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#reinforcement learning Dataset Open access

Sustainable Agro-Food Systems in the Digital Era: A Systematic Review of Economic, Technological, and Managerial Perspectives

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

This dataset supports the systematic review article "Sustainable Agro-Food Systems in the Digital Era: A Systematic Review of Economic, Technological, and Managerial Perspectives", which synthesises evidence from sixteen (16) studies examining how economic instruments, technological innovations, and managerial governance mechanisms interact synergistically to shape sustainable outcomes in agro-food systems. The review covers multiple thematic dimensions including economic instruments and policies (environmental protection taxes, supply chain finance, green credit lines, bioeconomy and ESG strategies), technological innovations (artificial intelligence, machine learning, remote sensing, Internet of Things, unmanned aerial vehicles, blockchain, quantum machine learning, multi-agent reinforcement learning, and generative artificial intelligence), and managerial and governance factors (economies of scale, government regulation, institutional support, stakeholder engagement, data governance, and farmer trust). The included studies span diverse geographic contexts, including Asia (China, India, Pakistan, Bangladesh, Nepal, Bhutan, Sri Lanka), Europe (Spain, Ireland, the Netherlands, Germany, United Kingdom), South Asia, and global datasets (FAO, NASA POWER). The dataset includes Figure 1 (PRISMA 2020 Flow Diagram illustrating the study selection process from initial database search to 16 included studies), Supplementary Document 1 (PRISMA 2020 Checklist with 27 items completed for the systematic review), Supplementary Document 2 (Risk of Bias Assessment Criteria detailing the quality appraisal framework adapted for diverse study designs spanning empirical quantitative studies, qualitative case studies, systematic reviews, narrative reviews, Delphi consultations, and simulation-based research), Supplementary Document 3 (Full Data Extraction Form containing standardised data from all sixteen included studies including author(s), year, geographic scope, study design, sectoral focus, key technologies investigated, economic instruments and policies examined, managerial and governance factors considered, quantitative findings, and risk of bias assessment). All files are available under a Creative Commons Zero (CC0 1.0) license.

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