Standardized Metrics in Regenerative Agriculture for Climate Change Adaptation and Mitigation

Abstract

Regenerative agriculture (RA) is an alternative approach in combating climate change adaptation; however, its effective implementation at scale depends on the development and adoption of standardized metrics. The methodology of this systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, in order to maintain a high level of transparency and rigor throughout the process of selecting and evaluating the included studies. This research identified the challenges and opportunities associated with implementing a robust monitoring, reporting and verification (MRV) framework, which combines direct measurements, proximal sensors and remote sensing to balance accuracy and costs. An innovative aspect of this work is the integration of both social and economic indicators for assessment of RA performance, highlighting the importance of incentives based on verifiable outcomes to support the long-term adoption of regenerative practices. In addition, innovations that can facilitate the scaling and validation of these metrics are explored, which encompasses the use of open and interoperable digital infrastructures to enhance connectivity and integration. This systematic approach contributes to the development of an integrated and adaptable setting for the evaluation and monitoring of RA, serving as a cornerstone for policy formulation and sustainable management strategies.

1. Introduction

Climate change is profoundly reconfiguring agri-food systems, and agriculture is simultaneously part of the problem and part of the solution. In recent years, the literature has shown that transitioning to production models oriented towards ecological processes can reduce vulnerability to climate shocks and contribute to reducing net emissions, by improving soil health, increasing the diversity of cropping systems and optimizing biogeochemical cycles [1,2]. Current approaches to assessing sustainability in agriculture remain fragmented and often focus on isolated dimensions, either ecological or economic, without capturing the systemic interactions that define the real performance of farms. Many analytical frameworks focus on biophysical indicators, such as greenhouse gas emissions or soil organic carbon content, but neglect socio-economic dimensions, such as income stability, working conditions or access to green finance, which are equally essential for a just transition [3,4]. This lack of integration limits the comparability of results across regions and makes it difficult to formulate coherent policies, aligned with the objectives of the Common Agricultural Policy (CAP) and the European Green Deal. Therefore, there is a need to develop a robust and standardized framework that is able to connect ecological processes with socio-economic outcomes and to transform scientific evidence into concrete public policy tools, designed to endorse the embracing of RA practices.

Regenerative agriculture brings together a set of agroecological practices aimed at restoring soil ecosystem functions and strengthening farm resilience. Among the most widely used are diversified crop rotations, reduced mechanical tillage, green crops, legume integration and agroforestry, all of which contribute to increasing soil organic carbon (SOC) and improving biodiversity. In order to assess the results of their implementation, standardized methods and indicators have been developed, such as organic carbon content, greenhouse gas emission balance, soil fertility, water use efficiency or farm profitability [4,5,6,7,8]. Regenerative agriculture (Figure 1) has thus become a remarkably dynamic operational and research framework; however, its impacts remain difficult to compare across regions and practices due to the absence of a coherent set of standardized metrics and methodological variation in study design and reporting [4,9,10,11]. As a result, quantitative syntheses and policy assessments suffer from a lack of interoperability of indicators, and conclusions on the real capacity for adaptation and mitigation are often contingent upon the choices of measurement and observation periods. This fundamental issue is being increasingly highlighted, which calls for a shift from disparate inventories to robust, replicable, and transparent monitoring, reporting, and verification frameworks [12,13].

From a mitigation point of view, the scientific consensus has significantly refined over the past five years. Thus, agricultural soils can function as an important carbon sink when management promotes sequestration and reduces losses; however, the realistic potential is constrained by biogeophysical factors, pedoclimatic conditions and reversibility risks. Recent analyses indicate a credible range of sequestration potential, emphasizing that optimistic estimates need to be carefully calibrated to local realities and implementation costs. At the same time, approaches like soil cover, minimized tillage, diverse sequence of rotational crops to enhance soil health and tree integration can simultaneously deliver carbon benefits and agroecological co-benefits if supported by rigorous monitoring. The current debate is not only about how much carbon enters the soil, but also how it is measured, at what depths, with which analytical methodologies and over what time horizons, aspects that condition the comparability between studies and the eligibility for carbon markets [14,15,16,17]. Regarding the adaptation component, studies show that regenerative practices can increase the resilience of agroecosystems by improving aggregate structure and stability, promoting higher matter levels, supporting efficient nutrient cycling and regulating water dynamics; all these effects translate into production stability under thermal and water stress conditions. However, signals regarding yields and net emissions remain heterogeneous between climates and soil types, which requires a taxonomy of indicators that considers the context and explicitly links biophysical outcomes to socio-economic outcomes at the farm level and along the value chain. In this regard, recent studies recommend moving from practice indicators to outcome indicators, capable of capturing both ecosystem services and economic and social performance in an integrated assessment framework [18,19,20]. Technological progress has paved the way for more comparable and scalable metrics; the integration of multi-source remote sensing, process modeling and stratified spatial sampling allows annual landscape-scale tracking of practices and outcomes, reducing estimation uncertainties and MRV costs. Platforms that combine satellite imagery, machine learning algorithms and in situ data can deliver standardized indicators of vegetation cover, tillage intensity, rotations or green crop coverage, providing an essential link between plot, farm and region. In parallel, the digitalization of MRV for low-emission agriculture is becoming a condition for the credibility of carbon credits and for the integration of agricultural outcomes into corporate and national decarbonization strategies [21,22,23].

A wide range of metrics have been used in the literature to assess regenerative agriculture, from biophysical indicators (soil organic carbon content, greenhouse gas emissions or water use efficiency) to socio-economic indicators, which measure the profitability, income stability and resilience of rural communities. However, most existing frameworks treat these dimensions separately, without coherently integrating the relationships between ecological processes, economic performance and social impacts. Also, methodological differences and the lack of common monitoring and verification protocols limit the comparability of results and the applicability of policies at a regional level. Therefore, the current literature remains fragmented and incomplete, requiring a systematic review to assess the degree of integration of these metrics and their relevance for the climate transition of agriculture.

Based on these findings, the present research aims to answer three essential questions. First, what types of standardized metrics are currently used in literature to evaluate RA under the pressures imposed by climatic changes. Second, to what extent do these metrics simultaneously capture adaptation and mitigation functions. Third, what are the main regional and sectoral differences in their application. The analysis also aims to identify methodological gaps and opportunities for the development of a coherent assessment framework, capable of connecting biophysical results with socio-economic and institutional ones. Finally, we aimed to explore the opportunities offered by emerging technologies (remote sensing or digital sensors) for the development of robust and globally comparable metrics. The purpose of this approach is to propose an operational classification of biophysical and socio-economic indicators, to inventory the monitoring, reporting and verification approaches reflected in the literature (in the period 2020–2025) and to contribute to the substantiation of a set of comparable, reportable and verifiable metrics, compatible with both scientific rigor and public policy requirements within the agriculture domain and climate change interactions.

