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<p>Agriculture stands at a critical inflection point, facing climate variability, resource depletion, and declining profitability. Small and marginal farmers—who form most of India’s agricultural workforce—struggle with low productivity, fragmented landholdings, and limited access to information, credit, and markets. Traditional systems are inadequate to meet the growing need for efficiency, resilience, and sustainability. In this context, digitalization presents both a necessity and an opportunity, offering data-driven solutions to bridge gaps in knowledge, infrastructure, and inclusion while transforming how agriculture is practiced and governed. Agriculture digitalization integrates technologies such as AI, IoT, blockchain, drones, and big data analytics to enhance productivity, sustainability, and farmer empowerment. While advanced economies have achieved efficiency through precision agriculture, India and other developing nations are leveraging digitalization for inclusive growth. India’s agricultural sector—employing nearly half the workforce—faces deep structural challenges but has progressed through multiple initiatives, laying the groundwork for a data-driven transformation. To measure and guide this transformation, The Agri Collaboratory, in partnership with CGIAR, IFC, and the World Bank Group, developed the Agriculture Digitalisation Framework (AgDx)—a farmer-centric, open-data framework assessing the adoption, penetration, maturity, and impact of digital agriculture in India and the Global South. AgDx 1.0 comprises six dimensions, 16 pillars, 39 indicators, and 121 sub-indicators, using Multiple Correspondence Analysis to benchmark 28 Indian states. Findings reveal no “Leader” states, though Telangana, Karnataka, and Maharashtra are “Advanced,” while northeastern states remain “Aspiring.” AgDx identifies digital progress and gaps, while establishing a scalable model for data-led policy, collaboration, cross-country learnings and benchmarking.</p>

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AgDx—The Agri Digitalization Framework: A Tool for Transformation

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • Navya Mehrotra,
  • Mahika Phartiyal,
  • Sampriti Guha,
  • Sanjiv Rangrass,
  • Nipun Mehrotra

摘要

Agriculture stands at a critical inflection point, facing climate variability, resource depletion, and declining profitability. Small and marginal farmers—who form most of India’s agricultural workforce—struggle with low productivity, fragmented landholdings, and limited access to information, credit, and markets. Traditional systems are inadequate to meet the growing need for efficiency, resilience, and sustainability. In this context, digitalization presents both a necessity and an opportunity, offering data-driven solutions to bridge gaps in knowledge, infrastructure, and inclusion while transforming how agriculture is practiced and governed. Agriculture digitalization integrates technologies such as AI, IoT, blockchain, drones, and big data analytics to enhance productivity, sustainability, and farmer empowerment. While advanced economies have achieved efficiency through precision agriculture, India and other developing nations are leveraging digitalization for inclusive growth. India’s agricultural sector—employing nearly half the workforce—faces deep structural challenges but has progressed through multiple initiatives, laying the groundwork for a data-driven transformation. To measure and guide this transformation, The Agri Collaboratory, in partnership with CGIAR, IFC, and the World Bank Group, developed the Agriculture Digitalisation Framework (AgDx)—a farmer-centric, open-data framework assessing the adoption, penetration, maturity, and impact of digital agriculture in India and the Global South. AgDx 1.0 comprises six dimensions, 16 pillars, 39 indicators, and 121 sub-indicators, using Multiple Correspondence Analysis to benchmark 28 Indian states. Findings reveal no “Leader” states, though Telangana, Karnataka, and Maharashtra are “Advanced,” while northeastern states remain “Aspiring.” AgDx identifies digital progress and gaps, while establishing a scalable model for data-led policy, collaboration, cross-country learnings and benchmarking.