Agricultural Data as a Case Study for Sectoral Data Law: From EU Horizontal Rules to a Spanish Agricultural Data Act

Abstract

The digital transformation of agriculture is rapidly turning the sector into a highly data-intensive domain. The European Union has responded with a broad horizontal framework encompassing the General Data Protection Regulation (GDPR), the Data Governance Act (DGA), the Data Act, the PSI Directive and the AI Act. However, this framework remains sector-neutral: it does not define ‘agricultural data’ as a legal category, nor does it explicitly recognize the specific position of farmers as data providers. This article pursues three objectives: (i) to map the EU legal and policy framework on data and AI as it applies to agriculture and identify regulatory gaps; (ii) to synthesize key concerns from the literature on agricultural data governance, with particular attention to the position of farmers and data spaces; and (iii) to develop an outline of a Spanish ‘Law on Agricultural Data and Digital Agricultural Services’ as an example of sectoral data legislation. The proposed Act—structured around a Preliminary Title and seven substantive Titles—would define agricultural data, recognize farmers as data providers, establish mandatory contractual protections, govern agricultural data spaces and cooperatives, introduce sector-adapted AI rules, address data sovereignty, and set up an institutional framework and graduated sanctions. The analysis argues that sectoral data law can complement EU horizontal rules, enhance legal certainty, and empower farmers without fragmenting the internal market. The article employs a doctrinal legal analysis and normative design-oriented methodology, drawing on secondary literature, policy documents, and EU and Spanish law; it does not rely on original empirical fieldwork.

1. Introduction

Digitalization is reshaping agriculture into a highly data-intensive activity. Sensors, connected machinery, farm management information systems, remote sensing services and traceability platforms continuously generate vast streams of agricultural data. These data flows underpin precision farming, agri-environmental monitoring and digital market services, and they are expected to play a central role in achieving the EU’s sustainability goals under the Farm to Fork Strategy (European Commission 2020b). The Farm to Fork Strategy is the European Union’s overarching plan to make food systems fair, healthy, and environmentally friendly, setting ambitious targets for the reduction in pesticides, fertilizers, and antibiotic use while promoting sustainable agricultural practices across the food chain.

However, the agricultural data economy also raises concerns. Farmers and smaller agri-food actors often have limited visibility over what happens to the data generated on their farms, face structural asymmetries in their dealings with machinery manufacturers and platform operators, and lack effective tools to control secondary uses. These concerns are not adequately addressed by the existing EU horizontal data and AI framework.

These challenges must be understood against the backdrop of broader theoretical debates in data governance scholarship. Critical scholars have examined how datafication reshapes power relations in agriculture, with digital platforms and machinery manufacturers acquiring structural advantages over farmers as a result of data asymmetries (Forney 2022). The emerging field of data justice highlights how prevailing data governance models frequently fail to deliver fair distribution, recognition, and participation for rural communities and smallholder farmers, both in the Global South and within EU member states (Ruder and Wittman 2025; Athena Infonomics and Development Gateway 2022). More broadly, the political economy of digital infrastructures points to the ways in which the design of data systems—including data standards, interoperability requirements, and platform architectures—embeds particular distributions of economic power and renders alternative arrangements more or less feasible (Atik 2022). These theoretical frameworks inform the normative ambitions of the proposed Agricultural Data Act and provide the analytical lens through which the regulatory gaps identified in this article are interpreted.

Spain offers a particularly interesting case study. The country has adopted a comprehensive Digitization Strategy for the Agri-Food and Forestry Sector and Rural Areas, with successive action plans, but has not yet enacted dedicated legislation on agricultural data. This combination—an ambitious policy push alongside a legal gap—makes Spain a suitable context for exploring the design of sectoral data legislation.

This article argues that agriculture is a revealing sector for examining the limits of horizontal EU data and AI law and for exploring the potential of sectoral data legislation. It pursues three objectives:

• To map the EU legal and policy framework on data and AI as it applies to agriculture and identify where it remains under-determined or leaves significant discretion to Member States.
• To synthesize key concerns and proposals from the literature and practice on agricultural data governance, with particular attention to the position of farmers and data spaces.
• To develop an outline of a Spanish ‘Law on Agricultural Data and Digital Agricultural Services’ as an example of sectoral data legislation that could complement EU rules and address identified gaps.