2. Materials and Methods

The research methodology approached for the systematic review was built according the PRISMA framework [24]. The process aimed to ensure transparency, reproducibility and comprehensive coverage of relevant sources, with a focus on the literature published between 2020 and 2025 in prestigious journals indexed in international databases. The selection was based on the Web of Science database (accessed on 27 August 2025), which concentrates a large part of recent publications on regenerative agriculture and climate change. The search topic was based on aspects related to “regenerative agriculture”. The search yielded a total of 884 publications. Results were processed using VOSviewer (version 1.6.20) to perform the keyword analysis, with a minimum co-occurrence threshold of two set for keyword selection (77 results) (Figure 2). The threshold of at least two co-occurrences was selected to ensure a balance between thematic relevance and network clarity, avoiding excessive overlap of rare or isolated terms. Figure 2 illustrates the keyword co-occurrence network, highlighting the main thematic cores associated with regenerative agriculture, agroecological practices and soil management, sustainability assessment and monitoring metrics, climate adaptation and emission reduction, as well as socio-economic dimensions and supporting policies.

The search strategy used a combination of keywords such as “regenerative agriculture”, “standardized metrics”, “climate change adaptation”, “climate change mitigation”, “soil carbon sequestration”, “sustainable agriculture”, “resilience”, “greenhouse gas emissions”, “monitoring reporting verification”, “remote sensing” and “digital agriculture”. The terms were combined using logical operators to maximize the relevance of the results, for example “regenerative agriculture AND standardized metrics OR climate change”. To maintain the coherence of the analysis framework, only peer-reviewed and open access articles that explicitly discuss metrics or indicators related to regenerative agricultural practices, with applicability in assessing climate change adaptation and mitigation, were included. Publications that address sustainable agriculture in a general sense, without direct references to standardized metrics or comparable assessment tools, were excluded. The selection process involved a first stage of filtering in relation to summaries, followed by full reading of documents to confirm relevance. Selected articles were then classified thematically, by categories of metrics associated with adaptation, mitigation or socio-economic dimensions, with a focus on the degree of standardization and applicability in different regional contexts. To ensure the accuracy and transparency of the process, the selection flow was summarized in a PRISMA diagram briefly showing the methodology steps for the studies incorporated in the analysis (Figure 3).

Data extracted from the selected literature were then analyzed depending on relevance. Spatio-temporal patterns in relation to regenerative agriculture were structured in a context analysis, while for content analysis, the most relevant findings were organized in four chapters bringing together information on the main aspects investigated in this review (metrics for regenerative agriculture, socio-economic indicators, MRV framework, current issues (gaps and challenges and opportunities and innovations)). This structuring allowed for a qualitative and comparative synthesis, aiming to answer the formulated research questions and identify methodological gaps, convergences and opportunities for standardization of metrics in the field of regenerative agriculture. We would like to specify that the main dataset used for developing the methodological framework was built using the Web of Science Core Collection, while complementary searches were performed in other scientific databases to ensure comprehensive coverage of the relevant literature.

2.1. Context Analysis

The concepts of regenerative agriculture are often addressed in the specialized literature, while the actual applicability in combating climate change is rather an innovative approach towards sustainable agriculture. The core of articles selected provides an overview of the actual state, as well as the challenges that regenerative agriculture faces at the moment. The temporal criteria show a tendency to analyze the situation in sustainable agriculture, especially in the last five years (August 2020–2025). The studies have only begun to increase in the last 4–5 years, an aspect that illustrates the growing concerns for restructuring and improving agriculture with the aim of transitioning it towards a more sustainable version (Figure 4A).

According to Figure 4B, it can be said, in terms of spatial distribution, that there is a relatively balanced distribution at the global level. This aspect is relevant because it underscores the efforts in the transition to regenerative agriculture.

2.2. Content Analysis

As the pressure on agri-food systems increases due to climatic effects, agriculture became the star of its fundamental transformation, being called upon to integrate sustainability principles not only as a response to regulatory requirements or social expectations, but also as a driver of regenerative innovation. In this sense, regenerative agriculture is emerging as an essential framework for action, and the development of standardized metrics becomes crucial to measure the real impact on soil carbon and ecosystem resilience through biodiversity. Standardization facilitates comparability and transparency and stimulates responsible investments, connecting agricultural practices with sustainable financing objectives and European and international strategies for climate neutrality.

Within the content analysis, the most relevant results were organized into four main directions, each bringing together essential information for understanding and systematizing the field of regenerative agriculture. The first direction concerns the metrics used to evaluate regenerative agriculture, which constitute the foundation of any standardization process. The second direction analyzed concerns socio-economic indicators, which highlight the pivotal role of rural communities and farmers in driving the transition toward regenerative practices. The third direction is represented by the MRV framework, essential for validating and recognizing the results of regenerative agriculture at a global level. The last direction of the analysis integrates current problems and methodological gaps, but also opportunities and innovations identified. This dynamic highlights not only the present challenges, but also the capacity to fundamentally transform agricultural systems in the field and provide real solutions for climate change adaptation and mitigation.

3. Results

3.1. Dual Role of Agriculture in Climate Change

Agriculture plays a complex and seemingly contradictory role in climate change (Figure 5). In addition, it is a valuable contributor to GHG and ecosystem degradation, and, on the other hand, it represents one of the most promising nature-based solutions for adaptive and mitigative responses to climate change. Within the European Union, the agriculture is directly accountable for around 11% of GHG (mainly methane and nitrous oxide) and also captures carbon through photosynthesis and storage in soils and vegetation, indicating the potential for agriculture to play a “dual climate role” [12,25,26,27,28].

RA pushes beyond the concept of sustainability and is emerging as a transdisciplinary approach, oriented towards the restoration of ecosystems and the revitalization of natural resources. RA’s description in the literature is not consistent; it is sometimes focused on practices and sometimes on results, but essentially reflects a philosophy that privileges adaptation to the local context, a reduction in synthetic inputs, animal–crop integration, soil conservation and the promotion of ecosystem functions, biodiversity and soil health. This definition is also supported by studies that highlight the fact that regenerative agriculture uses soil-based methods as a starting point for the regeneration of multiple ecosystem services [4,29]. It can therefore act as an ecological solution to climate challenges, in addition to revitalizing soil and biodiversity, providing socio-ecological benefits without reducing farmers’ yields or incomes. The importance of regenerative agricultural practices can also be reflected in the regeneration of the agricultural ecosystem by sequestering carbon, restoring soil health and increasing climate variability resilience [10,30].

In light of recent research, the dual role of agriculture in the climate crisis is becoming the central element of academic and policy debate. On the one hand, conventional agricultural systems continue to be major sources of GHG emissions, the degradation of soils and the loss of biodiversity; on the other hand, by adopting regenerative practices, agriculture holds the capacity to become an essential tool in the context of adaptation and mitigation strategies, actively contributing to carbon sequestration, restoring natural capital and strengthening the resilience of rural communities. This duality is not an irreconcilable paradox but a starting point for understanding that the transition depends on the ability to replace harmful practices with regenerative solutions, validated by scientific evidence and quantified by standardized metrics. Thus, the integration of regenerative agriculture into global climate strategies requires not only a recognition of this dual role, but also a systematic effort to develop and harmonize indicators that can credibly measure both negative contributions and emerging benefits. Only through a robust monitoring, reporting and verification framework can agriculture be ensured to move from a structural problem to a strategic solution. Consequently, its dual role should not be seen as a contradiction, but as an opportunity for transformation, from a sector traditionally perceived as vulnerable and polluting to an essential vector of the green transition and long-term sustainability.

