Dos dados à decisão: transformar a agricultura através de sistemas e governação mais inteligentes 23 Data to Decisions: Transforming Agriculture Through Smarter Systems and Governance BY JAMES HENDERSON Agricultural Data Manager − Global Partnership for Sustainable Development Data Across global farms and forests, a quiet revolution is unfolding. Satellites scan canopies, sensors track soil moisture, drones map crop health, and digital platforms crunch vast datasets to offer real-time advice to farmers. This digitalization has generated an unprecedented volume of agricultural data, accelerated by recent advances in artificial intelligence (AI). Yet for all this technological progress, one fundamental question persists: Is this data helping to make better decisions and for whom? As digital technologies proliferate across agricultural landscapes, the promise of data-driven farming is clear. But so too are the risks. In my work with the Global Partnership for Sustainable Development Data (the Global Partnership), I’ve witnessed both the transformative potential and concerning pitfalls of agricultural digitalization. The critical insight: data alone creates no value. Without effective data use, even the most comprehensive datasets remain inert. Data must be transformed through active use into useful information and into applicable knowledge that serves farmers, policymakers, and food systems alike. The gap between data collection and data use represents one of the most significant missed opportunities in agricultural transformation today. The Data Paradox: Abundance Amid Scarcity Despite the explosion of agricultural data globally, a troubling paradox exists: many countries, especially low- and middle-income countries (LMICs), still lack the most basic and up-to-date agricultural statistics needed for effective decision-making. The digital revolution has created an illusion of data abundance while masking critical gaps. What’s often missing is timely and accurate agricultural data at subnational levels. While we have access to petabytes of satellite imagery, turning that into actionable insights requires skills, context-specific data, and ground validation. Without this, we still struggle to answer critical questions: How many farmers are growing which crops, where? What yields are they achieving? What inputs are they using? What prices are they receiving? Satellite data can help fill many, but not all, of these gaps but only if paired with the right expertise and supporting systems. These data gaps are particularly severe in LMICs, where agricultural statistical systems have faced chronic underinvestment. In many cases, comprehensive agricultural censuses have not been conducted for more than a decade, if at all. Annual surveys, where they exist, often rely on paperbased methods that are more costly and time-consuming than digital approaches. Even when digitization is used, paper workflows can introduce inconsistencies or delays, depending on how they’re managed. These surveys also tend to have limited geographic coverage and collect too few variables to support complex planning and policy. Equally problematic is the erosion of trust in official agricultural statistics due to these very real shortcomings. Many potential data users from ministries and researchers to agribusinesses and farmer organizations express skepticism about the accuracy and timeliness of national census results. This leads to a vicious cycle: when agricultural censuses aren’t trusted, stakeholders develop ad-hoc or parallel systems for decision making, further fragmenting the data landscape. Perhaps most fundamentally, many countries lack core data infrastructure such as farmer registries or land cadastres. Without adequate georeferencing, even advanced statistical techniques can’t produce reliable estimates. High-income countries rely on these systems as foundations for effective data use. LMICs attempting to build advanced analytics on incomplete foundations risk generating misleading results, no matter how sophisticated the tools. Compounding these challenges is the misalignment of development funding with national food security priorities. When countries do receive support from development partners to improve agricultural statistics, it often comes via short-term projects focused on the traceability of export crops rather than staple foods most important for domestic food security. Coffee, cocoa, and cotton data systems
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