The focus is not on drafting statutory language but on articulating the conceptual and structural elements of a possible Agricultural Data Act and situating it within the broader evolution of EU data law.

2. Materials and Methods

This article combines doctrinal legal analysis of EU and Spanish law with a policy-oriented and normative legislative design approach. The design-oriented methodology is well-established in legal scholarship concerned with the normative evaluation and construction of regulatory frameworks: rather than testing empirical hypotheses, it focuses on identifying functional needs, evaluating existing instruments, and articulating structural proposals for legislative intervention (Atik 2022). This approach is appropriate to the article’s objective of developing a conceptual and structural outline for sectoral data legislation, and is complementary to doctrinal analysis rather than in tension with it. The methodology has four main components.

First, the EU legal analysis focuses on the instruments that directly shape rights, obligations and governance structures around data and AI in the agricultural sector: the GDPR (European Union 2016), the DGA (European Union 2022a), the Data Act (European Union 2023), the PSI Directive (European Union 2019a) and the AI Act (European Union 2024). Key provisions are examined with particular attention to their applicability in agricultural contexts and the gaps they leave open.

Second, a targeted literature review on agricultural data governance is undertaken. Literature was selected on the basis of four criteria: thematic relevance to agricultural data governance and EU data law; disciplinary diversity (including legal analysis, science and technology studies, political economy, and agri-food studies); recency, with priority given to works published in the last five years; and institutional authority, including OECD reports and formally endorsed voluntary instruments. Works considered include OECD reports (Jouanjean 2020; McFadden et al. 2022), academic studies on ‘data ownership’ debates (Atik 2022), voluntary instruments such as the EU Code of Conduct on Agricultural Data Sharing (Ryan et al. 2021), critical scholarship on data justice in agri-food systems (Ruder and Wittman 2025; Athena Infonomics and Development Gateway 2022), and governance scholarship on the social dimensions of datafication (Forney 2022).

Third, the Spanish national context is examined through the Digitization Strategy for the Agri-Food and Forestry Sector (MAPA 2019), its successive Action Plans (MAPA 2021) and the relevant components of the Recovery, Transformation and Resilience Plan (Government of Spain 2021).

Fourth, a normative, design-oriented method is adopted to elaborate an outline of the Spanish Agricultural Data Act. This involves drawing on the functional elements of existing EU data and AI instruments, adapting them to the specific governance challenges of the agricultural sector, and proposing a structured statutory architecture.

During the preparation of this article, a generative AI language model (Claude 3.5 Sonnet, Anthropic, 2024) was used to support text drafting and language refinement. The author has reviewed and edited the output and takes full responsibility for the content and conclusions of this publication.

This methodological approach has limitations. The article does not rely on original empirical fieldwork; it draws on secondary literature and existing data. The choice of secondary sources is deliberate and appropriate to the doctrinal and normative objectives of the work: legal analysis and legislative design do not require primary data collection, and the conclusions of the article are calibrated accordingly. The article does not purport to provide empirical evidence of how farmers experience current regulatory gaps, or of whether the proposed Act would achieve its intended effects in practice; these are important questions for future empirical and evaluative research. The article also does not undertake an exhaustive comparative analysis of agricultural data legislation across EU Member States or third countries; comparative references are selective and illustrative, designed to situate the Spanish proposal within broader regulatory trends rather than to provide systematic comparative evidence. Within these limits, the chosen combination is appropriate to the article’s objective: to integrate doctrinal, policy and normative dimensions in a single analytical framework.

3. EU Legal and Policy Framework on Data and AI Relevant for Agriculture

3.1. GDPR and Agricultural Personal Data

Regulation (EU) 2016/679 (GDPR) (European Union 2016) provides the core framework for the processing of personal data in the EU. Many agricultural data flows involve personal data—where information about farm operations is traceable to an identified or identifiable natural person. In these cases, GDPR applies in full: farmers as data subjects enjoy rights of access, rectification, erasure, restriction and data portability.

