Farm Data Governance for Secure Data Sharing
Farm Data Governance: Data Ownership, Farmer Consent, and Secure Exchange in the Agri Data Ecosystem
Today’s farm is no longer just land, seed, water, and machinery. Every planting, cultivation, harvesting, irrigation, input use, tractor movement, sensor reading, and interaction with a bank or insurance provider creates a data footprint. This data gains real value when it can support better decisions, more precise resource use, product traceability, and the design of agricultural financial services. In the OECD’s framework, agricultural data includes farm administrative and production data, agronomic data, land data, farm management data, and machinery data. This broad scope shows that data governance is far more than an issue of administrative IT. As water economics, food security, and technology investment become increasingly dependent on the farm, the way data is managed becomes part of the infrastructure of production and trust.
For the farm operator, farm data can be an operational asset. But if this asset is transferred without a clear contract, valid consent, and a secure transmission path, it can become a source of distrust. A farmer may share data on crops, soil, machinery, or sales with a platform in order to receive agronomic advice, insurance, bank credit, or access to a supply chain. However, the farmer must know for what purpose the data is being used, for how long, by which recipient, and with what right to withdraw permission. This is the line between productive digitalization and uneven data extraction. In its precise sense, farm data governance is a set of legal, contractual, technical, and institutional rules that organize the collection, storage, processing, transfer, deletion, anonymization, auditing, and valuation of data.
– OECD: “Agricultural data and its use for better decision-making are at the center of the digital transformation of agriculture.”
The importance of this issue for food security begins with everyday decision-making. Sensor data, remote sensing, precision machinery, market data, input data, and financial records, when reliably combined, create a more accurate picture of production risk and the farm’s real needs. This picture can make input use more targeted, strengthen product traceability, and improve the delivery of financial and insurance services tailored to the realities of the farm. Yet the same data that is valuable for innovation and resource efficiency can undermine farmer trust and reduce the adoption of digital tools if exchanged without technical standards, valid consent, and sufficient security.
From Absolute Ownership to the Right to Control Farm Data
In authoritative sources, the issue of farm data is rarely resolved through a simple and absolute concept of ownership. The OECD makes clear that farm data rights sit at the intersection of contract law, competition, intellectual property, privacy, and data protection, and none of these areas alone provides a complete answer. For this reason, the practical question in Agri Data is more often framed around access control, rights of use, portability, confidentiality, security, auditability, and value sharing. This conceptual shift matters because, in the digital farm, data is usually produced by a combination of people, machines, platforms, sensors, and public systems.
The EU Code of Conduct on Agricultural Data Sharing uses the concept of the data originator to clarify this complexity. The term refers to the person or entity that has created or collected the data itself, used a technical tool to generate the data, or commissioned a data processor to do so. The value of this concept is that, instead of creating a broad dispute over ownership, it establishes the starting point for contract, consent, access rights, and transfer rights. In practice, the farmer who generates data through connected machinery, soil sensors, or a farm management application should remain at the center of decisions about the use and transfer of that data.
– EU Code of Conduct on Agricultural Data Sharing: “Data should be collected and used only for the specific purpose agreed upon in the contract.”
The principle of purpose limitation is a core pillar of trust in farm data contracts. If data has been collected for crop nutrition recommendations, using the same data for credit scoring, advertising, or transfer to a third party must be defined separately and transparently. A good contract is not merely a lengthy legal document. It must make the type of data, purpose of processing, recipient, retention period, deletion or anonymization method, farmer access rights, cost and timing of data transfer, and procedure for handling security incidents understandable. Wherever the contract is ambiguous, the asymmetry between the farm operator and the technology provider grows, and the value of data remains concentrated on the platform side rather than serving farm productivity.
The legal evolution of the European Union shows that this debate has moved beyond voluntary recommendations. The EU Data Act establishes a path for users of connected products and related services to access the data generated by those products and services and to transfer it to third parties selected by the user. This rule has direct relevance for agricultural machinery, sensors, connected equipment, and farm platforms, because much of the valuable agricultural data is generated during the operation of these tools. When the user can receive data in a usable format and transfer it to an advisor, bank, insurer, or another provider, the risk of vendor lock-in is reduced and competition in digital agricultural services is strengthened.