3.2. Metrics for Regenerative Agriculture

In regenerative agriculture, metrics are not just numbers, but vital tools that tell us whether changes are actually working. At the same time, they are a direct reflection of how this agricultural model reshapes ecosystems and local economies and contributes to combating climate change (Figure 6). Measuring the performance of regenerative agriculture requires a coherent set of metrics that reflect both biophysical processes (such as soil carbon stocks and flows), ecological impacts (diversity, soil health, water quality) and socio-economic outcomes (productivity, costs, financial resilience). Recent studies suggest that fundamental metrics include soil organic carbon (SOC) content and its dynamics over clearly defined time windows, greenhouse gas fluxes (CO₂, CH₄, N₂O) per unit of output or hectare, soil health indicators (organic matter, aggregate stability, microbial biomass), functional biodiversity metrics, and economic indicators that assess on-farm cost-benefit. These elements are recognized as priorities for MRV and integration into Environmental, Social, Governance (ESG) reporting, as they allow for comparability across practices and locations, the assumption of additionality, and the estimation of permanence of carbon sequestration [4,11,19,31,32].

Empirical evidence reinforces the significance of integrating biophysical, ecological, social and economic indicators into MRV frameworks. Recent studies illustrate how combining SOC measurements, GHG fluxes, biodiversity indicators and economic data at the farm level allows for a more accurate assessment of sustainability outcomes [33,34]. As an example, the study by Billah et al. [35] provides empirical evidence regarding the integration of climate-smart agriculture (CSA) interventions, highlighting the importance of integrating biogeophysical, ecological and socio-economic indicators for farm sustainability. The authors identify constraints such as limited access to information and extension services, but also highlight effective support strategies, including farmer training and incentives for innovation, providing concrete examples of CSA practices applicable in diverse agricultural contexts [35].

Globally, empirical evidence supports the use of these metrics (see

Table 1. Summary of metrics and effects associated with regenerative agricultural practices in different geographic regions.

Region	Regenerative Agricultural Practices	Monitored Indicators	Reported Effects	Methodological Notes
Europe	cover crops, minimum tillage, diversified crop rotations	SOC (Mg C/ha), biodiversity, aggregate stability	significant SOC accumulation in 0–30 cm and 0–100 cm layers, biodiversity restoration, reduced erosion	emphasis on time windows (years–decades) and standardized depth layers
United States	cover crops, reduced tillage	SOC, GHG (CO2, N2O), soil health indicators	average SOC increases, improved soil health, regional N2O emission variability may offset carbon storage gains	simultaneous measurement of SOC and GHG fluxes recommended
Africa	zai pits, agroforestry, water conservation	SOC, soil fertility, socio-economic indicators	SOC and fertility improvements in small-scale systems, increased income resilience	indicators should be adapted to local socio-ecological contexts
Asia and Australia	agroforestry, perennial systems	SOC, ecological functions, soil moisture, plant physiological indicators	measurable SOC increases; strong dependence on climate and management practices	inclusion of moisture and physiological metrics is crucial
Global	integrated MRV and ESG frameworks	SOC, GHG, biodiversity, economic indicators	SOC recognized as a key metric, biodiversity increases (microbial diversity up to +50%), alignment with carbon market standards	requires harmonized MRV protocols, clear rules on sampling depth, time windows, and reporting units

). In Europe, regional studies have shown significant soil organic carbon (SOC) accumulations following the adoption of conservation practices (cover crops, reduced tillage, diversified rotations), and research analyzing SOC dynamics under different land-use regimes provides useful quantitative values for MRV benchmarks [36,37,38,39]. These works repeatedly emphasize the importance of time windows (years–decades) and depth layers (0–30 cm, 0–100 cm) in realistic estimations of sequestration. For example, SOC is a valuable indicator [40,41,42,43]. Studies in this direction indicate that the adoption of cover crops can increase SOC and stabilize and preserve it for a longer period [32,44,45,46]. Biodiversity, too, is equally relevant; in just a few years, regenerative systems will be able to restore biodiversity and improve natural habitats, increase microbial diversity (by up to 50%) and reduce erosion [30,47].

In the United States, for instance, extensive studies of cover crops and reduced tillage practices document average increases in SOC and positive effects on some soil health indicators; however, they also signal trade-offs, notably regional variations in nitrous oxide (N₂O) emissions that may partially offset carbon storage benefits if not accounted for at the GHG balance level [48,49,50]. Several studies indicate that additional N₂O emissions can partially or even completely offset the benefits of SOC storage. For example, Li et al. [50] estimate that these additional emissions can cover between 75% and 310% of the carbon stored, depending on the scenario. Other studies show that the benefits of climate mitigation through SOC are often overestimated if increases in N₂O are not taken into account [48,49].

These studies clearly recommend that metrics include simultaneous measurements of SOC and GHG fluxes to avoid overestimating climate potential. Moreover, the importance of agroforestry has been documented in the literature, as well as of grassland restoration in accumulating SOC and providing ecosystem services, which provides estimates of storage potential and implications for land policy (e.g., opportunities for converting degraded grasslands and avoiding deforestation). This body of studies provides parameters for regional-scale models and emphasizes the need to include variables related to previous land use and the risk of leakage in metrics [43,51,52,53].

In Africa, the diversity of local solutions (from water conservation techniques and zai pits to agroforestry) improves SOC and fertility in small-scale systems. These studies provide data on the variation in carbon stocks by land use type and emphasize the importance of adapting metrics to local socio-ecological practices and conditions, as well as the role of income resilience indicators for smallholder farmers [54,55,56]. On the other hand, in Asia and Australia, data from agroforestry and perennial systems indicate measurable gains in carbon stocks and ecological functions, but also show a strong dependence on climate and management conditions (irrigation, fertilization, species planted). In this regard, the recommendations rely on the inclusion of soil moisture measurements, but also plant physiological indicators in the metrics package, as they significantly modulate the rate of SOC stabilization and the emission–sequestration balance [57,58,59].

Thus, two practical consequences for the development of metrics are observed: on the one hand, the package of indicators must be comprehensive enough to capture the benefits (biodiversity, soil health, hydrological services) and trade-offs (N₂O emissions, leakage potential), and on the other hand, clear rules must be adopted regarding time windows, sampling depths and reporting units (Mg C/ha, kg N₂O-N/ha/year, biodiversity indicators per area unit, economic indicators per farm). This combination makes metrics useful for both research and public policy, for accessing climate finance and for land-based carbon markets; however, it also requires the development of nationally and internationally harmonized MRV protocols to ensure credibility and comparability [7,32].

3.3. Socio-Economic Indicators in Regenerative Agriculture

Socio-economic indicators are key aspects that reflect the impact of agriculture on the well-being of farmers and communities, covering both the economic and social dimensions. They include, among others, farm profitability, food security, access to innovative finance and social equity. Each indicator should not be considered in isolation, but interacts with the others; for example, improving access to finance can increase profitability and contribute to reducing social inequalities, and ensuring food security depends on the economic performance of farm and equity in the resources distribution. Thus, an integrated approach to socio-economic indicators allows for complete understanding of sustainability and resilience in agricultural systems [60,61].