However, GDPR does not provide a specific legal status for farmers as data providers in respect of non-personal data generated on their land or with their machinery. The rights of farmers in relation to non-personal agricultural data—which constitute a significant and growing proportion of data generated in the sector—are governed mainly by contract law and, increasingly, by the Data Act.

3.2. Data Governance Act and Data Intermediaries

Regulation (EU) 2022/868 (DGA) (European Union 2022a) aims to increase trust in data sharing by regulating data intermediaries and establishing mechanisms for the re-use of certain categories of protected public sector data. For agriculture, the DGA is relevant in at least two respects: it enables the re-use of protected public sector data under harmonized conditions, and it provides the regulatory framework for data intermediaries that could serve as trusted operators of agricultural data spaces. However, the DGA does not define ‘agricultural data’ nor does it specifically address the position of farmers.

3.3. Data Act and Access to Machine-Generated Data

Regulation (EU) 2023/2854 (Data Act) (European Union 2023) establishes harmonized rules on fair access to and use of data generated by connected products and related services. In agriculture, where a large proportion of data is produced by connected machinery and sensors, the Data Act has significant potential to rebalance relationships between farmers and equipment manufacturers or platform operators. Its user access rights, portability obligations and third-party access mechanisms are particularly relevant. However, it does not define agricultural data nor address sector-specific governance arrangements such as agricultural data spaces.

3.4. Open Data and the PSI Directive

Directive (EU) 2019/1024 (European Union 2019a), together with sector-specific legislation on environmental and meteorological data, promotes the availability of high-value public sector datasets. For agriculture, these instruments facilitate access to climate, soil and land use data that are essential inputs for digital services. They do not, however, regulate the relationship between farmers and private data service providers, nor do they address non-personal data generated by private agricultural activities.

3.5. AI Act and High-Risk AI in Agriculture

Regulation (EU) 2024/1689 (AI Act) (European Union 2024) establishes a risk-based framework for artificial intelligence systems across all sectors of the economy and society. The risk-based approach classifies AI systems into four tiers: AI with unacceptable risk (prohibited), high-risk AI (subject to stringent requirements including conformity assessments, transparency, and human oversight), limited-risk AI (subject to transparency obligations), and minimal-risk AI (not specifically regulated). This graduated approach applies horizontally across all sectors—including agriculture, energy, health, and finance—and does not create sector-specific exemptions or tailor its categories to the particularities of any one industry.

While agriculture is not singled out, several categories of high-risk systems are highly relevant for agricultural contexts—particularly AI used in critical infrastructure, in the management of essential services, and in systems affecting access to public benefits such as agricultural subsidies. Member States retain significant discretion in implementing oversight structures and enforcement mechanisms, which creates space for supplementary national rules adapted to agricultural specificities.

3.6. EU Strategies for Data and Sustainable Food Systems

The European Strategy for Data (European Commission 2020a) explicitly envisages the creation of sectoral data spaces, including an agricultural data space. In parallel, the Farm to Fork Strategy (European Commission 2020b) positions data-driven agriculture as a key enabler of sustainable food systems. Together, these strategies suggest that the EU expects data governance frameworks to be adapted to sectoral needs, while the horizontal legislative framework has not yet caught up with this vision.

4. Regulatory Gaps and the Case for Sectoral Legislation

4.1. Agricultural Data Governance and Farmers as Data Providers

An increasing body of literature explores how digitalization and datafication are transforming governance in agriculture. OECD work highlights issues around farmers’ trust in digital tools, perceived risks of data sharing, and the lack of legal clarity on data ownership and secondary uses (Jouanjean 2020; McFadden et al. 2022). Atik (2022) argues that ‘data ownership’ debates are often misleading and that a more productive approach is to design comprehensive governance frameworks that clarify roles, rights and responsibilities. Ryan et al. (2021) examine the EU Code of Conduct on Agricultural Data Sharing—a voluntary instrument that promotes fairer contractual practices but has limited reach. More recently, Forney (2022) has examined how digital platforms govern farmers through data, highlighting the social dimensions of datafication in rural contexts and the ways in which data extraction reinforces structural dependencies between farmers and agri-tech firms. Critical scholarship on data justice argues that many prevailing models fail to deliver fair distribution, recognition and participation for farmers and rural communities (Ruder and Wittman 2025; Athena Infonomics and Development Gateway 2022). Drawing on food sovereignty frameworks and community-based data governance models, this scholarship calls for approaches that place farmers at the center of data governance rather than treating them as passive data subjects.

