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Impact claims without evidence are marketing.

Measurement, Reporting, and Verification (MRV) is the trust layer of the Kokonut Framework. It is how a farm activity becomes evidence, how evidence becomes a public record, and how public records become useful for DAO members, grant reviewers, developers, researchers, capital allocators, and AI agents. At Kokonut, MRV is not a PDF produced after the fact. It is a live data pipeline:
Farm activity → structured payload → IPFS record → Farm Registry event → EAS attestation → public Data Hub → annual impact report.
At Adelphi, this pipeline is already active. Farm data flows to hub.kokonut.network/projects/41, where harvest records, MRV events, and impact metrics can be inspected by anyone.

Built for farm operators, DAO members, grant reviewers, data scientists, Impact Guild contributors, and agent builders.


MRV at a glance

Measure

Soil probes, satellite imagery, drone surveys, and field observations capture what is happening on the farm.

Report

Measurements are converted into structured Farm Registry payloads with timestamps, farm IDs, measurement types, and source-specific data.

Verify

Payloads are pinned to IPFS/Filecoin and anchored through EAS attestations, creating public records that Kokonut cannot retroactively alter.

Use

Verified data powers farm management, DAO funding decisions, annual EBF reports, CRISP risk scoring, AI agents, and public impact dashboards.
MRV is valuable because it serves both the farm and the network. Operators get better irrigation, nutrient, and harvest decisions. DAO members and partners get verifiable evidence instead of self-reported claims.

What MRV proves

MRV answers five trust questions every regenerative farm eventually faces:
QuestionMRV evidenceWho needs it
Is the farm real and active?Farm registry record, geospatial data, field logs, harvest recordsDAO members, funders, partners
Is the land improving?NDVI, NDRE, ReCI, MSAVI, soil moisture, soil temperature, electrical conductivityImpact Guild, agronomists, researchers
Are harvest claims credible?Crop cycle logs, harvest milestones, production forecasts, Data Hub recordsDAO members, buyers, operators
Is impact verifiable?IPFS payloads, EAS attestations, annual EBF reports, CRISP risk assessmentsGrant reviewers, institutions, public goods funders
Can the model scale?Standardized payloads, API endpoints, agent-readable data, reusable reporting workflowsDevelopers, AI agents, new farms

How MRV works

The pipeline is designed so that each layer adds a different kind of trust:
  1. Sensor and field data capture the event.
  2. Structured payloads make the event machine-readable.
  3. IPFS/Filecoin CIDs make the evidence content-addressed.
  4. Farm Registry events make the data queryable.
  5. EAS attestations make the record tamper-resistant.
  6. The Data Hub makes the result public.
  7. Annual reports turn raw data into ecological, economic, social, and sustainability insights.

The three-tier monitoring stack

Kokonut’s MRV methodology operates across three complementary sensing tiers. Each tier captures what the others cannot.
Ground sensing measures what is happening below and around the crop.Soil moisture probes provide continuous subsurface readings across three core variables:
  • Volumetric Water Content (VWC): Measures how much water is present in the soil relative to total soil volume, helping operators detect irrigation needs before stress is visible above ground.
  • Electrical Conductivity (EC): Measures the soil’s ability to conduct electricity, acting as a proxy for salinity and nutrient availability.
  • Soil Temperature: Tracks root-zone temperature, which affects germination, microbial activity, nutrient cycling, and plant growth.
These readings feed into the ground field of each KokonutMRVPayload.

Tools used at Adelphi

ToolPurposeData output
SilviPer-plant GPS tracking and health logsPlant-level location and phenology records
Atlantis AppField data collection, project management, and third-party attestationsStructured reports with photos, timestamps, and attestation-ready evidence
QGISVegetation index calculation from drone orthomosaicsRaster analysis and NDVI/NDRE/MSAVI grid outputs
Pix4DcaptureDrone flight planning and orthomosaic generationGeoreferenced imagery for the QGIS pipeline
Landsat 8 + SentinelLandscape-scale remote sensingVegetation trends, crop health signals, and periodic monitoring snapshots

The MRV payload

Each farm operating under the Kokonut Framework reports against a common payload structure. This lets tools, dashboards, AI agents, and annual reports all read from the same source of truth.
interface KokonutMRVPayload {
  farm_id: string;                  // Farm registry identifier
  timestamp: string;                // ISO 8601
  measurement_type: "ground" | "remote" | "community";

  // Ground sensing
  ground?: {
    volumetric_water_content: number; // % — moisture in soil volume
    electrical_conductivity: number;  // dS/m — salinity / nutrient proxy
    soil_temperature: number;         // °C
  };

  // Remote sensing
  remote?: {
    ndvi: number;    // general vegetation health
    reci: number;    // chlorophyll / nitrogen proxy
    ndre: number;    // dense or mature canopy monitoring
    msavi: number;   // early season / bare soil adjustment
    source: "landsat_8" | "sentinel" | "drone";
    image_url?: string; // IPFS CID or signed URL
  };

  // Community analytics
  community?: {
    crop_cycle_stage: string;
    plant_health_notes: string;
    water_analysis: string;
    soil_texture: string;
    disease_flags: string[];
  };

  // Attestation
  attested_by?: string;       // EAS attestation UID
  attestor_address?: string;  // Wallet that signed the attestation
}

From measurement to verification

Data collected

Soil probes, satellite images, drone imagery, harvest records, and community field logs are assembled into a structured MRV payload.