Indicators of social and economic conditions are central to the assessment of regenerative agriculture (Figure 7), as they provide insight into the influence of agricultural practices on the well-being of farmers, rural communities and long-term economic stability. These indicators allow us to not only quantify ecological and climate benefits, but also understand how these benefits translate into tangible outcomes for people and society. These indicators reflect that implementing regenerative practices can have important effects on farm profitability, food security, income stability, employment and social cohesion [13,30,62].

A key indicator of the success of regenerative agriculture is farm profitability, assessed by the economic yield per unit area, the cost–benefit ratio or the net income per farm. This reflects not only the quantity of products obtained, but also the efficient utilization of available resources and the capacity of the agricultural system to generate sustainable benefits in the long term. Recent studies show that the adoption of regenerative practices (such as diversified crop rotations, reduced mechanical work on the soil, the implementation of cover crops and the integration of legumes) can lead to increases in profitability, compared to conventional systems. For instance, in their study, Jordon et al. [40] indicated the potential of cover crops and arable land rotations (UK) to promote carbon sequestration and storage (within 30 years) but also mitigate up to a quarter of agricultural GHG emissions. This increase is due to several interconnected factors, including reduced costs of chemical fertilizers and pesticides, increased agricultural yields by improving soil fertility, reduced risk associated with drought or climate fluctuations and increased resilience of the agricultural ecosystem [40,63,64,65].

Moreover, the impact on profitability is not limited to the individual farmer level, but has regional and global implications. At a regional scale, increased profitability can stimulate investment in agricultural infrastructure, access to sustainable technologies and diversification of economic activities in rural areas. For example, farmers implementing complex rotations or cover crops reduce the vulnerability of their systems to market price fluctuations, which has income-stabilizing effects at the community level and contributes to reducing rural migration. At the same time, these practices allow integration into green financing schemes, including carbon credits and subsidies for regenerative-climate practices, creating a multiplier effect on the local economy and food security [4,66,67]. Thus, farm profitability in regenerative agriculture should not be seen only as a simple economic indicator. It is a point of convergence between production efficiency, soil health, cost reduction, climate resilience and green financing opportunities, and is a central pillar in the socio-economic assessment of the transition to sustainable and climate-resilient agricultural systems [19].

Socio-economic indicators reflect the interplay between economic and social dimensions in promoting agricultural sustainability, including farm profitability, food security, access to innovative finance, and social equity. These indicators are interdependent, so improving one aspect, such as access to finance, can positively influence profitability and social equity. An integrated approach facilitates the complete evaluation of agriculture impact on the well-being of farmers and communities (Figure 7).

Food security is another key indicator, measuring the ability of regenerative systems to ensure stable yields despite climate variability. Examples from East Africa, Latin America and Asia show that regenerative practices minimize the likelihood of crop failure by conserving water, improving soil fertility and diversifying crops. This makes rural communities more resilient and increases the amount of food available, even in the face of drought or extreme weather events [68,69,70]. An example in this regard is the study by Liu et al. [71], which highlights that diversified rotations increase the resilience of agricultural systems to abiotic and biotic stresses, improving water management, soil health and fertilizer use efficiency, which strengthens long-term production [71]. On the other hand, on a global scale, the World Economic Forum Report highlights that the adoption of regenerative-climate practices in vulnerable regions (such as Sub-Saharan Africa, Latin America or South Asia) increases not only carbon sequestration and biodiversity, but also farmers’ possibility to continue production even in the face of challenges imposed by climate variability, thus directly supporting urban–rural food security and bolstering the economic resilience of rural and agricultural communities [72,73]. Another relevant example is the study by von Cossel et al. [74], which complements this evidence, noting that agroforestry improves the resilience of agroecosystems through erosion control, habitat functionality, and diversity of ecosystem responses to disturbances such as drought, flooding, or pest infestations, all key aspects for local food stability [74].

Food security and resilience of rural communities are not just side effects of regenerative practices, but strategic outcomes certified by measurable parameters: yield stability, water retention, soil health and reduced economic vulnerability. Combining these outcomes creates the basis for adapted public policies, increased financing in the agricultural transition and building sustainable food systems capable of withstanding increased climate pressures.

At the same time, employment and the well-being of agricultural workers are essential indicators of the regenerative impact. Regenerative practices make agriculture more labor-intensive in a skilled way; they require consultancy, operation of new technologies, certification and MRV monitoring. Also, the substantial reduction in pesticide exposure has remedial effects on the health and productivity of people involved in production. Recent reviews of the academic literature highlight that the systematic adoption of these practices requires trained personnel involved in the long-term management of regenerative systems [75,76,77].

Another important aspect refers to the importance of access to innovative finance, an aspect that cannot be underestimated. Access to green finance and carbon markets opens up another socio-economic dimension; carbon credit schemes, payments for ecosystem services and subsidies dedicated to regenerative practices contribute to diversifying farmers’ incomes and reducing exposure to agricultural price volatility. Studies reveal that, without dedicated financial support, the transition to regenerative methods remains blocked, despite the obvious ecological advantages.

Access to innovative financing is an essential pillar in the transition to regenerative agriculture, where investment in the necessary technologies and practices (diversified rotations, soil covers, agroforestry, MRV monitoring) involves significant initial costs, but generates environmental and socio-economic durability in the medium and long term. A study conducted in Poland by Kurdyś-Kujawska et al. [78] highlights the willingness of farmers to access blended financing, preferring moderate amounts that provide financial flexibility and reduce the risks associated with debt, suggesting the potential of blended finance instruments to facilitate the transition to sustainable agricultural systems [78]. Blended finance as a mechanism for mobilizing private investment in combination with public or philanthropic funds can support tough transitions towards sustainability. Havemann et al. [79] underline the need to recognize the complementary roles of public and private financiers to bridge all gaps between the necessary resources and current capacities in sustainable agriculture [79].

In southern Africa, a landscape-based analysis by Smith et al. [80] shows that blended finance may, however, require context-specific adaptation. In conservancy lands, de-risking financial support (which reduces investor risk by guaranteeing certain socio-economic outcomes) could boost local economies, community empowerment and alignment with Sustainable Development Goal 15 (SDG 15—life on land) [80,81]. Also, recent research by Mapanje et al. [82] on financing sustainable agriculture in Sub-Saharan Africa highlights the potential of financial technologies to support the expansion of financial inclusion of smallholder farmers through credit, savings, insurance and digital payments, thereby strengthening the adoption of regenerative practices. These digital tools can facilitate more efficient, inclusive, and accessible pathways to financing in rural contexts [82,83,84,85,86].