A common thread across this literature is that farmers should be recognized as data providers: actors whose activities, land and animals generate the data that underpin digital services and agri-environmental governance. In the absence of a specific legal status, farmers’ rights over non-personal agricultural data are defined primarily by contracts drafted by more powerful counterparties, with limited regulatory backstop beyond the general provisions of the Data Act.

4.2. Identified Regulatory Gaps

Sectoral data legislation refers to binding legal rules specifically designed for a defined economic or social sector—here, agriculture—that operate alongside the general (horizontal) EU framework as lex specialis. Unlike horizontal instruments such as the GDPR or the Data Act, which apply across all sectors without distinction, sectoral legislation can address the particular governance challenges, market structures, power asymmetries, and public interest dimensions of a specific domain. The case for sectoral legislation in agriculture rests on the identification of regulatory gaps that the horizontal framework, by design, cannot fully address.

The interaction of EU horizontal law, national policies and agricultural practices reveals several such gaps:

• There is no legal definition of ‘agricultural data’ in EU or Spanish law, creating legal uncertainty about the scope of applicable rules.
• The role of the farmer as data provider remains implicit and under-protected, particularly in relation to non-personal data and secondary uses.
• Contracts for digital agricultural services often lack transparency and balanced treatment of data issues; the EU Code of Conduct is voluntary and has limited reach.
• Emerging agricultural data spaces lack a clear hard-law framework on governance structures, access conditions and value-sharing mechanisms.
• The AI Act leaves practical questions unresolved in agricultural contexts—on risk categorization, sector-specific impact assessments and oversight structures.
• There is limited attention to strategically sensitive agricultural datasets from a security and sovereignty perspective.

These gaps do not necessarily imply that a sectoral Agricultural Data Act is the only solution, but they demonstrate normative space and practical need for sector-specific legislative action.

5. Spanish Policy Context on Agricultural Digitalization

Spain has been relatively proactive in adopting a strategic approach to agricultural digitalization. In 2019, the Ministry of Agriculture, Fisheries and Food (MAPA) presented the Digitization Strategy for the Agri-Food and Forestry Sector and Rural Areas (MAPA 2019), setting out a comprehensive vision for data-driven agriculture aligned with EU digital and sustainability goals. The Strategy is implemented through successive Action Plans (2019–2020, 2021–2023, 2024–2026) (MAPA 2021), which encompass measures to improve connectivity, promote digital skills, support innovation hubs and encourage data sharing among agricultural actors.

These policy documents increasingly recognize the importance of data use, interoperability and digital services. However, they do not themselves constitute legislation: they express political commitments and funding priorities rather than legally enforceable rights or obligations. Spanish law incorporates the EU data and AI acquis—GDPR, DGA, Data Act, PSI Directive and, prospectively, the AI Act—but there is no statute that explicitly defines ‘agricultural data’, recognizes the legal status of farmers as data providers, or establishes a governance framework for agricultural data spaces.

This combination of a strong policy push for digitalization and the absence of a dedicated legal framework makes Spain a suitable and timely case study for exploring the design of a sectoral Agricultural Data Act.

6. Outline of a Spanish Law on Agricultural Data and Digital Agricultural Services

6.1. Object, Scope and Definitions (Preliminary Title)

The Act would begin by defining its objective: to regulate the generation, access, use, sharing and governance of agricultural data in Spain; to establish rights and obligations for actors involved in digital agricultural services; and to promote a fair, trustworthy and sustainable agricultural data economy.

Key definitions would include: (i) agricultural data—any digital representation of facts or information generated in the context of agricultural, livestock, forestry or aquaculture activities; (ii) data provider—the person whose activities, land, animals or infrastructure generate agricultural data; (iii) digital agricultural services; (iv) agricultural data space; and (v) strategically sensitive agricultural data. These definitions would be aligned with, but more specific than, those in the DGA and the Data Act (European Union 2022a; European Union 2023).