Payload pinned to IPFS/Filecoin

The full evidence package is pinned to IPFS/Filecoin. The returned CID is content-addressed: the same data always produces the same identifier, and different data cannot produce that same record.

Submitted to the Farm Registry API

The payload is submitted to POST /farms/{farm_id}/mrv. The API returns a mrv_id and records the event before attestation.

EAS attestation created on-chain

The attestation encodes fields such as farm_id, event_type, ipfs_cid, impact_score, attestor_role, and timestamp. The record is signed by the attestor wallet and anchored on-chain.

Data appears on the public Data Hub

Once attested, the MRV event appears in the farm’s public record at hub.kokonut.network/projects/41, traceable to the original evidence package and on-chain attestation.
The purpose of on-chain verification is not hype. It prevents retroactive editing of the impact record and gives funders, DAO members, researchers, and communities a shared source of truth.

What gets reported annually

MRV events aggregate into annual impact reporting across four dimensions aligned with the Ecological Benefits Framework and the Kokonut 8 Forms of Capital.
DimensionWhat is measuredExample metrics
EnvironmentalEcological health and carbon performanceCarbon sequestration rates, NDVI averages, biodiversity index changes, MRV event count
EconomicFarmer and community financial impactGross revenue, public goods allocation, jobs created, household income changes
SocialCommunity wellbeing and capacityWorkshops, participants trained, women in leadership roles, educational program reach
SustainabilityLong-term resilience across all 8 forms of capitalAnnual sustainability audit, organic certification status, SDG attestations
Annual reports for Adelphi are published through the public Data Hub. Historical MRV records, harvest forecasts, and aggregated impact metrics should remain traceable to their underlying EAS attestations.

EBF and CRISP: the interpretation layer

Raw MRV data is the evidence. EBF and CRISP help interpret what that evidence means.

EBF — Ecological Benefits Framework

EBF asks: what ecological and community benefits did this farm produce, and how should they be reported across environmental, economic, social, and sustainability dimensions?

CRISP — Carbon Risk Scoring

CRISP asks: how likely is the farm to deliver the carbon and ecological outcomes it claims, given operational, climate, policy, financial, and developer risks?
Together, they turn continuous farm-level MRV into an auditable impact profile that DAO members, public goods funders, grant reviewers, and institutional partners can use.

How AI agents use MRV

Farms generate more data than human operators can process manually. Satellite passes can happen every few days. Soil probes can read hourly. Crop cycles produce repeated harvest records. Each event creates work: ingest, calculate, validate, attest, report, and sometimes trigger a DAO or marketplace action. The Kokonut × AI Agents architecture is designed to automate the slowest parts of this workflow:

MRV Agent

Ingests satellite or drone data, calculates vegetation indices, and submits MRV payloads to the Farm Registry.

Harvest Agent

Converts harvest logs into structured records, links them to crop cycles, and prepares attestable outputs.

Impact Scoring Agent

Reads verified MRV and harvest records, calculates impact scores, and supports EBF and CRISP reporting.
The Kokonut Agentic Marketplace is in active development. The current MRV page should distinguish between the live MRV methodology at Adelphi and the automation layer being built to increase reporting frequency and reduce manual bottlenecks.

Why this matters for each reader

Farm operators

MRV improves decisions around irrigation, nutrients, crop stress, harvest timing, and long-term soil management.

DAO members and capital allocators

MRV provides evidence for funding proposals, treasury decisions, risk assessment, and replication decisions.

Grant reviewers and partners

MRV replaces subjective impact claims with timestamped records, public dashboards, and third-party-verifiable evidence.

Developers and agent builders

MRV provides a standard schema, API surface, attestation format, and data layer for tools, dashboards, and AI agents.

Impact Guild contributors

MRV creates clear work: validate field reports, review evidence packages, improve data standards, and produce annual impact reporting.

Communities

MRV makes local work visible, making it easier for communities to prove what they grow, restore, teach, and sustain.

Known improvement areas

MRV should be credible about what is live, what is planned, and what still needs contribution.
AreaCurrent directionContribution opportunity
Data schemasFarm Registry and MRV payloads are documentedImprove compatibility, validation, and versioning
AttestationsEAS-compatible farm events are part of the pipelineDefine schemas, roles, review processes, and indexing
DashboardsAdelphi records are published through the Data HubImprove public views, filters, charts, and export formats
AI automationAgentic Marketplace architecture is in developmentBuild MRV, harvest, and impact scoring agents
Impact reportingEBF and CRISP guide annual reportsRefine methodology and evidence requirements
Field workflowCommunity analytics capture human judgmentImprove operator forms, photo standards, and QA checklists

Next steps

Explore Adelphi Data

See live MRV records, harvest data, and impact metrics from Kokonut’s first live farm.

Build with Kokonut

Use the Farm Registry schema, MRV payload format, attestation fields, and developer primitives.

Kokonut × AI Agents

Learn how agents automate MRV ingestion, vegetation index calculation, attestations, payments, and impact scoring.

Ecological Impact Frameworks

Understand how EBF and CRISP convert raw MRV evidence into annual reports and carbon risk assessments.

Adelphi Farm

See the live reference implementation of the Kokonut Framework, MRV stack, and public data model.

Open Collaboration

Join the Impact Guild, improve the MRV methodology, help validate data, or build tools for the next farm.