In the context of RA transition, social equity and the inclusion of small farmers are fundamental pillars for systemic justice and long-term success. If policies and financing are designed only for large farms, there is a risk that existing inequalities will deepen, turning regenerative agriculture into a gift only for the already well-capitalized. The scientific literature draws attention to the need to deliberately design inclusive approaches that ensure equal access to vital resources such as land, financing and technical know-how [13,87,88]. An example of this is the study by Gordon et al. [87], who warn about the risk of elite capture of regenerative concepts, which can culturally and economically marginalize already vulnerable groups. This calls for acknowledgment of the role of Indigenous people and communities of color, including participatory structuring of the regenerative agricultural process and an agroecological perspective centered on food sovereignty [87]. Therefore, integrating social equity and the integration of smallholders in the transition to RA is not only an ethical dimension, but an essential condition for the success and long-term sustainability of this paradigm. Policies and financial mechanisms that ensure equitable access to resources, knowledge and markets can transform agricultural regeneration from a privilege for large farms into a collective strategy capable of reducing inequalities and providing sustainable ecological and socio-economic benefits on a global scale.

Furthermore, integrating socio-economic indicators into common public policies is key to effectively scaling up regenerative agriculture. Indicators such as income stability, community cohesion and resource productivity are not just parameters assessed in isolation, but constitute the foundations on which institutions can build clear justifications for public funding. The importance of adopting synthetic metrics, such as ISPAS (Sustainable Agricultural Productivity), IREA (Emission Reduction), ISAC (Combined Sustainability) and IESA (Agricultural Area Efficiency), which integrate economic, social and environmental dimensions, is underlined, providing policymakers with robust tools for monitoring and adjusting CAP and European Green Deal measures. For instance, the Common Agricultural Policy not only supports the economic viability of farms, but also promotes social inclusion by redistributing funds to small farms and through social conditionality linking payments to compliance with labor standards and stimulating the participation of young people and women in agriculture [89,90].

The link between socio-economic indicators and the effective implementation of European policies is a crucial element for the RA transition to become a realistic and sustainable one. In the current framework of the CAP and the EU Green Deal, these indicators should not be seen only as post-factum evaluation tools, but as active benchmarks that can guide the process of developing, financing and monitoring public measures. For example, the integration of indicators such as farm profitability, income stability, rural employment or access to green financing in support mechanisms and eco-schemes can contribute to a fairer distribution of resources and a better correlation between economic, social and environmental objectives. In this sense, regenerative agriculture becomes not only an ecological direction, but a concrete instrument of economic and social cohesion, capable of reducing the structural vulnerabilities of the European rural space. The systematic use of these indicators would allow a more precise calibration of public interventions, through an evidence-based approach and continuous evaluation of results. At the same time, the adoption of synthetic indicators that combine economic, social and ecological dimensions, such as ISPAS or IESA, would provide decision-makers with an integrated picture of progress towards the objectives of the Green Deal. In this way, the correlation between socio-economic indicators and the European policy framework becomes not just a technical requirement, but an essential condition for the substantiation of coherent measures, capable of transforming sustainability into a principle of agricultural governance, not just a declarative intention.

A comparative analysis of agri-environmental indicators (AEIs) highlights the need for a transdisciplinary framework that covers not only ecological aspects, but also the socio-economic dimension, including agricultural education, pesticide reduction and water quality improvement. The coherent implementation of these metrics at regional and national levels allows for the substantiation of public policy decisions and the prioritization of investments towards farms that adopt regenerative practices [91]. At the same time, the role of institutions and the historical framework of agricultural policies, such as the CAP, in creating conditionality that links payments to the attainment of environmental and climate-related goals—thus strengthening the link between public financial support and the practical implementation of regenerative systems—is also highlighted [92]. In addition, farmers’ well-being and sense of competence influence the sustainable adoption of regenerative practices. Public policies that integrate these psychosocial dimensions, together with financial and technical support, succeed in transforming financing and regulation into a catalyst for the regenerative transition, while strengthening equity and sustainability at the community level [93].

Therefore, public policies, through instruments such as the CAP or the EU Green Deal, become crucial catalysts in the transformation of regenerative-climate agriculture. The adoption and monitoring of socio-economic indicators allow for the substantiation of decisions, the prioritization of investments and the scaling up of regenerative practices in an equitable, sustainable and efficient way. This integrated approach ensures that the benefits of regeneration do not remain the privilege of well-capitalized farmers, but become widely accessible, thus strengthening the ecological, economic and social resilience of rural communities.

In

Table 2. Socio-economic indicators in regenerative agriculture.

Key Socio-Economic Aspect	Example Indicators/Metrics
farm profitability	economic yield per ha, cost–benefit ratio, reduction in input costs
income stability	yearly income variance, revenue diversification
food security and community resilience	crop diversification, yield variability, local supply stability
labor and farmer well-being	skilled labor demand, pesticide exposure rates, occupational health
access to innovative financing and carbon markets	green loans, carbon credit revenue, ecosystem service payments
social inclusion and equity	land access, financial support received, training participation
socio-economic indicators for policy alignment	standardized metrics for profitability, resilience, equity

, we summarize the main metrics related to socio-economic indicators in regenerative agriculture.

3.4. Monitoring, Reporting, Verification Framework in Regenerative Agriculture

In the absence of a robust MRV framework, regenerative agriculture risks remaining a theoretical promise, lacking the necessary tools to validate real-world impacts. MRV thus emerges as an essential mechanism for quantifying the measurable effects of regenerative practices on soil carbon sequestration, emission reduction, maintaining ecosystem health, and strengthening agricultural resilience (Figure 8). From this perspective, monitoring provides raw data and relevant indicators, reporting integrates and standardizes information in a form usable for public policies and carbon markets, and verification ensures the credibility and transparency of the entire process [94,95].

The monitoring dimension has been significantly enriched in recent years by the integration of digital technologies, especially remote sensing. Globally, the diversity of agricultural contexts has led to the adaptation and implementation of specific MRV technologies, each of which plays a crucial role in strengthening the credibility and efficiency of regenerative practices. In North America, the integration of medium- and high-resolution satellite imagery has become a central method for monitoring practices such as crop rotation and cover crops. These technologies allow for the identification and quantification of changes at the agricultural landscape level and provide essential data for models estimating soil carbon storage. In a study carried out by Jones et al. [21], the use of satellite images combined with deep learning algorithms allows for the identification of crop rotations, the presence of cover crops and the intensity of soil work, reaching accuracy levels between 78 and 80% for crop detection and crop identification, respectively. This approach demonstrates the scalable potential of monitoring and the ability to provide continuous and comparable data, indispensable for assessing regenerative progress [21].

In Europe, recent research highlights a variety of MRV tools that highlight the importance of RA practices in carbon sequestration and improving the resilience of agricultural ecosystems. In Spain, a study in Navarra by Antón et al. [96] demonstrated, through systematic monitoring of soil samples, that the addition of organic matter and controlled management are the most efficient methods in increasing SOC stocks, while conservation agriculture is more effective in arid areas, thus confirming the need for adaptation to the pedoclimatic context [96]. In Germany, pilot projects based on the integration of satellite remote sensing and proximity sensors have been used to quantify soil carbon changes and assess the impact of crop rotations combined with land cover, providing a model for integrating digital technologies into the objective verification of regenerative impacts [97,98,99,100]. In France, MRV approaches have included both the use of national soil databases and regional-scale modeling to assess the role of agroforestry and diversified rotations in reducing net greenhouse gas emissions, confirming through integrated analyses that these practices contribute to climate change mitigation and also to increasing functional biodiversity [101,102,103,104].