6.2. Recognition of Farmers as Data Providers and Basic Rights (Title I)

The core innovation of the Act would be the recognition of farmers and cooperatives as data providers and the granting of a set of basic rights. Title I would include:

• Right to information: clear, prior disclosure of what data are collected, for which purposes and by whom.
• Right to decide on non-essential uses: effective consent mechanisms for uses beyond the main contracted service.
• Right to effective portability: interoperable access to data concerning their activities, in line with the Data Act.
• Right to limit future uses: revocation of authorizations for secondary uses without prejudice to legal obligations.

Title I would also impose duties on data holders and users—duties of diligence, data quality and security—and a prohibition of exploitative practices such as excessive lock-in or unauthorized secondary commercialization.

6.3. Contracts for Digital Agricultural Services (Title II)

Title II would address the contractual dimension of agricultural data governance, applying to contracts between providers of digital agricultural services and farmers or cooperatives. It would establish: (i) reinforced transparency obligations on data-related aspects; (ii) mandatory contractual clauses identifying parties, data categories, purposes and data retention periods; and (iii) prohibited unfair terms, including clauses granting unlimited royalty-free rights over agricultural data or authorizing unilateral modification of data use conditions.

An illustrative example is instructive: a farmer who purchases or leases a connected machine typically faces standard-form contracts drafted by the manufacturer, with no meaningful negotiating position. Denying the standard terms may mean losing access to essential functionalities or after-sales support. Title II would directly address this structural asymmetry by establishing minimum standards that apply regardless of contractual choice.

6.4. Governance of Agricultural Data Spaces and Data Cooperatives (Title III)

Title III would provide a regulatory framework for agricultural data spaces and collective data governance entities. For data spaces, it would establish a registration system for recognized spaces, impose minimum governance requirements (including multi-stakeholder boards and transparency obligations), and introduce value-sharing obligations for spaces generating income from aggregated agricultural data (AgriDataSpace Consortium 2024). For data cooperatives and trusts, it would recognize their legal status, specify requirements of democratic governance and fiduciary duty, and enable them to act as collective bargaining agents in negotiations with digital service providers.

6.5. AI Systems Using Agricultural Data (Title IV)

Title IV would articulate sector-specific rules for AI systems and advanced analytics based on agricultural data, complementing the AI Act. It would cover transparency and documentation requirements—in language understandable to non-specialist users—context-specific impact assessments for high-impact AI systems, and reinforced human oversight and contestability mechanisms for AI-informed decisions affecting access to subsidies, credit or insurance.

6.6. Security, Sovereignty and Strategically Sensitive Datasets (Title V)

Title V would address the strategic dimension of certain agricultural datasets (Baezner and Robin 2018). It would mandate the government to adopt a list of strategically sensitive categories, establish requirements of secure storage and processing, regulate access conditions for non-EU entities and cross-border transfers, and coordinate with existing cybersecurity and critical-infrastructure frameworks.

6.7. Institutional Governance, Enforcement and Sanctions (Titles VI–VII)

Title VI would designate a competent national authority for agricultural data governance, define its functions (compliance supervision, registry management, guidance and certification), and create a multi-stakeholder advisory council with representation of farmers’ organizations, cooperatives, industry, civil society, academia and regional authorities.

Title VII would set out a graduated sanctioning regime, coordinated with GDPR, the DGA, the Data Act and the AI Act, distinguishing minor, serious and very serious infringements and providing for proportionate administrative sanctions, complementary measures (suspension from registries or certification schemes), and exclusion from public funding programs in cases of repeated or very serious violations.

6.8. Constitutional, Competence, and EU Law Hierarchy Considerations

A Spanish Agricultural Data Act would need to be assessed against the distribution of competences under the Spanish Constitution. Relevant Title I provisions of Article 149 CE—particularly paragraphs 13 (bases and coordination of general economic planning), 18 (common administrative procedural legislation) and 1 (basic conditions guaranteeing equality)—would likely provide the constitutional basis for key provisions. The Act would need to be framed as basic State legislation (legislacion basica), preserving space for Autonomous Communities to develop supplementary rules in areas linked to their competences in agriculture, rural development and regional economic promotion. A more detailed examination of constitutional case law is identified as a priority for further research.