– European Union Data Act: “Users of a connected product or related service in the Union may access the data generated.”
Farmer Consent and Trust in Data Exchange
Consent in the Agri Data ecosystem should not be reduced to a simple checkbox at the end of a registration form. Under the GDPR, consent must be freely given, specific, informed, and unambiguous. This definition is especially important for farm data, particularly on family farms or in cases where the data can be linked to an individual. Valid consent must be connected to the purpose of processing, the type of data, the recipient, the retention period, the ability to withdraw consent, and the relevant version of the contract. If the farm operator does not know which bank, insurer, platform, or institution will receive crop or machinery data after collection, consent becomes, in practical terms, a vague and weak authorization.
– EU General Data Protection Regulation: “Consent must be a freely given, specific, informed and unambiguous indication of the data subject’s wishes.”
India’s Digital Personal Data Protection Act also defines consent as free, specific, informed, unconditional, and unambiguous, with a clear affirmative action. This definition has practical importance for India’s Digital Agriculture Mission and AgriStack, because the country’s agricultural data architecture is based on linking identity, location, and crop data to public services and agricultural decision-support tools. AgriStack has three foundational registries: georeferenced village maps, a registry of crops sown, and a farmers’ registry. Without a consent management layer, audit logs, and precise separation of processing purposes, such a structure can shift from a service-delivery infrastructure into a source of concern about data control.
Trust is not built merely by announcing general principles. It must be visible in technical and operational mechanisms. Consent management in Agri Data must record who gave permission, when, for which data, for what purpose, to which recipient, and under which version of the contract. Withdrawal of consent must also have a clear path, because consent without the ability to withdraw it looks, to the farm operator, more like a permanent transfer of data control. The EU Code of Conduct emphasizes that contracts should not be changed without the prior consent of the data originator, and this principle is fundamental to preventing unilateral changes to data terms.
– EU Code of Conduct on Agricultural Data Sharing: “Contracts should not be changed without the prior consent of the data originator.”
Australia’s example shows that even voluntary codes can be useful when they focus on reducing contractual asymmetry. In the first version of the Farm Data Code, the National Farmers’ Federation placed the direct relationship between the farmer and the service, technology, or equipment provider at the center and presented the code’s purpose as increasing trust in data sharing. This model does not create a horizontal legal obligation like the Data Act, but it does create a shared language for contractual transparency, control, portability, security, and regulatory compliance. In markets that do not yet have specialized farm data legislation, such codes can serve as an intermediate stage for bringing order to contracts.
Technical Infrastructure for Secure Data Exchange and Standards
Without technical standards, farm data governance turns into a set of elegant but low-impact contracts. Data accessibility means that the farm operator or an authorized representative can receive farm data in a structured, commonly used, machine-readable format. In practice, this requires APIs, standardized exports, service levels, and transfer rights. Portability is not only a legal right; it also depends on data format, documentation, transfer costs, delivery time, and the absence of contractual restrictions. If platforms keep historical farm data in closed formats, the farm operator faces costs and barriers when switching providers, receiving financial services, or connecting to a supply chain.
ISO 11783, also known as ISOBUS, organizes the communication layer among machinery, sensors, actuators, control units, and display or storage units. The importance of this standard is that it moves farm machinery and equipment away from closed data islands and closer to interoperable components in an operational system. Alongside it, the ADAPT Standard version 1.0 from AgGateway provides a data schema, data type definitions, and unit-of-measure abbreviations for the commercial transfer of agricultural production data. Such standards reduce the cost of integration among farm management systems, input suppliers, agronomic advisors, banks, insurers, and financial platforms.
– International Organization for Standardization: “The standard harmonizes the method and format of data transfer among sensors, actuators, and control components.”
