Our DCF methodology
A rigorous, best-in-class methodology built on in-house expertise
Our Deforestation and Conversion-Free (DCF) methodology delivers robust, transparent, and independently assured risk assessments that support your company’s commitments to protect forests and other natural ecosystems after your chosen cut-off date.
Alignment with the emerging voluntary standards
DCF standards are being developed and are beginning to be adopted. Our methodology combines adherence to those industry standards with trusted, peer-reviewed local datasets. We integrate sector-specific guidance from the Accountability Framework Initiative (AFi), the Consumer Goods Forum (CGF), the European Forest Institute (EFI), and commodity-specific protocols (e.g., ECA for cocoa, ECF for coffee, Fediol for oilseeds, and GPSNR for rubber).
Supply shed modelling to minimise false positives
If you don't have exact farm location data, Meridia's unique supply shed modelling enables you to get trusted sourcing data before verifying against DCF commitments. Our supply sheds modelling offers much greater realism and accuracy vs crude buffer radii.
Coverage of country‑specific laws & regulations
Verify checks your supply chain against country-specific laws and regulations—covering natural land changes, protected areas, Indigenous Peoples' territories, human rights, environmental protections, and forced labour and embargoes (currently Brazil only). This country-tailored layer ensures alignment with frameworks like the Accountability Framework Initiative (AFi) and others.
Rigorous data quality checks
Accurate geospatial data is the foundation of credible verification. Our methodology performs integrity, consistency, and plausibility tests to flag errors, such as inaccurate GPS points, polygon overlaps, duplicate records, and implausible plot shapes, helping you address risks before formal reporting.
Built on over a decade of field experience
Meridia's DCF methodology is grounded in ten years of mapping, verification, and operational experience with farmers, communities, and landscapes worldwide. This on-the-ground expertise enhances our understanding of local land rights, farm mapping, and data collection challenges, ensuring that our risk assessments are practical, reliable, and applicable at scale.
Extra proprietary protected areas layer, in addition to IBAT
While the IBAT classification offers a useful baseline, it does not fully capture the nuances of local legal frameworks or permitted land uses. Our methodology conducts a manual review and categorisation of all protected areas, taking into account the specific legal context and the conditions under which agricultural activities may be permitted–ensuring a more accurate, legally grounded assessment of protected areas and their implications for lawful production.
Targeted remediation for high-risk suppliers
Enable your field teams and suppliers to mitigate risk on the ground with concrete actions for each critical risk and step-by-step guides. All in a format that's easy to follow, leveraging our decade of field experience.
How Verify assesses DCF compliance risks
We apply zero gross deforestation and zero gross conversion standards after the selected cut-off date. Deforestation refers to the loss of natural forest; conversion refers to the change from any natural ecosystem (e.g., forest, savannah, grassland, wetland, peatland) to non-natural land use. Where required by country/biome or customer policy, earlier cut-off dates are applied (e.g., Amazon 31 July 2008).
Deforestation
We define deforestation as the loss of natural forest cover occurring after a specified cut-off date, differentiated by biome. For example, the Amazon biome uses a cut-off of 31 July 2008, whereas other biomes use 31 December 2020.
Our methodology uses high-resolution forest loss datasets, such as PRODES and Hansen Global Forest Change, to run polygon-overlap checks against farm plot boundaries. Any overlap with forest loss after the relevant cut-off triggers a critical deforestation compliance risk.
Conversion
Conversion refers to the change of any native natural ecosystem—including forest, savannah, grassland, wetland, and peatland—into a non-natural land use after the same biome-specific cut-off dates. This expands beyond forests to cover all native vegetation types, aligning with broader DCF commitments. We use datasets such as MapBiomas Alerts to detect native vegetation conversion and run polygon-overlap analyses to flag any non-compliant land-use changes.
Legality
We identify farm plots that pose critical risks to your supply chain and highlight where supporting documentation is required to meet DCF commitments. Local land use laws can be complex, restricting certain agricultural uses to protect ecosystems and communities, and non-compliance poses significant legal and reputational risks.
Our methodology assesses compliance with these legal requirements, including wider territorial deforestation/conversion, and extends beyond land use laws to address social and ethical dimensions critical for DCF compliance:
Data quality
Our methodology performs integrity, consistency, and plausibility tests to flag errors, such as inaccurate GPS points, polygon overlaps, duplicate records, and implausible plot shapes, helping you address risks before formal reporting.
Supporting accountability with clear, actionable outputs
When conducting tests on farms, each test provides a risk score ranging from low to critical. To speed up the screening process, Meridia developed an actionable scoring system. By combining all test results into a single score for each farm, this greatly simplifies understanding and comparing risk levels across numerous farms.
Our methodology produces risk scores that allow users to:
- Detect and prioritise farm plots or suppliers that are not compliant with the EUDR
- Review detailed risk context and remediation recommendations
- Engage suppliers effectively through data-driven insights
- Provide credible, verifiable evidence of compliance progress to buyers, coalitions, and regulators
Risk score reclassification
Built into the query builder, users can flag, review, and reclassify farm plots directly from the map or results table—adding evidence, notes, and a full audit trail for complete traceability. Access is restricted to portal admins to ensure control and compliance, while still enabling local nuance and field-based corrections.
This feature strengthens due diligence documentation, reduces false risk signals, and aligns with EUDR requirements by ensuring that classification decisions are transparent, defensible, and integrated into future analyses.
Fully transparent and versioned
Transparency is central to effective compliance. Every test in our methodology is fully documented and versioned, detailing the data sources, thresholds, and criteria used. This enables your organisation and auditors to precisely trace how risk scores are generated, supporting reproducibility and audit readiness.
Tailored to your supply chain needs
Choose your own cut-off date from the most frequently used: 2008, 2015, 2020; unit of analysis (farm plot, supply shed, or jurisdictional level). We adapt to your company's maturity, data availability, and business requirements.
EY-approved and independently audited
Our methodology is independently audited by EY to the globally recognised ISAE 3000 standard. This rigorous external assurance validates the integrity, accuracy, and transparency of our approach, providing confidence that our risk assessments meet international best practice and regulatory expectations.
Flexible and usable by everyone
Designed to fit organisations of varying maturity and data availability, our methodology supports multiple levels of analysis, from precise farm plot mapping to jurisdictional and supply shed scales. We prioritise ease of use, enabling teams without GIS expertise to generate reliable, repeatable assessments and reports efficiently.
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