Recent studies have highlighted technical adaptations for specific crops, such as the research by Biswal et al. [105], which shows that NDVI is the most accurate indicator for estimating yield at the maximum vegetative stage of potato crops, providing a robust tool for early assessment of yield and crop health. Another example is the study of Yu et al. [106], which highlights how the integration of advanced agricultural monitoring technologies can strengthen MRV systems for climate-smart agriculture, facilitating data-driven decision-making and supporting the implementation of effective adaptive practices. These examples provide not only empirical evidence of the effectiveness of remote sensing, but also applicable models for scaling up technological integration in diverse agricultural systems. Taken together, these examples illustrate how the implementation of robust, locally tailored MRV methodologies strengthens the scientific basis for European agricultural and climate policies, demonstrating the global applicability of RA as a useful tool for climate change mitigation and adaptation.

Reporting is equally critical because it transforms the data collected into structured information that can be used to generate carbon credits, assess compliance with international standards, and inform agricultural policy decisions. Brummitt et al. [95] recently documented an MRV pipeline implemented across hundreds of thousands of hectares of agricultural land in the US, where field data were combined with biogeochemical models and methodological adjustments to account for uncertainties and indirect effects. The results showed that, after rigorous corrections, almost 300,000 tons of CO₂-equivalent could be certified, demonstrating the applicability of standardized reporting at scale [95]. In addition, the verification process adds an additional layer of integrity, by involving independent bodies capable of auditing the methodologies used and guaranteeing the credibility of the results. In Brazil, for instance, a study published by Perosa et al. [107] proposed a hybrid, public–private governance model, which involves the collaboration of farmers with certification bodies and local authorities. This form of verification reduces costs, but also vulnerability to incorrect reporting, strengthening trust in carbon certification schemes and associated policies [107].

In Asia, and particularly in India and China, the focus is on using predictive models and mobile digital applications to collect and report data directly from farmers. This solution, supported by government policies and international initiatives, addresses the challenges of the large number of small and dispersed farms and allows for rapid centralization of data for verification. At the same time, the integration of satellite image analysis offers a scalable solution for monitoring large areas and reducing uncertainties in estimating yields and carbon flows [108,109,110,111,112].

Africa offers a different perspective, where MRV solutions are adapted to limited infrastructure and extreme climatic conditions. Projects focus on the utilization of portable sensors and wide-coverage satellite imagery to estimate soil degradation and monitor the influence of regenerative practices on crop productivity and resilience. These technologies, supported by international partnerships and multilateral organizations, contribute to integrating African farmers into global carbon markets and amplify the resilience of rural communities to climate shocks [70,113,114].

An emerging dimension of the MRV framework is the implementation of advanced digital technologies, which promise to automate a significant part of the process and reduce financial barriers. Research efforts already exist and are being tested to use spectral sensors, autonomous vehicles for sampling, and artificial intelligence models for digital mapping of soil carbon, technologies that are in the process of being standardized internationally [115,116,117].

Thus, the role of MRV technologies differs from one continent to another; however, their convergence shows that the future of RA is dependent on a global technological infrastructure that allows for data standardization and international recognition of results. The diversity of these examples illustrates not only methodological progress, but also the need for contextual adaptation, which makes MRV an indispensable scientific and political framework for validating and scaling up regenerative agriculture on a global scale. This being said, MRV can no longer be conceived of as a technical tool, but as a strategic element of the transition to regenerative agricultural systems. Through scalable digital monitoring, standardized reporting and independent verification, MRV gives scientific and institutional legitimacy to these practices and creates the premises for their integration into green financing schemes and international climate policies. Without it, the benefits of regenerative agriculture would remain difficult to demonstrate, and public and private investments would be delayed in being mobilized on a large enough scale to produce systemic change.

While these examples illustrate the diversity and adaptability of MRV technologies, a structured synthesis is needed to highlight both commonalities and regional specificities.

Table 3. Main MRV approaches used across continents.

Region	MRV	Focus of Application	Strengths	Context Specificities
America	medium- and high-resolution satellite imagery, deep learning algorithms	monitoring crop rotations, cover crops, soil disturbance	high scalability, continuous monitoring, good accuracy (78–80%)	requires advanced digital infrastructure
Europe	satellite remote sensing, proximity sensors, soil sampling, national soil databases, regional modeling	carbon sequestration monitoring, agroforestry, diversified rotations	strong institutional support, multi-scale monitoring	variable pedoclimatic contexts require site-specific adaptation
Asia	mobile digital apps, predictive models, satellite imagery	data collection from small farms, large-scale reporting	rapid data centralization	high farm fragmentation, variable technical capacity
Africa	portable sensors, wide-coverage satellite imagery	soil degradation monitoring, productivity and resilience assessment	affordable and scalable technologies	limited infrastructure, climatic variability

summarizes the main MRV approaches used across continents, identifying their technological focus, strengths, context-dependent challenges, and transferable lessons that can inform global strategies for scaling up regenerative agriculture.

3.5. Current Issues, Gaps, Challenges, Opportunities and Innovations

Recent studies highlight that regenerative agriculture faces conceptual, economic and technical challenges, a lack of a unified definition, high initial costs and monitoring difficulties in diverse pedoclimatic contexts [4,65,118]. In parallel, opportunities include the use of digital technologies and hybrid MRV, flexible outcome-based indicator frameworks and access to climate finance and voluntary carbon markets, which support the transition to sustainable systems [94,95,119,120]. Thus, an emerging consensus recommends an integrated approach, combining flexibility of metrics, local adaptation and technological and economic support.

Currently, the adoption of regenerative agriculture faces numerous challenges in defining, implementing, and evaluating outcomes (Figure 9). The lack of a unified definition often generates confusion, and different conceptual frameworks propose different practices and objectives, making it difficult to compare, validate, and report metrics across systems, regions, and studies. In addition, the knowledge gap between expected effects and empirical evidence is significant. Although regenerative agriculture is promoted for its benefits in terms of carbon sequestration, biodiversity, or soil resilience, many studies do not provide robust quantified data, and the generalization of locally identified effects at a wider scale remains uncertain and underdeveloped [11,121,122].

Widespread adoption is also hampered by clear economic and social challenges. The transition to regenerative practices involves high upfront costs (new equipment, crop adaptation, access to technologies), and benefits can take years to become visible, which discourages farmers, especially those without capital or financial support. The focus of agricultural policies on short-term productivity, coupled with the lack of incentive mechanisms (subsidies, access to carbon credit markets, tailored insurance), perpetuates the conventional status quo and delays the transition to regenerative practices [30,77,123,124].

Another key gap lies at the interface between local knowledge and standardized global approaches. In many regions, especially in countries with different agricultural traditions, there are no platforms for integrating local soil and environmental knowledge into comparable metrics, and global strategies do not sufficiently consider local specificities. At the same time, the available digital tools are often developed without the direct involvement of farmers or local actors; they are designed for monocultures and productivity, not for complex and personalized agroecological systems [30,121,125,126]. Although the global framework of indicators for regenerative agriculture aims for a standardized set for assessing performance, there is often a discrepancy between these indicators and farmers’ local knowledge, reflecting regional specificities and different farm typologies. The study by Eshetae et al. [127] in the Guinea Savannah agroecological zone of Ghana illustrates these conflicts in concrete terms: small and medium-sized farmers use practices adapted to local resources and risks, which do not always align with global indicators of productivity or carbon sequestration [127]. This situation highlights the need to integrate local knowledge in the definition and application of indicators to increase the relevance, acceptability and effectiveness of climate-smart agriculture interventions.

