Regenerative agriculture assessment lens
The regenerative agriculture assessment lens (thereafter referred to as 'the lens' in this section) offers a structured approach to assess regenerative conditions and outcomes at the landscape scale. It is designed as a thematic entry point for landscape-level assessment, providing the private sector, governments, and civil society with reliable information to guide and incentivize regenerative improvements at scale.
The lens is intended to support users in measuring and tracking regenerative agriculture conditions within their landscapes, demonstrating how changes can compound over time into longer-term outcomes. By assessing agricultural systems as part of a broader landscape, users can understand not only whether regeneration is occurring, but why. This understanding enables the identification of opportunities for targeted improvement, uncovers vulnerabilities requiring additional support, and fosters coordination and shared accountability across diverse actors.
Download the indicator package that constitutes the lens below:
Coordinate with LandScale to use the regenerative agriculture assessment lens
The regenerative agriculture assessment lens is available for use by interested initiatives. Please coordinate with LandScale to align on usage, pricing, and next steps for getting started.
Regenerative agriculture lens structure
The lens was developed through a gap analysis of the LandScale assessment framework, findings from literature reviews, and targeted consultations. As a result, it builds on existing indicators from the LandScale assessment framework and introduces new ones, including soil, agrobiodiversity, community, institutional support and incentive structures, economic resilience and diversification, and livestock health and welfare.
The lens follows the same holistic structure as the LandScale assessment framework, organized across four pillars:
Ecosystems
Human well-being
Governance
Production
However, several adaptations have been made to ensure it is tailored to the assessment of regenerative agriculture. Specifically:
New indicators and metrics have been introduced to address identified gaps.
The applicability status of certain indicators and metrics has been adapted to better reflect their relevance to regenerative agriculture.
Some indicators and metrics from the assessment framework have been removed where they were not directly applicable to regenerative agriculture contexts.
As a result, the lens is composed of a combination of inherited, adapted, and new indicators and metrics, when compared to the LandScale assessment framework. Refer to the LandScale assessment framework section for details on the lens' foundational structure as well as the different types of indicators and metrics it supports.
Supplemental measurement guidance for metrics
The table below presents additional technical guidance to support consistent measurement of metrics that were newly introduced or adapted for this lens. This guidance complements the indicator package document available for download at the top of this section.
1.1.3.5 Total area (ha) & percentage (%) of managed lands in the landscape that are currently degraded
This metric captures areas of managed land that were once designated for economic activity but are now degraded, unproductive, and not actively being restored. Refer to footnote 5 in the indicator package document at the top of this section for the definition of managed land.
1.1.3.6 Managed land degradation rate
This metric requires a baseline year and annual measurements to show how the degradation rate has changed over a minimum three-year period. Refer to footnote 5 in the indicator package document at the top of this section for the definition of managed land.
1.2.6.1 Richness (number of unique species and varieties) of cultivated crop and livestock species across managed lands in the landscape
This metric captures the diversity of species and varieties in the landscape; therefore, there is no single prescribed reporting method, and results will depend on the available data. To ensure richness is fully represented, include both commercial and non-commercial crop and livestock varieties. Applying a biodiversity index (E.g., Simpson or Shannon) is the recommended methodology. If an index is used, the full list of species and varieties and their relative abundance should be included. The chosen methodology must be described in detail.
1.2.6.2 Area (ha) and percentage (%) of managed land under multispecies or multifunctional production systems, disaggregated by type (e.g., agroforestry, intercropping, crop-livestock integration, silvopasture, polyculture)
To avoid duplication when multiple production systems are present in the same area, identify the primary (most representative) production system and use it to define the category. Document the rationale used to determine representativeness, and note any additional production systems present so they are acknowledged without creating duplicate categories.
1.2.6.3 User-defined metric(s) on associated biodiversity in managed areas, such as presence, abundance, or habitat suitability of key functional groups (e.g., pollinators, soil fauna, beneficial insects, birds, nitrogen-fixing organisms)
Biodiversity in managed areas can be measured in multiple ways, and there is no single prescribed reporting method for this metric. The chosen approach will depend on data availability, scope, and the functional groups relevant to the landscape. User-defined metrics must be described with specific units of measurement.
