet for online reporting) and expertise of the stakeholder responsible for the reporting.

CIRCASA [211] made recommendations for cropland that could be used for or adapted to other ecosystems. The CIRCASA team suggested that reporting should primarily be through gridded data extraction (e.g. of the modelled outputs) for any spatially defined area (e.g. a field, farm, small region, sourcing area of an industry, given crop type, or country) and any time period (e.g. one year for CAP or insetting programs up to several years or decades for NDCs or offsetting projects). Inherently, all SOC stock change estimates should be provided with the same unit (e.g. g C m⁻² or g CO₂eq m⁻² per time period considered); any claims put forth by projects must always be substantiated by statistically rigorous evidence and be independently verified by a third party.

Concerning baseline setting, we recommend an adaptive framework, with supporting guidelines/tools, that would accommodate both generic (i.e. calculated for a given type of pedoclimatic conditions × crop rotation × practices) and project-specific baselines. The operational processing chain described above would allow both. First, it could produce information on several years prior to implementation of, for instance, a carbon farming program but also during the project's life cycle. Second, because the proposed approach is based on remote sensing and hybrid modeling it will allow to simulate scenarios for plots/farms in the same pedoclimatic region (or landscape) that consider the adoption level of recommended carbon farming practices, and this for a range of crop rotations.
<figure><img src="image_30.png" /></figure>

Verification of the practices implemented during performance-based projects could benefit from modern technologies. For instance, activity data could be collected through mobile phones [212,213], online portals [214] or connection to Farm Management Information Systems (FMIS) [215,216] with application programming interfaces (APIs) [217]. Yet, our own recent experience has shown that activity data in FMIS may lack reliability/consistency and may require to be checked by a third party (e.g. an agricultural council).

For verification of SOC stock changes, we recommend an approach based on soil re-sampling (e.g. surveys, grids, demonstration farms) and remote sensing using standardized protocols. To optimize the cost/accuracy ratio of verification, well-designed initial and final soil sampling schemes are needed [37, 218,219]. Further, the process should be based on consistent sampling procedures and comparable analytical methods [147, 220–224]. A principal decision is whether to adopt a design-based or model-based statistical inference for output-based verification. The important advantage of design-based statistical inference (i.e. statistical sampling theory) is that it is entirely model-free, hence makes no assumptions [225,226]. It yields unbiased estimates of SOC stock change for an area of interest while the associated estimation accuracy is quantified. However, it requires a probability sample from the area of interest, with all inclusion probabilities known and greater than zero. Model-based approaches essentially rely on statistical regression (e.g. machine learning [227] or kriging [228]). They may also use uncertainty propagation methods [229,230] when outputs of process-based models are used to infer the SOC stock change and uncertainties in model inputs, parameters and structure need to be accounted for. It is important to note that, due to an averaging out effect, uncertainty decreases when spatial averages of SOC stock change are computed [159, 219]. The uncertainty decrease is largest when errors have a low spatial correlation. More information about design-based and model-based statistical inference for verification of output-based projects is provided elsewhere (Supplementary Information 1).

For verification of results emanating from "advanced" Tier 3 approaches, pluri-annual high-resolution maps of biomass and SOC stock changes produced by hybrid modeling approaches (e.g. AgriCarbon-EO) could be useful as these will provide insights into spatio-temporal dynamics of

C stock changes, for instance to help identify areas that preferentially store or lose C. Such an integrated approach would allow for a substantial reduction in the number of soil samples required to detect significant SOC stock changes (at a pre-defined confidence level), ultimately providing more representative and accurate estimates.

## Conclusions

Current MRV systems use a diversity of guidelines and approved methodologies. These consider a wide range of procedures to manage, for example, additionality, uncertainty, persistence, baselines, measurement, reporting and verification. A selection of current MRV guidelines and approved methodologies, as applied to various ecosystems in defined geographies, was characterized according to twenty-six "key characteristics" using a pre-defined number of classes/options for each characteristic. Subsequent multi-dimensional scaling showed that the considered MRV methodologies are fairly uniformly distributed in the two-dimensional space and that there are no clear clusters or extremes, and some patterns were observed. The assessment, however, was not unambiguous as it required simplifications. In retrospect, having a binary categorization of characteristics like leakage, permanence, and additionality could not capture the nuance as to how the different protocols actually account for those "complex" issues. Although a protocol can set these requirements, this does not mean that they actually do a "sufficient job at" accounting for them [231]. In the future, it could be worthwhile to organize regional stakeholder workshops to jointly revise the list of key characteristics while also assigning weights to each characteristic with a view to evaluate the sensitivity of the results of the multidimensional scaling procedure to various choices.

As a follow up to this review, building on the elements shown in Figure 3, the next phase of the ORCaSa project will be to develop a prototype for an operational, modular and multi-ecosystem MRV system that would be applicable in different contexts (e.g. national or subnational reporting, CAP, voluntary carbon market, insetting/supply chain) and at different levels of complexity (i.e. Tier 1 to 3), depending on the context of application and the availability/accuracy of input data. An important element of that work will be to develop
<figure><img src="image_31.png" /></figure>

decision trees to guide the choice of MRV system components that will be proposed by ORCaSa.

There are still numerous research and governance issues to consider with respect to improving MRV approaches [232,233]. These are being addressed in the framework of the Soil Carbon International Research Consortium (IRC) which aims to provide better access to research, methods and practices for soil carbon (see https://www.impact4soil.com/), and a strategic research agenda is currently being developed.

## Acknowledgements

This study was carried out in the framework of the EU ORCaSa project (https://irc-orcasa.eu). We thank the participants of two international stakeholder workshops for their valuable contributions. Our special thanks go to the anonymous reviewers whose insightful comments and suggestions helped us to improve the original submission.

## Disclosure statement

The authors have no financial or non-financial competing interests associated with this research or the publication of this manuscript.

## Funding

The ORCaSa project has received funding from the Horizon Europe Programme under grant agreement n° 101059863.

## Data availability statement

The materials and data that support the findings of this study are available at https://doi.org/10.17027/isric_e6dac18.

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