In light of these challenges, several directions for solutions are indicated in the specialized literature. First, an outcome-based, flexible and context-adaptable metric framework is needed, which allows for global comparability and at the same time local relevance. Such a framework has been proposed in the food industry, offering flexibility in the selection of indicators, minimizing the reporting burden and the possibility of independent verification. At the same time, it is recommended to create rigorous farm-to-policy and farm-to-analysis systems, through controlled longitudinal experiments, to build a solid evidence base regarding the long-term outcomes of regenerative practices in various contexts [30,121,126]. For the proposed solutions to be effective, they must be directly related to the identified challenges, responding to both conceptual uncertainties and economic, institutional and technical difficulties that hinder the transition to regenerative practices. A flexible metric framework directly addresses the lack of a unified definition, as it does not impose a fixed set of practices, but rather allows for the measurement of globally comparable end results while maintaining local relevance. Thus, conceptual divergences can be harmonized by reference to common objectives, not rigid procedures.

Second, digital technologies, remote sensing, and soil carbon copilot-type models offer promising solutions to efficiently and at scale monitor soil carbon storage, soil health and the dynamics of agroecological regimes. The application of this kind of platform reduces costs, increases accuracy and supports informed decisions. Third, there is a clear need for supporting policies and innovative economic mechanisms. Expanding financial infrastructure, such as thematic subsidy schemes, access to voluntary carbon credit markets (which can initially offset the costs of the transition) and insurance tailored to climate risks, can reduce the risks perceived by farmers and promote the adoption of regenerative practices [95,97,100,123]. Integrating digital technologies, as well as hybrid MRV systems, helps in reducing initial and operational costs by automating data collection and processing, making it more accessible to farmers and facilitating the rapid scale-up of regenerative practices.

Ultimately, interdisciplinary and participatory collaboration, mobilizing farmers, researchers, policymakers and consumers, is essential. This partnership will make it possible to reconcile scientific rigor, local relevance and practical feasibility, creating frameworks adapted to local realities but connected to global climate adaptation and mitigation objectives [128,129,130].

Unifying measurement methods in regenerative agriculture requires solutions that simultaneously address soil–climatic variability, monitoring costs, and credibility requirements in value chains and carbon markets (Figure 10). An effective approach is to move from fixed sets of indicators to a minimum core of outcomes (such as soil organic carbon dynamics, aggregate stability, water retention, and soil biodiversity), complemented by locally adapted indicators, established through thresholds and benchmarks from extensive regional databases. This flexibility increases the relevance and comparability of measurements across contexts [131,132,133,134].

To reduce costs and increase comparability, the technical solution with the greatest traction could be a hybrid MRV combining direct ground sampling with proximal sensors, satellite/aerial remote sensing and process-based models, with each component having a clear role in calibration and extrapolation. In recent years, it has been indicated that such flows reduce sample density without sacrificing accuracy, allow multi-annual scaling of estimates and make visible results like crop rotations, grassing, cover-cropping and tillage intensity at national scales. In parallel, for agriculture, it is emphasized that the design must include explicit quantification and reporting of uncertainty, so that the results are auditable and usable in management decisions and in the generation of carbon credits [21,95].

A promising direction is the digitization of measurements, this requires open data standards and interoperability between platforms. Integrating sample registers, sampling metadata (depth, season, analytical method) and model versions into an audit log allows for replicability and third-party inspection. Current industry proposals to move to outcome-based metrics can be anchored in such protocols, with common ontologies for indicators, uniform versioning schemes and public APIs for multi-actor aggregation (farmers, companies, auditors). Business and standardization initiatives already show convergence towards these principles; however, the alignment needs to remain flexible, allowing for regional adjustments of indicators and sampling methodologies [2,115,135,136].

A methodological solution to the “indicators vs. functions” problem is to explicitly map indicator–function–ecosystem service relationships and validate them over the long term in the field, so that composite soil health scores are traceable to agronomic and climatic outcomes. Recent studies directly link physical, chemical and biological indicators (such as pH, macroporosity, potentially mineralizable nitrogen, soil fauna) to productivity and resilience, and the integration of biological indicators remains a research priority, which justifies their inclusion in the core metrics [137,138]. Modern remote sensing and machine learning support an operational solution for monitoring results over large areas and over time: classification of rotations and cover crops and estimation of tillage intensity, pasture biomass and soil moisture, validated through networks of reference plots. Scale-up demonstrations of precision agriculture provide protocols replicable in other regions, provided that the quality of validation data and transparency of models are maintained [21,139].

Regarding the public policy dimension, one solution is to harmonize the monitoring framework at national and EU levels with benchmarks and dashboards that aggregate key indicators of soil degradation and functions, fixed sampling methodologies and minimum reporting frequencies. Technical documents and institutional syntheses must converge towards the need for a universal protocol; however, they must be applied differentially across landscapes and cropping systems to allow comparability across regions and years without losing local relevance [130,140,141].

For standardized metrics to drive change, they need to be linked to the right incentives and supply chains. One possible solution is for companies to shift reporting from practices adopted to measured outcomes, with contracts that pay for verifiable improvements in soil and ecosystem services, not just procedural compliance. This pivot can only be sustainable if MRV remains transparent, with reported uncertainties, reduced costs through technology, and farmer–researcher participation in defining relevant indicators at the farm level [142,143]. Each of the proposed solutions, flexible metric frameworks, digital technologies, economic mechanisms and intersectoral collaboration, provides a direct response to the identified problems: the lack of a clear definition, the gap between local knowledge and global standardization, high costs and insufficient transition incentives.

Thus, solutions to the problems of standardized metrics in regenerative agriculture lie at the intersection of three principles: common core results, contextualization through local benchmarks, and hybrid, open, and auditable MRV. Implemented together, these principles can transform indicators from simple lists into management and value creation tools capable of linking soil science to farmers’ daily decisions and credible market demands. The recent literature on minimum indicator sets and continental analyses highlights both convergences and divergences in the main conclusions. In general, there is a consensus on the importance of monitoring fundamental soil health indicators (organic carbon, aggregate stability, water retention) and some essential socio-economic variables for assessing the transition to regenerative agriculture [35,144]. However, divergences arise in the exact selection of secondary indicators and in how to integrate the local context: some continental studies propose a fixed core applicable at a large scale, while pilot farm-based research recommends maximum flexibility and adaptation to the specificities of each region [106,127]. This comparison highlights that, although there is a common starting point for consensus, the practical application of indicator sets requires local adjustments and a careful interpretation of contextual priorities.

4. Discussion

In the debate on standardized metrics for regenerative agriculture, treated as simultaneous climate adaptation and mitigation tools, a systemic perspective is required that explicitly links biophysical measurements to socio-economic indicators and MRV institutional architectures. This integration is necessary to transform indicators from simple descriptive metrics into operational policy and market tools [4,30]. In terms of metric choice, an effective solution could be the transition to core and context architectures, in which a minimal core of soil health and agronomic performance indicators is accompanied by sets adapted to specific pedoclimates and cropping systems. This approach can balance the need for large-scale comparability with local relevance, and studies proposing minimal indicator suites or continental analyses provide practical starting points for consensus [19,145,146].