Examples of acceptable methods include:
Applying a biodiversity index (e.g., Simpson or Shannon), supported by a list of species included and their relative abundance. This is the recommended methodology for reporting overall biodiversity in managed areas.
Using a Habitat Suitability Index for key functional species or groups. This is the recommended methodology when the landscape initiative has goals related to conservation or habitat restoration in managed areas, and for conducting an ecological analysis.
Assessing soil biodiversity through physical, molecular, and/or genetic methods. This is the recommended methodology when the landscape initiative has goals related to soil health in managed areas.
Listing and counting species by functional group. This is the recommended methodology for reporting biodiversity in managed areas when data is limited.
The chosen methodology must be described in detail.
1.3.1.4 User-defined metric(s) on water usage distribution across the landscape
There are multiple approaches to measuring water usage distribution; the choice should reflect the data available and the assessment’s objectives. User-defined metrics must be described with specific units of measurement.
Approaches may include:
Rights-based approach: Report how water rights or entitlements are distributed across stakeholders (e.g., who holds the largest volumes or has priority access, who owns or manages water infrastructure, whether different legal/tenure categories exist).
Use-based approach: Report water volume or area by primary use categories (e.g., agricultural, industrial, domestic/municipal, environmental, public services, etc.).
Metrics should be reported consistently using the same parameters to enable aggregation and comparability over time. The chosen methodology must be described in detail.
1.3.2.5 Concentration of agricultural contaminants (pesticide residues, nitrate, phosphate) (load/volume) in key water bodies
Monitoring agrochemical contaminants in water bodies requires advanced techniques carried out by specialized, often certified, laboratories. Sampling is the most common approach; however, when results are aggregated at the landscape level, they may not accurately reflect localized contamination levels. In such cases, reporting sampling points on a map is recommended. Because agrochemical application varies over time and space—depending on the crop, growth stages, and management practices—contaminant concentrations in water bodies also fluctuate. These factors must be considered when monitoring this metric and interpreting results. Therefore, the chosen methodology must be described in detail.
1.3.2.6 Distribution of algal blooms in key water bodies
There are multiple methods for monitoring algal blooms in water bodies. The most practical approach is remote sensing using satellite imagery, which can be complemented by aerial or ground-based sensors and laboratory analyses to improve the accuracy of results. Coordination with local agencies and institutions responsible for water quality monitoring is recommended. Because algal blooms fluctuate over time, monitoring should account for seasonal and temporal variation. The chosen methodology must be described in detail.
1.3.3.1 (Sinks) Rate of terrestrial carbon sequestration (tCO2e14/ha/yr) in aboveground and belowground biomass (litter, dead wood, harvested wood products and soil are optional)
Rate of carbon sequestration is an estimated measurement derived from field data and modeling. It is recommended to engage an expert to guide the estimation of carbon dynamics—either by using established software and calculators or by developing a tailored model suited to the landscape context. The chosen methodology must be described in detail.
1.3.3.2 (Sources) Rate of GHG emissions (tCO2e/yr) from deforestation and (optionally) forest degradation
The supplemental measurement guidance for this metric mirrors that of metric 1.3.3.1.
1.3.3.3 (Sinks) Rate of C sequestration in above and below ground biomass in woody perennials in forest plantations, agroforestry & lands under restoration (tCO2e/yr)
The supplemental measurement guidance for this metric mirrors that of metric 1.3.3.1.
1.3.3.4 (Sinks) Rate of C sequestration in soil organic carbon pool within agriculture, forest plantations, and other production land uses (such as agroforestry) & lands under restoration (tCO2e/yr)
The supplemental measurement guidance for this metric mirrors that of metric 1.3.3.1.
1.3.3.5 (Sources) Rate of GHG emissions (tCO2e/yr) from agricultural production & primary processing per unit of production15 (including crops and livestock)
The supplemental measurement guidance for this metric mirrors that of metric 1.3.3.1.
1.3.5.1 Soil erosion rate (tons/ha/year)
There are multiple methods for measuring the soil erosion rate in a landscape. Direct measurements are the most accurate but require field sampling and are labor-intensive at the landscape scale. Indirect methods rely on modeling and estimation using geospatial data such as soil erodibility, slope steepness, and land cover, typically derived from satellite imagery and existing soil datasets or maps. The FAO recommends the Revised Universal Soil Loss Equation (RUSLE) model, which is well-suited for landscape-scale measurements. Alternatively, soil erosion rate potential may be used to report this metric when direct or indirect measurements are not available. The chosen methodology must be described in detail.