A critical dimension of constitutional feasibility concerns institutional capacity. The establishment of a dedicated national supervisory authority—as envisaged in Title VI—would require adequate budgetary resources, specialized expertise in both agricultural and data law matters, and effective coordination mechanisms with existing regulators such as the Spanish Data Protection Agency (AEPD) and the sectoral bodies competent for agricultural policy. The drafting and implementation of the Act would also need to engage key stakeholder groups—including farmers’ organizations, cooperatives, agri-tech companies, and civil society—to ensure legitimacy and workability. The possibility of resistance from digital service providers and large agri-food operators seeking to limit mandatory transparency and data-sharing obligations should be anticipated and addressed through robust participatory drafting procedures and clear enforcement mechanisms.

The proposed Act must also be assessed against its position within the EU legal hierarchy. The key EU data and AI regulations—GDPR, DGA, Data Act, and AI Act—are directly applicable EU law that takes precedence over national legislation and enjoys direct effect in Member States. The proposed Agricultural Data Act would be required to operate strictly within this hierarchy: it cannot derogate from or conflict with directly applicable EU rules, and national courts and authorities would be obliged to interpret it in conformity with EU law. The Act is therefore designed not as an autonomous parallel framework but as a complementary instrument that fills the normative spaces left open by EU horizontal instruments, giving concrete legal form to the sector-specific possibilities those instruments create or leave to Member State discretion. This interpretive conformity obligation, combined with the lex specialis character of the Act, ensures that its provisions reinforce rather than undermine the coherence of the EU data governance framework.

7. Discussion

The proposed Spanish Agricultural Data Act illustrates how sectoral data law can complement EU horizontal rules without fragmenting the internal market. Rather than creating conflicting obligations, it would operate as lex specialis, addressing specific problem constellations—farmers’ bargaining position, data space governance, sector-adapted AI oversight—where horizontal rules leave gaps or provide insufficient certainty.

From a policy perspective, such an Act could increase legal certainty for all actors—farmers, cooperatives, industry and digital providers—and strengthen public trust in data-driven agricultural innovation. It could also align data governance with sustainability, food system resilience and rural development goals, providing a concrete vehicle for implementing the aspirations of EU data and AI strategies at sectoral level.

At the same time, sectoral legislation carries genuine risks that must be acknowledged and addressed. First, there is a risk of regulatory fragmentation: if other Member States were to adopt divergent sectoral agricultural data laws, the resulting patchwork could impede the development of a genuine European agricultural data space and impose compliance costs on operators active across multiple jurisdictions. Second, there is a risk of overlap and normative inconsistency with EU horizontal instruments, particularly in areas such as data portability, AI transparency, and data intermediary governance, where the proposed Act would need to be carefully calibrated to avoid duplicating or complicating existing obligations. Third, there are implementation constraints: the Act’s effectiveness depends on the availability of an adequately resourced supervisory authority, the active participation of farmer organizations in governance structures, and the willingness of digital service providers to engage constructively with the new framework rather than seeking to circumvent it through contract design or jurisdictional arbitrage. These risks underscore the importance of procedural safeguards—participatory drafting, ongoing evaluation, and the role of an independent authority and advisory council—careful legal articulation of the Act’s relationship with EU law, and a phased implementation approach that allows for learning and adjustment.

Comparatively, the proposal aligns with and can be further situated within an emerging regulatory trend toward sectorally differentiated data governance in the EU. In the energy sector, Directive (EU) 2019/944 (European Union 2019b) has established sector-specific rules on smart metering data and customer access rights, addressing power asymmetries between energy consumers and network operators in ways that the general data framework cannot. In the health sector, the European Health Data Space Regulation (European Commission 2022) explicitly positions itself as a sectoral data framework building on GDPR, creating sector-specific categories of electronic health data, cross-border access mechanisms, and a dedicated governance architecture. Both analogies are instructive for agriculture: like the energy and health sectors, agriculture combines strong public interest considerations (food security, environmental sustainability, rural development), significant data asymmetries between individual actors and large platforms or corporations, and important data flows with cross-border and cross-sector implications. Agriculture could follow a similar trajectory to energy and health, with the proposed Act serving as a model for other Member States and potentially informing a future EU-level sectoral instrument. The ALI-ELI Principles for a Data Economy also offer a broader normative reference point for the design of data governance frameworks (American Law Institute and European Law Institute 2023).

