Dos dados à decisão: transformar a agricultura através de sistemas e governação mais inteligentes 25 out strong and inclusive governance, these systems could reinforce existing inequalities or even create new ones. The answer isn’t to reject innovation, nor rely on regulation alone. What’s needed is a stronger role for public institutions in shaping how agricultural data is governed, from investing in infrastructure to protecting rights. Collaboration between governments, private actors, and producers is essential. Only through co-designed, inclusive systems can digital transformation serve the public good. Global Data Power Dynamics While national systems are where policy decisions are made, global frameworks play a critical role in setting priorities and shaping how resources are allocated. UN agencies such as FAO, IFAD, WFP, and the UN Statistical Commission provide essential guidance on food security, agriculture, and rural development, helping countries align with international standards and goals. At the global level, FAO serves as the UN’s normative authority for agricultural statistics. It collects data from countries through standardized questionnaires and works to ensure international comparability across diverse contexts. In cases where data are missing or outdated, FAO may rely on publicly available sources or model-based estimates to maintain continuity in global datasets. These practices are necessary to produce consistent, timely information, but they can sometimes raise concerns among countries if figures appear that they did not directly report. This reflects not a failure of intent, but the inherent complexity of data governance in a globally connected system. To improve timeliness and coverage, FAO is also expanding its use of non-traditional data sources, including satellite imagery, web scraping, and social media analytics. These innovative methods offer valuable new insights, especially where conventional data are scarce. However, they also raise important questions about validation, transparency, and accountability. Who determines what gets published? How are these new sources assessed for reliability? These are not just technical considerations, but political ones, tied to questions of representation and trust. Translating global norms into meaningful local impact requires sustained engagement in international processes. Yet LMICs often face structural disadvantages in global data governance due to uneven diplomatic resources. While wealthier nations usually maintain well-staffed missions with ambassador-level representatives dedicated to specialized UN agencies, many LMIC’s countries rely on a single ambassador covering multiple geographic regions and topics, or they may lack permanent representation altogether. This disparity affects how countries participate in shaping global standards. When expert groups are convened to develop statistical methodologies, better-resourced delegations can send specialists who are equipped to engage on both the technical and political dimensions of data governance. In contrast, countries with limited representation may rely on generalist diplomats without specific statistical expertise. Despite their best efforts, this can limit their influence in decisions that ultimately shape the data landscape they are expected to operate within. From Data Potential to Systems That Deliver The digital transformation of agriculture has created powerful new possibilities. Advances in AI, remote sensing, and mobile platforms are generating data at unprecedented scale and speed. But the real opportunity lies not in volume, but in value. The key challenge is no longer simply collecting more data - it is building the systems, governance models, and institutional capacities to turn that data into meaningful, actionable insight for those who need it most. The disconnect between growing data availability and its limited real-world application reflects a deeper systems problem - one that spans infrastructure, financing, power dynamics, and trust. Yet it also presents a rare moment for reform. Three Priorities for Agricultural Data Governance To unlock the full potential of agricultural data while avoiding the risk of deepening inequality, three priorities stand out: 1. Reinforce Data Sovereignty and System Resilience: National governments must be empowered to steward their own data ecosystems - hosting data locally, ensuring interoperability, and building infrastructure that can withstand political or funding shocks. This is essential for long-term sustainability and sovereignty over national decision-making processes. 2. Shift Toward Strategic, Domestic-Led Investment: Traditional donor-driven data projects are often shortterm and narrowly scoped. Moving forward, investment models must prioritize long-term, nationally driven strategies. Countries should lead in defining their own priorities, supported by technical partners who can bring coherence, flexibility, and scale. Domestic political leadership should anchor both planning and financing efforts to ensure accountability and alignment with national development objectives. 3. Reduce Fragmentation and Foster Alignment Across Systems: Fragmented tools and uncoordinated invest-
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