Data provenance is also part of data security and quality. Data whose source is unclear—whether it came from a calibrated sensor, drone, machinery, farmer self-reporting form, bank, or government system—has limited value for financial assessment, insurance, and agricultural decision-support tools. Effective governance must retain metadata, source identifiers, timestamps, algorithm versions, and the chain of changes so that auditing is possible. This issue is even more sensitive for agricultural financial services, because credit or insurance decisions based on incomplete, untraceable, or manipulated data can harm the farm operator, the bank, and the entire value chain.
Data security in this ecosystem is not limited to protecting a central database. Encryption, access control, key management, event logging, penetration testing, incident notification, and security service levels must be built into the system architecture. As farm data becomes more connected to banks, insurers, financing platforms, supply chains, exports, and public systems, both the attack surface and the consequences of data exposure increase. Secure data exchange has meaning only when the identity of actors, access permissions, scope of use, transfer path, event retention, and responsibility for security breaches are defined at both the contractual and infrastructure levels.
The GS1 EPCIS standard and its related core vocabulary provide a common language for supply-chain events and traceability. In Agri Data, this layer connects farm data to proof of origin, value-chain visibility, product recalls, and export compliance. When data on production, harvesting, transportation, processing, and distribution is recorded as events that different actors can understand, farm data moves beyond the boundaries of the farm and becomes linked to market trust and the food chain. This connection has strategic value for products that require proof of origin, quality control, or route traceability.
– GS1: “EPCIS 2.0 supports existing and emerging use cases for traceability and supply-chain visibility.”
Global Financing Models and Agri Data Governance
The European Union has pursued Agri Data simultaneously through law, standards, and infrastructure financing. The preparatory AgriDataSpace project was implemented with a budget of €2 million, 15 partners, and 10 countries, with the aim of designing a reference architecture, governance model, and roadmap for the European agricultural data space. In the deployment phase, the AgriDataSpace roadmap refers to an €8 million budget for the 36-month CEADS initiative, defined for 2025 to 2028, with the goal of creating a secure and trusted agricultural data space across the European Union. This model shows that an agricultural data space is not simply the product of a software company; it is a shared infrastructure that requires public investment to support standardization, trust, risk reduction, and private-sector participation.
India has chosen a different path, approving the Digital Agriculture Mission with a budget of 28.17 billion Indian rupees, including 19.40 billion rupees from the central government. This mission includes AgriStack, the Krishi Decision Support System, and a comprehensive soil fertility and soil profile map, bringing it close to a public digital data infrastructure for agriculture. The logic of this model is that the government creates the foundational data layer, registries, and decision-support infrastructure, after which farmer-centered services are built on top of it. India differs from the European model in its stronger emphasis on public digital infrastructure and foundational registries. Yet in both approaches, trust, consent, security, and auditability are conditions for the ecosystem’s durability.
The American Ag Data Transparent model is more industry-led and certification-based. Under this approach, companies seeking the seal must provide their contracts and answer 11 questions about agricultural data ownership, use, portability, and security, followed by third-party review. The value of this model lies in reducing contractual ambiguity and making companies’ behavior more comparable, although it is not a substitute for a legal right of access or mandatory data portability. For emerging markets, combining certification-based transparency with binding rules on consent and portability may offer a more balanced path than relying entirely on either the market or the state.
– Ag Data Transparent: “Companies must answer 11 questions about agricultural data ownership, use, portability, and security.”
From an economic perspective, farm data is valuable to investors, banks, and insurers only when it is both reliable and usable based on valid consent. If farmers do not trust data sharing, adoption of digital tools declines, and the volume of data needed for financial, insurance, and supply-chain models does not emerge. If data remains locked in closed formats, integration costs and the cost of switching providers rise, while competition declines. Therefore, Agri Data financing must account not only for technical infrastructure but also for the costs of API maintenance, authentication, consent management, data cleaning, security monitoring, auditing, and farm operator support.
A Localization Path for Iran
Institutionally, Iran has a general starting point for data exchange. Article 7 of the National Data and Information Management Law assigns data exchange among government agencies or with businesses, subject to protective and security principles, to the National Information Exchange Center. Article 8 also addresses the online maintenance and updating of information databases and the determination of access levels. This general framework can provide a foundation for secure Agri Data exchange, but it is not sufficient for farm data. Agriculture requires data contracts, farmer consent, API standards, audit logs, access policies, and a clear distinction among statistical data, operational data, and contractual data.