Table A1. Comparative overview of EU horizontal data/AI law and the proposed Spanish Agricultural Data Act.

EU Instrument	Key Provisions for Agriculture	Remaining Gaps	Proposed Act Provisions	Relationship
GDPR (2016/679)	Personal data rights (access, portability, erasure) for farmers as data subjects	No status for farmers as data providers of non-personal data	Title I: Recognition of farmers as data providers; rights over non-personal agricultural data	Complementary—fills gap not covered by GDPR
Data Act (2023/2854)	User access rights to data from connected products; third-party access; portability	No definition of agricultural data; no sector-specific governance for data spaces	Titles I–III: Sector-adapted rights, mandatory contract clauses, data space governance	Complementary—applies GDPR portability logic to agricultural context
DGA (2022/868)	Regulated data intermediaries; framework for data altruism and data spaces	Does not address agricultural data specifically; limited on value-sharing	Title III: Registration, governance requirements and value-sharing for agricultural data spaces	Complementary—adds sectoral specificity to DGA framework
PSI Directive (2019/1024)	Re-use of public sector agricultural data; high-value datasets	Does not regulate private agricultural data relationships	Preliminary Title: Definition of agricultural data, including public sector datasets	Complementary—extends to private data
AI Act (2024/1689)	Risk-based framework; transparency and human oversight for high-risk AI	No sector-specific risk categories for agriculture; discretion on oversight structures	Title IV: Sector-adapted AI transparency, impact assessments and contestability rules	Complementary—fills implementation gap left by AI Act

(see Appendix A) provides a comparative overview of how the proposed Act relates to the main EU horizontal instruments, illustrating the complementarity rather than conflict between the two levels of regulation.

8. Conclusions

Agriculture is a rich and revealing case for understanding the opportunities and challenges of governing data and AI in a sector undergoing rapid digital transformation. The EU horizontal framework on data and AI is extensive and ambitious, but it does not adequately address the specificities of agricultural data, the structural power asymmetries between farmers and digital service providers, or the governance needs of emerging agricultural data spaces. Sectoral data legislation, operating as lex specialis alongside the horizontal framework, offers a coherent and legally sustainable response to these gaps.

The proposed Spanish Law on Agricultural Data and Digital Agricultural Services, structured around a Preliminary Title and seven substantive Titles, would make a concrete contribution on several fronts: it would establish a legally operative definition of agricultural data and recognize farmers as data providers; it would introduce mandatory contractual protections addressing the structural information and bargaining asymmetries that currently disadvantage farmers; it would provide a hard-law governance framework for agricultural data spaces and cooperatives; it would articulate sector-adapted AI transparency and oversight rules; it would address data sovereignty concerns relating to strategically sensitive agricultural datasets; and it would create an institutional architecture—a dedicated supervisory authority and a multi-stakeholder advisory council—capable of translating these norms into practice.

The proposed Act could enhance legal certainty and trust in data-driven agricultural innovation, empower farmers and cooperatives in their dealings with digital service providers, align data governance with sustainability and rural development goals, and provide a replicable template for other EU Member States and sectors facing analogous governance challenges. Its design reflects the principle that effective data governance requires not only horizontal rules of general application but also sector-specific frameworks attentive to the particular power relations, technical conditions, and socio-environmental stakes of defined domains.

Further work is needed to refine this proposal through empirical engagement with farmers, cooperatives, agri-tech firms, and regulatory authorities; comparative analysis with other EU Member States and sectors; and detailed examination of constitutional and competence questions, including the case law of the Constitutional Court. These limitations are acknowledged and do not undermine the core analytical contribution: sectoral data law can play a valuable and necessary role in realizing the promises of the EU data and AI strategies in ways that are sensitive to the specific challenges of the agricultural sector.