From a socio-economic perspective, the sustained implementation of regenerative practices depends on financial and also market access mechanisms that reward measurable results, not just declared practices. A particular emphasis is placed on the importance of integrating economic indicators (net income, production costs, access to green markets) and social capital into evaluation schemes, which would allow the design of incentives based on verifiable results [76,147].

Regarding MRV, evidence is converging towards hybrid and hierarchical models that combine in situ soil samples, proximal sensors, remote sensing and process-based models. Such configurations allow for local calibration and landscape-level scaling, reduced sampling density where models are well validated, and explicit reporting of uncertainty, crucial for auditability and acceptance in carbon markets [94,95,148,149].

Current challenges remain significant and include the lack of consensus on a minimum set of indicators, disproportionate costs for small farms, the difficulty of quantifying and communicating uncertainty, and the lack of international harmonization of MRV protocols. Recognition of these gaps is essential in creating practical proposals (such as digital registers, modular protocols or benchmark plots), but implementation at scale requires investment in data infrastructure and institutional capacity [145,150]. In terms of opportunities and innovations in the context of the transition to regenerative agriculture, they are abundant. For example, improvements in remote sensing and machine learning allow for dynamic classifications of crop rotations and indirect estimates of ecosystem services, and a digital registry and standardized data ontologies can open up the possibility of interoperability between commercial platforms and public registries. However, these technologies need to be robustly validated through reference plot networks to avoid systematic errors in scaling [95,151].

Priority future directions emerging from the literature review include initiating a multi-stakeholder consensus process to define a minimum core set of indicators with clear rules for contextual adaptation, developing and adopting open ontologies and interfaces for interoperability, investing in tiered MRV that combines benchmark sites, proximal sensors, and remote sensing, promoting financial incentive schemes based on measurable outcomes integrated into supply chains, and strengthening institutional capacity at national and regional levels for auditing and uncertainty management. A critical assessment of the effectiveness and affordability of advanced MRV systems is crucial for the widespread implementation of regenerative agriculture. While hybrid and hierarchical approaches are robust and scalable, elevated costs may hinder the adoption of practices by smallholder farmers. Policy instruments such as subsidies, cooperative MRV schemes or public–private partnerships can reduce these costs and incentivize participation, and linking financial support to verifiable outcomes ensures both adoption and transparency of ecological and socio-economic benefits. All of these directions require coordinated funding plans and cross-local validation studies to demonstrate the robustness of the methods before large-scale implementation [4,94].

Thus, transforming standardized metrics into effective tools for adaptation and mitigation requires a synchronized strategy between research, technology and public policy, based on scientific consensus, open digital infrastructures and market mechanisms that pay for verifiable results. The future is shaping up to be one in which metrics directly serve the decisions of farmers, buyers and policymakers; however, this requires concerted steps and a focus on validation, equity and transparency.

The analysis carried out, starting from the research questions stated in the introduction, shows that the recent literature mainly uses standardized metrics for the assessment of regenerative agriculture, focused on biophysical indicators of soil health and on essential socio-economic indicators. However, there are important differences in the way in which these metrics simultaneously cover the functions of adaptation and mitigation of climate change. Adapting indicators to the regional and sectoral context is important to ensure the relevance and comparability of results, and emerging technologies, such as digital sensors and remote sensing, offer significant opportunities for large-scale monitoring and transparent MRV. Based on these, an integrated operational framework is proposed that combines a minimum core of universal biophysical and socio-economic indicators, local adaptations through regional benchmarks and thresholds, the use of hybrid MRV and digital technologies to reduce costs and increase credibility, as well as the correlation of indicators with climate finance mechanisms and public policies. This framework provides a practical and implementable approach, designed to connect scientific results with policy decisions and farmers’ practices, thus supporting a comparable, reportable and verifiable assessment of regenerative agriculture in the context of the climate crisis.

5. Conclusions

The evaluation of standardized metrics in regenerative agriculture emphasizes the need for a systemic framework that integrates biophysical, socio-economic and institutional dimensions, so that indicators become functional approaches that enhance climate resilience through adaptation and mitigation actions. In this sense, metrics have to be structured in an architecture that combines a minimum core of standardized indicators with locally adapted sets in order to ensure comparability and contextual relevance. At the same time, the sustainable adoption of regenerative practices depends on the integration of socio-economic indicators and incentives based on verifiable results, which confers economic and social legitimacy and facilitates farmers’ access to green markets and carbon credit schemes. The MRV framework is a central element for the credible assessment of agricultural outcomes, allowing the combination of data from direct sampling, proximal sensors, remote sensing and process-based models, so as to balance the accuracy, cost and scalability of monitoring. Emerging machine learning technologies and open digital platforms enhance data transparency, interoperability and auditability, and the development of standardized digital registries offers significant opportunities for replicability and uncertainty control. This analysis revealed that the main challenges lie in the lack of standardized indicators and harmonized MRV protocols, while the main opportunities come from digital innovation, data interoperability and the integration of socio-economic dimensions in assessment frameworks.

Challenges identified include the lack of consensus on a minimum set of indicators, the high costs of monitoring for small farms, the difficulty of quantifying uncertainty and incomplete international harmonization of MRV protocols. At the same time, technological innovations, open standards, remote sensing, advanced sensors and common data ontologies create real opportunities for scaling and validating assessment systems. Future directions should focus on strengthening scientific consensus for the core of indicators, developing and implementing hybrid and tiered MRV, economic incentives based on results and increasing institutional capacity for audit and data management.

However, this study has some limitations, such as the diversity of data sources, the lack of a harmonized description of RA and limited empirical validation of indicators in different agro-climatic contexts. These aspects highlight the need for further field research and standardized monitoring, reporting and verification frameworks to ensure data comparability and reliability. From a policy perspective, it is essential to integrate these socio-economic and environmental metrics into EU policies, like the Common Agricultural Policy and the European Green Deal, to support evidence-based decisions and make regenerative agriculture a pillar of sustainable and inclusive development.

Evidence from regional and technical case studies from Europe, the Americas, Asia and Africa confirms that regenerative practices, adapted to local pedoclimatic conditions and supported by digital MRV tools, can improve soil health, carbon sequestration and climate resilience, providing a solid empirical basis for the proposed framework. Overall, standardized metrics, when integrated into a systemic, adaptable, and auditable framework, can transform regenerative agriculture from a series of experimental practices into a robust climate adaptation and mitigation tool capable of supporting farmer decisions, market strategies, and public policies. Based on the analysis of the recent literature, an operational framework for assessing regenerative agriculture is proposed that combines a minimum core of biophysical and socio-economic indicators, local adaptations through regional benchmarks, and the use of hybrid MRV and digital technologies. This framework allows us to connect scientific results with policy decisions and farmers’ practices, ensuring comparable, reportable and verifiable assessments and facilitating the shift toward sustainable and climate-resilient agricultural systems.