The 2024 General Agricultural Census shows that the country has the capacity to build foundational agricultural data. Items such as type of operation, farm operator characteristics, land, annual and permanent crops, pressurized irrigation, livestock, greenhouses, fish and shrimp farming ponds, honeybees, and sericulture fall within the scope of collected data. However, the official census page itself emphasizes the confidentiality of statistical information and limits the use of individual information to aggregate and public statistics. Therefore, census data should not be converted into credit or commercial data for banks, insurers, or platforms without a clear legal basis and valid consent.
The practical path for Iran should distinguish among three layers: public and statistical data, operational farm data, and contractual data that can be shared with banks, insurers, or platforms. Public data is used for policymaking, planning, and macro-level statistics and must remain consistent with statistical confidentiality. Operational farm data is generated through sensors, machinery, applications, inputs, production, and sales; without metadata, technical standards, and access control, it lacks the quality required for financial decisions or supply-chain use. Contractual data is exchangeable only when the farm operator knows the purpose, recipient, retention period, and the possibility of withdrawing consent.
– Operational Requirements for the Agricultural Data Ecosystem
To create a trusted ecosystem, the first requirement is a model farm data contract written in language that is understandable to the farm operator while also being enforceable for banks, insurers, platforms, and technology providers. Instead of using broad language about ownership, this contract must provide clear rules on access rights, rights of use, purpose limitation, portability, security, auditing, data deletion, and responsibility for security incidents. The second requirement is a consent manager: a system that records consent as a verifiable event and gives the farm operator the ability to withdraw or modify it. The third requirement is a standardized and documented API so that farm data can move among authorized actors with sufficient access control and security.
Iran’s risks must also be addressed from the beginning of the design process. The absence of valid and revocable consent weakens farmer trust and reduces adoption of digital tools. Vendor lock-in can keep historical farm data inside closed systems and prevent its transfer to another provider or a financial institution. Data quality and provenance are also serious risks. Incomplete data, uncontrolled self-reporting, uncalibrated sensors, or data without timestamps and source identifiers cannot be fully relied upon for credit assessment and underwriting. The lack of API standards also increases the cost of connecting banks, insurers, supply chains, and agricultural platforms.
The role of government in this path is not to take control of all data, but to set foundational rules and create institutional trust. The government can use the capacity of the National Information Exchange Center for secure and controlled exchange, but the specialized Agri Data layer must be developed with the participation of relevant ministries, statistical bodies, banks, insurers, agricultural technology companies, farm operator associations, and supply-chain actors. Banks and insurers need data, but their access to farm operator data should not occur without specific and purpose-based consent. Private investors will also enter this field only when rules on access, security, responsibility, portability, and the data value model are clear from the beginning.
A Practical Summary for Decision-Making
Farm data governance is not a purely legal or purely technical issue. It is an infrastructure for food security, resource efficiency, financial services, insurance, traceability, and the development of agricultural markets. The experience of the European Union shows that codes of conduct, data access law, data spaces, and standardization must work together. India’s experience shows that public digital agricultural infrastructure faces trust risks without consent and auditability. The experiences of Australia and the United States also show that contractual transparency and certification can reduce asymmetry between farmers and technology companies, but they are not sufficient to fully protect the right to access and transfer data.
For Iran, the logical starting point is to build a specialized Agri Data layer based on the general data law, statistical confidentiality, and the real needs of the agricultural value chain. This layer should use census and public data for policymaking, while connecting operational and contractual farm data to banks, insurers, advisors, or platforms through farmer consent, transparent contracts, standardized APIs, and audit logs. Such a path can embed farmer trust into the system architecture from the beginning while enabling financial innovation, product traceability, and more precise decision-making. The future of agricultural data will be built neither on ambiguous ownership nor on unrestricted sharing, but on transparent control, valid consent, and secure exchange.