1.3.5.2 Soil water holding capacity
There are two recommended methods for measuring soil water holding capacity:
Soil sample analysis: Collect soil samples and have them analyzed by qualified laboratories or institutions. It is recommended to coordinate with local agencies or research organizations that may already have soil data for the landscape. Sampling locations should be documented and reported on a map.
Remote sensing: Use satellite-based datasets (e.g., International Soil Reference and Information Center) to estimate soil water holding capacity where soil sampling is not possible or to complement existing sample data.
The chosen methodology must be described in detail.
1.3.5.3 Organic carbon concentration
Organic carbon concentrations are best measured by analyzing soil samples. It is recommended to coordinate with local agencies or research organizations that may already have soil data for the landscape, and to follow a standard procedure (e.g., the Soil Health Institute's standard operating procedure) as a reference. Sampling locations should be documented and reported on a map. The chosen methodology must be described in detail.
1.3.5.4 Carbon mineralization potential
Carbon mineralization potential is best measured by analyzing soil samples. It is recommended to coordinate with local agencies or research organizations that may already have soil data for the landscape, and to follow a standard procedure (e.g., the Soil Health Institute's standard operating procedure) as a reference. Sampling locations should be documented and reported on a map. The chosen methodology must be described in detail.
1.3.5.5 Aggregate stability
Soil aggregate stability can be measured by analyzing soil samples or through smartphone-based methods (e.g., collecting soil aggregates and using applications like the Slakes app to test them). It is recommended to coordinate with local agencies or research organizations that may already have soil data for the landscape, and to follow a standard procedure (e.g., the Soil Health Institute's standard operating procedure) as a reference. Sampling locations should be documented and reported on a map. The chosen methodology must be described in detail.
1.3.5.6 User-added metric(s) on soil biological activity
Soil biological activity is best measured by analyzing soil samples. It is recommended to coordinate with local agencies or research organizations that may already have soil data for the landscape. Sampling locations should be documented and reported on a map. If direct soil sampling is not feasible, indirect measurements may be used as proxies. User-defined metrics must be described with specific units of measurement. The chosen methodology must be described in detail.
1.3.5.7 Percentage (%) of farms that report perceived improvements in soil health
Primary data may be collected to report this metric. Alternatively, partnering with other actors in the landscape who have access to farmers—such as certifiers, NGOs, agencies running workshops, farmers' associations, or government bodies—can provide valuable data. Because this metric reflects qualitative perceptions, the methodology—including all factors and parameters used to define it—must be described in detail.
3.1.2.3 User-defined metric(s) of presence and quality of grievance mechanisms or other safeguards for land-related conflicts
Grievance mechanisms and safeguards implemented by all relevant actors in the landscape should be reported in this metric, as appropriate to the local context. Examples include Indigenous or communal land tenure legal frameworks, or farmers’ associations’ land and resource conflict resolution mechanisms. User-defined metrics must be described with specific units of measurement.
3.2.6.1 Number of government incentive programs for land management practices in the landscape, disaggregated by incentive type (e.g., direct payments, subsidies, training, technical assistance) and by land management practice (e.g., conventional, sustainable, regenerative)
Report a list of all incentive programs in the landscape, capturing those implemented by different levels or agencies of government. Complementary information about incentive program quality can be reported in metric 3.2.6.2.
3.2.6.2 User-defined metric of quality of government incentive programs
Report supporting data on the quality of incentive programs listed in metric 3.2.6.1. This may include measurable results, number of recipients (disaggregated by relevant groups), and observed impacts. The chosen methodology used to collect and report this information must be described in detail.
3.2.6.3 Number of non-governmental actors promoting improved land management practices in the landscape, disaggregated by type (e.g., civil society, NGOs, research institutions, certifiers and standard-setting bodies)
Report a list of non-governmental actors operating in the landscape (do not include government entities). The metric should focus on the presence and number of actors. Additional information on their quality or effectiveness may optionally be included as supplemental context, but is not required.
