Beyond Precision Agriculture: AI and Data-driven Information Systems as Catalysts for Sustainable Agribusiness Transformation
Timilehin Fawibe *
Department of Data Analytics and Information Systems, Texas State University, San Marcos, Texas, United States.
Gbenga Samuel Ernest
Department of Agricultural Economics, Oklahoma State University, Stillwater, Oklahoma, United States.
Joshua Emmanuel
Department of Agricultural Economics, Kansas State University, Manhattan, Kansas, United States.
Simon Jacob
Department of Agricultural Economics, Oklahoma State University, Stillwater, Oklahoma, United States.
Olawale Akinleye
Department of Financial Planning, University of Georgia, Athens, Georgia.
Tosin Olorunwo
Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Athens, Georgia.
*Author to whom correspondence should be addressed.
Abstract
The dominant narrative in digital agriculture centres on discrete technologies, including sensors, unmanned aerial vehicles, global positioning systems, remote sensing, and machine learning, evaluated primarily for their capacity to increase yields and improve input-use efficiency. This narrows the analytical lens to the farm gate and understates a more consequential shift: individual technologies generate value for agribusiness only when information systems capture, integrate, and route the data they produce into real operational and strategic decisions. This review critically examines the AI- and data-driven agribusiness transformation literature through a single organising pathway: data, analytics, artificial intelligence, information systems, decisions, sustainable agribusiness outcomes; it asks whether the field's technical achievements have matched comparable progress in systems integration and governance. A transparent narrative search and appraisal protocol synthesised evidence across ten thematic domains: agricultural big-data integration, predictive and prescriptive analytics, farm management information systems (FMIS), AI-enabled supply-chain analytics, climate-risk adaptation, sustainability and ESG analytics, data governance and cybersecurity, digital infrastructure and adoption, circular agriculture, and decision-support systems. The evidence shows that sensing and algorithmic capabilities have matured faster than the organisational and governance capacity required to convert those capabilities into adopted agribusiness decisions. Data-governance barriers, rather than technological ones, now dominate constraints on information-system integration in the literature published since 2023. Research, investment, and policy attention should therefore shift further toward the interoperability, governance, and organisational design layers of digital agriculture, rather than continuing to concentrate primarily on sensor accuracy or model performance.
Keywords: Agribusiness intelligence, farm management information systems, agricultural big data, artificial intelligence, data governance, sustainable agriculture, ESG analytics