4.1.1.4 User-defined metric(s) on productive loss attributed to biotic, abiotic, and novel stressors
Productive loss calculations vary by crop and are influenced by multiple factors. Representative crops can be selected, and the stages of productive loss should be clearly defined (e.g., during harvest, transportation, processing, storage). It is recommended to collaborate with farmers' associations, traders, and/or other relevant actors, and to engage an expert in productive loss calculations. When possible, productive loss should be disaggregated by stressor type and production system. Avoid double-counting (e.g., scenarios such as extreme weather events that increase vulnerability to disease must be carefully accounted for). User-defined metrics must be described with specific units of measurement. The chosen methodology must be described in detail.
4.1.2.3 Area (ha) and percentage (%) managed lands using irrigation systems, disaggregated by type (e.g., rainfed, gravity-fed, drip, pumped)
It is recommended to consult with actors in the landscape responsible for monitoring irrigation (e.g., agricultural councils, government agencies, water districts). Results for this metric should be reported for a specified timeframe, ideally using multi-year data (e.g., the past two to five years), since irrigation patterns and volumes fluctuate over time and vary by crop type and their respective water requirements.
4.1.5.1 Average net farm income per farming household across the landscape
It is recommended to consult with relevant actors and agencies that produce data on farm income, such as government bodies (e.g., agricultural departments), research institutions, or international organizations (e.g., the FAO, the World Bank, etc.). Where available, use official statistics or recent reports specific to the landscape or country context. Alternatively, primary data may be collected.
4.1.5.2 Percentage (%) of households with 3 or more distinct income sources (e.g., crops, livestock, off-farm employment, nature-based activities, governmental subsidies)
The supplemental measurement guidance for this metric mirrors that of metric 4.1.5.1.
4.1.5.3 Percentage (%) of households earning income from at least one nature-based source, disaggregated by type (e.g., PES schemes, agrotourism, carbon or biodiversity credits, other ecosystem service-related payments)
It is recommended to consult with relevant actors in the landscape who may have access to nature-based income data (e.g., local NGOs, producer organizations, government agencies). If a household earns income from multiple nature-based sources, it should be counted only once to avoid duplication. Assign the household to the primary (most representative) income source, based on the highest annual income contribution and, where relevant, the longest duration or continuity of income, and document the criteria used to determine representativeness. Because many nature-based income sources are temporary or seasonal, their duration and timing should also be recorded and described.
4.1.5.4 Percentage (%) of households earning income from governmental subsidies
It is recommended to consult with the local government agencies that issue subsidies, as well as regional partners who can provide information about the types of subsidies present in the landscape. Results should reflect the percentage of households receiving any form of subsidy income. Where relevant and useful, subsidies may be disaggregated by type (e.g., agricultural, conservation, energy, social support) to provide additional context, following the disaggregation rules outlined in metric 4.1.5.3.
4.1.5.5 User-defined metric(s) on access to reliable markets
There are multiple ways to report this metric, depending on data availability and context. Approaches may include quantitative methods (e.g., a market reliability index) or qualitative assessments that describe the physical, economic, and social structures enabling market access in the landscape. User-defined metrics must be described with specific units of measurement. The chosen methodology must be described in detail.
4.1.5.6 Percentage (%) of producers with access to business or technical support services (e.g., cooperatives, processors, extension agents, finance)
It is recommended to consult with actors in the landscape who may have access to support services data (e.g., producer organizations, NGOs, extension agencies, government programs). Results should reflect the percentage of producers with access to at least one form of business or technical support service. Where relevant and useful, services may be disaggregated by type to provide a more comprehensive view of support availability across the landscape. Each producer should be counted only once when calculating the overall percentage, even if they access multiple service types. When disaggregating by service type, producers may appear in more than one category, but the overall percentage should reflect unique producers only.
4.1.5.7 User-defined metric(s) for economic resilience and diversification
There are multiple ways to report this metric, depending on data availability and context. User-defined metrics must be described with specific units of measurement. The chosen methodology must be described in detail.
4.1.6.1 User-defined metric(s) on the health & welfare of farmed and working animals
There are multiple ways to report this metric, depending on data availability and context. User-defined metrics must be described with specific units of measurement. The chosen methodology must be described in detail.
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